Equilibrium optimization algorithm for network reconfiguration and distributed generation allocation in power systems - Shaheen, A.M.; Elsayed, A.M.; El-Sehiemy, Ragab A.; Abdelaziz, Almoataz Y. (Applied soft computing, 2021)
It is imperative to distribution system operators to provide quantitative as well as qualitative power demand and satisfy consumers’ satisfaction. So, it is important to address one of the most promising combinatorial optimization problems for the optimal integration of power distribution network reconfiguration (PDNR) with distributed generations (DGs). In this regard, this paper proposes an improved equilibrium optimization algorithm (IEOA) combined with a proposed recycling strategy for configuring the power distribution networks with optimal allocation of multiple distributed generators. The recycling strategy is augmented to explore the solution space more effectively during iterations. The effectiveness of the proposed algorithm is checked on 23 standard benchmark functions. Simultaneous integration of PDNR and DG are carried out considering the 33 and 69-bus distribution test systems at three different load levels and its superiority is established. Verification of the proposed technique on large scale distribution system with a variety of control variables is introduced on a 137-bus large scale distribution system. These simulations lead to enhanced distribution system performance, quality and reliability. While, the integration represents a challenge for complexity and disability to achieve optimal solutions of the considered problem especially for multi-objective framework. To solve this challenge, a multi-objective function is developed considering total active power loss and overall voltage enhancement with respecting the system limitations. The proposed algorithm is contrasted with harmony search, genetic, refined genetic, fireworks, and firefly optimization algorithms. The obtained results confirm the effectiveness and robustness of the proposed technique compared with the competitive algorithms. [Display omitted] •A newly proposed meta-heuristic IEOA is introduced in this work.•The proposed IEOA is applied on 23 standard benchmark functions.•The proposed technique verification is compared with other previous techniques.•The suggested IEOA is validated on three distribution systems.•The findings of simultaneous PDNR/DG allocation show the IFOA effectiveness.