Security-constrained Unit Commitment Research Articles

Linearization Method for Large-Scale Hydro-Thermal Security-Constrained Unit Commitment

Security-constrained unit commitment (SCUC) is one of the most fundamental optimization problems in power systems. The objective of SCUC is to minimize the operating cost while respecting both system-wide and generator-specific constraints. It leads to a large-scale and mixed-integer programming (MIP) model with a large number of binary decision variables which is difficult to solve. This paper, based on the convex hull theory of single-unit, proposes a linearization method for the hydro-thermal SCUC problem with decoupled thermal units and variable-head hydro units. Then, the strategy of embedding two types of convex hulls in a multi-unit commitment and the heuristic method of constructing a feasible solution are designed, by which the multi-UC is approximated from large-scale mixed-integer programming to linear programming that can be solved in polynomial time. Finally, we theoretically prove that the optimal solution of the proposed LP model is always better than that of the Lagrangian Relaxation model. Numerical experiments on several large-scale test systems demonstrate the effectiveness and efficiency of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes a linear programming model for the SCUC problem by lifting up to a higher-dimensional space. It realizes an important innovation in reducing the computational complexity of SCUC from the perspective of linearization. The proposed method can be well applied to large-scale long-term unit commitment problems. To better use this method, the following two properties should be highlighted: 1) the error of the proposed method is less than the Lagrangian relaxation method and decreases with the increasing system scales and 2) the computational efficiency of the proposed method is 10-100 times faster than that of the MIP model. We have tested many practical power systems and find that the error of the proposed LP model is usually very small compared with the precise MIP while the computational performance is significantly improved. In some practical cases, the decision makers usually do not want to find the precise optimal solution while only an approximation under a fast speed, because the boundary condition is imprecise. The proposed method is useful. Besides, for the cases that need the precise optimal solution, the proposed method can provide a high-quality initial solution for the MIP model to accelerate the convergence.

An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids

An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids

Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment

Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment

Construct watchlist security constraints for monthly unit commitment considering wind power uncertainty

AbstractMonthly unit commitment (UC) is a decision‐making process that determines the optimal generation scheduling of mid and long‐term power system, often operating as a subproblem within monthly electricity market operations and long‐term maintenance scheduling optimization in China. Dealing with transmission constraints in monthly security‐constrained unit commitment (SCUC) is considered to be challenging, but its simplification can be achieved through eliminating inactive security constraints. Additionally, the uncertainty associated with wind power needs to be given due consideration in monthly scheduling. Here, a three‐step watchlist construction strategy is proposed to facilitate the rapid screening of overloaded power flow constraints in monthly unit commitment, including three lists (i.e. list of risk, list of concern, and list of interest) under different conservatism. Importantly, the shift factor method is utilized to screen the potential overloaded constraints arising from the redispatch process for hedging the effects of large wind uncertainties. Results obtained from the testing of IEEE‐118 system indicates that more than 99% of active power flow constraints could be successfully screened out when wind power fluctuation is at 20%. As such, the proposed scheme provides an effective method to consider uncertainty associated with large‐scale wind power generation integrated into the power grid.

Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations

Day-ahead operation involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, South-Carolina (SC) synthetic grid 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation. Simulation results demonstrate a high training accuracy to identify commitment schedule while FL and post-process ensure ML predictions do not lead to infeasible solutions with minimal loss in solution quality.

Fast Simultaneous Feasibility Test for Security Constrained Unit Commitment

We present a novel fast security analysis method to perform a simultaneous feasibility test (SFT) for security constrained unit commitment (SCUC). Given an SCUC solution, SFT computes base and contingent line flows, overloads, and sensitivities for formulating the violated constraints in the SCUC solver. We use the Sherman-Morrison-Woodbury (SMW) formula to handle topology changes across hours and contingencies. We first developed this SMW-based SFT for contingencies relative to a static network as a generalization of line outage distribution factors (LODFs) achieving the speed of LODFs and modeling complex contingencies as in commercial SFTs. Speedup of the SMW method over commercial methods comes from moving computation from the solve phase to a startup phase. We now extend the SMW technique to network changes across hours and contingencies, permitting deployment on a single compute node due to speedup of the startup phase. The fast SMW-based SFT can be used in a callback, improving over the traditional SCUC-SFT iteration due to a stronger guarantee of satisfying security constraints and to overall speedup since the SCUC solver is invoked only once. The memory required by the SMW-based SFT is large but manageable, and we formulate a technique to decrease this by 80% when implemented.

Multi-Objective Security Constrained Unit Commitment via Hybrid Evolutionary Algorithms

Multi-Objective Security Constrained Unit Commitment via Hybrid Evolutionary Algorithms

DC-based security constraints formulation: A perspective of primal-dual interior point method

The DC network security constraints have been extensively studied in numerous power system problems, such as optimal power flow (OPF), security-constrained economic dispatch (SCED), and security-constrained unit commitment (SCUC). Power transfer distribution factors (PTDFs) are widely applied in DC network constraints. However, the PTDF matrix is extremely dense, making it difficult to solve security-constraint optimization problems. This paper investigates the computational inefficiency of PTDF-based security constraints from the perspective of matrix sparsity of primal–dual interior point method (IPM), and proposes a matrix transformation to restore sparsity during IPM iterations. It turns out that the transformation method is equivalent to solving the original optimization problem expressed in pure voltage angle. The regular B-θ formulation is also a variant of the proposed transformation. Numerical studies show that sparsity rather than the size of variables and constraints is the key factor impacting the speed of solving convex quadratic problems, i.e., OPF and SCED problems. The proposed transformation significantly outperforms the PTDF-based approach, achieving a 100x reduction in non-zero elements of the coefficient matrix and a 40x speed increase in barrier processes in a 25,000-bus network.

Risk-constrained probabilistic coordination in coupled transmission and distribution system

High penetration of renewable energy has instigated stochastic power injection at interconnection between transmission system (TS) and distribution system (DS). This paper delves into the intricate collaborative risk based stochastic dispatching challenges due to contingencies and the integration of renewable sources in the TS and DS. A framework of risk constrained probabilistic problem (RCPC) is presented, which is divided into Probabilistic coordination (PC) and risk-based stochastic security-constrained unit commitment (RSSCUC). The PC is a stochastic linear problem and RSSCUC is a stochastic mixed integer non-linear problem. Therefore, the probabilistic analytical target cascading along with polynomial chaos expansion (PCE) has been utilized for solving the PC problem for coupled TS and DS. Here, PCE has provided the proposed algorithm with the needed capability to present stochastic shared variables as coefficients for PC. Further, the benders decomposition algorithm has been utilized for solving RSSCUC, by dividing it into master problem and sub problem to generate feasibility cuts. The proposed technique reduces the computational burden on the system, as it requires single stochastic coordination problem for all scenarios as an alternative of each coordination problem for each scenario and polynomial coefficients instead of multiple scenarios for each shared variable between TS and DS. Different case studies have been performed utilizing the 6-bus system and IEEE 118-bus system as TSs, 7-bus, 9-bus, 85-bus and 69-bus systems as DSs. Results depict the efficacy of the proposed method.

An improved dynamic programming algorithm for security-constrained unit commitment under spatial-temporal decomposition framework

The security-constrained unit commitment with AC power flow constraints (AC-SCUC) is a typical NP-hard problem, which is difficult to be solved by traditional methods in a reasonable time. In this paper, it is decomposed into a number of tractable subproblems in both spatial and temporal dimensions, and then solved by an improved dynamic programming (IDP) algorithm. Different from traditional dynamic programming (DP) algorithms, which need to enumerate each unit status at each period, we just need to determine the start and end periods of each consecutive status. Such that the state space can be reduced greatly. Moreover, compared with traditional DP algorithms, which acquire the optimal cost of each decision by iteration, IDP directly obtains them through a closed-form solution technique without iteration, and thus has higher computational efficiency. Case studies on various test systems demonstrate the effectiveness of the proposed algorithm.

A fast ES-based method for solving SCUC problem

Security-Constrained Unit Commitment (SCUC) is the foundation for the safe and stable operation of the power system. However, existing methods for solving SCUC problems are either computationally expensive or inaccurate. An Encoding Strategy SCUC (ES-SCUC) approach that consists of a classifier and a linear system solution is proposed in this paper. First, the classifier predicts the ES, including tight constraints and on/off values, and then the ES is substituted into the Linear system to get an approximate solution. Experimental results show that the ES-SCUC solves for case300 SCUC problems within milliseconds. Compared to the state-of-the-art solver, it achieves speedups of two orders of magnitude with high accuracy.

Optimization of integrated energy system considering multi-energy collaboration in carbon-free hydrogen port

Renewable energy is highly efficient, clean, and low-carbon, and it has become the key to energy transformation. The lack of renewable energy consumption capacity has become a major restriction on the development of renewable energy generation industry, and the application of hydrogen storage technology to port integrated energy systems (IES) is considered an effective solution to the problem of grid-connection of renewable energies. The application of hydrogen storage technology to improve renewable energy consumption and integrated energy use has important research significance. This paper studies optimization of the IES considering multi-energy collaboration in carbon-free hydrogen ports. Security constrained unit commitment (SCUC) is a key issue in the operation of IES. A mixed integer linear programming (MILP) model is constructed with objectives of minimizing the operating cost of energy system. Furthermore, a customized enhanced particle swarm optimization (PSO) method is designed to solve the SCUC optimization problem. Enhanced PSO can obtain satisfying solutions in large-scale instances within a short time. Extensive numerical experiments have been conducted and the results demonstrate the applicability and efficiency of enhanced PSO in solving problems.

Ensemble Provably Robust Learn-to-Optimize Approach for Security-Constrained Unit Commitment

Security-constrained unit commitment (SCUC) is the basis for power systems and markets operation, which is solved periodically via mixed-integer programming (MIP) with limited input data changes to historical solved instances. This paper proposes an ensemble provably robust learn-to-optimize approach (EPR-L2O) for transforming the MIP-based SCUC into the tractable convex problem with predictable solving time by identifying the integer part of MIP, denoted by unit status integer solution. It formulates the SCUC model for unit status integer solution generation, prunes the generated integer solutions, clusters the units based on their operation status, and proposes the provably robust learning approach to training the unit status integer solution identifiers, which are provably robust against the input adversarial perturbation. After learning, the integer solutions from different identifiers are assembled to identify the integer variables of SCUC. Case study verifies that: 1) EPR-L2O achieves 6.91, 19.71, and 22.14 times acceleration in the 30-bus, 118-bus, and 300-bus systems than the Branch-and-Bound; 2) compared to cross-entropy-based learning, EPR-L2O reduces the MIP gap by 0.031%, 0.21%, and 0.0084% in the 30-bus, 118-bus, and 300-bus systems, indicating its operation economy and robustness.

A Security-constrained Unit Commitment-based Optimal Battery Usage Pattern Model

This paper discusses multiple strategies of security-constrained unit commitment (SCUC)-based optimal battery usage pattern models to drive the charging and discharging of different batteries and help the operation cycles satisfy their preferences according to battery characteristics. Previous studies mainly focus on either battery storage integration as a controllable resource into the grid with high penetration of renewable energy, or battery degradation costs as additional terms in the objective function of SCUC. In comparison, this paper first considers whether battery operation cycles satisfy their preferences according to specific battery characteristics. It discusses a full charging/discharging strategy for Ni-Cd batteries, which have a memory effect and prefer deep cycles. Then, the paper explores a shallow cycle strategy for Lithium-ion, lead acid, and Ni-H batteries, which have no memory effect and prefer shallow cycles for long service life. Subsequently, it is extended to a hybrid strategy when batteries with/without memory effects are at the same node. The proposed model minimizes the total operation cost of SCUC while including the penalties for battery usage strategies. The original problem is a mixed integer non-linear programming (MINLP) one, and then it can be mathematically converted into a mixed integer linear programming (MILP) one. Consequently, the models with multiple strategies are tested through IEEE 6-node power system. Computational results show that, compared to the base case without any strategy, the proposed optimal battery usage pattern approaches have an advantage in driving Ni-Cd batteries toward deep cycles, guiding Lithium-ion batteries towards shallow cycles, and improving the flexibility of performance with hybrid applications.

Flexible-reliable linear AC security constrained unit commitment considering uncertainties and renewable energy sources

In this paper, AC security-constrained unit commitment (AC-SCUC) is presented in the transmission network considering flexibility and reliability limits in the presence of renewable resources, energy storage and demand response plan. This minimization scheme considers the sum of expected operating costs and network reliability minus the benefit of network flexibility as the objective function. This problem is related to AC power flow equations, the operation model of storage, demand response, renewable sources, and non-renewable generation units, technical limits of the network, including indices of operation, reliability, flexibility, security. and the stability of the network. In this plan, load, renewable power, energy price, and availability of network equipment are uncertainties. In this paper, in order to accurately evaluate reliability and flexibility indices, stochastic planning is adopted to model the aforementioned uncertainties. The design has non-linear and non-convex constraints; therefore, to reach a unique solution in low computing time, the linear approximation model is incorporated. AC-SCUC linear modeling along with simultaneous modeling of operation, economic, flexibility, reliability, security, and stability indices along with stochastic modeling of uncertainties to accurately evaluate flexibility and reliability indices are considered as the contributions of this scheme. The obtained numerical results indicate the ability of the proposed scheme in improving the economic and technical condition of the transmission network with regard to the optimal management of energy sources, storage devices, and demand response. So, the scheme can obtain high reliability conditions for the transmission network by considering the high penalty price. Also, by considering a high incentive price for flexibility, non-renewable resources, storage devices, and demand response have been able to achieve high flexibility for the network. Optimal energy management of resources, storage devices, and demand response has significantly enhanced the network's operation status.

A two-stage SCUC model for distribution networks considering uncertainty and demand response

Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.

Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Grids

With increasing installation of wind and solar generation, conventional synchronous generators in power systems are gradually displaced resulting in a significant reduction in system inertia. Maintaining system frequency and rate-of-change-of-frequency (RoCoF) within acceptable ranges becomes more critical for the stability of a power system. This paper first discusses the impact of inter-area oscillations on the system rate-of-change-of-frequency (RoCoF) security; then, the limitations on locational RoCoF accounting for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G - 1$</tex-math></inline-formula> contingency stability are derived. To capture highest locational RoCoF during the oscillation, multiple measurement windows are introduced. By enforcing these frequency related constraints, a location based RoCoF constrained security constrained unit commitment (LRC-SCUC) model is proposed. Furthermore, an effective piecewise linearization (PWL) technique is employed to formulate a RoCoF linearization problem and linearize the nonlinear function representing the location based RoCoF constraints in SCUC. Case studies are carried on IEEE 24-bus system to demonstrate the effectiveness of proposed LRC-SCUC model. The results also show that deploying virtual inertia techniques not only reduces the total cost, but also improves the system market efficiency.

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.

Security-constrained unit commitment: A decomposition approach embodying Kron reduction

We address the day-ahead scheduling of electricity production units throughout a network imposing N-1 security constraints, which ensures uneventful operation under any single-branch failure. For realistic electric energy systems, this optimization problem, which is mixed-integer linear or nonlinear but convex, involves millions of continuous variables, millions of constraints, and thousands of binary variables. This problem is intractable if state-of-the-art branch-and-cut solvers are used. As a solution methodology, we propose a Benders-type decomposition technique with a dynamically enriched master problem. Such master problem incorporates scheduling (binary) decisions and decisions pertaining to under-contingency operating conditions. The subproblems represent the operation of the system under no failure and single-branch failure. As the algorithm progresses, the master problem incorporates additional under-contingency operating conditions, which increases its computational burden. We use Kron reduction to compact (reducing variables and constraints) the description of the under-contingency operating conditions in the master problem without losing accuracy, which renders major computational gains. The methodology proposed allows solving, within reasonable computing times, instances intractable with state-of-the-art branch-and-cut solvers and decomposition algorithms.

Proactive Security-Constrained Unit Commitment Against Typhoon Disasters: An Approximate Dynamic Programming Approach

Proactive security-constrained unit commitment (SCUC) is one of the effective resilience-oriented responses to keep the secure operation of the power system during natural disasters. The approaches proposed in the previous research works cannot guarantee high-quality response timely to various emergencies during typhoon disasters. This paper proposes a proactive SCUC model solved by an approximate dynamic programming (ADP) algorithm to improve system resilience during typhoon disasters while meeting the requirements of the computational time. Both the uncertainty of the typhoon motion path and the wind speed-dependent transmission line failure probability are considered in the scenario generation. The optimization process is formulated as a Markov decision process (MDP), and a piecewise linear function (PLF) based ADP is employed for solving the model. With the exogenous information extracted from generated scenarios and recorded in the slopes of PLFs, the ADP algorithm can result in a near-optimal solution within a short computational time. Simulations are carried out using the modified IEEE RTS-79 system. The significant computational time saving and high-quality solution demonstrate the effectiveness of the proposed approach.