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A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization

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  • Paraskevas Koukaras

    (Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece
    School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece)

  • Paschalis Gkaidatzis

    (Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece)

  • Napoleon Bezas

    (Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece)

  • Tommaso Bragatto

    (ASM Terni S.p.A., 05100 Terni, Italy)

  • Federico Carere

    (ASM Terni S.p.A., 05100 Terni, Italy)

  • Francesca Santori

    (ASM Terni S.p.A., 05100 Terni, Italy)

  • Marcel Antal

    (Distributed Systems Research Laboratory, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania)

  • Dimosthenis Ioannidis

    (Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece)

  • Christos Tjortjis

    (Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece
    School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece)

  • Dimitrios Tzovaras

    (Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece)

Abstract

Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.

Suggested Citation

  • Paraskevas Koukaras & Paschalis Gkaidatzis & Napoleon Bezas & Tommaso Bragatto & Federico Carere & Francesca Santori & Marcel Antal & Dimosthenis Ioannidis & Christos Tjortjis & Dimitrios Tzovaras, 2021. "A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization," Energies, MDPI, vol. 14(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3599-:d:576496
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    References listed on IDEAS

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