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This study presents a novel method for optimal energy trading within microgrids considering renewable energy (RE) integration. The proposed approach uses the hybridization of particle swarm optimization and gravitational search algorithms (PSO-GSA) with Nash Bargaining to optimize power flow and energy trading between interconnected MGs and the main utility grid. Unlike existing solutions, the proposed model promotes cooperative energy trading among MGs and the main grid, considering network constraints and RE's inherent uncertainty; consequently, it addresses key challenges related to model design, pricing fairness, power flow optimization, and network constraints. The proposed method is implemented in MATLAB® considering the interconnection of four different MGs, called cooperative MGs, which optimally enable energy trading within the cooperative MGs and main utility. Simulation results, case studies and comparisons demonstrate the relevance of the proposed hybrid PSO-GSA in terms of maximizing the RE utilization, reducing load burden on the main grid and substantial cost reductions, with monthly energy costs decreased by $60,720 compared to the base case of $94,551. Also, the robustness and efficiency of the proposed PSOGSA algorithm are evaluated by comparing it with other well-established metaheuristic optimization methods under the same system data, control variables, and constraints. This research significantly contributes to MG energy trading by proactively optimizing economic operations and increasing RE utilization while adapting cooperation among interconnected MGs. [ABSTRACT FROM AUTHOR]

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system. • This paper introduces PV inverters and SVC-based reactive power voltage regulation for renewable DG-integrated systems. • Reactive power control in the DG-integrated system is adopted using Reinforcement Learning-Driven PPO. • The validation of the proposed method uses the IEEE-33 bus system to integrate RERs-based DGs and achieve PPO-based control. • The proposed method achieved 68% voltage control, reduced power loss, and reduced the voltage out-of-control ratio to 0.044. • This research compares the suggested approach to different methodologies to demonstrate its validity and applicability. [ABSTRACT FROM AUTHOR]