Multi-Period Flexibility Forecast for Low Voltage Prosumers
This work addresses the need for demand-side flexibility in electric distribution grids, enabling system operators and market players to better manage residential resources, though it appears incremental in applying existing optimization and classification methods to this specific domain.
The paper tackles the problem of modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heaters, by developing a computational method that efficiently learns and defines the feasibility flexibility space, using an Evolutionary Particle Swarm Optimization algorithm and support vector data description to classify feasible operating trajectories.
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.