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Abstract: A group of buildings in the pharmaceutical industry located in Southern Germany is experiencing a trend of growing cooling loads to be met by the chilled water plant composed of 10 chillers of greatly varying cost effectiveness. With a capacity shortfall inevitable, the question arises whether to install an additional chiller or improve the utilization of the existing chillers, in particular those with low operating costs per unit cooling, through the addition of a chilled water thermal energy storage (TES) system. To provide decision support in this matter, an optimization environment was developed and validated that adopts mixed integer programming as the approach to optimizing the chiller dispatch for any load condition, while an overarching dynamic programming based optimization approach optimizes the charge/discharge strategy of the TES system. In this fashion, the chilled water plant optimization is decoupled but embedded in the TES control optimization. The approach was selected to allow for arbitrary constraints and optimization horizons, while ensuring a global optimum to the problem. Optimization scenarios have been defined that include current load conditions as well cooling loads that are elevated by 25% from current conditions in order to reflect the expected growth in cooling demand in the near future; both scenarios analyzed the impact of storage capacity by investigating several TES tank capacities. The annual optimization runs revealed that – based on the elevated cooling load scenario – the smallest TES system pays back the incremental investment necessary for the TES system in about three years; based on today’s cooling loads the static payback is approximately six years. As the efficiency and cost of operating the existing chillers vary over a wide range, the TES system allows for a reduction in operating costs for the chilled water plant by avoiding the operation of inefficient chillers (such as the single-stage absorption type) and shifting the demand to more efficient ones (such as the electrically driven centrifugal chillers). The adoption of a TES system will not only provide economic benefits as measured in operating cost savings, but also operational merits such as the avoidance of numerous safety measures necessary for a cooling plant without storage (e.g., always operating at least two chillers), and a cost effective addition of supplemental cooling capacity. Moreover, the overall system reliability and availability will be significantly improved through the addition of a thermal energy storage system. Future work will address the development of near-optimal heuristics suitable for implementation in the actual pharmaceutical industry buildings. [Copyright &y& Elsevier]

Abstract: This paper is the first part of a two-part investigation of a novel approach to optimally control commercial building passive and active thermal storage inventory. The proposed building control approach is based on simulated reinforcement learning, which is a hybrid control scheme that combines features of model-based optimal control and model-free learning control. An experimental study was carried out to analyze the performance of a hybrid controller installed in a full-scale laboratory facility. The first part presents an overview of the project with an emphasis on the theoretical foundation. The motivation of the research will be introduced first, followed by a review of past work. A brief introduction of the theory is provided including classic reinforcement learning and its variation, so-called simulated reinforcement learning, which constitutes the basic architecture of the hybrid learning controller. A detailed discussion of the experimental results will be presented in the companion paper. [Copyright &y& Elsevier]

In contrast to building energy conversion equipment, less improvement has been achieved in thermal energy distribution, storage and control systems in terms of energy efficiency and peak load reduction potential. Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid and time-of-use electricity rates are designed to encourage shifting of electrical loads to off-peak periods at night and on weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure (passive storage) or by using active thermal energy storage systems such as ice storage. Recent theoretical and experimental work showed that the simultaneous utilization of active and passive building thermal storage inventory can save significant amounts of utility costs to the building operator, yet increased electrical energy consumption may result. The article investigates the relationship between cost savings and energy consumption associated with conventional control, minimal cost and minimal energy control, while accounting for variations in fan power consumption, chiller capacity, chiller coefficient-of-performance, and part-load performance. The model-based predictive building controller is employed to either minimize electricity cost including a target demand charge or electrical energy consumption. This work shows that buildings can be operated in a demand-responsive fashion to substantially reduce utility costs with marginal increases in overall energy consumption. In the case of energy optimal control the reference control was replicated, i.e., if only energy consumption is of concern, neither active nor passive building thermal storage should be utilized. On the other hand, cost optimal control suggests strongly utilizing both thermal storage inventories. [ABSTRACT FROM AUTHOR]

This paper evaluates the benefits of combined optimal control of both passive building thermal capacitance and active thermal energy storage systems to minimize total utility cost in the presence of forecasting uncertainty in the required short-term weather forecasts. Selected short-term weather forecasting models are introduced and investigated with respect to their forecasting accuracy as measured by root mean square error, mean bias error, and the coefficient of variation. The most complex model, a seasonal autoregressive integrated moving average (SARIMA), shows the worst performance, followed by a static predictor model that references standard weather archives. The best prediction accuracy is found for bin models that develop a characteristic daily profile from observations collected over the past 30 or 60 days. The model that projects yesterday's patterns one day into the future proved to be a surprisingly poor predictor. We test the predictor models in the context of a predictive optimal control task that optimizes building global temperature setpoints and active thermal energy storage charge/discharge rates in a closed-loop mode. For the four locations investigated in this article—Chicago, IL, Denver, CO, Omaha, NE, and Phoenix, AZ—it was determined that the 30-day and 60-day bin predictor models lead to utility cost savings that are only marginally inferior compared to a hypothetical perfect predictor that perfectly anticipates the weather during the next planning horizon. In summary, the predictive optimal control of active and passive building thermal storage inventory using time-of-use electrical utility rates with significant on-peak to off-peak rate differentials and demand charges is a highly promising control strategy when perfect weather forecasts are available. The primary finding of this paper is that it takes only very simple short-term prediction models to realize almost all of the theoretical potential of this technology. [ABSTRACT FROM AUTHOR]

Presents a simplified ice storage system model where the icemaking mode is reflected by a higher power consumption per unit cooling than in chilled-water mode. Performance of the control strategies for ice storage systems; Parameters influencing ice storage system performance; Optimal control for maximal operating cost savings.

This paper describes an investigation of the economic performance of a central chiller plant with thermal energy storage (TES) operating in a hotel in California and an office building in Wisconsin for the following three conventional control strategies: chiller-priority, constant-proportion, and storage-priority control. Since all control strategies depend on properly selected design parameters, the storage and chiller capacities as the primary design parameters were varied over a wide range, and the life-cycle economic feasibility of each possible design was assessed over a 20-year economic life using the net present value method. Measured cooling and weather data for both buildings were used in annual calculations to determine annual utility cost savings on the basis of one of four artificial rate structures. Trends in the life-cycle economic performance of TES systems as a function of chiller and storage capacities were revealed, and design guidelines were developed. [ABSTRACT FROM AUTHOR]

High-performance buildings, such as zero-energy buildings (ZEBs), are an important step toward a reduction in greenhouse-gas emissions. Because ZEBs may exhibit large differences between demand and on-site generated electricity, residual electrical loads imposed by the building may fluctuate between positive and negative values. Furthermore, such buildings can be characterized by large temporal changes in residual load, commonly caused by clouds passing on a sunny day. Today, electricity grid operators can easily deal with a single ZEB with this behavior. But what happens if large portfolios of ZEBs have the same behavior? In this study, a highly efficient office building with a total floor area of 8,355 m2 located in Denver, Colorado, was designed and simulated using a detailed building energy modeling approach. Combining the building energy model with a photovoltaic model showed that the building reached net positive status on an annual basis. Further analysis of residual loads and strategies for their reduction revealed the limited potential of demand-side management in ZEBs and the high flexibility of batteries. Using a multiple-objective optimization approach for optimizing several simplified electric and thermal storage systems allowed the comparison of different strategies for residual load reduction. Although electrical storage may not yet be economical given today's system costs, results show that the residual loads can be effectively managed and reduced, and at the same time, an increase in photovoltaic self-consumption can be achieved. The analysis concludes with the presentation of a multiple-objective optimal solution (Pareto front) for a battery storage model, indicating what utility incentives would be required to achieve cost-effectiveness for a range of price scenarios for battery systems. [ABSTRACT FROM AUTHOR]

This paper describes an investigation of machine learning for supervisory control of active and passive thermal storage capacity in buildings. Previous studies show that the utilization of active or passive thermal storage, or both, can yield significant peak cooling load reduction and associated electrical demand and operational cost savings. In this study, a model-free learning control is investigated for the operation of electrically driven chilled water systems in heavy-mass commercial buildings. The reinforcement learning controller learns to operate the building and cooling plant based on the reinforcement feedback (monetary cost of each action, in this study) it receives for past control actions. The learning agent interacts with its environment by commanding the global zone temperature setpoints and thermal energy storage charging/discharging rate. The controller extracts information about the environment based solely on the reinforcement signal; the controller does not contain a predictive or system model. Over time and by exploring the environment, the reinforcement learning controller establishes a statistical summary of plant operation, which is continuously updated as operation continues. The present analysis shows that learning control is a feasible methodology to find a near-optimal control strategy for exploiting the active and passive building thermal storage capacity, and also shows that the learning performance is affected by the dimensionality of the action and state space, the learning rate and several other factors. It is found that it takes a long time to learn control strategies for tasks associated with large state and action spaces. [ABSTRACT FROM AUTHOR]

A module for ice-based thermal energy storage (TES) systems has been developed and integrated within EnergyPlus. The TES module uses building load and system thermodynamics (BLAST) models for two direct ice systems (ice-on-coil external melt and ice harvester) and one indirect ice system (ice-on-coil internal melt). The TES systems are integrated as part of the EnergyPlus cooling plant components and are able to operate for any charge/discharge rates provided as input data. In this paper, the structure of the TES module as implemented in EnergyPlus is described. In addition, typical input–output variables from the added TES module are illustrated. Moreover, the operation of the TES systems is discussed for various conventional control strategies. [Copyright &y& Elsevier]