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Highlights • Future residential demand with heat pumps are estimated in four cities. • Local wind and solar generation mixes are compared with and without storage. • Cost comparisons of distributed technology options including storage are studied. Abstract To reduce greenhouse gas emissions, the electricity sector is going through two main transitions. First, the electric grid is integrating variable renewable generation, such as wind and solar. Second, demands are changing as heating systems are shifting from gas-based to high efficiency electric heat pumps. This paper provides a comparative analysis of future energy scenarios with distributed technology options including (1) wind and solar generation; (2) heat pumps for heating and cooling; and (3) battery and thermal storage in representative residential blocks in four cities, including New York City, New York; Minneapolis, Minnesota; Tallahassee, Florida; and Fort Collins, Colorado. These cities are located in three climate regions with different weather patterns which result in different demand profiles and different local renewable resources. Future energy demand scenarios with 100% penetration of air source or ground source heat pumps for heating and cooling are estimated for the four residential city blocks. Under a future scenario with all electric demand with air source heat pumps and high renewable energy penetration, this study finds that (1) the optimal wind and solar generation mix varies with location and amount of storage and (2) battery storage is more cost effective than thermal storage, ground source heat pumps, and overbuilt renewable generation. [ABSTRACT FROM AUTHOR]

• Backup capability of solar and storage differs by region in the U.S. • Small batteries can meet a limited set of critical loads in most counties. • Electric cooling and heating loads are typically hardest loads to backup. • Household building attributes like efficiency can impact results up to 20% • Resiliency value needs to be high to motivate adoption of solar-plus-storage. Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer's resilience value of PVESS must be high to motivate adoption of these systems. [ABSTRACT FROM AUTHOR]

Electricity prices change substantially over time in the wholesale markets. The fluctuations are mainly caused by power grid states, fuel price fluctuations, and market conditions. Real-time pricing (RTP) is an effective way to reduce risks utility companies face from volatile electricity prices. RTP allows participating customers to reduce electric bills by changing use patterns. This paper analyzes the pricing components and characteristics of representative RTP programs for industrial customers based on publicly available information. Our main goal is to study the reasons behind their success and failure. Knowing the details about RTP programs increases customers' awareness of their advantages and assists them in deciding whether they should enroll. It also helps utility companies develop new programs or improve existing ones. Case studies in the manufacturing sector are presented and the savings of RTP are compared with another popular dynamic pricing program, time-of-use (TOU) pricing under different scenarios. The results show that the savings by switching from flat rates to TOU and RTP are highly program dependent. Eighteen out of the 35 base-case scenarios resulted in positive savings by switching to TOU, and 29 out of 35 base-case scenarios resulted in positive savings by switching to RTP. [ABSTRACT FROM AUTHOR]

In the United States, buildings account for 35% of total energy-related carbon dioxide emissions, making them important contributors to decarbonization. Carbon intensities in the power grid are time-varying and can fluctuate significantly within hours, so shifting building loads in response to the carbon intensities can reduce a building's operational carbon emissions. This paper presents a rule-based carbon responsive control framework that controls the setpoints of thermostatically controlled loads responding to the grid's carbon emission signals in real time. Based on this framework, four controllers are proposed with different combinations of carbon accounting methods and control rules. To evaluate their performance, we performed simulation studies using models of a 27-home, all-electric, net zero energy residential community located in Basalt, Colorado, United States. The carbon intensity data of four future years from the Cambium data set are adopted to account for the evolving resource mix in the power grid. Various performance metrics, including energy consumption, carbon emission, energy cost, and thermal discomfort, were used to evaluate the performance of the controllers. Sensitivity analysis was also conducted to determine how the control thresholds and intervals affect the controllers' performance. Simulation results indicate that the carbon responsive controllers can reduce the homes' annual carbon emissions by 6.0% to 20.5%. However, the energy consumption increased by 0.9% to 6.7%, except in one scenario where it decreased by 2.2%. Compared to the baseline, the change in energy cost was between −2.9% and 3.4%, and thermal discomfort was also maintained within an acceptable range. The little impact on energy cost and thermal discomfort indicates there are no potential roadblocks for customer acceptance when rolling out the controllers in utility programs. [ABSTRACT FROM AUTHOR]

• A framework for data center to bid into electricity market and follow regulation signal in real-time. • A risk-constrained bidding strategy is developed to determine the optimal energy and frequency regulation bids. • Dummy load is introduced to increase the amount of regulation service provision of data center. • Bi-linear server power consumption model and rule-based control enable data center to follow regulation signal accurately. • The proposed regulation service provision framework reduces electricity bill by 12%. The rapid adoption of cloud storage and computing services led to unprecedented growth of data centers in the world. As bulk energy consumers, large-scale data centers in the U.S. rack up billions in electricity costs annually. Fortunately, the operational flexibility of data centers can be leveraged to provide valuable frequency regulation services in smart grids to mitigate the indeterminacy of the renewable generation resources. Specifically, this paper aims to leverage computational flexibility provided by servers, such as dynamic voltage frequency scaling and dummy loads. This paper develops a comprehensive framework for data center's frequency regulation service provision in both hour-ahead market and real-time operations. A risk constrained hour-ahead bidding strategy along with a real-time data center power consumption control algorithm are developed to minimize electricity bills and the total response time of the requests. The introduction of dummy load, realistic bi-linear server power consumption model, and probabilistic forecast of electricity and frequency regulation service prices enable the data center to accurately follow frequency regulation signals, while reducing the financial risks associated with electricity market participation. The simulation results show that the proposed frequency regulation provision framework results not only in significant cost reduction for data centers, but also limits degradation in quality of service. Meanwhile, the stability and reliability of a power grid will be improved by the frequency regulation service provision. [ABSTRACT FROM AUTHOR]

• A novel approach to harness offshore renewable energy sources is proposed. • Cycle technical description from cryogen generation to end-use of energy is provided. • Using liquid air improves thermal cycle efficiency and allows waste heat recovery. • Liquid air favors indirect integration of renewable energy sources in the electric grid. • A Round-Trip Efficiency of up to 70% is possible with the proposed decoupled system. A novel approach using decoupled processes to harness offshore renewable energy, from marine hydrokinetics, ocean waves, wind, and solar to produce liquid air, is presented in this paper. Offshore renewables interconnection using submarine medium- and high-voltage direct current technologies are used to produce liquid air that can be transported to end-use locations using repurposed liquefied natural gas tankers. Two important possibilities arise from using the proposed technology. The first possibility allows the integration with conventional thermal cycles to improve efficiency. This approach can be used to leverage efficiencies of thermal systems that have already reached a plateau in the maximum achievable efficiency via design and operation optimization. The second possibility is related to the incorporation of low temperature cycles to recover waste heat from other thermal processes. This is important considering that waste heat accounts for more than 60% of the consumed energy in the United States. A detailed technical description of the complete cycle from cryogen generation to end-use of energy is provided. Estimation of efficiency enhancement for thermal plants using liquid air including waste heat recovery is presented. [ABSTRACT FROM AUTHOR]

• Develop physics-based model for an electric-boosting glass furnace. • Define demand response (DR) strategy balancing electricity and natural gas. • Optimal DR decisions for two case studies of practical interest are analyzed. The glass industry is highly energy-intensive, accounting for 1% of total industrial energy consumption in the United States. Most of the energy consumption in the glass manufacturing process is attributable to the significant heat required to melt raw materials. An electric boosting system which can transfer extra heat (5%–20% of total energy) to the glass melt in addition to the energy from natural gas combustion can be implemented in a glass furnace. Electric boosting is thermally efficient, reduces direct pollutant emissions, and prolongs furnace superstructure lifespan. However, a high level of electric boost is not always economically desirable, considering the volatility of electricity prices. Balancing between natural gas and electricity consumption in a demand response strategy can reduce the energy cost and mitigate strain on the electrical grid. In this paper, a physics-based model is developed to describe the dynamic behavior of a prototype electric boosting glass furnace. We present a dynamic optimization strategy to optimally balance between using natural gas and electric power under electricity price fluctuations. Case studies on the effect of varying energy prices and emissions regulations are analyzed. [ABSTRACT FROM AUTHOR]