Unit commitment by tabu search

Mantawy, A.H.; Abdel-Magid, Y.L.; Selim, Shokri Z.

doi:10.1049/ip-gtd:19981681

Cited by 166 publications

(49 citation statements)

References 10 publications

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“…The problem can then be decomposed into two sub problems, a combinatorial problem in U and V and a non-linear optimization problem in P . TS are used to solve the combinatorial optimization and the non-linear optimization is solved via a quadratic programming routine [18]. The flowchart for TS is shown in Fig.…”

Section: Overviewmentioning

confidence: 99%

“…But it will take much time to reach the near-global minimum. The TS [18][19][20], [23] is an iterative improvement procedure that starts from some initial feasible solution and attempts to determine a better solution in the manner of a greatest-decent algorithm. However, TS is characterized by an ability to escape local optima by using a short-term memory of recent solutions.…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

Christober¹,

Rajan²

2011

Journal of Electrical Engineering

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Section: Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

Christober¹,

Rajan²

2011

Journal of Electrical Engineering

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“…In addition, Aihara and Yokoyama [16] developed an operation scheduling method to simulate the Japanese weekly demand and supply considering large introduction of PV using dynamic programming. Unfortunately, dynamic programming faces some problems including the dimensionality, especially in the case of large-scale system [23].…”

Section: Introductionmentioning

confidence: 99%

Annual Assessment of Large-Scale Introduction of Renewable Energy: Modeling of Unit Commitment Schedule for Thermal Power Generators and Pumped Storages

et al. 2017

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Abstract:The fast-increasing introduction of renewable energy sources (RESes) leads to some problems in electrical power network due to fluctuating generated power. A power system must be operated with provision of various reserve powers like governor free capacity, load frequency control and spinning reserve. Therefore, the generator's schedule (unit commitment schedule) should include the consideration of the various power reserves. In addition, it is necessary to calculate the annual operational costs of electric power systems by solving the unit commitment per week of thermal power generators and pumped storages in order to compare and examine the variance of the operational costs and the operating ratio of the generators throughout the year. This study proposes a novel annual analysis for the thermal power generator and pumped storages under a massive introduction of RESes. A weekly unit commitment schedule (start/stop planning) for thermal power generator and pumped storages has been modeled and calculated for one year evaluation. To solve the generator start/stop planning problem, Tabu search and interior point methods are adopted to solve the operation planning for thermal power generators and the output decision for pumped storages, respectively. It is demonstrated that the proposed method can analyze a one-year evaluation within practical time. In addition, by assuming load frequency control (LFC) constraints to cope with photovoltaic (PV) output fluctuations, the impact of the intensity of LFC constraints on the operational cost of the thermal power generator has been elucidated. The increment of the operational cost of the power supply with increasing PV introduction amount has been shown in concrete terms.

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“…Various meta-heuristics are investigated, such as Artificial Neural Networks (ANN) [11], Genetic algorithm (GA) [12]- [16], Evolutionary Programming (EP) [17], Simulated Annealing (SA) [18] [19], Shuffled Frog Leaping algorithm [20], Particle Swarm Optimization (PSO) [21], Tabu Search (TS) [19] [22], Fuzzy Logic [23], harmony search algorithm (HSA) [24] and artificial bee colony algorithm (ABC) [25]. The practical advantage of meta-heuristic methods over deterministic methods lies in both their effectiveness and general applicability.…”

Section: Introductionmentioning

confidence: 99%

Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem

Farag¹,

El-Shorbagy²,

Eldesoky³

et al. 2015

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This paper presents a new algorithm for solving unit commitment (UC) problems using a binaryreal coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems 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. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit; while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results. KeywordsUnit Commitment (UC), Genetic Algorithm (GA), k-Means Clustering Technique M. A. Farag et al.

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Unit commitment by tabu search

Cited by 166 publications

References 10 publications

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

Annual Assessment of Large-Scale Introduction of Renewable Energy: Modeling of Unit Commitment Schedule for Thermal Power Generators and Pumped Storages

Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem

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Unit commitment by tabu search

Cited by 166 publications

References 10 publications

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

Annual Assessment of Large-Scale Introduction of Renewable Energy: Modeling of Unit Commitment Schedule for Thermal Power Generators and Pumped Storages

Binary-Real Coded Genetic Algorithm Based &lt;i&gt;k&lt;/i&gt;-Means Clustering for Unit Commitment Problem

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Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem