An Inexpensive Retrofit Technology for Reducing Peak Power Demand in Small and Medium Commercial Buildings

This article describes a low cost retrofit technology that uses collective control of multiple rooftop air conditioning units to reduce the peak power consumption of small and medium commercial buildings. This retrofit technology uses a model predictive control to select an operating schedule for the air conditioning units that maintains a temperature set point subject to a constraint on the number of units that may operate simultaneously. A proto-type of this new control system was built and deployed in a large gymnasium to coordinate four rooftop air conditioning units. Based on data collected while operating this proto-type, we estimate that the cost savings achieved by reducing peak power consumption is sufficient to repay the cost of the proto-type within a year. Moreover, it is possible to reduce the cost of this proto-type technology by a factor of at least six and thereby create a retrofit package that pays for itself within two months of operation. The effectiveness of the control in this demonstration strongly suggests that widespread deployment of the proposed technology could significantly reduce peak demand originating with small and medium buildings. Over 27% of the energy used by most small and medium sized commercial building is dedicated to air conditioning units, and most of these units continue to rely on simple, uncoordinated controls that independently maintain the temperature for their assigned sections within the building. One consequence of this uncoordinated control is that in the peak heating and cooling seasons it is almost certain that all of the air conditioning units within a building will operate at the same time. This causes large peaks in the power used for temperature control, and these peaks can be significantly reduced by the proposed retrofit technology. This reduction in peak demand benefits the utility company by reducing the need for expensive peaking plants and their associated infrastructure, and it benefits the building owner by reducing electricity costs. These advantages and the short payoff period suggest a significant commercial potential for the proposed control technology

Integration of Photovoltaics into Building Energy Usage through Advanced Control of Rooftop Unit

As the United States sees the continued expansion of photovoltaic (PV) and other distributed solar generation technologies into the distribution grid, there is an increased need to find approaches to mitigate integration challenges associated with renewable resources. Depending on the renewable resource, the integration challenges will vary. Much of the challenge with integration is associated with the uncontrolled oscillations of output power, for example, from a PV array. Both solar and wind resources rely on environmental conditions to produce power. However compared to wind, solar generation resources such as PV typically produce more second to minute oscillations due to cloud patterns. With low levels of penetration, the impact is minimal. This paper focuses on developing advanced control strategies for building equipment like the rooftop units along with energy storage technologies to support seamless PV integration into buildings. A forecasting approach for PV is presented along with model-based control strategies for using load to support the integration of PV. The forecasting model takes as input solar irradiance and module temperature to estimate the output power of PV based on an interconnected voltage. The first step is to poll the cloud patterns for the day and utilize this information to project the cloud density each hour. The trained neural network defines relationship of this cloud cover to the amount of expected solar irradiance that is measured. Temperature data is collected from weather application and is inserted as an initial temperature to the PV model and thermal model. The model develops the corresponding PV curves based on the current module temperature reading and the solar irradiance data provided. The model predicts the average power output of the PV array over the next one-hour time window. A control algorithm for the rooftop unit is presented that utilizes this PV forecast to optimize the energy consumption to match the PV peak generation. The model is validated using irradiance, temperature, and PV output power measurements from Oak Ridge National Laboratory’s 50kW PV array

Simulation Based Design and Testing of a Supervisory Controller for Reducing Peak Demand in Buildings

We describe a supervisory control strategy for limiting peak power demand by small and medium commercial buildings while still meeting the business needs of the occupants. The objective of the supervisory control is to operate no more tha

Utilizing Thermal Mass in Refrigerated Display Cases to Reduce Peak Demand

Refrigeration systems in supermarkets and convenience stores operate continuously to maintain proper product storage conditions, and these refrigeration systems can account for 40% or more of the electrical energy consumption of a supermarket or convenience store. Peak refrigeration system electricity demand typically occurs during the afternoon and summer months, corresponding to when the general demand and price for electricity are the highest. In order to reduce peak demand and its associated costs, it may be possible to shift refrigeration system electrical energy use by utilizing the thermal mass of the stored product. Refrigeration system capacities vary from 30-60KW in small store to over 400KW for large stores with annual energy consumption varying from 1-1.5 million kWh. During early morning hours, when electricity demand and price are lower and refrigeration system efficiency is greater, the temperature set point of the refrigeration system can be reduced in order to pre-cool the stored products to below their normal storage temperature. Subsequently, refrigeration system operation can be reduced during the mid to late afternoon when electricity demand and price are high, and product temperature may be allowed to drift upwards. This operating strategy is particularly feasible with products that are not adversely affected by variations in temperature, such as water and canned or bottled beverages. The key to utilizing the thermal storage is to understand the storage potential and associated time constants of the display cases. To determine the feasibility of reducing peak demand by shifting the refrigeration load to off-peak times, experimental and analytical analyses were performed. Simulated product, consisting of one-pint containers filled with a 50% ethylene glycol and 50% water solution, were stored in a medium-temperature vertical open refrigerated display case. Product temperature rise as a function of time was determined by turning off the refrigeration to the display case, while product temperature pull-down time was subsequently determined by turning on the refrigeration to the display case. It was found that the thermal mass of the product in the display case was such that during a 2.5 hour period with no refrigeration, the average product temperature increased by 10°F. In addition, it took approximately 3.5 hours for the product to recover to its initial temperature after the refrigeration was turned on. Using transient heat conduction analyses for one-dimensional objects (such as infinite slabs, infinite cylinders, or spheres), cooling or heating times of various objects may be estimated. For example, analytical methods predict that heating a cylindrical product by 10°F (from 30°F to 40°F) would take approximately 2.2 hours, which is in good agreement with the experimental results obtained in this study. From the analysis, it appears that the thermal mass of the stored product in refrigerated display cases is sufficient to allow product temperatures to safely drift for a significant time under reduced refrigeration system operation. Thus, strategies for shifting refrigeration system electrical demand can be developed. The use of an advanced refrigeration system controller that can respond to utility signals can enable demand shifting with minimal impact

Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings

The primary energy consumer of smart buildings are Heating, Ventilation, and Air-Conditioning (HVAC) systems, approximately 30% of the building energy use, which usually operate on a fixed schedule. Currently, most modern buildings still condition rooms with a set-point assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Occupancy-based controls can achieve significant energy savings by temporally matching the building energy consumption and building usage, conservative user behavior can save a third of expended energy.  In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant. With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, three techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we propose a method based on uncertain basis functions for modeling and prediction purposes. In literature, basis functions (e.g., radial basis functions, wavelets) are fixed; instead, we assume that the basis functions are random. We consider basis functions with three different distributions, which are Gaussian, Laplace and Uniform, respectively. Finally, we introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver (GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy in buildings. All above estimates can be used to adaptively update the temperature set-points for HVAC control strategy.  To demonstrate effectiveness of proposed approach, a simulation-based experimental analysis is carried out using occupancy data. We define the estimation accuracy as the total number of correct estimations divided by the total number of estimations, and both Root Mean Squared Error (RMSE) and estimation accuracy analysis are provided. All the proposed estimation techniques could achieve at least 70% accuracy rate. Generally, accuracy for binary states estimation is much higher than that of detailed occupancy. For GSPS model, more training data improves performance of estimation. It should be remarked that although some mismatch exist for non-zero jumps, estimation performance tracks the zero base line (non-occupied status) perfectly. Therefore, the estimation techniques are effective for binary estimation with over 90% accuracy. Finally, the estimated occupancy is applied into temperature set algorithm to generate reference temperature curve. By adjusting temperature set curve, we can achieve significant energy without sacrificing customer’s comfort.  In this paper, we propose three real-time occupancy estimation methods that can be incorporated into HVAC controls . We have shown the effectiveness of all the proposed approaches by simulation examples. We have seen great potential of energy saving by integrating the proposed technique into real HVAC control system.   Â

A Hardware-in-Loop Simulation of DC Microgrid using Multi-Agent Systems

Smart-grid is a complex system that incorporates distributed control, communication, optimization, and management functions in addition to the legacy functions such as generation, storage, and control. The design and test of new smart-grid algorithms require an efficient simulator. Agent-based simulation platforms are the most popular tools that work well in the control and monitoring functionalities of the power electric network such as the microgrid. Most existing simulation tools necessitate either simulated or static data. In this paper, we propose a hardware-in-loop simulator for de-microgrid. The simulator reads the power generated by the PV panels and the battery SoC using Raspberry PI. A physical agent that runs on Raspberry PI sends the real-time data to a de-microgrid simulator that runs on a PC. As a proof of concept, we implemented a load-shedding algorithm using the proposed system