This paper starts by reviewing demand response models in MISO’s energy management and market systems. It then discusses a recent enhancement that improves the ability of demand response to participate in MISO energy and ancillary service markets. Some future enhancements are also discussed.

This paper presents a novel fast security analysis method to perform simultaneous feasibility test (SFT) for security constrained unit commitment (SCUC). The method uses the Sherman-Morrison-Woodbury formula to calculate DC power flow with the incremental topology changes across time intervals and with the contingencies for each interval. The extremely fast SFT allows the SCUC Mixed Integer Programming (MIP) solver to check network security constraints in a callback, leading to a significant performance improvement over the traditional MIP-SFT iteration method.

Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) is a promising alternative. However, directly learning good solutions is difficult in view of the combinatorial nature of UC. This paper synergistically integrates ML within our recent decomposition and coordination method of Surrogate Lagrangian Relaxation to learn subproblem solutions of deterministic UC. Compared to the original problem, a subproblem is much easier to learn. Nevertheless, because of many types of constraints, finding “good enough” subproblem solutions for a given set of multipliers is still challenging. To overcome this, dimensionality is reduced via aggregating multipliers and removing unnecessary variables. Multiplier distributions are novelly specified for effective training. Furthermore, to exploit solutions obtained in daily operations, offline supervised learning and online self-learning are unified at the switching of the innovatively designed loss functions. Although testing of this first attempt to integrate two methods is limited to the IEEE 118-bus system, results demonstrate that ML obtains good-enough subproblem solutions efficiently, leading to near-optimal overall solutions. Our method opens a new direction for integrating ML and mathematical optimization to solve complicated UC and beyond.

This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established by the IEEE Technology and Innovation Subcommittee to first review the state-of-the-art of the security-constrained unit commitment (SCUC) business model, its mathematical formulation, and solution techniques in solving electricity market clearing problems. The Task Force then investigated the emerging challenges of future market clearing problems and presented efforts in building benchmark mathematical and business models.

Unit Commitment (UC) is an important problem in power system operations. It is traditionally scoped for 24 hours with one-hour time intervals. To improve system flexibility by accommodating the increasing net-load variability, sub-hourly UC has been suggested. Such a problem is larger and more complicated than hourly UC because of the increased number of periods and reduced unit ramping capabilities per period. The computational burden is further exacerbated for systems with large numbers of virtual transactions leading to dense transmission constraints matrices. Consequently, the state-of-the-art and practice method, branch-and-cut (B&C), suffers from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) is enhanced by embedding ordinal-optimization concepts for a drastic reduction in subproblem solving time. Rather than formally solving subproblems by using B&C, subproblem solutions that satisfy SAVLR’s convergence condition are obtained by modifying solutions from previous iterations or solving crude subproblems. All virtual transactions are included in each subproblem to reduce major changes in solutions across iterations. A parallel version is also developed to further reduce the computation time. Testing on MISO’s large cases demonstrates that our ordinal-optimization embedded approach obtains near-optimal solutions efficiently, is robust, and provides a new way on solving other MILP problems.