Stochastic and robust optimal operation of energy-efficient building with combined heat and power systems

Energy efficiency and renewable energy become more attractive in smart grid. In order to efficiently reduce global energy usage in building energy systems and to improve local environmental sustainability, it is essential to optimize the operation and the performance of combined heat and power (CHP) systems. In addition, intermittent renewable energy and imprecisely predicted customer loads have introduced great challenges in energy-efficient buildings' optimal operation. In the deterministic optimal operation, we study the modeling of components in building energy systems, including the power grid interface, CHP and boiler units, energy storage devices, and appliances. The mixed energy resources are applied to collaboratively supply both electric and thermal loads. The results show that CHP can effectively improve overall energy efficiency by coordinating electric and thermal power supplies. Through the optimal operation of all power sources, the daily operation cost of building energy system for generating energy can be significantly reduced. In order to address the risk due to energy consumption and renewable energy production volatility, we conduct studies on both stochastic programming and robust optimizations to operate energy-efficient building systems under uncertainty. The multistage stochastic programming model is introduced so that the reliable operation of building energy systems would be probabilistically guaranteed with stochastic decisions. The simulation results show that the stochastic operation of building systems is a promising strategy to account for the impact of uncertainties on power dispatch decisions of energy-efficient buildings. In order to provide absolute guarantee for the reliable operation of building energy systems, a robust energy supply to electric and thermal loads is studied by exploring the influence of energy storage on energy supply and accounting for uncertainties in the energy-efficient building. The robustness can be adjusted to control the conservativeness of the proposed robust operation model. For the purpose of achieving adaptability in the robust optimal operation and attaining robustness in the stochastic optimal operation of building energy systems, we also develop an innovative robust stochastic optimization (RSO) model. The proposed RSO model not only overcomes the conservativeness in the robust operation model, but also circumvents the curse of dimensionality in the stochastic operation model.


Dissertation, Mississippi State University, 10/2014



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