Multi-period flexibility forecast for low voltage prosumers

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Pinto, R (Author) Other; Bessa, RJ (Author) Other; Manuel Matos (Author) FEUP

Journal

Title: Energy

Vol. 141

Pages: 2251-2263

ISSN: 0360-5442

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 28 Citations

Scopus - 29 Citations

Other information

Authenticus ID: P-00N-9Q4

DOI: 10.1016/j.energy.2017.11.142

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13