Stochastic risk-embedded energy management of a hybrid green residential complex based on downside risk constraints considering home crypto miners, adaptive parking lots and responsive loads: A real case study

Abstract

Hybrid energy systems can improve the economical and technical aspects of different infrastructures due to their high efficiency and flexibility. In this study, the energy management of a hybrid green residential complex composed of renewable resources, electric boiler/chiller, and electrical storage is optimized considering adaptive parking lots, home crypto miners, and responsive loads. Since the reliable operation of such structures requires accurate predictions, various parameters including local loads, solar radiation, wind speed, arrival/departure time of vehicles, initial charge of batteries, and price of markets are modeled using scenario generation procedures in the first step. Afterward, the downside risk method is applied to analyze the risk of decisions under the desired robustness level. The proposed planning is implemented on a real network and the simulations are compared with the conditional value-at-risk approach for validation. The results show that in a specified robustness level, the risk-averse attitude increases the cost by about 4.47%, whereas the obtained crypto currency by crypto miners is about 34.67% higher than the risk-neutral strategy due to the availability of local sources. Furthermore, the parking lots can act as bulk storage units and 10% of load flexibility decrease the cost by about 3.53%.