A Unit Commitment Algorithm With Relaxation-Based Neighborhood Search and Improved Relaxation Inducement

Ma, Ziming; Zhong, Haiwang; Xia, Qing; Chen, Kang; Wang, Qiang; Cao, Xin

doi:10.1109/tpwrs.2020.2981374

Cited by 13 publications

(10 citation statements)

References 42 publications

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“…A UC problem can be transformed into the MILP model by linearizing the quadratic function, and the optimum solution can be obtained when solving the small and medium-sized UC. However, its execution time increases exponentially with system scale growth, and the latest improved MILP can solve a UC problem of a power system with less than 500 generating units [17]. QI-ADP, BDE, and IBAFSA can solve a UC problem of a system with 1000 generating units and obtain a near-optimal solution.…”

Section: Characteristic Comparison Of Proposed Methods With Othersmentioning

confidence: 99%

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Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment

Zhu

Gao

2020

IEEE Access

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As the power systems in some large developing and developed countries are getting bigger, solving large-scale unit commitment (UC) is an urgent need and significant task to ensure their economic operation and contribute green energy consummation to society. In this paper optimization models covering economy and environmental protection are established, and an improved binary artificial fish swarm algorithm (IBAFSA) is presented to solve the large-scale UC problems. The parameters of IBAFSA are improved by Lé vy flight and adaptive average visual distance to search space more actively, and a double threshold selection strategy is used to enhance the effectiveness of population evolution in the optimization. Meanwhile, a heuristic greedy search algorithm among the best individuals of all generations in the iterative process of the optimization is proposed, which is beneficial to improve computation convergence and reach the optimum solution. A fast constraints processing mechanism based on the heuristic modifying strategy of unit violation is established to handle the coupling between system spinning reserve constraint and unit minimum up and down time constraint. The effectiveness of the proposed approach is verified by the UC simulations of test systems of 10-1000 units, the IEEE 118-bus system, and a large-scale power system of 270 units. The numerical simulating results show that the proposed UC solution method can achieve the near-optimal solutions in a reasonable time, improve the economic and environmental benefits of a largescale power system, and is a general method to adapt to the changes of the objective function and constraints of a UC optimization.

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Section: Characteristic Comparison Of Proposed Methods With Othersmentioning

confidence: 99%

“…In [16], a systematic approach based on MILP is introduced to tighten formulations in the data pre-processing stage, which can decrease the computation. A relaxation-based neighborhood search and improved relaxation inducement are developed to improve the solution efficiency of UC in [17].…”

Section: Introductionmentioning

confidence: 99%

Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment

Zhu

Gao

2020

IEEE Access

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“…The purpose of UC is to reduce the expenses of start-up, shut-down, and operations. Network balance, generator technical needs, and network security are among the constraints [1].…”

Section: Identification Of the Uc Problemmentioning

confidence: 99%

“…Unit Commitment (UC) is considered to be one of the most significant remarkable problems in a power system, as it aims to decide the best schedule and the rate of the generating unit's production in the power system for a specific time interval by facing given forecasted load data [1]. The only optimizing pattern in deciding the UC schedule is the generation cost, which must be minimized over a planning cycle while satisfying all the system constraints resulting from the physical capacities of the generating unit and the network design of the transmission system.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting

Salman

Kuşaf

2021

Sustainability

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Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks.

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“…A systematic approach is presented in [4] to construct feasible solutions to security-constrained UC (SCUC) problems based on analytical feasibility conditions within the LR framework. Relaxation-based neighborhood search and improved relaxation inducement are proposed in [5] to reduce the computation time of UC. A surrogate LR approach is developed in [6] to accelerate the convergence by penalizing constraint violations via quadratic functions.…”

Section: Introductionmentioning

confidence: 99%

A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems

Zhou

Zhai

et al. 2023

Journal of Modern Power Systems and Clean Energy

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This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment (TCUC). Lagrangian relaxation (LR) and mixed-integer linear programming (MILP) are popular approaches to solving TCUC. However, with many binary unit commitment variables, LR suffers from slow convergence and MILP presents heavy computation burden. The proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems. A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible (ASF) approaches, which is then leveraged to efficiently solve new TCUC instances online. On/off statuses of considerable units can be fixed in the online calculation according to the database, which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances. A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables, which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure changes. Numerical tests illustrate the efficiency of the proposed approach.

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A Unit Commitment Algorithm With Relaxation-Based Neighborhood Search and Improved Relaxation Inducement

Cited by 13 publications

References 42 publications

Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment

Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment

Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting

A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems

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