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A comprehensive and modular set of appliance operation MILP models for demand response optimization

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  • Henggeler Antunes, Carlos
  • Alves, Maria João
  • Soares, Inês

Abstract

Demand response programs are essential to enable accommodating larger shares of variable power generation based on renewable sources, the deployment of which is imperative for decarbonizing the economy and mitigating global warming. Consumers/prosumers are increasingly exposed to and may benefit from time-differentiated energy prices aimed to induce changes in regular consumption patterns. These changes are also beneficial for retailers and grid operators in face of the variability of wholesale market prices, renewable energy availability and grid conditions. The optimization models to be implemented in autonomous home energy management systems require a rigorous modeling of appliance operation to generate effective load scheduling solutions, respecting their physical operation principles and use patterns in everyday life. A balance should be sought between the detail level of optimization models and the computational requirements to generate usable solutions having in mind their implementation in low-cost processors. This paper presents a comprehensive and modular set of mixed-integer linear programming models aimed at enabling their seamless incorporation in home energy management systems, allowing for the integrated optimization of all energy resources (exchanges with the grid, load management, electric vehicle and stationary battery, local microgeneration). Detailed energy consumption optimization models for shiftable, interruptible and thermostatic loads are presented, also including the power cost component and ways of dealing with user's discomfort. The modular models are presented in a building block manner enhancing the flexibility of their utilization in overall models with different objective functions encompassing the economic and comfort dimensions. Computational results are presented for a case study using actual data, which considers a time-of-use tariff with six periods. In addition to comparing with a plain tariff scheme, different consumer profiles are simulated to assess the impact of comfort requirements on cost. These results show that whenever consumers have the flexibility to change their consumption patterns, they are able to lower the net electricity bill by having an energy management system endowed with the models herein proposed to make optimized decisions on their behalf.

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  • Henggeler Antunes, Carlos & Alves, Maria João & Soares, Inês, 2022. "A comprehensive and modular set of appliance operation MILP models for demand response optimization," Applied Energy, Elsevier, vol. 320(C).
  • Handle: RePEc:eee:appene:v:320:y:2022:i:c:s0306261922005189
    DOI: 10.1016/j.apenergy.2022.119142
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    References listed on IDEAS

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    1. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    2. Mohseni, Amin & Mortazavi, Seyed Saeidollah & Ghasemi, Ahmad & Nahavandi, Ali & Talaei abdi, Masoud, 2017. "The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid," Energy, Elsevier, vol. 139(C), pages 315-328.
    3. Elkazaz, Mahmoud & Sumner, Mark & Naghiyev, Eldar & Pholboon, Seksak & Davies, Richard & Thomas, David, 2020. "A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers," Applied Energy, Elsevier, vol. 269(C).
    4. Zhang, Xiongfeng & Lu, Renzhi & Jiang, Junhui & Hong, Seung Ho & Song, Won Seok, 2021. "Testbed implementation of reinforcement learning-based demand response energy management system," Applied Energy, Elsevier, vol. 297(C).
    5. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    6. Soares, Inês & Alves, Maria João & Antunes, Carlos Henggeler, 2020. "Designing time-of-use tariffs in electricity retail markets using a bi-level model – Estimating bounds when the lower level problem cannot be exactly solved," Omega, Elsevier, vol. 93(C).
    7. Morales-España, Germán & Martínez-Gordón, Rafael & Sijm, Jos, 2022. "Classifying and modelling demand response in power systems," Energy, Elsevier, vol. 242(C).
    8. Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
    9. Carlos Henggeler Antunes & Maria João Alves & Billur Ecer, 2020. "Bilevel optimization to deal with demand response in power grids: models, methods and challenges," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 814-842, October.
    10. Soares, Ana & Gomes, Álvaro & Antunes, Carlos Henggeler, 2014. "Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 490-503.
    11. Adhikari, Rajendra & Pipattanasomporn, M. & Rahman, S., 2018. "An algorithm for optimal management of aggregated HVAC power demand using smart thermostats," Applied Energy, Elsevier, vol. 217(C), pages 166-177.
    12. Salgado, Marcelo & Negrete-Pincetic, Matias & Lorca, Álvaro & Olivares, Daniel, 2021. "A low-complexity decision model for home energy management systems," Applied Energy, Elsevier, vol. 294(C).
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