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.
Highlights
The modern-day electrical distribution systems are embracing the use of renewable energy sources for their environmental and economic benefits
The study presented in this paper focuses on determining the optimal short-term operational scheduling of a battery energy storage systems (BESS), based on the local available renewable energy sources (RES) and the energy demand in an active distribution network, that aims at total power losses minimization
In order to validate the load flow algorithm implemented by the authors, the MATPOWER toolbox has been used as reference, given its increased performance in power flow computation based on the Newton-Raphson method
Summary
The modern-day electrical distribution systems are embracing the use of renewable energy sources for their environmental and economic benefits. The new technologies integration leads to increased complexity in the efficient coordination of existing resources in active distribution networks, urging the resort to advanced optimization methods Based on their capability of solving large-scale linear and nonlinear problems, meta-heuristic optimization techniques grew in popularity in the past decades. The study presented in this paper focuses on determining the optimal short-term operational scheduling of a BESS, based on the local available RES and the energy demand in an active distribution network, that aims at total power losses minimization. In this regard, the authors propose a Mutation-Improved GWO algorithm to overcome the risk of convergence in local optimums of the standard technique.
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