• Energy Research

Abstract The proportion of electric generation from renewable resource has been growing rapidly in recent years, especially in Microgrids in remote areas. However, the intermitted nature of the renewable energy resource has posed a great pressure on the reliability of power grids and asked a great amount of ancillary services, especially for frequency regulation (FR). Heating, ventilation, and air-conditioning (HVAC) systems are promising resources to solve this challenge because they can take full use of thermal storage capacity of buildings. In this paper, a pump in HVAC system in a hotel is employed to provide the frequency regulation by adjusting its frequency to follow the automatic generation control (AGC) signals, RegA and RegD signals from Pennsylvania–New Jersey–Maryland (PJM). Performance scores are adopted to assess the control performance. In addition, the thermal capacity of the cooling coil is considered to evaluate its side-effect when providing this service. The results indicate that the pump can provide nearly 30kW total capacity with a relatively high average performance score. Moreover, the impact to indoor air temperature is almost neglectable with the thermal capacity of the cooling coil and indoor air hedging against the fluctuation of pump frequency.

Abstract Renewable electricity generations have been growing rapidly in recent years worldwide as a result of efforts to address the global energy issue. However, it has also imposed increasing challenges on the balance of power grids due to their intermittent nature. Building sector, as the largest consumer, is expected increasingly to play an important role in providing ancillary services (AS) to relieve stress and reduce the needs of investment for power balance of the smart grids. Grid-responsive building therefore becomes a worthwhile and essential feature of buildings today and in the future. This paper provides an overview of the classification, benefits, applications as well as methods/technologies for HVAC systems in non-residential buildings to provide ancillary services, as fast responses (i.e., in the magnitude of seconds or minutes) to the needs of smart grids. In addition, the challenges of applications and potential solutions are investigated and summarized. The potential capacities of HVAC systems in the entire non-residential building sector in Hong Kong for providing frequency regulation (FR) and spinning reserve are also quantified.

Abstract Ensuring the power balance and reliability of power grids is an increasing challenge due to the increasing involvement of intermittent renewable power generations. The use of existing heating, ventilation and air-conditioning (HVAC) systems in buildings has attracted increasing attention to implement continuous demand response in providing frequency regulation service, which can enhance instantaneous power balance and reliability of power grids without extra huge investment. However, the energy flexibility of buildings is not consistent, and the capacity available for frequency regulation service changes over time due to the changes of working conditions. In this study, a hierarchical optimal control strategy, consisting of a regulation bidding controller and a power use following controller, is proposed. It optimizes the power use baseline and regulation capacity, and controls HVAC systems to provide qualified frequency regulation service, considering the tradeoff between financial reward (regulation capacity) and thermal comfort while satisfying the operating constraints of HVAC systems. The proposed control strategy is validated on a simulation test platform. Results show that the strategy can maximize the use of regulation capacity provided by HVAC systems while ensuring the indoor environment control quality under a given guarantee rate.

Abstract Renewable electricity generations are promising to address the global energy issue while they also place great pressure on the reliability of power grids due to their intermittent nature. In recent years, existing heating, ventilation and air-conditioning (HVAC) systems in buildings have attracted increasing attention to implement continuous demand response in providing frequency regulation service, which can enhance instantaneous power balance and reliability of power grids without extra huge investment. When providing frequency regulation service, the power use of HVAC systems would follow the regulation signals. On the other hand, these signals, acting as continuous disturbances, affect naturally the building indoor environment control at the demand side. In this paper, a novel control strategy is proposed, which can prevent the sacrifice of the building indoor environment when providing the service. The core element of this control strategy is a frequency disturbance compensation scheme, which is developed based on the concept of “disturbance-observer-based control”. Experimental results show that the use of the proposed strategy can achieve significant improvement in the building indoor environment control without sacrificing the quality of frequency regulation service. In addition, the wear level of the valve was not affected significantly when adopting the frequency disturbance compensation scheme.

Abstract The increased generation of renewable power is challenging in terms of the power balance and reliability of power grids due to the intermittent nature of renewable sources. Heating, ventilation, and air-conditioning (HVAC) systems in buildings, at demand side, are promising candidates for providing frequency regulation. In this study, we systematically assessed the use of variable-speed pumps in HVAC systems for frequency regulation. The dynamic characteristics of the pump were investigated. The results show that the power of the pump can response to the frequency change rapidly (returning stable within 1s), while the response of flow rate is slightly slower. A frequency regulation control strategy is proposed and implemented on a test rig. From the viewpoint of power grids, the experimental results show that the pump can provide high-quality frequency regulation (performance scores of 0.989 and 0.968). From the viewpoint of buildings, the experimental results indicate that the fluctuation magnitude of the air-handling unit outlet air temperature increases (from 3.3 K to 7.1 K) with increasing automatic generation control (AGC) signal frequency, whereas the fluctuation magnitude of the indoor air temperature increases to some extent and then decreases when the frequency of the AGC signal is above a certain level.

Abstract Heating, ventilation and air-conditioning systems (HVAC), at demand side, have been regarded increasingly as promising candidates to provide frequency regulation service to smart power grids. To assess the performance of the frequency regulation service provided by the demand side, dedicated frequency regulation test signals have been proposed that are relatively demanding and critical to power grids considering the quality of the service provided by the demand side. However, other practical signals might be demanding and critical to buildings at the demand side considering the impact of the service on indoor environment control. In this study, a set of criteria is proposed to assess the demanding level of frequency regulation signals to power grids and buildings at demand side, respectively. The impacts of providing frequency regulation service (to power grids) on indoor environment control are quantified when HVAC systems are following practical signals with different demanding levels to buildings. The results show that indoor air temperature can have a relatively large offset when HVAC systems are following frequency regulation signals demanding to buildings. In addition, the indoor air temperature offset will increase when regulation capacity provided increases. Two dedicated test signals for buildings are therefore recommended to verify the environment control performance of buildings when providing frequency regulation service to power grids.

Abstract With the development of data-driven models, extracting information from historical data for better energy forecasting is critically important for energy planning and optimization in buildings. Feature engineering is a key factor in improving the performance of forecasting models. Adding load pattern labels for different daily energy consumption patterns resulting from different time schedules and weather conditions can help improve forecasting accuracy. Traditionally, pattern labeling focuses mainly on finding a day similar to the forecasting day based on calendar or other information, such as weather conditions. The most intuitive approach for dividing historical time-series load into patterns is clustering; however, the pattern cannot be determined before the load is known. To address this problem, this study proposes a novel short-term load forecasting framework integrating an early classification algorithm that uses a stochastic algorithm to predetermine the load pattern of a forecasting day. In addition, a hybrid multistep method combining the strengths of single-step forecasting and recursive multistep forecasting is integrated into the framework. The proposed framework was validated through a case study using actual metered data. The results demonstrate that the early classification and proposed labeling strategy produce satisfactory forecasting accuracy and significantly improve the forecasting performance of the LightGBM model.

Abstract The proposed energy performance diagnosis is intended to identify poor energy performance in a building and pinpoint the causes to provide suggestions for building operators to implement timely repair and maintenance. Many previous studies have probed the complicated problem of building energy performance diagnosis to achieve energy conservation and better performance. However, few of them have been successful because most of these methods rely on a large amount of data from an Energy Management and Control System (EMCS), and these data are unreliable. A detailed description of the methodology based on energy consumption data is presented in this paper along with the development of a prototype integrated toolkit. Weekly, daily and hourly diagnoses are developed at the whole building level, system level and component level, respectively. To validate the feasibility and applicability of the method, a case study on an office building demonstrating the proposed method was completed and was able to detect underperformance operation and energy waste.