Decentralised optimisation of cogeneration in virtual power plants

Within several projects we investigated grid structures and management strategies for active grids with high penetration of renewable energy resources and distributed generation (RES & DG). Those ”smart grids” should be designed and managed by model based methods, which are elaborated within these projects. Cogeneration plants (CHP) can reduce the greenhouse gas emissions by locally producing heat and electricity. The integration of thermal storage devices is suitable to get more flexibility for the cogeneration operation. If several power plants are bound to centrally managed clusters, it is called “virtual power plant”. To operate smart grids optimally, new optimisation and model reduction techniques are necessary to get rid with the complexity. There is a great potential for the optimised management of CHPs, which is not yet used. Due to the fact that electrical and thermal demands do not occur simultaneously, a thermally driven CHP cannot supply electrical peak loads when needed. With the usage of thermal storage systems it is possible to decouple electric and thermal production. We developed an optimisation method based on mixed integer linear programming (MILP) for the management of local heat supply systems with CHPs, heating boilers and thermal storages. The algorithm allows the production of thermal and electric energy with a maximal benefit. In addition to fuel and maintenance costs it is assumed that the produced electricity of the CHP is sold at dynamic prices. This developed optimisation algorithm was used for an existing local heat system with 5 CHP units of the same type. An analysis of the potential showed that about 10% increase in benefit is possible compared to a typical thermally driven CHP system under current German boundary conditions. The quality of the optimisation result depends on an accurate prognosis of the thermal load which is realised with an empiric formula fitted with measured data by a multiple regression method. The key functionality of a virtual power plant is to increase the value of the produced power by clustering different plants. The first step of the optimisation concerns the local operation of the individual power generator, the second step is to calculate the contribution to the virtual power plant. With small extensions the suggested MILP algorithm can be used for an overall EEX (European Energy Exchange) optimised management of clustered CHP systems in form of the virtual power plant. This algorithm has been u