Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Performance Enhancement of Distribution Systems via Distribution Network Reconfiguration and Distributed Generator Allocation Considering Uncertain Environment

The emergence of dispersed generation,smart grids,and deregulated electricity markets has increased the focus on enhancing the performance of distribution systems.This paper proposes a method to reduce the energy loss and improve the reliability of distribution systems by performing distribution network reconfiguration(DNR)and distributed generator(DG)allocation.In this study,the intermittent nature of renewable-based DGs and the load profile are considered using a probabilistic method.The study investigates different annual plans based on the seasonal power profiles of DGs and the load to minimize the combined cost function of annual energy loss and annual energy not served.The proposed method is implemented using the firefly algorithm(FA),which is one of the meta-heuristic optimization algorithms.Several case studies are investigated using the IEEE 33-bus distribution system to highlight the effectiveness of the method.

Sequential Reconfiguration of Unbalanced Distribution Network with Soft Open Points Based on Deep Reinforcement Learning

With the large-scale distributed generations(DGs)being connected to distribution network(DN), the traditional day-ahead reconfiguration methods based on physical models are challenged to maintain the robustness and avoid voltage offlimits. To address these problems, this paper develops a deep reinforcement learning method for the sequential reconfiguration with soft open points(SOPs) based on real-time data. A statebased decision model is first proposed by constructing a Marko decision process-based reconfiguration and SOP joint optimization model so that the decisions can be achieved in milliseconds.Then, a deep reinforcement learning joint framework including branching double deep Q network(BDDQN) and multi-policy soft actor-critic(MPSAC) is proposed, which has significantly improved the learning efficiency of the decision model in multidimensional mixed-integer action space. And the influence of DG and load uncertainty on control results has been minimized by using the real-time status of the DN to make control decisions. The numerical simulations on the IEEE 34-bus and 123-bus systems demonstrate that the proposed method can effectively reduce the operation cost and solve the overvoltage problem caused by high ratio of photovoltaic(PV) integration.

Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns

Distribution system planners usually provide dedicated feeders to its different class of customers,each of whom has its own characteristic load pattern which varies hourly and seasonally.A more realistic modeling should be devised by considering the daily and seasonal variations in the aggregate load patterns of different class of customers.This paper addresses a new methodology to provide integrated solution for the optimal allocation of distributed generations and network reconfiguration considering load patterns of customers.The objectives considered are to maximize annual energy loss reduction and to maintain a better node voltage profile.Bat algorithm(BA)is a new bio-inspired search algorithm which has shown an advance capability to reach into the promising region,but its exploration is inadequate.The problem is solved by proposing the improved BA(IBA).The proposed method is investigated on the benchmark IEEE 33-bus test distribution system and the results are very promising.