Optimization-based assessment of PV, wind, and demand response in residential virtual power plants

Growing energy demand, driven by increasing residential consumption and renewable energy integration, highlights the importance of optimizing the operation of residential virtual power plants (VPPs). This study develops a detailed framework to evaluate the potential of solar photovoltaic (PV) systems, wind generation, and demand response (DR) strategies in residential settings. Six distinct scenarios are formulated, each representing different combinations of renewable generation and controllable load flexibility. A linear programming optimization problem is designed and solved iteratively for each day across a full year for every scenario, ensuring realistic operational behavior over seasonal variations. To assess system performance, two novel indices, the grid contribution index (GCI) and the self-consumption index (SCI), are proposed, measuring the interaction with the grid and the utilization of local resources, respectively. A total of six scenarios were analyzed, with the combined solar–wind–demand response case achieving the best results (52.53 % cost reduction and a self-consumption index of 0.4543) compared to 33.49 % and 14.62 % for PV-only and wind-only cases, respectively. In addition, sensitivity analyses are conducted by varying PV output, wind availability, maximum inflow limits, and the percentage of shiftable loads to capture the effect of user-defined parameters on system performance. Doubling PV capacity reduced costs from $1167.57 to $713.93 and increased self-consumption from 0.165 to 0.309, while higher inflow limits and shiftable loads further improved grid independence. The results demonstrate that scenarios combining PV, wind, and DR achieve the highest self-sufficiency levels and the greatest cost reductions, highlighting the effectiveness of integrated resource management in enhancing the economic and operational benefits of residential VPPs.