Optimal Battery Energy Storage System Scheduling Based on Mutation-Improved Grey Wolf Optimizer Using GPU-Accelerated Load Flow in Active Distribution Networks

Abstract

In this paper, a novel Mutation-Improved Grey Wolf Optimizer (MIGWO) model is introduced in order to solve the optimal scheduling problem for battery energy storage systems (BESS), considering the mass integration of renewable energy sources (RES), such as solar and wind generation, in active distribution networks. In this regard, four improvements are applied to the conventional GWO algorithm to modify the exploration-exploitation balance for an enhanced convergence rate. The validity and performance of the proposed model are tested on 23 classical benchmark functions and compared to the original algorithm. The new technologies present in active distribution networks lead to increased complexity in the efficient coordination of existing resources, making it necessary to resort to advanced optimization and calculation methods. As operational planning and control functions in power systems are computationally demanding and require multiple power flow calculations, the necessity of simultaneous (parallel) computing techniques emerged. In order to reduce the computing time, an accelerated GPU parallel computing technique is also applied in the proposed model. The MIGWO algorithm is further applied on the modified IEEE-33 bus system aiming to minimize the total power losses, based on the optimal coordination of BESS operation scheduling and RES generation for multiple load demand and local generation scenarios, as well as for various initial state-of-charge values of BESS.INDEX TERMS Active distribution network, battery energy storage system, grey wolf optimization, parallel computing. NOMENCLATURE ABBREVIATIONS

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

“…For instance, references [6] and [14] have not considered operational constraints on the number of charge/discharge times in the storage problem and the number of hours allowed in the demand response program. References [8,13,16,19] have considered limiting the number of charge/discharge times to increase the life of storage devices, but the modeling effect of this limitation is not shown in the results. Reference [20] has not considered the effect of limiting the number of hours allowed to a demandside management program.…”

“…The equation (8) shows shiftable demands at the time of t. The constraint (9) represents the limits of the substation demand changes at the time of t in the demand response program. Equation (10) assures which is the sum of shifting demands obtained from the demand response program is equal to the sum of the initial demands.…”

“…In [7] the authors have used energy storage to regulate the frequency of smart grids, energy arbitrage, peak reduction, reduction of power fluctuations of renewable energy sources (RES), which is not possible without energy storage. In [8] the authors presented an improved gray wolf optimization algorithm to solve the optimal scheduling problem for battery energy storage systems (BESS), taking into account the mass integration of renewable energy resources, such as solar and wind energies in active distribution networks. Reference [9] proposes a data-based approach to virtual power plant resource planning, in which the amount of battery energy storage and customer choice of demand response are synergistically optimized to maximize the profitability of virtual power plants in the electricity market.…”

A Novel Radiality Maintenance Algorithm for the Metaheuristic Based Co-Optimization of Network Reconfiguration With Battery Storage

“…The BESS model for the day-ahead scheduling problem is presented in ( 10)−( 14) [10], [25]. The BESS state-ofcharge SOC t is calculated using (10), which is a dynamic linear equality constraint as each interval value depends upon the previous interval value.…”

“…This essentially results in a total of 264 decision variables that need to be optimized simultaneously. The BESS constraints in solving the co-optimization problem are handled according to [25]. The proposed RMA, in combination with the improved APSO, achieves a total power loss of 2.4859 MW with a population size of 50 and 200 iterations.…”