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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

25 ENERGY STORAGE↗
DOE OSTI2018

Impacts of High Variable Renewable Energy Futures on Wholesale Electricity Prices, and on Electric-Sector Decision Making

Increasing penetrations of variable renewable energy (VRE) can affect wholesale electricity price patterns and make them meaningfully different from past, traditional price patterns. Many long-lasting decisions for supply- and demand-side electricity infrastructure and programs are based on historical observations or assume a business-as-usual future with low shares of VRE. Our motivating question is whether certain electric-sector decisions that are made based on assumptions reflecting low VRE levels will still achieve their intended objective in a high VRE future. We qualitatively describe how various decisions may change with higher shares of VRE and outline an analytical framework for quantitatively evaluating the impacts of VRE on long-lasting decisions. We then present results from detailed electricity market simulations with capacity expansion and unit commitment models for multiple regions of the U.S. for low and high VRE futures. We find a general decrease in average annual hourly wholesale energy prices with more VRE penetration, increased price volatility and frequency of very low-priced hours, and changing diurnal price patterns. Ancillary service prices rise substantially and peak net-load hours with high capacity value are shifted increasingly into the evening, particularly for high solar futures. While in this report we only highlight qualitatively the possible impact of these altered price patterns on other demand- and supply-side electric sector decisions, the core set of electricity market prices derived here provides a foundation for later planned quantitative evaluations of these decisions in low and high VRE futures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Exploring Wholesale Energy Price Trends: The Renewables and Wholesale Electricity Prices (ReWEP) tool (Ver. 2024.1)

The Renewables and Wholesale Electricity Prices (ReWEP) visualization tool from Berkeley Lab has been updated with nodal electricity pricing and wind and solar generation data through the end of 2023. ReWEP users can explore trends in wholesale electricity prices and their relationship to wind and solar generation. ReWEP includes nodal pricing trends across locations, regions, and different timeframes. The tool consists of maps, time series, and other interactive figures that provide: (1) a general overview of how average pricing, negative price frequency, and extreme high prices vary over time, and (2) a summary of how pricing patterns are related to wind and solar generation. Interactive functionality allows investigation by year, season, time of day, and region, where region is defined as the Independent System Operators (ISO) or Regional Transmission Organizations (RTO) region. ReWEP also contains prices throughout much of the western United States from the Western Energy Imbalance Market and the Western Energy Imbalance Service Market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Exploring Wholesale Energy Price Trends: The Renewables and Wholesale Electricity Prices (ReWEP) tool (Ver. 2024.1)

The Renewables and Wholesale Electricity Prices (ReWEP) visualization tool from Berkeley Lab has been updated with nodal electricity pricing and wind and solar generation data through the end of 2023. ReWEP users can explore trends in wholesale electricity prices and their relationship to wind and solar generation. ReWEP includes nodal pricing trends across locations, regions, and different timeframes. The tool consists of maps, time series, and other interactive figures that provide: (1) a general overview of how average pricing, negative price frequency, and extreme high prices vary over time, and (2) a summary of how pricing patterns are related to wind and solar generation. Interactive functionality allows investigation by year, season, time of day, and region, where region is defined as the Independent System Operators (ISO) or Regional Transmission Organizations (RTO) region. ReWEP also contains prices throughout much of the western United States from the Western Energy Imbalance Market and the Western Energy Imbalance Service Market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Short-term Electricity Price Forecasting with Constrained Regressors

The volatility of electricity price presents a challenge to market participants as their decision-making process are highly depend on the accuracy of price forecasts. However, there is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. The distribution shift caused by spikes in electricity price data differentiates the forecasting tasks from other renewable energy sources. Moreover, the observations may be compromised by cyberattacks and thus not available in the testing phase. To this end, we propose a Similarity-Enhanced Electricity Decomposition Forecasting model (SEED-Forecaster) to address the missing response problem and spikes capturing in short-term electricity price forecasting. The effectiveness of the proposed framework is tested on real-world electricity price data from California Independent System Operator (CAISO). Numerical results of case studies show that the proposed SEED-Forecsater can enhance forecasting performance, particularly in capturing electricity spikes, even under conditions without regressors during testing stage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Retail Electricity Price and Cost Trends: 2024 Update [Slides]

Berkeley Lab’s "Retail Electricity Price and Cost Trends" summarizes recent trends in retail electricity price levels and price drivers in the United States. This report is intended to serve as a reference document for the diverse set of decision-makers impacted by changes in retail electricity prices and to provide a factual basis for assessing recent changes in retail electricity prices and key underlying drivers. National, regional, and state trends are reported for 2019 through 2023 using publicly-available data for: -Average retail electricity prices, retail sales, and utility revenues -Utility capital expenditures, operations and maintenance costs, and fuel and purchased power costs -Retail electricity sales impacts from behind-the-meter resources The report also includes qualitative case studies highlighting recent and/or regionally-specific issues contributing significantly to retail electricity price trends

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

How Do Electricity Pricing Programs Impact the Selection of Energy Efficiency Measures? - A Case Study with U.S. Medium Office Buildings

Building owners usually select energy efficiency measures (EEMs) by referring to return on investment (ROI). Current studies tend to apply static energy price to estimate ROI. However, more and more buildings are adopting dynamic electricity pricing programs. To understand how electricity pricing programs impact the selection of EEMs, this paper presents an analysis of the ROIs of EEMs under different pricing programs using U.S. medium office buildings as an example. Eight EEMs in four typical cities are selected as case studies. Considering five electricity pricing programs scenarios (one static program and four dynamic programs), EEMs are selected based on their ROIs. The main findings are: (1) The ROIs of EEMs change under different pricing programs. (2) In Honolulu, Buffalo, and Denver, replacing interior fixtures with higher-efficiency fixtures has a significantly higher ROI than the rest EEMs under all five pricing programs. However, the ROI of this EEM in Honolulu ranges from 28% to 47% for different pricing programs. (3) Similarly, in Fairbanks, replace heating coil with higher-efficiency coil produce higher ROI than the rest under all five pricing programs. (4) For other EEMs, their ROI rankings vary according to electricity pricing programs.

demand response↗
DOE OSTI2024

Transmission Value in 2023: Market Data Shows the Value of Transmission Remained High in Certain Locations Despite Overall Low Wholesale Electricity Prices

In 2023 additional electricity transmission would have provided the most value for links that crossed between grid interconnection regions in the United States (the Western Interconnection, the Eastern Interconnection, the Texas Interconnection) or crossed between system operator regions within the same interconnection. Many multi-interconnection or multi-region links had values of greater than $\$20$/MWh, or up to $\$175$ million/yr per 1 GW expanded transmission (subject to limits to the depth of the market at each side of the link). In contrast, many links within regions, or between regions in the northeast, had relatively low values in 2023, following the overall decline in wholesale electricity prices in 2023 compared with 2021-2022. The most valuable link in 2023, at $\$61$ /MWh, was between Texas and the Southwest. Multiple events in 2023 (high natural gas prices in the western U.S., and high summer temperatures in Texas and the Southwest) were observed to have driven this high value. Of particular note, high prices in Texas occurred at a largely distinct set of hours from high prices in the Southwest, helping to drive up the value of transmission in total and demonstrating significant value to both regions. This example demonstrates the unique value of transmission (compared to other solutions, such as building local generation resources) in delivering benefits to multiple regions given its ability to connect areas of the country that inevitably face differing circumstances.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A robust statistical analysis of the role of hydropower on the system electricity price and price volatility

Abstract Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage and large reservoir storage) for providing energy in times of high demand on the grid (S. Rehman, L M Al-Hadhrami, and M M Alam), (2015 Renewable and Sustainable Energy Reviews , 44 , 586–98). This study examines the impact of hydropower on system electricity price and price volatility in the region served by the New England Independent System Operator (ISONE) from 2014-2020 (ISONE, ISO New England Web Services API v1.1 .” https://webservices.iso-ne.com/docs/v1.1/ , 2021. Accessed: 2021-01-10). We perform a robust holistic analysis of the mean and quantile effects, as well as the marginal contributing effects of hydropower in the presence of solar and wind resources. First, the price data is adjusted for deterministic temporal trends, correcting for seasonal, weekend, and diurnal effects that may obscure actual representative trends in the data. Using multiple linear regression and quantile regression, we observe that hydropower contributes to a reduction in the system electricity price and price volatility. While hydropower has a weak impact on decreasing price and volatility at the mean, it has greater impact at extreme quantiles (>70th percentile). At these higher percentiles, we find that hydropower provides a stabilizing effect on price volatility in the presence of volatile resources such as wind. We conclude with a discussion of the observed relationship between hydropower and system electricity price and volatility.

13 HYDRO ENERGY↗
DOE OSTI2020

Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control

Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while maintaining resident's comfort. The success of such DRL-based techniques largely depends on the articulation of the problem in terms of states, actions, and reward function. Inclusion of the electricity pricing information in the problem formulation can play an important role in saving the cost of HVAC operation. However, less attention has been given in the literature on formulating well-crafted state features based on electricity pricing. In this work, we propose an approach for training the DRL model with a specific focus on feature engineering based on electricity pricing. During training, we generate random but sufficiently realistic electricity price signals so that the pre-trained DRL model is robust and adaptive to the dynamic and variable electricity prices. The validation results are encouraging and show the potential of ≈12%-15% savings in the one day cost of HVAC operation, proving the usefulness of including electricity pricing related features as state features.

Kurte, Kuldeep↗
DOE OSTI2022

On Harmonizing Today’s Regulated Tariffs and Future Dynamic Electricity Pricing

A novel method for harmonizing the advantages of dynamic retail electricity pricing with the protections of regulated electricity tariffs is discussed and demonstrated. The method socializes and protects customers from long-term locational price variability that is unfair to those customers who are, by no fault of their own, served at congested locations on a distribution system. However, the method preserves short-term (e.g., diurnal) price variability that might induce helpful, mitigative responses from retail electricity customers. Because the method causes actual price recovery to track a customer class’s approved, regulated price recovery, the method may remove regulators’ objections to dynamic electricity pricing and thereby hasten adoption of market-based retail electricity pricing and transactive energy systems.

Consumer protection, Demand response, Market resea↗
DOE OSTI2026

The relative influences of hydrologic information and dams’ hydropower scheduling decisions on electricity price forecasts

Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.

Electricity markets↗
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 OSTI2019

Non-residential Electricity Prices: A Review of Data Sources and Estimation Methods

This paper presents a review of electricity price data sources and estimation methods for non-residential consumers, including a detailed empirical study of tariff-based prices collected as part of Lawrence Berkeley Lab's Tariff Analysis Project (TAP). We compare the TAP data to two other data sources: utility-level annual sales, revenues and consumer counts published by the Energy Information Administration (EIA), and the Typical Bills reports published by the Edison Electric Institute. A brief overview of the types of rate structures included in the TAP database illustrates the often complex relationship between the consumer's energy use and the costs they see on their electricity bills. We provide mathematical definitions of the different types of electricity price that are useful in cost-benefit analysis, construct estimation methods appropriate to each data set, compare the results, and evaluate the relative strengths and weaknesses of each approach. We also examine how price variation is driven by differences in season, region, industry business model, and variables characterizing consumer energy use. Tables of regional price estimates for each data source, based on 2015 data, are included in the Appendix to this paper.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Game-Theoretic Approach for Electricity Pricing Between Distribution System Operator and Load Aggregators

Transactive energy has emerged and gained more and more attention in recent years along with smart grid two-way communication technologies. Electricity pricing strategies play a critical role in influencing and shaping the customers’ energy load profile. In this paper, we investigate the electricity pricing strategy between a distribution system operator (DSO) and load aggregators (LAs) by adopting a bi-level Stackelberg game approach. With the purpose of maximizing its own operating revenue as well as better serving its customers, the upper level DSO will determine the price considering how lower level LAs will respond to it. Peak-to-average ratio of the total demand is modeled as a constraint and peak load is penalized at the upper optimization level. Classical backward induction is used to obtain a Nash equilibrium for the formulated bi-level Stackelberg game. Numerical results demonstrate the effectiveness of the proposed game approach in leveraging flexible demand potential to benefit both DSO and LAs.

Chen, Yang↗
DOE OSTI2025

Factors influencing recent trends in retail electricity prices in the United States

This study analyzes the primary drivers of recent state-level trends in U.S. retail electricity prices. We summarize pricing trends, explore descriptive relationships, and employ regression models to quantify the influence of various factors. Although the recent national rise in retail prices has largely tracked inflation, state-level trends vary widely. We identify a number of factors that explain trends in subsets of states. States with the greatest price increases typically exhibited shrinking customer loads—partially linked to growth in net metered behind-the-meter solar—and had renewables portfolio standards (RPS) in concert with relatively costly incremental renewable energy supplies. By contrast, recent utility-scale wind and solar deployment that occurred outside RPS programs (but that benefited from tax incentives) had no discernible impact on increased retail prices. Hurricanes, storms and wildfires also contributed to sizable price increases in some states, most notably in California, where wildfire risk mitigation and liability insurance were major cost drivers. Fluctuations in natural gas prices—particularly following the onset of the Ukraine-Russia war—further contributed to sharp price increases through 2022–2023 in many states, with moderation in 2024. The relative influence of these factors varies across states and over time, and relationships may change in the future. Nonetheless, the findings underscore the diverse set of price determinants and highlight the need for continued research to inform effective policy and ensure customer affordability.

Customer load↗
DOE OSTI2021

Default Effects And Follow-On Behaviour: Evidence From An Electricity Pricing Program

We study default effects in the context of a residential electricity-pricing program. In the large-scale randomized controlled trial we analyse, one treatment group was given the option to opt-in to time-varying pricing while another was defaulted into the program but allowed to opt-out. We provide dramatic evidence of a default effect on program participation, consistent with previous research. A novel feature of our study is that we also observe how the default manipulation impacts customers’ subsequent electricity consumption. Passive consumers who did not opt-out but would not have opted in-comprising more than 70$\%$ of the sample-nonetheless reduce consumption in response to higher prices. Observing of this follow-on behaviour enables us to assess competing explanations for the default effect. We draw conclusions about the likely welfare effects of defaulting customers onto time-varying pricing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗