HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response

Nikolaos Kampelis, Nikolaos Sifakis, Dionysia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori and Cristina Cristalli
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Nikolaos Kampelis: Energy Management in the Built Environment Research Lab, School of Environmental Engineering, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece
Nikolaos Sifakis: Renewable and Sustainable Energy Systems Laboratory, School of Environmental Engineering, Technical University of Crete, Kounoupidiana, GR 73100 Chania, Greece
Dionysia Kolokotsa: Energy Management in the Built Environment Research Lab, School of Environmental Engineering, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece
Konstantinos Gobakis: Energy Management in the Built Environment Research Lab, School of Environmental Engineering, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece
Konstantinos Kalaitzakis: Electric Circuits and Renewable Energy Sources Laboratory, Technical University of Crete; Kounoupidiana, GR 73100 Chania, Greece
Daniela Isidori: Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy
Cristina Cristalli: Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy

Energies, 2019, vol. 12, issue 11, 1-23

Abstract: Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.

Keywords: HVAC optimization; demand response; near-zero-energy building; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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