Neural Computing and Applications
This paper applies jellyfish search optimization algorithm (JSOA) to maximize electric sale revenue for renewable power plants (RNPPs) with the installation of battery energy storage systems (BESS). Wind turbines (WTs) and solar photovoltaic arrays (SPVAs) are major power sources; meanwhile, the BESS can store energy generated at low-electricity price hours and supply the electricity to loads at other high-electricity price hours. In the first four cases with one operating day, JSOA and three other algorithms are implemented. JSOA reaches greater total revenue than the three other ones. BESS can support a renewable power plant (RNPP) to get more revenue by $495.2. Applying JSOA for two other cases with the change of requested saving energy levels and capacity of BESS, the results indicate that for increasing 10% saving energy or the reduction of 10% BESS capacity, the profit can be reduced by 10% of the maximum profit. In the last study case, BESS is connected between two plants in Vietnam, the Adani Phuoc Minh wind power plant and the Adani Phuoc Minh solar power plant, over one operating year. BESS supports the two power plants, reaching a profit of $733,322.5, about 4.15% of total revenue from the system without BESS. Considering BESS’s investment costs, the profit of BESS over ten operating years is greater than the costs of the cheapest BESS technology by $3,703,225. However, the profit is smaller than other more modern BESS technologies. So, using BESS can bring a high profit to RNPPs, and the selection of BESS technologies impacts the economic issue of the RNPPs.
