Funding: NWO, ESI-pose
Running Period: 2016 - 2020
Staff: Prof.dr. Johann Hurink, Prof.dr.ir Gerard Smit
Ph.D. student: Martijn Schoot Uiterkamp
This project addresses the joint optimisation of various energy streams within distribution grids with integrated energy storage. The focus of this project is on three issues:
- optimal integration and use of different forms of local storage in distribution grids,
- simultaneous optimisation of all relevant forms of energy,
- balancing energy demand and supply locally to keep the energy as low as possible in the grid-hierarchy.
Controlling distribution grids with large scale and distributed infeed of renewable resources and distributed storage becomes difficult or even impossible with centralized approaches. An interesting alternative is to ‘invert’ the control system and start the control at local level. This leads to the concept of micro-grids, which have been proposed as a solution to the grand challenge of integrating large amounts of micro-generation (primarily from renewables) in the distribution grid. By a careful coordination of local loads, distributed storage and local micro-generation, the aggregated load of the micro-grid will produce less stress on the utility network, compared to the conventional direct in-feed of micro-generation. In this project micro-grids are considered as small subparts of a distribution grid (e.g. an LV feeder in a neighbourhood behind a MV/LV transformer). In this view a micro-grid is still connected to the main grid and does not have to be autonomous all the time, but the goal is to balance the energy streams within a micro-grid and to keep the energy as locally as possible. In this project we plan to develop optimisation algorithms for micro-grids based on profiles (energy demand/supply patterns over time), thereby using all forms of energy (electricity, gas, heat, ..). Profiles of local assets (e.g. CHPs, storages, controllable appliances, or converters) are steered taking into account the objectives, (comfort) constraints and restrictions of the micro-grid with the goal to achieve a resulting overall energy profile of the micro-grid that is ‘friendly’ for the main utility grid. By integrating forecasting and planning methods, it is possible to predict and manipulate the profile of a micro-grid for the coming time period. Moreover, through a coordinated steering of different micro-grids, peaks or shortages of energy can be reduced or avoided in the transport grid.