Integrating Active, Flexible and Responsive Tertiary Prosumers into a Smart Distribution Grid
Duration: 10/2012 – 9/2015 Funding: FP7-ICT
The current energy grid is still based on a centralized and inflexible management of supply and demand for energy. But the modern energy market is becoming much more dynamic. The current grid is not prepared for a distributed energy market, in which individual households cause energy supply peaks by generating their own solar power on a sunny day. On the one hand, energy supply rates fluctuate very quickly. Renewable energy sources, such as wind and solar power, are especially unpredictable. On the other hand, the demand for energy is quite uncontrollable. Imagine the spike in demand when all across the country people come home to charge their electric cars. The energy grid must be prepared for such spikes because at this moment, the market has no way to influence and soften these peaks.
The INERTIA project proposed an Internet-of-Things-approach to the problem of energy management: Consumers gradually become ‘prosumers’; households that generate their own green energy, sell the energy when they have too much to use by themselves, and buy extra (grey) power when they need it. All these ‘prosumer hubs’ become active nodes (rather than passive elements), and are equipped with the technology to provide contextual information to the grid. In order to be able to ‘share’ energy, the different stakeholders must share information on supply and demand. Having such an information network in place, a truly free energy market emerges. Energy will be more realistically priced, based on actual supply and demand.
Within INERTIA, Almende developed the Pi5 aggregator control software tools and approach: a secure agent-based energy demand prediction framework for the smart grid. By applying a 15-minute energy forecast, it enables the negotiation of energy deals at MV level. Thereby, it is possible to actively manage an energy budget within given/negotiated boundaries by making prioritized use of equipment based on both comfort and energy budget restrictions.
The project contributed to our expertise in applying multi-agent-based systems and simulations to energy markets, and to use collaborative decision-making models to dynamically solve challenges in energy supply and demand in real-time conditions which are difficult to forecast.