Optimal planning of urban-scale rooftop PV systems for maximizing renewable energy integration by reducing grid congestion-induced curtailments: A case study

Urban-scale rooftop PV systems are experiencing rapid growth. Existing planning methods primarily focus on optimizing their locations and capacities to maximize either distributed renewable energy generation or associated economic benefits. However, large-scale deployment of distributed rooftop PV systems can cause complex transmission congestion, leading to significant curtailments of centralized renewable energy and thereby reducing the overall integration of both distributed and centralized renewable energy sources. To address this issue, this study proposes an optimal planning method for urban-scale rooftop PV systems aimed at maximizing total renewable energy integration by reducing grid congestion-induced curtailments. To handle the high-dimensional optimization problem, the method employs mixed-integer linear programming to determine the optimal locations and capacities of distributed PV systems at individual buses within the transmission network. The impacts of PV deployment on transmission congestion are quantified using power transfer distribution factors. This quantification helps prioritize or avoid deploying PV systems on buses that could alleviate or exacerbate congestion, leading to a reduction in transmission grid congestion-induced curtailments of centralized renewable energy. Using a case study of 9246 buildings in Nanjing, the proposed method was validated against a baseline approach that aimed solely at maximizing distributed rooftop PV generation under different budget levels. Results demonstrated that the proposed method reduced centralized renewable energy curtailment by 61.47%, thereby enhancing total renewable energy integration within the transmission network. The proposed method offers a practical solution for planning distributed PV systems, effectively mitigating transmission congestion and maximizing renewable energy integration.