About 41% of total energy consumption in the United States of year 2014 is used for heating and air conditioning, which is about 40 quadrillion (40 × 10 15 ) British thermal units (BTUs). Despite the fact that people have been working on fault detection and diagnosis (FDD) for heating, ventilation, and air conditioning (HVAC) systems for a long time, very few publications have focused on scalability and low cost. To address this challenge, we will propose an approach that focuses on control system data. Several machine learning algorithms are introduced for data exploration (e.g., DBSCAN, k-means) and analysis, rank-ordered weather data are used to define comparable days, a control system data focused model free approach is presented as well, and finally, fault detection is carried out by implementing anomaly algorithms (local outlier factor and isolation forest). Hidden faults were detected with a detection rate up to 90%+. The threshold parameter can be determined by selecting an acceptable true positive and false positive rate pair, which can be visualized using receiver operating characteristic (ROC) curves and is demonstrated in this article. A simulation model that is used to generate about 250 GB of data is used to evaluate the performance of various algorithms.

In this paper, the results of a feasibility analysis are summarized to assess the energy, economic, and environmental benefits of electrification and renewable energy integration for an urban district of Old City, Philadelphia, Pennsylvania. First, the energy demand for the district is reduced through the implementation of cost-effective energy efficiency measures (EEMs) appropriate for Philadelphia’s climate. Then, a combination of distributed generation (DG) systems including wind, photovoltaics, and hydropower is evaluated to determine optimized hybrid systems that meet the energy demand of non-electrified and electrified districts. The analysis indicates that the implementation of common and proven EEMs to all the district buildings can reduce its annual energy consumption and CO 2 emissions by 13% and 13.8%, respectively. These EEMs are estimated to be cost-effective based on the current electricity price of $0.082/kWh offered by Philadelphia’s utility, Philadelphia Electric Company (PECO). Through cost optimization analysis, a hybrid DG system combining wind and hydropower connected to the grid is found to lower the cost of energy for the non-electrified district to $0.007/kWh, lower than the current PECO rate, with the added benefit to lower carbon emissions by 10%. Moreover, the analysis considered the case of an electrified district which reduces life cycle costs by 3.5%. By implementing electrification and EEMs, the electricity usage decreases by 14% and peak demand by 19.5% as well as CO 2 emissions by 18%. Through cost optimization to design a hybrid DG system that can meet energy demands of the electrified district, wind and hydropower connected to the grid is found to achieve a cost of energy of $0.008/kWh and carbon emissions reduction of 34.9%.

Midland, Texas is one of the fastest-growing urban population centers in the country and has one of the lowest costs of electricity. This study aims to assess the potential of a grid-connected carbon-neutral community in an oil-rich city using energy efficiency measures and hybrid distributed generation (DG) systems. The community consists mostly of residential buildings including detached homes and apartment buildings. Moreover, a cost-optimization analysis of various DG technologies is carried out to meet both electrical and thermal loads of the community in Midland. The energy efficiency measures are selected for two main objectives: (i) reduce the total energy needs and (ii) electrify most of the buildings within the community. Improvement of heating, ventilating, and air conditioning systems and their controls are the main energy efficiency measures considered for all the buildings part of the community. DG systems are constrained by the renewable energy resources identified to be prevalent within the site of the community. It is found that photovoltaic (PV) systems are the most cost-effective, while wind and combined heat and power (CHP) would not be competitive compared to the current grid energy prices. Specifically, the optimization results indicate that PV, when implemented on a large scale, can provide adequate power to meet the energy needs of the community while also meeting carbon neutrality. A PV system size of 3400 kW is found to be required for the grid-connected community to be carbon neutral. While under this scenario a 100% reduction in carbon emissions is technically feasible, the cost of energy is estimated to be $0.194/kWh, almost double the current grid electricity price. However, if the capital cost of PV is decreased by 70% from its current level, the cost of energy due to the DG addition can be reduced significantly. In particular, a 1050-kW PV system was found to reduce the cost of energy below the grid electricity price of $0.10/kWh and achieves 31% reduction in carbon emissions for the community. Moreover, the 70% reduction in PV capital costs allows the carbon-neutral design for the community to be a cost-competitive solution with the grid.

In this study, optimal carbon-neutral designs are evaluated for grid-connected communities that include net-zero energy (NZE) homes in Boulder, CO. Specifically, the economic and environmental benefits are assessed for residential communities with various mixtures of both NZE and non-NZE homes. Optimization techniques based on life cycle costs including both capital costs and operating costs are used to design NZE homes as well as carbon-neutral communities. Using both energy efficiency measures and rooftop PV systems, the analysis estimates the capital costs required for NZE homes. Moreover, optimally sized distributed renewable systems are determined to achieve carbon-neutral operation for various types of residential communities. First, the impact of occupancy behavior in designing NZE homes as well as carbon-neutral communities is evaluated using three use patterns of appliances (i.e., refrigerator, television, dishwasher, and clothes washer) as well as domestic hot water. Then, different fractions of NZE homes are considered when designing carbon neural communities. The analysis indicates that occupant behavior can significantly affect the design of NZE homes as well as the capital costs to achieve this design. For instance, good behavior can result in 21.28% capital cost savings while bad behavior can result in a 10.42% increase in capital cost. Moreover, the analysis indicates while the communities made up partially or fully of NZE homes can attain carbon-neutral target with lower distributed generated (DG) capacities than non-NZE communities, they require high total capital costs at least based on current costs for distributed renewable technologies and utility electricity prices. Finally, it is found to be more cost-effective to share distributed power systems for communities rather than individual homes with their own rooftop PV system to attain carbon-neutral design.

Buildings of the future are expected to not only be energy efficient but also able to offer grid services through implementation of demand-side management strategies by utilizing existing and new technologies that enhance electrical load flexibility. With the high penetration of variable renewables, grid operators have to balance between variable supply with controllable and adaptable demand. This article reviews the current literature on grid-interactive efficient buildings (GEBs) that can provide grid services. In particular, the review identifies categories and examples of measures and technologies that are suitable for GEBs using various criteria. These criteria include demand-side management strategies, potential to provide grid services, technology maturity, as well as ability to model the technologies to perform detailed analyses and assessments in whole-building simulation software.

Switchable building envelope systems, including passive and active systems, have recently seen an increase interest in the literature. Unlike static insulation, switchable insulation systems (SISs) have the ability to adjust the thermal properties of envelope elements. Advanced control strategies for SISs are evaluated in this analysis using genetic algorithm-based optimization techniques. In particular, this study investigates the potential heating and cooling energy savings for deploying optimal controls specific to SIS technologies when applied to residential roofs located in representative US climates. Moreover, energy use and peak demand savings obtained by optimal controls are compared with those obtained from the 2-step rule-based controls. Overall, the analysis results indicate that the maximum monthly additional savings obtained by optimal controls can reach up to 32% compared with 2-step rule sets when an annual analysis is conducted for a residential building located in Golden, CO.

Stationary fuel cells provide potential opportunities for energy savings when integrated with buildings. Through smart dispatch of both electrical power and heat generated by the fuel cells and managing the building loads, the buildings can achieve more efficient operation. In this paper, we develop an optimal energy dispatch controller to operate a fuel cell-integrated building. The controller leverages the inherent thermal storage and the dispatchable fuel cell to reduce its operating cost and to allow the building to participate in grid services. The proposed controller is implemented on two types of commercial buildings, a large office building and a large hotel, and the effectiveness of the controller is demonstrated through simulations. The results also indicate that the potential saving varies significantly with different system parameters, including season, fuel prices, and equipment sizing, which provide helpful insights for building operators and other stake holders.

Thermostatically controlled loads (TCLs) have shown great potential for demand response (DR) in electric grid operations. However, it has been commonly seen that DR events using TCLs may cause load synchronization and unwanted oscillatory effects, especially in homogeneous populations. In an attempt to mitigate the negative impacts of DR events, a decentralized method is proposed that modifies each thermostat behavior based on the activity of a small number of nearby TCLs. This feedback introduces the possibility of instability in the aggregate behavior. A stability analysis is performed on a linearized model of the aggregate system and the results of that analysis compared to simulation results. The proposed modification of thermostat behavior results in fourfold reduction in the post-DR peak while suppressing ensuing oscillations at the expense of a modest increase in compressor cycling. The linearized model also provides insight into the aggregate behavior of the population.

In this work, a social welfare maximization problem is solved to determine the optimal scheduling of end-user controllable loads, smart appliances, and energy storage. The framework considers multiple retail energy suppliers as well as the AC power flow constraints of the distribution system. The demand side management program is focus on residential and commercial end-users. We have formulated a day-ahead residential bidding/buyback scheme modeled as an optimal power flow problem. This demand side program schedules end-user’s controllable loads or smart appliances and takes advantage of the flexibility of an energy storage system. The demand side management scheme minimizes retail company’s operating costs in the wholesale market, and it also considers distribution network constraints, assuring the appropriate quality of service. We have used a dual decomposition method to decouple some constraints while maximizing social welfare. We have also introduced a demand response call event with the main objective to take into consideration the system operational constraints. Through the coordination via local marginal prices, we have obtained a decentralized and distributed bidding/buyback scheme proposing a demand side management program that preserves the integrity of the private information of the different participants.

The integration of variable and intermittent renewable energy generation into the power system is a grand challenge to our efforts to achieve a sustainable future. Flexible demand is one solution to this challenge, where the demand can be controlled to follow energy supply, rather than the conventional way of controlling energy supply to follow demand. Recent research has shown that electric building climate control systems like heat pumps can provide this demand flexibility by effectively storing energy as heat in the thermal mass of the building. While some forms of heat pump demand flexibility have been implemented in the form of peak pricing and utility demand response programs, controlling heat pumps to provide ancillary services like frequency regulation, load following, and reserve have yet to be widely implemented. In this paper, we review the recent advances and remaining challenges in controlling heat pumps to provide these grid services. This analysis includes heat pump and building modeling, control methods both for isolated heat pumps and heat pumps in aggregate, and the potential implications that this concept has on the power system.

This paper develops means to analyze and cluster residential households into homogeneous groups based on the electricity load. Classifying customers by electricity load profiles is a top priority for retail electric providers (REPs), so they can plan and conduct demand response (DR) effectively. We present a practical method to identify the most DR-profitable customer groups as opposed to tailoring DR programs for each separate household, which may be computationally prohibitive. Electricity load data of 10,000 residential households from 2017 located in Texas was used. The study proposed the clustered load-profile method (CLPM) to classify residential customers based on their electricity load profiles in combination with a dynamic program for DR scheduling to optimize DR profits. The main conclusions are that the proposed approach has an average 2.3% profitability improvement over a business-as-usual heuristic. In addition, the proposed method on average is approximately 70 times faster than running the DR dynamic programming separately for each household. Thus, our method not only is an important application to provide computational business insights for REPs and other power market participants but also enhances resilience for power grid with an advanced DR scheduling tool.

This paper evaluates the potential energy cost savings when high R-value static insulation layers as well as dynamic insulation materials (DIMs) are applied to residential housing located in Barcelona Spain. The analysis considers three dwelling prototypes to characterize the existing housing stock in Barcelona including detached attached and apartments. In addition three vintages for each housing prototype are defined: before 1979 when building envelope insulation took effect in Spain between 1980 and 2006 and after 2006 when the building envelope insulation code became more restrictive. Using a modified 3R2C network model to determine thermal loads the performance of both static and dynamic insulation systems is evaluated when applied to exterior wall for various housing prototypes in Barcelona. The dynamic insulation R-value is selected based on a 2-step control strategy. The analysis results indicate that DIMs with the largest R-value step (i.e. difference between the high and the low R-values) achieve the highest savings in source energy reaching up to 19% reduction in source heating and cooling energy for the entire housing stock of Barcelona. The annual energy savings achieved by DIMs are valued to be 181 M€/year for the entire existing housing stock in Barcelona. In addition electrical peak demand reduction associated with retrofitting exterior walls for the existing Barcelona housing stock can result in future avoidance of building new power plants and can provide additional 144 M€ and 162 M€ for respectively static and dynamic insulation systems. Considering the current energy mix applying dynamic wall insulation systems for Barcelona existing housing stock could reduce annual CO 2 emissions by more than 300 000 tons or 6.80% of the total carbon dioxide currently emitted to heat and cool homes.

Solar PV Integration in the distribution network has become necessary to overcome the growing demand and to reduce emissions. The large percentage penetration of solar PV in the distribution network has more limitations to maintain the stability of the network. This paper emphasizes the strategy of location and the impact of solar PV integration in an urban distribution feeder. The urban distribution feeder has more service sector loads, having a majority of the demand during the daytime. To locate the solar PV, the diurnal load factor is proposed to maximize the utilization. With the proper location of solar PV in the appropriate nodes with more day demand, solar energy can be used locally without storage. This facilitates the effective utilization of urban distribution infrastructure, creating more network space for further load growth. The power flow and loading impact of solar PV generation are demonstrated on the 15-node Indian urban distribution feeder. Load profiles of various loads in different seasons of the year are normalized and grouped for the accurate analysis. Time series load flow analysis is performed with a variable load pattern to analyze the dynamic performance of the network.