Short-term thermal unit commitment-a new method
TL;DR: A method and an algorithm based on this new method that produces the same unit commitment schedule for the 20-unit system as a frequently used DP-STC algorithm in 15 s of computation time versus 524 s, respectively.
Abstract: A method and an algorithm based on this new method are presented. The effectiveness of the algorithm is illustrated by studying a 20-unit midwestern utility system, the EPRI 174-unit synthetic utility system D, and the EPRI 155-unit synthetic utility system E. The algorithm produces the same unit commitment schedule for the 20-unit system as a frequently used DP-STC algorithm in 15 s of computation time versus 524 s, respectively. The computation time is approximately linear with the number of hours in the unit commitment horizon. For the EPRI 174-unit system the algorithm requires only 205 s of computation time on a VAX 11/780 for a 48-hour horizon. >
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TL;DR: In this paper, a new mixed-integer linear formulation for the unit commitment problem of thermal units is presented, which requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving.
Abstract: This paper presents a new mixed-integer linear formulation for the unit commitment problem of thermal units. The formulation proposed requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving. Furthermore, the modeling framework provided by the new formulation allows including a precise description of time-dependent startup costs and intertemporal constraints such as ramping limits and minimum up and down times. A commercially available mixed-integer linear programming algorithm has been applied to efficiently solve the unit commitment problem for practical large-scale cases. Simulation results back these conclusions
1,601 citations
Cites methods from "Short-term thermal unit commitment-..."
...As a consequence, several solution techniques have been proposed such as heuristics [4]–[6], dynamic programming [7]–[9], mixed-integer linear programming (MILP) [10], [11], Lagrangian relaxation [12]–[18], simulated annealing [19]–[21], and evolution-inspired approaches [22]–[26]....
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TL;DR: In this article, a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of modern unit commitment (UC) problem for past 35 years based on more than 150 published articles.
Abstract: With the fast-paced changing technologies in the power industry, new power references addressing new technologies are coming to the market. So there is an urgent need to keep track of international experiences and activities taking place in the field of modern unit-commitment (UC) problem. This paper gives a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of UC problem for past 35 years based on more than 150 published articles. The collected literature has been divided into many sections, so that new researchers do not face any difficulty in carrying out research in the area of next-generation UC problem under both the regulated and deregulated power industry.
898 citations
TL;DR: Several optimization techniques have been applied to the solution of the thermal unit commitment problem as discussed by the authors, ranging from heuristics such as complete enumeration to the more sophisticated ones such as augmented LaGrangian.
Abstract: Several optimization techniques have been applied to the solution of the thermal unit commitment problem. They range from heuristics such as complete enumeration to the more sophisticated ones such as Augmented LaGrangian. The heuristics have even reappeared as expert systems. The problem to solve is the optimal scheduling of generating units over a short-term horizon, typically 168 hours. This paper is an overview of the literature in the unit commitment field over the past twenty five years. >
518 citations
TL;DR: In this paper, a price-based unit commitment (PBUC) problem based on the mixed integer programming (MIP) method was formulated for a GENCO with thermal, combined-cycle, cascaded-hydro, and pumped storage units.
Abstract: This paper formulates the price-based unit commitment (PBUC) problem based on the mixed integer programming (MIP) method. The proposed PBUC solution is for a generating company (GENCO) with thermal, combined-cycle, cascaded-hydro, and pumped-storage units. The PBUC solution by utilizing MIP is compared with that of Lagrangian relaxation (LR) method. Test results on the modified IEEE 118-bus system show the efficiency of our MIP formulation and advantages of the MIP method for solving PBUC. It is also shown that MIP could be applied to solve hydro-subproblems including cascaded-hydro and pumped-storage units in the LR-based framework of hydro-thermal coordination. Numerical experiments on large systems show that the MIP-based computation time and memory requirement would represent the major obstacles for applying MIP to large UC problems. It is noted that the solution of large UC problems could be accomplished by improving the MIP formulation, the utilization of specific structure of UC problems, and the use of parallel processing.
437 citations
Cites methods from "Short-term thermal unit commitment-..."
...The sequential method and unit decommitment were proposed to solve the UC problem in [19] and [20], respectively....
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TL;DR: In this article, a branch-and-bound method for scheduling thermal generating units is presented, where a simple rule is defined to compute the lower bound of each candidate schedule for interim computation usage, and the branching process takes place on the subschedule with the lowest lower bound.
Abstract: A branch-and-bound method for scheduling thermal generating units is presented. The decision variables are the start and stop times and the generation levels of the units. A simple rule is defined to compute the lower bound of each candidate schedule for interim computation usage, and the branching process takes place on the subschedule with the lowest lower bound. The heap data storage structure and space saving encoded data representations for partially fulfilled unit commitment schedules are utilized to facilitate the branch-and-bound procedure. By successive branching and bounding, the unit commitment schedule with the minimum cost can be obtained. Two examples, a 10 unit, 24 h and a 20 unit, 36 h case, are shown to illustrate the effectiveness of the proposed algorithm. >
320 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.
430 citations
TL;DR: A new Lagrangian relaxation method is proposed, which produces optimal solutions for unit commitment and the flexibility of the algorithm is such that numerous developments can be envisaged, such as simultaneous management of pumping units, probabilistic determination of the spinning reserve, etc.
Abstract: Unit commitment is a complex problem which, until now and for real size systems, has been solved using heuristic methods only. This paper proposes a new Lagragian relaxation method, which produces ??-optimal solutions. The flexibility of the algorithm is such that numerous developments can be envisaged, such as simultaneous management of pumping units, probabilistic determination of the spinning reserve, etc.
428 citations
TL;DR: In this article, the authors compared the performance of four unit commitment methods, three of which are based on the dynamic programming approach, and presented the modeling of inter-area flow constraints by linear flow network, so that such multi-area representation recognizes any existing transmission limitations.
Abstract: This paper compares the performance of four unit commitment methods, three of which are based on the dynamic programming approach. The paper also presents the modeling of inter-area flow constraints by linear flow network, so that such multi-area representation recognizes any existing transmission limitations which are normally ignored in most unit commitment methods. To assure realistic results, data for two large systems (up to 96 units) were used: EPRI Scenario System C and a midwestern utility. Unit commitment results for these two systems are presented in the paper and they demonstrate the importance of multiple area representation of a system in unit commitment. Such representation affects the schedule costs and assures the determination of realizable schedules.
278 citations
TL;DR: A branch-and-bound algorithm is proposed using a Lagrangian method to decompose the problem into single generator problems and a sub gradient method is used to select the Lagrange multipliers that maximize the lower bound produced by the relaxation.
Abstract: Two major decisions are made when scheduling the operations of a fossil-fuel power-generating system over a short time horizon. The “unit commitment” decision indicates what generating units are to be in use at each point in time. The “economic dispatch” decision is the allocation of system demand among the generating units in operation at any point in time. Both these decisions must be considered to achieve a least-cost schedule over the short time horizon. In this paper we present a mixed integer programming model for the short time horizon power-scheduling problem. The objective of the model is to minimize the sum of the unit commitment and economic dispatch costs subject to demand, reserve, and generator capacity and generator schedule constraints. A branch-and-bound algorithm is proposed using a Lagrangian method to decompose the problem into single generator problems. A sub gradient method is used to select the Lagrange multipliers that maximize the lower bound produced by the relaxation. We present...
268 citations
TL;DR: A truncated dynamic programming method for the commitment of thermal units over a period of up to 48 hours is described, such that the total cost is minimal.
Abstract: Fuel cost savings can be obtained by proper commitment of the available generating units. This paper describes a truncated dynamic programming method for the commitment of thermal units over a period of up to 48 hours. The commitment is such that the total cost is minimal. The total cost includes both the production cost and costs associated with the startup and shutdown of units. The startup cost for each unit is dependent on the amount of time the unit has been shutdown prior to startup. A variety of spinning reserve requirements are observed and crew constraints on the startup and/or shutdown of two or more units in the same plant are imposed on some of the plants. In addition, operation of individual units must satisfy the specified minimum up times and minimum shutdown times.
238 citations