Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid

Josue N. Otshwe (), Bin Li, Songsong Chen, Feixiang Gong, Bing Qi and Ngouokoua J. Chabrol
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Josue N. Otshwe: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Bin Li: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Songsong Chen: Beijing Key Laboratory of Demand Side Multi-Energy Complementary Optimization and Supply-Demand Interaction, China Electric Power Research Institute Co., Ltd., Beijing 100035, China
Feixiang Gong: Beijing Key Laboratory of Demand Side Multi-Energy Complementary Optimization and Supply-Demand Interaction, China Electric Power Research Institute Co., Ltd., Beijing 100035, China
Bing Qi: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Ngouokoua J. Chabrol: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 7, 1-14

Abstract: Distributed power resources (DPRs) offer a transformative opportunity to improve the efficiency, sustainability, and reliability of modern power infrastructures through their integration. This work presents a novel method based on a mix of renewable energy sources, energy storage technologies, and conventional generators for the optimization of DPR operations under dynamic market settings. Maximizing economic gains is the major objective while preserving system resilience and stability. To handle the complexity of DPR interactions, we offer a strong, hierarchical control architecture encompassing main, secondary, and tertiary levels. System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. Results indicate cost reductions of approximately 54.7%, which were achieved by effectively prioritizing renewable sources and optimizing energy storage usage. This research contributes both theoretically and practically to accelerating the transition toward sustainable, resilient, and economically viable power systems.

Keywords: distributed power resources; market environment; optimization; mixed-integer linear programming (MILP); particle swarm optimization (PSO); renewable energy; energy storage; cost minimization; control strategies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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