Deterministic optimization of the thermal Unit Commitment problem: A Branch and Cut search

Marcovecchio, Marian G.; Novais, Augusto Q.; Grossmann, Ignacio E.

doi:10.1016/j.compchemeng.2014.03.009

Cited by 24 publications

(14 citation statements)

References 40 publications

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“…This is an extremely challenging optimisation problem due to the enormous number of possible combinations of the status (on/off) of the generators within a considered network (Bhardwaj et al, 2012). This kind of formulations is applied to electricity and heat generation (Marcovecchio et al, 2014). Hytowitz and Hedman (2015) proposed a stochasticbased formulation to consider the uncertainty related to the generation of electricity through the use of photovoltaic panels under uncertain conditions.…”

Section: Introductionmentioning

confidence: 99%

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

Silvente

Kopanos

Dua

et al. 2018

Chemical Engineering Research and Design

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This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information.

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Section: Introductionmentioning

confidence: 99%

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

Silvente

Kopanos

Dua

et al. 2018

Chemical Engineering Research and Design

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“…Various mathematical programming and heuristic. Various approaches include mathematical programming and heuristic approaches such as dynamic programming [2], neural networks [3], simulated annealing [4][5][6], evolutionary programming [7][8][9] constraint logic programming [10], genetic algorithm [11][12][13],Lagrangian relaxation [14][15][16], branch and bound [17], tabu search [18,19], particle swarm optimization [22][23][24][25][26] approaches have been devoted to solve the UCproblem.This paper considers the generation scheduling problem which includes wind power generation along with thermal generating stations. In this problem, by increasing the reserve requirements, the impacts of wind power generation are modeled when specifying the reserve inequality for this problem.…”

Section: Introductionmentioning

confidence: 99%

Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm

Saravanan¹,

Subramanian²,

Dharamalingam³

et al. 2016

IJCA

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Integrating wind power with any other energy source in power system has many operational and scheduling complications because of its inconsistent nature in the process ofwind forecasting. In this paper, a new metaheuristic optimization method named Grey Wolf Optimization algorithm is involved for solving the problem of generation scheduling (GS) to obtain best possible solution in power systems taking into account the load balance, reserve requirement, wind power availability constraints, inequality and equality constraints. The proposed GWO method is applied to a test system involves 40 conventional units and 2 wind farms. The system performance of GWO algorithm is establishedbyevaluating the results obtained for different number of trails and various iterationsfor five different populations. Calculation of the solution for different populations in the systemdiscloses that the best optimal scheduleachieved by Grey Wolf Optimization algorithm.

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“…However, this approach to generate instances has been widely used in the literature (Carrion and Arroyo, 2006;Marcovecchio et al, 2014;Morales-Espana et al, 2013;Ostrowski et al, 2012). In this section, particular instances of the two case studies analyzed in Section 5 are used to illustrate some typical results obtained in terms of computational performance and commitment of units with this type of benchmark problems.…”

Section: Motivating Examplesmentioning

confidence: 99%

Symmetry breaking in MILP formulations for Unit Commitment problems

Lima

Novais

2016

Computers & Chemical Engineering

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Please cite this article as: Ricardo M. Lima, Augusto Q. Novais, Symmetry Breaking in MILP Formulations for Unit Commitment Problems, Computers and Chemical Engineering (2015), http://dx.doi.org/10. 1016/j.compchemeng.2015.11.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. • Symmetry issues are discussed in the context of UC formulations.• Three sets of SBC are proposed and analyzed.• Three well-known UC MILP models are used to implement the SBC.• The results presented are completely disruptive compared with the results presented in the literature.• The SBC derived have a clear advantage over general methods available in two MILP solvers. *HighlightsPage 2 of 43 show that problems that could not be solved to optimality within hours can be solved with a relatively small computational burden if the symmetry breaking constraints are assumed.The proposed symmetry breaking constraints are also compared with the symmetry breaking methods included in two MILP solvers, and the symmetry breaking constraints derived in this work have a distinct advantage over the methods in the MILP solvers.

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Deterministic optimization of the thermal Unit Commitment problem: A Branch and Cut search

Cited by 24 publications

References 40 publications

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty

Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm

Symmetry breaking in MILP formulations for Unit Commitment problems

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