Building Load Control and Optimization


Researchers and practitioners have proposed a variety of solutions to reduce electricity consumption and curtail peak demand. This research focuses on load control by improving the operations in existing building HVAC (Heating, Ventilating and Air-Conditioning) systems and by aggregating individual loads based on optimization studies. Emphasis is placed on electricity rates and climate data in California, where electricity costs have been of particular concern. The optimization problem in this research is multi-objective in the sense that we aim to reduce building load while maintaining an acceptable level of comfort. The first part of this research focuses on optimizing controls in a single building. A simple three-zone VAV system model is built in EnergyPlus (E+). The cost function structures and the potential difficulties associated with simulation-based optimization are discussed. Discontinuity and nonlinearity are of major concern. Two optimization algorithms are tested and applied to a variety of problems: Direct Search (DS) and Genetic Algorithms (GA). An E+ simulation based GA optimization environment is developed in Matlab. DS is found to be efficient with small problems in this research, while GA works in almost any situation with the price of intensive computation. A few operations guidelines are proposed. The second part of this research presents three ways of optimizing load control in an aggregation pool: Enumeration, multi-GA and model-based nonlinear optimization. Enumeration relies on expert rules to find a small set of feasible solutions through automated E+ simulations and search exhaustively for the optimal solution. Multi-GA solves the aggregation problem in the Matlab GA environment with sequential E+ simulations as the function evaluator. Because simulation-based optimization is very computationally intensive in handling multiple buildings, the model-based approach develops for each aggregation participant a time series model and several regression models to predict individual load profiles under load control. It then applies an interior-point-method-based commercial solver LOQO to these simplified building models. This system is fast and easy to scale up. Certain precision is lost due to modeling simplifications, but the results are still satisfactory for practice purposes. Overall, load aggregation offers load diversification opportunities among participants and improves the aggregated load profile. Load shedding later individual load profiles in a way that enhances the aggregation performance.