In order to achieve a Danish energy supply based on 100% renewable energy from combinations of wind, biomass, wave and solar power in 2050 and to cover 50% of the Danish electricity consumption by wind power in 2020, it requires more renewable energy in buildings and industries (e.g. cold stores, greenhouses, etc.), and to coordinate the management of large numbers of distributed energy resources with the smart grid solution. This paper presents different predictive control (Genetic Algorithm-based and Model Predictive Control-based) strategies that schedule controlled loads in the industrial and residential sectors, based on dynamic power price and weather forecast, considering users’ comfort settings to meet an optimization objective, such as maximum profit or minimum energy consumption. Some field tests were carried out on a facility for intelligent, active and distributed power systems, which is built around a small power grid with renewable power generations (two wind turbines and solar panels), a vanadium battery for storage, EV-charging infrastructure for EVs, and an intelligent office building. The simulation and field tests demonstrated that GA-based and MPC-based predictive control strategies are able to achieve load shifting and enable end users to participate in market-based power systems, and thus profit from optimal consumption of energy in relation to price and supply of ancillary services in the power system, as well as improve grids with integration of high penetration of renewable energy sources, which could lead to reducing reinforcements in the future power systems.
Proceeding for Powergen & Renewable Energy World Europe 2013, 2013