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Results for “security constrained unit commitment (SCUC)”

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16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling distributed energy resource aggregations in security constrained unit commitment and economic dispatch

The Federal Energy Regulatory Commission (FERC) recently issued Order 2222, which requires all wholesale electricity markets in the US to allow distributed energy resources (DERs) to participate in the market as aggregated resources. These DER aggregations may be composed of many individual resources that are offered and dispatched by the market as a single entity. We present here a model of a distributed energy resource aggregator (DERA) that is scheduled by a market operator’s security constrained unit commitment (SCUC) and security constrained economic dispatch (SCED). The DERA model includes constraints for battery energy storage systems (BESSs), demand response resources (DRRs), and a simple distributed energy resource (DER). This paper describes a model for each resource type and presents two methods for the DERA to generate market offer curves: a profit-maximizing optimization to compute cost curves and a direct cost algorithm to determine dispatch costs for each resource and combine into cost curves. Once all participating DERAs are scheduled in SCUC/SCED, the model is then modified to dispatch individual DERs to maximize profit or minimize schedule deviation of the DERAs. A simulation of a representative day illustrates the DERA offers, the scheduled generation, and the DERA dispatch. Findings show the potential for unavoidable schedule deviations due to internal DER constraints and due to economic incentives to deviate from the SCUC/SCED schedules. This highlights the importance of DERA offer construction on market efficiency and system reliability. Novel aspects of our approach include: (1) We consider the asymmetry of price incentives impacting DERAs from the wholesale market compared to those impacting consumers from the retail market, as imposed by current regulations and laws. (2) We model aggregate consumer response through statistically parameterizable utility functions rather than a potentially impractical approach of modeling each individual consumer. (3) We show how to use the DERA operational dispatch model to create offers into the wholesale electricity market. (4) We show how DERAs may fail to meet their scheduled dispatch because the market offer format may not permit them to fully express their operational features such as intertemporal costs and constraints to the market.

aggregations↗
DOE OSTI2019

FESTIV (Flexible Energy Scheduling Tool for Integrating Variable generation) [SWR-14-11]

Flexible Energy Scheduling Tool for Integrating Variable generation (FESTIV) is a tool that simulates the realistic representation of steady-state power system operations and scheduling performed by an independent system operator, regional transmission organization, or a vertically integrated regulated utility. The tool integrates security-constrained unit commitment (SCUC), security-constrained economic dispatch (SCED), and automatic generation control (AGC) into one model. These are the three modes of power system scheduling that are used to meet the changing net demand. Each of these submodels have different time characteristics and are used to meet the changing demand at different time resolutions and horizons. This allows multiple timescales to be studied where FESTIV accounts for intertemporal coupling between each submodel and the submodels themselves. It does this while having different objectives between the submodels; commitment of resources, dispatch and reserve commitment, and finally control.

Ela, Erik↗
DOE OSTI2025

Tightest Mixed-Integer Programming Formulations for Quadratic SCUC Optimization

In this project, we developed new, tighter Mixed-Integer Programming (MIP) formulations for the combined Alternating Current (AC) Security-Constrained Unit Commitment (SCUC) and Security-Constrained Optimal Power Flow (SCOPF). The work addresses a critical challenge in power system operations: efficiently determining which generation units to commit and how to optimally dispatch them while maintaining network reliability constraints for both normal and contingency scenarios. Our efforts: 1. Advance the Understanding of SCUC/SCOPF Modeling: By introducing tighter MIP formulations and leveraging cutting-edge optimization tools (Julia/JuMP, PowerModels.jl), this project has pushed forward the state of the art in efficient power systems scheduling. 2. Enhance Technical and Economic Feasibility: The methods developed provide more accurate and potentially faster solutions to large-scale, realistic scheduling and dispatch problems in electric power systems, which can translate into improved reliability and potentially lower costs for grid operations. 3. Benefit to the Public: Greater efficiency in power system operations leads to cost savings for utilities and end-users. Improved reliability and integration of advanced modeling approaches can facilitate the adoption of clean energy resources and better accommodate uncertainties in renewable generation. Because this technology could impact bulk power markets and reliability, these innovations have far-reaching public benefits in terms of cost savings, reliability, and sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Fast simultaneous feasibility testing and applications for electrical power grids

Fast simultaneous feasibility testing (SFT) for management of an electrical power grid is achieved through various innovations. The computation problem relates to evaluation of candidate solutions for external power flows into a power grid, with respect to predetermined constraints and contingencies. Storage and computations are reduced by formulating the problem in terms of transactional nodes (e.g. third party connections for generators and loads) instead of the larger number of bus nodes. Further advantages are achieved by precomputing matrices that can be reused across multiple SFT invocations, organizing matrices and operations to reduce storage and computation, and eliminating branches that have no contingency violations. The fast SFT further enables new applications for integrating SFT with a Security-Constrained Unit Commitment (SCUC) optimizer for efficient single pass solutions; operating SFT as a service to multiple SCUC solvers; or integration with Security-Constrained Economic Dispatch (SCED) optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Potential of Wind Power to Provide Flexible Ramping Products and Operating Reserve: Preprint

With increasing penetration levels of variable renewable energy sources, such as wind and solar, electric power systems need flexible resources that can provide fast-response ramping capabilities to counter the variability and uncertainty of renewable generation. Although the California Independent System Operator and Midcontinent System Operator have lauched ramp products, determining how wind power can participate using their flexible ramping models is still largely unclear. This paper proposes a security-constrained unit commitment (SCUC) model considering a flexible ramping constraint representing the system's flexible ramping requirements that account for the net load probabilistic forecast uncertainty. Through this explicit modeling of the uncertainty, the true cost of the flexible ramp service can be determined after solving the SCUC problem. In addition, the potential of wind power to provide operating reserves is analyzed. Numerical case studies demonstrate that with wind power providing both flexible ramping products and operating reserve, the system operating costs and ramp shortages will be reduced significantly.

17 WIND ENERGY↗
DOE OSTI2023

Developing VSC-HVDC Oscillation Damping Control Constraints in Unit Commitment

High-voltage direct current (HVDC) systems within the existing power grids will play a pivotal role in enabling high share of renewable power integration and transportation electrification. There is a need for transmission planning models to accurately capture the impact of VSC-HVDC oscillation damping control on the system planning and model the constraints in the security constrained unit commitment (SCUC) problem. In this paper, the SCUC problem in a hybrid DC-AC grid considering damping on electromechanical inter-area oscillation is studied. An electromechanical oscillation-driven transmission capability constraints are derived from system swing equation and incorporated into the SCUC. Multiple system damping control scenarios are set based on the extracted plausible bounds of damping coefficient and inertia constant to linearize the nonlinear expressions. The SCUC problem with proposed constraints is tested on the IEEE 73-bus reliability test system (RTS) case via PLEXOS. The impact of the proposed constraints on regional generation, inertia, power flow, annual system cost, and renewable energy curtailment are fully assessed.

damping control↗
DOE OSTI2022

Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges

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.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Developing VSC-HVDC Oscillation Damping Control Constraints in Unit Commitment

High-voltage direct current (HVDC) systems within the existing power grids will play a pivotal role in enabling high share of renewable power integration and transportation electrification. There is a need for transmission planning models to accurately capture the impact of VSC-HVDC oscillation damping control on the system planning and model the constraints in the security constrained unit commitment (SCUC) problem. In this paper, the SCUC problem in a hybrid DC-AC grid considering damping on electromechanical inter-area oscillation is studied. An electromechanical oscillation-driven transmission capability constraints are derived from system swing equation and incorporated into the SCUC. Multiple system damping control scenarios are set based on the extracted plausible bounds of damping coefficient and inertia constant to linearize the nonlinear expressions. The SCUC problem with proposed constraints is tested on the Reliability Test System Grid Modernization Lab Consortium (RTS-GMLC) via PLEXOS. The impact of the proposed constraints on regional generation, inertia, power flow, annual system cost, and renewable energy curtailment are fully assessed.

damping control↗
DOE OSTI2019

Analysis of Wind Ramping Product Formulations in a Ramp-Constrained Power Grid

Flexible ramping products are designed to compensate the variability and uncertainty of load and intermittent generation. Since their market implementation by the California Independent System Operator (CAISO) and Midcontinent System Operator (MISO), flexible ramping products have garnered much attention. However, it is still unclear how to best formulate wind power plants’ participation in the ramping requirement. This paper investigates different wind ramping product formulations and increasing wind power penetration in the context of a security-constrained unit commitment (SCUC) model. We demonstrate that the ramping model that captures both the intra- and inter-temporal output ramp capability of individual wind power plants reflects the true ramp contribution of the wind fleet. With increasing wind penetration, wind generation curtailments can support the grid’s ramping needs. In addition, we found that increased wind penetration has the potential of lowering ramping and production costs. Numerical case studies performed on the TAMU 2000-bus synthetic network support the findings.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

Machine Learning↗
DOE OSTI2019

Approximate Mixed-Integer Programming Solution with Machine Learning Technique and Linear Programming Relaxation

Security constrained unit commitment (SCUC) is solved in the day-ahead electricity market to determine electricity generation schedule for the following date. The associated mixed integer programming (MIP) problem is known computationally hard. Heuristics like rounding can be computationally efficient for solving large MIP problems however often produces lower quality solutions due to discrepancy between MIP solution and linear programming relaxation (LPR) solution, i.e., variables taking different discrete values between MIP and LPR solutions. In this work, we applied the machine learning-based algorithms—Classification and Regression Tree (CART) model and random forest (RF) model—to obtain an LPR-based approximation that close to an MIP solution without actually solving the MIP problem. By computing feature importance derived from the CART and RF models, we assessed the link between coefficients in the MIP model, the LPR solution, and the optimal solution of the MIP problem. We applied the CART and RF approaches to solve SCUC problems using day-ahead energy market cases from Midcontinent Independent System Operator. The results show that our method is computationally efficient and could achieve an approximation sufficiently close to the SCUC solution with <0.15% error and ~78% reduction in the discrepancies between LPR and MIP solutions.

Lin, Xinming↗
DOE OSTI2025

Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)

Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗
DOE OSTI2024

Integrated solution techniques for security constrained unit commitment problem

Apparatus and methods are disclosed for solving Mixed Integer Programming (MIP) problems, such as Security Constrained Unit Commitment (SCUC) problems used by power grid authorities to perform day-ahead market clearing. In certain examples, a plurality of threads of a software tool implementing a concurrent optimizer can be executed concurrently and sequentially to generate new solutions to a SCUC problem for an upcoming planning horizon. Data can be shared among the concurrently executing threads, such as intermediate/incumbent solutions and hints regarding the fixing of variables and constraints to reduce the size of the SCUC problem. In some examples, the threads are seeded with historical solutions from prior planning horizons. The software tool can select a best solution from the solutions generated by the threads, and determine dispatch instructions for a device coupled to the power grid for the upcoming planning horizon based at least in part on the selected solution.

Pan, Feng↗
DOE OSTI2019

Transmission Constraint Filtering in Large-Scale Security-Constrained Unit Commitment

When solving the security-constrained unit commitment problem, one of the most complicating factors is handling the large number of transmission constraints, corresponding to both base case and N - 1 line contingency scenarios. Although it is well known that only a few of these constraints need to be enforced, identifying this critical subset of constraints efficiently remains a challenge. In this letter, we propose a novel and simple iterative contingency-screening procedure that is able to eliminate 99.4% of the constraints selected by existing iterative methods, allowing for the solution of much larger-scale problems. We report computational results in realistic instances with up to 6468 buses and 9000 transmission lines. Furthermore, the method was also independently implemented and evaluated at MISO, where it performed faster than alternative methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

SCUC-DER Integration Report: Integrating Distributed Energy Resources (DER) Using Advanced Unit Commitment Models and DER Aggregation Methodologies

Distributed energy resources (DERs) are continuing to grow due to regulatory, policy, and market shifts, and it needs to be ensured that small DERs are given a level playing field with traditional resources. Legacy market processes such as unit-commitment problems were designed for a power grid consisting largely of centralized power plants. In contrast, DERs consist of many small devices with distinct operating characteristics that may or may not be connected at the transmission interconnection points, limiting their visibility to the independent system operators (ISOs) who operate wholesale electricity markets. This report details the development and initial results from a simulation platform that integrates state-of-the-art security-constrained unit commitment software, detailed feeder models, and a DER aggregator model to quantify potential DER integration issues. Quantitative results to date illustrate potential infeasible scheduling solutions from SCUC when the DERA includes aggregations of energy-limited energy storage resources. Likewise, if aggregations are not penalized for dispatch deviations, they may have incentives to deviate from the SCUC-determined resource schedules. Assumptions about the amount of aggregated demand response resources (DRRs) and the ability of DERAs to follow profit incentives have an important impact – DRRs have significant flexibility and can typically feasibly meet their SCUC schedule, but on the other hand, their profit incentives can cause unscheduled increases in load before and after DRR dispatch. Computation time results on the SCUC solver and simulation platform only show a modest increase in SCUC solution time as the number of DERAs is increased, but results to date only reflect the RTS-GMLC test system; results may show more significant solver slowdown in larger transmission systems. The largest contribution to simulation time is attributed to the DERA offer generation method, which is suggested for future improvements.

24 POWER TRANSMISSION AND DISTRIBUTION↗