Evolutionary hybrid approaches for generation scheduling in power systems

Dahal, Keshav; Aldridge, C. J.; Galloway, Stuart

doi:10.1016/j.ejor.2005.12.018

Cited by 7 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…A set of experiments was conducted using a genetic algorithm-based solution technique to obtain an optimised maintenance schedule as described in [12,13] for four scenarios. The evaluation (fitness) function is formulated as a weighted sum of the objective function and the penalty function for violations of the constraints in this example, taking a similar approach to that described in [13].…”

Section: Application Of Cost Models In Maintenance Schedulingmentioning

confidence: 99%

Modelling generator maintenance scheduling costs in deregulated power markets

Dahal

Al-Arfaj²,

Paudyal

2015

European Journal of Operational Research

View full text Add to dashboard Cite

Generating companies use the maintenance cost function as the sole or main objective for creating the maintenance schedule of power generators. Usually only maintenance activities related costs are considered to derive the cost function. However, in deregulated markets, maintenance related costs alone do not represent the full costs of generators. This paper models various cost components that affect the maintenance activities in deregulated power markets. The costs that we model include direct and indirect maintenance, failures, interruptions, contractual compensation, rescheduling, and market opportunity. The loss of firm's reputation and selection of loyalty model are also considered using the Analytic Hierarchy Process (AHP) within an opportunity cost model. A case study is used to illustrate the modelling activities. The enhanced model is utilised in generator maintenance scheduling cases. The experimental results demonstrate the importance and impact of market related costs in maintenance schedules.

show abstract

“…Based on some experimentation and previous knowledge on GAs application with other problem [20], the parameters of genetic operation are set as follow:…”

Section: Genetic Algorithm Designmentioning

confidence: 99%