Reliability Constrained Optimal Sizing and Examining Capacity Credit and Alternatives for Renewable Energy Sources

The growing penetration of renewable energy resources (RES) is changing its role from supplementary to alternative energy resources. If not properly planned, this transformation can significantly increase uncertainty due to the intermittent and non-dispatchable nature of resources such as solar irradiation and wind speed, potentially jeopardizing reliability of power supply. In this paper, a multi-objective approach is introduced to simultaneously optimize reliability and cost. Also, to deal with multiple types of RESs, a new concept, cost credit, is proposed as a supplement or alternative to capacity credit. Cost credit is a parameter that can be used to quantify the cost during planning and increase the reliability of the system. The overall objective is to combine and size the RESs, i.e., photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS), to meet the customer demand based on the total cost and reliability of the system. Two optimization methods, multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II), are explored for grid connected and stand-alone systems. The best combined size that gives optimal reliability and cost, is then obtained from their outputs utilizing a Fuzzy technique. Then, capacity and cost credit are assessed for the obtained optimal solution. Finally, sensitivity analysis is conducted to examine the impact of changing different parameters, purchasing/selling price, capacity of grid-connected, and swept area of RES, on the system size.

Automated summary

This paper introduces a multi-objective approach to optimize reliability and cost simultaneously, addressing the challenges brought by the rapid penetration of renewable energy resources. The concept of cost credit is proposed to enhance the reliability of renewable energy systems. The study explores two optimization methods, multi-objective particle swarm optimization and non-dominated sorting genetic algorithm, and utilizes a Fuzzy technique to determine the optimal combined size. Sensitivity analysis is conducted to examine the impact of different parameters on the system size.