Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

Climate change has detrimental impacts on the environment and sustainability, leading to employing alter-native energy systems such as combined cooling, heat and power (CCHP). The concept of integrated energy systems (IES) allows the coordination of several components, such as electric vehicles (EV), to serve various demands simultaneously. This paper focuses on coordinating the CCHP and electric vehicle parking lot (EVPL) integrated with photovoltaic (PV) technology as renewable energy (RE). The residential energy hub (REH) is modeled to integrate these components to meet the demands and minimize REH's operating costs and carbon emissions. EVPL functions as dynamic electrical storage besides serving EVs. Stochastic programming is used to model RE, EV, loads, and electricity price uncertainties. Demand response (DR) is applied for shiftable electrical loads. The thermodynamic model of heating and cooling loads is developed with flexibility as integrated demand response (IDR) based on the building's desired temperature. The emission cost model with penalty factors enforces REH to use less-pollutant energy sources. Subsequently, a risk-aversion strategy, namely downside risk constraint (DRC), is implemented to diminish the associated risk as the consequence of uncertainties for the decision-maker. Different constraint level is applied to provide various conservative decision-making strategies for the operator. Summer and winter scenarios with and without DR and flexible thermal loads were used to evaluate the model's accuracy. The scheduling problem is solved in IEEE 33-bus test system. The results reveal that the DR could reduce the operation cost by 5% in summer and 8% in winter. Moreover, zero risk for summer and winter is gained at the cost of 10.4% and 3.1% increment in operating costs.

Automated summary

This paper investigates the integration of combined cooling, heat and power (CCHP) system and electric vehicle parking lot (EVPL) with photovoltaic (PV) technology as renewable energy (RE). Stochastic programming and demand response are used to optimize the system's operation and reduce carbon emissions. A risk-aversion strategy is implemented to mitigate the risks associated with uncertainties. The results show that demand response can reduce operation costs by 5% in summer and 8% in winter.