Abstract
Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high $R/X$ ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.
Author supplied keywords
- Active distribution network (ADN)
- alternating current optimal power flow (ACOPF)
- centralized
- convexification; demand response (DR)
- direct current optimal power flow (DCOPF)
- distributed and decentralized solution methods
- distributed energy resource (DER)
- distribution locational marginal price (DLMP)
- distribution system operator (DSO)
- distribution-level electricity market
- linearization
- microgrid (MG)
- peer-to-peer (P2P) trading
Cite
CITATION STYLE
Wang, X., Li, F., Bai, L., & Fang, X. (2023). DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions. Proceedings of the IEEE, 111(7), 725–743. https://doi.org/10.1109/JPROC.2022.3177230
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