Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

Nikos Kampelis, Elisavet Tsekeri, Dionysia Kolokotsa, Kostas Kalaitzakis, Daniela Isidori and Cristina Cristalli
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Nikos Kampelis: Energy Management in the Built Environment Research Lab, Environmental Engineering School, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece
Elisavet Tsekeri: Energy Management in the Built Environment Research Lab, Environmental Engineering School, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece
Dionysia Kolokotsa: Energy Management in the Built Environment Research Lab, Environmental Engineering School, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece
Kostas Kalaitzakis: Electric Circuits and Renewable Energy Sources Laboratory, Technical University of Crete, GR 73100 Chania, Greece
Daniela Isidori: Research for Innovation, AEA srl. via Fiume 16, IT 60030 Angeli di Rosora, Marche, Italy
Cristina Cristalli: Research for Innovation, AEA srl. via Fiume 16, IT 60030 Angeli di Rosora, Marche, Italy

Energies, 2018, vol. 11, issue 11, 1-22

Abstract: Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessary open and transparent market framework linking energy costs to the actual grid operations. DR allows consumers to directly or indirectly participate in the markets where energy is being exchanged. One of the main challenges for engaging in DR is associated with the initial assessment of the potential rewards and risks under a given pricing scheme. In this paper, a Genetic Algorithm (GA) optimisation model, using Artificial Neural Network (ANN) power predictions for day-ahead energy management at the building and district levels, is proposed. Individual building and building group analysis is conducted to evaluate ANN predictions and GA-generated solutions. ANN-based short term electric power forecasting is exploited in predicting day-ahead demand, and form a baseline scenario. GA optimisation is conducted to provide balanced load shifting and cost-of-energy solutions based on two alternate pricing schemes. Results demonstrate the effectiveness of this approach for assessing DR load shifting options based on a Time of Use pricing scheme. Through the analysis of the results, the practical benefits and limitations of the proposed approach are addressed.

Keywords: demand response; artificial neural network; power predictions; energy management; genetic algorithm; optimisation; microgrid; smart grid (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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