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unit commitment problem: A new formulation and solution method

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第May期57卷 222-231页

作者： Tumuluru, Vamsi Krishna Huang, Zhe Tsang, Danny H. K. Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China

In this paper, we present a new formulation and solution method for the well known unit commitment problem (UCP) for scheduling the thermal generators in a day-ahead electricity market. Compared to the traditional approach, our approach has several advantages such as: (a) reducing the combinatorial complexity (i.e., the size of the binary state space) significantly, (b) eliminating the need for linearizing the constraints associated with the minimum ON time and minimum OFF time for any thermal generator, (c) eliminating the need for defining new binary decision variables to represent the startup and shutdown decisions for any thermal generator in each hour and (d) eliminating the need to linearize the non-linear cost functions associated with any thermal generator (e.g., time dependent exponential startup cost function). According to our formulation, the UCP can be stated as finding a feasible path of ON-OFF states for each generator (i.e., a sequence of unit commitment states that satisfy the corresponding minimum ON time and minimum OFF time constraints over the scheduling horizon) such that the total generation cost is minimized while meeting the demand and reserve requirement in each hour for the next day. We show how a near optimal solution for the UCP can be constructed using our solution method which is based on the Lagrangian relaxation (LR) method. Although only a near optimal solution is found, we show that our solution is comparable to that obtained when the UCP is modeled as a mixed integer linear program (MILP). (C) 2013 Elsevier Ltd. All rights reserved.

关键词： unit commitment problem Power scheduling Thermal generators Lagrangian relaxation

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unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm

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APPLIED SOFT COMPUTING 2013年第9期13卷 3873-3883页

作者： Datta, Dilip Tezpur Univ Sch Engn Dept Mech Engn Napaam 784028 Tezpur India

The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems. The problem becomes even more complicated when dynamic power limit based ramp rate constraint is taken into account. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which genetic algorithm (GA) has been investigated widely since long back. Such proposals have been made for solving only the integer part of the UCP, along with some other approaches for the real part of the problem. Moreover, the ramp rate constraint is usually discussed only in the formulation part, without addressing how it could be implemented in an algorithm. In this paper, the GA is revisited with an attempt to solve both the integer and real parts of the UCP using a single algorithm, as well as to incorporate the ramp rate constraint in the proposed algorithm also. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed GA is found quite satisfactory in comparison with the previously reported results. (C) 2013 Elsevier B. V. All rights reserved.

关键词： unit commitment problem Ramp rate constraint Genetic algorithm

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unit commitment problem Using An Efficient PSO Based Algorithm 11

Unit Commitment Problem Using An Efficient PSO Based Algorit...

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11th International Conference on Advanced Computational Intelligence (ICACI)

作者： Zhai, Yu Mu, Nankun Liao, Xiaofeng Le, Junqing Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

ISBN: (纸本)9781538677322

Electric generators consume most of the world's fossil energy in power plant. In power plants, better solving unit commitment problem (UCP) means saving more fossil energy. Nowadays, most of the algorithms to solve the UCP cannot get good results, so it is necessary to study more efficient algorithms. Towards this end, this paper presents a novel algorithm to solve UCP. The proposed algorithm combines particle swarm optimization and simulated annealing algorithm to solve UCP better. At the same time, a convex optimization algorithm is proposed to solve the corresponding economic load distribution problem. We have done a lot of experiments to prove the advantage of this algorithm, which can solve UCP efficiently.

关键词： unit commitment problem economic load distribution particle swarm optimization simulated annealing algorithm

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unit commitment problem by using JayaDE Optimization Algorithm 7

Unit Commitment Problem by using JayaDE Optimization Algorit...

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7th IEEE India International Conference on Power Electronics (IICPE)

作者： Kundu, Shiena Kumar, Nishant Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

ISBN: (纸本)9781509045303

The large requirement of power for fulfilling daily demands cannot be supplied with the help of a single unit or stored power, a large number of generating units are required to complete this demand. The scheduling of these units so as to generate the forecasted power at a minimum cost is known as unit commitment problem. It is one of the basic optimization problems in power systems. Different algorithms like lagrangian relaxation, particle swarm optimization and genetic algorithm etc. have been used to address this problem. In this paper a better solution to unit commitment problem is proposed. The proposed JayaDE algorithm is a hybrid of Jaya optimization algorithm and Differential Evolution algorithm. The proposed solution results in minimum generation cost as compared to cost involved while using other existing optimization algorithms. Results are shown for 10, 20 and 40 unit system and comparison of total cost and total computational times are listed for different algorithms.

关键词： Algorithm. Differential evolution Hybrid Java optimizaton unit commitment problem

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Solving Fuel-Based unit commitment problem Using Improved Binary Bald Eagle Search

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Journal of Bionic Engineering 2024年第6期21卷 3098-3122页

作者： Sharaz Ali Mohammed Azmi Al-Betar Mohamed Nasor Mohammed A.Awadallah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial Intelligence Research Center(AIRC) Ajman UniversityP.O.Box 346AjmanUnited Arab Emirates

The unit commitment problem(UCP)corresponds to the planning of power generation *** objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand,which also minimizes the total operating cost while adhering to different constraints such as power generation limits,unit startup,and shutdown *** this paper,four different binary variants of the Bald Eagle Search(BES)algorithm,were introduced,which used two variants using S-shape,U-shape,and V-shape transfer *** addition,the best-performing variant(using an S-shape transfer function)was selected and improved further by incorporating two binary operators:swap-window and *** variation is labeled Improved Binary Bald Eagle Search(IBBESS2).All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-,10-,20-,40-,60-,80-,and *** comparative evaluation,34 comparative methods from existing literature were compared,in which IBBESS2 achieved competitive scores against other optimization *** other words,the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-,40-,60-,80-,and 100-unit ***,IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms,especially in large-scale unit commitment *** Friedman statistical test further validates the results,where the proposed IBBESS2 is ranked the *** conclusion,the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.

关键词： Swarm intelligence Bald eagle search unit commitment problem Optimization

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A stochastic model for the unit commitment problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 1996年第3期11卷 1497-1506页

作者： Takriti, S Birge, JR Long, E Department of Industrial and Operations Engineering University of Michigan Ann Arbor MI USA

We develop a model and a solution technique for the problem of generating electric power when demands are not certain. We also provide techniques for improving the current methods used in solving the traditional unit commitment problem. The solution strategy can be run in parallel due to the separable nature of the relaxation used. Numerical results indicate significant savings in the cost of operating power generating systems when the stochastic model is used instead of the deterministic model.

关键词： unit commitment problem stochastic programming Lagrangian relaxation dynamic programming

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Fuzzy and simulated annealing based dynamic programming for the unit commitment problem

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第3期36卷 5081-5086页

作者： Patra, S. Goswami, S. K. Goswami, B. Assam Engn Coll Gauhati 781013 Assam India Jadavpur Univ Kolkata W Bengal India

A dynamic programming technique with a fuzzy and simulated annealing based unit selection procedure has been proposed in this paper for the solution of the UC problem. The curse of dimensionality of the dynamic programming technique is eliminated by minimizing the number of prospective solution paths to be stored at each stage of the search procedure. Heuristics like priority ordering of the units, unit grouping, fast economic dispatch based on priority ordering, avoidance of repeated economic dispatch through memory action have been employed to make the algorithm fast. The proposed method produced comparable results with the best performing methods found in the literature. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Dynamic programming unit commitment problem Heuristic procedures Fuzzy based unit selection Simulated annealing

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A binary-real-coded differential evolution for unit commitment problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期42卷 517-524页

作者： Datta, Dilip Dutta, Saptarshi Tezpur Univ Sch Engn Dept Mech Engn Napaam 784028 Tezpur India Royal Grp Inst Royal Sch Engn & Technol Dept Mech Engn Gauhati 781035 India

The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem encountered in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which differential evolution (DE) is one of the widely investigated metaheuristics. However, DE is usually applied for solving the integer part of the UCP, along with some other schemes for the real part of the problem. In this paper a binary-real-coded DE is proposed as a complete solution technique of the UCP. Some repairing mechanisms are also incorporated in the DE for speeding up its search process. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed DE is found quite satisfactory in comparison with the previously reported results. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： unit commitment problem Differential evolution Repairing mechanisms

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Adaptive general variable neighborhood search heuristics for solving the unit commitment problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 78卷 873-883页

作者： Todosijevic, Raca Mladenovic, Marko Hanafi, Said Mladenovic, Nenad Crevits, Igor Univ Valenciennes LAMIH UMR CNRS 8201 F-59313 Valenciennes 9 France Serbian Acad Arts & Sci Math Inst Belgrade 11000 Serbia

The unit commitment problem (UCP) for thermal units consists of finding an optimal electricity production plan for a given time horizon. In this paper we propose hybrid approaches which combine Variable Neighborhood Search (VNS) metaheuristic and mathematical programming to solve this NP-hard problem. Four new VNS based methods, including one with adaptive choice of neighborhood order used within deterministic exploration of neighborhoods, are proposed. A convex economic dispatch subproblem is solved by Lambda iteration method in each time period. Extensive computational experiments are performed on well-known test instances from the literature as well as on new large instances generated by us. It appears that the proposed heuristics successfully solve both small and large scale problems. Moreover, they outperform other well-known heuristics that can be considered as the state-of-the-art approaches. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Power systems unit commitment problem Mixed integer nonlinear problem Variable neighborhood search

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Binary real coded firefly algorithm for solving unit commitment problem

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INFORMATION SCIENCES 2013年 249卷 67-84页

作者： Chandrasekaran, K. Simon, Sishaj P. Padhy, Narayana Prasad Kamaraj Coll Engn & Technol Dept EEE Virudunagar 620001 India Natl Inst Technol Dept EEE Tiruchirappalli 620015 Tamil Nadu India Indian Inst Technol Dept EE Roorkee 247667 Uttar Pradesh India

This paper presents a new biologically-inspired binary real coded firefly (BRCFF) algorithm to solve the unit commitment problem (UCP) by considering system and generating unit constraints. The firefly (FF) algorithm is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication. Upon solving UCP, the proposed binary coded FF algorithm determines the ON/OFF status of the generating units, while the economic dispatch problem (EDP) is solved using the real coded FF algorithm. The manner of firefly communication through luminescent flashes and their synchronization is imitated and suitably implemented in UCP. An effective constraint handling mechanism is introduced to solve complicated system and unit constraints. Finally, the proposed algorithm is applied to 3, 12, 17, 26, and 38 generating unit systems for a 24 h scheduling horizon and a comparative study is conducted using other recently reported results. Numerical results clarify and verify the significance of the proposed algorithm. The results obtained indicate that the proposed biologically-inspired algorithm could be an important player in swarm-based optimization. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Binary real coded firefly algorithm Economic dispatch problem unit commitment problem

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