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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Potential Impacts of Dynamic Electricity Pricing in California: Load Shape and Customer Bill Impacts Under Elastic Customer Response

The increasing penetration of renewable energy in California has intensified grid management challenges, exemplified by the “duck curve” and the resulting need for steep ramping and curtailment of renewables. To address these issues, dynamic electricity tariffs that vary in near-real time are being considered to incentivize customers to shift demand and support the grid. This study extends previous work on the bill impacts of such tariffs in the absence of load response by quantifying the system-level and customer impacts of load response based on customer price elasticity. Customer-level load response modeling was conducted using meter data from 411,000 customers across residential, commercial, and industrial sectors. Customer demand elasticity was estimated using literature-based values, with scenarios ranging from low to high elasticity, including an automation-enhanced scenario. Results indicate that universal adoption of, and response to, dynamic tariffs can significantly reduce peak net load (by 15%) and maximum ramping requirements (by 20%) with moderate elasticity, delivering demand response resources comparable to or exceeding current programs at all elasticity levels. Bill analysis shows that, when responding elastically to dynamic prices, most non-PV customers experience modest savings, while PV customers may see higher effective rates due to lower compensation for exports during low-price periods. Emissions analysis reveals a reduction in per-kWh emissions system-wide, with a total absolute load increase of 2% accompanied by a negligible absolute emissions increase. The study concludes that while dynamic tariffs offer substantial grid benefits, customer bill savings under modeled response behaviors may be too modest to drive widespread adoption without additional incentives or enabling technologies. Future research should model flexible loads and advanced control technologies with greater fidelity to better represent the potential opportunities of dynamic tariffs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

California Price Response Potential Study

California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1983

Communications network design and costing model technical manual

This computer model provides the capability for analyzing long-haul trunking networks comprising a set of user-defined cities, traffic conditions, and tariff rates. Networks may consist of all terrestrial connectivity, all satellite connectivity, or a combination of terrestrial and satellite connectivity. Network solutions provide the least-cost routes between all cities, the least-cost network routing configuration, and terrestrial and satellite service cost totals. The CNDC model allows analyses involving three specific FCC-approved tariffs, which are uniquely structured and representative of most existing service connectivity and pricing philosophies. User-defined tariffs that can be variations of these three tariffs are accepted as input to the model and allow considerable flexibility in network problem specification. The resulting model extends the domain of network analysis from traditional fixed link cost (distance-sensitive) problems to more complex problems involving combinations of distance and traffic-sensitive tariffs.

Logan, K. P.↗
DOE OSTI2010

Storage Viability and Optimization Web Service

Non-residential sectors offer many promising applications for electrical storage (batteries) and photovoltaics (PVs). However, choosing and operating storage under complex tariff structures poses a daunting technical and economic problem that may discourage potential customers and result in lost carbon and economic savings. Equipment vendors are unlikely to provide adequate environmental analysis or unbiased economic results to potential clients, and are even less likely to completely describe the robustness of choices in the face of changing fuel prices and tariffs. Given these considerations, researchers at Lawrence Berkeley National Laboratory (LBNL) have designed the Storage Viability and Optimization Web Service (SVOW): a tool that helps building owners, operators and managers to decide if storage technologies and PVs merit deeper analysis. SVOW is an open access, web-based energy storage and PV analysis calculator, accessible by secure remote login. Upon first login, the user sees an overview of the parameters: load profile, tariff, technologies, and solar radiation location. Each parameter has a pull-down list of possible predefined inputs and users may upload their own as necessary. Since the non-residential sectors encompass a broad range of facilities with fundamentally different characteristics, the tool starts by asking the users to select a load profile from a limited cohort group of example facilities. The example facilities are categorized according to their North American Industry Classification System (NAICS) code. After the load profile selection, users select a predefined tariff or use the widget to create their own. The technologies and solar radiation menus operate in a similar fashion. After these four parameters have been inputted, the users have to select an optimization setting as well as an optimization objective. The analytic engine of SVOW is LBNL?s Distributed Energy Resources Customer Adoption Model (DER-CAM), which is a mixed-integer linear program (MILP) written and executed in the General Algebraic Modeling System (GAMS) optimization software. LBNL has released version 1.2.0.11 of SVOW. Information can be found at http://der.lbl.gov/microgrids-lbnl/current-project-storage-viability-website.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A Case Study about Energy and Cost Impacts for Different Community Scenarios Using a Community-Scale Building Energy Modeling Tool

The United States building sector consumed approximately 75% of electricity in 2019. By implementing renewable energy technologies and control strategies into buildings, future buildings will serve as energy generators as well as consumers. To accommodate this transition, communications among buildings and between buildings and the grid could provide more possibilities to optimize the energy performance of buildings. This paper develops a community-scale building energy model tool and conducts a case study adopting behind-the-meter distributed energy resources, sharing energy in different buildings, and using different electricity tariff structures. Three scenarios are studied: (1) electricity only supplied by the grid, (2) photovoltaic (PV) panels installed on and available to some but not all buildings, and (3) a connected community. To consider the impacts of locations and energy tariffs, this paper selects four cities and three electricity tariffs to evaluate the energy and cost performances of these three scenarios. The results show that the PV panels in Scenario 2 reduce 25% to 33% of the community-level electricity consumption and 20% to 30% of the community-level electricity cost compared with Scenario 1 in all studied locations and energy tariffs. By considering power management in the connected community (Scenario 3), the electricity consumption and cost can be further reduced by 6% to 7% and 5% to 11%, respectively, compared with Scenario 2.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Electricity Bill Calculator (elecprice) v0.1.0

The Electricity Bill Calculator Library is a generic tool for manipulating the tariffs of electricity, with an emphasis on commercial Time-Of-Use (TOU) rates in the U.S. and residential Net Energy Metering tariffs in CA. Typical uses of the package are price signal generation and bill computation. To generate price signals from a specific tariff and given a time window, the tool creates a pandas dataframes containing the various components of the electricity charges, at each time step. This is particularly useful for Demand Response (DR) applications, such as price-based optimization of energy in buildings. The tool also computes bills given a building's power consumption and a specific tariff and returning the corresponding cost of electricity, as well as a breakdown per type of rate.

Prakash, Anand Krishnan↗
DOE OSTI2018

Residential Electricity Prices: A Review of Data Sources and Estimation Methods

This paper presents an empirical study of residential electricity prices based on a compilation of tariff data collected for Lawrence Berkeley Lab's Tariff Analysis Project (TAP). We also review other data sources commonly used to estimate residential sector electricity prices, including utility data published by the Energy Information Administration (EIA), the Typical Bills reports published by the Edison Electric Institute, and household-level electricity consumption and expenditure data included with the EIA's Residential Energy Consumption Surveys. We define three different types of price that can be estimated from the data, construct estimation methods, compare the results and evaluate the relative strengths and weaknesses of each approach. We examine several sources of variation including seasonal, regional, industry structure, and the magnitude of baseline household consumption. The tariff data are also used to explore the impact of moving households from standard tariffs to time-of-use pricing, and to summarize how this affects predictions of electricity bill savings for both demand reduction and demand shifting. (auth)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Lessons from wind policy in Portugal

Wind capacity and generation grew rapidly in several European countries, such as Portugal. Wind power adoption in Portugal began in the early 2000s, incentivized by a continuous feed-in tariff policy mechanism, coupled with public tenders for connection licenses in 2001, 2002, and 2005. These policies led to an enormous success in terms of having a large share of renewables providing electricity services: wind alone accounts today for ~23.5% of electricity demand in Portugal. We explain the reasons wind power became a key part of Portugal's strategy to comply with European Commission climate and energy goals, and provide a detailed review of the wind feed-in tariff mechanism. We describe the actors involved in wind power production growth. We estimate the environmental and energy dependency gains achieved through wind power generation, and highlight the correlation between wind electricity generation and electricity exports. Finally, we compare the Portuguese wind policies with others countries' policy designs and discuss the relevance of a feed-in tariff reform for subsequent wind power additions.

17 WIND ENERGY↗
DOE OSTI2015

Effects of Home Energy Management Systems on Distribution Utilities and Feeders Under Various Market Structure; NREL (National Renewable Energy Laboratory)

The combination of distributed energy resources (DER) and retail tariff structures to provide benefits to both utility consumers and the utilities is not well understood. To improve understanding, an Integrated Energy System Model (IESM) is being developed to simulate the physical and economic aspects of DER technologies, the buildings where they reside, and feeders servicing them. The IESM was used to simulate 20 houses with home energy management systems on a single feeder under a time-of-use (TOU) tariff to estimate economic and physical impacts on both the households and the distribution utilities. Home energy management systems (HEMS) reduce consumers’ electric bills by precooling houses in the hours before peak electricity pricing. Utilization of HEMS reduce peak loads during high price hours but shifts it to hours with off-peak and shoulder prices, resulting in a higher peak load. used to simulate 20 houses with home energy management systems on a single feeder under a time-of-use (TOU) tariff to estimate economic and physical impacts on both the households and the distribution utilities. Home energy management systems (HEMS) reduce consumers’ electric bills by precooling houses in the hours before peak electricity pricing. Utilization of HEMS reduce peak loads during high price hours but shifts it to hours with off-peak and shoulder prices, resulting in a higher peak load.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Hardware-in-the-Loop Simulation of a Distribution System with Air Conditioners under Model Predictive Control: Preprint

Many have proposed that responsive load provided by distributed energy resources (DERs) and demand response (DR) are an option to provide flexibility to the grid and especially to distribution feeders. However, because responsive load involves a complex interplay between tariffs and DER and DR technologies, it is challenging to test and evaluate options without negatively impacting customers. This paper describes a hardware-in-the-loop (HIL) simulation system that has been developed to reduce the cost of evaluating the impact of advanced controllers (e.g., model predictive controllers) and technologies (e.g., responsive appliances). The HIL simulation system combines large-scale software simulation with a small set of representative building equipment hardware. It is used to perform HIL simulation of a distribution feeder and the loads on it under various tariff structures. In the reported HIL simulation, loads include many simulated air conditioners and one physical air conditioner. Independent model predictive controllers manage operations of all air conditioners under a time-of-use tariff. Results from this HIL simulation and a discussion of future development work of the system are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Techno-economic and environmental assessment of solar-based electrical vehicles charging stations integrated with hydrogen production

With the growing interest in adopting both commercial and residential electric vehicles (EVs) utilizing green renewable energy, the techno-economic assessment of EV charging stations with solar energy is a critical aspect of the transition to sustainable transportation. However, battery storage capacity for variable solar energy production is becoming increasingly less cost-effective due to the higher Lithium price. Complementing solar energy production and battery storage, the potential of combining green hydrogen fuel energy integration with solar energy for EV charging stations can provide a sustainable alternative to reach carbon-free transportation. This research aims to assess the technical and economic viability of grid-connected Photovoltaic (PV)-based EV charging stations across Kentucky based on existing EV load profiles. The study considers different PV system sizes, locations, orientations, and the impact of net metering policies and electricity tariffs on the profitability of the PV systems. Further, using excess PV energy, Lithium-Ion batteries will be charged, which will be assessed considering the techno-economic and environmental impacts of green EV charging stations. To further enhance green renewable energy production, we also investigated the feasibility of on-grid PV-based EV charging stations with Lithium-Ion batteries charged during low tariff periods. In comparison with these scenarios, we studied the potential benefits of integrating green hydrogen energy production using excess solar energy with and without governmental incentives. Furthermore, we assess the environmental impact of these green EV charging stations across Kentucky. Our study indicates that EV stations across Kentucky have similar techno-economic feasibility with insignificant deviation in the levelized cost of electricity, payback period, and PV energy fraction with/without Li-Ion batteries. However, with green hydrogen production, the marginal H 2 production cost indicates the first scenario (on-grid PV energy charging) is the most significant, roughly 10 USD/Kg, which is similar to 1.5 times higher than the second (charging during low-tariff periods) and the third scenarios (power grid to satisfy the demand deficit - approximately 6.5 USD/Kg). From the environmental perspective, the second scenario would be a good option for either hybrid EV/H 2 or EV charging stations in Kentucky, factoring in the hydrogen production cost and the carbon footprint.

08 HYDROGEN↗
DOE OSTI2012

Solar America Initiative State Working Group (Final Scientific/Technical Report)

Through the support from the Department of Energy, NARUC has educated thousands of stakeholders, including Public Utility Commissioners, commission staff, and State energy officials on solar energy technology, implementation, and policy. During the lifetime of this grant, NARUC staff engaged stakeholders in policy discussions, technical research, site visits, and educational meetings/webinars/materials that provided valuable education and coordination on solar energy technology and policy among the States. Primary research geared toward State decision-makers enabled stakeholders to be informed on current issues and created new solar energy leaders throughout the United States. Publications including a Frequently Asked Questions guide on feed-in tariffs and a legal analysis of state implementation of feed-in tariffs gave NARUC members the capacity to understand complex issues related to the economic impacts of policies supportive of solar energy, and potential paths for implementation of technology. Technical partnerships with the National Renewable Energy Laboratory (NREL) instructed NARUC members on feed-in tariff policy for four States and solar PV resource assessment in seven States, as well as economic impacts of solar energy implementation in those States. Because many of the States in these technical partnerships had negligible amounts of solar energy installed, this research gave them new capacity to understand how policies and implementation could impact their constituency. This original research produced new data now available, not only to decision-makers, but also to the public at-large including educational institutions, NGOs, consumer groups, and other citizens who have an interest in solar energy adoption in the US. Under this grant, stakeholders engaged in several dialogs. These educational opportunities brought NARUC members and other stakeholders together several times each year, shared best practices with State decision-makers, fostered partnerships and relationships with solar energy experts, and aided in increasing the implementation of smart policies that will foster solar technology deployment. The support from the Department of Energy's Office of Energy Efficiency and Renewable Energy has created solar energy leaders in the States; leaders who will serve to be a continuing valuable resource as States consider adoption of new low-carbon and domestic energy supply to meet the energy needs of the United States.

14 SOLAR ENERGY↗
DOE OSTI2024

One Year In: Tracking the Impacts of NEM 3.0 on California’s Residential Solar Market

On December 15, 2022, the California Public Utilities Commission passed an overhaul of the net metering program for the state’s investor-owned utilities. The changes replaced the long-standing net energy metering (NEM) tariffs with a net billing tariff (NBT) structure—colloquially known as “NEM 3.0”—which significantly reduces the compensation for behind-the-meter solar photovoltaic (PV) systems. The NEM tariffs remained open for new interconnection applications until April 15, 2023, but after that date, all new interconnection applications were submitted under NBT. Now, one year later, we have an opportunity to evaluate how the California solar market has evolved under this new compensation regime. As a precursor to its annual Tracking the Sun report, Berkeley Lab has released a short technical brief describing key trends in the California residential solar market since the roll-out of the new NBT structure. The purpose of this analysis is to provide empirical insights into how the market has evolved over the past year, confirming some expectations while also revealing several striking surprises.

14 SOLAR ENERGY↗
DOE OSTI2022

Optimizing Price-Informed Operation of a Battery Storage System in an Office Building

New prescriptive efficiency requirements amended to US model energy codes historically have been evaluated using an average, blended electricity rate, which obscures demand charges. Post 2019, new measures can be evaluated using a representative time-of-use (TOU) tariff yet more sophisticated analysis methods are needed to consider a variety of TOU tariffs and assess price-informed control. To address these needs, this study couples building prototype simulation model with varying-in-sophistication battery storage operating strategies for different electricity TOU tariffs. The analysis compares the impact of operating a battery storage system following simpler rule-of-thumb methods versus a semi-optimized priced-informed heuristic approach. The investigation demonstrates that the heuristic approach results in greater electricity cost savings and is practical to implement.

Lei, Xuechen↗
DOE OSTI2015

Effects of Home Energy Management Systems on Distribution Utilities and Feeders Under Various Market Structures: Preprint

The combination of distributed energy resources (DER) and retail tariff structures to provide benefits to both utility consumers and the utilities is poorly understood. To improve understanding, an Integrated Energy System Model (IESM) is being developed to simulate the physical and economic aspects of DER technologies, the buildings where they reside, and feeders servicing them. The IESM was used to simulate 20 houses with home energy management systems on a single feeder under a time of use tariff to estimate economic and physical impacts on both the households and the distribution utilities. HEMS reduce consumers’ electric bills by precooling houses in the hours before peak electricity pricing. Household savings are greater than the reduction utility net revenue indicating that HEMS can provide a societal benefit providing tariffs are structured so that utilities remain solvent. Utilization of HEMS reduce peak loads during high price hours but shifts it to hours with off-peak and shoulder prices and resulting in a higher peak load.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Economic Sizing of Batteries for the Smart Home: Preprint

This paper presents a methodology to economically size a home battery based on parametric analysis using home energy management system (HEMS) software to optimally dispatch the battery along with controllable loads under several use cases. We account for connected equipment, controls, renewable resources, and other factors such as building characteristics and utility tariffs. The paper defines an analytical pathway for such a sizing tool, develops initial sizing guidance, and clarifies technical and market opportunities for home batteries in the context of existing and emerging equipment and control technologies. A parametric analysis that included 132 scenarios has been performed based on different combinations of pertinent parameters. Results indicate that four variables dominate the decision-making process: application scenarios (new construction or retrofit), utility tariffs, existence of HEMS, and the anticipated payback time. Life-cycle cost analysis indicated that in the absence of utility incentives, batteries plus HEMS have a payback time of longer than 10 years for new construction under a time-of-use rate structure and feed-in tariff; larger batteries have a longer payback time but may provide more benefits to utilities on reducing power backfeed under certain circumstances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Customer outcomes in Pay-As-You-Save programs

We review the energy and financial outcomes of households participating in several programs based on successive versions of the Pay As You Save¯ (PAYS¯) system. PAYS¯ programs offer non-debt financing for energy efficiency (and sometimes other technologies) in residential buildings through a tariff attached to the home’s utility meter, designed to be offset by project savings. We find that the five programs we study generally serve customers living in zip codes with levels of income and education below the national average and unemployment rates above the national average, demonstrating their potential to improve equity in energy efficiency adoption. Using weather-normalized analysis of energy consumption data, we show that most customers of Midwest Energy’s program reduce annual electricity and gas consumption, averaging 15% and 26% reductions respectively. Changes in energy consumption calculated using this method represent a combination of project effects and changes in occupant behavior. These results are similar to existing analyses of PAYS¯ programs in North Carolina, Arkansas, and Tennessee. About half of participating Midwest households generate sufficient energy cost savings to cover their monthly tariff. Various factors, including changes in occupant behavior, program error, causes independent of the customer or program, or some combination thereof may explain lower-than-expected cost reductions in some projects. Given the inherent variability in annual household electricity consumption, we feel these programs are enabling energy efficiency improvements and their attendant co-benefits, including occupant health and comfort and reduced carbon emissions, while reasonably balancing energy savings and tariff costs. Pairing PAYS¯ with additional financial assistance, as well as promoting cost-effective measures such as air and duct sealing, could further broaden program participation by enabling additional projects to meet PAYS¯ program eligibility rules.

Deason, Jeff↗
DOE OSTI2018

An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling

Energy-aware scheduling in manufacturing operations with time-of-use electricity tariffs is a challenging problem. Sustainable manufacturing is gaining significant momentum: companies are not only improving their product quality, but also optimizing the production processes to improve energy consumption (i.e., minimizing energy cost) in order to manage environmental challenges which contribute to global climate change. The purpose of this paper is to study a preemptive scheduling problem on a single-machine to minimize the total completion time and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multiobjective mathematical programming model. To solve these objectives, we develop several new holistic ant colony optimization algorithms. The proposed model is solved via several methods including weighted sum method (WSM) using CPLEX, and multiobjective ant colony optimization based on a dominance ranking (ACO-DR) or based on a dominance ranking procedure and crowding distance comparison (ACO-DRC) or based on a heuristic approach to obtain an approximate Pareto-front and also provide information on when to start and resume each job for any solution on the Pareto-front. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we demonstrate how the results of the multiobjective model could be utilized in decision making using the multiobjective optimization on the basis of ratio analysis (MOORA) method. Furthermore, this proposed model and heuristics allows decision makers to operate in challenging-data enabled environments in industrial internet of things ecosystem, and assists in optimizing production planning to improve energy cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗