Research on Optimization Algorithm of Distribution Network Reconstruction Based on Cuckoo Search-Particle Swarm Optimization
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E3S Web of Conferences 218, 02036 (2020) https://doi.org/10.1051/e3sconf/202021802036 ISEESE 2020 Research on optimization algorithm of distribution network reconstruction based on cuckoo search-particle swarm optimization Jin Chen1, Bing Li1, Gang Miao1, Gang Fang1,a and Long Xu1 1 State Grid Urumqi Electric Power Corporation, Urumqi, Xinjiang, China Abstract. As the distribution network environment becomes more complex, improving the optimization effect and running time is the key to distribution network reconfiguration. In this paper, firstly, the minimum branch power loss is used as the evaluation index of the result, and the hybrid algorithm of PSO algorithm and CS algorithm is selected as the optimization method of the reconstruction problem. Secondly, the mathematical model of the distribution network introduced by DG is simplified, and the static distribution network weight is used. It is a simplified way of power flow solving by introducing DG into the distribution network model. Based on the CS-PSO algorithm, the optimization calculation of the distribution network reconfiguration with DG is carried out. Finally, a calculation example proves the rationality and effectiveness of the method. optimization in each time period with its own 1 Introduction characteristics, consider the three factors of network loss, voltage quality, and balanced load, and use particle swarm Power system is an important infrastructure of modern optimization algorithm to simplify the calculation of the society, and its safe and reliable operation is an important mathematical model of EV introduction into the guarantee for people's normal social and economic life distribution network. Literature [6] takes the combination [1,2]. Distribution network reconstruction is to change the of distribution network switch and photovoltaic cell distribution network topology by opening or closing a sequence arrangement as the optimization variable, and large number of circuit breakers installed on the the change of photovoltaic cell sequence affects the distribution network to achieve the effects of reducing reconstruction effect, so as to achieve the optimal network branch loss, uniform three-phase load, and ensuring power loss from both the distribution network reconstruction and quality. Reconfiguration can effectively improve the safe the adjustment of photovoltaic cells. Literature [7] uses the operation of the distribution network. power flow characteristics after the reconfiguration of the Literature [3] calculates stochastic trend of the model distribution network to optimize the large DG access by using Cornish - Fisher series expansion to the primary location and its output, uses random power flow and mathematical formula of the solution to satisfy the load Monte Carlo to simulate uncontrollable DG, and combines voltage and line current flows through the constraint of DG to achieve power flow optimization and reduce lines probability. With one day as the calculation period, power consumption and improve reliability. considering the random output of DG and the timing change of EV charging and discharging, the minimum network loss is taken as the objective function, and the 2 CS-PSO algorithms for distribution improved genetic algorithm is adopted as the optimization network reconstruction method. Literature [4] analyses the dynamic probability model with time as the variable including wind energy, solar power and EV, uses Monte Carlo statistical random 2.1 Distribution network reconstruction model power flow method to solve the dynamic model, and uses Distribution network reconstruction is to use the change the improved PSO algorithm to optimize the of the branch switch in the feeder to obtain a new network reconstruction model. Literature [5] fully considers the structure, and then optimize the power flow path to driving characteristics of household EVs, uses the achieve functions such as load balancing, improvement of statistics of the U.S. department transportation to predict power quality, and reduction of active power loss. In the size and timing of EV charging, and builds an EV essence, it is a set of seeking optimal switch combinations. charging model. Draw the charging load curve through the Depending on the preference, the objective function can charging model, divide the time segment according to the include reducing network loss and balancing load. This characteristics of the curve, make reconstruction a Corresponding author: [email protected] © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). E3S Web of Conferences 218, 02036 (2020) https://doi.org/10.1051/e3sconf/202021802036 ISEESE 2020 paper chooses the lowest active power loss on the 2.2 Cuckoo algorithm distribution network line as the objective function, and it can be expressed as: The CS algorithm is proposed by observing the Nb reproduction phenomenon of cuckoos in the ecosystem. 2 (1) min PRIloss i i i When the cuckoo reproduces, it puts its own eggs in the i1 nests of other birds for incubation. When other birds do In the formula, is the total number of branches in not find them, the eggs will be kept for hatching, but when the system; i is the branch number; is the resistance of other birds find foreign eggs in the nest, and then this egg branch i; is the effective value of the current flowing often faces the risk of being destroyed by other birds, through branch i; is the breaking variable of branch i, which means that the optimized solution of the egg has a i =0 means the line is disconnected, and i =1 means probability of being discarded. This is also a feature of the the branch switch is closed. CS algorithm. Randomly generated particles have the In the traditional distribution network, the validity of probability of being discarded, so the randomness and the solution can be judged as long as the power flow uncertainty of the algorithm are improved. At the same constraints and topology constraints are met. The specific time, the Levy flight mechanism [8] followed by birds was constraints are as follows: also introduced as the basis for particle jumping. (1) Topological radioactivity constraint: the system The following conditions are set for the behaviour of structure satisfies the radial structure; each egg, that is, the particle search behaviour: (2) Power flow constraint: PGi is the active power (1) Each cuckoo generates an egg, which is then component in the generator output at node i, Q i s t h e randomly placed in a nest at a certain location; Gi (2) High-quality eggs in other nests are retained, reactive power component in the generator output at node thereby evolving the next generation i, P represents the active power out of the network at Di (3) The total number of other nests n that can be used node i, represents the reactive power out of the network at by cuckoos is fixed, that is, the total number of eggs node i, QDi is the amount of reactive power flowing out produced is fixed. The probability that the eggs produced by cuckoos in the nest will be recognized and discarded of the network from node i, bij is the conductance of by other birds is pa 0,1 branch j, gij is the susceptance of branch visits, bshij, is Under the above set conditions, plus Levy's flight the capacitance to ground of branch visits; cirepresents motion law, the cuckoo's nest search trajectory and nest all nodes adjacent to node i; ij is the line break between coordinates iteration formula is: xii t1 x t Levy nodes j, ij =1 represents line connection, ij =0 represents line disconnection. (5) x t 2 In the formula, i represents the position of the i- PPGi Di ij gVVVg ij i i j ijcos ij b ij sin ij jci th nest in the t-th generation, corresponds to the QQ bb/2 VVVb2 cos g sin Gi Di ij ij sh, ij i i j ij ij ij ij multiplication of the dimensions, Levy represents the jci i 1, , N step distance, is a random number of the flight trajectory, (2) and the length and direction of the trajectory change are (3) Constraints on branch transmission power: Si, given arbitrarily. is a given value greater than zero, Si,max are the apparent power and allowable maximum and can be selected according to actual usage, generally value of branch i, respectively. Nb represents the total =0.01. number of branches. The Levy route trajectory function is as follows: SS,1,, i N (3) ii,max b s (4) Node voltage constraints: Ui, Ui,min and Ui,max are 1 respectively the node voltage value of node i and its upper v (6) and lower limits; N represents the total number of nodes In the formula, =+1,0 2 the parameters in the network. and v are the random quantities provided by the normal UUUiiii,min ,max ,1,, N (4) distribution function and obey the function law of the According to the branch relationship between the following formula: nodes in the branch data, the nodes are connected into a network structure in the program. Through the operation 2 1sin/2 ~N 0, , of the switch, the network structure is transformed into a 1/2 1/22 tree structure, and the power flow is calculated by Newton 2 ~N 0,vv , 1 Raphson iteration method to obtain the node voltage and (7) phase angle of each node, and the current of each branch is obtained. In this way, the network loss of the distribution network is obtained, and it is judged whether the obtained 2.3 Particle Swarm Algorithm topology structure meets the requirements of the objective The PSO algorithm [9] was published by Kennedy and function. Eberhart in 1995. It originated from the study of bird flock 2 E3S Web of Conferences 218, 02036 (2020) https://doi.org/10.1051/e3sconf/202021802036 ISEESE 2020 activity foraging.