Planning in smart grids

e electricity supply chain is changing, due to increasing awareness for sustainability and an improved energy e›ciency. e traditional infrastructure where demand is supplied by centralized generation is subject to a transition towards a Smart Grid. In this Smart Grid, sustainable generation from renewable sources is accompanied by controllable distributed generation, distributed storage and demand side load management for intelligent electricity consumption. e transmission and distribution grid have to deal with increasing žuctuations in demand and supply. Since realtime balance between demand and supply is crucial in the electricity network, this increasing variability is undesirable. Monitoring and controlling/managing this infrastructure increasingly depends on the ability to control distributed appliances for generation, consumption and storage. In the development of controlmethodologies, mathematical support, which consists of predicting demand, solving planning problems and controlling the Smart Grid in realtime, is of importance. In this thesis we study planning problems which are related to the Unit Commitment Problem: for a set of generators it has to be decided when and how much electricity to produce to match a certain demand over a time horizon. e planning problems that we formulate are part of a control methodology for Smart Grids, called TRIANA, that is developed at the University of Twente. In a rst part, we introduce a planning problem (the microCHP planning problem), that considers a set of distributed electricity generators, combined into a Virtual Power Plant. A Virtual Power Plant uses many small electricity generating appliances to create one large, virtual and controllable power plant. In our setting, these distributed generators are microCHP appliances, generating Combined Heat and Power on a domestic scale. Combined with the use of a heat bušer, operational žexibility in supplying the local heat demand is created, which can be used in the planning process, to decide when to generate electricity (which is coupled to the generation of heat). e power output of a microCHP is completely determined by the decision to generate or not. e microCHP planning problem combines operational dependencies in sequential discrete time intervals with dependencies between dišerent generators in a single time interval, and searches for a combined electricity output that matches a desired form. To illustrate the complexity of this problem, we prove that the microCHP planning problem isNP-complete in the strong sense. We model the