Modified African Buffalo Optimization for Strategic Integration of Battery Energy Storage in Distribution Networks

Abstract

This article presents a two-layer optimization scheme for simultaneous optimal allocation of wind turbines (WTs) and battery energy storage systems (BESSs) in power distribution networks. The prime objective of this formulation is to maximize the renewable hosting capacity of the system. For outerlayer, a new objective function is developed by combining multiple objectives such as annual energy loss in feeders, back-feed power, BESSs conversion losses, node voltage deviation, and demand fluctuations caused by renewables subject to various system security and reliability constraints. Furthermore, a modified variant of African buffalo optimization (ABO) introduced to overcome some of the limitations observed in its standard variant. The proposed modifications are first validated and then introduced for simultaneous optimal integration of multiple distributed energy resources in distribution systems. The proposed modified ABO is employed to determine the optimization variables of outer-layer. Whereas, a heuristic is proposed to solve the inner-layer optimization problem aiming to determine the optimal dispatch of BESSs suggested by outer-layer optimization. By considering the high investment and operating cost of BESSs, minimum energy storage capacity has been ensured during the planning stage. To present the efficacy of developed model, it is implemented on a 33-bus, benchmark test distribution system for various test cases. The comparative simulation results show that the proposed optimization model and modified ABO is very promising to improve the performance of active distribution systems.

Optimal Coordinated Energy Management in Active Distribution System with Battery Energy Storage and Price-Responsive Demand

“…e coordinated scheduling of BESSs and MT for maximizing the DPF is a complex nonlinear multiconstraint optimization problem, which is difficult to handle by using conventional analytical-based methods. For this kind of complex combinatorial optimization problems, AI-based nature-inspired metaheuristic or evolutionary methods which may include genetic algorithm (GA) [28], gravitational search algorithm (GSA) [29], African buffalo optimization (ABO) [30], and many more were used to solve it [31]. e ABO algorithm was developed by Odili et al in 2015 [32].…”

“…On the other hand, it suffers from the inability to provide global optimum and to tackle complex engineering optimization problems. To overcome some of these limitations, Singh et al [30] introduced a modified variant of ABO named MABO that offers the potential to seek a global or near-global optimum solution for the complex distribution power system optimization problems. Besides, the modifications provide well-balanced and well-coordinated exploration and exploitation capabilities.…”

Bi-Level Optimal Allocation of Flexible Resources for Distribution Network Considering Different Energy Storage Operation Strategies in Electricity Market

“…where R is the MT's ramp rate; R max is the maximum ramp rate. (13) where SOC t , SOC min , and SOC max are the real-time, minimum and maximum state of charge(SOC), respectively; in this paper, SOC min and SOC max take 0.2 and 0.9 respectively; P dis. max and P ch.…”

“…In the research on distribution network planning, bi-level optimization model is widely adopted [13]- [18]. In [13], a bi-level optimization scheme is proposed for optimal allocation of WT and ESS in distribution networks, and the objective of the optimization model is optimal economy and reliability.…”

“…In the research on distribution network planning, bi-level optimization model is widely adopted [13]- [18]. In [13], a bi-level optimization scheme is proposed for optimal allocation of WT and ESS in distribution networks, and the objective of the optimization model is optimal economy and reliability. Reference [14] considers the uncertainties od DRE and load, and the author establishes a bi-level planning model for integrated energy systems incorporating demand response.…”