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Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries.

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries.

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs.

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid.

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid.

Maintaining thermal comfort in built environments is important for occupant health, well-being, and productivity, and also for efficient HVAC system operations. Most of the existing personal thermal comfort learning methods require occupants to provide feedback via a survey to label the monitored environmental or physiological conditions in order to train the prediction models. However, the accuracy of these models usually drops after the training process as personal thermal comfort is dynamic and changes over time due to climatic variations and/or acclimation. In this paper, we present a hidden Markov model (HMM) based learning method to capture personal thermal comfort using infrared thermography of human face. We chose human face since its blood vessels has a higher density and it is not covered while performing regular activities in built environments. Learning algorithm has 3 hidden states (i.e., uncomfortably warm, comfortable, uncomfortably cool) and uses discretization for forming the observed states from the continuous infrared measurements. The approach can potentially be used for continuous monitoring of thermal comfort to capture the variations over time. We tested and validated the method in a four-day long experiment with 10 subjects and demonstrated an accuracy of 82.8% for predicting uncomfortable conditions.

Direct current (DC) power distribution has recently gained traction in buildings research due to the proliferation of on-site electricity generation and battery storage, and an increasing prevalence of internal DC loads. The research discussed in this paper uses Modelica-based simulation to compare the efficiency of DC building power distribution with an equivalent alternating current (AC) distribution. The buildings are all modeled with solar generation, battery storage, and loads that are representative of the most efficient building technology. A variety of parametric simulations determine how and when DC distribution proves advantageous. These simulations also validate previous studies that use simpler approaches and arithmetic efficiency models. This work shows that using DC distribution can be considerably more efficient: a medium sized office building using DC distribution has an expected baseline of 12% savings, but may also save up to 18%. In these results, the baseline simulation parameters are for a zero net energy (ZNE) building that can island as a microgrid. DC is most advantageous in buildings with large solar capacity, large battery capacity, and high voltage distribution.

Direct current (DC) power distribution has recently gained traction in buildings research due to the proliferation of on-site electricity generation and battery storage, and an increasing prevalence of internal DC loads. The research discussed in this paper uses Modelica-based simulation to compare the efficiency of DC building power distribution with an equivalent alternating current (AC) distribution. The buildings are all modeled with solar generation, battery storage, and loads that are representative of the most efficient building technology. A variety of parametric simulations determine how and when DC distribution proves advantageous. These simulations also validate previous studies that use simpler approaches and arithmetic efficiency models. This work shows that using DC distribution can be considerably more efficient: a medium sized office building using DC distribution has an expected baseline of 12% savings, but may also save up to 18%. In these results, the baseline simulation parameters are for a zero net energy (ZNE) building that can island as a microgrid. DC is most advantageous in buildings with large solar capacity, large battery capacity, and high voltage distribution.

In the face of unprecedented challenges of upcoming fossil fuel shortage and reliability and security of the grid, there is an increasing interest in adopting distributed, renewable, energy resources, such as microgrids (MGs), and engaging flexible electric loads in power system operations to potentially drive a paradigm shift in energy production and consumption patterns. Prior work on MG dispatch has leveraged decentralized technologies like combined heat and power (CHP) and heat pumps to promote efficiency and economic gains; however, the flexibility of demand has yet to be fully exploited in cooperation with the grid to offer added benefits and ancillary services. The object of the study is to develop microgrid optimal dispatch with demand response (MOD-DR), which fills in the gap by coordinating both the demand and supply sides in a renewable-integrated, storage-augmented, DR-enabled MG to achieve economically viable and system-wide resilient solutions. The key contribution of this paper is the formulation of a multi-objective optimization with prevailing constraints and utility trade-off based on the model of a large-scale MG with flexible loads, which leads to the derivation of strategies that incorporate uncertainty in scheduling. Evaluation using real datasets is conducted to analyze the uncertainty effects and demand response potentials, demonstrating in a campus prototype a 17.5% peak load reduction and 8.8% cost savings for MOD-DR compared to the non-trivial baseline, which is on par with the Oracle for perfect predictions.

In the face of unprecedented challenges of upcoming fossil fuel shortage and reliability and security of the grid, there is an increasing interest in adopting distributed, renewable, energy resources, such as microgrids (MGs), and engaging flexible electric loads in power system operations to potentially drive a paradigm shift in energy production and consumption patterns. Prior work on MG dispatch has leveraged decentralized technologies like combined heat and power (CHP) and heat pumps to promote efficiency and economic gains; however, the flexibility of demand has yet to be fully exploited in cooperation with the grid to offer added benefits and ancillary services. The object of the study is to develop microgrid optimal dispatch with demand response (MOD-DR), which fills in the gap by coordinating both the demand and supply sides in a renewable-integrated, storage-augmented, DR-enabled MG to achieve economically viable and system-wide resilient solutions. The key contribution of this paper is the formulation of a multi-objective optimization with prevailing constraints and utility trade-off based on the model of a large-scale MG with flexible loads, which leads to the derivation of strategies that incorporate uncertainty in scheduling. Evaluation using real datasets is conducted to analyze the uncertainty effects and demand response potentials, demonstrating in a campus prototype a 17.5% peak load reduction and 8.8% cost savings for MOD-DR compared to the non-trivial baseline, which is on par with the Oracle for perfect predictions.

We model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of $85–$1,230 billion in present-value terms, with a central estimate of $400 billion. Air-pollution-related health benefits are estimated at $52–$272 billion, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers.

We model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of $85–$1,230 billion in present-value terms, with a central estimate of $400 billion. Air-pollution-related health benefits are estimated at $52–$272 billion, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers.

Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today's methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of "smart" meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as "M&V 2.0", are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs.This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V 2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. These findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption.

Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today's methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of "smart" meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as "M&V 2.0", are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs.This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V 2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. These findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption.

This paper explores various questions regarding very high penetration of intermittent renewable to electricity grid. The study was performed using one-year hourly demand data of California's electricity grid together with the hourly-simulated output of various solar and wind technologies distributed throughout the state. The result shows that wind-solar complementarities carry significant multidimensional benefits to the future grid as compared to a stand-alone wind/solar based grid. Specifically at 20% total energy loss, it was shown that their optimal complementarities lead to very high renewable penetration with smaller storage and backup requirement. Our study of system dispatch shows that storage provides flexible dispatch strategy, which makes it applicable for various services depending on season of the year including reduction of summer backup capacity need. We have also found that the existing peak load based long term planning reserve criteria may no longer be applicable to very high renewable grid. Finally, comparing our result to literature data, we conclude that some of the pessimistic views of storage technology are borne out of our lack of understanding about its role and design requirement.

Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the baseline energy use). Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions. However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.

This paper investigates the financial incentives of load shifting under a time-of-use rate and Net Energy Metering pertaining to the solar net-zero energy apartment community, West Village in Davis, California. By "smart-scheduling" the electricity and domestic hot water demand of the dishwasher, clothes washer, dryer, sinks and showers solely to off-peak periods, the peak demand is reduced by 18%, the part-peak demand by 32% and the off-peak demand increased by 12%. With this shifted schedule customers accrue twice as many credits as they would receive under a non-shifted schedule with the same time-of-use rate, totaling to $2975 of "free" electricity per year for one 12. unit building. But, under current rates smart-scheduling is found to be worthwhile only during the months from May through October, when 96% of the credits are accumulated. If the rate schedule is altered to include peak-periods during the winter months, the credit savings will double again in value. These comparisons are prepared using two photovoltaic simulation programs (PolySun by Vela Solaris and System Advisory Model by the National Renewable Energy Laboratory) and for apartments using an electric heater and a heat pump for domestic hot water. By quantifying these savings, PV generating customers are informed that a time-of-use rate can benefit them significantly, especially if the surplus generation is maximized and sold to the grid during peak day time hours. With this information, housing developers can create effective incentives for residents, and utility companies, policy makers and designers of smart-scheduling household appliances can encourage a more reliable, clean and economical national grid. © 2013 Elsevier Ltd.

This paper investigates the financial incentives of load shifting under a time-of-use rate and Net Energy Metering pertaining to the solar net-zero energy apartment community, West Village in Davis, California. By "smart-scheduling" the electricity and domestic hot water demand of the dishwasher, clothes washer, dryer, sinks and showers solely to off-peak periods, the peak demand is reduced by 18%, the part-peak demand by 32% and the off-peak demand increased by 12%. With this shifted schedule customers accrue twice as many credits as they would receive under a non-shifted schedule with the same time-of-use rate, totaling to $2975 of "free" electricity per year for one 12. unit building. But, under current rates smart-scheduling is found to be worthwhile only during the months from May through October, when 96% of the credits are accumulated. If the rate schedule is altered to include peak-periods during the winter months, the credit savings will double again in value. These comparisons are prepared using two photovoltaic simulation programs (PolySun by Vela Solaris and System Advisory Model by the National Renewable Energy Laboratory) and for apartments using an electric heater and a heat pump for domestic hot water. By quantifying these savings, PV generating customers are informed that a time-of-use rate can benefit them significantly, especially if the surplus generation is maximized and sold to the grid during peak day time hours. With this information, housing developers can create effective incentives for residents, and utility companies, policy makers and designers of smart-scheduling household appliances can encourage a more reliable, clean and economical national grid. © 2013 Elsevier Ltd.