Multiple energy planning in the energy hub considering renewable sources, electric vehicles and management in the daily electricity market with wind multi-objective optimization algorithm

This article presents a decision-making framework leveraging randomness to manage short-term intelligent energy hub (EH) within regenerated power systems integrating EH input electricity and natural gas. EH outputs, addressing electricity and heat needs, are managed via battery storage systems (BSS) and electric vehicle (EV) fleets in regulation markets. The energy hub operator optimizes decisions concerning natural gas and energy, minimizing costs in electricity supply and thermal energy carriers across day-ahead (DA) markets and regulations. Emphasizing the benefits of demand-side management planning in cost reduction, the model incorporates a conditional value at risk (CVaR) method to assess and manage uncertainties. Moreover, leveraging electric vehicle battery storage capacity is explored to mitigate wind and solar energy production uncertainties. An energy optimization strategy based on a Multi-Objective Wind-Driven Optimization (MOWDO) is proposed, demonstrating the effectiveness of the model and method across various system conditions and scenarios. Results indicate that increasing the number of EVs from 2000 to 10,000 reduces the expected cost by 1.5 % and the CVaR by 1.5 %. Additionally, optimizing the BSS discharge cost coefficient from $45/MWh to $15/MWh decreases the expected cost by 0.5 % and the CVaR by 0.3 %. The system achieves a 20 % reduction in peak-hour electricity purchases by leveraging the BSS and EV fleet during high price conditions. These findings underscore the positive impact of demand-side management programs in enhancing renewable resource utilization amidst uncertainty.