Utility Resource Planning Research Articles

Predicting city-scale daily electricity consumption using data-driven models

• We studied how city electricity use is influenced by weather and COVID-19 pandemic. • Seven data-driven models were applied and evaluated for data of three cities. • Gradient boosting tree model delivers the most accurate prediction with CVRMSE of 4%−6%. • 1 °C increase of ambient temperature drives up the three cities electricity usage by around 5%. • COVID-19 curtailment reduced city-scale electricity usage by 2%−12%. Accurate electricity demand forecasts that account for impacts of extreme weather events are needed to inform electric grid operation and utility resource planning, as well as to enhance energy security and grid resilience. Three common data-driven models are used to predict city-scale daily electricity usage: linear regression models, machine learning models for time series data, and machine learning models for tabular data. In this study, we developed and compared seven data-driven models: (1) five-parameter change-point model, (2) Heating/Cooling Degree Hour model, (3) time series decomposed model implemented by Facebook Prophet, (4) Gradient Boosting Machine implemented by Microsoft lightGBM, and (5) three widely-used machine learning models (Random Forest, Support Vector Machine, Neural Network). Seven models are applied to the city-scale electricity usage data for three metropolitan areas in the United States: Sacramento, Los Angeles, and New York. Results show seven models can predict the metropolitan area's daily electricity use, with a coefficient of variation of the root mean square error (CVRMSE) less than 10%. The lightGBM provides the most accurate results, with CVRMSE on the test dataset of 6.5% for Los Angeles, 4.6% for Sacramento, and 4.1% for the New York metropolitan area. These models are further applied to explore how extreme weather events (e.g., heat waves) and unexpected public health events (e.g., COVID-19 pandemic) influence each city's electricity demand. Results show weather-sensitive component accounts for 30%–50% of the total daily electricity usage. Every degree Celsius ambient temperature increase in summer leads to about 5% (4.7% in Los Angeles, 6.2% in Sacramento, and 5.1% in New York) more daily electricity usage compared with the base load in the three metropolitan areas. The COVID-19 pandemic reduced city-scale electricity demand: compared with the pre-pandemic same months in 2019, daily electricity usage during the 2020 pandemic decreased by 10% in April and started to rebound in summer.

Planning for low-cost renewable energy

Renewable energy is increasingly cost-competitive with conventional generation technologies. Traditional approaches to utility resource planning and resource investment and procurement (“acquisition”) will, however, prevent utilities from taking fuller advantage of low-cost renewable resources. This paper describes three areas where enhancements in utility resource planning can more accurately reflect the benefits and costs of renewable generation: (1) how operational uncertainties associated with wind and solar generation are characterized in planning models; (2) how reliability services provided by wind and solar generation are valued in planning models; and (3) alignment between cost assumptions used in planning and market conditions. The paper also describes three areas where enhancements in utility resource acquisition can better align decision-making with market outcomes: (1) reducing the time between planning-procurement cycles; (2) shifting from a needs-based to an opportunity-based approach to planning and acquisition; and (3) ensuring comparability among different technology and ownership options. Nascent efforts to resolve challenges in these six areas illustrate the need for utilities and state regulators to expand and upgrade their analytical capabilities.

The Case against Carbon Prices

Professor Dr. Anthony Patt is head of the Climate Policy Group in the Department of Environmental Systems Science at ETH Zurich, Switzerland. His research focus is on technological pathways and public policies for climate change mitigation and adaptation. He is a Coordinating Lead Author in Working Group III of the Intergovernmental Panel on Climate Change. He obtained his PhD at Harvard University's Kennedy School of Government. He is the author of “Transforming Energy: Solving Climate Change with Technology Policy” (Cambridge University Press, 2015). Download : Download high-res image (204KB) Download : Download full-size image Professor Dr. Johan Lilliestam is head of the Renewable Energy Policy Group in the Department of Environmental Systems Science at ETH Zurich, Switzerland. His research focus is policies and strategies for a transition to a completely renewable energy system, including the effects of interactions between different energy policies. He obtained his PhD at the Central European University in Budapest, Hungary. Before joining the ETH, he worked at the Potsdam Institute for Climate Impact Research (PIK) in Potsdam, Germany, and the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria. He currently holds a grant from the European Research Council for his work on policy interactions. Download : Download high-res image (202KB) Download : Download full-size image

Energy Storage Likely to Increase in Utility Resource Planning

Today energy storage is a diverse asset class, and technological advances and rapid cost declines have now resulted in a wide array of energy storage technologies increasingly being deployed on utility systems across the country. Utilities from Hawaii to New York have been finding energy storage an extremely attractive and cost‐effective way to solve grid needs.

English

Long term load forecasting is an important aspect of electric utility resource planning and utility expansion. This paper presents a simple regression analysis based model involving population and per capita GDP for long term forecasting of India’s sector-wise electrical energy demand. The model requires an input, the year of the forecast, and predicts the sector-wise energy demand. Sector-wise energy consumption during the years 1990 -2012 forms the data for developing the forecasting model. It presents the forecasted sector-wise energy demands up to the 2025.

Incorporating energy efficiency into electric power transmission planning: A western United States case study

Driven by system reliability goals and the need to integrate significantly increased renewable power generation, long-range, bulk-power transmission planning processes in the United States are undergoing major changes. At the same time, energy efficiency is an increasing share of the electricity resource mix in many regions, and has become a centerpiece of many utility resource plans and state policies as a means of meeting electricity demand, complementing supply-side sources, and reducing carbon dioxide emissions from the electric power system. The paper describes an innovative project in the western United States to explicitly incorporate end-use efficiency into load forecasts – projections of electricity consumption and demand – that are a critical input into transmission planning and transmission planning studies. Institutional and regulatory background and context are reviewed, along with a detailed discussion of data sources and analytical procedures used to integrate efficiency into load forecasts. The analysis is intended as a practical example to illustrate the kinds of technical and institutional issues that must be addressed in order to incorporate energy efficiency into regional transmission planning activities.

Survey of Western U.S. electric utility resource plans

We review long-term electric utility plans representing ~90% of generation within the Western U.S. and Canadian provinces. We address what utility planners assume about future growth of electricity demand and supply; what types of risk they consider in their long-term resource planning; and the consistency in which they report resource planning-related data. The region is anticipated to grow by 2% annually by 2020 – before Demand Side Management. About two-thirds of the utilities that provided an annual energy forecast also reported energy efficiency savings projections; in aggregate, they anticipate an average 6.4% reduction in energy and 8.6% reduction in peak demand by 2020. New natural gas-fired and renewable generation will replace retiring coal plants. Although some utilities anticipate new coal-fired plants, most are planning for steady growth in renewable generation over the next two decades. Most planned solar capacity will come online before 2020, with most wind expansion after 2020. Fuel mix is expected to remain ~55% of total generation. Planners consider a wide range of risks but focus on future demand, fuel prices, and the possibility of GHG regulations. Data collection and reporting inconsistencies within and across electric utility resource plans lead to recommendations on policies to address this issue.

A critical examination of models and projections of demand in water utility resource planning in England and Wales

Demand modelling plays a vital role in water resource management yet has rarely been critically reviewed. This paper adopts a critical realist framework for a historical analysis of demand modelling practices and their role in long-term water resource management in England and Wales from 1945 to 2010. It then focuses on recent domestic demand models in the English and Welsh private water sector. A critique of scientific realist assumptions regarding demand models is presented and the role of the current regulatory environment in encouraging a highly strategic use of demand models is discussed. Policy recommendations toward more effective modelling practices are made.

The future of utility customer-funded energy efficiency programs in the USA: projected spending and savings to 2025

We develop projections of future spending on, and savings from, energy efficiency programs funded by electric and gas utility customers in the USA, under three scenarios through 2025. Our analysis, which updates a previous LBNL study, relies on detailed bottom-up modeling of current state energy efficiency policies, regulatory decisions, and demand-side management and utility resource plans. The three scenarios are intended to represent a range of potential outcomes under the current policy environment (i.e., without considering possible major new policy developments). Key findings from the analysis are as follows:

Electric utility resource planning using Continuous-Discrete Modular Simulation and Optimization (CoDiMoSO)

Electric utility resource planning traditionally focuses on conventional energy supplies such as coal, natural gas, and oil. Nowadays, planning of renewable energy generation as well as its side necessity of storage capacities have become equally important due to the increasing growth in energy demand, insufficiency of natural resources, and newly established policies for low carbon footprint. In this study, we propose to develop a comprehensive simulation based decision making framework to determine the best possible combination of resource investments for electric power generation and storage capacities. The proposed tool involves a combined continuous-discrete modular modeling approach for processes of different nature that exist within this complex system, and will help the utility companies conduct resource planning via employed multiobjective optimization techniques in a realistic simulation environment. The distributed power system considered here has four major components including (1) energy generation via a solar farm, a wind farm, and a fossil fuel power station, (2) storage via compressed air energy storage system, and batteries, (3) transmission via a bus and two main substations, and (4) demand of industrial, commercial, residential and transportation sectors. The proposed approach has been successfully demonstrated for the electric utility resource planning at a scale of the state of Florida.

Energy efficiency as a preferred resource: evidence from utility resource plans in the Western US and Canada

This article examines the future role of energy efficiency as a resource in the Western US and Canada, as envisioned in the most recent resource plans issued by 16 utilities, representing about 60% of the region’s load. Utility and third-party-administered energy-efficiency programs proposed by 15 utilities over a 10-year horizon would save almost 19,000 GWh annually, about 5.2% of forecast load. There are clear regional trends in the aggressiveness of proposed energy savings. California’s investor-owned utilities (IOUs) had the most aggressive savings targets, followed by IOUs in the Pacific Northwest, and the lowest savings were proposed by utilities in Inland West states and by two public utilities on the West Coast. The adoption of multiple, aggressive policies targeting energy efficiency and climate change appears to produce sizeable energy-efficiency commitments. Certain specific policies, such as mandated energy savings goals for California’s IOUs and energy-efficiency provisions in Nevada’s Renewable Portfolio Standard, had a direct impact on the level of energy savings included in the resource plans. Other policies, such as revenue decoupling and shareholder incentives and voluntary or legislatively mandated greenhouse gas emission reduction policies, may have also impacted utilities’ energy-efficiency commitments, though the effects of these policies are not easily measured. Despite progress among the utilities in our sample, more aggressive energy-efficiency strategies that include high-efficiency standards for additional appliances and equipment, tighter building codes for new construction and renovation, as well as more comprehensive ratepayer-funded energy-efficiency programs are likely to be necessary to achieve a region-wide goal of meeting 20% of electricity demand with efficiency in 2020.

Long Term Energy Consumption Forecasting Using Genetic Programming

Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this paper, a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. The empirical results demonstrate successful load forecast with a low error rate.

Managing carbon regulatory risk in utility resource planning: Current practices in the Western United States

Concerns about global climate change have substantially increased the likelihood that future policy will seek to minimize carbon dioxide emissions. As such, even today, electric utilities are making resource planning and investment decisions that consider the possible implications of these future carbon regulations. In this article, we examine the manner in which utilities assess the financial risks associated with future carbon regulations within their long-term resource plans. We base our analysis on a review of the most recent resource plans filed by 15 electric utilities in the Western United States. Virtually all of these utilities made some effort to quantitatively evaluate the potential cost of future carbon regulations when analyzing alternate supply- and demand-side resource options for meeting customer load. Even without federal climate regulation in the US, the prospect of that regulation is already having an impact on utility decision-making and resource choices. That said, the methods and assumptions used by utilities to analyze carbon regulatory risk, and the impact of that analysis on their choice of a particular resource strategy, vary considerably, revealing a number of opportunities for analytic improvement. Though our review focuses on a subset of US electric utilities, this work holds implications for all electric utilities and energy policymakers who are seeking to minimize the compliance costs associated with future carbon regulations.

Balancing Cost and Risk: The Treatment of Renewable Energy in Western Utility Resource Plans

Markets for renewable energy have historically been motivated by policy efforts, but a less widely recognized driver is poised to also play a major role in the coming years: utility integrated resource planning. A review of the planning efforts of 12 western utilities reveals that resource plans are becoming increasingly sophisticated in their treatment of renewable resources and the costs and risks that they both entail and mitigate. However, further improvements are possible.

Efficient formulation of electric utility resource planning models

We show that the z-substitutes method for the efficient formulation of electric utility resource planning models may be used in more general circumstances, expanding considerably its range of practical applications. Numerical tests on a sample model provided a 40% to 75% reduction in problem solution time.

Incentive Regulation for Public Utilities

Preface. 1. Incentive Regulation in Theory and Practice T.P. Lyon. 2. Incentive Regulation in Telecommunications: Why States Don't Choose Price Caps J.M. MacDonald, J.R. Norsworthy, Wei-Hua Fu. 3. Dynamic Pricing under Static Regulation: The Case of UBP T.A. Abbott III, M.A. Crew. 4. An Economic Analysis of Transfer Pricing and Imputation Policies for Public Utilities A.C. Larson, S.G. Parsons. 5. Long-Distance Telecommunications: Expectations and Realizations in the Post-Divestiture Period D.L. Kaserman, J.W. Mayo. 6. The Law and Economics of Privacy: Applications to Regulated Utilities W.E. Kovacic. 7. Pricing Priority Service: Theory versus Utility Practice M.A. Crew, C. Fernando. 8. Energy Utility Resource Planning, Conservation, Valuation, and Environmental Costing R.A. Michelfelder. 9. Incentives in Pipeline Pricing and Capacity J.M. Chermak, R.H. Patrick. 10. Issues in Incentive Regulation: TFP in State Regulatory Reform -- Theory Versus Practice M.O. Bidwell Jr., E.J. Goldrick, A.W. McGrath.

A simplified technique for screening utility DSM options for their capacity and operational benefits

Demand-side management (DSM) is becoming a universally accepted means for reducing utility-capacity needs and improving system operational performance. A technique is presented which utilizes an inverted load-duration curve (ILDC) in an efficient way to integrate utility DSM effects properly without using the chronological load-modification process. Such a simplified method will provide the opportunity to screen a large number of DSM scenarios and obtain a reliable estimate of their potential capacity and operational benefits. A few select cost-effective DSM programs may then be chosen and evaluated with supply-side options in integrated utility-resource planning.

Integrated resource planning with renewable resources

There is no “one size fits all” role for renewables in utility resource plans, so the benefits and potential of renewables depend on careful analysis of the specifics of a given utility's situation. As the state of the modeling art advances, and as implications of analysis are better understood, regulatory policy will move in a direction consistent with analysis and away from interim solutions like regulatory set-asides, targets and “green RFPs”.

Buying and selling power in the age of competition

The American electric power industry is currently in the throes of striving for workable competition. Driven by the converging forces of deregulation, technological revolution, and evolving customer expectations, electric utilities are having to compete as never before at both the wholesale and retail levels. At times, the transition has been particularly wrenching, with some utilities going through painful internal restructuring and others being caught up in external consolidation. Increasingly, the key to success in this more competitive environment is anticipating and actively responding to the forces that are shaping the new markets for electric power. Here, the author describes how such market management is the subject of pioneering research being conducted in EPRI's Utility Resource Planning and Management Program. On the wholesale side, this research centers on the development of powerful analytical methods to support utility decisions regarding new opportunities in the bulk power market. Research on retail market management is concentrated on developing innovative price-differentiated services that can enhance the value of electricity to customers and also foster a utility's long-term financial competitiveness. >

Most Value Planning: Estimating the Net Benefits of Electric Utility Resource Plans

Abstract Most U.S. utility regulatory commissions require that electric utilities minimize cost when comparing supply-side and demand-side resources. However, utilities should also consider the effects of resource plans upon the benefits, or “value,” that electricity consumers receive. A method for extending the traditional “least cost” objective to include value changes in electric utility resource planning is presented. This method uses the concept of consumers surplus to quantify the benefits of electric rate changes, “take-back” of energy savings in conservation programs, and increases in energy services provided by load building or valley-filling programs. In general, the method rewards resource portfolios that cause rates to move closer to marginal cost or increase the energy services provided. To illustrate its range of application, the “most value” objective is applied to screening of demand-side programs, evaluation of resource plans, and acid rain compliance planning. These examples confirm that optimizing net value can yield different resource portfolios than minimizing cost.