Electric Air Conditioning Research Articles

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

A seat at the energy table

Abstract Energy injustice persists due to the difficulty of meeting competing interests in a rapidly evolving system. Transformative energy technologies need to fit into existing structures while also improving upon those systems. To aid in complex decisions, this paper provides a simple checklist of four necessary and often missed concepts for energy justice. Energy choices should be Supported, Environmental, Affordable, and Tolerable to provide everyone a SEAT at the energy table, in other words to promote energy justice. For the purposes of this framework, supported indicates that supply chains and infrastructure to meaningfully participate are available, environmental considerations cover climate change and health-affecting pollutants as well as disposal affecting soil and water, affordability rather straight-forwardly requires equal economic access to the energy transition, and tolerable requires personal and political buy-in. This structure is intended to aid anyone that makes decisions, i.e. not only highly specialized academics. Many technologies that address one or two of these concepts are touted as solutions to injustice, but without additional structure, they may have disbenefits in other areas. By using a simple structure to illuminate this consistent difficulty, future research and policy will be better able to promote true justice instead of tangentially addressing it in ways that fail to capture the complexities of the system. The tension between benefits and unintended consequences is explored for several energy technologies at varying stages of implementation, including electric vehicles, cooking, time-of-use pricing, and air conditioning. Existing policy mechanisms that have tried to address only some barriers have not necessarily resulted in an equitable transition. This indicates that equity needs to be considered explicitly, and that all four chair legs need to be evaluated. This method highlights the principles most easily considered in non-specialist decisions and maps them to concepts that are easy to grasp.

Flexible energy utilization potential of demand response oriented photovoltaic direct-driven air-conditioning system with energy storage

The surge in air conditioning electricity consumption exacerbates grid peak load. To counteract grid peaking pressures and accommodate a high penetration rate of renewable energy, a photovoltaic direct-driven air-conditioning system (PVACS) integrated with energy storage was suggested. The power response characteristics of the air conditioner based on indoor temperature set-point regulation were clarified with an on-site test. The test results indicated a significant flexible energy utilization potential through intelligent temperature management. Whereas, its flexibility is limited by high ambient temperature or inadequate air conditioner capacity. Then, the effects of demand response were simulated, aiming to achieve the synchronization between air conditioner power and photovoltaic (PV) power. Simulation results showed that the demand response strategies effectively reduced peak power by 45.2% and increased self sufficiency ratio and self consumption ratio to 0.83 and 0.91, respectively. Moreover, the role of short-term electricity energy storage was considered for further promoting solar energy accommodation and grid peak shaving. Optimized energy storage further reduced peak power by more than 23.4%, while mitigating the temporal supple-demand mismatch.

A hierarchical framework for integration of smart buildings in fully-renewable multi-microgrids and distribution systems: Towards more sustainable societies

Using smart buildings would be a crucial step towards sustainability. Although in the literature, some research effort has focused on integration of smart buildings into power systems; the integration of fully-renewable smart buildings into fully-renewable microgrids and distribution systems has not been addressed before. In this paper, a tri-level hierarchical stochastic framework is developed for the integration of fully renewable smart buildings in fully renewable multi-microgrids (MGs) and distribution systems. The studied smart buildings have electric water heater, air conditioner, PV and battery. In the first level, the model of each smart building is separately solved to find optimal schedule of its resources and the desired electricity exchange with its corresponding MG. In the second level, each MG solves its operational planning model, considering its transactions with the connected smart buildings and finally, in the third level, distribution system operator solves its operational planning model, accepting the electricity exchange requests taken from all MGs. The proposed methodology is applied to a 69-bus 100 % renewable distribution system with 4 100 % renewable microgrids, integrated with 100 % renewable smart buildings. The results confirm the efficacy of the proposed methodology for the integration of 100 % renewable smart buildings in 100 % renewable multi-MGs. The results testify that that building-to-grid (B2G) capability decreases the operation cost of all smart buildings; the average decrease in operation cost of buildings, caused by B2G, is 38 %. The results on the studied case indicate that that the impact of batteries on operation cost of smart buildings is marginal. The results also show that the higher threshold of comfort index in smart buildings, results in their higher operation cost. The results of this research may be helpful for both smart building operators and power system operators. This research is in line with the global energy transition and decarbonisation targets as the considered smart buildings, microgrids and distribution system are fully renewable.

Demand response strategies for air-conditioning systems based on direct load control: Considering envelope structures

In recent years, the surge in rural air-conditioning (AC) electricity consumption has led to an overall increase in peak loads in both winter and summer, presenting a major challenge to the reliability and stability of low-voltage distribution networks. Demand response (DR) strategy not only relieves the peak load, but also reduces the energy consumption and achieves the purpose of energy saving and emission reduction. Among the many DR strategies, direct load control (DLC) stands out as an effective way to quickly reduce building AC loads. Meanwhile, the building envelope is also one of the important factors affecting the AC load. A typical rural house in the Xi’an area of Shaanxi Province was used as a case study. A ground source heat pump system was developed, installed and used as an experimental platform in order to aid the study. Therefore, in this study, the combination of external envelope and DLC strategies was investigated to establish a simulation model of energy consumption in rural building complexes. Two DLC load control strategies were implemented under different external envelope conditions for annual AC energy simulation. The results show that the thickness of the external insulation layer has the greatest impact on the AC load of the building compared to the window-wall ratio and orientation. With a thickness of 0 mm–75 mm insulation, the DCCM strategy can effectively reduce AC energy consumption by 16%–18%; With a thickness of 0 mm–75 mm insulation, the TSCM strategy can effectively reduce AC energy consumption by 5%–8%.

Air-conditioning anthropogenic heat in high-density residential areas: Spatial patterns and impacts

The overheated urban environment has drastically increased air-conditioning (AC) usage, which induces extensive heat emissions from outdoor units. This phenomenon is more adverse in high-density residential areas as the air-conditioning anthropogenic heat (ACAH) would further cause localized heat accumulation in canyons, negatively impacting AC energy consumption and residents’ comfort. While previous research has explored the impact of ACAH on urban canyons, there is a lack of studies that systematically investigate the pattern, impacts, and mitigations of ACAH at a refined scale in high-density residential areas. This study aims to investigate the temperature distribution under ACAH and quantify its impact on cooling energy consumption and pedestrian thermal discomfort. High-density residential areas were categorized by different heights of buildings and directions of outdoor unit placement. Numerical simulations were conducted to study ambient temperature distribution. Results showed that the worst heat accumulation was at the windward position of the AC outdoor unit, and the outdoor temperature on the middle floor was 2.3 °C higher than that on the upper floor, resulting in a higher energy consumption of 6.3 W/m2. The worst case with taller windward buildings caused 55 % of the pedestrian areas to be more uncomfortable. Mitigation strategies were also proposed from multiple perspectives, including heat source optimization (e.g., cross-layout of AC outdoor units) and offering subsidies for AC electricity consumption gaps. The study provides a systematic research perspective on spatial heat accumulation patterns in high-density residential areas, which is instructive for mitigating urban warming and promoting sustainable urban development.

Utilization of typha-boards for preserving the historic wooden houses typical of the region in the Thai city of Chiang Mai (Thailand)

ABSTRACT The quality of life in the historic wooden houses typical of the city of Chiang Mai in northern Thailand is significantly reduced by climate changes and the intense air pollution during the Haze Season (time of slash-and-burn). The traditional indirect cooling system through strong natural infiltration ventilation in traditional houses is severely limited in its function due to changes in the local urban microclimate. The low comfort level leads to increased use of electric air conditioning, resulting in substantial energy consumption and the formation of urban heat islands and more and more often to the loss of historic Thai wooden houses by demolition.

Impact of optimized gas-liquid separator on temperature and frost distribution in electric vehicle heat pump AC

In the context of low temperatures, the outdoor evaporator surface in an electric vehicle's heat pump air conditioning system tends to accumulate frost unevenly. This uneven frosting occurs due to the varying heat exchange capacity of the refrigerant's two phases within the evaporator. Any reduction in the heat exchanger's capacity would lead to a negative impact on system performance. To address these challenges, this study introduces a gas–liquid separator placed in front of the outdoor evaporator. The aim was to enhance both the outdoor evaporator's performance and the overall system efficiency while investigating frost formation and heat distribution in the outdoor evaporator within a heat pump air conditioning system. The research outcomes demonstrate that the gas–liquid separator can effectively enhance evaporating pressure by regulating the opening degree of the gas-phase bypass valve. Optimal performance is achieved with the gas-phase branch bypass valve set between 20% and 30%, ensuring a stable operational condition and higher evaporating pressure. Moreover, the incorporation of a gas–liquid separator could lead to significant improvements in system performance, with increases of 6.9% in heat capacity and 7.4% in COP observed. Additionally, this enhancement extends to the outlet air temperature, showing improvements ranging from 3.6% to 11.2%.

Compressor speed control for optimizing energy matching of PV-driven AC systems during the cooling season

Photovoltaic-driven air conditioning (PVAC) systems innovatively utilize PV power for building cooling, reducing PV power fluctuation impact on the utility grid. This study introduced a novel PVAC system that optimizes the energy matching between PV generation and air conditioning electricity demand through directly controlling of the compressor speed in accordance with the real-time PV power generation. A PID control method was employed to regulate the compressor speed of PVAC, and three control strategies were proposed and successfully implemented during the experiments. The system's efficiency was evaluated using metrics such as the self-consumption rate (SCR), self-sufficiency rate (SSR), real-time zero energy proportion (RZEP), and the mean absolute error (MAE) of indoor temperature. The results of this study confirm that the proposed control method significantly enhances the power matching between the PV power generation and the air conditioning electricity demand. Implementing the recommended strategy, SCR increased from 0.53 to 0.625 and SSR from 0.716 to 0.808, all while maintaining indoor thermal comfort. Moreover, the proposed approach mitigates the impact of distributed PV generation on the utility grid and potentially diminishes energy storage needs, which can contribute to a lower system investment requirements and operating costs.

Two-Layer Optimization Strategy of Electric Vehicle and Air Conditioning Load Considering the Benefit of Peak-to-Valley Smoothing

To satisfy the interests of multiple agents and those of comprehensive indicators such as peak-to-valley differences and load fluctuations occurring on the network side, this paper presents a flexible load demand-side response optimization method that considers the benefits of peak-to-valley smoothing. First, load aggregation modelling of air conditioning and electric vehicles was conducted, and the complementarity of the power consumption behavior of different types of flexible loads was used to improve the responsiveness of the load aggregator. Second, considering demand-side responses and taking into account the interests of both supply and demand, the load fluctuation and peak-to-valley difference on the network side are reduced, and a flexible load double-layer optimization model incorporating the peak-to-valley smoothing benefit is established. Finally, the effectiveness of the proposed optimization model is verified by using the KKT condition and the big M method to evaluate this two-layer optimization problem as a single-layer optimization problem. Comparative examples show that the proposed two-layer optimization method can take advantage of the complementarity of air conditioning and electric vehicles to improve the income of load aggregators. Moreover, the proposed method can effectively reduce the load peak-to-valley difference and load fluctuation of the distribution network by introducing the peak-to-valley smoothing benefit model.

Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031.

Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence

The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.

Evaluation of the Effects of Window Films on the Indoor Environment and Air-Conditioning Electricity Consumption of Buildings

The objective of this study was to evaluate the effects of window films on indoor environmental conditions and electricity consumption of air conditioning. The research focused on the performance of different window films (HAG, RG), taking into account variations from different building orientations. The findings of this research indicated that building orientation could significantly influence the duration of direct sunlight entering the interior, with the areas closer to the glass being more susceptible to the effects of outdoor temperature and solar radiation. The clear glass with heat-absorbing film (HAG) and reflective film (RG) both reduced the indoor temperature and indoor illuminance while increasing indoor comfort. The RG could accumulate less heat on the glass surface compared with the HAG. The glass temperature of the RG will be lower than the HAG. The electricity-saving ratios of the HAG were 1.4%, 1.9%, 1.4%, and 1.2%, respectively, when facing the east, south, west, and northwest orientations compared with the clear glass (OG). The electricity-saving ratios of the RG were 3%, 4.2%, 4.2%, and 10.3%, respectively.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.

Decomposition of air conditioning electricity consumption considering the dependence between the temperature and electricity consumption.

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022.

Decomposition of air conditioning electricity consumption based on effective duration.

As the share of air conditioning electricity consumption within total grid electricity consumption grows, the decomposition of such consumption becomes increasingly crucial for assessing electricity usage patterns, devising consumption scheduling strategies, and maintaining the stability of the power grid. Although there is a strong correlation between apparent temperature and air conditioning electricity consumption, the literature currently available seldom explores the impact of apparent temperature on this consumption. Moreover, there is a scarcity of effective assessment indices to evaluate the efficacy of air conditioning electricity consumption breakdown. This study introduces a method for decomposing electricity consumption from air conditioning units, utilizing effective duration as a basis to tackle these issues. By employing an apparent temperature model as a constraint, this approach identifies the effective operating time of air conditioning and constructs a constrained convex optimization problem to estimate air conditioning power usage. Additionally, a novel evaluation index for the effectiveness of air conditioning electricity consumption decomposition is proposed, which includes penalties for negative decomposed consumption, alongside the traditional consistency index. Comparative experiments are conducted using real electricity consumption data from Fujian Province. Empirical results indicate that the methodology for air conditioning electricity consumption decomposition presented in this paper aligns more closely with actual conditions. Furthermore, the evaluation metrics introduced for the decomposition of air conditioning electricity consumption are adept at precisely gauging the quality of the air conditioning electricity consumption data.

Occupancy-based one-year-ahead heating, ventilation, and air-conditioning electricity consumption optimization using machine learning

The world is increasingly urbanized. While, more than a half of its population lives in big cities, buildings are responsible for nearly the 40 % of the global annual energy consumption. Recent advances in smart grids and the high penetration of renewable energy sources have increased the need for long-term buildings' electricity consumption prediction to support operational decision making, demand response, and the installation of distributed generation systems. In addition, to meet international environmental goals, electric utilities usually offer financial incentives to reduce consumption levels. Heating, ventilation, and air-conditioning (HVAC) systems use the half of the buildings' energy to maintain users' comfort. In this scenario, predicting and optimizing their long-term electricity consumption without neglecting comfort conditions become vital to negotiate beneficious service-level agreements (SLAs) within the framework of demand-side management strategies. In this paper, we predict and optimize the one-year-ahead HVAC electricity consumption profile of a reference office building based on a machine learning approach. Although the occupants' number influences the HVAC electricity consumption, there has long been a lack of high-resolution, non-Boolean, long-term occupancy estimators. In this context, we first forecast the one-year-ahead occupants' number schedule and then use it, together with traditionally used historical weather data, to predict the one-year-ahead HVAC electricity consumption. The consumption profile is then optimized based on the estimated one-year-ahead occupants’ count schedule. The best results, obtained with a feedforward neural network, achieve 33 % annual electricity saving without neglecting summer cooling conditions in a hot zone like Miami, being efficient even in energetic-demanding scenarios such as extended working hours.

Space-Level air conditioner electricity consumption and occupant behavior analysis on a university campus

In China, despite only 2 % of the population residing on university campuses, these campuses account for 8 % of the total energy consumption. Understanding the space-level energy consumption characteristics and energy-related occupant behaviors on a university campus can help improve campus buildings' energy efficiency. This study investigates 21 office spaces on a university campus to better understand the space-level electricity consumption of air-conditioners (AC) and the occupant behaviors (OB), thermal comfort needs, and window operation. First, Internet of Things (IoT) sensors were installed in the 21 office spaces to collect the indoor temperature and humidity, the electricity consumption of the AC, and the status of doors. The data were collected for the entire cooling season. The space-level occupancy patterns were then analyzed based on the quality of the doors, including the first occupant’s arrival time and the last occupant’s departure time. Moreover, the indoor temperature, humidity, and illuminance were combined to evaluate occupants’ thermal and visual comfort need. In addition, statistical and clustering analyses were conducted on air conditioner operations' duration, frequency, and electricity consumption. The results showed that the spaces were typically occupied from 9:21 to 22:25, with 10 h and 53 min daily. The average indoor temperature was 26.1 °C, ranging from 22.1 °C (5th percentile) to 34.9 °C (95th percentile) when the room was occupied and ranging from 21.7 °C (5th percentile) to 32.5 °C (95th percentile) when the AC was on. The air conditioner's electricity consumption ranges from 11.7 to 152.2 kWh/m2 with a median of 68.9 kWh/m2 and from 46.84 to 1883.7 kWh/person with a median of 464.7 kWh/person. The different occupancy densities often lead to a significant difference in the electricity consumption of air conditioners. The findings of this study can be used to support the development of prototype energy models and occupant-centric control (OCC) models for campus office buildings.

STUDY OF ELECTRICAL INSTALLATION PLANNING AT PRATAMA JAGOI BABANG HOSPITAL

Planning for Jagoi Babang Pratama Hospital is a Regional General Hospital owned by the Government which is located in Jagoi Babang District, Bengkayang Regency, West Kalimantan Province. Jagoi Babang Primary Hospital has a total land area of 10,275 m2. The Study of Electrical Installation Planning at Pratama Jagoi Babang Hospital will be carried out in Building C, which at the time this research was appointed, development planning was being carried out and the location of Building C was on the 1st floor. The construction of this hospital is very dependent on electrical installations, especially lighting and air conditioning installations to support the needs of hospital staff and patients. For the smooth running of hospital activities, calculations are needed to design correct and proper electrical installations, according to the Indonesian National Standard based on General Electrical Installation Requirements (PUIL) of 2011 and Regulation of the Minister of Health of the Republic of Indonesia Number 24 of 2016 concerning technical requirements for hospital buildings and infrastructure which is stated in article 23. In planning the electrical installation of Pratama Jagoi Babang Hospital, researchers made several calculations, namely lighting illumination, air conditioning capacity, safety size, cross-sectional area, total load, and voltage drop. Based on the results of calculations and analysis of the number of lamps counted, 470 lamps were obtained with the type of DL LED 14 Watt, DL LED 18 Watt, TL LED 18 Watt. Then the total load required is 8168 Watt = 8.168 kW while for planning Air Conditioners with a total load required is 140.633 kW with a total of 35 Air Conditioners.

Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification

In electric buses, heating, ventilation and air conditioning are responsible for up to 50% of the energy consumption. It is therefore necessary to identify improved thermal settings to minimize the energy consumption, while guaranteeing good thermal comfort. Hence, an accurate prediction of the passengers’ thermal sensation (TS) is needed. One of the most widely used models for TS prediction is the PMV-PPD model, which has been shown to provide reliable results in uniform, steady-state climatic conditions. Since these are not present in an urban bus, the accuracy of the PMV-PPD model diminishes. Additionally, some of the parameters needed are difficult to obtain (i.e., clothing insulation). This paper presents seven different machine learning models (ML) for the prediction of TS using three different sets of parameters. The first set comprises five parameters similar to the PMV-PPD model, the second uses only two, and the third uses all parameters available. To obtain the necessary data, climatic measurements in an electric bus in Berlin, Germany, were made. These measurements were performed in summer for ambient temperatures between 14.7 °C and 32.0 °C. Person-related information as well as the thermal comfort assessment were obtained via surveys. Despite the relatively small data set, four of our seven ML models performed well with a median accuracy between 70.3% and 69.4%. This could also be observed when using only two parameters. Hence, the efforts to gain experimental data can be reduced significantly. For the PMV-PPD model, a median shift of +1 was observed for mild and warm TS. The median accuracy rises from 48.8% without shift to 68.8% with shift.