Lambda of Lagrangian relaxation solution to unit commitment problem

Bakirtzis, Anastasios G.; Zoumas, C.E.

doi:10.1049/ip-gtd:20000173

Cited by 48 publications

(27 citation statements)

References 18 publications

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“…If the units have already been shut down, there will be a minimum time before they can be restarted and the constraint is given in (6).…”

Section: A) Minimum Up Timementioning

confidence: 99%

“…The MIP methods [3][4] for solving the unit commitment problems fail when the number of units increases because they require a large memory and suffer from great computational delay. The LR approach [5][6][7][8] to solve the short-term UC Problems was found that it provides faster solution but it will fail to obtain solution feasibility and solution quality problems and becomes complex if the number of units increased. The BB method [9] employs a linear function to represent fuel cost and start-up cost and obtains a lower and upper bounds.…”

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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“…If the units have already been shut down, there will be a minimum time before they can be restarted and the constraint is given in (6).…”

Section: A) Minimum Up Timementioning

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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“…The UC problem has been extensively studied in the Several heuristic approaches based on exact methods have been used such as dynamic programming, mixed-integer programming, benders decomposition, lagrangian relaxation and branch and bound methods, see e.g. [17], [9], [30], [4]. The main drawbacks of these traditional techniques are the high computational time required for large complexity and dimensionality problems.…”

Section: Introductionmentioning

confidence: 99%

“…Recently a stochastic dynamic programming approach to schedule power plants was proposed [26]. In [4] a solution using lagrangian relaxation is proprosed. However, the problem becomes too complex as the number of units increases and there are some difficulties in obtaining feasible solutions.…”

Section: Introductionmentioning

confidence: 99%

An optimal control approach to the Unit Commitment problem

Fontes¹,

Fontes²,

Roque³

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Abstract-The Unit Commitment (UC) problem is a wellknown combinatorial optimization problem arising in operations planning of power systems. It is typically formulated as nonlinear mixed-integer programming problem and has been solved in the literature by a huge variety of optimizations methods, ranging from exact methods (such as dynamic programming, branch-and-bound) to heuristic methods (genetic algorithms, simulated annealing, particle swarm). Here, we start by formulating the UC problem as a mixed-integer optimal control problem, with both binary-valued control variables and real-valued control variables. Then, we use a variable time transformation method to convert the problem into an optimal control problem with only real-valued controls. Finally, this problem is transcribed into a finite-dimensional nonlinear programming problem and solved using an off-the-shelf optimization solver.

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“…The major methods include priority list method (PLM) (Senjyu et al 2006), dynamic programming (DP) (Su and Hsu 1991), branch-and-bound methods (BBM) (Cohen and Yoshimura 1983), integer and mixedinteger linear programming (MILP) (Samer and John 2000;Khodaverdian et al 1986), and Langrangian relaxation (LR) (Ongsakul and Petcharaks 2004;Bakirtzis and Zoumas 2000). Among these methods, PLM is simple and fast, but the quality of final solution is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Unit commitment problem using enhanced particle swarm optimization algorithm

Yuan

Nie

et al. 2010

Soft Comput

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This paper proposes an enhanced PSO (EPSO) approach to solve the unit commitment (UC) problem in electric power system, which is an integrated improved discrete binary particle swarm optimization (DBPSO) with the Lambda-iteration method. The EPSO is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. The implementation of EPSO for UC problem consists of three stages. First, the DBPSO based on priority list is applied for unit scheduling when neglecting the minimum up/down time constraints. Second, heuristic search strategies are used to handle the minimum up/down time constraints and decommit excess spinning reserve units. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. To verify the advantages of the EPSO method, the EPSO is tested and compared to the other methods on the systems with the number of units in the range of 10 to 100. Numerical results demonstrate that the EPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time.

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Lambda of Lagrangian relaxation solution to unit commitment problem

Cited by 48 publications

References 18 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

An optimal control approach to the Unit Commitment problem

Unit commitment problem using enhanced particle swarm optimization algorithm

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