Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam

Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.Nghiên cứu giải pháp tối ưu hoá quá trình điều khiển và vận hành trạm sạc tích hợp điện mặt trời tại Việt Nam.

Motivation for research

COP26 and PDP VIII – the commitments of Vietnam to sustainable development

At the COP26 conference, Vietnam demonstrated its commitment to combating global climate change by pledging to achieve net-zero emissions by mid-century and signing the Global Coal to Clean Power Transition Statement To support these goals, the Vietnamese government has developed a detailed roadmap featuring eight essential tasks focused on fostering sustainable and low-emission economic growth.

The transition from fossil fuels to renewable energy sources (RESs) is crucial for reducing greenhouse gas (GHG) emissions across various sectors, including energy and transportation Key initiatives include decreasing reliance on fossil fuel vehicles and promoting research, development, and adoption of electric vehicles (EVs) The Ministry of Transport is tasked with assessing the feasibility of phasing out fossil fuel vehicles by 2040 and creating a roadmap for transitioning to clean energy transportation.

Worth mentioning, on 15 th May 2023, the Vietnamese government adopted the Power Development Plan VIII (PDP VIII), showing a strong commitment towards decarbonization.

The PDP VIII sets a new RES development direction by increasing the amount of renewable power generation capacity (i.e., up to 48 percent of the total capacity by

By 2030, the electricity distribution plan aims to increase renewable energy's share to 65.8-71 percent while eliminating coal power, reducing its contribution from 20 percent to zero by 2050 The PDP VIII shifts focus from grid-connected solar projects to promoting solar energy for self-consumption, particularly through rooftop installations on residential and commercial buildings The plan sets an ambitious target of having 50 percent of office buildings and residential homes utilizing rooftop solar power for on-site energy use by 2030.

The commitments made at COP26 and the PDP VIII highlight the Vietnamese government's strong commitment to sustainable development, particularly in the energy and transportation sectors This research aims to explore strategies to enhance the growth of renewable energy sources (RESs) and promote clean transportation solutions within the context of Vietnam.

1.2 The transition to electric two-wheeler mobility in Vietnam’s urbans

In Hanoi and Ho Chi Minh City, public transport meets only 15% and 9% of travel demand, respectively, resulting in a heavy reliance on private vehicles, particularly gasoline-powered motorcycles, which account for approximately 80% of urban traffic This dominance of fossil fuel vehicles contributes to significant traffic congestion, elevated greenhouse gas emissions, and worsening air pollution in these cities.

Figure 1 Private vehicle ownership in Vietnam and other countries

Traffic congestion in Hanoi is significantly influenced by several factors, including inadequate traffic infrastructure, insufficient public transport options, and the socioeconomic conditions prevalent in Vietnam Many individuals opt for private motorcycles as their primary mode of transportation in urban areas due to these challenges and the impact of weather conditions.

The increasing prevalence of private vehicles in urban areas leads to significant traffic congestion, noise pollution, and deteriorating air quality To mitigate these challenges, various solutions have been suggested, including stricter exhaust emission standards, restrictions on private vehicle usage, enhancements to public transportation, and the promotion of less polluting vehicles Among these strategies, the electrification of transport stands out as a viable option, offering the dual benefits of reducing air pollution and diversifying energy sources Electric vehicles produce zero tailpipe emissions and operate with greater efficiency compared to traditional internal combustion engine vehicles.

GHG emissions reductions when coupled with a low- carbon electricity sector, reducing fossil fuels dependence, lesser noise and being able to provide ancillary services to the energy system [99].

Regarding electric mobility transition in Vietnam, in the first periods, with low purchase price (about 400

Electric bicycles, priced around USD 99, have gained popularity among students and the elderly due to their low speed and exemption from driver's license and vehicle registration requirements However, their appeal is diminished by lower quality and performance compared to gasoline-powered alternatives.

Figure 3 The fifteen most polluted cities in Southeast

Electric two-wheelers (E2Ws) have gained significant public interest as major manufacturers like Honda, Yamaha, Piaggio, and Vinfast enter the market Their modern designs, high quality, and diverse features—including e-Sim technology, anti-theft systems, cruise control, operation history records, and waterproof capabilities—combined with competitive pricing, have made E2Ws increasingly appealing to consumers.

In 2019, Vietnam witnessed a significant increase in high-quality electric two-wheelers (E2Ws), with production rising from 0.9 million units in 2017 to five million by 2019, according to the International Association of Public Transport (UITP) The country had eleven manufacturers producing E2Ws, achieving a total output of 52,938 units that year.

(Registry Department) Annually, the growth rate of the E2W market is up to 30 - 40

The shift from traditional motorcycles to electric two-wheelers (E2Ws) is driven by the rising popularity of motorbikes, socio-economic factors, and inadequate transport infrastructure E2Ws maintain the advantageous characteristics of motorcycles for urban commuting while offering the benefits of electric mobility Additionally, they hold the potential to enhance intelligent transportation systems, improving connectivity to public transport services at transit hubs.

The rapid increase in emerging vehicles is expected to place significant strain on the distribution grid, which is primarily designed for traditional load demand growth To effectively manage this transition, it is essential to explore research-based solutions that address these challenges.

1.3 Rooftop solar power development in Vietnam and its impacts

With a high average annual total radiation, Vietnam is considered a place with special potential for developing solar power (Figure 4).

Figure 4 Map of average daily global horizontal irradiance (GHI) in Vietnam

Figure 5 Top 10 countries by PV installed capacity in 2020

In 2015, the installed solar capacity for power generation was a mere 4 MWp, with about 900 kWp connected to the grid By 2018, this capacity grew to 106 MWp, and surged dramatically to around 5 GWp in 2019, including nearly 0.4 GWp from rooftop photovoltaic (PV) systems By the end of 2020, the nation's total PV capacity reached approximately 16,500 MW, positioning it among the top 10 countries globally Additionally, by December 2020, there were 105,212 rooftop solar systems installed nationwide, contributing a total capacity of 9,730.87 MWp.

High levels of photovoltaic (PV) penetration can lead to significant challenges for grid operation and security When PV integration surpasses a certain threshold, it can destabilize the grid, resulting in frequency and voltage fluctuations This situation may overload existing infrastructure and create mismatches between energy demand and supply.

[14], about 365 GWh of solar output was curtailed in 2020 as a result of balancing supply and demand.

To enhance the hosting capacity of the power grid, utilities can implement mitigation techniques and adopt grid optimization solutions that synchronize photovoltaic (PV) operations with the overall grid Additionally, upgrading and flexibly managing the power grid is essential for effectively integrating renewable energy sources (RESs).

Another method to promote PV usage without affecting the distribution grid operation is to encourage self-consumption, which was clearly mentioned in the PDP

PV-integrated charging stations – A solution for both E2W and rooftop solar development

The urban distribution grid in Vietnam faces two significant factors that will impact its future: the rise of unplanned, deferrable charging loads and the increasing use of distributed renewable energy sources (RESs) that are intermittent and encourage self-consumption Addressing these challenges presents both opportunities and obstacles for the energy system.

Studies show that any form of EV such as

HEV (Hybrid EV), PHEV (Plug-in hybrid EV),

PEV (Plug-in EV) has lower emissions than

ICE counterparts Noteworthily, the amount of emissions depends on the proportion of clean energy supplied to the vehicle [42], [45], [50].

Figure 6 A PV-integrated E2W charging station.

Charging electric vehicles (EVs) from a grid primarily powered by fossil fuels like coal or natural gas results in considerable emissions Therefore, the significant reduction of greenhouse gas (GHG) emissions through EV adoption occurs only when these vehicles are charged using renewable energy sources (RESs) or from a grid with a high proportion of renewable electricity.

Sustainable energy sources such as wind power, solar power, hydropower, biogas, and tidal energy are essential for powering electric vehicles (EVs) In urban areas of Vietnam, rooftop photovoltaic (PV) systems present a particularly appealing solution due to their numerous advantages.

1) Vietnam has a large potential of PV power with solar radiation reaching up to 5kWh/m 2 and annual sun hour being from 1600-2600 hours [55] Rooftop PV is encouraged as it does not consume many land resources [119], [126].

2) The costs of PV module, hardware and inverter are continuously decreasing.

By Q1/2019, the cost of PV module was less than $0.3/Wp [89].

3) Since PV modules can be installed on the roofs close to the charging stations or placed on/used as the parking lot’s roof, PV power can be accessed easily.

4) The combination of EV charging and solar power can reduce the amount of

Injecting photovoltaic (PV) power into the distribution grid can effectively meet charging demands while alleviating the adverse effects of charging loads and high levels of renewable energy sources (RESs) on the grid This approach not only enhances grid stability but also promotes the integration of sustainable energy solutions.

5) EV batteries can be used as energy storage devices, which could contribute to optimal exploitation of solar energy [9], [19].

6) Charging cost from PV power is cheaper than from the grid Electricity is locally generated and consumed, which can be seen as a solution for the decrease of feed-in tariffs (FIT) [55].

7) PV systems have low operation and maintenance costs.

8) PV-integrated charging stations can help reduce GHG emissions and contribute to sustainable development.

Figure 7 Charging station block diagram.

Figure 7 illustrates a block diagram of a PV-integrated charging station, which supplies electricity from solar power or the grid to vehicle batteries When photovoltaic (PV) power exceeds charging demand, the excess electricity is fed back into the grid Conversely, if charging demand surpasses PV output, the grid compensates for the shortfall Additionally, if AC/DC converters are bidirectional, electric vehicle (EV) batteries can serve as energy storage devices, offering ancillary services.

Research goals, scope, and research questions

This work conducts research on optimal charging solutions for PV-integratedE2W charging stations The objectives, scope, and questions of the research are described belows.

1) This work aims at coordinating hundreds of E2Ws charging considering both V2G and non-V2G.

This article presents solutions focused on load leveling, valley filling, and peak shaving, which are designed to enhance grid support and mitigate the effects of electric vehicle (EV) charging on the distribution grid and other electrical loads.

3) Uncertainties such as charging behaviour should be considered

4) The proposed solutions in this work should consider the high number of vehicles in the E2W charging stations.

This article examines the charging and discharging processes of lithium-ion (Li-ion) batteries, which are commonly used in electric two-wheelers (E2Ws) in Vietnam It emphasizes the importance of aligning battery capacity and charging power with the specifications of popular E2Ws in the region.

Electric two-wheelers (E2Ws) often come with portable single-phase chargers designed for residential use, operating at a unidirectional power level Therefore, charging these vehicles at a standard residential voltage of 220 V is essential for efficient operation.

While electric two-wheelers (E2Ws) have smaller battery capacities (0.2–5 kWh) and charging powers (0.25-4 kW) compared to electric cars, which typically have an average capacity of 50 kWh and charging powers ranging from 7.2-19.2 kW for AC and 50-400 kW for DC, the cumulative effect of charging multiple E2Ws can create a significant demand on the distribution grid This aggregated power load can influence other electrical demands and overall system efficiency, making it essential to consider charging strategies in these scenarios.

The charging station utilizes a rooftop photovoltaic (PV) system in conjunction with the grid for its power supply This study does not explore topics like island mode operation or the optimization of power dispatch, nor does it cover the optimal sizing of the PV system.

5) In this research, it is assumed that forecasted data of PV generation and conventional load are available and sufficiently accurate.

With the mentioned objectives and scope, the following questions may need further clarification:

1) The integration of charging loads, RESs, conventional loads, and the grid introduces microgrids Thus, it is necessary to develop smart and flexible solutions for these systems.

2) The charging algorithm needs to meet the charging requirements while reducing impacts on the distribution grid and conventional loads.

3) The algorithm should also be tailored to facilitate ancillary services or grid support.

Proposed solutions must recognize that electric two-wheelers (E2Ws) often outnumber electric vehicles (EVs) at charging stations, highlighting the need for tailored infrastructure to accommodate this disparity.

5) Uncertainties such as charging behaviors should be taken into account.Furthermore, the proposed algorithms should be verified through diverse case studies and empirical test.

Research methodology

The disertation is carried out based on the following research methods:

1) Synthesis Research: This method involves in gathering and synthesizing information in literature, and implementing data collection related to technical specifications.

Mathematical modeling and simulations are essential tools for analyzing different operational scenarios of photovoltaic (PV)-integrated electric vehicle (E2W) charging stations These simulations offer valuable insights into system performance and the effectiveness of scheduling algorithms.

3) Empirical Testing: This work plans to perform practical tests and gather empirical measurement This helps in research validation practically.

4) Expert Consultation: Consultation from experts in the field can provide valuable insights for the research findings.

Research contributions and outline of the thesis

In this work, a research on the scheduling algorithms for E2W charging stations is conducted The main contributions of this work can be expressed in the following points:

This article presents effective strategies to address increasing load demands and promotes the growth of rooftop photovoltaic (PV) systems in urban settings It aims to alleviate the negative effects of electric vehicles (EVs) and solar energy on the distribution grid, ultimately minimizing the necessity for grid upgrades or reinforcements.

2) In this work, two scheduling algorithms are proposed The effectiveness of the algorithms in terms of improving load profile, filling the valleys, and shedding peak loads are verified.

3) An empirical test bench has been successfully set up for evaluating the real- time responses of E2W charging station following long-term charging plan from the scheduler.

The dissertation is structured into five chapters, beginning with an introduction that outlines the research's motivation, objectives, scope, and contributions Chapter 1 provides a comprehensive review of the architectures and control algorithms utilized in electric vehicle (EV) charging stations In Chapter 2, both mathematical and simulation models for the charging station are developed Chapter 3 introduces a power allocation algorithm focused on load leveling, while Chapter 4 presents and validates an optimal algorithm based on a receding horizon framework.

4 Finally, a test bench is established in Chapter 5 to verify real-time responses of E2W charging to scheduled commands.

This section is mainly based on:

[1] Huu D.N and Ngoc V.N (2021) Analysis Study of Current Transportation Status in Vietnam’s Urban Traffic and the Transition to Electric Two-Wheelers Mobility Sustainability, 13(10), 5577.

[2] Ngoc Van Nguyen and Huu Duc Nguyen (2022) PV-Integrated Electric Two- wheeler Charging Stations: A Solution towards Green Cities TNU Journal of

OVERVIEW OF ELECTRIC VEHICLE CHARGING

Charging station architectures

Generally, charging a group of EVs can be implemented by centralized, decentralized, or hierarchical architectures (Figure 1.1).

An EV aggregator at a charging station manages the charging and discharging processes of electric vehicles (EVs) Direct aggregators formulate specific charging strategies for each EV, while indirect aggregators disseminate information signals to coordinate charging among the vehicles.

Indirect Aggregator Indirect control signal a) Centralized b) Decentralized – Type 1 c) Decentralized – Type 2

Indirect control Local signal controller d) Hierarchical e) Hierarchical

Indirect control Local signal controller f) Hierarchical g) Hierarchical

In centralized architecture, an aggregator directly manages the EV charging schedule by gathering the charging needs and specifications of electric vehicles (EVs) This aggregator then solves an optimization problem to establish the optimal charging profile, which is subsequently communicated to the vehicle owners.

This architecture necessitates that vehicle owners relinquish some control over their vehicle charging, enabling optimal solutions through comprehensive system information It facilitates the consideration of various constraints affecting electric vehicles (EVs) and the grid However, it raises privacy concerns regarding data such as charging times, behavior, and travel distances Furthermore, the system's reliability is jeopardized if a failure occurs at the aggregator.

In a centralized architecture, scalability poses a major challenge, particularly as the number of electric vehicles (EVs) grows or when scheduling is divided into smaller time slots This increase in operational complexity results in a heightened demand for computational capabilities.

In decentralized architecture, electric vehicles (EVs) utilize local controllers to establish their charging schedules, providing scalability and practical implementation advantages Although decentralized solutions may not always yield optimal strategies, they demonstrate greater resilience against system failures compared to centralized models, particularly when controllers are designed to function during network disruptions Furthermore, the decentralized architecture can be classified into two types based on the structure of the communication network.

In a decentralized architecture, Type 1, electric vehicles (EVs) independently determine their charging schedules and engage in communication with other EVs to achieve a global equilibrium This method necessitates continuous sharing of charging plans among vehicles, leading to significant communication demands, particularly in environments with a large number of EVs.

In a decentralized architecture of Type 2, an indirect aggregator is utilized to gather specific information and disseminate it to electric vehicles (EVs) This method alleviates the communication load, leading to a substantial reduction in the infrastructure requirements compared to the Type 1 architecture.

In a decentralized architecture, local controllers manage scheduling by creating charging profiles based on data from indirect aggregators or other electric vehicles (EVs) When these controllers receive consistent information and share a common goal, they generate a unified charging schedule for the EVs For example, if the controllers aim to minimize charging costs according to time-of-use (TOU) electricity rates, all EVs may charge during off-peak hours and halt charging when prices are high, thereby influencing the overall optimization objectives.

The hierarchical approach effectively merges the advantages of centralized and decentralized architectures by utilizing a tree-like structure that designates control and computation tasks to both direct and indirect aggregators Each aggregator oversees a group of electric vehicles (EVs), influencing the decisions of other aggregators within shared environments such as parking lots, apartment complexes, or transit hubs A direct central aggregator is tasked with formulating an optimal charging plan for all sub-aggregators, who then create specific charging profiles for the EVs under their management Conversely, an indirect central aggregator disseminates information to sub-aggregators, enabling them to establish charging schedules for each EV This system allows for efficient communication and task distribution, ensuring optimal charging strategies across various EV groups.

In the revised communication network depicted in Figure 1.1f-g, the central aggregator is removed, allowing sub-aggregators to connect directly This design enhances system resilience by ensuring that if one connection fails, alternative links maintain network integrity However, it is important to note that if an aggregator experiences a failure, the electric vehicles (EVs) under its management will lose control.

1.1.4 Proposal of E2W charging station architecture

In a decentralized architecture, the coordination of electric vehicle (EV) charging and discharging relies on signal broadcasting, enabling vehicles to actively manage their charging plans An EV aggregator broadcasts price signals that influence the charging behavior of vehicles, ultimately helping to achieve broader system objectives However, this architecture requires continuous updates and new signal broadcasts, as simultaneous charging during low-price periods can create new peak loads and impact the aggregator's goals.

A decentralized architecture for electric car charging stations can influence charging behavior due to pricing; however, this model may not be suitable for electric two-wheelers (E2Ws) as their lower energy and power consumption diminishes the effectiveness of price signals In scenarios involving numerous E2W chargers in a parking lot, a centralized controller is essential for efficient management Vehicle owners should only need to communicate basic charging requirements and departure times to the controller or select from options suggested by an aggregator.

Centralized architecture faces significant scalability challenges, particularly as the number of vehicles rises This increase in vehicles leads to a heightened demand for computing power, making the resolution of optimization problems increasingly complex and time-consuming.

The centralized approach can be impractical for large-scale, real-time applications, necessitating algorithms to focus on minimizing computational complexity and time.

A hierarchical architecture can enhance scalability in electric vehicle (EV) charging systems Each Electric-to-Wire (E2W) group should be overseen by a centralized controller that devises charging plans for the vehicles under its management These sub-controllers can either operate as a network or be governed by a central controller, allowing for the efficient management of charging stations servicing thousands of vehicles without overwhelming the central system However, implementing a hierarchical architecture necessitates higher initial investments and effective strategies for managing and coordinating the sub-controllers.

EV charging station control algorithms

Research on algorithms for electric two-wheeler (E2W) charging stations is currently limited, with most studies concentrating on electric car charging algorithms However, the control algorithms developed for electric car charging stations can be adapted and applied to E2W charging stations, offering valuable insights and potential advancements in this area.

Many studies view the electric vehicle (EV) charging challenge as a constrained optimization problem, where charging rates and durations are treated as key decision variables This optimization problem encompasses a range of constraints set by grid operators, aggregators, vehicle specifications, and vehicle owners.

Realizing an optimal algorithm is challenging, especially for non-convex OPs. However, certain OPs were solved using diverse techniques as in [122], [128],

Charging station control issues can be categorized into technical and economic aspects, with perspectives varying among grid operators, EV aggregators, and vehicle owners Notably, advancements in technical performance can enhance economic metrics, highlighting the interdependence of these two dimensions.

Table 1.1 Classification of charging station problems

- Maximizing convenience for vehicle owners

1 Minimizing cost of electricity generation

3 Maximizing revenue of EV aggregator

1.2.1 Algorithms focus on technical aspects

Control algorithms focusing on technical goals have been widely discussed in research The technical objectives are follows:

Load regulation algorithms have been extensively studied to flatten aggregated load profiles, which encompass both conventional and charging loads This flattening process mitigates peak loads, alleviating the overloading issues faced by transformers, transmission lines, and other electrical infrastructure Additionally, a smoother load profile minimizes the necessity for abrupt adjustments in generator output, allowing generators to function at optimal efficiency and stability.

The control of EV charging to fill low load periods during nighttime was widely addressed in literature Although the impact of a single EV charging/discharging on

𝑡 the distribution grid is relatively minor, the aggregated impact of a high number of

When considering load regulation, the constraints include energy requirement, charging/discharging power, overloading constraints, and voltage constraints at the point of common coupling (PCC).

The most intuitive approach for load leveling (or valley filling) is to minimize the total load variance with the objective function as in (1.1) [108]. min ∑𝑀 (𝐷 𝑆 +

Where: 𝐷 𝑆 is non-EV load

𝑃 𝑖 denotes the charging power of 𝐸𝑉 𝑖 at timeslot 𝑡.

𝑀 is the total number of timeslots of Δ 𝑇 during the scheduling time.

Where 𝐼𝑆𝑂𝐶 𝐸𝑉 , 𝐹𝑆𝑂𝐶 𝐸𝑉 , 𝐴 𝑖 , 𝑃 𝑖 , 𝑃 𝑖 , 𝜂 𝑖 are the State of Charge (SOC) at

𝑖 𝑖 𝑚𝑖𝑛 𝑚𝑎𝑥 arrival time, the SOC at departure time, battery capacity, minimum charging power, maximum charging power, and charging efficiency of 𝐸𝑉 𝑖 , respectively.

Diverse approaches to load regulation exist, with one study successfully minimizing total load variation to flatten the load curve and fill in valleys, while another study influenced vehicle owners' charging behavior through strategic electricity pricing.

The aggregator can transmit control signals, including fluctuating electricity price signals based on overall load demand Each electric vehicle (EV) aims to reduce charging costs by scheduling their charging during low-price electricity periods, effectively utilizing low-load times With each iteration, EVs adjust their charging profiles accordingly Additionally, a study highlighted the significance of discrete charging rates for EVs Game theory emerges as a valuable approach for optimizing individual EV charging behaviors, facilitating better coordination of charging processes.

[117], a non-coordinative game was established to coordinate a high number of

EVs that were weakly linked through a common electricity price Notably, the low-load valley was filled at the

Nash equilibrium (NE), where no EV benefited if it unilaterally deviated from the selected charging strategy [93].

An online algorithm for regulating electric vehicle (EV) load was developed based on decentralized control architecture This algorithm broadcasts a reference signal derived from the total real-time load, allowing each EV to determine its charging status by comparing its state of charge (SOC) with the received reference This innovative solution operates without relying on forecasts, making it immune to forecasting errors.

The valley filling and peak shaving algorithm, developed through dynamic programming and game theory, optimizes the charging schedule for electric vehicles (EVs) This process utilizes a forward induction dynamic programming algorithm to determine the most efficient charging times for each EV.

In [81], a non-iterative approach was proposed for sequential scheduling of each

The algorithm designed for electric vehicle (EV) charging seeks to reduce both the variance and peak load on the system It establishes the charging profile for each EV only upon connection However, a significant limitation arises when multiple EVs connect at the same time, resulting in increased waiting times.

In a recent study, researchers implemented a sequential scheduling method to create a decentralized system that effectively reduces the mean squared error between real-time aggregated loads and a reference point This reference point is estimated offline using data from both electric vehicle (EV) and non-EV loads.

Various load regulation algorithms effectively address dual objectives by incorporating essential constraints, including transformer overload limits, grid power supply capabilities, and voltage regulations at the Point of Common Coupling (PCC).

Recent studies have explored various methods to manage transformer load levels and ensure efficient energy distribution for electric vehicles (EVs) One approach integrated transformer load levels into pricing signals, while another utilized an ant-based swarm algorithm to alert EVs when total load exceeded transformer capacity Additionally, the bisection method was employed to identify a load level that satisfied EV energy demands, effectively addressing transformer overload by proportionally reducing the energy requirements of EVs.

Improving operational efficiency is crucial, with the goal of maximizing the use of renewable energy sources (RESs) at charging stations This involves minimizing grid electricity consumption and effectively coordinating charging and discharging processes in alignment with the day-ahead energy plan.

Research [131] presented a decentralized algorithm and proposed a token-based

The IT infrastructure optimizes energy services by utilizing generation and consumption tokens, aiming to maximize average generation utilization It ensures that the actual power consumption of electric vehicles (EVs) remains below the total power allocated for charging, promoting efficient energy management.

In a study utilizing game theory, the balance of real-time electricity generation planning was explored, focusing on electric vehicle (EV) owners who initially participated in a non-cooperative game to forecast their day-ahead electricity demand, thereby minimizing costs This information enabled the aggregator to formulate the power generation or purchasing strategy for the following day Subsequently, vehicle owners engaged in a real-time game to modify their charging behaviors in accordance with the anticipated demand.

Summary

Chapter I conducts research on the architectures and control algorithms for EV charging stations, then proposes appropriate architecture for E2W charging stations in the context of Vietnam.

Each architectural design presents unique benefits and challenges Centralized systems excel in global optimization but face limitations in scalability, increased vulnerability to failures, and privacy issues Conversely, decentralized architectures offer enhanced scalability and minimize control loss during failures, yet struggle with achieving global optimization and require continuous signal updates to maintain equilibrium.

E2W charging presents unique challenges due to its lower energy demands compared to electric cars, making decentralized price signal broadcasting less effective in influencing charging behavior A more efficient solution involves utilizing a central controller to coordinate the charging of E2Ws To manage the potential computational load from hundreds or thousands of vehicles, it is advisable to segment the charging station into multiple sub-stations, each overseen by its own sub-controller.

Current research on algorithms for electric two-wheeler (E2W) charging stations is limited, but existing algorithms for electric car charging can provide useful insights It is essential for E2W charging algorithms to account for specific characteristics, including the high volume of vehicles, modest battery capacities, and lower charging power compared to electric cars.

This chapter is mainly based on:

Ngoc Van Nguyen, Khac Nhan Dam, and Huu Duc Nguyen (2022) conducted a comprehensive study on the architectures and control algorithms specific to electric vehicle and electric two-wheeler charging stations in Vietnam Their research, published in a prominent journal, addresses the growing need for efficient charging solutions in the context of the country's expanding electric vehicle market.

DC ACCB12 SOLAR INVERTER WITH MPPT

AC DC EV 01 SOC/SOH ev01 CB23

MODELING OF PV-INTEGRATED ELECTRIC-

Chapter objectives

In this chapter, a PV-integrated E2W charging station is modeled to simulate and assess the operation of the stations as well as components such as E2W batteries,

PV system, power converters The models can also be utilized to deploy control algorithms Thus, this chapter focuses on:

- Modeling power electronic converters such as grid-tie solar inverter, boost/buck converters, battery chargers/dischargers.

The following features should be included:

- Maximum Power Point Tracking (MPPT)

Both real-time and long-term models are considered in this chapter, allowing to investigate real-time responses and integrate long-term scheduling algorithms.

Charging station block diagram

Figure 2.1 depicts an EV charging station connected to the grid, a PV system, and conventional loads.

Figure 2.1 Charging station block diagram.

The station operates using a photovoltaic (PV) system and the distribution grid, where electricity generated from PV panels is converted to AC power via a solar inverter and supplied to the AC bus This bus connects various components, including conventional loads and electric two-wheelers (E2Ws) Importantly, if the vehicle-to-grid (V2G) feature is enabled, E2Ws can both absorb and discharge energy to the AC bus, necessitating the use of bi-directional chargers.

Realtime model

This model is suitable for investigating the charging/discharging response of E2Ws in real-time The model consists of components as follows:

2.3.1 PV module and PV array

To model PV modules, either a one- diode model or a two-diode model can be exploited [72] Figure 2.2 illustrates the equivalent circuit diagram for the one-diode model.

Figure 2.2 The one-diode model and

Thevenin equivalent circuit One-diode model is established based on the following equations [72]:

Output current of the module:

𝑘 𝑖 represents short-circuit current of the cell at 25°C and 1000 W/m 2

𝑇 𝑛 is the nominal temperature which is 298 (K)

𝑞 is the charge of one electron which is approximately 1.6×10 -19 coulombs (C)

𝑛 is the ideality factor of the diode.

𝐾 is the Boltzmann constant which is 1.38×10 -23 (J/K)

𝐸 𝑔0 is the bandgap energy of the semiconductor material (eV)

𝑁 𝑠 is the number of cells connected in series.

𝑁 𝑃 is the number of PV modules connected in parallel.

𝑉 𝑡 is the diode thermal voltage (V)

For example, a PV system can be modeled based on PV panel specifications as in Table 2.1.

Voltage at maximum power point 𝑉 𝑚𝑝 (V) 35.6

Current at maximum power point 𝐼 𝑚𝑝 (A) 4.65

Based on the one-diode model, PV module can be built on MATLAB/Simulink as shown in Figure 2.3.

A 150 kWp photovoltaic (PV) system can utilize 900 solar panels organized into 36 parallel strings, with each string comprising 25 panels connected in series.

To fully harness solar energy, Maximum Power Point (MPP) tracking algorithms are essential These algorithms enable solar panels to consistently operate at peak power output, adapting to fluctuations in irradiance and temperature.

Battery modeling is essential in electric vehicle (EV) research, enabling designers to enhance storage systems and forecast battery performance These models are integral to the Battery Management System (BMS), where they help estimate real-time state of charge (SOC) and overall battery functionality Given that most EVs rely on high-energy-density lithium-ion batteries, it is crucial to develop precise models for this battery type.

Battery modeling encompasses three main types: electrochemical models, artificial neural network models, and equivalent circuit models Electrochemical models focus on the chemical reactions occurring within batteries and use various parameters to simulate polarization However, establishing these models can be challenging due to the influence of environmental conditions on electrochemical processes, which also restricts their application in real-world scenarios.

Artificial neural network models leverage the non-linear and self-learning capabilities of neural networks, integrating experimental data to identify relationships between various battery parameters However, a significant limitation is that these neural networks necessitate extensive experimental data to accurately predict battery performance.

Equivalent circuit models simulate battery dynamics using components like resistors, capacitors, and voltage sources Notable models include R int, RC, PNGV, and Thevenin The RC model focuses on battery polarization through capacitance, omitting resistance The PNGV model addresses intricate charge/discharge relationships but is complex to simulate In contrast, the Thevenin model incorporates both capacitance and resistance, offering a simpler implementation.

Various mathematical representations of batteries are detailed in the battery models referenced in [84] Equivalent circuit models, characterized by their simplicity and minimal parameters, facilitate the derivation of state-space equations [65] Consequently, these models are extensively utilized in both simulation and real-time control systems Experimental evidence supports the effectiveness of the equivalent circuit model for LiFePo and LiMnCo batteries [26].

Figure 2.4 shows the equivalent circuit model of a battery.

Figure 2.4 The equivalent circuit model of a battery

The open-circuit voltage (VOCV) and internal resistance (𝑅𝑖𝑛𝑡) of a battery fluctuate with the state of charge (SOC) and can be sourced from the manufacturer's specifications Additionally, the output voltage of the battery is influenced by specific factors outlined in relevant studies.

Based on equations 2.6-2.10, the open-circuit voltage and internal resistance of the battery (consisting of multiple cells) can be determined.

In Figure 2.5, the open-circuit voltage, discharge resistance

(Rchg) are obtained from the manufacturer's data for the Saft

Lithium Ion Battery 7 Ah, based on the working temperature and

SOC The specific data for the battery can be referred to as in

2.3.3 DC-DC boost converter and maximum power point tracking (MPPT) algorithm

A boost converter is a type of power electronic converter designed to increase voltage from a lower level to a higher level It typically comprises key components such as an inductor, switch, diode, and capacitor The operational model of the boost converter is illustrated in Figure 2.6.

Figure 2.6 DC-DC boost converter block

The RefGen function is responsible for tracking the maximum power point based on the voltage and current values from PV array.

2.3.3.2 Maximum power point tracking algorithms

Maximum Power Point Tracking (MPPT) is a control algorithm designed to optimize the power output of photovoltaic (PV) systems by analyzing variations in voltage and current This article explores two widely used MPPT algorithms: Perturb and Observe (P&O) and Incremental Conductance (INC), highlighting their effectiveness in locating the maximum power point of PV arrays.

 The Perturb and Observe (P&O) algorithm:

This algorithm operates by altering the operating point of photovoltaic (PV) panels and monitoring the resulting power changes By analyzing these variations, the algorithm optimizes the operating point to enhance power output effectively.

The P&O algorithm is simple and widely used, although it may have some drawbacks, such as oscillations around the MPP and slower tracking speed in rapid environmental condition change.

The flowchart of the P&O algorithm is shown in Figure 2.7 [132].

The Incremental Conductance (INC) algorithm operates based on the following principles [59] (Figure 2.8).

- The slope of the power curve is zero at the

- The slope is positive on the left side of the

- The slope is negative on the right side of the

, at the MPP , at the left of MPP , at the right of MPP

, at the MPP , at the left of MPP , at the right of MPP

(2.13) Figure 2.9 Flowchart of INC algorithm

Figure 2.9 illustrates the flowchart of the INC algorithm The MPP can be tracked by comparing the instantaneous conductance (𝐼/𝑉) to the incremental conductance (𝑑𝐼/𝑑𝑉).

The inverter control blocks are established in Simulink as follows [83]:

Voltage transformation and current transformation blocks to alpha-beta coordinate system and d-q coordinate system are shown in Figure 2.10.

Figure 2.10 Transformation to the 𝑑𝑞 coordinate system

Position of the rotating frame (𝑤𝑡) coordinate system (Figure 2.11).

Control signal block of the PWM generation block (Figure 2.12).

Figure 2.12 Control signal block for generating PWM signals.

The PWM signal generation block and power circuit of the inverter are as in Figure 2.13.

Figure 2.13 PWM signal generation and the inverter power circuit.

To implement battery charging/discharging, a buck/boost converter can be used. The PWM control signal for the buck/boost circuit is generated from a charger controller as in Figure 2.14.

Long-term model

This model effectively schedules the charging of electric vehicles (EVs) across various time periods By breaking down a working day or shift into discrete time intervals, the model gathers essential data and performs scheduling to optimize specific objectives throughout the entire scheduling horizon.

The charging station accommodates 𝑁 electric bicycles and motorcycles, with each vehicle denoted as 𝐸𝑉 𝑖 At any given time 𝑡, 𝐶 𝑆 represents the total energy stored in the batteries, while 𝐷 𝑆 indicates the net load, calculated as the total power of non-EV loads minus the power generated by the photovoltaic (PV) system The overall load demand, including both charging and net load, is represented by 𝐸 𝑆 The operational day is segmented into timeslots of Δ 𝑇, and 𝑃 𝑆 signifies the aggregate charging power for all electric vehicles at time 𝑡 Notably, all chargers are equipped with Vehicle-to-Grid (V2G) technology, allowing for efficient energy management at the charging station.

With V2G (Vehicle-to-Grid) capability, chargers enable bidirectional energy exchange between the grid and electric vehicles (EVs) The charging behavior is influenced by the sign of 𝑃 𝑆: when 𝑃 𝑆 is greater than zero, the batteries are receiving energy, whereas when 𝑃 𝑆 is less than zero, energy is being returned to the grid.

The power constraints for charging/discharging can be described as follows:

Where, 𝑃 𝐶𝑖 , 𝑃 𝐶𝑖 are the maximum allowable charging/discharging power for

𝑃 𝐵𝑖 , 𝑃 𝐵𝑖 are the maximum allowable charging/discharging power for the battery

The total charging/discharging power of all EVs in the station must not exceed the supply capacity of the grid, which is expressed by the constraints:

The maximum power (𝑃 𝑒𝑥) that a microgrid can exchange with electric vehicles (EVs) is determined by the difference between the maximum allowable exchange power with the distribution grid (𝑃 𝑓𝑒𝑒𝑑𝑒𝑟) and the net load.

The non-EV loads are considered non-dispatchable, necessitating that the charging station modifies the overall charging load demand of electric vehicles (EVs) to comply with specific constraints.

Constraints on the energy requirements of the EVs:

Where, 𝑅 𝑆 denotes total required energy of E2Ws in the station.

𝐹𝑆𝑂𝐶 𝐸𝑉 𝑖 is the SOC of 𝐸𝑉 𝑖 at the departure time.

𝐼𝑆𝑂𝐶 𝐸𝑉 𝑖 is the initial SOC of 𝐸𝑉 𝑖

𝑡 0 is the starting time of the working day

𝑡 𝑚 is the end time of the working day

𝑃 𝑖 is charging/discharging power of 𝐸𝑉 𝑖 at time 𝑡.

Summary

In this chapter, a PV integrated E2W charging station in real-time and long-term operational mode is modeled.

The real-time model facilitates the analysis of charging station operations, while the long-term model serves as a framework for implementing optimal charging scheduling algorithms in subsequent chapters.

CHARGING POWER ALLOCATION ALGORITHM FOR

Chapter objectives

This chapter introduces a smart charging strategy for electric two-wheelers (E2W) at charging stations that integrates a photovoltaic (PV) system, the distribution grid, and conventional loads The proposed algorithm enhances the aggregated load profile, supports the grid, and addresses the challenges posed by high levels of rooftop PV and electric vehicle (EV) loads It generates optimized charging profiles for the scheduling period, showcasing its effectiveness in improving load profiles and minimizing computational demands through various case studies and scenarios.

E2W charging stations cater to numerous electric vehicles (EVs) with limited battery capacities and charging power To optimize efficiency, a grouping strategy is recommended, where E2Ws are categorized according to their energy requirements throughout the scheduling period The charging process is organized into three distinct stages.

- Stage 1: Finding a common profile for the entire charging station This profile is determined based on the objective of improving the total load curve.

- Stage 2: From the profile of the station, allocate the power at each timeslot to the groups to determine the group charging profiles.

- Stage 3: Based on the group profiles, determine individual charging profile considering relevant constraints.

The grouping approach, combined with a three-stage power allocation process, streamlines the solution of the constrained OP by eliminating the need to repeatedly solve for each E2W charging profile This significantly decreases the computational demands associated with scheduling hundreds of E2Ws.

Input data requirements

To evaluate the effectiveness of a charging scheduling algorithm, it is essential to supply specific input data, which encompasses vehicle technical specifications, charging behaviors, and predictions for load and photovoltaic (PV) power output.

3.2.1 Electric bike and electric motorcycle specifications

According to the European Commission (EC), 2-wheel, 3-wheel vehicles and quadricycles are classified into L-category (Table 3.1).

It could be realized that an e-bike can travel up to 25 km/h using a motor of up to

Electric bikes typically feature motors ranging from 250 W to 1000 W for high-speed models, utilizing 12–48 V batteries with energy capacities between 0.2 and 1 kWh In contrast, electric mopeds can reach speeds of up to 45 km/h, powered by motors between 1 and 4 kW, and generally operate on 48 V batteries with capacities of 1 to 5 kWh.

E-bikes are highly energy-efficient, consuming only 5–15 Wh/km based on factors like drivetrain efficiency, riding style, tire features, and the total weight of the bike and rider In comparison, electric cars consume significantly more energy, ranging from 150–200 Wh/km.

Table 3.1 The family of L-category vehicles [3]

In Vietnam, e-bikes typically feature 48 V-12 Ah batteries and motors with a power output below 500 W, allowing for a maximum travel distance of 50-60 km In contrast, electric mopeds are equipped with larger 60 V-20 Ah batteries and more powerful motors ranging from 800 to 1200 W.

W) and have 70-80 km travel range (Table 3.2).

Maximum travel distance (km) e-bikes

The charging scheduling problem for electric vehicles (EVs) faces two significant challenges: the unpredictability of EV charging behaviors and the lengthy computation time required to determine optimal solutions for large-scale EV scheduling.

Numerous studies, both theoretical and statistical, have explored charging behavior, which is defined by various factors including arrival time (connection time), departure time (disconnection time), charging duration, average distance traveled since the last charge, initial state of charge (SOC), and final SOC.

The charging behavior of private vehicles is influenced by the starting time and duration of charging Daily driving end times align with a normal (Gaussian) distribution, while daily driving distances adhere to a log-normal distribution, consistent with previous studies Specifically, the probability density functions for first drive times and last return times exhibit Gaussian distributions with parameters (μ; σ) of (7.5; 3.24) and (17.5; 3.41), respectively Additionally, the distribution of daily driving mileage follows a log-normal pattern, characterized by parameters (μ; σ) of (3.37; 0.5).

A study utilizing driving pattern data from the National Household Travel Survey (NHTS) revealed that the arrival and departure times to and from the office or home follow a normal distribution.

[15], [26], [41], [102], [147] also stated that home arrival/departure times and the daily trip distances turned out to be quite similar to a normal distribution.

In both residential and workplace settings, authors in [16] suggested that the arrival and departure times of electric vehicles (EVs) in areas with a high concentration can be effectively modeled using appropriate probability density functions (PDFs), such as the normal distribution The parameters of these normal distributions can be derived from the historical data available for the region under study.

In some other works, charging behavior is proposed to follow different distribution forms such as the gamma function, Birnbaum–Saunders distribution [9], extreme- value distribution [100] etc.

Most studies on electric vehicle (EV) charging behavior typically model arrival and departure times using probability distributions, often normal or log-normal In this research, the arrival and departure time distributions for electric two-wheelers (E2W) are assumed to follow a normal distribution.

Where 𝑓 is the probability of arrival/departure time, and à and σ) being are mean value and standard deviation, respectively.

A study on state of charge (SOC) behavior revealed that over 90% of electric vehicle (EV) charging sessions involved fully recharging the batteries, regardless of their initial SOC levels EV owners prioritize achieving a full charge promptly to enhance vehicle autonomy The analysis indicated that 87.5% of sampled EVs were fully recharged, while only 2.4% of charges ended prematurely with a SOC below 50% Additionally, around 10.1% of charging sessions resulted in a SOC between 50% and 90%, often due to the need for the vehicle to depart before reaching a full charge.

A study found that 75% of electric vehicle (EV) owners prefer home charging, typically charging their vehicles overnight after arriving home, often during peak evening load times EVs generally remain idle for longer periods at night than needed for charging, and starting the charging process earlier allows it to conclude sooner, maximizing efficiency.

"valley filling" during the late-night and early-morning periods [144].

A study indicated that electric vehicle (EV) owners typically arrive home around 6 PM and depart for work by 7:30 AM on weekdays, reflecting common commuting patterns Notably, the probability densities of home arrival and departure times vary significantly between weekdays and weekends; weekdays show sharp peaks, while weekends exhibit more flattened curves.

Figure 3.1 Arrival/departure distribution function - trip home [63]

Figure 3.2 Probability densities of home arrival and departure times [27]

The probability density functions for the first drive time and last return time of electric vehicle (EV) users exhibit a normal distribution, with mean and standard deviation values of (7.5; 3.24) and (17.5; 3.41) respectively Observations indicate that most EV users initiate charging after arriving home from work around 18:00, with over 90% starting to charge between 13:00 and 23:00, leading to mean values of (18; 5) Additionally, studies report means and standard deviations for home arrival time, departure time, and daily trip distance as (19.55; 1.67), (7.47; 0.383), and (39.5; 15.8) Slight variations were noted in another study, showing values of (19.89; 1.92) for connection time and (7.56; 2.33) for disconnection time.

Home-charging behavior typically follows a normal distribution, suggesting that electric vehicles (EVs) are generally parked at home until their departure the next morning, with evening trips being an exception However, this assumption does not impact the effectiveness of the scheduling solution, as real-time updates on EV mobilities can be incorporated seamlessly.

Charging power allocation algorithm for E2Ws

Charging loads from electric vehicles (EVs) are often deferrable due to their longer parking durations compared to the required charging time This flexibility allows for optimal dispatching of charging loads, which can enhance both the economic and technical advantages for charging station owners and distribution operators.

This study introduces a charging power allocation algorithm designed to enhance the overall load profile for electric two-wheelers (E2Ws) The algorithm effectively meets the energy requirements of E2Ws while adhering to charging and discharging constraints, ultimately aligning the total load profile with a specified target.

This method is based on the "water filling" concept, which allocates power for charging at each time step The allocatable power is determined by the expected energy needs throughout the scheduling period, relying on accurate forecasts of conventional load, renewable energy generation, and vehicle data.

The "water filling" method for power allocation in electric vehicles has been explored in previous studies, focusing on minimizing load variance by treating charging demand like water filling a valley In contrast to these studies, this research introduces a three-stage power allocation process to establish the charging profile for each electric two-wheeler (E2W) at the charging station.

The proposed algorithm consists of three stages, which are described as follows:

Stage 1 focuses on establishing the complete charging profile of the station by calculating the difference between the desired total load profile—which encompasses both netload and charging load—and the netload itself.

Stage 2 of the charging process distributes power to electric two-wheelers (E2W) based on the overall charging profile of the station This allocation is determined by grouping vehicles according to their energy requirements, ensuring that all vehicles within the same group share similar charging power profiles.

 Stage 3: After obtaining group profiles, stage 3 allocates the group charging power at each timeslot to the individual E2W.

All three stages must satisfy constraints relating to the energy requirements, charging/discharging power limits, depth of discharge (DOD) etc.

When E2W groups are classified and the energy requirements of the vehicles are similar, power allocation may conclude at stage 2, allowing the group profile to be utilized for all vehicles within that group However, using this group profile could lead to minor inaccuracies in the charging needs of the E2Ws, potentially impacting the satisfaction of vehicle owners.

3.3.1 Mathematical formulation of the algorithm

Inherited from the long-term model in Chapter 2, in this section, a charging power allocation algorithm is developed with the aim of improving the total load profile.

When a charging station is linked to the electrical grid, conventional building loads, and a rooftop photovoltaic (PV) system, and all chargers are equipped with Vehicle-to-Grid (V2G) capabilities, the average power consumption throughout a typical workday can be estimated using a specific equation.

The variance of the total load (including netload and charging loads) can be calculated by:

Thus, for the purpose of load leveling, an ideal charging reference vector or preliminary total charging power at timeslots could be defined as:

In order to refine the preliminary total charging power at each timeslot The refinement process must consider the constraints for the aggregated charging load as in equations (2.16) - (2.19).

To ensure that the total charging power of electric two-wheelers (E2Ws) at the station aligns with the ideal charging reference vector, it is crucial to meet the conditions outlined in equation (2.20) Consequently, the refinement process involves adjusting the initial total charging power across various timeslots to comply with the constraints specified in equations (2.16) to (2.19).

After the reallocation, the final charging reference vector is obtained as in (3.5):

In the next stage, the charging reference vector is allocated to groups of E2Ws.

Assuming that 𝑁 vehicles in the charging station are divided into 𝐾 groups The vector of EV group number is:

𝒏 = [𝑁 1 𝑁 2 ⋯ 𝑁 𝐾 ]𝑇, ∑𝐾 𝑁 𝑗 = 𝑁(3.6) The charging power matrix of the groups which is called charging reference matrix is defined as:

The allocation of the charging reference vector to E2W groups should consider constraint on the equality between aggregated group charging power and total charging power of the station as in (3.8).

Each column vector in the charging reference matrix corresponds to the charging profile of one group For the 𝑗 𝑡ℎ group, the constraint on the amount of required energy is:

Constraint on group charging/discharging power:

Maximum group battery capacity constraint:

The stage 2 algorithm utilizes the charging reference vector to establish the charging reference matrix while adhering to specific constraints This matrix is subsequently used to define the individual charging patterns.

The selection of vehicles within a group is determined by the necessary charging energy, with the assumption that all electric two-wheelers (E2Ws) are fully charged by the end of the scheduling period Consequently, the vehicles in the group will have approximately the same initial state of charge (SOC) To streamline the process, the algorithm in stage 3 calculates the charging power for each vehicle based on the group's charging pattern.

After preliminarily assigning as in (3.12), a correction process should be performed to meet the following constraints:

At all time-steps, the aggregated charging power of vehicles in the group is approximately equal to the total charging power of the group:

[ 𝑀 ] [𝑃 𝑖,𝑗 ] Constraint on the amount of required energy for each vehicle:

Individual charging and discharging rate constraint:

Permitted depth of discharge constraint:

The stage 1 algorithm, as outlined in Section 3.3.1, comprises two key functions Function 1 is designed to determine the charging reference vector by utilizing data on vehicles present at the station, projected conventional load data, and anticipated photovoltaic (PV) power output.

Function 2 is a constraint checking function These functions are described as follows:

Stage 1 – Total charging pattern finding.

Function 1 - Charging reference vector finding This function includes the following steps:

Acquisition of E2W parameters, non-EV load profile, PV power output It is assumed that the forecast is accurate enough.

Calculation of netload (as in 2.15), total charging energy demand (2.20), average total load demand (3.2) and ideal charging reference vector (3.4) or preliminary total charging power at each time-step.

To ensure compliance with power constraints (2.16), (2.17), (2.18), and (2.19) during each timeslot, check for any violations If violations are found, implement necessary corrections and redistribute the charging power from unsatisfactory periods to others, ensuring that no period exceeds the power constraints This process results in an optimized total charging power pattern, which is then assigned to the charging reference vector.

Function 2 - Total battery capacity checking This function includes the following steps:

The initial step involves calculating the total energy stored in the batteries and assessing any maximum battery capacity violations during each timeslot If a violation is detected at any point, the charging energy for that unsatisfactory period is redistributed to other periods.

The reallocation process should consider charging power limits.

After the correction and reallocation, recalculate the total charging power pattern which is utilized as charging reference vector for stage 2 algorithm.

Stage 2 – Group charging pattern finding.

In stage 2, the algorithm allocates the total charging power to electric vehicle (EV) groups during each timeslot, utilizing the charging reference vector established in stage 1 (Figure 3.6).

Function 3 - Preliminary group charging pattern finding.

Summary

In Chapter 3, we introduce a novel three-stage power allocation algorithm that simplifies the process of power distribution for electric two-wheelers (E2Ws) Unlike centralized methods that rely on complex multivariable optimization, our approach focuses on the desired total load profile to efficiently implement power allocation The algorithm involves identifying preliminary charging profiles, performing constraint checks, and reallocating power until all constraints are satisfied This method significantly reduces both computational complexity and time, particularly in scenarios where the number of E2Ws at a charging station exceeds that of electric car charging stations, resulting in a greater number of optimal variables to manage.

The total load profile is crafted to achieve load leveling, peak clipping, and valley filling This profile can be pre-computed for diverse objectives, allowing for complete adaptability to various optimization goals.

The simulation results across various scenarios indicate that implementing the proposed algorithm leads to a substantial enhancement in the total load profile This improvement notably reduces load fluctuations, effectively fills in the valley load, and decreases peak load levels.

This chapter is mainly based on:

[1] Huu D.N and Ngoc V.N (2021) A Two-Level Desired Load Profile Tracking Algorithm for Electric Two-Wheeler Charging Stations Eng Technol Appl Sci

[2] Huu D.N and Ngoc V.N (2022) A Three-Stage of Charging Power Allocation for Electric Two-Wheeler Charging Stations IEEE Access, 10, 61080–61093.

CHAPTER IV: OPTIMAL CHARGING ALGORITHM BASED ON

Chapter objectives

While 2 or 3-stage power allocation algorithms effectively tackle the charging scheduling challenges for a large number of electric vehicles (EVs), particularly at E2W charging stations, they do exhibit certain limitations.

Firstly, the charging scheduling problem is assumed to follow the average total load profile over the scheduling horizon Mathematically, this solution cannot be claimed as optimal.

The algorithm is validated using deterministic vehicle data at the charging station, with fixed parameters like arrival and departure times However, since charging behavior is inherently unpredictable, it is crucial to address its dynamic impacts Specifically, the charging schedule must be adaptively adjusted when new vehicles arrive or existing vehicles depart.

The algorithm's key advantages lie in its effective grouping and power allocation methods, which significantly reduce computational demands compared to traditional optimization for each vehicle This section introduces a real-time scheduling algorithm that dynamically groups vehicles based on the current timeslot E2W data is continuously updated to reflect any changes in vehicle status, ensuring that scheduling is executed promptly with each timeslot transition.

The grouping approach effectively minimizes computational complexity in the coordinated charging of large-scale electric vehicle (EV) populations Ensuring low computational complexity is essential for achieving real-time performance in charging coordination for practical applications Additionally, the scheduling framework must be designed to accommodate realistic dynamics and uncertainties.

To effectively determine the desired state of charge (SOC) and the expected departure time for drivers, a user interface (UI) is essential for gathering this information Users should input these details each time their vehicle connects to the charging station However, the design of the UI is beyond the scope of this research Therefore, it is assumed that the scheduler has access to the required data regarding the desired SOC and expected departure times for vehicles.

To summarize, the proposed algorithm in this Chapter has the following characteristics:

1) Grouping approach: This approach allows group level optimization, reducing computational complexity Noteworthily, grouping is implemented whenever the current timeslot changes Thus, it is dynamic.

2) Reduction of computational complexity: By optimizing at group level, the algorithm contributes to releasing computational complexity, making it suitable for practical applications.

3) Real-time performance: The algorithm considers dynamic changes such as the vehicle arrival/departure time It updates the scheduling data and re-schedules.

4) Handling dynamics and uncertainties: The framework is capable of handling realistic dynamics and uncertainties associated with charging coordination.Adaptive charging plans are implemented when new vehicles connect/leave.

Mathematical formulation, control framework and algorithm flowchart

In most studies on EV scheduling, a scheduling day is typically discretized into

The study utilizes 96 equally divided timeslots, each lasting 15 minutes, which is a prevalent temporal resolution for demand side management (DSM) schemes When a vehicle arrives at time 𝜏 𝑎 and departs at time 𝜏 𝑑, it is allocated to the corresponding arrival timeslot 𝑡 𝑎 and departure timeslot 𝑡 𝑑.

To be explicit, for a particular EV, the charging process must be scheduled from the timeslot next to the arrival time 𝜏 𝑎 to the timeslot right before the departure time

To enhance the accuracy of charging scheduling, it is essential to minimize the duration of the timeslot However, excessively short timeslots can lead to increased problem complexity and a higher frequency of rescheduling.

Figure 4.1 Scheduling and implementing timeline.

For the purpose of load leveling, the optimization objective aims to minimize load fluctuation This can be interpreted as total load variance minimization [16],

[28], [33], [34], [35], [41], [56], [66], [75] Thus, the objective function would be as follows: min 𝐹 = 1

The strategy targeting the objective function (4.3) seeks to align the total load in each timeslot with the average load throughout the parking period, thereby achieving a maximally flat load profile.

The load variance minimization problem is recognized as a convex optimization challenge, offering two key benefits: it can be efficiently solved with commercial solvers for real-time dispatch, and its objectives can seamlessly integrate into other electric vehicle (EV) charging functions, such as reducing system operating costs or enhancing aggregator profits Additionally, the global optimal solution for quadratic programming (QP) problems can be effectively determined in polynomial time.

[136] Thus, the resulting OP is polynomial time-space complexity.

Solving the QP problem (4.3) gives the optimal charging profile for EVs during the scheduling horizon, usually one day, for the given netload forecast.

In this study, the optimization problems were solved using MATLAB Quadratic Programming (quadprog) with the interior-point-convex algorithm.

The standard form of a QP is:

Note that 𝑥 𝑇 denotes the transpose of 𝑥, and 𝐴𝑥 ≤ 𝑏 means that the inequality is taken elementwise over the vectors 𝐴𝑥 and 𝑏.

The quadprog function expects a problem of the above form, defined by the parameters {𝐻, 𝑓, 𝐴, 𝑏, 𝐴 𝑒𝑞 , 𝑏 𝑒𝑞 , 𝑙, 𝑢}; 𝐻 and 𝑓 are required, the others are optional.

Alternate QP formulations must be manipulated to conform to the above form; for example, if the inequality constraint was expressed as 𝐴𝑥 ≥ 𝑏, then it can be rewritten

Considering 𝐸𝑉 𝑖 , if 𝑥 𝑡 is the charging power of 𝐸𝑉 𝑖 at timeslot 𝑡, then the objective function aiming at load variance minimization over the scheduling horizon can be expressed as: min ∑𝑡𝑑

𝑡 𝑎 is the arrival timeslot of 𝐸𝑉 𝑖

𝑡 𝑑 is planned departure timeslot of 𝐸𝑉 𝑖 , and

With the assumption that the forecast data for conventional load and solar power is accurate enough, 𝜇 𝑎𝑣𝑒 takes on a definite value at each timeslot.

After determining the parameters {𝐻, 𝑓, 𝐴, 𝑏, 𝐴 𝑒𝑞 , 𝑏 𝑒𝑞 , 𝑙, 𝑢} from the equations, it is essential to select an appropriate MATLAB solver In this study, the interior point method is chosen as the preferred solver for optimization tasks.

The complexity of optimizing charging profiles for electric two-wheelers (E2Ws) at a charging station is determined by the number of timeslots and E2Ws present The proposed algorithm utilizes a group-based strategy, identifying an optimal charging profile for a representative E2W from each group and adjusting it for the other E2Ws As a result, the scale of the optimization challenge is affected by the number of groups and timeslots involved.

To tackle the challenge of real-time charging scheduling for Electric Two-Wheelers (E2Ws) amidst behavior uncertainty, we propose a receding horizon framework This approach enables the charging station to continuously update the status of E2Ws present, accounting for both existing vehicles and any new arrivals or departures during the current timeslot.

Research has demonstrated that receding horizon control (RHC) effectively adapts static optimization methods for real-time scheduling, allowing for the consideration of dynamic system changes Typically, RHC operates by optimizing over the next T time-steps, executing the decision for the initial time-step, and subsequently re-solving the optimization problem for the following steps.

𝑇 time-steps by incorporating new measurement data available at that moment(Figure 4.2) In short, RHC scheme repeats the process of optimization, execution,and adaptation.

Figure 4.2 Illustration of receding horizon time window [64]

In each iteration of the optimization process, the problem is adjusted to account for load variations For instance, the optimization is initially conducted over a 24-hour period, specifically from hour 1 to hour 24 In the subsequent iterations, the optimization window shifts, with the second iteration focusing on the period from hour 2 to hour 24, and this pattern continues throughout the entire time horizon.

There are two approaches to consider: the complete Receding Horizon Control (RHC) algorithm and the partial RHC algorithm The complete RHC algorithm schedules all Electric Two-Wheelers (E2Ws) at each iteration, while the partial RHC algorithm maintains the original schedule for existing loads and only addresses newly arrived charging requests Although the partial scheme is less optimal than the complete one, it offers the advantage of reduced computational complexity.

The receding horizon approach offers significant advantages by allowing the integration of real-time updates, such as new charging requests and unforeseen charging terminations in E2W scheduling For instance, customers can connect their electric vehicles (EVs) to the charging station at any point during the scheduling horizon, while existing processes may unexpectedly end, like a charging session interrupted by an unplanned trip.

Charger efficiency is influenced by operating power and working voltage, yet it remains relatively constant for simplicity Additionally, for the purpose of scheduling, onboard batteries are treated as ideal energy storage systems, neglecting their transient performance during charging and discharging.

To prolong battery lifespan, it is essential to establish upper and lower limits for the State of Charge (SOC) to prevent overcharging and deep discharging, both of which can damage battery integrity Research consistently indicates that the minimum SOC should be set at 20%, while the maximum SOC should fall between 80% and 100%.

This study assumes that electric two-wheelers (E2Ws) will not discharge when their state of charge (SOC) falls below 20% and can be charged up to a maximum of 100% It is also assumed that the smart charging station can identify the nominal battery capacity and the connection SOC, as well as the permitted charging and discharging power through a vehicle information system (VIS) This system is responsible for relaying essential E2W information to the station controller, allowing for effective communication with the E2W owner Consequently, an E2W can be characterized by a set of parameters as outlined in the study.

The equation EV = [EV ID, ta, td, ISOC, FSOC, A] (4.12) defines the vectors for Electric Two-Wheelers (E2Ws), which are collected and stored in a data table known as the existing EV table This table is regularly updated to reflect the arrival or departure of new vehicles during the current timeslot, ensuring that the information remains accurate and relevant.

EV table is used as the input data for the charging scheduling algorithm.

Several principles should be considered when scheduling include:

Case study and simulation results

In this section, several case studies are proposed and analyzed to assess the algorithm’s effectiveness.

An additional scenario, where the E2Ws do not allow discharging, is also included This is because battery wear and tear cost may be a crucial factor preventing

V2G acceptance of EV owners Hence, each case study includes the following scenarios:

 Scenario 1 - Charging loads do not participate in the microgrid This scenario is analyzed to evaluate netload variance in case of RESs participation.

 Scenario 2 - Max speed charging (or uncontrolled charging): E2Ws are charged at the maximum

Figure 4.3 Flowchart of the algorithm possible power as soon as they are connected and stop charging when reaching their required

 Scenario 3 - Average charging: E2Ws are charged at a constant rate At the departure time, E2Ws reach their required SOC.

 Scenario 4.1 - Smart charging: E2Ws follow the proposed algorithm

 Scenario 4.2 – Smart charging: E2Ws follow the proposed algorithm and allow bi-directional power exchange (both charging and discharging are permitted).

To assess the effectiveness of the real-time RHC-based algorithm and compare it with the power allocation algorithm discussed in Chapter 3, two case studies were conducted: the first focuses on a charging station for university staff, designed to accommodate 150-170 vehicles throughout the entire working day; the second examines a charging station for students, which can support up to 250 vehicles during two study periods within a single working day.

The investigation of case study a) follows the same assumptions outlined in Chapter 3 In contrast, case study b) is designed to accommodate two distinct study periods: the morning session from 7:00 to 11:30 and the afternoon session from 12:30 to 17:00.

Other input data such as PV power, conventional load and E2W specifications are similar to the case study in Chapter 3.

4.3.1.1 Charging station for university staffs

This case study is conducted to compare with the previously proposed power allocation algorithm However, smart charging scenario includes both V2G and non- V2G instances.

The simulation results are as follows:

Figure 4.4 Average charging pattern Figure 4.5 Max rate charging pattern a) Neload profile b) Total load profile - max rate charging c) Total load profile - average rate charging

Figure 4.6 Total load profile in scenarios 1, 2, 3

Figure 4.7 Charging profile – RHC based algorithm (scenario 4.1)

Figure 4.8 Total load profile – RHC based algorithm (scenario 4.1)

Figure 4.9 Charging profile – RHC based algorithm (scenario 4.2)

Figure 4.10 Total load profile – RHC based algorithm (scenario 4.2)

In the max speed charging scheme (scenario 2), electric two-wheelers (E2Ws) initiate high-power charging immediately upon connection, leading to a peak load at the start of working hours, coinciding with peak net load periods This results in a marked contrast between peak and off-peak loads, as illustrated in the data For instance, in January, the peak load reaches 89.1 kW around 7:00 AM, which is 13.33 times greater than the valley load of 6.69 kW observed at 12:00 PM.

Besides, since E2Ws complete charging early in the morning, the utilization of

PV power charging efficiency is relatively low, particularly during peak solar radiation months like May, June, and July During these times, the total energy demand may fall below the high output of PV systems, resulting in excess solar energy being injected into the distribution grid.

In scenario 3, evenly distributing the charging power from arrival to departure keeps the total load shape consistent with the net load, as illustrated in Figure 4.6c Nevertheless, the overall load rises since it encompasses both the conventional load and the additional charging load.

The smart charging scheme significantly enhances the total load profile, particularly evident in scenario 4.2, where electric two-wheelers (E2Ws) discharge energy during peak periods, effectively reducing overall peak load In contrast, scenario 4.1 shows that E2Ws do not contribute to peak load reduction, as they avoid charging during peak times to prevent further load increases Instead, they charge at high rates during off-peak hours that align with high solar power generation, demonstrating effective valley filling However, in scenario 4.2, E2Ws refrain from discharging energy in the final working hours to maintain the required state of charge (SOC) for departure, resulting in higher power demand during these times.

L oa d va ri an ce ( W 2 ) timeslots than in the earlier ones Peak shaving and valley filling are clearly observed in Figure 4.10.

The simulation results also indicate that compared to the three-stage power allocation algorithm, scenario 4.2 produces a total load profile that closely resembles.

Table 4.1 Load variance in different scenarios

Scenario 1 Scenario 2 Scenario 3 Scenario 4.1 Scenario 4.2

Jan 111,071,069 496,987,981 111,071,069 13,190,716 651,266 Feb 148,246,204 580,699,035 148,246,204 23,622,939 1,458,466 Mar 145,483,222 556,286,750 145,483,222 21,170,792 4,380,515 Apr 161,290,670 576,055,083 161,290,670 26,484,600 8,871,876 May 182,823,353 620,486,235 182,823,353 36,571,416 16,478,368 Jun 176,123,091 606,921,755 176,123,091 34,269,423 17,557,115 Jul 139,741,318 554,705,620 139,741,318 22,239,232 10,175,335 Aug 149,118,945 550,593,284 149,118,945 25,958,136 15,714,134 Sep 177,426,398 568,487,987 177,426,398 39,495,142 29,439,929 Oct 206,897,743 568,674,450 206,897,743 59,060,602 50,952,454 Nov 193,474,507 571,720,137 193,474,507 47,711,259 36,736,745 Dec 156,611,432 555,227,993 156,611,432 27,477,741 13,319,280

Load variance in different scenarios

- Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Scenario 1 Scenario 2 Scenario 3 Scenario 4.1 Scenario 4.2

Figure 4.11 Load variance in different scenarios

Table 4.1 and Figure 4.11 illustrate the load variance across various scenarios during the working hour period Notably, the maximum speed charging scheme results in the highest load fluctuations, whereas the average charging scheme maintains a more stable load.

RC based algorithm (Scenario 4.2) Three-stage power allocation

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Implementing the RHC-based algorithm significantly enhances the overall load profile, with the most notable improvements observed in scenario 4.2 In this scenario, the load variance is lower compared to scenario 4.1, leading to a more favorable load profile The reduction in peak load during high-demand hours is achieved by allowing electric two-wheelers (E2Ws) to discharge their energy, contributing to this improved performance.

Figure 4.12 Load variance in the two proposed algorithms

Figure 4.12 compares the load variance in scenario 4.2 with that of the three-stage power allocation algorithm, revealing no significant difference between the two However, the RC-based algorithm excels in dynamic and real-time scheduling, effectively managing fluctuations and uncertainties This makes it a more practical solution for scheduling large-scale electric two-wheeler (E2W) populations.

Appendix 6.1 depicts the charging profile and battery capacity of several E2Ws in scenarios 4.1 and 4.2 The results show that E2Ws charge at a high power during timeslots with high solar power generation The significant difference can be observed in the charging power profiles between scenario 4.1 and scenario 4.2, where E2Ws in scenario 4.2 can discharge energy to support the micro grid.

This case study is proposed to investigate the performance of the RHC based algorithm considering the dynamic factors of arrival/departure times and studying periods within the workday.

L oa d va ri an ce ( W 2 )

The station has the capacity to accommodate 225 electric two-wheelers (E2Ws) during each study period The arrival and departure times of these vehicles are assumed to follow a normal distribution, as detailed in Table 4.2.

Table 4.2 Arrival/ departure time probability distribution parameters

(mean: 6:45, standard deviation: 12 minutes) Departure time – Morning shift 𝜇 = 11.25; 𝜎 = 0.2

(mean: 11:15, standard deviation: 12 minutes) Arrival time – Afternoon shift 𝜇 = 12.25; 𝜎 = 0.2

(mean: 12:15, standard deviation: 12 minutes) Departure time – Afternoon shift 𝜇 = 16.75; 𝜎 = 0.2

In the study of electric two-wheelers (E2Ws), the morning shift IDs range from 1 to 225, while the afternoon shift IDs span from 226 to 450, with an average arrival time of 16:45 and a standard deviation of 12 minutes To analyze the data, a random generator creates 10 ID patterns for various instances, including arrival and departure times for both shifts From these generated patterns, one random ID is selected to serve as the input data for the algorithm.

The selected arrival/departure pattern can be referred to Appendix 1.1.

Conventional load data is presented in Appendix 3.1.

Regarding scheduling horizon, a day is discretized into 96 timeslots of 15 minutes Timeslot 1 corresponds to the first arrival at 6:15 and timeslot 46 corresponds to the last departure at 17:30.

The initial State of Charge (SOC) for Electric Two-Wheelers (E2Ws) was established using research data on charging behaviors A random generator is employed to create multiple sets of E2W populations, followed by two random selections for morning and afternoon shifts, as detailed in Appendix 2.2.

Average monthly electricity consumption data from EVN Hanoi indicates that June, July, and August experience peak demand, while the lowest consumption occurs from January to March.

Solar power output can be referred to Appendix

4.1 The simulation results are as follows:

Figure 4.14 Max rate charging pattern a) Neload profile b) Total load profile – max rate charging c) Total load profile (average rate charging)

Figure 4.15 Total load profile in scenarios 1, 2, 3

The netload of the system, illustrated in Figure 4.15a, shows that during high solar power generation periods (timeslot 20 to 30), the PV system not only meets the conventional load but also produces surplus power for distribution grid injection This integration of the PV system greatly decreases electricity consumption from the grid, although it results in significant fluctuations in the load profile Peak load occurs in the early morning (timeslot 7) and late afternoon (timeslot 40), with a low point noted around timeslot 24.

Summary

This chapter introduces and validates an optimal charging algorithm specifically designed for photovoltaic-integrated electric two-wheeler (E2W) charging stations The algorithm's performance and effectiveness have been thoroughly analyzed through multiple case studies.

This study enhances the receding horizon framework for electric two-wheelers (E2W) charging by iteratively updating E2W data, enabling effective load leveling and the generation of individual charging profiles In contrast, the previous work [10] primarily aimed at minimizing overall daily peak power without clearly presenting individual charging profiles Specifically, the receding horizon approach in [10] focused on coordinating vehicle charging to keep power consumption aligned with current or anticipated peak power levels.

The proposed RHC-based algorithm combines the benefits of a group-based approach with the RHC framework and mathematical optimization tools to address the real-time optimal charging problem for electric vehicles (EVs) By utilizing the RHC framework, the algorithm effectively manages uncertainties in charging behavior Scheduling occurs in the current timeslot, allowing the charging station to dynamically update the EV table and implement grouping as the timeslot changes.

OP assigns group profiles to individuals, facilitating corrections that alleviate the computational burden This approach enables the efficient scheduling of a large number of E2Ws, a crucial feature of E2W charging stations.

Research results under various case studies have revealed interesting conclusions.

Smart charging schemes significantly enhance the aggregated load profile, with Vehicle-to-Grid (V2G) scenarios demonstrating superior benefits over non-V2G scenarios Unlike non-V2G, which fails to alleviate peak load, V2G facilitates peak shaving by allowing electric two-wheelers (E2Ws) to discharge during high demand periods Both V2G and non-V2G schemes effectively fill load valleys and achieve load leveling, outperforming uncontrolled and average charging methods.

Non-continuous working shifts, such as those at student charging stations, can lead to significant load fluctuations, particularly when high solar output coincides with shift changes In factories with consecutive shifts, sudden surges in vehicle charging during transitions require careful management For shift-working factories, a flat load characteristic allows for average charging to be a practical solution, as it minimally impacts the overall load profile In apartment buildings, electric two-wheelers (E2Ws) parked overnight benefit from smart charging schemes that primarily enhance nighttime load profiles, yet these schemes do not effectively utilize solar energy during the day These challenges highlight the necessity for alternative solutions like Battery Energy Storage Systems (BESS) Overall, case studies involving university charging, office charging, overnight apartment charging, and three-shift factory charging have demonstrated the algorithm's effectiveness in load leveling, valley filling, and peak shaving.

This chapter is mainly based on:

[1] Huu D.N and Ngoc V.N (2022) A Three-Stage of Charging Power Allocation for Electric Two-Wheeler Charging Stations IEEE Access, 10, 61080–61093.

[2] Huu D.N and Ngoc V.N An Optimal charging algorithm based on receding horizon framework for electric two-wheeler charging stations (ongoing submission).

[3] Ngoc V.N and Huu D.N (2023) Optimal valley filling algorithm for ElectricTwo-wheeler Charging Stations Eng Technol Appl Sci Res (Accepted).

REALTIME RESPONSES OF E2W CHARGING AND

Chapter objectives

This chapter aims at establishing a testing workbench to implement observation, measurement, and validation of the charging/discharging responses of E2Ws in the charging station.

The coordination of charging for electric two-wheelers (E2Ws) requires the optimization of charging and discharging schedules, determining power levels for various timeslots throughout the scheduling horizon To implement these long-term schedules effectively, it is essential to study real-time responses to commands based on long-term operations This chapter simulates an E2W charging station in real-time and establishes a test bench, along with the development of a control program designed to charge and discharge E2Ws at predetermined power levels.

The contributions of this chapter are as follows:

1) Real-time charging/discharging response simulation.

2) Developing an empirical test bench for an E2W charging station.

3) Conducting experimental tests to assess the feasibility of long-term scheduling algorithm.

4) Measuring and evaluating the real-time charging/discharging.

Real-time charging/discharging simulation

The long-term charging schedule primarily yields charging profiles for individual electric two-wheelers (E2Ws) at specific timeslots In this section, we explore the real-time charging and discharging responses of E2Ws using the model introduced in Chapter 2 Charging and discharging commands represent predetermined power values assigned to each timeslot; a positive power value indicates that the E2W battery is being charged, while a negative value signifies that the battery is discharging.

Figure 5.1 presents the daily charging power profile of the charging station, detailing the power levels at each time step throughout the scheduling period The analysis includes three specific charging power values: 5.6 kW, indicating energy consumption, and -1.95 kW and -6.56 kW, reflecting energy discharge during designated timeslots.

Figure 5.1 Typical total charging profile.

Figure 5.2 Total charging current at 5.6 kW charging power command

Figure 5.2 and Figure 5.3 demonstrate the current variations at the charging station when the power command shifts from 5.6 kW (charging) to -1.95 kW (discharging), indicating that the current response closely aligns with the reference current.

Figure 5.4 Total charging power at charging commands of 5.6 kW, -1.95 kW, -6.56 kW

Figure 5.5 PV power, grid power, conventional load and charging load

Figure 5.4 displays the charging responses to commands of 5.6 kW, -1.95 kW, and -6.56 kW, while Figure 5.5 highlights the real-time characteristics of solar power (P_PV), conventional load (P_Load), power exchange with the distribution grid (P_Grid), and the charging station's power consumption (P_Batt).

A power balance is consistently maintained between solar energy, conventional loads, charging loads, and grid power consumption During the observation period, photovoltaic (PV) power effectively supports both conventional loads and charging stations.

Figure 5.6 SOC and battery voltage of a typical E2W

Figure 5.6 illustrates the fluctuations in State of Charge (SOC) and battery voltage within a standard charging station The charging rate, reflected in the steepness of the SOC curve over time, is influenced by the power levels during charging or discharging Throughout this process, continuous monitoring of battery voltage and current is essential to ensure they remain within safe limits, preventing any exceedance of the maximum allowable voltage.

The simulation results confirm that the charging station can effectively meet the long-term charging schedule through its charging/discharging power commands, highlighting the algorithm's feasibility in real-time scenarios.

Testing workbench set up

5.3.1 The technical scope of the test bench

The initial phase of test bench development involves defining the prototype's scale and technical scope, which is essential for establishing the experimental scale, charging and discharging power, and required features to guarantee the test bench's functionality, reliability, and safety This stage also highlights the necessary technical specifications, measurement parameters, and testing procedures.

The experimental charging station, illustrated in Figure 5.7, comprises essential components such as DC-DC converters, inverters, batteries, and solar panels Additionally, measuring equipment is required to collect crucial data, including State of Charge (SOC), charging and discharging power, battery voltage, and current.

Figure 5.7 Test bench block diagram

In Vietnam, most electric bicycles utilize batteries with a voltage range of 36-48 V and a capacity of 12 Ah, featuring motor power below 250 W and a maximum design speed of 25 km/h, as outlined in QCVN 75:2019/BGTVT and QCVN 68:2013/BGTVT These e-bikes can cover distances of approximately 50-60 km In contrast, electric motorcycles are equipped with larger batteries (48-60 V; 20 Ah) and higher motor power (800-1200 W), allowing them to achieve maximum distances of around 70-80 km Most chargers for electric two-wheelers operate on single-phase AC power from the residential grid, with a typical charging power of about 400 W and a charging time of 3 to 5 hours, while discharge power can reach up to 1200 W.

The test bench is specifically designed to analyze real-time charging and discharging responses, with the experimental model utilizing a power range of 0 W to 400 W The selected batteries are chosen based on their specifications to effectively support this range.

12.8 V, 30 Ah The battery type is LiFePO4, allowing continuous discharge current up to 30 A (maximum continuous discharging power of 380 W), instantaneous discharge current up to 100 A, and maximum charging current of 10 A It's also worth noting that to optimize the lifetime of cells, the battery pack is integrated with an active voltage balancing circuit.

For Lithium battery charging, the most popular method is the Constant Current- Constant Voltage (CCCV) because of its simplicity and easy implementation [7],

[110] It should be noted that more than 80 % of the battery capacity is filled during

The CCCV charging method experiences approximately 50% of its total charging time during the constant voltage (CV) stage To mitigate the lengthy charging duration associated with this method, several solutions have been proposed, including the multistage constant current (MCC) technique, pulse charging, boost charging, and variable current profiles, all aimed at optimizing the regulation of charging current.

CC phase Higher current levels are usually chosen for the earlier CC stages.

The charging rate is directly influenced by the charging power, with the primary control occurring during the Constant Current (CC) stage Consequently, tests are conducted on the test bench to analyze various charging responses during this critical phase.

Converters are chosen to ensure that their rated power, voltage range, and current ratings align with the specifications of the batteries By adjusting the charging and discharging current, the power for both charging and discharging can be modified effectively.

5.3.2 Test bench design and operation

Figure 5.8 showcases the test bench design, featuring a charging station that incorporates distinct buck/boost converters It utilizes two types of inverters: a grid-tie solar inverter and a single-phase grid-tie inverter, which efficiently convert DC link voltage into AC voltage.

To ensure optimal performance of the inverter during battery discharging, it is essential to utilize buck/boost converters that adjust the battery voltage to meet the inverter's minimum input voltage requirements Similarly, during the battery charging process, these converters play a crucial role in converting the DC voltage from the DC link or AC/DC converter to the appropriate level necessary for effective battery charging.

For small-scale E2W charging stations, a single-phase grid-tie inverter is ideal due to its modest charging power and battery capacity In contrast, larger-scale operations can benefit from multiple sub-stations connected to different phases, facilitating easy expansion in power and capacity to accommodate more vehicles However, integrating new sub-stations into a three-phase grid requires careful consideration of phase load balancing and coordination among sub-controllers, which is beyond the scope of this dissertation.

In the experimental setup, the limitations of the small-scale pilot prototype and available equipment result in a unidirectional single-phase grid-tie inverter To address this, an AC/DC converter is utilized to transform AC electricity from the grid.

DC charging Besides, an additional path of charging can be realized from the DC link as in Figure

5.8 If charging is executed from the DC link, the total charging power must be lower than the total discharging power of the remaining batteries Worth mentioning, in the case of using bidirectional grid-tie inverters, the employment of AD/DC converter is not necessary.

To charge Battery 1, the grid voltage is first converted into DC voltage using an AC/DC converter This DC voltage is then regulated for voltage and current through R12 and a programmable buck converter before being directed to Battery 1 via R14.

To discharge Battery 1, its DC voltage is first routed through R11 and then enhanced by a boost converter The elevated DC voltage passes through R13 and the programmable buck converter, which adjusts the voltage level for the inverter The inverter then transforms the DC voltage into AC, allowing energy from Battery 1 to be fed into the grid.

Summary

This chapter investigates the real-time charging and discharging responses of a charging station, utilizing commands derived from a long-term scheduling algorithm Both simulation results from the Simulink model and experimental data from the test bench confirm the feasibility of optimized charging algorithms for electric two-wheelers (E2Ws) over an extended scheduling horizon under real-world conditions Notably, the response time for these charging and discharging commands is minimal when compared to the length of a single timeslot.

The controller's memory stores charging and discharging voltage and current setpoints in data tables, which are communicated to programmable power converters using Modbus RTU This approach capitalizes on the benefits of Modbus communication and the dependability of industrial control systems, ensuring scalability The test bench's setup and operation have confirmed the practicality of this solution in real-world scenarios, although there are still limitations that require further enhancement.

1) In the test bench, due to limitations in the power capacity and available commercial equipment, the grid-connected converter is unidirectional. However, in case of using a bidirectional grid-connected converter, which allows power exchange in both directions (from DC link to the grid and vice verse), the AC/DC converters can be eliminated Additionally, if the bidirectional converter supports control and data acquisition through industrial communication standards, the operation of the charging station may become more flexible.

2) At present, the test bench does not utilize monitoring devices for charging voltage, SOC, and State of Health (SOH), which would enable recording and observation of data over a long period The SOC and battery voltage are only measured in real-time.

3) The test bench primarily focuses on the constant current (CC) charging stage. The constant voltage (CV) stage has not been evaluated Further research and evaluation of the CV charging stage could provide valuable insights into the overall charging performance.

4) The coordination of battery power control, PV power, and grid power has not been investigated in the test bench This issue is important as it determines how these different power sources interact and complement each other during charging and discharging Further research in this concern would be beneficial to optimize the overall performance and efficiency of the charging station, taking advantage of the combined power sources effectively.

The test bench serves as a pilot prototype designed to validate the feasibility of optimal charging algorithms under practical conditions, successfully achieving the research objectives Future developments of the test bench will address the other limitations previously mentioned.

This chapter is mainly based on:

Ngoc Van Nguyen and Huu Duc Nguyen (2022) conducted a study published in the Journal of Science and Technology - HaUI, which validated real-time responses related to the long-term charging schedules of photovoltaic-integrated electric two-wheeler charging stations.

[2] Ngoc Van Nguyen and Huu Duc Nguyen (2023) Empirical verification of real- time charging responses following long-term scheduling for electric two- wheeler charging stations (Ongoing submission).

This dissertation explores optimal solutions for photovoltaic integrated charging stations in Vietnam, presenting key research findings that contribute to the advancement of sustainable energy infrastructure in the region.

1) Centralized architecture is suiable for E2W charging stations In the case of larger scale, a hierarchical architecture which coordinates mutilple centralized charging stations may be adopted.

2) Real-time and long-term models are considered in the study with the former aiming at investigating real-time charging responses while the latter aiming at integrating optimal charging algorithms.

3) In the dissertation, two scheduling algorithms aiming at improving total load profile are proposed and verified The algorithms can accommodate high numbers of E2Ws and handling dynamics and uncertainties.

4) Simulation results reveal that attention should be paid to charging stations depending on specific locations.

5) In the study, a testing workbench has been set up for empirical verification.

As an extension of this thesis, several tasks for future work are possible and listed below:

1) Exploring additional uncertainties that could impact on the algorithms These uncertainties could be technical specifications variations of E2Ws and the accuracy of forecasted PV power generation and non-EV loads Further addressing the uncertainty in charging requirements of vehicle users.

2) Addressing other issues such as: island mode operation, optimal power dispatch of power sources in the station; optimal PV sizing.

3) Further quantitatively evaluating the computational time of the proposed algorithms with a certain harware infrastructure.

4) Further investigating detailed design of converters as well as sizing components quantitatively MPPT algorithms for small-scale rooftop PV systems should also be investigated in detail.

5) Further developing the test bench and conducting experiments under various scenarios.

Ngoc Van Nguyen and Huu Duc Nguyen (2022) conducted a study validating real-time responses in relation to the long-term charging schedules of photovoltaic-integrated electric two-wheeler charging stations Their research, published in the Journal of Science and Technology - HaUI, highlights the effectiveness of integrating solar energy into electric vehicle infrastructure, emphasizing the importance of optimizing charging schedules for enhanced efficiency and sustainability.

Ngoc Van Nguyen, Khac Nhan Dam, and Huu Duc Nguyen (2022) conducted a study focusing on the architectures and control algorithms essential for electric vehicle and electric two-wheeler charging stations in Vietnam Their research addresses the specific needs and challenges of the Vietnamese context, aiming to enhance the efficiency and effectiveness of charging infrastructure.

Journal of Science and Technology - HaUI, 58(4), 55–64.

[3] Huu D.N and Ngoc V.N (2022) A Three-Stage of Charging Power Allocation for Electric Two-Wheeler Charging Stations IEEE Access, 10, 61080–61093.

[4] Ngoc Van Nguyen and Huu Duc Nguyen (2022) PV-Integrated Electric Two- wheeler Charging Stations: A Solution towards Green Cities TNU

Journal of Science and Technology, 227(3), 25–32.

[5] Heckmann W., Duc N.H., Granford Ruiz D et al (2022) Smart Energy Buildings: PV Integration and Grid Sensitivity for the Case of Vietnam.

Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems, SCITEPRESS - Science and Technology Publications, 117–124.

[6] Huu D.N and Ngoc V.N (2021) A Two-Level Desired Load Profile Tracking Algorithm for Electric Two-Wheeler Charging Stations Eng Technol Appl Sci

[7] Huu D.N and Ngoc V.N (2021) Analysis Study of Current Transportation Status in Vietnam’s Urban Traffic and the Transition to Electric Two- Wheelers Mobility Sustainability, 13(10), 5577.

A study by Huu D.N and Ngoc V.N (2021) explores the trend of transport electrification in Vietnam, focusing on the feasibility of photovoltaic (PV)-integrated charging stations for electric two-wheelers Presented at the 11th International Conference on Power, Energy, and Electrical Engineering (CPEEE) in Shiga, Japan, this research highlights the potential for sustainable energy solutions in the electric transportation sector The findings emphasize the importance of innovative charging infrastructure to support the growing demand for electric vehicles in the region.

Ngoc Van Nguyen and Huu Duc Nguyen (2020) conducted a technical and economic assessment of photovoltaic (PV) based charging stations designed for electric bicycles at Electric Power University Their findings were published in the EPU Journal of Science and Technology for Energy, Volume 25.