  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.
