Showing papers on "Power system simulation published in 2022"

A Comprehensive Review of Security-constrained Unit Commitment

Abstract: Security-constrained unit commitment (SCUC) has been extensively studied as a key decision-making tool to determine optimal power generation schedules in the operation of electricity market. With the development of emerging power grids, fruitful research results on SCUC have been obtained. Therefore, it is essential to review current work and propose future directions for SCUC to meet the needs of developing power systems. In this paper, the basic mathematical model of the standard SCUC is summarized, and the characteristics and application scopes of common solution algorithms are presented. Customized models focusing on diverse mathematical properties are then categorized and the corresponding solving methodologies are discussed. Finally, research trends in the field are prospected based on a summary of the state-of-the-art and latest studies. It is hoped that this paper can be a useful reference to support theoretical research and practical applications of SCUC in the future.

A Criterion and Stochastic Unit Commitment Towards Frequency Resilience of Power Systems

Abstract: High-capacity long-distance transmission lines, such as high-voltage direct-current (HVDC) lines, are vulnerable to extreme weather. Their failures may cause significant power loss and frequency drop in the receiving-end power systems, which should be considered in the power system scheduling. In this paper, the failure rates of transmission lines subject to extreme weather conditions are firstly modeled to obtain the probability of failure events. Then, a novel criterion defined by the integration of abnormal frequency during the primary and secondary frequency regulation process is proposed to measure frequency security. This criterion is capable to reflect the cumulative frequency deviation effect and distinguish the contributions of different frequency regulation reserve providers, e.g., generators and flexible loads. Moreover, the linearity of the criterion makes it easy to be incorporated into optimization problems. Finally, a two-stage stochastic frequency constrained unit commitment (FCUC) model is developed to optimally schedule generators and flexible loads while satisfying the frequency security constraints under transmission line failure events. The proposed FCUC model is efficiently solved by a regularized L-shape algorithm. The proposed model and techniques are validated based on the modified IEEE 118 and 300 bus systems with realistic meteorological data.

A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

Abstract: Optimizing the schedule of thermal generators is probably the most important task when the operation of power systems is managed. This issue is known as the unit commitment problem in operational research. It has been profoundly studied in the literature, where several techniques have been proposed to address a computationally tractable solution. In turn, the ongoing changes of paradigms in energy markets focus the attention on the unit commitment problem as a powerful tool to handle new trends, such as the high renewable energy sources penetration or widespread use of non-conventional energy-storage technologies. A review on the unit commitment problem is propo- sed in this paper. The easy understanding of the diverse techniques applied in the literature for new researchers is the main goal of this state-of-art as well as identifying the research gaps that could be susceptible to further developments. Moreover, an overview of the evolution of the Mixed Integer Linear Programming formulation regarding the improvements of commercial solvers is presented, according to its prevailing hegemony when the unit commitment problem is addressed. Finally, an accurate analysis of modeling detail, power system representation, and computational performance of the case studies is presented. This characterization entails a significant development against the conventional reviews, which only offer a broad vision of the modeling scope of their citations at most.

Quantum Distributed Unit Commitment: An Application in Microgrids

Abstract: The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment problem complexity is expected to increase in the future because of the trend toward the increase of penetration of intermittent renewables. Even though quantum computing has proven effective for solving a host of problems, its applications for power systems’ problems have been rather limited. In this paper, a quantum unit commitment is innovatively formulated and the quantum version of the decomposition and coordination alternate direction method of multipliers (ADMM) is established. The above is achieved by devising quantum algorithms and by exploiting the superposition and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible solutions. The main contributions of this paper include: 1) the innovative development of a quantum model for Unit Commitment; 2) development of decomposition and coordination-supported framework which paves the way for the utilization of limited quantum resources to potentially solve the large-scale discrete optimization problems; 3) devising the novel quantum distributed unit commitment (QDUC) to solve the problem in a larger scale than currently available quantum computers are capable of solving. The QDUC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing.

Deep Learning to Optimize: Security-Constrained Unit Commitment With Uncertain Wind Power Generation and BESSs

Abstract: This paper proposes a new model of scenario-based security-constrained unit commitment (SCUC) with BESSs. By formulating such a model as a mixed-integer programming (MIP) problem, we can obtain the optimal control strategy of units and BESSs to reduce the operating cost. To solve this MIP with the proposed model, we propose a new learning-based approach to tackle the SCUC problem. The proposed convolutional neural network (CNN)-based SCUC algorithm (CNN-SCUC) has two main stages. First, CNN-SCUC trains a CNN to obtain solutions to the binary variables corresponding to unit commitment decisions. Then, the continuous variables corresponding to unit power outputs are solved by a small-scale convex optimization problem. In contrast to existing work, CNN-SCUC eliminates the need of explicitly considering the scenario-based security constraints in the optimization problem, and thus greatly reduces the computational complexity. The average gap to the optimal solution is as small as 0.0267%. The algorithm is also scalable in the sense that the computational time is reduced from about 1236.32 seconds to 0.8379 seconds in a 10-unit and 200-scenario system. Besides, the computation time remains almost constant when the number of scenarios increases. Case studies show that compared with the traditional scenario-based SCUC model, more than 4.70% operating cost reduction is achieved by incorporating BESSs in the system.

Battery Storage Formulation and Impact on Day Ahead Security Constrained Unit Commitment

Abstract: This paper discusses battery storage formulations and analyzes the impact of the constraints on the computational performance of security constrained unit commitment (SCUC). Binary variables are in general required due to mutual exclusiveness of charging and discharging modes. We use valid inequalities to improve the SOC constraints. Adding batteries to the MISO day ahead market clearing cases reveals the impact of binary variables and the valid inequalities on SCUC solving time. Warm start and lazy constraint techniques are applied to improve the performance and make the valid inequalities more effective, reducing computation time to acceptable levels for implementation.

An IGDT-Based Day-Ahead Co-Optimization of Energy and Reserve in a VPP Considering Multiple Uncertainties

Abstract: The increasing of grid-connected variable renewable energy (VRE) on the demand side causes balance problems in the power system. Thus, dealing with the uncertainty, variability, and consequently, flexibility requirement is becoming an urgent challenge to the power system operators. Virtual power plant (VPP), which bundles different types of small-scale distributed energy resources (DERs) into a single unit for optimization will effectively mitigate those uncertainties. An optimal VPP energy management method is proposed in this article for optimal energy and operating reserve (OR) scheduling. The studied VPP is a cluster of dispersed generating units (including dispatchable and stochastic power sources), flexible loads, as well as storage units. VPP operator has to make decisions based on the uncertainty coming from the stochastic VRE, load demand, as well as market electricity price. Thus, a dynamic risk reserve quantification method is proposed to cover both VRE power and load forecast uncertainties, while information gap decision theory is applied in the unit commitment procedures to study the impact of price uncertainty on the decision-making of VPP operators. Finally, the proposed method is implemented and verified with a case study, and optimal decisions are discussed.

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

Abstract: With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

A unit commitment model for optimal vehicle-to-grid operation in a power system

Abstract: Integrating plug-in electric vehicles (PEVs) into a smart grid can pose some challenges, particularly when a significant number of these vehicles are simultaneously charged and discharged. However, smart management of PEVs in a vehicle-to-grid (V2G) system can result in benefits to the grid such as load leveling, and cost reduction. This paper proposes a unit commitment model for a V2G system connected to a smart power grid. The model considers different penetration levels of PEVs and investigates the economic and technical effects of using PEVs to support the grid. The proposed methodology incorporates controlled charging and discharging as well as accounting for battery degradation in the unit commitment problem. The model is tested using an IEEE 24 bus network to determine the impact of high PEV penetration on generation cost. A comparison between a system without V2G and a system with V2G is presented to highlight the benefits of the proposed approach. The results show that the optimal scheduling of PEVs leads to reduction in generation cost and is effective in leveling the load profile through valley filling and peak load reduction.

Multitime Scale Co-Optimized Dispatch for Integrated Electricity and Natural Gas System Considering Bidirectional Interactions and Renewable Uncertainties

Abstract: This article presents a model predictive control (MPC) based multitime scale co-optimized dispatch for integrated electricity and natural gas system (IEGS) considering the bidirectional interactions and renewable uncertainties. In the proposed model, optimal dispatch is extended into three substages optimization problems of day-ahead, intraday, and real-time to coordinate the economy and accuracy of operations. The different optimization strategy is designed in each dispatching stage according to the operating characteristics of multienergy coupled units and high-penetration intermittent wind power. In the day-ahead stage, the unit commitment is tackled to arrange the start-stop plan of slow-response units, maintained in intraday and real-time stages to ensure full-term optimal economic operation. In the intraday stage, MPC-based rolling optimization is implemented to obtain the unit output scheme with the lowest operating cost target. Furthermore, the output scheme is rolling adjusted in the real-time stage based on the ultrashort-term forecast value, and a closed-loop feedback mechanism is introduced to achieve accurate power balance. In each stage, stochastic decision-making is executed based on the stochastic scenario method, efficiently capturing the uncertainty in IEGS. Besides, the proposed nonlinear model is transformed into a mixed-integer second-order cone programming problem using McCormick relaxation, which can be efficiently solved by commercial solvers. Simulation results on IEEE39-Gas20 and IEEE118-GAS40 test systems demonstrate the superiority of the proposed method in operational economy and wind power utilization, and also verify the effectiveness of the method to accurately track random fluctuations without obvious computational burden.

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

Abstract: 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.

A Novel Gradient Based Optimizer for Solving Unit Commitment Problem

Abstract: Secure and economic operation of the power system is one of the prime concerns for the engineers of 21st century. Unit Commitment (UC) represents an enhancement problem for controlling the operating schedule of units in each hour interval with different loads at various technical and environmental constraints. UC is one of the complex optimization tasks performed by power plant engineers for regular planning and operation of power system. Researchers have used a number of metaheuristics (MH) for solving this complex and demanding problem. This work aims to test the Gradient Based Optimizer (GBO) performance for treating with the UC problem. The evaluation of GBO is applied on five cases study, first case is power system network with 4-unit and the second case is power system network with 10-unit, then 20 units, then 40 units, and 100-unit system. Simulation results establish the efficacy and robustness of GBO in solving UC problem as compared to other metaheuristics such as Differential Evolution, Enhanced Genetic Algorithm, Lagrangian Relaxation, Genetic Algorithm, Ionic Bond-direct Particle Swarm Optimization, Bacteria Foraging Algorithm and Grey Wolf Algorithm. The GBO method achieve the lowest average run time than the competitor methods. The best cost function for all systems used in this work is achieved by the GBO technique.

Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework

Abstract: As an important application in the power system operation and electricity market clearing, the network-constrained unit commitment (NCUC) problem is usually executed by Independent System Operators (ISO) in an open-looped predict-then-optimize (O-PO) process, in which an upstream prediction (e.g., on renewable energy sources (RES) and loads) and a downstream NCUC are executed in a queue. However, in the O-PO framework, a statistically more accurate prediction may not necessarily lead to a higher NCUC economics against actual RES and load realizations. To this end, this paper presents a closed-loop predict-and-optimize (C-PO) framework for improving the NCUC economics. Specifically, the C-PO leverages structures (i.e., constraints and objective) of the NCUC model and relevant feature data to train a cost-oriented RES prediction model, in which the prediction quality is evaluated via the induced NCUC cost instead of the statistical forecast errors. Therefore, the loop between the prediction and the optimization is closed to deliver a cost-oriented RES power prediction for NCUC optimization. Lagrangian relaxation is adopted to accelerate the training process, making the C-PO applicable for real-world systems. Case studies on an IEEE RTS 24-bus system and an ISO-scale 5655-bus system with real-world data show that the proposed C-PO can effectively improve the NCUC economics as compared to the traditional O-PO.

Enhanced Flexible Ramping Product Formulation for Alleviating Capacity Shortage in Look-ahead Commitment

Abstract: The roll-out of a flexible ramping product provides independent system operators (ISOs) with the ability to address the issues of ramping capacity shortage. ISOs procure flexible ramping capability by committing more generating units or reserving a certain amount of headrooms of committed units. In this paper, we raise the concern of the possibility that the procured flexible ramping capability cannot be deployed in real-time operations due to the unit shut-down in a look-ahead commitment (LAC) procedure. As a solution to the issues of ramping capacity shortage, we provide a modified ramping product formulation designed to improve the reliability and reduce the expected operating cost. The trajectories of start-up and shutdown processes are also considered in determining the ramping capability. A new optimization problem is formulated using mixed integer linear programming (MILP) to be readily applied to the practical power system operation. The performance of this proposed method is verified through simulations using a small-scale system and IEEE 118-bus system. The simulation results demonstrate that the proposed method can improve the generation scheduling by alleviating the ramping capacity shortages.

An Efficient Power System Planning Model Considering Year-Round Hourly Operation Simulation

Abstract: High renewable energy penetration increases the electricity seasonal imbalance in the long-term timescale. Power system planning needs to consider the optimal configuration of various flexibility resources and electricity balance in different timescales. The coupling of multiple timescales largely increases the computation complexity of the power system planning problem. Thus, this paper presents an efficient source-grid-storage co-planning model which incorporates a year-round hourly operation simulation. To improve the computation efficiency of the planning model, from the temporal scale, a self-adaptive compact panorama time series (CPTS) model is applied, which greatly reduces the number of variables related to short-term decisions. From the spatial scale, a network-constrained relaxed clustered unit commitment (NC-RCUC) model is introduced, which significantly reduces the number of variables related to unit commitment decisions. Case studies on the modified Garver’s 6-node system and HRP-38 system prove the validation and efficiency of the proposed model (“HRP” stands for high renewable penetration). The studies on the China power grid in 2035 demonstrate the future planning results of generation, transmission and storage in China power systems based on the proposed model.