TY - JOUR
T1 - A systematic approach for the joint dispatch of energy and reserve incorporating demand response
AU - Zhang, Menglin
AU - Ai , Xiaomeng
AU - Fang, Jiakun
AU - Yao, Wei
AU - Zuo, Wenping
AU - Chen, Zhe
AU - Wen, Jinyu
PY - 2018/11
Y1 - 2018/11
N2 - The intermittent nature of wind power increases the need for flexibility of the power system. This paper proposes the systematic approach for the joint dispatch of energy and reserve incorporating demand response, including the formulation of the two-stage optimization, dynamic scenario generation, and inactive constraint identification. The incentive-based demand response model is adopted to improve flexibility by its cooperation with conventional units. The dynamic scenario generation method is developed to provide reasonable input for the two-stage optimization, considering the temporal correlations of the wind power. Three indicators are proposed to evaluate the quality of scenarios. To speed up the solution, the inactive constraint reduction has been applied to reduce the computational burden raised by the number of the scenarios and the system scale. Finally, the modified IEEE 118-bus test system with fifty incentive-based demand response aggregators is utilized to evaluate the effectiveness of the proposed method to improve operational economics and to promote wind power utilization. Simulation results show that 89.53% of the transmission line constraints can be removed, leading to a maximal reduction of 69.81% of the computational time. Compared to the conventional sampling method, the dynamic scenario set performs better in terms of three proposed indicators, and can reduce the total cost by 1.99%. Neglecting the constraint of response times, the economic efficiency would be overestimated by 0.98%.
AB - The intermittent nature of wind power increases the need for flexibility of the power system. This paper proposes the systematic approach for the joint dispatch of energy and reserve incorporating demand response, including the formulation of the two-stage optimization, dynamic scenario generation, and inactive constraint identification. The incentive-based demand response model is adopted to improve flexibility by its cooperation with conventional units. The dynamic scenario generation method is developed to provide reasonable input for the two-stage optimization, considering the temporal correlations of the wind power. Three indicators are proposed to evaluate the quality of scenarios. To speed up the solution, the inactive constraint reduction has been applied to reduce the computational burden raised by the number of the scenarios and the system scale. Finally, the modified IEEE 118-bus test system with fifty incentive-based demand response aggregators is utilized to evaluate the effectiveness of the proposed method to improve operational economics and to promote wind power utilization. Simulation results show that 89.53% of the transmission line constraints can be removed, leading to a maximal reduction of 69.81% of the computational time. Compared to the conventional sampling method, the dynamic scenario set performs better in terms of three proposed indicators, and can reduce the total cost by 1.99%. Neglecting the constraint of response times, the economic efficiency would be overestimated by 0.98%.
KW - Co-optimization of energy and reserve
KW - Incentive-based demand response
KW - Dynamic scenarios
KW - Scenario evaluation
KW - Inactive constraint reduction
UR - http://www.scopus.com/inward/record.url?scp=85053129632&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2018.09.044
DO - 10.1016/j.apenergy.2018.09.044
M3 - Journal article
SN - 0306-2619
VL - 230
SP - 1279
EP - 1291
JO - Applied Energy
JF - Applied Energy
ER -