Multi-period Market Operation of Transmission-Distribution Systems Based on Heterogeneous Decomposition and Coordination

• Main Authors: Cong Liu, Jingyang Zhou, Yi Pan, Zhiyi Li, Yifei Wang, Dan Xu, Qiang Ding, Zhiqiang Luo, Mohammad Shahidehpour
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Energies
• Subjects: HGDC; transmission and distribution market; MI-SOCP; bidding willingness; optimal operation mode
• Online Access: https://www.mdpi.com/1996-1073/12/16/3126

The integration of shiftable/curtailment distribution generators (DGs) along with quick-response storage has not only increased the transaction’s flexibility but also puzzled the bidding willingness of transmission-connected market players (TMPs). In this paper, the method of heterogeneous decomposition and coordination (HGDC) is applied to decompose the integrated transmission-distribution market framework into a bi-level problem with a transmission wholesale market master problem and several distribution retail market subproblems in a decentralized organization structure. The price-based bidding willingness of demand-side resources’ (DSRs’) aggregator is simulated considering the relation between distribution system operator’s (DSO’s) operation modes and its equivalent market transactive price. Besides the traditional mixed-integer linear programming (MILP) model, the active reconfiguration model of DSOs based on mixed-integer second-order conic programming (MI-SOCP) is proposed to rearrange its operation switch status and elaborate its operation cost according to the market transaction. Multi-period optimal operation modes could be obtained through an HGDC-based iteration process by coordinating the transmission system operator (TSO) with DSOs and considering the market energy equilibrium and reserve requirements for security considerations. Karush-Kuhn-Tucker (KKT) conditions are used to testify the optimality and convergence of the bi-level model in theory. The T5-3D33 case is employed to illustrate the efficiency of the proposed model and method.