(418d) Real-Time Model Predictive Control for Energy and Demand Optimization of Multi-Zone Buildings

In a smart grid, electricity cost depends on not only the total cumulative energy consumption, but also the demand, which is the maximum power rate required by users. A simulation-based control system is established to reduce the energy and demand costs for commercial buildings. A virtual model for a single floor, five-zone building is simulated by Energyplus. Real-time data exchange between Energyplus and controller in Matlab is achieved by introducing the building controls virtual test bed (BCVTB) [1] as a middleware.

In this work, system identification technique is firstly implemented to obtain the temperature and power models to be used in the model predictive control (MPC) framework. The objective function in MPC is a combination of energy and demand costs. For the energy portion, an actual time-of-use rate with higher cost during peak hours is applied. Time-varying constraints in the MPC optimization problem are calculated from some given thermal comfort indices [2]. MPC controller calculates optimal values of zone setpoints for HVAC equipments based on predicted future behaviors of zone temperatures and power. Only the control action for the current step will be implemented before simulation proceeds to the next time step.

In order to shift the loads beyond peak hours, it is essential to utilize the building thermal storage capacity which can be shown by response analysis of the identified model. A weather estimator is designed based on historical weather data to predict the ambient temperature in future horizon. Impacts of other disturbances such as solar radiations and internal loads are studied as well.

Effectiveness of this work is demonstrated by a continuous weekly simulation. Pre-cooling effect during off-peak period and autonomous cooling discharging during on-peak period can be observed. Cost savings brought by real-time MPC are given by comparing with the baseline and other pre-programmed control strategies.

References

[1]M. Wetter and P. Haves, A Modular Building Controls Virtual Test Bed for the Integration of Heterogeneous Systems, 3rd National Conference of IBPSA-USA, 2008, pp. 69 -76.

[2]R. Z. Freire, G. H. C. Oliveira and N. Mendes, Predictive controllers for thermal comfort optimization and energy savings, Energy and Buildings, Vol. 40, No. 7, 2008, pp. 1353-1365