Market participants can only bid with lagged information disclosure under the existing market mechanism, which can lead to information asymmetry and irrational market behavior, thus influencing market efficiency. To promote rational bidding behavior of market participants and improve market efficiency, a novel electricity market mechanism based on cloud-edge collaboration is proposed in this paper. Critical market information, called residual demand curve, is published to market participants in real-time on the cloud side, while participants on the edge side are allowed to adjust their bids according to the information disclosure prior to closure gate. The proposed mechanism can encourage rational bids in an incentive-compatible way through the process of dynamic equilibrium while protecting participants’ privacy. This paper further formulates the mathematical model of market equilibrium to simulate the process of each market participant’s strategic bidding behavior towards equilibrium. A case study based on the IEEE 30-bus system shows the proposed market mechanism can effectively guide bidding behavior of market participants, while condensing exchanged information and protecting privacy of participants.

A virtual battery (VB) provides a succinct interface for aggregating distributed storage-like resources (SLR) to interact with a utility-level system. To overcome the drawbacks of existing VB models, including conservatism and neglecting network constraints, this paper optimizes the power and energy parameters of VB to enlarge its flexibility region. An optimal VB is identified by a robust optimization problem with decision-dependent uncertainty. An algorithm based on the Benders decomposition is developed to solve this problem. The proposed method yields the largest VB satisfying constraints of both network and SLRs. Case studies verify the superiority of the optimal VB in terms of security guarantee and less conservatism.

The Texas electric power crisis that occurred in February 2021 has drawn great attention internationally due to its severity and for not having been foreseen. In this rapid communication, we classify the 2021 Texas electric power crisis as an energy insufficiency-caused power crisis, which alerts of a new blackout mechanism. Different from capacity insufficiency-caused power crises in the past, the Texas electric power crisis of 2021 directly resulted from the long-duration extreme cold weather as well as fundamentally from the insufficiency of sustainable supply capability of energy. We begin this paper with a brief retrospect of the event and its consequences. Definitions of energy/capacity insufficiency-caused power crises are given, as well as an overview of the supply and demand during the event, based on realistic operation data. Quantitative simulations are then conducted to reveal the underlying reasons for the power crisis and reveal how to better prepare for the future. Finally, several insights and suggestions on handling the new mode of blackout in the future are proposed and discussed.

To incorporate the operating constraints of a virtual power plant (VPP) in transmission-level operation and market clearing, the concept of the VPP capability curve (VPP-CC) is proposed which explicitly characterizes the allowable range of active and reactive power outputs of a VPP. A two-step projection-based calculation framework is proposed to approximate the VPP-CC by the convex hull of critical points on its perimeter. The output of the proposed algorithm is concise and can be easily incorporated in the existing system operation and market clearing. Case studies based on the IEEE 33 and 123 test feeders show the computational efficiency of the proposed method outperforms existing methods by 4 $\sim$7 times. Additionally, many fewer inequalities are needed to depict the VPP-CC while achieving the comparative approximation accuracy compared to sampling-based methods, which will relieve the communication and computation burden.

Network-constrained unit commitment (NCUC) is one of the most widely used applications in power system and electricity market operations. According to empirical evidence, some of the transmission constraints in a NCUC are inactive. Identifying and eliminating these inactive constraints can improve the efficiency. In this paper, an efficient method is first proposed for identifying the inactive transmission constraints. The physical and economic insights of NCUC are carefully considered and utilized. Both the generating costs and power transfer distribution factor (PTDF) are considered. Not only redundant constraints but also non-binding constraints can be identified via the proposed method. An acceleration method that combines relaxation-based neighborhood search and improved relaxation inducement is proposed for further reducing the computation time. The case study shows that the proposed method can significantly reduce the number of transmission constraints and substantially improve the efficiency of NCUC without impacting the optimality.