Economic-environmental analysis of renewable-based microgrid under a CVaR-based two-stage stochastic model with efficient integration of plug-in electric vehicle and demand response

Abstract

• Developing the electric vehicles and demand response in the MG’ operation. • Analyzing the economic-environmental performance of flexible MG. • Developing a risk-aversion strategy in the MG performance with multiple technologies. • Applying the CVaR technique to manage the risk of the proposed probabilistic model. The optimal scheduling of microgrid systems as a promising milieu to improve energy efficiency and environmental benefits is faced with several challenges related to strong uncertainties. Hence, control the risk in the daily operation of the microgrid to overcome the effects of random variables such as renewable power, load, and energy prices in the proposed scheduling are urgently required. Motivated by mentioned challenges, this paper focuses on the optimal risk assessment of the microgrid incorporated with an electric vehicle parking lot, demand response program (as two emerging flexible resources), and high penetration of renewable energy (wind and solar energies) to enhance financial and environmental goals. To indicate the taken strategies (risk-taker or risk-aversion strategies) by the system operator in the daily scheduling in facing PV and wind power variation, real-time power market, load variation, and behavior of the electric vehicle drivers, the conditional value-at-risk as a risk measure criterion is employed. The proposed model is formulated as a mixed-integer linear two-stage stochastic and modeled in General Algebra Modeling System. A 33-bus smart microgrid test system is considered to demonstrate the model's effectiveness, and simulation results are presented for several cases. Results indicate that the simultaneous integration of electric vehicles and demand response in the microgrid reduces the total operation cost by 9.97%. Besides, the emission pollution and renewable power curtailment are respectively reduced up to 12.34% and 8.49% under the proposed approach. Also, under the risk-aversion strategy, the low expected operation cost is obtained in the higher risk level.