Robert D. Turney

Cost optimization of combined building heating/cooling equipment via mixed-integer linear programming

In this paper, we propose a mixed-integer linear program to economically optimize equipment usage in a central heating/cooling plant subject to time-of-use and demand charges for utilities. The optimization makes both discrete on/off and continuous load decisions for equipment while determining utilization of thermal energy storage systems. This formulation allows simultaneous optimization of heating and cooling subsystems, which interact directly when heatrecovery chillers are present. Nonlinear equipment models are approximated as piecewise-linear to balance modeling accuracy with the computational constraints imposed by online implementation and to ensure global optimality for the computed solutions. The chief benefits of this formulation are its ability to tightly control on/off switching of equipment, its consideration of cost contributions from auxiliary equipment such as pumps, and its applicability to large systems with multiple heating and cooling units in which a combinatorial problem must be solved to pick the optimal mix of equipment. These features result in improved performance over heuristic scheduling rules or other formulations that do not consider discrete decision variables. We show optimization results for a system with four conventional chillers, two heat-recovery chillers, and one hot water boiler. With a timestep of 1 h and a horizon of 48 h, the optimization problem can be solved to optimality within 5 minutes, indicating suitability for online implementation.

Distributed economic model predictive control for large-scale building temperature regulation

Although recent research has suggested model predictive control as a promising solution for minimizing energy costs of commercial buildings, advanced control systems have not been widely deployed in practice. Large-scale implementations, including industrial complexes and university campuses, may contain thousands of air handler regions each with tens of zones. A single centralized control system for these applications is not desirable. In this paper, we propose a distributed control system to economically optimize temperature regulation for large-scale commercial building applications. Since there is no clear time-scale separation, we propose a decomposition strategy that considers the complexities of thermal energy storage, zone interactions, and chiller plant equipment while remaining computationally tractable. One of the primary benefits of the proposed formulation is that the low-level airside problem can be decoupled and solved in a distributed manner; hence, it can be easily extended to handle large applications. Peak demand charges, a major source of coupling, are included. Iterations and communication between the low-level subsystems are not required. Since the time scale of regulatory controllers is significant, their dynamics are modeled. The interactions of the airside system with the waterside system are also considered, including discrete decisions, such as turning chillers on and off. Previously, heuristics have been used to make these decisions. We demonstrate the effectiveness of this control system architecture via a simulation study. In the presence of sufficient active thermal energy storage, we provide a simplification of the proposed control strategy that remains economically optimal.

Economic MPC and real-time decision making with application to large-scale HVAC energy systems

Abstract With the potential to decrease operating costs and improve energy efficiency, model predictive control (MPC) has been proposed as a replacement for traditional heuristic, PID, and other conventional control strategies for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Due to the size of large commercial HVAC systems, implementing MPC as a single monolithic optimization problem is not practical nor desirable given real-time operating requirements. In this paper, we present a hierarchical decomposition for economic MPC in large-scale commercial HVAC systems using a two-layer approach. We show a sample optimization for a campus of 25 buildings with 500 total zones and a central plant consisting of eight chillers. Then, we discuss an application of the ideas presented here in the recently completed

CONTROLLERS AND METHODS FOR PROVIDING COMPUTERIZED GENERATION AND USE OF A THREE DIMENSIONAL SURGE MAP FOR CONTROL OF CHILLERS

485-million replacement of the Stanford campus heating and cooling systems and conclude with some of the control theory challenges presented by this new class of applications.