An Improved Lagrangian Relaxation Algorithm for the Robust Generation Self-Scheduling Problem

Che, Ping; Tang, Zhenhao; Gong, Hua; Zhao, Xiaoli

doi:10.1155/2018/6303596

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…The generation self-scheduling problem plays an important role in the daily-operation planning of generation companies in the deregulated electricity market. A typical generation self-scheduling problem is deciding the operation of generation units according to electricity prices and the physical characteristics of generation units, with the objective of maximizing total profits [28,29]. Effective generation self-scheduling can promote the economic operation of generation companies.…”

Section: Work and Contribution Of The Papermentioning

confidence: 99%

Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling

2019

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We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase of generation emission intensity. By introducing a step function to express the variable tax rate, we formulate the generation self-scheduling problem under the proposed carbon-tax policy as a mixed integer nonlinear programming model. The objective function is to maximize total generation profits, which are determined by generation revenue and the levied carbon tax over the scheduling horizon. To solve the problem, a decomposition algorithm is developed where the variable tax rate is transformed into a pure integer linear formulation and the resulting problem is decomposed into multiple generation self-scheduling problems with a constant tax rate and emission-intensity constraints. Numerical results demonstrate that the proposed decomposition algorithm can solve the considered problem in a reasonable time and indicate that the proposed carbon-tax policy can enhance the incentive for generation companies to invest in low-carbon generation capacity.

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Section: Work and Contribution Of The Papermentioning

confidence: 99%

Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling

2019

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“…Many mathematical methods have been proposed to resolve scheduling problems [7]; some works have approached the FJSSP using the branch and bound method [8], linear programming [9], or Lagrangian relaxation [10]. These methods ensure global convergence and have worked very well in solving small instances, but their computational time makes them impractical for problems with dozens of scheduling operations [6].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Search Using Genetic Algorithms and Random-Restart Hill-Climbing for Flexible Job Shop Scheduling Instances with High Flexibility

et al. 2022

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This work presents a novel hybrid algorithm called GA-RRHC based on genetic algorithms (GAs) and a random-restart hill-climbing (RRHC) algorithm for the optimization of the flexible job shop scheduling problem (FJSSP) with high flexibility (where every operation can be completed by a high number of machines). In particular, different GA crossover and simple mutation operators are used with a cellular automata (CA)-inspired neighborhood to perform global search. This method is refined with a local search based on RRHC, making computational implementation easy. The novel point is obtained by applying the CA-type neighborhood and hybridizing the aforementioned two techniques in the GA-RRHC, which is simple to understand and implement. The GA-RRHC is tested by taking four banks of experiments widely used in the literature and comparing their results with six recent algorithms using relative percentage deviation (RPD) and Friedman tests. The experiments demonstrate that the GA-RRHC is a competitive method compared with other recent algorithms for instances of the FJSSP with high flexibility. The GA-RRHC was implemented in Matlab and is available on Github.

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A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination

et al. 2020

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The backbone of a conventional electrical power generation system relies on hydro-thermal coordination. Due to its intrinsic complex, large-scale and constrained nature, the feasibility of a direct approach is reduced. With this limitation in mind, decomposition methods, particularly Lagrangian relaxation, constitutes a consolidated choice to “simplify” the problem. Thus, translating a relaxed problem approach indirectly leads to solutions of the primal problem. In turn, the dual problem is solved iteratively, and Lagrange multipliers are updated between each iteration using subgradient methods. However, this class of methods presents a set of sensitive aspects that often require time-consuming tuning tasks or to rely on the dispatchers’ own expertise and experience. Hence, to tackle these shortcomings, a novel Lagrangian multiplier update adaptative algorithm is proposed, with the aim of automatically adjust the step-size used to update Lagrange multipliers, therefore avoiding the need to pre-select a set of parameters. A results comparison is made against two traditionally employed step-size update heuristics, using a real hydrothermal scenario derived from the Portuguese power system. The proposed adaptive algorithm managed to obtain improved performances in terms of the dual problem, thereby reducing the duality gap with the optimal primal problem.

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An Improved Lagrangian Relaxation Algorithm for the Robust Generation Self-Scheduling Problem

Cited by 3 publications

References 37 publications

Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling

Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling

A Hybrid Search Using Genetic Algorithms and Random-Restart Hill-Climbing for Flexible Job Shop Scheduling Instances with High Flexibility

A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination

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