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To achieve a Danish energy supply based on 100% renewable energy from combinations of wind, biomass, wave and solar power in 2050 and to cover 50% of the Danish electricity consumption by wind power in 2025, it requires to coordinate the management of large numbers of distributed and demand
response resources in the Smart Grid. This paper presents different predictive control (Genetic Algorithm-based and Model Predictive Control-based) strategies that schedule controlled loads in the industrial and residential sectors, based on dynamic power price and weather forecast, considering users’ comfort settings to meet an optimization objective, such as maximum profit or minimum energy consumption. It is demonstrated in this work that GA-based and MPC-based predictive control strategies are able to achieve load shifting for grid reliability and energy savings, including demand response through
precooling/preheating.
Original languageEnglish
JournalI E E E Transactions on Sustainable Energy
Publication date2012
Number of pages8
ISSN1949-3029
StateSubmitted

Keywords

  • Demand response, Distributed energy resources, Genetic algorithm, Load shifting, Model predictive control, Real time application, Smart grid, Wind power penetration
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