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
At the COP26 conference, Vietnam pledged significant commitments to combat global climate change, vowing to achieve net-zero emissions by mid-century and signing the Global Coal to Clean Power Transition Statement In support of these goals, the government has developed a detailed roadmap featuring eight essential tasks designed to promote sustainable and low-emission economic growth.
Promoting the shift from fossil fuels to renewable energy sources (RESs) is essential for reducing greenhouse gas (GHG) emissions across various sectors, including energy and transportation Key initiatives include decreasing reliance on fossil fuel vehicles and fostering research, development, and adoption of electric vehicles (EVs) The Ministry of Transport should assess the feasibility of phasing out fossil fuel vehicles by 2040 and create a comprehensive 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 2050, the electricity distribution plan aims to eliminate coal power, reducing its share from 20% to 0%, while increasing renewable energy sources to account for 65.8-71% of total capacity The PDP VIII shifts focus from grid-connected solar power 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% of office buildings and homes utilizing rooftop solar power for self-consumption 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 solutions that can enhance the growth of renewable energy sources (RESs) and clean transportation in Vietnam's evolving landscape.
The transition to electric two-wheeler mobility in Vietnam’s urbans
In Hanoi and Ho Chi Minh City, public transport currently 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, increased greenhouse gas emissions, and heightened 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 common in developing countries like Vietnam Research indicates that inadequate traffic infrastructure, insufficient public transport options, low- to middle-income levels, and challenging weather conditions lead many residents to prefer private motorcycles for urban commuting.
The increasing number of private vehicles contributes significantly to urban traffic congestion, noise, and air pollution To mitigate these challenges, various solutions have been proposed, including stricter exhaust emission standards, limiting private vehicle use, enhancing public transport systems, and promoting less polluting vehicles Notably, transport electrification stands out as a viable solution, offering benefits such as zero tailpipe emissions, improved energy efficiency compared to internal combustion engines, and the potential for energy diversification.
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, are popular 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 attention 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 Vietnam, the electric two-wheeler (E2W) market experienced significant growth, increasing from 0.9 million units in 2017 to five million units by 2019, according to the International Association of Public Transport (UITP) By the end of 2019, eleven companies were actively producing E2Ws, with a total output of 52,938 units.
(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 increasing adoption of motorbikes, socio-economic factors, and inadequate transport infrastructure E2Ws maintain essential motorcycle characteristics for urban traffic while offering the advantages of electric mobility They also have the potential to enhance intelligent transportation systems, improving connectivity to public transport services at transit hubs.
The rapid increase in emerging vehicles is anticipated to place significant strain on the distribution grid, which is primarily designed to handle the anticipated growth of traditional energy demands To effectively manage this transition, it is essential to explore research-based solutions that address these challenges.
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
China United States of America
Japan Germany India Italy Australia Vietnam Republic of Korea United Kingdom
In 2015, the installed solar capacity for power generation was merely 4 MWp, with approximately 900 kWp connected to the grid By 2018, this capacity had grown to 106 MWp, and by 2019, it surged to around 5 GWp, including nearly 0.4 GWp from rooftop photovoltaic systems By the end of 2020, the country's total photovoltaic capacity soared to approximately 16,500 MW, positioning it among the top 10 nations in the world for solar power capacity.
5) With regards to rooftop solar, there were 105,212 systems installed throughout the country by December 2020, with a total capacity of 9,730.87 MWp [142]
Excessive penetration of photovoltaic (PV) systems can lead to significant challenges for grid operation and security, including instability, frequency and voltage anomalies, infrastructure overloads, and mismatched supply and demand In 2020 alone, approximately 365 GWh of solar energy was curtailed to maintain this balance.
To enhance the hosting capacity of the power grid, utilities should 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 accommodating renewable energy sources (RESs).
To enhance the use of photovoltaic (PV) systems without disrupting the distribution grid, promoting self-consumption is essential, as outlined in the PDP VIII The goal is to have 50 percent of buildings and residential homes adopting rooftop solar power for self-consumption by 2030, presenting a significant challenge that requires comprehensive solutions to achieve.
PV-integrated charging stations – A solution for both E2W and rooftop solar
The urban distribution grid in Vietnam faces significant future impacts from two key factors: the rise of unplanned, deferrable charging loads and the increasing prevalence of distributed renewable energy sources (RESs), which are characterized by their stochastic and intermittent nature and are encouraged for self-consumption Addressing these emerging factors presents both challenges and opportunities 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 powered mainly by fossil fuels like coal or natural gas leads to substantial emissions Therefore, the real benefit of EVs in reducing greenhouse gas (GHG) emissions is realized only when they are charged using renewable energy sources (RES) or from a grid with a high percentage of renewable electricity.
Wind power, solar power, hydropower, biogas, and tidal energy are all viable sustainable energy sources 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
Integrating photovoltaic (PV) power into the distribution grid not only meets charging demands but also alleviates the adverse effects of charging loads and high levels of renewable energy sources (RESs) on the grid.
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
The block diagram of a PV-integrated charging station illustrates how electricity from solar power or the grid supplies vehicle batteries When photovoltaic (PV) power exceeds charging demand, surplus electricity is fed back into the grid Conversely, if charging demand surpasses PV output, the grid compensates for the shortfall Additionally, with bidirectional AC/DC converters, electric vehicle (EV) batteries can function as energy storage devices, offering ancillary services.
Research goals, scope, and research questions
This work conducts research on optimal charging solutions for PV-integrated E2W 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
The solutions proposed in this article focus on load leveling, valley filling, and peak shaving, which collectively 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
Electric two-wheelers (E2Ws) commonly utilize lithium-ion (Li-ion) batteries, making it essential to focus on the charging and discharging processes of these batteries It is crucial that the battery capacity and charging power align with the specifications of widely-used E2Ws in Vietnam.
Electric two-wheelers (E2Ws) commonly come with portable single-phase chargers that function as residential single-phase power sources Therefore, charging these vehicles at the standard residential voltage of 220 V is essential.
Despite the relatively small battery capacity and charging power of electric two-wheelers (E2Ws), which range from 0.2 to 5 kWh and 0.25 to 4 kW respectively, their cumulative charging can lead to significant aggregated power This impact can affect other loads, the distribution grid, and overall system efficiency Therefore, it is crucial to consider charging strategies in these scenarios.
The charging station utilizes a rooftop photovoltaic (PV) system alongside grid power However, this study does not explore 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) frequently outnumber electric vehicles (EVs) at conventional charging stations.
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 simulation are utilized to explore different operational scenarios of photovoltaic-integrated electric vehicle (PV-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 strategies to address increasing load demands and promotes the growth of rooftop photovoltaic (PV) systems in urban settings, while also minimizing the negative effects of electric vehicles (EVs) and solar energy on the distribution grid As a result, it reduces the necessity for upgrades or reinforcements to the existing grid infrastructure.
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 focuses on the review of architectures and control algorithms for 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 aimed at load leveling, while Chapter 4 presents and validates an optimal algorithm based on a receding horizon framework Finally, Chapter 5 establishes a test bench to assess the real-time responses of electric-to-wheel (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 plays a crucial role at charging stations by managing the charging and discharging processes of electric vehicles (EVs) Direct aggregators formulate specific charging strategies for each EV, while indirect aggregators communicate information signals to coordinate the charging activities among multiple EVs.
Indirect Aggregator Indirect control signal
Local controller a) Centralized b) Decentralized – Type 1 c) Decentralized – Type 2
Indirect control signal Local controller d) Hierarchical e) Hierarchical
In a centralized architecture for EV charging, an aggregator directly gathers the charging needs and specifications of electric vehicles (EVs) This aggregator then solves an optimization problem to establish an efficient charging profile, which is subsequently communicated to the vehicle owners.
This architecture necessitates that vehicle owners relinquish some control over their charging processes, which can lead to optimal solutions when comprehensive system information is accessible It facilitates the effective consideration of various constraints affecting electric vehicles (EVs) and the grid However, this system raises privacy concerns regarding data such as charging times, behaviors, and travel distances Furthermore, the system's reliability is at risk, as a failure at the aggregator could lead to a complete collapse.
In a centralized architecture, scalability poses a considerable challenge as the number of electric vehicles (EVs) grows and scheduling intervals become shorter This increase in complexity results in heightened demands for computational power.
In decentralized architecture, electric vehicles (EVs) utilize local controllers to establish their charging schedules, offering scalability and practical benefits despite potentially suboptimal strategies Unlike centralized systems, decentralized models demonstrate greater resilience against network failures, particularly when controllers are designed to function during such events Additionally, decentralized architecture can be classified into two types based on the communication network structure.
Decentralized architecture Type 1 (Figure 1.1b) enables electric vehicles (EVs) to autonomously determine their charging schedules while communicating with one another to achieve a global equilibrium However, this method necessitates continuous schedule exchanges among EVs, leading to significant communication challenges, especially in environments with a high density of electric vehicles.
The Type 2 decentralized architecture features an indirect aggregator that gathers targeted information and disseminates 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 using data from indirect aggregators or other electric vehicles (EVs) When these controllers share the same information and aim for a unified objective, they generate a synchronized charging schedule for the EVs For example, if the controllers focus on minimizing charging costs by leveraging time-of-use (TOU) electricity prices, all EVs will charge during periods of lower rates and halt charging during higher tariff times, which could impact the overall optimization goals.
The hierarchical approach effectively merges the advantages of centralized and decentralized architectures by utilizing a tree-like structure In this model, control and computation tasks are delegated to both direct and indirect aggregators, each responsible for managing a group of electric vehicles (EVs) These aggregators influence the decisions of other aggregators, facilitating coordination among EVs located in close proximity, such as those found in parking lots, apartment complexes, or transit hubs.
A direct central aggregator calculates the charging plan for all sub-aggregators to optimize overall performance, allowing each sub-aggregator to create specific charging profiles for the electric vehicles (EVs) they manage In contrast, an indirect central aggregator disseminates information to sub-aggregators, who then establish the charging schedules for each EV.
In Figure 1.1e, a central aggregator calculates the charging plan for all sub- aggregators who broadcast information, transfer calculation task to the local controllers at EVs
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, as alternative connections can compensate for disconnections between aggregators However, it's important to note that if an aggregator fails, 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 is facilitated through signal broadcasting, allowing vehicles to actively plan their charging schedules An EV aggregator plays a crucial role by offering and disseminating price signals that influence the charging behaviors of individual vehicles, ultimately aiding in the achievement of system-level objectives However, this system requires continuous updates and broadcasts of new signals, as simultaneous charging during low-price periods can lead to new peak loads, potentially disrupting the aggregator's goals.
The decentralized architecture for electric vehicle charging stations is influenced by pricing, which significantly affects charging behavior However, this model may not be suitable for electric two-wheelers (E2Ws), as their lower energy and power consumption renders price signals less impactful To efficiently manage the charging of numerous E2Ws in a parking lot, a centralized controller is essential Vehicle owners should provide basic charging requirements, such as their departure time, or select from options suggested by an aggregator, ensuring streamlined management of E2W charging.
Centralized architecture faces significant scalability challenges, as studies indicate that an increase in vehicle numbers leads to a heightened demand for computing power, making the resolution of optimization problems increasingly complex and time-consuming.
A centralized approach can be impractical for large-scale, real-time applications, necessitating algorithms that consider both computational complexity and processing time.
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, insights from control algorithms developed for electric car charging stations can be adapted and applied to enhance E2W charging solutions.
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, vehicles, 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], [136],
Charging station control issues can be categorized into technical and economic aspects, as illustrated in Table 1.1 These aspects are relevant to various stakeholders, including grid operators, EV aggregators, and vehicle owners Notably, advancements in technical performance can enhance economic metrics, highlighting the interconnectedness 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 aim to flatten the aggregated load profile, which includes both conventional and charging loads This reduction in peak load alleviates the overloading issues faced by transformers, transmission lines, and other electrical infrastructure Additionally, a more stable load profile minimizes the need for abrupt adjustments in generator output, allowing generators to function at optimal efficiency and stability.
Research highlights the importance of managing electric vehicle (EV) charging during off-peak nighttime hours While the effect of an individual EV on the distribution grid is minimal, the cumulative impact of numerous EVs can lead to substantial changes in grid dynamics.
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
Where: 𝐷 𝑡 𝑆 is non-EV load
𝑃 𝑡 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 Subject to, ∑ 𝑀 𝑡=1 𝜂 𝑖 𝑃 𝑡 𝑖 Δ 𝑇 = (𝐹𝑆𝑂𝐶 𝐸𝑉 𝑖 − 𝐼𝑆𝑂𝐶 𝐸𝑉 𝑖 )𝐴 𝑖 (1.2)
𝑃 𝑚𝑖𝑛 𝑖 ≤ 𝑃 𝑡 𝑖 ≤ 𝑃 𝑚𝑎𝑥 𝑖 (1.3) Where 𝐼𝑆𝑂𝐶 𝐸𝑉 𝑖 , 𝐹𝑆𝑂𝐶 𝐸𝑉 𝑖 , 𝐴 𝑖 , 𝑃 𝑚𝑖𝑛 𝑖 , 𝑃 𝑚𝑎𝑥 𝑖 , 𝜂 𝑖 are the State of Charge (SOC) at arrival time, the SOC at departure time, battery capacity, minimum charging power, maximum charging power, and charging efficiency of 𝐸𝑉 𝑖 , respectively
Various load regulation strategies exist, such as minimizing total load variation to flatten the load curve and influencing vehicle owners' charging behavior through electricity pricing Aggregators can broadcast control signals, like fluctuating electricity price signals based on total load demand, encouraging electric vehicles (EVs) to charge during lower-cost periods, thus filling low-load times Each EV updates its charging profile iteratively to optimize costs Additionally, some studies focus on discrete charging rates, while game theory emerges as a valuable method for coordinating EV charging by optimizing individual vehicle behavior A non-coordinative game has been proposed to manage numerous EVs linked by a shared electricity price, effectively addressing low-load valleys.
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 introduced, leveraging a decentralized control architecture This method broadcasts a reference signal based on real-time total load, allowing each EV to determine its charging status by comparing its state of charge (SOC) with the reference Notably, this solution operates in real-time without relying on forecasts, making it immune to forecasting errors.
The valley filling and peak shaving algorithm, developed through dynamic programming and game theory, effectively optimizes electric vehicle (EV) charging schedules Utilizing a forward induction dynamic programming approach, the algorithm determines the optimal charging times for each EV, enhancing energy efficiency and grid stability.
In [81], a non-iterative approach was proposed for sequential scheduling of each
The algorithm was designed to optimize the charging profile of electric vehicles (EVs) by minimizing both the variance and peak total load It established the charging strategy only when an EV was connected, but this approach led to delays when multiple EVs were connected at the same time.
In [104], the authors developed a decentralized scheme utilizing a sequential scheduling approach to reduce the mean squared error between real-time aggregated load and an offline estimated reference point, drawing on data from both non-EV and EV loads.
Various load regulation algorithms have successfully addressed dual objectives by incorporating essential constraints, including transformer overload limits, the power supply capacity of the grid, and voltage constraints 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 price signals, while another utilized an ant-based swarm algorithm to alert EVs when total load surpassed transformer capacity Additionally, research determined the optimal load level to satisfy EV energy demands by employing the bisection method, effectively addressing transformer overload constraints by proportionally reducing EV energy demand.
Improving operational efficiency is crucial, with the objective of maximizing the use of renewable energy sources (RESs) at charging stations This involves reducing electricity consumption from the grid and coordinating charging and discharging processes to align with the day-ahead energy plan.
A recent study introduced a decentralized algorithm alongside a token-based IT infrastructure designed to optimize energy services This innovative system utilizes generation and consumption tokens to enhance the average utilization of energy generation, while also ensuring that the power consumption of electric vehicles (EVs) remains below the total power allocated for their charging needs.
Utilizing game theory, a study explored the balance of real-time electricity generation planning Initially, electric vehicle (EV) owners participated in a non-cooperative game to forecast their next-day electricity demand, aiming to minimize costs This forecast informed the aggregator's decisions regarding power generation and purchasing 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 approach presents distinct advantages and disadvantages Centralized systems excel in global optimization but face challenges with scalability, vulnerability to failures, and privacy concerns In contrast, decentralized architectures offer better scalability and reduced risk of control loss during failures, yet they struggle with achieving global optimization and require ongoing updates for signal broadcasting to maintain equilibrium.
E2W charging, with its lower power and energy needs compared to electric cars, may not benefit from a decentralized architecture that relies on price signal broadcasting to influence charging behavior Instead, implementing a central controller at E2W charging stations is more effective for coordinating vehicle charging To manage the computational demands of charging hundreds or thousands of vehicles, it is advisable to segment the charging station into multiple sub-stations, each overseen by its own sub-controller.
Research on algorithms for electric two-wheeler (E2W) charging stations is still in its early stages However, existing algorithms designed for electric car charging stations can provide valuable insights for E2W applications It's essential for these algorithms to account for specific E2W characteristics, including the high volume of vehicles, limited battery capacity, and lower charging power relative to electric cars.
This chapter is mainly based on:
Ngoc Van Nguyen, Khac Nhan Dam, and Huu Duc Nguyen (2022) conducted a study on the architectures and control algorithms for electric vehicle and electric two-wheeler charging stations in Vietnam Their research, published in the Journal of Science and Technology - HaUI, provides valuable insights into optimizing charging infrastructure to support the growing demand for electric transportation in the country.
CHAPTER II: MODELING OF PV-INTEGRATED ELECTRIC-
Chapter objectives
This chapter presents a model of a photovoltaic (PV)-integrated electric two-wheeler (E2W) charging station, designed to simulate and evaluate the performance of the station and its components, including E2W batteries, the PV system, and power converters Additionally, the models serve as a foundation for implementing control algorithms, emphasizing the operational efficiency of the charging station.
- 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
DC AC SOLAR INVERTER WITH MPPT
SOC/SOH ev(N) SOC/SOH ev01
Figure 2.1 Charging station block diagram
The station utilizes a photovoltaic (PV) system in conjunction with the distribution grid to generate electricity Solar panels convert sunlight into DC power, which is then transformed into AC power by a solar inverter, supplying energy 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 energy from and discharge energy back 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]:
𝑅 𝑠ℎ (2.4) 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 effectively utilize 900 solar panels arranged in strings and arrays These panels can be configured in parallel, comprising 36 strings, with each string containing 25 solar panels connected in series.
To harness the full potential of solar energy, Maximum Power Point (MPP) tracking algorithms are utilized These algorithms ensure that solar panels consistently operate at their 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 behavior These models are integral to the Battery Management System (BMS), facilitating real-time state of charge (SOC) estimation and performance assessment Given that most EVs rely on high-energy-density lithium-ion batteries, it is vital 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 simulating chemical reactions and polarization within batteries using various parameters However, establishing these models can be challenging due to the influence of environmental conditions on electrochemical processes, which also limits their effectiveness in real-world applications.
Artificial neural network models leverage the self-learning and non-linear characteristics of neural networks, along with experimental data, to identify relationships among various battery parameters However, a significant limitation is that these models necessitate a substantial amount of experimental data to accurately predict battery performance.
Equivalent circuit models simulate battery dynamics using elements like resistors, capacitors, and voltage sources Key models include Rint, RC, PNGV, and Thevenin The RC model captures battery polarization through capacitance but omits resistance The PNGV model addresses intricate charge/discharge relationships, though its complexity poses simulation difficulties In contrast, the Thevenin model incorporates both capacitance and resistance, making it straightforward and easy to implement.
The article discusses various mathematical representations of battery models, particularly highlighting equivalent circuit models that are characterized by a limited number of parameters, facilitating the derivation of state-space equations These models are prevalent in both simulation and real-time control systems Extensive experiments indicate that the equivalent circuit model is effective for LiFePo and LiMnCo batteries, as illustrated in Figure 2.4.
Figure 2.4 The equivalent circuit model of a battery
The open-circuit voltage (VOCV) and internal resistance (Rbat_int) of a battery fluctuate with the state of charge (SOC) and can be sourced from the manufacturer's specifications The battery's output voltage is defined by specific parameters outlined in relevant studies.
𝑉 𝑂𝐶𝑉 , 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔 (2.10) 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
(Rdis) and charge 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, a switch, a diode, and a capacitor, which work together to efficiently elevate voltage levels.
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 an essential control algorithm designed to optimize the power output of photovoltaic (PV) systems by analyzing fluctuations in voltage and current This article explores two widely-used MPPT algorithms: the Perturb and Observe (P&O) method and the Incremental Conductance (INC) technique, highlighting their effectiveness in locating the maximum power point of PV arrays.
▪ The Perturb and Observe (P&O) algorithm:
The basic principle of this algorithm is to perturb the operating point of PV panels and observe the change in power
Based on this observation, the algorithm adjusts the operating point to increase the power output
The P&O algorithm is a widely utilized method known for its simplicity, but it does have limitations, including oscillations around the Maximum Power Point (MPP) and a slower response to rapid changes in environmental conditions.
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
Figure 2.9 Flowchart of INC algorithm
, at the MPP , at the left of MPP , at the right of MPP
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
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 facilitates the scheduling of electric vehicle (EV) charging across various time periods A typical workday or shift, referred to as the scheduling horizon, can be divided into multiple time intervals By gathering essential information, the model optimizes the scheduling process to meet 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, the aggregated battery energy is represented by 𝐶 𝑡 𝑆, while 𝐷 𝑡 𝑆 indicates the total power of non-EV loads after accounting for the power generated by the PV system, known as netload The total load demand, comprising both charging load and netload, is denoted as 𝐸 𝑡 𝑆 The operational day is segmented into timeslots of Δ 𝑇, and the total charging power for all EVs at time 𝑡 is represented by 𝑃 𝑡 𝑆 Additionally, all chargers are equipped with Vehicle-to-Grid (V2G) capabilities, allowing for efficient energy management at the charging station.
With the integration of Vehicle-to-Grid (V2G) technology, energy exchange between electric vehicles (EVs) and the grid can occur bidirectionally The charging behavior of EVs is influenced by the sign of \( P_{tS} \); specifically, \( P_{tS} > 0 \) indicates that the batteries are receiving energy, while \( P_{tS} < 0 \) signifies that the batteries are discharging energy back to the grid.
The power constraints for charging/discharging can be described as follows:
𝐶ℎ𝑎𝑟𝑔𝑖𝑛𝑔: 𝑃 𝑡 𝑆 ≤ 𝑚𝑖𝑛{∑ 𝑁 𝑖=1 𝑃 𝑚𝑎𝑥 𝐶 𝑖 , ∑ 𝑁 𝑖=1 𝑃 𝑚𝑎𝑥 𝐵 𝑖 } (2.16) 𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔: 𝑃 𝑡 𝑆 ≥ 𝑚𝑎𝑥{∑ 𝑁 𝑖=1 𝑃 𝑚𝑖𝑛 𝐶 𝑖 , ∑ 𝑁 𝑖=1 𝑃 𝑚𝑖𝑛 𝐵 𝑖 } (2.17) Where, 𝑃 𝑚𝑎𝑥 𝐶 𝑖 , 𝑃 𝑚𝑖𝑛 𝐶 𝑖 are the maximum allowable charging/discharging power for the 𝑖 𝑡ℎ charger, respectively
𝑃 𝑚𝑎𝑥 𝐵 𝑖 , 𝑃 𝑚𝑖𝑛 𝐵 𝑖 are the maximum allowable charging/discharging power for the battery of the 𝑖 𝑡ℎ EV
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:
Charging and discharging processes in microgrids can be defined by specific power equations For charging, the condition is expressed as \( P_{tS} \leq P_{max\_feeder} - D_{tS} = P_{max\_ex} \) Conversely, for discharging, the condition is \( P_{tS} \geq -P_{max\_feeder} + D_{tS} = -P_{max\_ex} \) Here, \( P_{max\_ex} \) represents the maximum power that the microgrid can exchange with electric vehicles (EVs), determined by the difference between the maximum allowable exchange power between the microgrid and the distribution grid (\( P_{max\_feeder} \)) and the net load.
In scenarios where non-EV loads are considered non-dispatchable, the charging station must manage the overall charging load demand of electric vehicles (EVs) to comply with specific constraints This adjustment is crucial to ensure that the operational limits are maintained and not breached.
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 𝑡 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 foundation for incorporating optimal charging scheduling algorithms in subsequent sections.
CHARGING POWER ALLOCATION ALGORITHM FOR
Chapter objectives
This chapter aims to develop a smart charging strategy for electric two-wheelers (E2W) at charging stations that integrate 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 over a specified scheduling horizon Through various case studies and scenarios, the algorithm showcases its effectiveness in improving load profiles while reducing computational demands.
E2W charging stations cater to numerous electric vehicles (EVs) that possess limited battery capacities and charging power To optimize energy management, a grouping strategy is introduced, organizing E2Ws according to their energy requirements throughout the scheduling period The charging process is executed in three distinct stages, enhancing efficiency and ensuring effective energy distribution.
- 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
By employing a grouping strategy and a three-stage power allocation method, the need to repeatedly solve the constrained OP for each E2W charging profile is removed This approach significantly decreases the computational requirements when scheduling numerous E2Ws.
Input data requirements
To evaluate the effectiveness of a charging scheduling algorithm, it is essential to provide specific input data, which encompasses vehicle technical specifications, charging behaviors, and predictions of 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 bicycles typically feature motors ranging from 250 W to 1000 W for high-speed models, powered by 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, utilizing motors that range from 1 to 4 kW and are usually equipped with 48 V batteries that have capacities from 1 to 5 kWh.
Furthermore, e-bikes have an extremely low energy consumption in the range of 5–
The energy consumption of electric bikes typically ranges around 15 Wh/km, influenced by factors such as drivetrain efficiency, riding habits, tire characteristics, and the combined weight of the bike and rider In comparison, electric cars consume significantly more energy, averaging between 150 to 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 maximum power of 500 W, allowing for a 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, enabling a travel range of 70-80 km.
Maximum travel distance (km) e-bikes
The charging scheduling problem for electric vehicles (EVs) faces two primary challenges: the unpredictability of EV charging behaviors and the prolonged time required to determine optimal solutions in large-scale EV scheduling scenarios.
There are numerous studies, both theoretical and statistical, on charging behavior
Charging behavior typically involves key factors such as connection and disconnection times, total 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, with the daily driving end time fitting a normal (Gaussian) distribution Studies have shown that the daily driving distance adheres to a log-normal distribution Specifically, the probability density function for the first drive time and last return time follows a Gaussian distribution, with parameters (μ; σ) of (7.5; 3.24) and (17.5; 3.41), respectively Additionally, the daily driving mileage is characterized by a log-normal distribution, with parameters (μ; σ) of (3.37; 0.5).
A study utilizing driving pattern data from the National Household Travel Survey (NHTS) revealed that the probability distribution of arrival and departure times to and from home or the office aligns with a normal distribution This finding is supported by additional research in the field.
[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
Authors in [16] suggested that the arrival and departure behavior of electric vehicles (EVs) in areas with a high concentration can be effectively modeled using statistical probability density functions (PDFs), such as the normal distribution By analyzing historical data from the study region, the parameters of these normal distributions can be accurately determined.
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 analyze arrival and departure times using probability distributions, typically normal or log-normal distributions This study specifically assumes that the arrival and departure time distributions for electric two-wheelers (E2W) follow a normal distribution.
(3.1) Where 𝑓 is the probability of arrival/departure time, and à and σ 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 aimed for a full recharge, regardless of the initial SOC EV owners prioritize quick battery recharging to enhance vehicle autonomy The analysis showed that 87.5% of sampled EVs were fully charged, while only 2.4% of charges ended prematurely with a SOC below 50% Additionally, around 10.1% of charging sessions reached a SOC between 50% and 90%, often due to the need for the vehicle to leave before the battery was fully charged.
A study found that 75% of electric vehicle (EV) owners prefer home charging, typically overnight, and often plug in their vehicles immediately upon arriving home This charging habit frequently aligns with peak electricity demand in the evening Additionally, EVs tend to remain idle for extended periods at night, exceeding the necessary charging time Starting the charging process earlier could lead to earlier completion, thereby reducing the opportunity for "valley filling" during late-night and early-morning hours.
A study found that electric vehicle (EV) owners typically arrive home around 6:00 PM and leave for work by 7:30 AM on weekdays, aligning with common weekday travel patterns Additionally, the probability densities of home arrival and departure times vary significantly between weekdays and weekends, with sharp peaks noted on weekdays and more gradual curves during holidays.
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 means and standard deviations of (μ; σ) as (7.5; 3.24) and (17.5; 3.41) respectively Research indicates that most EV users begin charging after returning home from work at around 18:00, with over 90% starting their charging sessions between 13:00 and 23:00 Additional 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), respectively Slight variations in these parameters were noted, with connection and disconnection times recorded as (19.89; 1.92) and (7.56; 2.33) in another study.
Home-charging behavior typically adheres to a normal distribution, with electric vehicles (EVs) assumed to remain parked at home until the next morning's departure, while infrequent evening trips are disregarded However, this assumption does not significantly impact the effectiveness of the scheduling solution, provided that the real-time mobility data of EVs is continuously updated.
Charging power allocation algorithm for E2Ws
Electric vehicle (EV) charging loads are often deferrable, as the parking duration usually exceeds the actual charging time This flexibility allows for optimized dispatching of charging loads, which can enhance both economic and technical advantages for charging station owners and distribution operators.
This study introduces a charging power allocation algorithm designed to enhance the total load profile for electric two-wheelers (E2Ws) The algorithm effectively meets the energy requirements of E2Ws while adhering to charging and discharging constraints, ultimately shaping the total load profile to align with a predefined target.
This method is based on the "water filling" principle, which determines the available power for charging at each time interval The allocatable power is calculated by considering the expected energy needs throughout the scheduling period, relying on precise forecasts of conventional load, renewable energy generation, and vehicle data.
The "water filling" method for power allocation in electric vehicles has been explored in various studies This algorithm functions by distributing charging demands into the grid, akin to pouring water into a valley, with the primary goal of reducing load variance while accounting for both traditional energy consumption and electric vehicle charging needs.
[149] However, unlike those studies, this research performs power allocation through three stages to determine the charging profile of individual E2W in the charging station
The proposed algorithm consists of three stages, which are described as follows:
Stage 1 focuses on establishing the overall charging profile of the station by calculating the difference between the desired total load profile, which encompasses both net load and charging load, and the net load itself.
Stage 2 of the charging process distributes power to Electric Two-Wheelers (E2W) groups according to the overall charging profile of the station The grouping is determined by the energy requirements of the vehicles, ensuring that those within the same group have comparable 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 This allows for the application of the group profile to the vehicles without necessitating a third-stage algorithm However, utilizing the group profile may lead to minor inaccuracies in the charging needs of E2Ws, potentially impacting vehicle owner satisfaction.
3.3.1 Mathematical formulation of the algorithm
In this section, we develop a charging power allocation algorithm aimed at enhancing the total load profile, building on the long-term model discussed in Chapter 2 The charging station is assumed to be connected to the grid, conventional building loads, and a rooftop PV system, with all chargers equipped with V2G capabilities The average power of the total load throughout a working day can be estimated using a specific equation.
The variance of the total load (including netload and charging loads) can be calculated by:
𝑀∑ 𝑀 𝑡=1 (𝐷 𝑡 𝑆 + ∑ 𝑁 𝑖=1 𝑃 𝑡 𝑖 − 𝜇 𝑎𝑣𝑒 𝑆 ) 2 ≥ 0 (3.3) 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 redistributing 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 subsequent phase, the charging reference vector is assigned to clusters of electric two-wheelers (E2Ws) It is assumed that N vehicles at the charging station are categorized into K distinct groups, represented by the vector of the EV group number.
𝒏 = [𝑁 1 𝑁 2 ⋯ 𝑁 𝐾 ] 𝑇 , ∑ 𝐾 𝑗=1 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:
𝑃 𝑡 𝐺 𝑗 ≥ 𝑚𝑎𝑥 {∑ 𝑁 𝑖=1 𝑗 𝑃 𝑚𝑖𝑛 𝐶 𝑖,𝑗 , ∑ 𝑁 𝑖=1 𝑗 𝑃 𝑚𝑖𝑛 𝐵 𝑖,𝑗 }, 𝑗 = 1,2, … , 𝐾; 𝑡 = 1,2, … , 𝑀 (3.10) 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 Subsequently, this charging reference matrix is employed to identify the individual charging patterns.
The selection of electric two-wheelers (E2Ws) in a group is determined by the necessary charging energy, ensuring that all vehicles are fully charged by the end of the scheduling period Consequently, the vehicles within the group typically share a similar initial state of charge (SOC) To streamline the process, the algorithm in stage 3 calculates the charging power for each vehicle based on the overall group charging pattern.
𝑁 𝑗[𝑃 1 𝐺 𝑗 𝑃 2 𝐺 𝑗 … 𝑃 𝑀 𝐺 𝑗 ] 𝑇 , 𝑗 = 1,2, … , 𝐾; 𝑖 = 1,2, … , 𝑁 𝑗 (3.12) 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:
Constraint on the amount of required energy for each vehicle:
= ∑ 𝑀 𝑡=1 𝑃 𝑖,𝑗 𝑡 ∆ 𝑡 , 𝑗 = 1,2, … , 𝐾; 𝑖 = 1,2, … , 𝑁 𝑗 (3.14) Individual charging and discharging rate constraint:
Permitted depth of discharge constraint:
The stage 1 algorithm, as detailed in Section 3.3.1, comprises two key functions Function 1 aims to determine the charging reference vector by utilizing information about vehicles at the station, projected conventional load data, and anticipated photovoltaic (PV) power output Meanwhile, Function 2 serves as a constraint checking function to ensure the validity of the results.
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) to (2.19) during each timeslot, check for any violations and implement necessary corrections Subsequently, redistribute the charging power from periods that do not meet the requirements to others, ensuring that no time slot exceeds the established power limits This process results in an enhanced 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 in this process involves calculating the total energy stored in batteries and assessing any violations of maximum battery capacity during each time slot When violations are identified, the charging energy during the affected periods is redistributed to other timeslots This reallocation must take into account the limits on charging power.
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
Based on the charging reference vector obtained after stage 1, stage 2 manages to allocate the total charging power at each timeslot to EV groups (Figure 3.6) The stage
2 algorithm consists of the following functions:
Function 3 - Preliminary group charging pattern finding
Summary
In Chapter 3, we introduce a novel three-stage power allocation algorithm that streamlines the process of power distribution without relying on complex multivariable optimization problems Unlike traditional centralized methods, our approach focuses on the desired total load profile for effective power allocation The algorithm involves identifying preliminary charging profiles, checking constraints, and reallocating power until all requirements are met This method significantly reduces both computational complexity and time, particularly in scenarios where the number of electric two-wheelers (E2Ws) at charging stations exceeds that of electric car charging stations, resulting in a greater number of optimal variables to manage.
The total load profile is strategically developed to achieve load leveling, peak clipping, and valley filling Its capability for pre-computation allows it to be tailored for various optimization objectives, making it highly adaptable for diverse applications.
The simulation results across various scenarios demonstrate that the implementation of the proposed algorithm significantly enhances the total load profile, effectively minimizing load fluctuations, filling in load valleys, and reducing peak loads.
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.
OPTIMAL CHARGING ALGORITHM BASED ON
Chapter objectives
While 2 or 3-stage power allocation algorithms effectively tackle the charging scheduling challenges for a significant number of electric vehicles (EVs), particularly at E2W charging stations, they still face 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, charging behavior is inherently dynamic, necessitating further exploration of its impacts As new vehicles arrive or depart, the charging schedule must be adaptively adjusted to accommodate these changes.
The algorithm's key benefits lie in its grouping and power allocation methods, which significantly reduce computational demands compared to optimizing each vehicle individually This section introduces a real-time scheduling algorithm that dynamically groups vehicles based on the current timeslot The E2W data is continuously updated with any changes in vehicle status, and scheduling is executed whenever there is a change in the current timeslot.
The grouping approach effectively minimizes computational complexity in the coordinated charging of large-scale electric vehicle (EV) populations, which is essential for real-time performance in practical applications To ensure efficient decision-making processes, the scheduling framework must also accommodate realistic dynamics and uncertainties.
To effectively manage the desired state of charge (SOC) and drivers' expected departure times, a user interface (UI) is essential for gathering the required information each time a vehicle connects to the station Although the design of this UI is not covered in this research, it is assumed that the scheduler has access to the necessary 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
Most studies on electric vehicle (EV) scheduling divide a scheduling day into 96 equal timeslots, each lasting 15 minutes, which is a common temporal resolution for demand side management (DSM) schemes When a vehicle arrives at time 𝜏 𝑎 and departs at time 𝜏 𝑑, it is assigned 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 optimize charging scheduling accuracy, 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 𝑜𝑏𝑗 = 1
The objective function (4.3) focuses on minimizing the difference between the total load at each timeslot and the average load during the parking period This approach aims to achieve a load profile that is as flat as possible, ensuring a more balanced energy distribution over time.
The load variance minimization problem has been established as a convex optimization problem, offering two significant advantages Firstly, it allows for rapid and efficient solutions using commercial solvers, which is crucial for real-time dispatch Secondly, the objectives can seamlessly integrate as constraints in other electric vehicle (EV) charging functions, such as minimizing system operating costs or maximizing aggregator profits Additionally, it is well-recognized that the global optimal solution for quadratic programming (QP) problems can be efficiently determined in polynomial time, resulting in an overall optimization problem with 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: min𝑥
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 requires a specific problem format defined by the parameters {𝐻, 𝑓, 𝐴, 𝑏, 𝐴 𝑒𝑞 , 𝑏 𝑒𝑞 , 𝑙, 𝑢}, where 𝐻 and 𝑓 are mandatory, while the others are optional To use alternate quadratic programming (QP) formulations, it is necessary to adjust them to fit this required structure; for instance, an inequality constraint such as 𝐴𝑥 ≥ 𝑏 can be reformulated accordingly.
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 ∑ (x 𝑡 − (𝜇⏟ 𝑎𝑣𝑒 𝑆 + 𝑃 𝑡 𝑃𝑉 − 𝑃 𝑡 𝑛𝑜𝑛𝐸𝑉 )
𝑡 𝑎 is the arrival timeslot of 𝐸𝑉 𝑖
𝑡 𝑑 is planned departure timeslot of 𝐸𝑉 𝑖 , and
𝑎 + 𝐴 𝑖 (𝐹𝑆𝑂𝐶 𝐸𝑉𝑖 −𝐼𝑆𝑂𝐶 𝐸𝑉𝑖 ) Δ 𝑇 ) (4.11) 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 a MATLAB solver In this study, the interior point method is chosen as the preferred approach for optimization.
The complexity of the optimization problem is determined by the number of timeslots and electric two-wheelers (E2Ws) at the charging station The proposed algorithm utilizes a group-based strategy to establish an optimal charging profile for a representative E2W from each group, subsequently adjusting it to accommodate the other E2Ws Therefore, the scale of the optimization challenge is affected by both the number of groups and timeslots involved.
To tackle the challenge of real-time charging scheduling for electric two-wheelers (E2Ws) amidst behavioral uncertainties, a receding horizon framework is proposed This approach allows charging stations to continuously update the status of E2Ws, incorporating both existing vehicles and any new arrivals or departures during the current timeslot.
Research indicates that receding horizon control (RHC) effectively extends static optimization methods to real-time scheduling, accommodating dynamic system changes RHC operates by optimizing over the next T time-steps, executing the decision for the first time-step, and then re-optimizing for the following T time-steps using newly available measurement data In essence, the RHC framework involves a continuous cycle of optimization, execution, and adaptation.
Figure 4.2 Illustration of receding horizon time window [64]
The optimization problem is updated at each iteration to reflect changes in loads throughout a 24-hour period, defined as [1, 24] In the first iteration, the optimization is performed over the full time horizon, while subsequent iterations adjust the optimization horizon to [2, 24], [3, 24], and so forth, ensuring a dynamic approach to load management.
The article discusses two approaches to the RHC algorithm: the complete 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 retains the existing schedule for current loads and only addresses newly arrived charging requests Although the partial scheme is suboptimal compared to the complete scheme, it offers the advantage of reduced computational complexity.
The receding horizon approach offers a significant advantage by allowing for the integration of updated information in E2W scheduling This includes newly received charging requests and unforeseen terminations of charging sessions For instance, customers can connect their electric vehicles (EVs) to the charging station at any point during the scheduling period, while existing processes may be abruptly halted, such as when a user needs to end a charging session unexpectedly for an impromptu trip.
Charger efficiency is influenced by operating power and working voltage, but changes are minimal, allowing us to treat them as constant for simplicity Additionally, for the sake of scheduling, onboard batteries are assumed to be ideal energy storage systems, neglecting their transient performance during charging and discharging.
To prolong battery life, 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 indicates that the minimum SOC should be set at 20%, while the maximum SOC typically falls between 80% and 100%.
[32], [36], [41], [48], [105] In this study, it is assumed that E2Ws do not discharge if their SOC value is below 20 % and E2Ws can be charged up to a maximum of 100 %
The smart charging station can identify the onboard battery's nominal capacity and the state of charge (SOC) through a vehicle information system (VIS) This system communicates essential E2W information to the station controller, allowing for the determination of permitted charging and discharging power By interacting with the VIS and the E2W owner, the E2W can be defined by a specific vector of parameters.
Case study and simulation results
This section presents and analyzes various case studies to evaluate the effectiveness of the algorithm Additionally, it includes a scenario where electric two-wheelers (E2Ws) are not permitted to discharge, highlighting the significance of battery wear and tear costs as a potential barrier.
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 SOC
▪ 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 However, only G2V is permitted
▪ Scenario 4.2 – Smart charging: E2Ws follow the proposed algorithm and allow bi-directional power exchange (both charging and discharging are permitted)
To evaluate 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 case study focuses on a charging station for university staff, designed to accommodate 150-170 vehicles throughout a full working day The second case study examines a charging station for students, which can accommodate 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 a max speed charging scheme for electric two-wheelers (E2Ws), charging begins at high power immediately upon connection, leading to a peak load at the start of working hours This creates a significant disparity between peak and off-peak loads, particularly evident in January, where 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 at 12:00 PM.
Electric two-wheelers (E2Ws) often finish charging early in the morning, leading to inefficient use of photovoltaic (PV) power for charging During months with high solar radiation, such as May, June, and July, the total load during peak PV power output can become negative, indicating that excess PV power is being injected into the distribution grid.
In scenario 3, evenly distributing the charging power from arrival to departure maintains the total load shape, aligning it with the net load (Figure 4.6c) Nevertheless, the overall load experiences an increase due to the combination of conventional load and charging load.
The smart charging scheme significantly enhances the total load profile, particularly in scenario 4.1, where electric two-wheelers (E2Ws) do not discharge, failing to alleviate peak load times (Figure 4.8) Notably, during peak netload periods, such as around timeslot 5 in the morning and timeslot 37 in the afternoon, E2Ws refrain from charging to prevent further peak load increases (Figure 4.7) Instead, they charge at a high rate during off-peak hours that align with peak solar power generation, effectively demonstrating the benefits of valley filling.
In scenario 4.2, the peak shaving effect is evident as the electric two-wheelers (E2Ws) discharge energy during peak periods, significantly reducing the overall peak load This scenario shows the most improvement in the total load profile compared to others However, in the final timeslots of the working day, the E2Ws refrain from discharging to maintain the necessary state of charge (SOC) for departure, leading to increased power demand during these times The effects of peak shaving and valley filling are clearly illustrated in the accompanying figures.
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
Figure 4.11 Load variance in different scenarios
Table 4.1 and Figure 4.11 illustrate the load variance across various scenarios, highlighting that the maximum speed charging scheme results in the most significant load fluctuations, whereas the average charging scheme maintains a more stable load.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec L oad v ar ian ce ( W 2 )
Load variance in different scenarios
Scenario 1 Scenario 2 Scenario 3 Scenario 4.1 Scenario 4.2 improve the overall load profile The load profile shows the most notable improvement when implementing the RHC based algorithm Among scenarios 4.1 and 4.2, the load variance in scenario 4.2 is lower, resulting in a better overall load profile This is because in scenario 4.2, E2Ws permit discharging to reduce the peak load during high- demand hours
Figure 4.12 Load variance in the two proposed algorithms
Figure 4.12 compares load variance in scenario 4.2 with the three-stage power allocation algorithm, revealing no significant differences between the two methods However, the RC-based algorithm excels in dynamic and real-time scheduling, effectively managing fluctuations and uncertainties, which enhances its practicality for scheduling large-scale 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
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec L oad v ar ian ce ( W 2 )
Three-stage power allocation RC based algorithm (Scenario 4.2)
The station has the capacity to accommodate up to 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
The study analyzes electric two-wheelers (E2Ws) with a mean arrival time of 16:45 and a standard deviation of 12 minutes Morning shift E2Ws are assigned IDs from 1 to 225, while the afternoon shift IDs range from 226 to 450 A random generator is utilized to create 10 ID patterns for various scenarios, including arrival and departure times for both morning and afternoon shifts Subsequently, one random ID pattern is chosen as 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 behavior A random generator was employed to create multiple E2W population sets, followed by random selections for both morning and afternoon shifts, as detailed in Appendix 2.2.
Summary
This chapter presents a validated optimal charging algorithm for photovoltaic (PV)-integrated electric two-wheeler (E2W) charging stations The algorithm's performance and effectiveness are thoroughly analyzed through multiple case studies.
This study enhances the approach of previous work [10] by employing an iterative scheduling method for electric two-wheeler (E2W) charging, utilizing updated E2W data Unlike work [10], which primarily aims to minimize the overall daily peak power, this research not only addresses peak power reduction but also incorporates load leveling and generates individual charging profiles for improved efficiency.
The receding horizon policy discussed in [10] aimed to synchronize vehicle charging to ensure power consumption aligns with current or anticipated peak power levels; however, the individual charging profiles were not clearly outlined in the study.
The proposed RHC-based algorithm combines the benefits of a group-based approach with the RHC framework and mathematical optimization tools to effectively address the real-time optimal charging problem By utilizing the RHC framework, the algorithm can manage uncertainties in charging behavior, allowing for dynamic scheduling at each timeslot As the timeslot progresses, the charging station updates the EV table and adjusts the grouping, ensuring optimal charging solutions are continuously implemented.
OP assigns group profiles to individuals, facilitating efficient corrections This approach significantly reduces computational demands, enabling the practical scheduling of numerous Electric Two-Wheelers (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, particularly when comparing Vehicle-to-Grid (V2G) and non-V2G scenarios While non-V2G does not alleviate peak load, V2G effectively enables electric two-wheelers (E2Ws) to discharge energy during high demand periods, facilitating peak shaving Both V2G and non-V2G strategies excel at filling load valleys and leveling demand, outperforming uncontrolled and average charging methods.
Non-continuous working shifts at charging stations, particularly for students, can lead to significant load fluctuations, especially when high solar output coincides with shift changes In contrast, charging stations for shift-working factories require careful management during transition periods due to a sudden influx of vehicles A flat load characteristic in these factories suggests that average charging can be an effective and straightforward solution without drastically impacting the overall load profile For apartment building charging stations, where electric two-wheelers (E2Ws) are parked overnight, smart charging schemes primarily enhance the load profile at night, failing to optimize solar energy utilization during the day These challenges highlight the need for additional solutions, such as battery energy storage systems (BESS), to effectively manage energy demands.
Through multiple case studies, including university charging with two non-continuous study periods, office charging, overnight charging at apartment buildings, and charging in three-shift factories, the algorithm's effectiveness in load leveling, valley filling, and peak shaving has been successfully validated.
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 Electric Two-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 optimizing charging and discharging schedules to determine power output across various timeslots To implement these long-term schedules effectively, it's 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 Additionally, a control program is developed to manage the charging and discharging of E2Ws at specified 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
This section explores the real-time charging and discharging responses of electric two-wheelers (E2Ws) based on the long-term charging schedule, which provides individual vehicle charging profiles at specific timeslots Utilizing the real-time model introduced in Chapter 2, predetermined power values dictate the charging commands: a positive power value indicates that the E2W battery is charging, while a negative value signifies that the battery is discharging.
Figure 5.1 illustrates the total charging power profile of the charging station on a typical day
This profile illustrates the charging power at each time step throughout the scheduling horizon, highlighting the charging responses with three specific power values: 5.6 kW, -1.95 kW, and -6.56 kW across three timeslots Positive values indicate energy consumption by the station, while negative values reflect energy discharge from the station.
Figure 5.1 Typical total charging profile
Figure 5.2 Total charging current at 5.6 kW charging power command
The current response at the charging station is depicted in Figures 5.2 and 5.3, highlighting the transition when the power command shifts from 5.6 kW (charging) to -1.95 kW (discharging) This abrupt change demonstrates that the current supplied closely aligns with the reference current throughout the process.
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 response 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 power consumption of the charging station (P_Batt).
A power balance is consistently maintained among solar power, conventional loads, charging loads, and grid consumption During the observation period, photovoltaic (PV) power effectively supplies energy to 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 in a typical charging station The charging rate and the steepness of the SOC curve over time are influenced by the charging or discharging power levels Throughout the charging and discharging processes, continuous monitoring of battery voltage and current is essential to ensure they remain within the maximum allowable limits.
The simulation results confirm that the charging station can effectively meet the long-term charging schedule using the derived charging and discharging power commands This highlights the practicality of the long-term charging scheduling algorithm while accounting for real-time responses.
Testing workbench set up
5.3.1 The technical scope of the test bench
The initial phase of test bench development involves defining the scale and technical scope of the prototype, which is essential for establishing the experimental scale, charging and discharging power, and required features to guarantee functionality, reliability, and safety This step is vital for identifying technical specifications, measurement parameters, and testing procedures.
The experimental charging station, illustrated in Figure 5.7, consists of 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 are equipped with batteries ranging from 36-48 V and a capacity of 12 Ah, conforming to regulations that limit motor power to below 250 W and a maximum speed of 25 km/h (as per QCVN 75:2019/BGTVT and QCVN 68:2013/BGTVT) These e-bikes can cover distances of approximately 50-60 km In contrast, electric motorcycles feature larger batteries (48-60 V; 20 Ah) and more powerful motors (800 W or higher).
1200 W), enabling them to travel a maximum distance of around 70-80 km
Most electric two-wheeler (E2W) chargers operate on single-phase AC power from residential sources, delivering an average charging power of approximately 400 W The typical charging duration is between 3 to 5 hours, while the discharge power can reach up to 1200 W.
The test bench is specifically designed to analyze the real-time charging and discharging responses of batteries In the experimental model, the charging and discharging power is varied from 0 W to 400 W, utilizing batteries with appropriate specifications.
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 typically sees around 50% of the total charging time spent in the constant voltage (CV) stage To mitigate the lengthy charging times associated with this method, various solutions have been proposed, including multistage constant current (MCC) techniques, pulse charging, boost charging, and variable current profiles, all aimed at optimizing the charging current during the constant current (CC) phase Typically, higher current levels are utilized in the initial stages of the CC phase to enhance efficiency.
The charging rate is directly proportional to the charging power, with the primary control of both occurring during the Constant Current (CC) stage To evaluate various charging responses, tests are conducted on the test bench specifically focusing on the CC stage.
Converters are chosen to ensure 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
The test bench design, as shown in Figure 5.8, features a charging station that incorporates distinct buck/boost converters and is equipped with two types of inverters: a grid-tie solar inverter and a single-phase grid-tie inverter, which transforms DC link voltage into AC voltage.
To ensure optimal inverter performance, it's essential to use buck/boost converters on the test bench, as they adjust the battery voltage to meet the inverter's minimum input voltage requirements during discharging Additionally, these converters play a crucial role in charging the battery by converting the DC voltage from the DC link or AC/DC converter to the appropriate level needed for effective battery charging.
For small-scale electric two-wheeler (E2W) charging stations, a single-phase grid-tie inverter is ideal due to its modest charging power and battery capacity In contrast, larger-scale stations can incorporate multiple substations connected to different phases, facilitating easy expansion in power and capacity to accommodate more vehicles However, when adding new substations to a three-phase grid, it is crucial to consider phase load balancing and the collaboration between sub-controllers, which is beyond the scope of this dissertation.
In the experimental setup, the single-phase grid-tie inverter is unidirectional due to the limitations of the small-scale pilot prototype and available equipment To facilitate DC charging, an AC/DC converter is used to convert AC electricity from the grid Additionally, charging can occur from the DC link, provided that the total charging power is less than the total discharging power of the remaining batteries It is important to note that if bidirectional grid-tie inverters are utilized, the use of an AC/DC converter becomes unnecessary.
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 it flows through R14 to Battery 1.
To discharge Battery 1, its DC voltage is first processed through R11 and then increased by a boost converter The elevated DC voltage subsequently passes through R13 and a programmable buck converter, which adjusts it to the suitable 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 Simulation results from the Simulink model, along with experimental data from the test bench, confirm that the optimized charging algorithms for electric two-wheelers (E2Ws) are practical in real-world scenarios Notably, the response time for these charging and discharging commands is minimal when compared to the length of a single timeslot.
The charging and discharging voltage and current setpoints are stored in data tables within the controller's memory and 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 setup and operation have validated the practicality of this solution in real-world scenarios.
However, the test bench still has some limitations that need to be improved, such as:
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, designed as a pilot prototype, effectively verifies the feasibility of optimal charging algorithms under practical conditions, aligning with the research objectives Future developments of the test bench will address the other mentioned limitations.
This chapter is mainly based on:
Ngoc Van Nguyen and Huu Duc Nguyen (2022) conducted a study validating real-time responses in relation to long-term charging schedules for PV-integrated electric two-wheeler charging stations Their research was published in the Journal of Science and Technology - HaUI, Volume 58, Issue 5.
[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, highlighting key research outcomes 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 long-term charging schedules for photovoltaic-integrated electric two-wheeler charging stations Their research, published in the Journal of Science and Technology - HaUI, emphasizes the importance of optimizing charging strategies to enhance the efficiency of electric two-wheelers powered by solar energy.
Ngoc Van Nguyen, Khac Nhan Dam, and Huu Duc Nguyen (2022) conducted a study on the architectures and control algorithms for electric vehicle and electric two-wheeler charging stations in Vietnam Their research, published in the Journal of Science and Technology - HaUI, highlights the importance of developing efficient charging solutions to support the growing demand for electric vehicles in the country The findings emphasize innovative design and control strategies that can enhance the performance and accessibility of charging infrastructure.
[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
[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
Huu D.N and Ngoc V.N (2021) conducted a study on the growing trend of transport electrification in Vietnam, focusing on the viability of photovoltaic (PV)-integrated charging stations for electric two-wheelers Their research highlights the importance of sustainable energy solutions in the transportation sector, particularly in the context of electric mobility The findings emphasize the potential benefits of integrating renewable energy sources into charging infrastructure, supporting the transition to electric vehicles in Vietnam.
Conference on Power, Energy and Electrical Engineering (CPEEE), Shiga,
Ngoc Van Nguyen and Huu Duc Nguyen (2020) conducted a comprehensive technical and economic assessment of photovoltaic (PV) based charging stations specifically designed for electric bicycles at Electric Power University Their findings, published in the EPU Journal of Science and Technology for Energy, highlight the viability and advantages of integrating renewable energy solutions into electric bicycle infrastructure, emphasizing both efficiency and cost-effectiveness.