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The paper illustrates the status quo of a research project for the development of a control system enabling CHP units for a demand-oriented electricity production by an intelligent management of the heat storage tank. Thereby the focus of the project is twofold. One is the compensation of the fluctuating power production by the renewable energies solar and wind. Secondly, a reduction of the load on the power grid is intended by better matching local electricity demand and production.
In detail, the general control strategy is outlined, the method utilized for forecasting heat and electricity demand is illustrated as well as a correlation method for the temperature distribution in the heat storage tank based on a Sigmoid function is proposed. Moreover, the simulation model for verification and optimization of the control system and the two field test sites for implementing and testing the system are introduced.
The coupling of the heat and power sector is required as supply and demand in the German electricity mix drift further and further apart with a high percentage of renewable energy. Heat pumps in combination with thermal energy storage systems can be a useful way to couple the heat and power sectors. This paper presents a hardware-in the-loop test bench for experimental investigation of optimized control strategies for heat pumps. 24-hour experiments are carried out to test whether the heat pump is able to serve optimized schedules generated by a MATLAB algorithm. The results show that the heat pump is capable of following the generated schedules, and the maximum deviation of the operational time between schedule and experiment is only 3%. Additionally, the system can serve the demand for space heating and DHW at any time.
This article presents a two-level optimisation approach for the management of controllable and distributed converters with storage systems across different energy sectors. It aims at the reduction of electrical peak load and at the economical optimisation of the electrical energy exchange with the grid, based on a dynamic external incentive, e.g. through dynamic energy price tariffs. By means of a secure, standardised and lean communication with two different internal price signals, an optimal flexibility provision shall be achieved. The two-level optimisation approach consists of a centralised and several distributed decentralised entities. At the centralised level, the distributed flexibilities are invoked for optimal scheduling on the basis of an internal price algorithm for stimulating the decentralised entities. Based on that internal incentive and on the expected demands for electricity, heating and cooling, the decentralised optimisation algorithms provide optimal generation schedules for the energy converters. The suggested interaction between the central and decentral entities is successfully tested and the principle potential for peak shaving and the adaption to dynamic energy-related market prices could be demonstrated and compared to different energy management strategies such as the standard heat-led operation. Further, variations of the system parameters such as load shifting potential, installed capacity and system diversification are evaluated against cost saving potential for the energy supply and overall system performance.