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Firefly algorithm with multiple workers for the power system unit commitment problem

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第6期24卷 4773-4789页

作者： Koodalsamy, Banumalar Bairavan Veerayan, Manikandan Koodalsamy, Chandrasekaran Simon, Sishaj Pulikottil Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi Tamil Nadu India Natl Inst Technol Puducherry Dept Elect & Elect Engn Karaikal India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

This paper proposes an improved firefly (FF) algorithm with multiple workers for solving the unit commitment (UC) problem of power systems. The UC problem is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under system and unit constraints. Nowadays, highly developed computer systems are available in plenty, and proper utilization of these systems will reduce the time and complexity of combinatorial optimization problems with large numbers of generating units. Here, multiple workers are assigned to solve a UC problem as well as the subproblem, namely economic dispatch (ED) in distributed memory models. The proposed method incorporates a group search in a FF algorithm and thereby a global search is attained through the local search performed by the individual workers, which fine tune the search space in achieving the final solution. The execution time taken by the processor and the solution obtained with respect to the number of processors in a cluster are thoroughly discussed for different test systems. The methodology is validated on a 100 unit system, an IEEE 118 bus system, and a practical Taiwan 38 bus power system and the results are compared with the available literature.

关键词： Distributed memory models economic dispatch problem firefly algorithm multiple workers parallel computing unit commitment problem

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A Polynomial-Time Algorithm with Tight Error Bounds for Single-Period unit commitment problem

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Journal of the Operations Research Society of China 2023年第1期11卷 1-28页

作者： Ruo-Tian Gao Shu-Cherng Fang Cheng Lu Wen-Xun Xing Big Data Center of State Grid Corporation of China Beijing 100053China Department of Industrial and System Engineering North Carolina State UniversityRaleigh 27695-7906USA School of Economics and Management North China Electric Power UniversityBeijing 102206China Department of Mathematical Sciences Tsinghua UniversityBeijing 100084China

This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem,which can be formulated as a mixed-integer quadratic programming problem and proven to be *** theoretical bounds for the absolute errors and relative errors of the approximate solutions generated by the proposed algorithm are *** results support the effectiveness and efficiency of the proposed algorithm for solving large-scale problems.

关键词： Nonlinear programming Lagrangian dual unit commitment problem Mixed-integer quadratic programming Convex relaxation

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Parallel matheuristics for the discrete unit commitment problem with min-stop ramping constraints

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2020年第1期27卷 219-244页

作者： Dupin, Nicolas Talbi, El-ghazali Univ Lille Ctr Rech Informat Signal & Automat Lille UMR 9189 CRIStAL F-59000 Lille France

The discrete unit commitment problem with min-stop ramping constraints optimizes the daily production of thermal power plants, subject to an operational reactivity of thermal units in a 30-minute delay. Previously, mixed integer programming (MIP) formulations aimed at an exact optimization approach. This paper derives matheuristics to face the short time limit imposed by the operational constraints. Continuous relaxations guide the search for feasible solutions exploiting tailored variable fixing strategies. Parallel matheuristics are derived considering complementary strategies in parallel. Tests were performed on more than 600 real-life instances. Our parallel matheuristic provides high-quality solutions and outperforms the MIP approach in the time limits imposed by the industrial application. This paper illustrates a special interest for matheuristics in industrial highly constrained problems: many tailored neighborhood searches can be derived from an MIP formulation, and their combination in a parallel scheme improves the solution quality as well as the consistency of the heuristic.

关键词： matheuristic mixed integer programming unit commitment problem hybrid heuristics variable fixing LP-guided heuristics kernel search parallel metaheuristics

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A Solution to the unit commitment problem Using Imperialistic Competition Algorithm

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IEEE TRANSACTIONS ON POWER SYSTEMS 2012年第1期27卷 117-124页

作者： Hadji, Moosa Moghimi Vahidi, Behrooz Amirkabir Univ Technol Dept Elect Engn Tehran Iran

This paper presents a new approach via a new evolutionary algorithm known as imperialistic competition algorithm (ICA) to solve the unit commitment (UC) problem. In ICA the initial population individuals (countries) are in two types: imperialists and colonies that all together form some empires. Imperialistic competitions among these empires converge to a state in which there exists only one empire. In the proposed ICA for the UC problem, the scheduling variables are coded as integers;therefore, the minimum up/down-time constraints can be handled directly. A new method for initializing the countries is proposed. To verify the performance of the proposed algorithm, it is applied to systems with number of generating units in range of 10 up to 100 in one-day scheduling period.

关键词： Generation scheduling imperialistic competition algorithm optimization methods unit commitment problem

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Deterministic optimization of the thermal unit commitment problem: A Branch and Cut search

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COMPUTERS & CHEMICAL ENGINEERING 2014年 67卷 53-68页

作者： Marcovecchio, Marian G. Novais, Augusto Q. Grossmann, Ignacio E. Univ Nacl Litoral Argentina UNL Consejo Nacl Invest Cient & Tecn INGAR CONICET Santa Fe Argentina LNEG Lisbon Portugal Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

This paper proposes a novel deterministic optimization approach for the unit commitment (UC) problem, involving thermal generating units. A mathematical programming model is first presented, which includes all the basic constraints and a set of binary variables for the on/off status of each generator at each time period, leading to a convex mixed-integer quadratic programming (MIQP) formulation. Then, an effective solution methodology based on valid integer cutting planes is proposed, and implemented through a Branch and Cut search for finding the global optimal solution. The application of the proposed approach is illustrated with several examples of different dimensions. Comparisons with other mathematical formulations are also presented. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Energy optimization unit commitment problem Deterministic optimization Branch and Cut algorithm

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Solution proposal to the unit commitment problem incorporating manifold uncertainties

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第21期14卷 4763-4774页

作者： Zhai, Fang Shi, Libao Tsinghua Univ Shenzhen Int Grad Sch Natl Key Lab Power Syst Shenzhen Shenzhen 518055 Peoples R China

The widespread uncertainties have made the interaction of renewable energy sources and power grid more complicated and difficult to handle. This study proposes a solution proposal to the unit commitment problem incorporating multiple uncertainties, consisting of probability, possibility, and interval measures. To handle the manifold uncertainties in a focused and efficient manner, the evidence theory (ET) is applied to fuse these uncertain variables into Dempster-Shafer (DS) structure. Furthermore, the uncertainty of power loss is considered and modelled by applying the ET and the extended affine arithmetic framework. Regarding the highly non-linear mix-discrete constrained multivariable characteristic of the established optimisation model, an enhanced binary grey wolf optimiser algorithm is introduced, in which some key issues such as how to compare the objective function values with DS structure, how to deal with the inequality constraints with DS structure, and how to repair the constraints during the solution process are elaborately discussed. Finally, the IEEE 30-bus test system and a real-sized 183-bus China power system are studied to demonstrate the validity and scalability of the proposed model and method.

关键词： power grids uncertainty handling optimisation renewable energy sources power generation dispatch power generation scheduling affine transforms Dempster-Shafer structure extended affine arithmetic framework nonlinear mix-discrete established optimisation model enhanced binary grey wolf optimiser algorithm DS structure solution process unit commitment problem manifold uncertainties widespread uncertainties renewable energy sources power grid multiple uncertainties interval measures evidence theory uncertain variables power loss uncertainty constrained multivariable characteristic inequality constraints IEEE 30-bus test system real-sized 183-bus China power system

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Evolutionary Algorithms for the unit commitment problem

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2008年第3期16卷 239-255页

作者： Uyar, A. Sima Tuerkay, Belgin Istanbul Tech Univ Fac Elect & Elect Dept Comp Engn TR-34469 Istanbul Turkey Istanbul Tech Univ Fac Elect & Elect Dept Elect Engn TR-34469 Istanbul Turkey

This paper compares three evolutionary computation techniques, namely Steady-State Genetic Algorithms, Evolutionary Strategies and Differential Evolution for the unit commitment problem. The comparison. is based on a set of experiments conducted on benchmark datasets as well as on real-world data obtained from the Turkish Interconnected Power System. The results of two state-of-the-art evolutionary approaches, namely a Generational Genetic Algorithm and a Memetic Algorithm for the same benchmark datasets are also included in. the paper for comparison. The tests show that Differential Evolution is the best performer among all approaches on the test data used in the paper. The performances of the other two evolutionary algorithms are also comparable to Differential Evolution. and the results of the algorithms taken from literature showing that all EA approaches tested here are applicable to the unit commitment problem. The results of this experimental study are very promising and promote further study.

关键词： unit commitment problem evolutionary algorithms steady-state genetic algorithms generational genetic algorithms differential evolution evolutionary strategies

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New self-adaptive bat-inspired algorithm for unit commitment problem

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2014年第6期8卷 505-517页

作者： Niknam, Taher Bavafa, Farhad Azizipanah-Abarghooee, Rasoul Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Bat-inspired algorithm (BA) is a new evolutionary meta-heuristics algorithm inspired by a known technique of bats for finding prey. This study presents a self-adaptive BA to solve the unit commitment (UC) problem. The applied self-adaptive technique increases the population diversity and improves the exploration power of BA which results in better solutions and higher speed of convergence in solving the UC problem. This study, also, applies simple methods to handle the minimum on-/off-time constraint and spinning reserve requirement in generation of all solutions directly and without using any penalty function. The performance of the proposed method is verified by applying 10 up to 100-unit systems as well as a Taiwan power (Taipower) 38-unit system in a 24 h scheduling horizon.

关键词： power system simulation power generation dispatch power generation scheduling power systems spinning reserve requirement on- off-time constraint evolutionary meta-heuristics algorithm unit commitment problem self-adaptive bat-inspired algorithm

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A binary bat algorithm with improved crossover operators and Cauchy mutation for unit commitment problem

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JOURNAL OF SUPERCOMPUTING 2024年第8期80卷 11261-11292页

作者： Pang, Aokang Liang, Huijun Lin, Chenhao Yao, Lei Hubei Minzu Univ Coll Intelligent Syst Sci & Engn 39 Xueyuan Rd Enshi 445000 Hubei Peoples R China Enshi Power Supply Co State Grid Hubei Elect Power Co 96 Hangkong Ave Enshi 445000 Hubei Peoples R China

Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches.

关键词： Bat algorithm Binary algorithm unit commitment problem Local mutation Crossover operator

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Modified Binary Differential Evolution Algorithm to Solve unit commitment problem

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2018年第8期46卷 900-918页

作者： Dhaliwal, Jatinder Singh Dhillon, Jaspreet Singh St Longowal Inst Engn & Technol Elect & Instrumentat Engn Dept Longowal Punjab India

unit commitment problem with huge number of constraints is considered as an important optimization problem encountered in the electric power systems. This paper proposes a modified binary differential evolution (MBDE) inspired by the idea of estimation of distribution and differential evolution algorithms to solve the multi-constrained unit commitment problem. The updating strategy of the standard differential evolution (DE) is reserved in the proposed MBDE so that the excellent characteristics of DE, such as easy implementation and parameter tuning, are inherited. A new probability estimator operator is applied in MBDE, which efficiently preserves the diversity of population and boost the global search ability. Further, this paper also explores various priority methods based on unit characteristics for unit scheduling and allocating the power to committed units. The effectiveness of proposed method has been investigated on the small scale to large scale power systems ranging from 10 to 100 generating units for 24hours scheduling period. Results are validated by performing comparison with previously published research papers. Simulated results achieved by proposed method are found superior to the previously reported algorithms used to solve the unit commitment problem. A Wilcoxon signed rank test for paired samples also proves predominance of the proposed MBDE algorithm.

关键词： unit commitment problem economic power dispatch modified binary differential evolution

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