Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources. (9th April 2022)

• Record Type: Journal Article
• Authors: Hassan, Mohamed H.; Elsayed, Salah K.; Kamel, Salah; Rahmann, Claudia; Taha, Ibrahim B. M.
• Other Names: Bicer Yusuf guestEditor.
• Is Part Of: International journal of energy research. Volume 46:Number 8(2022)
• Page Start: 11291
• Page End: 11325
• Publication Date: 2022-04-09
• Subjects: Bonobo optimizer algorithm -- carbon emission -- chaotic sequences -- optimal power flow -- stochastic renewable energy sources; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.7928
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs.