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A two-layer algorithm based on PSO for solving unit commitment problem

SOFT COMPUTING 2020年第12期24卷 9161-9178页

作者： Zhai, Yu Liao, Xiaofeng Mu, Nankun Le, Junqing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China

It is well known that electric generators consume huge amounts of energy every year. Nowadays, research for the unit commitment problem (UCP) has become a very important task in a power plant. However, the existing optimal methods for solving UCP are very easy to fall into local optimum, resulting in poor performance. Moreover, as no separate layering of economic load distribution, the existing algorithms are very inefficient. Toward this end, a new algorithm named improved simulated annealing particle swarm optimization (ISAPSO) is proposed in this paper. The proposed algorithm consists of a two-layer structure which is designed to simplify the complex problem of UCP. Specifically, in the upper layer, the algorithm based on elitist strategy PSO and SA is much easier to jump out of the local optimum when solving UCP and thus gets a better solution. In the lower layer, convex optimization approach is used to improve the search efficiency of ISAPSO. Furthermore, several methods are also designed to solve the problem-related constraints, which can save a lot of computing resources. Finally, the experimental results show that the cost performance of ISAPSO is better than that of the existing algorithms.

关键词： unit commitment problem Economic load distribution Particle swarm optimization Simulated annealing algorithm

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Study on unit commitment problem considering wind power and pumped hydro energy storage

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 63卷 91-96页

作者： Ming, Zeng Kun, Zhang Liang, Wang North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

The stochastic and intermittent characteristic of wind power present great challenge to the operation of power system. A novel unit commitment problem (UCP) model is proposed in this paper. As the status of thermal units need to be described by binary number, Binary Particle Swarm Optimization (BPSO) algorithm is proposed to find the optimum schedule scheme. Case studies on the 10 units system illustrate the efficiency of the proposed approach. Crown Copyright (C) 2014 Published by Elsevier Ltd. All rights reserved.

关键词： unit commitment problem Wind power Pumped hydro energy storage system BPSO

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A fuzzy reinforcement learning approach to thermal unit commitment problem

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NEURAL COMPUTING & APPLICATIONS 2019年第3期31卷 737-750页

作者： Navin, Nandan Kumar Sharma, Rajneesh Netaji Subhas Inst Technol Div Instrumentat & Control Engn New Delhi 110078 India

unit commitment problem (UCP) aims at optimizing generation cost for meeting a given load demand under several operational constraints. We propose to use fuzzy reinforcement learning (RL) approach for efficient and reliable solution to the unit commitment problem. In particular, we cast UCP as a multiagent fuzzy reinforcement learning task wherein individual generators act as players for optimizing the cost to meet a given load over a twenty-four-hour period. unit commitment task has been fuzzified, and the most optimal unit commitment solution is generated by employing RL on this fuzzy multigenerator setup. Our proposed multiagent RL framework does not assume any a priori task or system knowledge, and the generators gradually learn to produce most optimal output solely based on their collective generation. We look at the UCP as a sequential decision-making task with reward/penalty to reduce the collective generation cost of generators. To the best of our knowledge, ours is a first attempt at solving UCP by employing fuzzy reinforcement learning. We test our approach on a ten-generating-unit system with several equality and inequality constraints. Simulation results and comparisons against several recent UCP solution methods prove superiority and viability of our proposed multiagent fuzzy reinforcement learning technique.

关键词： unit commitment problem Fuzzy Q learning Economic load dispatch Multiagent fuzzy RL

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A new genetic-based tabu search algorithm for unit commitment problem

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ELECTRIC POWER SYSTEMS RESEARCH 1999年第2期49卷 71-78页

作者： Mantawy, AH Abdel-Magid, YL Selim, SZ King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. The proposed algorithm, which is mainly based on genetic algorithms incorporates tabu search method to generate new population members in the reproduction phase of the genetic algorithm. In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the algorithm, a simple short term memory procedure is used to counter the danger of entrapment at a local optimum by preventing cycling of solutions, and the premature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algorithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods in the literature. (C) 1999 Elsevier Science S.A. All rights reserved.

关键词： tabu search algorithm unit commitment problem genetic algorithm

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Tighter MIP formulations for the discretised unit commitment problem with min-stop ramping constraints

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2017年第1-2期5卷 149-176页

作者： Dupin, Nicolas Univ Lille Ctr Rech Informat Signal & Automat Lille UMR 9189 DGACRIStAL F-59000 Lille France

This paper elaborates compact MIP formulations for a discrete unit commitment problem with minimum stop and ramping constraints. The variables can be defined in two different ways. Both MIP formulations are tightened with clique cuts and local constraints. The projection of constraints from one variable structure to the other allows to compare and tighten the MIP formulations. This leads to several equivalent formulations in terms of polyhedral descriptions and thus in LP relaxations. We analyse how MIP resolutions differ in the efficiency of the cuts, branching and primal heuristics. The resulting MIP implementation allows to tackle real size instances for an industrial application.

关键词： OR in energy unit commitment problem Ramping constraints Mixed integer programming Polyhedron Constraint reformulation

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Fix-and-optimize procedures for solving the long-term unit commitment problem with pumped storages

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ANNALS OF OPERATIONS RESEARCH 2019年第1-2期274卷 241-265页

作者： Franz, Alexander Rieck, Julia Zimmermann, Juergen Tech Univ Clausthal Inst Management & Econ Operat Res Grp D-38678 Clausthal Zellerfeld Germany Univ Hildesheim Inst Econ & Comp Sci Operat Res Grp D-31141 Hildesheim Germany

In this paper, we consider a long-term unit commitment problem with thermal and renewable energy sources, where system operating costs have to be minimized. The problem is enhanced by adding pumped storages, where water is stored in reservoirs, being turbinated or pumped up if it is beneficial in terms of reducing the operating costs. We present a tight mixed-integer linear programming model with a redefinition of decision variables and a reformulation of constraints, e.g., for the spinning reserve. The model serves as a basis for a new decomposition method, where fix-and-optimize schemes are used. In particular, a time-oriented, a unit-oriented, and a generic fix-and-optimize procedure are presented. A computational performance analysis shows that the mixed-integer linear model is efficient in supporting the solution process for small- and medium-scale instances. Furthermore, the fix-and-optimize procedures are able to tackle even large-scale instances. Particularly, problem instances with real-world energy demands, power plant-specific characteristics, and a one-year planning horizon with hourly time steps are solved to near-optimality in reasonable time.

关键词： unit commitment problem Pumped storages Hydrothermal coordination Volatile residual demand patterns Mixed-integer linear programming model Fix-and-optimize procedure

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Optimized electric vehicle charging integrated in the unit commitment problem

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OPERATIONAL RESEARCH 2022年第5期22卷 5137-5204页

作者： Semertzidis, Georgios Stamatakis, Dimitrios Tsalavoutis, Vasilios Tolis, Athanasios, I Natl Tech Univ Athens Sch Mech Engn Athens Greece

Displacing combustion engine vehicles with electric ones has recently emerged for reducing adverse environmental impacts and dependencies on fossil fuels. However, high electric vehicle penetration might disrupt the smooth operation of power sectors due to increased peak loads. A thorough investigation is therefore required, considering the charging of multiple electric vehicles, as flexible loads, in the unit commitment problem. A novel approach for resolving this Operational Research problem is hereby presented, combining power flow and transmission constraints with various scenarios of electric vehicles' penetration. A variant of Differential Evolution, aided by heuristic repair mechanisms, Priority Lists and advanced State-of-the-Art constraint handling techniques, is implemented to obtain feasible, near-optimal solutions. Well-established power systems including transmission constraints were used as benchmarks for testing the method proposed. The results are compared with those of a Mixed Integer-Linear algorithm based on the same formulation. They indicated that low and average demand cases might be resolved efficiently using the evolutionary approach proposed. As for large scale fleets, they might be handled by power systems at near optimal states exhibiting viable and resilient production schedules.

关键词： Electric vehicles Differential evolution unit commitment problem Mixed integer linear programming Optimized charging of electric vehicles DC power flow

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On the complexity of the unit commitment problem

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ANNALS OF OPERATIONS RESEARCH 2019年第1-2期274卷 119-130页

作者： Bendotti, Pascale Fouilhoux, Pierre Rottner, Cecile Sorbonne Univ CNRS LIP6 4 Pl Jussieu F-75005 Paris France EDF R&D 7 Blvd Gaspard Monge F-91120 Palaiseau France

This article analyzes how the unit commitment problem (UCP) complexity evolves with respect to the number n of units and T of time periods. A classical reduction from the knapsack problem shows that the UCP is NP-hard in the ordinary sense even for T=1. The main result of this article is that the UCP is strongly NP-hard. When the constraints are restricted to minimum up and down times, the UCP is shown to be polynomial for a fixed n. When either a unitary cost or amount of power is considered, the UCP is polynomial for T=1 and strongly NP-hard for arbitrary T. The pricing subproblem commonly used in a UCP decomposition scheme is also shown to be strongly NP-hard for a subset of units.

关键词： unit commitment problem Complexity Dynamic programming Subproblem of decomposition scheme

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A long-term unit commitment problem with hydrothermal coordination for economic and emission control in large-scale electricity systems

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OR SPECTRUM 2020年第1期42卷 235-259页

作者： Franz, Alexander Rieck, Julia Zimmermann, Juergen Tech Univ Clausthal Inst Management & Econ Operat Res Grp D-38678 Clausthal Zellerfeld Germany Univ Hildesheim Inst Business Econ & Informat Syst Operat Res Grp D-31141 Hildesheim Germany

The paper describes a long-term scheduling problem for thermal power plants and energy storages. In addition, renewable energy sources are integrated by considering the residual demand. Besides the classical minimization of the production costs, emission-related costs are taken into account. Thereby, emission costs are determined by market prices for CO2 emission certificates (i.e., using the EU emissions trading system). For the proposed unit commitment problem with hydrothermal coordination for economic and emission control, an enhanced mixed-integer linear programming model is presented. Moreover, a new heuristic approach is developed, which consists of two solution stages. The heuristic first performs an isolated dispatching of thermal plants. Then, a re-optimization stage is included in order to embed activities of energy storages into the final solution schedule. The considered approach is able to find outstanding schedules for benchmark instances with a planning horizon of up to one year. Furthermore, promising results are also obtained for large-scale real-world electricity systems. For the German electricity market, the relationship of CO2 certificate prices and the optimal thermal dispatch is illustrated by a comprehensive sensitivity analysis.

关键词： Emission control Hydrothermal coordination Power generation scheduling unit commitment problem

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Effect of modelling choices in the unit commitment problem

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2024年第1期15卷 1-63页

作者： Wuijts, Rogier Hans van den Akker, Marjan van den Broek, Machteld Univ Utrecht Utrecht Netherlands Univ Groningen Groningen Netherlands

In power system studies the unit commitment problem (UC) is solved to support market decisions and assess system adequacy. Simplifications are made to solve the UC faster, but they are made without considering the consequences on solution quality. In this study we thoroughly investigated the impacts of simplifications on solution quality and computation time on a benchmark set consisting of almost all the available instances in the literature. We found that omitting the minimum up- and downtime and simplifying the startup cost resulted in a significant quality loss without reducing the computation time. Omitting reserve requirements, ramping limits and transmission limits reduced the computation time, but degraded the solution significantly. However, the linear relaxation resulted in less quality loss with a significant speed-up and resulted in no difference when unserved energy was minimized. Finally, we found that the average and maximum capacity factor difference is large for all model variants.

关键词： Power system modeling unit commitment problem Model relaxations

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