A robust optimization approach for integrated community energy system in energy and ancillary service markets. (1st April 2018)

• Record Type: Journal Article
• Authors: Zhou, Yizhou; Wei, Zhinong; Sun, Guoqiang; Cheung, Kwok W.; Zang, Haixiang; Chen, Sheng
• Is Part Of: Energy. Volume 148(2018)
• Page Start: 1
• Page End: 15
• Publication Date: 2018-04-01
• Subjects: Integrated community energy system -- Uncertainty -- Robust optimization -- Energy market -- Ancillary service market -- Gaussian process; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.01.078
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Distributed energy resources within local energy systems can be reorganized into a single entity, namely, into an integrated community energy system. This integration provides adequate scale to participate in wholesale markets. This paper proposes a day-ahead scheduling strategy for the integrated community energy system in a joint energy and ancillary service markets. The uncertainty of energy market prices, ancillary service market prices, wind power, and photovoltaic power are taken into account. The proposed integrated community energy system organizes combined cooling, heating, and power systems in different areas, and aggregates diverse distributed energy resources. Meanwhile, regulation up, regulation down, spinning, and non-spinning reserves are simultaneously employed in the proposed model. The robust optimization approach is adopted to handle uncertainty, and confidence intervals of uncertain parameters are predicted by a Gaussian process method. Finally, simulations of a real regional multi-energy system demonstrate the effectiveness and applicability of the proposed model. Highlights: Energy and ancillary service optimization for integrated community energy system. Incorporate regulation up, regulation down, spinning, and non-spinning reserves. Develop a robust optimization model that handle multiple uncertainty. Operational strategies under different robustness can be obtained.