Partial surrogate cuts method for network-constrained optimal scheduling of multi-carrier energy systems with demand response. (1st April 2020)

• Record Type: Journal Article
• Authors: Chen, Honglin; Liu, Mingbo; Liu, Yingqi; Lin, Shunjiang; Yang, Zhibin
• Is Part Of: Energy. Volume 196(2020)
• Publication Date: 2020-04-01
• Subjects: Multi-carrier energy system -- Demand response -- Energy storage system -- Network-constrained model -- Partial surrogate cuts (PSC) method; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117119
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The integration of different energy systems is believed to enable synergy effects such as reducing costs. The concepts of multi-carrier energy systems have critically influenced the investigations of energy management, and their optimal operations with demand response have gained much importance. However, less research has addressed network constraints, which are important, but would transform the overall problem to one of mixed integer nonlinear programming (MINLP) problem, whose solution would be challenging. This paper develops a network-constrained optimal scheduling model for a heating and power energy system with consideration of the demand response and energy storage systems, and detailed constraints such as the maximum number of switching operations for a storage device are considered. To solve this problem, convex relaxations and reformulation techniques are used so that the problem can leverage popular optimization solvers. To further improve the computational efficiency, the partial surrogate cuts (PSC) method is proposed. Its key idea is to partition the continuous variables into linear and nonlinear variables, and then take full advantage of the model structure. Case studies based on practical systems are implemented to evaluate the formulation and demonstrate the effectiveness of the proposed approach. Highlights: The influences of the network constraints on the optimal scheduling are studied. More detailedformulations for energy storage systems are modeled. Convexification of the constraints is applied to make the problem more tractable. A new PSC method is proposed to further improve the computational efficiency.