A systematic approach for the joint dispatch of energy and reserve incorporating demand response. (15th November 2018)

• Record Type: Journal Article
• Authors: Zhang, Menglin; Ai, Xiaomeng; Fang, Jiakun; Yao, Wei; Zuo, Wenping; Chen, Zhe; Wen, Jinyu
• Is Part Of: Applied energy. Volume 230(2018)
• Page Start: 1279
• Page End: 1291
• Publication Date: 2018-11-15
• Subjects: Co-optimization of energy and reserve -- Incentive-based demand response -- Dynamic scenarios -- Scenario evaluation -- Inactive constraint reduction; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.09.044
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A systematic approach for the joint dispatch of energy and reserve is proposed. A new demand response model is proposed to balance the flexibility and reliability. Dynamic scenario generation and evaluation methods are proposed for wind profiles. The inactive constraint reduction method is used to reduce the calculation burden. Abstract: 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%.