An Improved Beetle Swarm Algorithm Based on Social Learning for a Game Model of Multiobjective Distribution Network Reconfiguration

Abstract

With the increased distributed generation (DG) and electric vehicle (EV) load penetration in distribution networks, it is more difficult to ensure the safe and economic operation of the distribution networks because of the great volatility and randomness of DG and EV loads. In this paper, the uncertainties of wind power, photovoltaics, conventional loads, and EV loads are considered. Photovoltaics and conventional loads are related to solar radiation, and they are subtracted to form the net load. Then, the Wasserstein distance is used to divide the scene, and K-means clustering is used to reduce the scene, so the reconstruction analysis is carried out in the limited typical scene. In addition, to minimize network loss, load balance and maximum voltage deviation, a multiobjective game reconstruction model of the distribution network is established under the condition of satisfying network constraints. Moreover, the social beetle swarm optimization algorithm considering two social behaviors is adopted to solve the complex problem. Finally, the simulation results are verified on the standard IEEE-33 system and IEEE-118 system. The results show that the proposed strategy and algorithm can effectively reduce the network loss, improve the node voltage level and ensure that the load is not overloaded.

Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations

“…One such endeavor is simultaneous control of DSRs and allocation of DGs. As such, recent studies to the implementation of an effective integration strategy has been presented; for example; manta ray foraging optimization [1]; harmony search algorithm (HSA) with an objective of minimizing real power loss and improving voltage profile [29]; combined GA and branch exchange [30]; artificial bee colony optimizer based on maximization of system loadability [31]; improved spotted hyena algorithm [32], improved elitist-jaya algorithm (IEJAYA) [5], FWA [33], firefly (FF) algorithm [34], sinecosine algorithm [35], Harris Hawks Optimizer (HHO) [36], invasive weed optimizer [37], salp swarm algorithm [38] and an improved beetle swarm optimization algorithm [39]. In [40], multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) have been applied effectively for DGs allocation in distribution systems.…”

An efficient allocation of D-STATCOM and DG with network reconfiguration in distribution networks

“…The proposed approach presents new insights into the voltage regulation problem with large scale penetration of renewable energy sources. A multi-objective based ONR problem by considering the DG units and electric vehicles by handling the uncertainties related to them has been proposed in [18].…”

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

“…Majority of DNR studies are based on heuristic optimization algorithms. To this end, a new social beetle swarm optimization algorithm considering two social behaviors is developed in [6] to solve the multiobjective DNR problem which minimizes network loss, load balance index, and maximum voltage deviation. The same method is coupled with grey target decision-making in [7] to improve the process of selecting the best beetle and solve the problem of conflicting objectives.…”