Smart deployment of energy storage and renewable energy sources for improving distribution system efficacy

Abstract

Climate change, global warming, the depletion of fossil fuels, and rising energy demand are the main forces behind the increase in renewable energy sources. However, the unpredictability of power output from these renewable energy sources presents distribution system integration issues such as limited feeder capacity, unstable voltage, and network power loss. This study analyses the African vulture optimisation algorithm to determine the best allocation of distribution generators, with an emphasis on reducing the ageing of distribution transformers and delaying investment in feeders. The optimization technique provides faster global convergence and outperforms existing bio-inspired algorithms verified with benchmark uni-modal functions as a result of a larger crossover between the exploration and exploitation phases. The key aim is to decrease active power loss while simultaneously enhancing security margin and voltage stability. The IEEE 69-bus RDS system is utilised to validate the case studies for appropriate allocation of photovoltaic, wind turbine generation, and battery energy storage systems units, as well as offering the ideal energy management approach. During simulation, uncertainty on the characteristics of renewable energy source is accounted for. The results demonstrate the efficacy of the proposed algorithm with a substantial improvement in voltage profile, the benefit of lower CO2 emissions, an increase in security margin of up to 143%, and the advantage of extending the feeder investment deferral period by more than 50 years. In addition, the distribution transformer ageing acceleration factor improves significantly in the case of an increase in load demand.

Keywords

• photovoltaic
• distribution transformer ageing
• battery energy storage system
• wind turbine generation
• african vulture optimisation
• feeder investment deferral