Cost Optimization in Virtual Power Plant 3.0 for Enhancing Economic Performance and Improved System Efficiency using Mixed Integer Programming

The increasing penetration of distributed energy resources (DERs) necessitates improvements in power system reliability, economic efficiency, and integration with energy market operations. Virtual power plants (VPPs) present a promising solution for optimizing the integration of DERs and load resources through a sharing economy model. This study presents an in-depth analysis of the VPP 3.0 model, focusing on cost optimization and system efficiency. The problem is formulated as a mixed-integer programming (MIP) model and solved using MATPOWER 7.0, thereby, demonstrating its applicability for real-world implementations. A case study on a 33 bus distribution system is conducted to assess the economic and operational performance of the proposed VPP model. The study evaluates locational marginal prices (LMPs), and active power losses at each branch, along with the OPF results for the VPP system. The analysis explores the sensitivity of these metrics under various operational scenarios, highlighting the VPP system’s capacity to improve operational efficiency. This study further evaluates the efficacy of the MIP approach in comparison to various meta-heuristic approach, emphasizing on its superior optimization capability. By employing MIP, the VPP system achieves improved power flow optimization and realizes over a 50% reduction in operational costs compared to the standard system under varying loading conditions.