Enhancing PV distributed generator planning in medium‐voltage DC distribution networks: A multi‐design techno‐economic analysis with load demand response

Distributed generators have grown in significance for the technical and financial operation of electric power systems in recent years. The integration of these generators into the electrical distribution network (EDN) has experienced rapid growth, primarily driven by advancements in renewable energy sources (RESs), particularly photovoltaic distributed generators (PVDGs). With the increasing implementation of solar energy as a RES, designing the optimal integration of PVDGs into medium voltage direct current (MVDC) networks is crucial to comprehensively analyzing technical and economic factors. To address this issue, a new multiple objective function (MOF) is proposed, which combines various techno‐economic parameters such as total active power loss (APL), voltage deviation (VD), and the investment cost of PVDGs (ICPVDG) installed in the test system. The objective is to minimize the MOF simultaneously to achieve the optimal incorporation of PVDGs. This study aims to solve the allocation problems related to the location and size of PVDG units in the modified MVDC test systems IEEE 27 and 33‐bus. Simulation results demonstrate the superior accuracy and effectiveness of the equilibrium optimization algorithm (EOA) in achieving the minimum MOF. The utilization of multiple PVDG units reduces overall active power losses and improves voltage profiles across all buses. Distributed generators are crucial for electric power systems, especially photovoltaic distributed generators (PVDGs). To optimize PVDG integration into medium voltage direct current (MVDC) networks, a new multiple objective function (MOF) is proposed. This function combines techno‐economic parameters like total active power loss, voltage deviation, and investment cost. The study aims to solve allocation problems in modified MVDC test systems IEEE 27 and 33‐bus. Simulation results show superior accuracy and effectiveness of the equilibrium optimization algorithm.