Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration

In this paper, we formulate a stochastic long-term optimization planning problem that addresses the cooperative optimal location and sizing of renewable energy sources (RESs), specifically wind and photovoltaic (PV) sources and battery energy storage systems (BESSs) for a project life span of 10-years. The aim is to enhance the integrated capacity of green energy in the electric power distribution system (DS) while adhering to topological, technical, and economic constraints and considering the annual load growth. Moreover, to account for uncertainties related to various input random variables such as wind speed, solar irradiation, load power, and energy prices, Monte Carlo Simulation (MCS) is employed to generate multiple scenarios. The backward reduction method (BRM) is then applied to streamline the number of generated scenarios, reducing computational efforts. To solve the optimization planning model, a hybrid optimization algorithm is proposed, combining the non-dominating sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO). This hybrid approach aims to simultaneously minimize three long term objective functions from the economic, environmental, and technical point of view: total expected investment, operational, and carbon emission cost, power loss, and voltage deviation. The effectiveness of the planning model and the performance of the solver method are validated using the 69-bus benchmark test system. The adopted system is configured into three cases, including basic DS, DS with RESs, and DS with a combination of RESs and BESSs. Simulation results demonstrate the capability of the proposed planning model in achieving the following improvements: RESs without ESS achieved 3.35 MVA penetration while reducing DS dependency by 31.44 %. Moreover, the technical objectives improved: power loss by 39.14 % and voltage deviation by 45.45 %. Post-BESS deployment, green energy capacity reached 3.65 MVA, enhancing technical objectives by 3.74 % and 9.00 %, with a marginal 0.82 % expense increase compared to the case with RESs alone. •Develop a long-term planning model that integrates both BESSs and RESs, over a 10-year project lifespan toward enhancing the penetration level of green energy.•Employed MCS-BRM to address the uncertainties associated with a combination of stochastic input variables.•Proposed a novel Hybrid NSGAII-MOPSO-TOPSIS approach to ascertain the optimal location, sizing, and scheduling of wind farms, PV