Dual Demand Side Management in the Innovation City of Bottrop

  Copyright: RWTH Aachen Example of loadshifting potential. Surplus of electrical energy can be used to charge storage devices.

The goal of 2DSM is to develop an energy management strategy that exploits the flexibility of the coupled thermal and electrical supply systems, and from storage technologies. This coupling is realized by electro-thermal heating systems i.e. Combined Heat and Power Units (CHP) and Heat Pumps (HP). The storage includes thermal storage capacity of water tanks and the thermal mass of the buildings. Thermal storage allows for a more flexible operation of the CHPs and HPs by shifting in time their operation, and thus their electrical power demand, with respect to the heat demand. This way electrical energy may be used when more available, thus supporting the integration of intermittent, renewable, energy sources (Figure 1).

To support the design and performance assessment of the energy management algorithms, a multi-physics simulation platform has been developed. The platform enables a holistic simulation of city district energy systems, comprising thermal and electrical models, as well as the control. The thermal simulation is based on simplified models of the buildings, based on the VDI standard 6007, and their heating systems. The user behavior is modeled at household level using a stochastic model. The electrical grid is modeled in detail down to the point of common coupling of the individual buildings. As a result, the effect of different control strategies of the thermal energy systems on the electrical grid can be thoroughly evaluated.

The control architecture is based on multi-agent systems, in order to incorporate local generation units and flexible loads within building energy systems (BES) while providing scalability as well as data privacy. The control system comprises of a planning and a near real-time control phase. In the planning phase, the agents, each one representing a participating BES, agree upon a load schedule for the next day, considering forecasted power output of renewable energy sources. A global optimization goal is aimed for, i.e. maximal integration of renewable energy, while maintaining the thermal comfort of the resident, as well as satisfying the constraints of the electrical network. In the second phase, the short-term balancing algorithm coordinates the switching of the heating systems in order to avoid transient effects due to simultaneous switching. Furthermore, it compensates for deviations from the anticipated behavior and intervenes if the deviations cross preset limits.