Optimal stochastic-probability management of resources and energy storage in energy hub considering demand response programs and uncertainties

Because the probability decision-making variables in the energy hub (EH) cause technical, economic and envi-ronmental challenges, this study presents a multi-objective planning approach based on stochastic-probability optimization algorithms. Uncertainties include the price of electricity carriers, renewable power generation, and electrical loads uncertainty (specifically electric vehicles (EVs)) based on probability distribution functions. The multi-objective optimization function includes minimizing the operation cost, minimizing the greenhouse gas emissions cost, and minimizing the EH risk cost. The stochastic-probability optimization method is pro-grammed in both primary and secondary levels. At the primary level, the optimal operation of the EH based on the Monte Carlo method and K-means clustering is considered. To change the consumption pattern and provide a suitable platform for customer participation in optimal energy distribution, demand response (DR) programs based on energy market interactions have been modeled. At the secondary level, the EV unit optimal operation based on the Alternating direction method of multipliers (ADMM) and considering their probabilistic behavior is presented. EH risk analysis has also been developed based on the conditional value at risk (CVaR) method. The results of this study show the effectiveness of the proposed method in different scenarios and a 15.1% reduction in the total operation cost.

Automated summary

This study presents a multi-objective planning approach based on stochastic-probability optimization algorithms for energy hubs, addressing technical, economic, and environmental challenges. By considering uncertainties such as electricity carrier prices, renewable power generation, and electrical load uncertainty, the proposed method reduces the total operation cost in different scenarios.