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Multi-period flexibility forecast for low voltage prosumers

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  • Pinto, Rui
  • Bessa, Ricardo J.
  • Matos, Manuel A.

Abstract

Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

Suggested Citation

  • Pinto, Rui & Bessa, Ricardo J. & Matos, Manuel A., 2017. "Multi-period flexibility forecast for low voltage prosumers," Energy, Elsevier, vol. 141(C), pages 2251-2263.
    • Handle: RePEc:eee:energy:v:141:y:2017:i:c:p:2251-2263
    DOI: 10.1016/j.energy.2017.11.142
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    5. Konstantinos Kotsalos & Ismael Miranda & Jose Luis Dominguez-Garcia & Helder Leite & Nuno Silva & Nikos Hatziargyriou, 2020. "Exploiting OLTC and BESS Operation Coordinated with Active Network Management in LV Networks," Sustainability, MDPI, vol. 12(8), pages 1-25, April.
    6. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
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