Optimal reserve determination in the presence of A-CAES and wind, considering a stochastic approach for EVs and driver's responsibility

Determination of the optimal reserve requirement of clean energy systems is one of the main challenges of system scheduling. Electric vehicles (EVs) could reduce public transportation emission due to fossil fuels in the cities, although power systems confront uncertainties in the presence of EVs. Adiabatic compressed air energy storage (A-CAES) also has merits like no fossil fuel consumption, low costs, and fast start-up. It could provide various applications like energy and reserve to reduce power system costs. This paper presents a probabilistic method for optimal determining of spinning reserve in the presence of wind, A-CAES, and EVs for the day-ahead market. The optimal reserve level will determine via simultaneously optimizing the total operation cost and total expected energy not supplied. For the A-CAES facility, we consider air pressure limitations, thermal storage capacity limitations, and power output limitations. Besides, the availability, responsibility, driving patterns, and the variety of electric vehicles are also considered. The impact of incentive on system cost is analyzed either. Dc power flow is used to model the transmission flow limits. The problem is formulated as mixed-integer linear programming. Finally, the well-known 24 bus test system is used to verify the efficiency of the proposed model. At the end we found A-CAES is not suitable for participation in the reserve market and it’s better to use them for peak shaving. Most of EVs will participate in reserve market and 30 percent of incentive cost will cause the optimal cost of the system.