Heuristic solutions to the long-term unit commitment problem with cogeneration plants

Kjeldsen, Niels; Chiarandini, Marco

doi:10.1016/j.cor.2011.03.018

Cited by 23 publications

(14 citation statements)

References 19 publications

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“…The incredibly challenging task of perfectly balancing the supply and demand of electricity at all times Takriti and Krasenbrink (1999), Nowak and R € omisch (2000), , Takriti and Birge (2000), Doorman and Nygreen (2003), Viana et al (2003), Valenzuela and Mazumdar (2003), Thompson et al (2004), Sen et al (2006), Dahal et al (2007), Troncoso et al (2008), Cerisola et al (2009), Toczy lowski and Zoltowska (2009), Corchero and Heredia (2011), Kjeldsen and Chiarandini (2012), Murillo-S anchez et al (2013), Papavasiliou and Oren (2013), Zugno and Conejo (2015), Lorca et al (2016), van Ackooij and Malick (2016), Zugno et al (2016), and Morales and Pineda (2017) Valuation and/or optimal investment level for generation, storage, or transmission assets: single-firm level decision making, includes real options-based papers opens up many research questions that can be addressed with the tools and techniques of OR/MS. Indeed, the electric power industry research has long been an important stream in OR/MS scholarship.…”

Section: Electric Power Industry Researchmentioning

confidence: 99%

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Parker

Tan

Kazan

2019

Production and Operations Management

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The electric power industry is undergoing dramatic change driven by rapid technological transformation, concern over climate change, and evolving market structures. As a result, there is a need for new models to help the industry better utilize resources in a time of increasing uncertainty and to help government policy makers better understand the impacts of their regulatory decisions. We provide a structured review of the operations research and management science literatures to describe the current operational and policy issues in the electric power industry, with a particular focus on issues surrounding electricity market design, renewable integration, effects of climate policy on electric power infrastructure, rise of electric powered vehicles, energy storage, and the growing interdependence between natural gas and electric power sectors. We identify the current research frontier and classify the existing research into clusters with respect to managerial issues addressed. We offer a forecast for where the electric power industry is going and describe some important public policy issues. Finally, we highlight research opportunities and discuss how the management science community can contribute by considering the interaction between operational considerations and electric power policy.

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Section: Electric Power Industry Researchmentioning

confidence: 99%

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Parker

Tan

Kazan

2019

Production and Operations Management

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“…A similar approach is used in Ref. [33] to heuristically find the approximate solutions of OO MILP problem. In Refs.…”

Section: Discussionmentioning

confidence: 98%

“…In Ref. [33], heuristic techniques are used to find good feasible starting values for the MILP procedure and thus improve solving efficiency. The advantage is the possibility to take into account seasonal energy storage and the constraints that connect time-dependent variables over the entire year.…”

Section: Energy Supply Operation Optimizationmentioning

confidence: 99%

Greenhouse gases emission assessment in residential sector through buildings simulations and operation optimization

Stojiljković

Ignjatović

Vučković

2015

Energy

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“…The model was nonlinear and nonconvex but could be solved using GAMS/SBB. Kjeldsen and Chiarandini [26] developed heuristic approaches to the unit commitment problem with cogeneration plants. Theirs was a MILP model and the proposed heuristics were able to find solutions with 1-2% optimality gaps.…”

Section: Literature Reviewmentioning

confidence: 99%

Operational planning of combined heat and power plants through genetic algorithms for mixed 0–1 nonlinear programming

Gopalakrishnan

Kosanovic

2015

Computers & Operations Research

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a b s t r a c tThis paper is concerned with short-term (up to 24 h) operational planning in combined heat and power plants for district energy applications. In such applications, heat and power demands fluctuate on an hourly basis due to changing weather conditions, time-of-day factors and consumer requirements. Plant energy efficiency is highly dependent on ambient temperature and operating load since equipment efficiencies are nonlinear functions of these parameters. In operational planning strategies, nonlinear equipment characteristics are seldom taken into account, resulting in plants being operated at sub-par efficiencies. In order to operate plants at highest possible efficiencies, scheduling strategies which take into account nonlinear equipment characteristics need to be developed. For such strategies, a mixed 0-1 nonlinear programming formulation is proposed. The problem is nonconvex and hence global optimality conditions are unknown. Classical techniques like branch-and-bound may not produce integer feasible solutions, may cut off the global optima and have an exponential increase in CPU time for a linear increase in planning horizon size. As an alternative, a solution method through genetic algorithms is proposed in which genetic search is applied only on 0-1 variables and gradient search is applied on continuous variables. The proposed method is a nonlinear extension of the one originally developed by Sakawa et al. [Sakawa M, Kato K, Ushiro S. Operational planning of district heating and cooling plants through genetic algorithms for mixed 0-1 linear programming. Eur J Operat Res 2002;137(3):677-87].Numerical experiments show the proposed genetic algorithm method is more consistent in finding integer feasible solutions, finds solutions with lower optimality gaps and has reasonable CPU time as compared to branch-and-bound. From an application perspective, the proposed scheduling strategy results in 5-11% increase in plant energy efficiency.

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Heuristic solutions to the long-term unit commitment problem with cogeneration plants

Cited by 23 publications

References 19 publications

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Electric Power Industry: Operational and Public Policy Challenges and Opportunities

Greenhouse gases emission assessment in residential sector through buildings simulations and operation optimization

Operational planning of combined heat and power plants through genetic algorithms for mixed 0–1 nonlinear programming

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