Unit commitment using DP — An exhaustive working of both classical and stochastic approach
13 Jun 2013-pp 382-385
TL;DR: DP is used to solve the stochastic model for unit commitment problem and the commitment is in such a way that the total cost is minimal.
Abstract: In the present electricity market, where renewable energy power plants have been included in the power systems there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving unit commitment problem. The use of augmented Lagrangian technique by convergence of decomposition method was proposed in 1994, and in 2007 chance constrained optimization was used for providing a solution to the stochastic unit commitment problem. Dynamic Programming is a conventional algorithm used to solve deterministic problem. In this paper DP is used to solve the stochastic model. The stochastic modeling for generation side has been formulated using an approximate state decision approach. The programs were developed in MATLAB environment and were extensively tested for 4 unit 8 hour system. The results obtained from these techniques were validated with the available literature and outcome was satisfactory. The commitment is in such a way that the total cost is minimal.
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TL;DR: A three-phase approach based on price decomposition that yields quickly near-optimal solutions to large-scale real-world instances to be tested on Hydro-Québec's production system.
Abstract: Short-term hydro-generation planning can be efficiently modeled as a mixed integer linear program (MILP). Depending on the size of the system and the time horizon, the resulting MILP may be too large to be solved in reasonable time with commercial solvers. This paper presents a three-phase approach based on price decomposition that yields quickly near-optimal solutions to large-scale real-world instances. For any partition of the production system into subsystems, the first phase solves a linear program to estimate the marginal cost of electricity in each subsystem. The second phase solves local MILPs corresponding to each subsystem, and gives a solution that is almost feasible. The final phase slightly perturbs the solution to obtain a feasible solution that is proven to be near-optimal. Our method is tested on real instances corresponding to Hydro-Quebec's production system.
8 citations
Cites methods from "Unit commitment using DP — An exhau..."
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01 Jan 2013
TL;DR: In this article, a particle swarm optimization technique is used which is population based global searching optimization technique to solve the unit commitment problem, for committing the units optimally, which is arrived from the exploration on the bird and fish flocking movement behavior.
Abstract: The optimization problem while committing the units is minimizing the entire production cost, at the same time meeting the demand and fulfilling the equality and inequality limits. In order to supply adequate power to the consumers in a cost-effective and secured way the commitment of thermal units is the best option available. The commitment of generating units is done depending upon the prediction of upcoming demand. For getting a way out to the unit commitment problem there are numerous conventional and advanced programming practices used. For solving the deterministic problem the conventional dynamic programming algorithm is employed. In this paper DP is used to solve the unit commitment problem. In this paper Particle swarm optimization technique is used which is population based global searching optimization technique to solve the unit commitment problem, for committing the units optimally. It is arrived from the exploration on the bird and fish flocking movement behavior. For the straightforward implementation of the algorithm it is extensively used and rapidly developed and few particles are needed to be attuned. An algorithm was developed to attain a way out to the unit commitment problem using Particle Swarm Optimization technique. The effectiveness of the algorithm was tested on two test systems. The first system comprising of three units and the second system is an IEEE 30-bus system and the attained results using the two methods are compared for total operating cost.
4 citations
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01 Jan 2016
TL;DR: This thesis combines both ROR projects and storage dam projects and formulate the problem as a stochastic program to minimize the cost of energy generation and distribution under ROR Projects supply uncertainty.
Abstract: approved: ------------------------------------------Ebisa D. Wollega, Ph.D. Hydroelectric power system is a renewable energy type that generates electrical energy from water flow. An integrated hydroelectric power system may consist of water storage dams and run-of-river (ROR) hydroelectric power projects. Storage dams store water and regulate water flow so that power from the storage projects dispatch can follow a pre-planned schedule. Power supply from ROR projects is uncertain because water flow in the river, and hence power production capacity, is largely determined by uncertain weather factors. Hydroelectric generator dispatch problem has been widely studied in the literature; however, very little work is available to address the dispatch and distribution planning of an integrated ROR and storage hydroelectric projects. This thesis combines both ROR projects and storage dam projects and formulate the problem as a stochastic program to minimize the cost of energy generation and distribution under ROR projects supply uncertainty. Input data from the Integrated Nepal Power System are used to solve the problem and run experiments. Numerical comparisons of stochastic solution (SS), expected value (EEV), and wait and see (W&S) solutions are made. These solution approaches give economic dispatch of generators and optimal distribution plan that the power system operators (PSO) can use to coordinate, control, and monitor the power generation and distribution system. The W&S solution approach provided the least cost plan. The EEV solution was worse than the SS. The PSO may invest in advanced technologies to more accurately reveal the uncertainties in the planning process, to operate at the W&S operational cost. However, the tradeoff between using the SS solution and investing in new technologies to operate at W&S solution may require rigorous feasibility study.
1 citations
Cites background or methods from "Unit commitment using DP — An exhau..."
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TL;DR: The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.
Abstract: In Pakistan, hydroelectric power is one of the reliable sources of electricity with a capacity of 8,713 MW, which is 29% of the total energy mix. Hence, with such a vast resource capacity of hydroelectric power, its optimization and dispatch planning will have a great significance. This research work discusses the importance of hydroelectric power generation planning for both storage and run-of-river (ROR) hydropower plants, as well a; a solver-based optimization technique is proposed for the first time to resolve the intricate job of generation planning for hydroelectric power plants in MATLAB. A mathematical-optimization model is also developed, which uses a Mixed-Integer Linear-Programming (MILP) algorithm, based on the objective function of profit maximization, which considers a random varying revenue plan as model input. Three hydropower generators of different capacities and efficiencies are considered for the optimization problem. MILP based solution is proposed for both storage and ROR hydropower plants with two dispatch schedules, i.e., Normal dispatch schedule and optimum dispatch schedule. The objective functions are solved, and the profit (in dollars) from each dispatch schedule is calculated and compared. The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income. Hence, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.
1 citations
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References
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Book•
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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 branch-and-bound approach is presented for solving the unit commitment problem based on branch and bound techniques. But it does not require a priority ordering of the units.
Abstract: A new approach is presented for solving the unit commitment problem based on branch-and-bound techniques. The method incorporates time-dependent start-up costs, demand and reserve constraints and minimum up and down time constraints. It does not require a priority ordering of the units. The method can be extended to allow for a probabilistic reserve constraint. Preliminary computational results are reported.
415 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
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TL;DR: A method for determining the unit commitment schedule for hydro-thermal systems using extensions and modifications of the Branch and Bound method for Inteler Programming has been developed and significant features include its computational practicability for realistic systems and proper representation of reserves associated with different risk levels.
Abstract: A method for determining the unit commitment schedule for hydro-thermal systems using extensions and modifications of the Branch and Bound method for Inteler Programming has been developed. Significant features of the method include its computational practicability for realistic systems and proper representation of reserves associated with different risk levels. Contracts relating to power interchange have also been adequately modelled for such an approach.
369 citations
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TL;DR: A heuristic improvement of the truncated window dynamic programming has been studied for the unit commitment application and results indicate a substantial saving in the computation time without sacrificing the quality of the solution.
Abstract: A heuristic improvement of the truncated window dynamic programming has been studied for the unit commitment application. The proposed method employs a variable window size according to load demand increments, and corresponding experimental results indicate a substantial saving in the computation time without sacrificing the quality of the solution. An iterative process for the number of strategies saved in every stage is incorporated to fine tune the optimal solution. >
296 citations
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