A Branch-and-Bound Algorithm for Unit Commitment

Cohen, Arthur I.; Yoshimura, Motoki

doi:10.1109/tpas.1983.317714

Cited by 360 publications

(100 citation statements)

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“…In general, their efficiency heavily depends on the amount of modeling detail that is integrated in the problem. Early applications of MILP can be found in Garver (1962), Muckstadt and Wilson (1968) and Cohen and Yoshimura (1983), and in Cohen and Yoshimura (1983) it is stated that the model could be extended to allow for probabilistic reserve constraints. Hydro-thermal UC is considered in Dillon et al (1978), Pereira and Pinto (1983) and Shaw et al (1985) where constraints regarding hydro units such as flow equations, storage level of reservoirs, pump storage and min and max outflow of each reservoir are incorporated in the model.…”

Section: Modern Use Of Milp Techniquesmentioning

confidence: 99%

“…;Lauer et al (1982);Cohen and Yoshimura (1983);Huang et al (1998); Shafie-Khah and Parsa (2011);Makkonen and Lahdelma (2006). Many other constraints have been considered, such as Must Run/OffDillon et al (1978);Frangioni et al (2009Frangioni et al ( , 2011, Transmission (comprised OTS) Pereira and Pinto(1983); Fisher et al (2008); Lu and Shahidehpour (2005); Di Lullo (2013); Hedman et al (2011a, b); Ruiz (2012); Papavasiliou et al (2013b); O'Neill et al (2010); Ostrowski et al (2012); Ostrowski and Wang (2012); Liu et al (2012a, b); Korad and Hedman (2013); Hedman et al (2009, 2010); Zhang and Wang (2014), Modulation Dillon et al (1978), Starts Turgeon (1978), Hot/Cold Starts Lauer et al (1982), Ramp Rate Huang et al (1998); Shafie-Khah and Parsa (2011); Frangioni et al (2011); Carrión and Arroyo (2006); Lu and Shahidehpour (2005); Ostrowski et al (2012); Morales-España et al (2013a); Frangioni et al (2009); Jabr (2012); Arroyo and Conejo (2004); García-González and San (2007); Morales-España et al (2013b); García-González and San (2007); Morales-España et al (2014), Hydro-Thermal Shaw et al (1985); Shafie-Khah and Parsa…”

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confidence: 99%

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Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty

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Section: Modern Use Of Milp Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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“…As shown in Figure 1, most of load is supplied by base units and the remaining load is supplied by other units k p ′ . Base units and other units form the unit combination, which is represented as (12). In this way, the search space is limited and the computation time is reduced, which is suitable for large-scale unit application.…”

Section: Production Of All Possible Candidate Units For Each Time Periodmentioning

confidence: 99%

“…It involves the determination of an optimum start-up and shut-down schedule of generating units that minimizes operating cost, while satisfying a set of system constraints over a time period [1] [2]. Numerous efficient and robust UC methods have been developed and can be classified into two main categories [3]: The first category represents numerical optimization techniques such as priority list methods (PL) [4] [5] [6], dynamic programming method (DP) [7] [8], Lagrangian relaxation (LR) [9] [10] [11], and the popular branch-and-bound method (BB) [12] [13] [14] [15]. The PL method is fast but highly heuristic and gives schedules with relatively high operation costs.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Unit Commitment Approach Incorporating Modified Priority List with Charged System Search Methods

Wu¹,

Chih-Cheng²,

Lin³

et al. 2017

SGRE

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This paper presents a new hybrid approach that combines Modified Priority List (MPL) with Charged System Search (CSS), termed MPL-CSS, to solve one of the most crucial power system's operational optimization problems, known as unit commitment (UC) scheduling. The UC scheduling problem is a mixed-integer nonlinear problem, highly-dimensional and extremely constrained. Existing meta-heuristic UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to largescale, heavily-constrained UC applications. In the first step of the proposed method, initial hourly optimum solutions of UC are obtained by Modified Priority List (MPL); however, the obtained UC solution may still be possible to be further improved. Therefore, in the second step, the CSS is utilized to achieve higher quality solutions. The UC is formulated as mixed integer linear programming to ensure the tractability of the results. The proposed method is successfully applied to a popular test system up to 100 units generators for both 24-hr and 168-hr system. Computational results show that both solution cost and execution time are superior to those of published methods.

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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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A Branch-and-Bound Algorithm for Unit Commitment

Cited by 360 publications

References 12 publications

Large-scale Unit Commitment under uncertainty

Large-scale Unit Commitment under uncertainty

A Hybrid Unit Commitment Approach Incorporating Modified Priority List with Charged System Search Methods

Unit commitment problem using enhanced particle swarm optimization algorithm

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