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DOE OSTI2020

Assessing concurrent effects of climate change on hydropower supply, electricity demand, and greenhouse gas emissions in the Upper Yangtze River Basin of China

Hydropower importantly provides flexible low-carbon electricity, however, climate change will affect the hydropower system through altering hydrologic regimes while also affecting electricity demands for heating and cooling that hydropower resources serve. This study assesses the effect of climate change on hydropower and electricity demand in the Upper Yangtze River Basin (UYRB) in China on the regional net electric load and greenhouse gas (GHG) emissions. This is accomplished by using climate projections from five global climate models (GCMs) to simultaneously force (1) a physically-based hydrological model and a statistically-based hydropower model to estimate the future generating capacity of 21 large hydropower plants in the UYRB and (2) an empirical electricity demand model accounting for socioeconomic and climatic factors. Under climate change, the projected hydropower generation in the UYRB tends to increase in the 21st century but is far less than the increase in electricity demand, increasing the gap between demand and supply. Future increases in overall electricity demand are driven by GDP growth, but climate change will alter the distribution of the seasonal electricity demand. Climate warming decreases electricity demand for heating in winter and increases electricity demand for cooling in summer, but ultimately increases demand. Meanwhile, there is an increasing mismatch between electricity demand and hydropower supply associated with inter- and intra-annual variations, owing to the temporal climate change and increase in compound climate extremes (droughts and heatwaves). Finally, meeting the gap between supply and demand due to climate change is estimated to contribute 79.0–184.6 and 50.6–316.2 MMT CO 2e /yr of additional GHG emissions by the mid and end of 21st century, respectively.

13 HYDRO ENERGY↗

DOE OSTI2022

A New Evaluation Metric for Demand Response-Driven Real-Time Price Prediction Towards Sustainable Manufacturing

Abstract The increasing industry energy demand highlights the urgency of demand response management, while the emerging smart manufacturing technologies pave the way for the implementation of real-time price (RTP)-based demand response management towards sustainable manufacturing. The demand response management requires scheduling of manufacturing systems based on RTP predictions, and thus the prediction quality can directly alter the effectiveness of demand response. However, since the general price prediction algorithms and prediction evaluation metrics are not specifically designed for RTP in demand response problems, a good RTP prediction obtained and evaluated by these algorithms and metrics may not be suitable for demand response scheduling. Therefore, in this study, the relationships between the effectiveness of demand response for manufacturing systems and evaluation results from six commonly used metrics are investigated. Meanwhile, a new metric called k-peak distance (KPD), considering the characteristics of the demand response problem, is proposed and compared with the other six metrics. Furthermore, an encoder-decoder long short-term memory recurrent neural network with KPD is proposed to provide better RTP prediction for manufacturing demand response problems. The case studies indicate that the proposed KPD metric shows a 1.8–3.6 times higher correlation with the demand response effectiveness compared to the other metrics. In addition, the production schedule based on the RTP prediction obtained from the proposed algorithm can improve the effectiveness of demand response by 23.4% on average.

Engineering↗

DOE OSTI2022

Latent demand for electricity in sub-Saharan Africa: A review

Universal access to electricity is an essential part of sub-Saharan Africa's path to development. With the United Nations setting Goal 7 of its sustainable development goals to be universal access to clean, reliable and affordable electricity, substantial research efforts have been made to optimize electricity supply based on projected demand in sub-Saharan African (SSA) countries. Our study reviews the literature on electricity demand, with a specific focus on latent demand (i.e., electricity demand that would exist if the necessary techno-economic conditions were met) in SSA. We found that out of 57 electricity demand papers reviewed, only 3 (5%) incorporated latent demand in their electricity demand projections. Furthermore, majority of the literature on electricity consumption and demand estimation in SSA use econometric models to identify determinants of electricity consumption and project future demand. We find that population density, urbanization, household income, electricity price, market value of crops and availability of natural resources to be significant determinants of electricity consumption in SSA. We conclude the review by proposing a methodology, and providing an initial proof of concept, for more accurately projecting latent demand in sub-Saharan Africa. Incorporating latent demand in electrification models would help inform energy sector stakeholders (e.g., investors and policymakers) about which sectors and geographic locations hold potential for wealth creation via electricity access.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States: Preprint

Geothermal resources at temperatures below 150 degrees C have great potential as energy sources for various direct-use applications including heating and cooling in residential and commercial buildings. This study geospatially quantifies U.S. heating and cooling demand in residential, commercial, and manufacturing sectors; heating demand in the agricultural sector; and cooling demand in data centers at the county level through end-use energy consumption, expenditure, and commissioned power analyses. Heating and cooling demand in the residential sector was estimated using energy consumption data obtained from the U.S. Energy Information Administration accounting for different U.S. climate zones. For commercial sector analysis, the end-use major fuel energy intensity at the census division level was disaggregated to the county level with respect to principal building activities. Heating and cooling demand analysis for the manufacturing sector was based on end-use energy consumption for direct-use total process categorized by the North America Industry Classification System. Fuel expenditures in the U.S. Department of Agriculture Farm Production Expenditures were examined for heating demand analysis in the agricultural sector, particularly for the greenhouse, nursery, and floriculture production category. Lastly, commissioned power for data centers in the United States were explored for cooling demand analysis. Results indicated a significant fraction of U.S. primary energy consumption is used for low-temperature heating and cooling applications. Heating and cooling demand in residential and commercial sectors is significantly affected by the number of housing units and climate zone designations, while heating and cooling demand in manufacturing and agricultural sectors and data centers are mainly dependent on the number of facilities and their locations. Maps were generated visualizing where heating and cooling demand is high and, overlain with geothermal resource maps, can indicate locations where geothermal energy can supply this heating and cooling demand.

cooling demand↗

DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States

cooling demand↗

DOE OSTI2018

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. Lastly, the aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Preparing Distribution Utilities for the Future - Unlocking Demand-Side Management Potential: A Novel Analytical Framework

The balance of supply and demand in the power systems has traditionally been served solely through generation and network capacity planning and operations. However, with increased requirements for flexibility due to the uptake in variable renewable generation sources such as wind and solar there is a need to increased demand-side flexibility. In addition, there are increased communications and flexibility capabilities emerging on the demand-side from the adoption of advanced metering infrastructures and smart meter deployment and intelligent loads such as smart thermostats and schedulable white goods (e.g. dishwashers and washing machines). Unlocking demand-side flexibility can bring system benefits from peak load reduction bringing about generation capacity and network upgrade deferral, to reducing demand and more efficient utilization of generation and network capacity. Unlocking demand-side flexibility is an evolving process for utilities and solutions must be tailored to each specific customer group. Demand-side management (DSM) is a broad set of tools that can include demand response (both dispatchable and non-dispatchable), energy efficiency and distributed energy resources and demand-side technologies. The National Renewable Energy Laboratory (NREL), in collaboration with BSES Rajdhani Power Ltd. (BRPL) and Deloitte, examined the potential of DSM in BRPL’s service territory, developing detailed information on customer classes and willingness to participate in DSM. The study developed modeling frameworks for load analysis and the analysis tools to assess the potential of time-of-use tariffs in motivating customers to reduce their peak period energy consumption. The study shows that BRPL customers, specifically their domestic customers, are willing to participate in DSM programs and that time-of-use pricing can help BRPL reduce their peak demand and help unlock demand-side flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Assessing the reliability of medical resource demand models in the context of COVID-19

Abstract Background Numerous medical resource demand models have been created as tools for governments or hospitals, aiming to predict the need for crucial resources like ventilators, hospital beds, personal protective equipment (PPE), and diagnostic kits during crises such as the COVID-19 pandemic. However, the reliability of these demand models remains uncertain. Methods Demand models typically consist of two main components: hospital use epidemiological models that predict hospitalizations or daily admissions, and a demand calculator that translates the outputs of the epidemiological model into predictions for resource usage. We conducted separate analyses to evaluate each of these components. In the first analysis, we validated various hospital use epidemiological models using a recent validation framework designed for epidemiological models. This allowed us to quantify the accuracy of the models in predicting critical aspects such as the date and magnitude of local COVID-19 peaks, among other factors. In the second analysis, we evaluated a range of demand calculators for ventilators, medical gowns, and COVID-19 test kits. To achieve this, we decoupled these demand calculators from the underlying epidemiological models and provided ground truth data for their inputs. This approach enabled a direct comparison of the demand calculators, comparing them against each other and actual usage data when available. The code is available athttps://doi.org/10.5281/zenodo.13712387. Results Performance varied greatly across the epidemiological models, with greater variability in COVID-19 hospital use predictions than for COVID-19 deaths as analyzed previously. Some models did not have any peaks. Among those that did, the models under-estimated date of peak approximately as often as they over-estimated, but were more likely to under-estimate magnitude of peak, with typical relative errors around 50%. Regarding demand calculator predictions, there was significant variability, including five-fold differences in predictions for gown models. Validation against actual or surrogate usage data illustrated the potential value of demand models while demonstrating their limitations. Conclusions The emerging field of demand modeling holds promise in averting medical resource shortages during future public health emergencies. However, achieving this potential necessitates focused efforts on standardization, transparency, and rigorous model validation before placing reliance on demand models in critical public health decision-making.

Medical Informatics↗

DOE OSTI2017

Exploring Demand Charge Savings from Commercial Solar

Commercial retail electricity rates commonly include a demand charge component, based on some measure of the customer’s peak demand. Customer-sited solar PV can potentially reduce demand charges, but the magnitude of these savings can be difficult to predict, given variations in demand charge designs, customer loads, and PV generation profiles. Moreover, depending on the circumstances, demand charges from solar may or may not align well with associated utility cost savings. Lawrence Berkeley National Laboratory (Berkeley Lab) and the National Renewable Energy Laboratory (NREL) are collaborating in a series of studies to understand how solar PV can reduce demand charge levels for a variety of customer types and demand charges designs. Previous work focused on residential customs with solar. This study, instead, focuses on commercial customers and seeks to understand the extent and conditions under which rooftop can solar reduce commercial demand charges. To answer these questions, we simulate demand charge savings for a broad range of commercial customer types, demand charge designs, locations, and PV system characteristics. This particular analysis does not include storage, but a subsequent analysis in this series will evaluate demand charge savings for commercial customers with solar and storage.

14 SOLAR ENERGY↗

DOE OSTI2019

Peak Demand Impacts From Electricity Efficiency Programs

Quantification of the costs and benefits of electricity efficiency programs have focused largely on the economic value of annual energy reductions. State public utility commissions PUCs and utilities are increasingly interested in assessing peak demand impacts of these programs. The U.S. Energy Information Administration defines peak demand as "The maximum load during a specified period of time." In practice, utilities and grid operators use a wide range of definitions for peak demand. With increasing need for a more flexible and resilient electricity system, and changing costs for generation, utilities and other efficiency program administrators must take into account all characteristics of efficiency programs — including peak demand reduction — to ensure a reliable system at the most affordable cost. In this study, Berkeley Lab explored a new metric, the program administrator (PA) cost of saving peak demand (PA CSPD). We collected data on costs, energy savings and peak demand savings for electricity efficiency programs for 36 investor-owned utilities and other PAs in nine states (Arizona, Arkansas, California, Colorado, Illinois, Massachusetts, Maryland, New York and Texas) for 2014 to 2017. While some utilities report demand reductions for their electricity efficiency programs, there has been little sustained effort in the United States to gather peak demand reduction data or benchmark the cost of achieving peak demand impacts. As a first of its kind analysis, we developed a framework for analyzing peak demand savings across utilities and states and begin to quantify the cost of saving peak demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Impact of Transport Electrification Demand and Charging Schedules on Electricity Markets and Nuclear Generators

As the U.S. pursues deep decarbonization targets, electric vehicles (EVs) are likely to become a major driver of demand growth and a major determinant of daily demand patterns. This study analyzes a possible future ERCOT-like electricity grid, and examines the impact of different types of EV charging schedules on grid and market outcomes. This analysis demonstrates the significant impact of EV charging patterns on capacity expansion simulations. Even without EVs, the overall daily demand profile in a market can have significant impacts on prices and grid stability in that system, especially if non-dispatchable renewable generators (e.g. wind and solar) make up a significant fraction of the generation mix. EV demand will not necessarily follow this preexisting demand profile, so its daily trends may significantly change what generation portfolio would optimally serve the system. Furthermore, the effects of EV demand can alter the profitability of different types of units, by altering the frequency of market events like extreme-demand hours or zero-price hours. These effects are explored in this study. The EV demand levels were derived from MARKAL simulations of the West-South-Central North American Electric Reliability Corporation (NERC) region for the year 2050, using a carbon tax of $100/ton. The baseline MARKAL simulation forecasted that 23% of the region’s annual electricity demand in 2050 would be attributable to EVs, and broke out demand projections for EV and non-EV end-use in that year. To model lower EV penetration into the system, an additional case was explored which assumed that EVs only achieved 75% of the demand level projected by MARKAL.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Energy demand science for a decarbonized society in the context of the residential sector

To develop a decarbonized society, two contradictory requirements must be met: (1) reducing energy demand and (2) creating flexibility in energy demand in order to respond to fluctuations in renewable electricity generation. To help meet these requirements, conventional energy efficiency studies should be extended to incorporate “energy demand science.” This paper presents a definition of “energy demand science” and then reviews the related history and research questions of energy demand science in the context of the residential sector. It then examines three key areas that must be integrated into the next-generation energy demand science: (1) energy demand measurement with detailed granularity and analysis using cutting-edge technology, (2) energy demand modeling that helps clarify the formation mechanism of energy demand, and (3) identification of the factors that influence people's decision making, which represents typical human-dimension research. Energy demand science consists of technical, human, natural environment, demographic, and land-use dimensions, and their integration is key for the establishment of a decarbonized society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Charge Management for an Inductively Charged On-Demand Battery-Electric Shuttle Service with High Penetration of Renewable Energy

This paper presents a charge management control strategy for an on-demand battery-electric shuttle van operating at the National Renewable Energy Laboratory (NREL) campus and supported by day-time inductive charging at the vehicle's waiting spot. A new control algorithm has been proposed for reducing the demand charge costs incurred from wireless charging of the on-demand shuttle. A custom controller has been developed to monitor the shuttle, wireless charger, renewable energy generation, and various loads at NREL's campus, and regulate charging behavior for demand response. The intermittent renewable generation and sporadic operation of the on-demand shuttle service contribute to a high level of uncertainty in expected campus load profile, which must be carefully managed. The control algorithm predicts energy profile to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller has been designed and optimized using a Simulink model for the entire system. Next, it has been implemented and tested in real-time on the NREL campus. Two primary vehicle-use cases, charge sustaining and charge depletion operation, are tested under different load profiles and drive cycles to assess the controller's effectiveness at reducing peak demand and therefore demand charges. The proposed controller showed robust performance under different driving scenarios with high correlation between simulated and experimental data. The results showed that proper demand response can be achieved with an average of 94% reduction of charging loads during peak demand events.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗

DOE OSTI2022

The Demand-Side Resource Opportunity for Deep Grid Decarbonization

Technological innovations are creating a larger role for demand-side resources in electric grids. The rise of demand-side resources is occurring alongside the efforts to decarbonize grids. The confluence of these two trends has motivated research on the role of demand-side resources in grid decarbonization. Here, we synthesize the demand-side resource literature to develop a coherent and realistic vision for that role. We find that demand-side resources can supplement supply-side approaches to decarbonize grids more quickly and cost effectively. The role of demand-side resources is secondary to that of supply-side resources, and a critical analysis of tradeoffs is required whenever demand-side solutions substitute for supply-side solutions. Nonetheless, our review suggests that demand-side solutions can play a meaningful role, providing around 10%-30% of the resources required for deep decarbonization in domains such as energy, peak capacity, and deployed solar capacity. However, demand-side resources remain underutilized due to several solvable barriers. The urgency imposed by climate change requires that these barriers be addressed to fully realize the demand-side resource opportunity.

decarbonization↗

DOE OSTI2018

Modeling and Integration of Flexible Demand in Heat and Electricity Integrated Energy System

This paper is focused on utilizing customers' flexible energy demand, including both heat demand and electricity demand, to provide balancing resources and relieve the difficulties of integrating variable wind power with the combined heat and power. The integration of heat and electricity energy systems providing customers with multiple options for fulfilling their energy demand is described. Customer aggregators are introduced to supply downstream demand in the most economical way. Controlling customers' energy consumption behaviors enables aggregators to adjust their energy demand in response to supply conditions. Incorporating aggregators' flexible energy demand into the centralized energy dispatch model, a two-level optimization problem (TLOP) is first formed where the system operatormaximizes social welfare subject to aggregators' strategies, which minimize the energy purchase cost. Furthermore, the subproblems are linearized based on several reasonable assumptions. Optimal conditions of the subproblems are then transformed as energy demands to be described as explicit piecewise-linear functions of electricity prices corresponding to the demand bid curves. In this way, the TLOP is transformed to a standard optimization problem, which requires aggregators to only submit a demand bid to run the centralized energy dispatch program. All the parameters pertaining to the aggregators' energy consumption models are internalized in the bid curves. The proposed technique is illustrated in a modified testing system.

combined heat and power (CHP)↗

DOE OSTI2025

Occupant-Centric Demand Response for Thermostatically-Controlled Home Loads

Efficiently managing energy usage to balance supply and demand on the electric grid is crucial, especially with the widespread deployment of distributed variable renewable electricity generation. This paper introduces two duty-cycle control methods for heating systems, adjusting thermostat setpoints to limit and shift electricity demand. The control approaches employ innovative techniques, such as adaptive duty cycling, to prioritize household thermal comfort while reducing peak demand. These control methods can respond to signals from the electric grid, including demand targets and time-of-use tariffs, and were tested physically on an electric furnace and heat pump in a test home during winter conditions in 2021 and 2022. The results are given as average demand reductions and energy use impacts with respect to the average indoor-outdoor temperature difference during the control period. For heat pumps, demand limiting control reduced power by 18.5% and 23.3% for indoor-outdoor temperature differences of 30°F and 40°F. Preheating-based demand shifting achieved reductions of 34.8% and 33.2% for the same temperature differences. Electric furnace tests showed demand reductions of 33.8% and 25.3% for demand limiting, and 56.1% and 45.7% for preheating-based demand shifting. These findings highlight the potential for innovative control methods to enhance grid efficiency and reduce energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Solar + Storage Synergies for Managing Commercial-Customer Demand Charges

Demand charges, which are based on a customer’s maximum demand in kilowatts (kW), are a common element of electricity rate structures for commercial customers. Customer-sited solar photovoltaic (PV) systems can potentially reduce demand charges, but the level of savings is difficult to predict, given variations in demand charge designs, customer loads, and PV generation profiles. Lawrence Berkeley National Laboratory (Berkeley Lab) and the National Renewable Energy Laboratory (NREL) are collaborating on a series of studies to understand how solar PV can impact demand charges. Prior studies in the series examined demand charge reductions from solar on a stand-alone basis for residential and commercial customers. Those earlier analyses found that solar, alone, has limited ability to reduce demand charges depending on the specific design of the demand charge and on the shape of the customer’s load profile. This latest analysis estimates demand charge savings from solar in commercial buildings when co-deployed with behind-the-meter storage, highlighting the complementary roles of the two technologies. The analysis is based on simulated loads, solar generation, and storage dispatch across a wide variety of building types, locations, system configurations, and demand charge designs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Evaluating the contribution of plant metabolic pathways in the light to the ATP:NADPH demand using a meta-analysis of isotopically non-stationary metabolic flux analyses

Abstract Balancing the ATP: NADPH demand from plant metabolism with supply from photosynthesis is essential for preventing photodamage and operating efficiently, so understanding its drivers is important for integrating metabolism with the light reactions of photosynthesis and for bioengineering efforts that may radically change this demand. It is often assumed that the C3 cycle and photorespiration consume the largest amount of ATP and reductant in illuminated leaves and as a result mostly determine the ATP: NADPH demand. However, the quantitative extent to which other energy consuming metabolic processes contribute in large ways to overall ATP: NADPH demand remains unknown. Here, we used the metabolic flux networks of numerous recently published isotopically non-stationary metabolic flux analyses (INST-MFA) to evaluate flux through the C3 cycle, photorespiration, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and starch/sucrose synthesis and characterize broad trends in the demand of energy across different pathways and compartments as well as in the overall ATP:NADPH demand. These data sets include a variety of species including Arabidopsis thaliana , Nicotiana tabacum , and Camelina sativa as well as varying environmental factors including high/low light, day length, and photorespiratory levels. Examining these datasets in aggregate reveals that ultimately the bulk of the energy flux occurred in the C3 cycle and photorespiration, however, the energy demand from these pathways did not determine the ATP: NADPH demand alone. Instead, a notable contribution was revealed from starch and sucrose synthesis which might counterbalance photorespiratory demand and result in fewer adjustments in mechanisms which balance the ATP deficit.

59 BASIC BIOLOGICAL SCIENCES↗