The thesis aims to propose the Heuristic method algorithm applied to the problem of power grid restructuring with the objective of reducing power loss in case of having / without distributed power source connecting to the distribution grid. Study the effect of distributed power source when connecting to the distribution grid, affecting the power grid restructuring problem. Proposing algorithms according to the new method of Meta Heuristic for grid reconstruction problem with the objective of reducing power loss in the cases with / without and in case of considering the position and capacity of the power source scattered when connecting distribution grid.
MINISTRY OF EDUCATION AND TRAINING VIETNAM ACADEMY OF SCIENCE AND TECHNOLOGY GRADUATE UNIVERSITY SCIENCE AND TECHNOLOGY …………………………***……………………… Ng NGUYEN TUNG LINH BUILDING ARTIFICIAL INTELLIGENCE ALGORITHM FOR RECONFIGURATION DISTRIBUTION NETWORK PROBLEM Code major: Control theory and Optimization control (Automation and Control Engineering Technology) Code: 62 52 02 16 SUMMARY OF ENGINEERING DOCTORAL THESIS Ha Noi - 2018 INTRODUCTION Sep up the problem According to the statistics of Vietnam Electricity Corporation, total power loss in recent years is about 9-15% of electricity production volume, in which distribution of the electrical network holds 5-7% Then, researching methods for power loss reduction in the distribution network is very urgent demand Reconfiguaration distribution network is one of methods to minimize power loss which is researched most Currently, solution of reconfiguaration distribution network problem is optimal one under NP-hard class and then in order to solve this problem, there are some following methods to approach and solve the problem: - Seek by optimal mathematical method - Seek heuristic to seek enough good solution - Seek near correct solution by natural emulation algorithm such as: Simulated Annealing algorithm, genetic algorithm, herd optimum, etc Objectives and tasks of the thesis Propose algorithm by Heuristic method to apply for the problem of reconfiguaration distribution network with objective function for capacity loss reduction considered in the case that there is/is not dispersal power connected into electrical distribution network Research the effect of dispersal power when connect to the electrical distribution network, affecting the network reconstruction problem Propose algorithm by new Meta Heuristic method for reconfiguration distribution network problem with a goal of power losses reudction in the the case that there is/is not as well as consideration to the location and capacity of dispersal power source when it is connected to electrical distribution network Scope of research Reconfiguaration distribution network problem with objective function of capacity loss reduction in the case that there is/is not dispersal power source connected to the network The problem of electrical distribution network under consideration to the location and capacity of dispersal power source when connect to electrical distribution network with objective function of capacity loss reduction Researching method The research is applied Heuristic method and artificial intelligence algorithm for the problem of reconfiguaration distribution network Use emulation method to check accuracy of proposed algorithms through checking sample problems of IEEE New points of the thesis: the thesis achieves some the following researching contents: Propose method for the problem of reconfiguaration distribution network on “Heuristic” experience rules with objective function of capacity loss reduction under the consideration of two cases: there is not connection to dispersal power source and there is connection to dispersal power source Propose the improvement for algorithm of metallurgical emulation for the problem of reconfiguaration distribution network with subjective function of power loss reduction Propose using genetic algorithm for the reconfiguarationin the consideration of the location and capacity of dispersal power when connect to distribution network with objective function of capacity loss reduction Practical value of the thesis: Researching results of the thesis achieved some practical value in the problem of re-configuration solution and actual applications The method which proposes by the way of researching Heuristic, again can affirm that application of experience rules and optimal method for optimum problems are still used well in some cases The researching method according to MetaHeuristic for the problem of electrical network re-configuration is proposed by the author to use Simulated Annealing algorithm and genetic algorithm for the problem of reconfiguaration distribution network with objective function of capacity loss reduction in the case there is dispersal power and there is not dispersal power as well as the case considered to the location, dispersal power capacity connected to distribution network This is supporting tool for deciding design and operation of distribution network when participate into competitive electrical market Thesis layout: The thesis is divided into chapters Chapter 1: General view of distribution network and the problem of reconfiguaration distribution network Chapter 2: Heuristic method for the problem of electrical reconfiguaration distribution network Chapter 3: MetaHeuristic method for the problem of electrical distribution problem Chapter 4: Genetic algorithm for the problem of Reconfiguaration distribution network in the consideration of dispersal power planning CHAPTER 1: OVERVIEW OF ELECTRICAL DISTRIBUTION NETWORK AND THE PROBLEM OF ELECTRICAL DISTRIBUTION NETWORK RECONFIGURATION 1.1 Introduction of electrical distribution network 1.1.1 Characteristics of electrical distribution network Electrical distribution network is an important component in the supply of electricity from the production site to the electricity consumers, spreading across the whole territory of the country The distribution network can be designed with a loop structure or a beam structure, but for technical reasons and operating conditions, it is operated in a beam structure Thanks to the open operating structure, the relay protection system only uses over current relay Distribution network operating conditions must always meet the following conditions: - Open operating structure - All loads are provided with electricity, pressure drop within the allowed range - The relay protection system must be changed suitably - Lines, transformers and other equipment are not overloaded 1.1.2 Introduction of electrical reconfiguration distribution network a Introduction of electrical reconfiguration distribution network: Electrical reconfiguaration distribution network problem is the status control of switching equipments in the distribution network, in some operation cases to ensure for some objectives b Classification of electrical reconfiguaration distribution network *Classification by objective function: Problem 1: Determine electrical network by load diagram in certain duration for the operation cost to be minimum Problem 2: Detetrmine non – changeable electrical network in certain duration for power loss to be minimal Problem 3: Determine structure of electrical network at a certain time for capacity loss to be minimal Problem 4: Re-configure distribution network to balance load (among lines, transformers at stations) to improve loading capacity of electrical network Problem 5: Recover electrical network after incident or cutting off power for repairing Problem 6: Determine structure of electrical network by many objectives such as: capacity loss is minimum, highest loading balance, minimum loading transmission, minimum pressure drop at the end of network concurrently happens out, affects of dispersal power source to reconfiguration electrical distribution network, etc Problem 7: Determine electrical network to ensure for objectives of power reduction and stopping supply or improving reliability of power supply *Classification by researching methods: Classification of methods for electrical distribution network reconfiguration solutions Analytic method Heuristic method (Experience law) Meta Heuristic (Use AI algorithm) Figure 1.3 Classification of reconfiguration distribution network by researching method * Some researching results of reconfiguration distribution network Problem – Determination of reduction network structure P is the most important problem The problem determines the structure of effective capacity loss reduction network – problem is an important one, it is considered as one module to solve other problems in the system of reconfiguration distribution network It is proved through alogorithms of last researches Problem – Minimal function of operation cost This objective function is very function with distribution network with flexible and low load transmission cost in the operation; network structure can be changed in many times in the day This function is very suitable with distribution network with flexible and low load transmission cost, network structure can be changed in many time in the day Problem – Minimal function of power loss In the fact, evenly in developed industrial countries, load transmission cost affects greatly the decision on network structure changes because sometime these costs are bigger than gained benefits Thus, for problem – Determine non-changeable electrical network structure in surveying time for power loss to be minimal Problem – Balance capacity among lines and transformer stations This algorithm can be applied suitably for areas which are usually affected by overload or incertain loads, In [91], Tim Taylor, etc Problem – Re-structure distribution network after incident This is objective which is mentioned by almost scientists in their researches Problem – Re-structure network under objective function In the operation of distribution network, there are many operation objectives which the controlid must select suitably with characteristics of local electrical networks 1.1.3 Current status of electrical distribution network in Vietnam Current status of Vietnam’s electrical network - By history of development and in each country, there many grades of distribution voltages and these grades in regions are also different each other (6.6, 10, 15, 22, 35 kV) - Recloser and loaded cutter (LBS) are not controlled remotely and quantity is not considerable then switching cost is big and time for loading transmission is long Problems during the process of Vietnam’s electrical grip operation are presentated in problem to problem 1.1.4 Model of electrical reconfiguration distribution network a Mathematical model of reconfiguaration distribution network For mathematics, reconfiguration network is the problem for planning discrete curvilinear by the capacity line running on branches, at [78] the model as presented below n n n n Minimal function F = Cij I ij Rij Cij Lij i 1 j 1 (1.4) i 1 j 1 Satisfying with: n Sij D j (1.5) Sij Sij max DVij DVij max (1.6) (1.7) i 1 n Sft Sft max (1.8) ft (1.9) ft f t Objective function is interrupted, it is very difficult to solve reconfiguration distribution network by the method of mathematical analytics traditionally [11] b Some assumptions to simplize reconfiguaration distribution network Reactive power compensation when consider reconfiguration distribution network Ross Baldick [49]: “It is possible to pass away reactive power compensator in the network after solving the problem of electrical distribution network structure determination.” Some other assumptions for the problem of electrical distribution network reconfiguration - Switching operation to transmiss load does not make electrical system to be incertain - Voltage at loading buttons does not change and has value nearly equal to Uđm - When solve capacity distribution problem in the beam network, pass away power loss - Reliability of power supply in the distribution network seems not to change when network structure changes 1.2 Overview of researches on re-configuration solutions with objective functions of power loss reduction 1.2.1 Heuristics combination and optimization a Merlin and Back algorithms – close loop technique b Other algorithms 1.2.2 Pure algorithms on Heuristics a Algorithms of Civanlar and partners – Technique of branch change b Some other algorithms 1.2.3 Algorithms based on artificial intelligence a Use ANN to re-structure electrical distribution network b Re-structure network by gen algorithm c Re-structure network by algorithm of metallurgical emulation d Re-structure by Ant Colony Algorithm – ACS e Re-structure by Tabu Search – TS f Re-structure network by Fuzzy Logic method g Re-structure network by herd algorithm h Re-structure network by expert system 1.3 Comments and evaluation 1.3.1 Electrical reconfiguration distribution network with the objective of electrical network control - Almost reconfiguration problems approach different objectives but all of them use problem – to determine structure of electrical distribution network to reduce power loss as main modules during the re-solution problem - When solve problem 3, algorithms baseds on the researching method by the technique of branch change of Civanlar [17] or close loop technique of Merlin and Back [65] then they usually fall into local minimal and use algorithms of artificial intelligence and evolution in which the most effective one is Gen algorithm and algorithm of metallurgical emulation - Algorithms in problem find out solutions to reduce directly power loss function value for whole network and then it wastes a lot of time because the problem of power distribution must be solved in many times during the repeating process 1.3.2 Technique for solving electrical network re-structure problem - When approach electrical reconfiguration distribution network, scientists determine that mathematical analystics is not as effective as search algorithm - Search algorithms are used in the electrical reconfiguration distribution network can be divided into three main directions such as: heuristic search algorithm combines with optimal algorithm; algorithm only uses heuristic rule in expert system; use artificial intelligence including expert system, genetic algorithm, noron network, etc - Almost algorithms of network re-structure not show out that network structure has minimal power loss, not prove the point to find out minimal point in overall Under above reasons, in the own research, the author propses the search of electrical reconfiguration distribution network by directions: research by the way of using Heuristic and MetaHeuristic methods with objective function on power loss reduction CHAPTER 2: HEURISTIC METHOD FOR ELECTRICAL RECONFIGURATION DISTRIBUTION NETWORK 2.1 Heuristic method for electrical reconfiguration distribution network 2.1.1 Introduction Heuristic algorithm is an expansion to algorithm definition It presents problem solution with the following characteristics: - Usually seek good solution (but not surely for the best solution) - Solve problem by Heuristic algorithm easily and quickly to give out results rather than optimal algorithm - Heuristic algorithm is regularly presented naturally, nearly with the thinking way and action of human In the electrical reconfiguration distribution network, Heuristic algorithm was used since long time Since 1975 until now, there are nearly 80 researches on this problem by using Heuristic as published on famous magazines 2.1.2 Emulation of electrical reconfiguration distribution network Emulation of electrical network and conventions: Convention: - IPi, IQi are action and reactive current of the branch i; Ri electrical resistance of branch i, - Call k as electrical switch number to ensure for open electrical network operation On the branch, there is open electrical switch at the jth with the sign: MNj and j = k Convention choose a set of independent loops in order for each independent loop to cross unique one open electrical switch MNj; the positive direction is counter to the switchwise in the figure 2.1 as hereafter: - Vjh is the set of crossing branches between loop j and loop h; - Vjj is the set under the loop j; - Rj loop is resistance of loop j; - MNj is the branch with openning switch of the jth loop Indicator Aij states the correlation between the jth loop and natural distribution direction at the the ith branch in the open network Aij = 1: when direction of j loop is the same to directions of IPi and IQi; Aij = -1: When direction of j loop is counter to directions of IPi and IQi; Aij = 0: when the ith branch does not belong to j loop b Mathematical description of load re-distribution operation Following with the figure 2.3 Consider a simple distribution network consisting of: a single source and a closed loop at the MN branch, as shown in Figure 2.3 (or at the MN branch with IPMN = and IQMN = 0) We need to determine the key to open (on the circuit) to minimize power loss (Open key may include on-openning key) Assuming that the result is a switch on the MN branch and un switch on the branch AB, then the change in load distribution can be similar to the in / out of the two poles on the branch MN at the current until the current on the AB line is zero c Conditions for power loss to be minimal after re-distributing P branch load on branches of electrical networks after re-distributing load: P L K I Pi Bil I PlDG Aij I PjMN i 1 l 1 j 1 n sau K I PjMN j 1 K R MNj I QjMN j 1 2 n L K Ri I Qi Bil I QlDG Aij I QjMN i 1 l 1 j 1 Ri (2.5) R MNj 2.1.3 Comments and evaluation - With any distribution network, deriven from a certain (non-optimal) open configuration, if inject/ draw out at the opening switch of an electric current by the formula (2.11), (2.12) to create loop currents through branches, the objective function P will be smallest - The switch dependent ring number on the loop to inject in / draw out an electric current Theoretically, if switch is selected for the loop current to inject in or draw out equally to 0, then it is the optimal point - The expression (2.13) is the sum of the voltage drops on the branches of the j independent circuit if the circuit is pure (or homogeneous circuit) This shows that the optimum value of obtained current under (2.11) and (2.12) is the branch line of the closed distribution network When close all electrical switches, the losses P in the distribution network are minimal - - When there are DG resources involved in the distribution network, the optimal expression for the received current will be added to the second component (in expressions (2.11) and (2.12) 2.2 Propose Heuristic algorithm for electrical reconfiguration distribution network 2.2.1 Objective function of the problem Objective funciton of the problem: j j ( opt) vòng j I QjMN (opt) R vong I MN G I PjMN ( opt) R vong R j j j j K j 1 2 K (2.15) j 1 2.2.2 Proposal of new algorithm: Performance procedures follow with two stages as follows: This algorithm has the following characteristics: The objective function shown in this section takes into account the resistance factor of the independent loop This is a new objective function (previous studies often used to reduce the function P = I2R directly, or simply to find the branch to have the smallest current) G function is meaningful as a comparison criterion, so finding the distribution network configuration with increasing P has at least been taken to the problem of determining the power loss increase function (G function) on distribution network This enables the algorithm to be more stronger and faster in finding optimal configurations with P increasing less than loop network G function has just considered as the value of loss P, just considering the resistance factor of the distribution network (R loops), thus it is possible to consider the interactions between the electric switches and the DG to whole distribution network This is difference in comparison with previous studies If we ignore the resistance factor (the "distance" of the electrical switch comparing to the power supply), we usually select the branch with the smallest lines in the distribution network to open first This leads to open electric switches which are far from the power source, in the factthat it is not normally open (because if they are open, the rear loads will not have electricity) Therefore, finding optimum P can avoid the local minimum and does not take the time to re-examine whether all loads are being supplied with electricity It also makes sense to compare the value of the function G when there are many competitive pairs in the distribution network Begin Solve capacity distribution problem in the distribution network with DG and compensator, pass away line resistance sensor Determine independent rings by definitions of distribution network Calculate reduction of G function in each independent ring Calculate reduction of G function in each independent ring Choose closing/opening switch couple in independent ring with most G reduction Correct G Function reduce? Wrong Open remaining switches with the smallest current Process Closeopenning electrical switch and solve capacity distribution problem in distribution network with one closed loop (1 independent loop) Open electrical switch with the smallest current in closed loop Open switch with the next smallest current in closed loop Distribution capacity in the beam distribution network Correct Check pressure drop, overload ? Wrong Wrong Current through openning switch is the smallest one? Correct Correct Implement with independent loops Wrong Process End End Figure 2.7: Diagram of reconfiguaration distribution network algorithm has DG, seek the smallest P 2.3 Emulation and evaluation of researching results 2.3.1 Emulation of researching results Considering the distribution network of 16 nodes with 21 branches; there are open switches; there are DGs proposed by G.Celli in [39] described in Figure 2.7 a Describes the process of finding the network configuration without DG b Describe the process of finding the network configuration when there are DGs at node and node 13 c Describe the process of finding the network configuration when there is a DG generator at node d Describe the process of finding the network configuration when there is a DG generator at node 13 e Evaluation of simulation results: After performing simulations on the sample network and comparing with some other methodologies summarized in Table 2.7 Table 2.7 Results of surveying summary on the distribution network with 16 nodes DG1 – DG2 – P No Open switch PP node node 13 Remark (kW) (kW) (kW) 2, 8, 9, 15, 16, 20 144.17 G Celli [51] 0 No DG 2, 17, 16, 20, 10, 19 92.3 TOPO 0 Proposed 0 2, 17, 16, 20, 10, 19 92.3 method 2, 8, 10, 15, 18, 20 76.1 G Celli [51] 450 630 Have both DG 2, 17, 18, 20, 10, 19 66.3 TOPO 450 630 2, 17, 18, 20, 10, 19 66.3 PP propose 450 630 2, 8, 10, 15, 16, 20 102.6 G Celli [51] 450 DG1 works 2, 17, 16, 20, 10, 19 83.7 TOPO 450 and DG2 Proposed 2, 17, 16, 20, 10, 19 83.7 450 rests method 10 2, 9, 10, 15, 18, 20 82.9 G Celli [51] 630 DG1 rests and 11 2, 17, 18, 20, 10, 19 74.3 TOPO 630 G2 Proposed 12 2, 17, 18, 20, 10, 19 74.3 630 works method In the consideration of example on IEEE sample electrical network with one source and 33 nodes Parameters are presented in (11), using DG (11) Table 2.9 Comparison before and upon performing electrical network re-structure Method Loss (kW) Open switch Repe ated loop The system does no thave DG Initial Proposed method R Srinivasa [75] 203.679 136.87 136.87 SW33, SW34, SW35, SW36, SW37 SW7, SW9, SW14, SW32, SW37 SW7, SW9, SW14, SW32, SW37 - 10 Objective function of loss reduction is determined by capacity current as follows: NL P +Q2 Min f= k i R i i i with i NL i=1 Vi (3.1) * Proposed method Some definitions: Initial state of the network: The initial state before reconfiguration is selected as the initial state Initial temperature: Under Metropolis conditions, the initial temperature T0 is calculated as follows [87]: 𝑇0 = −∆𝐶/𝑙𝑛(0,95) (3.8) Accepted rate According to [87] we implement the metropolis process with the number of times equal to 10n where n is the total number of switches in the system and check the acceptance ratios at each temperature If the acceptance ratio at a state is less than 0.1 then the state is equilibrated at the temperature under the investigation In contrast, the Metropolis process is carried out according to the formula (3.9) 𝑇𝑜𝑡𝑎𝑙𝑎𝑐𝑐𝑒𝑝𝑡𝑒𝑑𝑐𝑎𝑠𝑒𝑠 𝐴𝑐𝑐𝑒𝑝𝑡𝑎𝑛𝑐𝑒𝑟𝑎𝑡𝑖𝑜 = (3.9) 𝑇𝑜𝑡𝑎𝑙𝑖𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑒𝑑𝑀𝑒𝑡𝑟𝑜𝑝𝑜𝑙𝑖𝑠𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑠 Disturbance mechanism From here, the TPM disturbance mechanism [105] is proposed to select the tie and sec switch through selected probability (Closed open) is calculated: SWi S Lj jloop (3.10) S Lj Price function: Price function is defined by the following formula: n Ls Ploss A( x) B x C ( x) 1 n Ls Ploss A( x) B x n Ls (3.11) Temperature reduction process: The proposed temperature reduction process is proposed for formular of the temperature reduction Ti 1 e i 10 T i (3.12) Ending condition: If during the process of the temperature drop, the following expression is satisfied: C x C x C x T Tk 2 T Tk 1 T Tk T T T With as very small number which can be selected randomly, C x T T Tk (3.13) is obsolete value of the function b Apply SA algorithm with loss reduction objective in consideration to connection of dispersal power (DG) into the network Objective function of the problem Min( Plosse ) Min(PRO, Losse PDG, Losse ) (3.18) Binding conditions: (3.19), (3.20), (3.21),( 3.22) 13 Electrical distribution reconfiguration distribution network becomes seeking solution: x=(x1,x2,x3,x4,…xn, PDG1, PDG2…PDGm) in order for objective function (3.10) to achieve value Proposed algorithm in figure 3.5 Begin Initialize x= (x=x1,x2,….Xn) Po, initial temperature T, counter i=0 Use disturbance mechanism TPM to create x, x’, calculate Pr loss Calculate difference x, x’ ∆C= C(x’) - C(x) Correct Decrease emperature Wrong Accepted ratio < 0.1 Correct Balance status Wrong Correct Freezing point Wrong Correct Optimal solution End Figure 3.3: Diagram of SA algorithm for reconfiguration distribution network Check and evaluate results in the sample IEEE network a Consider example in the case that there is not connected dispersal power Consider sample IEEE network diagram with 33 nodes as shown in Fig 3.6 of Baran and Wu [11] Select the parameters α = 1000; β = 1000; ε = 0,1; δ = 1; results are presented as follows: 14 Figure 3.8 The convergence characteristic Figure 3.9 Voltage diagram of nodes Table 3.2: Compare SA algorithm with other algorithms Method Initial structure Method (SA) Heuristc Method Gomes [37] HSA [45] Shirmohammadi [80] Zhu [106] HR[25] Loss (kW) Umin (p.u) 202.68 136.57 136.57 136.57 137.07 0.913 0.932 0.931 0.928 0.928 s33, s34, s35, s36, s37 s7, s9, s14, s32, s37 s7, s9, s14, s32, s37 s7, s9, s14, s32, s37 s7, s9, s14, s32, s37 136.66 139,52 137.54 0.921 0.934 0.921 s7, s10, s14, s32, s37 s11, s28, s32, s32, s33 s33, s14, s7, s36, s28 15 Open switches Begin Initialize x= (x=x1,x2,….Xn) Pdg1, Pdg2, Pdg3, ….Pdgn) Po, initial temperature T, counter i=0 Use disturbance mechanism TPM to create x, x’, calculate Pr loss (calculate value of C energy function)_(10) Calculate difference x, x’ ∆C= C(x’) - C(x) Decrease temperature Correct Wrong Accepted ratio < 0.1 Wrong Correct Balance status Wrong Correct Freezing point Wrong Correct Optimal solution and calculate P gf (3.16) End Figure 3.5 Schematic of SA algorithm for reconfiguration distribution network with DG consideration b Check on sample example of IEEE with electrical network having 16 nodes with DG connection Chedck on electrical network with 16 nodes in [23] Select parameters α = 750; β = 800; γ =0,1; ε = 0,1; δ = 1; 16 Fig 3.11 The convergence process of SA algorithm Fig 3.12 Voltage diagram of nodes Table 3.3: Summary of proposed method comparison with other methods No Open switch 10 11 12 2;8;9;15;16;20 2;16;17;10;20;19 2;16;17;10;20;19 2;8;10;15;18;20 2;17;18;20;10;19 2;17;18;20;10;19 2;8;10;15;16;20 2;17;16;20;10;19 2;17;16;20;10;19 2;9;10;15;18;20 2;17;18;20;10;19 2;17;18;20;10;19 ∆PkW 144.17 92.3 92.3 81.93 66.3 66.3 84.74 83.7 83.7 99.04 74.3 74.3 Mehtod G Celli[39] TOPO [83] SA G Celli[39] TOPO [83] SA G Celli[39] TOPO [83] SA G Celli[39] TOPO [83] SA DG1 Node 0 450 450 450 450 450 450 0 DG2 Node 13 0 630 630 630 0 630 630 630 Note No connection to DG Connection to both DG DG1 works, DG2 rests DG1 works, DG2 rests Consider example about an IEEE power with 33 load nodes whose parameters are shown in Fig [23], (a) (b) Figure 3.14 The convergence process of electrical network with 33 nodes does not include DGs (a) Figure 3.15 The convergence process of electrical network with 33 nodes does not include DGs (b) Table 3.5 Compare SA algorithm with PSO and PSS/ADEPT on the network of 33 nodes Repeated Mehtod Loss (kW) Open switch loop System does not have DG connection Initial 203.679 25-29, 18-33, 9-15, 12-22, 8-21 configuraiton PSO [54] 138.876 7-8, 25-29, 8-9, 14-15, 32-33 23 17 SA 138.876 7-8, 25-29, 8-9, 14-15, 32-33 PSS/ADEPT 111.45 7-8, 28-29, 9-10, 14-15, 32-33 The system has DG connection PSO [54] 111.45 7-8, 28-29, 8-9, 14-15, 32-33 15 SA 111.45 7-8, 28-29, 9-10, 14-15, 32-33 12 PSS/ADEPT 111.45 7-8, 28-29, 9-10, 14-15, 32-33 3.3 Comments and evaluation: Algorithm meaning: In this research, the author proposed to use the SA algorithm for the reconfiguration of electrical distribution network in two cases: there is no dispersal power connection and there is dispersal power connection In such contents, the contribution of the research was to partially improve the SA algorithm for better results, specifically: In the disturbance mechanism, the selection of closed loop to open, used the levels of switch in order to perform the process of closing and openning the closed loop, the price function of the system is taken into (3.10) to ensure for objective function (3.1) to achieve value and satisfies the constraints of the problem Through "penalty" coefficient, the process of reducing the temperature proposed by the authors is used by (3.13) shortening the time when the temperature is high, decreases rapidly and when a certain temperature level can be reached optimal configuration, the temperature will drop slowly, ensure no errors (errors) and facilitate easy local minimal point to be out of local optimum point to the overall optimization of the structure formation process The research also proposed the stop criteria of the system when reach the optimal structure (3.14) for the convergent problem 18 CHAPTER GENETIC ALGORITHM FOR ELECTRICAL RECONFIGURATION DISTRIBUTION NETWORK IN THE CONSIDERATION OF DISPERSAL POWER PLANNING 4.1 The method uses genetic algorithm (GA) for the electrical reconfiguration distribution network 4.1.1 Introduction to genetic algorithm 4.1.2 Some characterisitics of genetic algorithms 4.1.3 Some related studies Through the research of a number of related publications, we have received comments as belows: Comment: In generally, there are methods of locating and DG capacity in current scientific research that can be summarized into four groups: (1) Determine the location and capacity of the optimal dispersion sources on the original distribution network by optimal algorithms Then use the optimal algorithms to determine the network structure with the smallest loss (2) Determine the network structure with the smallest loss by optimal algorithms Then, determine the location and capacity of the optimal dispersion sources on the optimum distribution network (3) Determine the location of dispersal sources based on indicators such as voltage stability, voltage sensitivity at nodes Then use the optimal algorithm to determine the capacity, combining with the problem of network re-configuration (4) Use optimal algorithms to solve the problem of determining the location, dispersal source capacity and optimal beam operation 4.2 Apply genetic algorithm for reconfiguaration distribution network 4.2.1 Description of the problem and objective function Problem: The objective function of the power loss reduction on the distribution network, the maximum transmission power of the dispersal sources is also considered as the objective function of the multi-objectives: 𝑁 𝐷𝐺 𝐹 = min(𝑎 ∑𝑁𝑏𝑟 𝑖=1 𝑘𝑖 𝑅𝑖 |𝐼𝑖 | + (1 − a) ∑𝑗=1 (1 − 𝑃 𝑃𝐷𝐺𝑗 𝐷𝐺𝑗𝑚𝑎𝑥 )) (4.3) Binding conditions: (4.4), (4.5), (4.6), (4.7) 4.2.2 Propose method of using GA algorithm for electrical reconfiguration distribution network Method 1: The simultaneous search of the location, capacity of DG and optimal structure, based on the selection and combination of genetic algorithms The steps in the method are proposed according to the steps shown in Figure 4.3 Method 2: the problem determines the location and capacity of DGs which can be divided into two phases as follows: Phase 1: Determine the location and capacity of the DGs for the objective function (4.3) on the network to be the smallest one by closing all electric switches under open state, forming the closed distribution network structure with DG Phase 2: Determine the operating configuration of the open distributed power network with the smallest capacity loss from the distributed network structure with DG Description of objective function and bindings The objective function for phase is similar to the objective in the expression (4.3) The objective function for phase is represented by the expression (4.24) 19 𝑁𝑛𝑟 𝑁𝑏𝑟 𝑃𝑙𝑜𝑠𝑠 = ∑𝑁𝑏𝑟 𝑖=1 𝑘𝑖 ∆𝑃𝑖 = ∑𝑖=1 𝑘𝑖 𝑅𝑖 |𝐼𝑖 | = ∑𝑖=1 𝑘𝑖 𝑅𝑖 𝑃𝑖2 +𝑄𝑖2 𝑉𝑖2 (4.25) Recommended method: The proposed method is divided into two phases Phase 1: The variables that need to be optimized are the location and capacity of dispersal sources, so the vector of control variable has the form as follows: 𝑖 𝑋𝑖 = [𝑉𝑇1𝑖 , … , 𝑉𝑇𝑚𝑖 , 𝐷𝐺1𝑖 , … , 𝐷𝐺𝑚 ] (4.29) Phase 2: The variables that need to be optimized are the electric switches in the system, so the vector of control variable is formed likely: 𝑖 𝑋𝑖 = [𝑆1𝑖 , 𝑆2𝑖 … , 𝑆𝑁𝑂 ] (4.30) The algorithm is presented in figure 4.5 Begin Bắt đầu Đọc thông số lưới, định opening khôngswitch; gian select tìm kiếm dimensions khóa mở; Read network parameters, determine spaceXác for seeking parameters, of complex N, Chọn số:mutation kích thước thể N,ratio sốXkeep, biến Iter cần tối ưu, tỉ lệ đột biến Xm, tỉ lệ variables to bethông optimized, rate Xm,quần and selection max chọn lọc Xkeep, Số hệ Itermax suddenly - Khởi tạoCreate ngẫu nhiênchromosome quần thểcomplex nhiễmNsắc thể N (open switch, …, position, …, DG capacity, …) [khóa mở,…, vị DG trí DG, , công suất DG, ] Solve capacity distribution problem and calculate capacity loss for each chromosome Giải toán phân bố cơng suất tính tốn tổn thất cơng suất cho nhiễm sắc thể Select lọc Chọn - Giữ lại nhiễm sắc thể tốt rate tỉ lệXkeep chọn lọc Xkeep Retain the best chromosome based dựa on selection - Chọn cặp nhiễm sắc ghép chéo Select chromosome couple for thể crossđể combination Cross combination Ghép chéo Select randomly one gene inngẫu each parent chromosome Replaceselected gene bysắc one new - Chọn nhiên gencouple, cặp nhiễm thểgene cha mẹ combination by using single point method - ThayImplement thể gencross chọn gen - Thực ghép chéo sử dụng phương pháp đơn điểm Chromosome Đột biến gene cần quantity to biến be mutation Xm: (N-1) x Xmx Xm - Xác định số Determine lượng gen đột Xm: (N-1) x Nts Replace raddomly onegen selected gene chọn - Thay thể ngẫu nhiên số Check binding limitcủa of new chromosomes - Kiểm tra giới hạn ràng buộc nhiễm sắc thể Correct Iter = Iter + Iter