Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones

Feng, Zhong-kai; Niu, Wen-jing; Wang, Sen; Cheng, Chuntian; Song, Zhenguo

doi:10.3390/en12112179

Cited by 15 publications

(7 citation statements)

References 51 publications

(62 reference statements)

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“…C j = (X up j + X down j ) • 0.5 (14) where X up j and X down j are the upper and lower limits of the ith dimension.X k ij is the quasi-oppositional position of the ith wolf in the jth dimension at the kth iteration. r 3 is the random number uniformly distributed in the range of [0, 1].…”

Section: B the Proposed Igwo Methods 1) Quasi-oppositional Learningmentioning

confidence: 99%

Optimal Operation of Hydropower System by Improved Grey Wolf Optimizer Based on Elite Mutation and Quasi-Oppositional Learning

et al. 2019

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As one of the most important renewable energy, hydropower is often asked to satisfy the load demand of power system at peak periods. Thus, the optimal operation of hydropower system is modelled to minimize the standard deviation of the residual load series obtained by subtracting the total power outputs of all the involved hydropower plants from the original load curve. Hence, this paper develops an improved grey wolf optimizer (IGWO) to effectively address the complex constrained optimization problem. In the proposed method, the quasi-oppositional learning is used to enhance the convergence rate of the swarm; the elite mutation operator is used to increase the probability of escaping from local optima; the elastic-ball strategy and heuristic constraint handling method are used to help infeasible individuals rebound to feasible space. Numerical experiments of 12 classical test functions demonstrate the feasibility of the IGWO method in the global optimization problems. Then, the developed method is applied to solve the optimal operation of two cascade hydropower systems. The results indicate that the proposed method outperforms several traditional methods in smoothing the peak loads of power system. To sum up, an effective solution tool is provided for the hydropower system operation optimization problem. INDEX TERMS Hydropower system, grey wolf optimizer, quasi-oppositional learning, elite mutation, elastic-ball strategy, constraint handling method.

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Section: B the Proposed Igwo Methods 1) Quasi-oppositional Learningmentioning

confidence: 99%

Optimal Operation of Hydropower System by Improved Grey Wolf Optimizer Based on Elite Mutation and Quasi-Oppositional Learning

et al. 2019

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“…(2) Peak Operation In modern power systems, the hydropower system is often asked to respond to the peak loads of power grid to produce a relatively smooth load series for other kinds of energy systems (like thermal, solar, and wind) [74][75][76]. In this way, the total operational cost of the electrical power system in the long run can be sharply reduced [77][78][79]. Hence, the second objective function was chosen to minimize the mean square deviation of the residual load curve obtained by subtracting all power outputs of hydropower reservoirs from the original energy load curve, which could be expressed as:…”

Section: ) Power Generationmentioning

confidence: 99%

Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Feng

Liu

Jiang

et al. 2019

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In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system.

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“…As shown in Figure 14, there are no obvious differences in the average loads of SCA and HSCA, but the peak-valley differences and standard deviations of HSCA are much smaller than SCA. Generally, the smaller values of the peak-valley difference and standard deviation denote a smoother load series [60]. Therefore, it can be concluded that the HSCA approach has a stronger optimization ability in using the hydropower generation to reduce the peak loads of the power system.…”

Section: Engineering Backgroundmentioning

confidence: 99%

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

et al. 2019

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In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

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Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones

Cited by 15 publications

References 51 publications

Optimal Operation of Hydropower System by Improved Grey Wolf Optimizer Based on Elite Mutation and Quasi-Oppositional Learning

Optimal Operation of Hydropower System by Improved Grey Wolf Optimizer Based on Elite Mutation and Quasi-Oppositional Learning

Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

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