A two stage solution methodology for deterministic unit commitment problem
01 Jan 2016-pp 317-322
TL;DR: In this paper, a two-stage solution methodology for deterministic unit commitment problem is presented, in the first stage the ON/OFF status of units is determined by priority list method while in the second stage the economic load dispatch has been solved by Particle swarm optimization technique.
Abstract: This paper presents a two stage solution methodology for deterministic unit commitment problem. In the first stage the ON/OFF status of units is determined by priority list method while in the second stage the economic load dispatch has been solved by Particle swarm optimization technique. The proposed technique is applied in MATLAB environment on two different cases comprising of four and ten generating units respectively. It is observed that for both the cases the overall production cost obtained through the proposed technique is better than the results reported thus far.
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TL;DR: This paper introduces a new unit commitment problem, adapting extended priority list (EPL) method, and proposes a method to modify unit schedule using problem specific heuristics to fulfill operational constraints.
Abstract: This paper introduces a new unit commitment problem, adapting extended priority list (EPL) method. The EPL method consists of two steps, in the first step we get rapidly some initial unit commitment problem schedules by priority list (PL) method. At this step, operational constraints are disregarded. In the second step unit schedule is modified using the problem specific heuristics to fulfill operational constraints. To calculate efficiently, however, note that some heuristics is applied only to solutions can expect improvement. Several numerical examples demonstrate the effectiveness of proposed method.
156 citations
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TL;DR: The authors present a new optimization algorithm named doctor and patient optimization (DPO), designed by simulating the process of treating patients by a physician, which is successfully applied to solve the energy commitment problem for a power system supplied by a multiple energy carriers system.
Abstract: Regular assessments of events taking place around the globe can be a conduit for the development of new ideas, contributing to the research world. In this study, the authors present a new optimization algorithm named doctor and patient optimization (DPO). DPO is designed by simulating the process of treating patients by a physician. The treatment process has three phases, including vaccination, drug administration, and surgery. The efficiency of the proposed algorithm in solving optimization problems compared to eight other optimization algorithms on a benchmark standard test function with 23 objective functions is been evaluated. The results obtained from this comparison indicate the superiority and quality of DPO in solving optimization problems in various sciences. The proposed algorithm is successfully applied to solve the energy commitment problem for a power system supplied by a multiple energy carriers system.
12 citations
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TL;DR: An efficient iterative method with an inner unit commitment optimisation layer to achieve the optimised battery capacity is proposed and the method is applied to determine the battery capacity of the experimental Microgrid at Griffith University.
Abstract: This paper presents a battery capacity optimisation method with the aim of investment and operational cost reduction for grid-connected microgrids consisting of dispatchable generators, renewable energy resources and battery energy storage. The operating cost of grid-connected commercial Microgrids is mainly associated with the purchased energy from the grid and monthly peak demand. Hence, mitigating the peak value by the means of battery energy storage and dispatchable generators during the peak period can effectively reduce the operating cost. However, due to the high cost and short life span of the battery energy storage systems, the optimum design of energy storages is of the utmost importance to the Microgrids. This paper proposes an efficient iterative method with an inner unit commitment optimisation layer to achieve the optimised battery capacity. In order to implement the inner unit commitment optimisation, the Mixed Integer Quadratic Programming (MIQP) optimisation algorithm is applied and CPLEX solver is chosen to solve the optimisation problem. This approach is applicable and beneficial when dealing with high demands as it economically distributes the load requirement between the battery and dispatchable generators. Finally, the proposed method is applied to determine the battery capacity of the experimental Microgrid at Griffith University. The simulation results for the understudy case verified the efficiency and effectiveness of the proposed approach.
7 citations
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TL;DR: A modified firefly algorithm to address unit commitment issues is presented and results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation.
Abstract: Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on ...
5 citations
Journal Article•
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TL;DR: An attempt has been made to formulate a short term deterministic Unit Commitment problem in renewable integrated environment with battery storage involving hybrid Particle Swarm Optimization (PSO) technique to provide techno-economic solution to this complex optimization problem.
Abstract: The rising energy demand and climate change issues have warranted the inclusion of renewable energy resources with existing conventional fuel based generation system. The intermittent renewable generation require adequate battery support in order to minimize load deficit issues in electrical grid. Hence, an attempt has been made in this paper to formulate a short term deterministic Unit Commitment problem in renewable integrated environment with battery storage. Ten thermal generators are scheduled with a 500 MW wind energy generation system supported by 200 MWh battery with backup of four hours. A three stage solution methodology is evolved involving hybrid Particle Swarm Optimization (PSO) technique to provide techno-economic solution to this complex optimization problem. The charge/ discharge scheduling of battery energy storage integrated to wind generation system is taken up as a co-optimization problem. The generation of battery energy storage integrated wind energy system is so scheduled that it relieves the costlier thermal generating units in the most economic manner.
4 citations
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References
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01 Jan 1984
TL;DR: In this paper, the authors present a graduate-level text in electric power engineering as regards to planning, operating, and controlling large scale power generation and transmission systems, including characteristics of power generation units, transmission losses, generation with limited energy supply, control of generation, and power system security.
Abstract: Topics considered include characteristics of power generation units, transmission losses, generation with limited energy supply, control of generation, and power system security. This book is a graduate-level text in electric power engineering as regards to planning, operating, and controlling large scale power generation and transmission systems. Material used was generated in the post-1966 period. Many (if not most) of the chapter problems require a digital computer. A background in steady-state power circuit analysis is required.
6,152 citations
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TL;DR: In this paper, a dynamic programming formulation of the unit commitment problem is presented, which features the classification of generating units into related groups so as to minimize the number of unit combinations which must be tested without precluding the optimal path.
Abstract: A field-proven dynamic programming formulation of the unit commitment problem is presented. This approach features the classification of generating units into related groups so as to minimize the number of unit combinations which must be tested without precluding the optimal path. Programming techniques are described which maximize efficiency. Considerations are discussed which determine when generating units must be evaluated and when they may be ignored. The heuristic procedures described in this paper are concerned with supplying all apriori information to the program thereby minimizing its execution time. Results are presented from field testing on a medium size utility. Composite generating unit formulation is described for the economic allocation of constrained fuel to a group of units.
493 citations
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TL;DR: In this paper, a new unit commitment problem, adapting extended priority list (EPL) method is introduced, which consists of two steps, in the first step, in order to get rapidly some initial unit commitment problems by priority list method, operational constraints are disregarded.
Abstract: This paper introduces a new unit commitment problem, adapting extended priority list (EPL) method. The EPL method consists of two steps, in the first step we get rapidly some initial unit commitment problem schedules by priority list (PL) method. At this step, operational constraints are disregarded. In the second step unit schedule is modified using the problem specific heuristics to fulfill operational constraints. To calculate efficiently, however, note that some heuristics applied only to solutions can expect improvement. Several numerical examples demonstrate the effectiveness of proposed method.
406 citations
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TL;DR: The proposed enhanced adaptive Lagrangian relaxation (ELR) for a unit commitment (UC) problem consists of adaptive LR (ALR) and heuristic search and the total system production costs are less expensive than the others especially for the large number of generating units.
Abstract: This paper proposes an enhanced adaptive Lagrangian relaxation (ELR) for a unit commitment (UC) problem. ELR consists of adaptive LR (ALR) and heuristic search. The ALR algorithm is enhanced by new on/off decision criterion, new initialization of Lagrangian multipliers, unit classification, identical marginal unit decommitment, and adaptive adjustment of Lagrangian multipliers. After the ALR best feasible solution reached is obtained, the heuristic search consisting of unit substitution and unit decommitment is used to fine tune the solution. The proposed ELR is tested and compared to conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA), and genetic algorithm based on unit characteristic classification (GAUC) on the systems with the number of generating units in the range of 10 to 100. ELR total system production costs are less expensive than the others especially for the large number of generating units. Furthermore, the computational times of ELR are much less than the others and increase linearly with the system size, which is favorable for large-scale implementation.
379 citations
"A two stage solution methodology fo..." refers methods in this paper
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TL;DR: In this article, a general optimization method, known as simulated annealing, is applied to generation unit commitment, by exploiting the resemblance between a minimization process and the cooling of a molten metal, generated feasible solutions randomly and moves among these solutions using a strategy leading to a global minimum with high probabilities.
Abstract: A general optimization method, known as simulated annealing, is applied to generation unit commitment. By exploiting the resemblance between a minimization process and the cooling of a molten metal, simulated annealing generates feasible solutions randomly and moves among these solutions using a strategy leading to a global minimum with high probabilities. The method assumes no specific problem structures and is highly flexible in handling unit commitment constraints. A concise introduction to the method is given. Numerical results on test systems of up to 100 units are reported. >
365 citations
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