Two-stage Framework for Microgrids Energy Management Considering Demand Response Program and Compressed Air Energy Storages under Uncertainties
Subject Areas : International Journal of Smart Electrical Engineering
Alireza Azarhooshang
1
,
Sasan Pirouzi
2
,
Mojtaba Ghadamyari
3
1 - Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
2 - Power System Group, Semirom Branch, Islamic Azad University, Semirom, Iran
3 - Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Keywords: emission, Demand response program, Multi-Objective Energy Management, Islanded Microgrids, Compressed Air Energy Storage,
Abstract :
This paper presents a two-stage stochastic model for the management of microgrids. In the proposed model, the uncertainties related to the generation output of wind turbines, the consumption load, and the electrical energy price have been taken into account. The presented two-stage problem is modeled as a mixed-integer linear programming (MILP) problem and solved by the CPLEX solver of the GAMS software. In the first stage, the operation areas of individual microgrids are determined. To this end, the considered model is implemented on the 118-bus IEEE distribution system and the security constraints of the distribution system are considered. In the second stage, the microgrid operation problem is solved considering the microgrids' operation area. The second stage is solved as single-objective and multi-objective problems separately. Objective functions in the multi-objective case include the operating costs and the amount of pollutant emissions. The results show that an increase in the operating costs due to the reduction in the amount of emissions in the bi-objective case. It is worth noting that the multi-objective model provided in this study is solved using the fuzzy and the ε-constraint methods separately. The comparison indicates a more reduction in the operating costs in the ε -constraint method than the fuzzy method. However, the reduction of emissions in the fuzzy method is higher than that of the ε-constraint method. Further, more investigations prove the effectiveness of the DRP on the correction of the demand curve and the reduction in the operating costs.