Optimizing generation capacities incorporating renewable energy with storage systems using genetic algorithms

In grid advancement, energy storage systems are playing an important role in lowering the cost, reducing infrastructural investment, ensuring reliability and increasing operational capability. The storage system can provide stabilization services and is pivotal for backup power for emergencies. With a continuous rise in fuel prices and increasing environmental issues, the energy from renewable resources is gaining more popularity. The main drawbacks of some renewable sources are their intermittent energy generation and uncertain source availability, which has increased interest in energy storage systems (ESSs). This paper investigates the economic feasibility when ESSs are introduced in the electric grid with an expansion of a storage system as well as more percentage of the renewable energy integration and less percentage of fuel consumption by conventional power sources. The Artificial Neural Network is implemented to validate the forecasted load model. The uncertainties associated with the renewable energy system are handled by a chance-constrained model and solved by a genetic algorithm (GA) in MATLAB; selection criteria of GA for optimization process is also discussed in detail. The effectivity of the proposed methodology is verified by applying it to a case that lies in the western region of China.