Electric Utility Research Articles

U.S. vertically integrated electric utility greenhouse gas emissions and carbon risk premiums around the Paris Accord

We study the pricing of greenhouse gas emissions of vertically integrated producers of electricity around the Paris Accord (PA). We study whether emissions are priced by financial markets, providing a market-based incentive for firms to reduce their carbon footprints and if the heightened attention on climate change post-Paris Accord (PA) impacts the size of the “carbon risk premium.” We focus on electricity generators, because they are responsible for the largest share of emissions and emissions reductions in the U.S. and are highly exposed to regulatory, physical, and stranded asset risks. We find the cost of carbon risk is reflected in the returns of vertically integrated electric utilities. The post-PA period provides the strongest evidence that carbon risk is priced. We find that equity markets provide incentives for power producers to reduce emissions, as reductions in emissions are associated with reductions in required returns on equity (increases in equity market values). The challenge for regulators is how to respond in rate cases. Lowering a utility's regulated return to reflect lower market estimates of the return on equity would dilute the market-based incentive for emissions reductions. Adding a longer-term return incentive for continued investment in emissions reductions would reinforce the market incentive.

A mixed-methods examination of fixed-object crashes among electric utility company fleet vehicles

IntroductionOne of the most common and serious types of crashes among all motor-vehicle users involves collisions with fixed objects. This type of crash occurs frequently among utility vehicle workers while driving for work. The overarching objective of this research was to improve the safety of electric utility company vehicle operators by determining the circumstances under which utility vehicles are involved in crashes with fixed objects and to provide recommendations to help drivers and utility fleet company management prevent these types of crashes. MethodsThe study incorporated a mixed methods approach and gathered information from structured interviews with safety managers at electric power companies and an analysis of utility vehicle crashes using data supplied by electric power companies and a statewide database in Michigan. ResultsMany factors were found to contribute to fixed-object crashes including engagement in secondary tasks and job productivity pressure. Information was organized and evaluated to develop 11 recommendations for countermeasure strategies that are directed at various levels within the system in which the drivers operate. DiscussionThis study was the first to explore fixed-object crashes among electric utility fleet vehicles. The findings are unique in that they provide insight into the safety practices in an industry that has received limited academic attention and highlight the need to place greater emphasis on road safety practices in the workplace. Practical applicationCountermeasures generated for this study are applicable to companies in which fleet driver safety is a primary concern.

Has Real Time Spot Electricity Market in India Impacted Day-Ahead Spot Electricity Market?

Introduction of Real-Time Market (RTM) on June 1st, 2020, marked pivotal advancement in electricity trading in India featuring bi-hourly auctions and power transmission based on temporal blocks. Adherence to operational protocols outlined in Procedure for Scheduling Collective Transactions in RTM along with statutory directives like CERC Power Market Regulations 2010 ensures efficient market operation. Notable features include 15-minute contract trading, clandestine bidding and authorization from SLDC. Day-Ahead Market (DAM) facilitates electricity exchange through double-sided closed auctions ensuring compliance with regulatory protocols. In this study we explore whether Real Time Spot Electricity Market in India has impacted Day-Ahead Spot Electricity Market. Findings of the study provide crucial insights for market participants, stakeholders, open access consumers and electric utilities is India.

Production of energy from renewable sources and financial performance of European utilities: A panel-data analysis

Energy transition has become a priority for utilities worldwide.Renewable electricity generation represents one of the pillars of the European decarbonization policy and is included among eligible activities in the Taxonomy Regulation.The limited literature regarding the effects of investments in renewables on the financial performance of firms is fundamentally split in two lines: the first is focused on stock returns, the second concerns the determinants of the financial performance which is measured either through risk or accounting metrics. A limited set of studies has assessed the effects of investments in renewable power generation on the performance of utilities. In this article three linear regression analysis conducted on a panel-data composed of firm-year observations for 64 electric utilities from continental Europe between 2011 and 2020 are presented. The analysis has focused on: (i) profitability (Return-On-Asset), (ii) market valuation (Tobin's q) (iii) risk (Weighted Average Cost of Capital).The results show that the renewable generation share is significantly and positively correlated with the first two indicators, while not correlated with the cost of capital.This could represent a constraint to investments in renewable generation capacity with a negative impact on the achievement of the targets of the European decarbonization policy.

Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters

The adoption of smart meters and dynamic pricing programs is rapidly increasing among electric utility companies. In “Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters,” Keskin, Li, and Sunar analyze how utility companies should use smart meter data for better pricing decisions. Utility companies typically have access to consumption patterns and high-dimensional features on customer characteristics and exogenous factors. The authors identify that such feature data can exhibit different forms of heterogeneity—over time and over customers. They show that the different forms of feature heterogeneity significantly worsen the best profit performance that can be achieved by a data-driven dynamic pricing policy. The authors also develop a policy based on joint spectral clustering and contextual dynamic pricing and prove that this policy achieves near-optimal profit performance.

A Financial Analysis and Valuation of Leading Companies in the U.S. Electric Utility Sector

In the post-pandemic era, as societal life demands gradually recover and stabilize, the American public utilities sector has demonstrated positive growth momentum, garnering significant investor attention. This research focuses on four prominent enterprises within the industry, including Southern Company (SO), American Electric Power (AEP), Duke Energy (DUK), and Exelon Corporation (EXC). These entities not only epitomize the developmental trajectories of the electric power industry but also reflect the escalating influence of renewable energy over traditional fossil fuels and the ramifications of heightened environmental standards on corporate operations. Leveraging the 2023 annual reports as foundational data, this study conducts a thorough analysis and valuation of these firms' financial statement indices and price-to-earnings ratios, aiming to offer comprehensive insights and analyses regarding their financial standings and market prospects. The findings indicate that Southern Company exhibits robust market competitiveness and allure, alongside an optimistic outlook for future development, rendering it a viable candidate for sustained attention from investors seeking long-term value investments.

Intelligent MPPT for High Gain Zeta Converter for Standalone PV Application with Galvanic Isolation

In this paper, Standalone PV system is designed to operate without the need of electric utility grid which are commonly designed to supply specific DC electric loads. These type of PV system are generally powered using photovoltaic array and consist of solar charging modules, controllers and regulators. The objective of this work is to implement the Radial Basis Function Neural Network (RBFNN) based Maximum Power Point Tracking (MPPT) technique to attain constant DC link voltage of the ZETA converter. This method utilizes a ZETA converter to achieve better voltage gain and lower ripple in the output current and voltage. Using an Artificial Neural Network (ANN) controller to operate a high frequency converter, which improves power conversion efficiency. To ensure safety and prevent electrical faults, an isolation transformer provides galvanic isolation between the high frequency converter and the PV system. Galvanic isolation avoids electrical faults from being transmitted and safeguards connected loads. The rectifier is used to supply the Direct Current (DC) load with the output from the isolation transformer. The rectifier changes the transformer’s Alternating Current (AC) output into DC current to power DC loads. An intelligent MPPT is employed to stabilize the converter’s output voltage and address the intermittent characteristics of PV, and also assists in sustaining the DC link voltage without alterations and ripples from the converter. The efficiency of this work is authenticated via MATLAB simulation 2021a.

(Invited) Research and Development Trends in Sodium-Ion Batteries

Lithium-ion batteries (LIBs) are the most used energy storage technology for electronic devices and electric vehicles. However, the increasing demand for lithium may make it difficult to support the widespread use of LIBs in electric vehicles, utility grids, and other applications. Therefore, developing alternative technologies to LIBs is critical for large-scale energy storage. Studies suggest that using sodium-ion batteries with renewable energies can significantly reduce the cost of electricity. Despite the lower energy density, sodium-ion batteries are appealing for several reasons: (1) sodium is abundant, (2) less critical components are used, and (3) the manufacturing knowledge of LIBs can be used. Herein, we report for the first time a hydrothermal synthesis of a new class of sodium-rich cathode material with the formula Na3+xNixV2-x(PO4)2F2O that could enable the compensation of the irreversible sodium loss in the first charge in a full cell with hard carbon. Trends in sodium-ion batteries will be discussed, considering the new developments in layered oxides, oxy-fluoro-phosphates, and Prussian blue cathodes.

Performance Measurement System Design for Integrated Thinking: Evidence from Japanese Electric Utility Providers

Asian companies are increasingly practicing integrated reporting to create long-term economic, environmental, and social value for investors and other stakeholders. To accomplish such value creation, far-reaching changes in strategies, management control systems, and governance are required, which is achieved through a management approach called integrated thinking. However, findings from previous studies indicate barriers in designing performance measurement systems, leading to misalignment between strategies and managers' decision-making. Therefore, this research study aims to expand existing knowledge on integrated reporting by (1) developing a conceptual framework for the characteristics of performance measurement systems to support integrated thinking and (2) analyzing the current status of performance measurement systems used by Japanese electric utility providers. To support integrated thinking, performance measurement systems should contain multidimensional and connected key performance indicators with quantitative target values and action plans that are integrated into the management reward system. By utilizing primary data, the most recent integrated reports of all 11 Japanese electric utility providers were analyzed trough a manual content analysis and percentage analysis. On average, the requirements of performance measurement system design are fulfilled to a degree of 56 %, while the lowest degree being 39 % and the highest degree being 75 %. In most cases, companies use multidimensional key performance indicators with incomplete target values and vaguely defined action plans. Key performance indicators are insufficiently connected and integrated with rewards systems, indicating the outside-in approach of integrated reporting. Larger companies that explicitly refer to the <IR> Framework tend to use more sophisticated performance measurement systems. The findings suggest several areas for improvement, such as connectivity between KPIs as well as further integrating non-financial KPIs with the rewards system.

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

Transient response of a megawatt-scale solar photovoltaic in an electric distribution utility

There is an increasing trend among customers of an electrical distribution utility to adopt grid-tied solar photovoltaic systems. This shift offers multiple benefits to consumers, including lower monthly electricity bills and a contribution to the development of green energy. For the electrical distribution utility, various impacts may arise due to varying levels of solar energy penetration. This study investigates the effects of integrating varying levels of solar photovoltaic penetration into the commercial consumer network of Cagayan de Oro Electric Power and Light Company (CEPALCO) in the Philippines. Utilizing PowerWorld simulator, the research evaluates 11 different scenarios with solar penetration levels adjusted according to the percentage of load demand. Key findings include alterations in solar megavolt ampere of reactive power output, bus voltage levels, transformer power loading, and transmission line ampacity, with frequency levels remaining stable across scenarios. The optimal solar penetration level was identified at 70%, balancing the benefits of solar energy integration with the need to maintain grid stability and operational limits. This optimal level ensures the effective utilization of renewable energy sources without compromising the performance of CEPALCO’s electrical infrastructure. The research concludes with recommendations for maintaining grid stability and operational limits at the optimal solar penetration limits.

Multi-Criteria Decision Making In The Selection Of Electric Sports Utility Vehicles: Integrated Critic–Copras Method

The aim of this study was to assess and determine the most suitable electric sports utility vehicle (e-SUV) alternatives for consumers who are considering buying an e-SUV that is sold in Turkey. Accordingly, 10 different vehicles were selected using specific criteria from the e-SUVs available through distributor sales in Turkey. CRITIC and COPRAS methods were used to rank these vehicles. The results of the CRITIC method for determining the weights of the criteria were the following three: fast charging time, energy consumption, and price. The results of the analyses conducted using the COPRAS method and these weights determined the e-SUV rankings. Based on these results, the top three alternatives were Subaru Solterra, New MG ZS EV, and BMW iX3 eDrive20.

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Is the Russian Green Bond Market Strong Enough to Hedge in the Crisis Times?

The scope of this research has two facets. First, we study the spillover effects between the Russian green bonds and the leading capital market’s ‘indexes before and after the February 2022 events. Second, the identified level of asset connectedness permits to identify portfolio management implications for the analyzed assets. To reveal the spillover effects, we applied the vector autoregressive model and created a synthetic index to capture the dynamics of the green bonds market which included 14 green bond issues between 2021 and 2023 in Russia. We analyze oil & gas, electrical utilities, metals & extraction, chemical sectors collectively referred to as “pollution intensive indexes”. The paper contributes by discoveringthat the total connectedness index (TCI) between Russian green bond market and pollution intensive indexes changed over time and increased after the outbreak of the conflict. Additionally, the paper is novel on revealing the relationship between low hedging effectiveness and hedging ratio of green bond and energy, metals and extraction, sustainability and oil and gas indexes which indicate no need for hedging after February events. The optimal bivariate portfolio weights analysis shows that Russian green bonds market is an outstanding instrument for assets portfolio management during geopolitical conflict. These findings have implications for the government and other stakeholders to manage both the contagion and climate risks during the military conflict.

Enhancing Utility Sector Efficiency and Security: Integrating Digital Identity Systems Amidst Privacy and Ransomware Challenges

The integration of digital identity systems within gas and electric utilities has the potential to significantly enhance operational efficiency and customer service. However, this transformation brings forth critical challenges related to privacy and cybersecurity, including the rising threat of ransomware attacks. These attacks can severely disrupt operations and compromise data integrity, underscoring the need for both preventive measures and robust recovery strategies. This review paper delves into the intersection of these domains, analyzing the current landscape of digital identity systems in the utility sector, identifying key vulnerabilities, and evaluating existing regulatory frameworks. Through an examination of case studies and best practices, the paper offers recommendations to strengthen digital identity infrastructures, focusing on advanced encryption, multi-factor authentication, continuous monitoring, and effective ransomware recovery strategies. These insights aim to assist utility companies in safeguarding consumer data and ensuring the integrity of essential services

Renewable, Flexible, and Storage Capacities: Friends or Foes?

Problem definition: More than 99% of the new power generation capacity to be installed in the United States from 2023 to 2050 will be powered by wind, solar, and natural gas. Additionally, large-scale battery systems are planned to support power systems. It is paramount for policymakers and electric utilities to deepen the understanding of the operational and investment relations among renewable, flexible (natural gas-powered), and storage capacities. In this paper, we optimize both the joint operations and investment mix of these three types of resources, examining whether they act as investment substitutes or complements. Methodology/results: Using stochastic control theory, we identify and prove the structure of the optimal storage control policy, from which we determine various pairs of charging and discharging operations. We find that whether storage complements or substitutes other resources hinges on the operational pairs involved and whether executing these pairs is constrained by charging or discharging. Through extensive numerical analysis using data from a Florida utility, government agencies, and industry reports, we demonstrate how storage operations drive the investment relations among renewable, flexible, and storage capacities. Managerial implications: Storage and renewables substitute each other in meeting peak demand; storage complements renewables by storing surplus renewable output; renewables complement storage by compressing peak periods, facilitating peak shaving and displacement of flexible capacity. These substitution and complementary effects often coexist, and the dominant effect can alternate as costs change. A thorough understanding of these relations at both operational and investment levels empowers decision makers to optimize energy infrastructure investments and operations, thereby unlocking their full potential. Funding: This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute. This research was also supported by Kelley School of Business, Indiana University, and Haslam College of Business, University of Tennessee. O. Q. Wu thanks Grant Thornton for their generous support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0068 .

Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities.

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.

Holiday Peak Load Forecasting Using Grammatical Evolution-Based Fuzzy Regression Approach

Peak load forecasting plays an important role in electric utilities. However, the daily peak load forecasting problem, especially for holidays, is fuzzy and highly nonlinear. In order to address the nonlinearity and fuzziness of the holiday load behaviors, a grammatical evolution-based fuzzy regression approach is proposed in this paper. The proposed hybrid approach is based on the theorem that fuzzy polynomial regression can model all fuzzy functions. It employs the rules of the grammatical evolution to generate fuzzy nonlinear structures in polynomial form. Then, a two-stage fuzzy regression approach is used to determine the coefficients and calculate the fitness of the fuzzy functions. An artificial bee colony algorithm is used as the evolution system to update the elements of the grammatical evolution system. The process is repeated until a fuzzy model that best fits the load data is found. After that, the developed fuzzy nonlinear model is applied to forecast holiday peak load. Considering that different holidays possess different load patterns, a separate forecaster model is built for each holiday. Test results on real load data show that an averaged absolute percent error less than 2% can be achieved, which significantly outperforms existing methods involved in the comparison.

Synthesis, Studies of Inorganic Class of Mohrite (MOH) Crystals for Electrical, Electronic, Photonic, Phase Matching and Sensor Utilities by Theory and Practice

The high-quality single crystals of Mohrite (MOH) with dimensions 7 × 6 × 3 mm3 were productively grown-up by the customary solution growth methodology. The fully grown crystals were focused to the appropriate structural, optical and electrical investigations. The MOH is monoclinic nature of the specimen with P21/c as the space group and lattice parameters are a as 6.2385 Å, b as 12.6076 Å, c as 9.2655 Å and beta is 106.526°. The crystalline structural authentication with single crystal XRD analysis and compared the same with software based associated results. FITR spectra reveal the vibrational modes of as grown crystals. Optical absorption spectral data divulges that the as-grown crystalline sample has higher optical transparent nature in the visible-NIR region. Photoluminescence intensity is remarkably enriched, shows high crystallinity of MOH. The results of UV, PL, electrical and photo conductivity, SEM, theoretical studies suggest MOH in photonic utility and device appliances especially in UV filters and in electronic filters, room temperature based sensor study for MOH is enabled and reported for red LED based work.

A proposed quality control process for front-end planning to minimize risk of field change orders in underground electrical construction

Controlling capital costs and cost overruns due to construction field change orders (FCOs) is essential for the electric power industry to provide affordable energy services. Conversion from overhead to underground systems due to security and climate change factors will increase the risk of FCOs due to site conditions. The failure in collaboration by front-end planning (FEP) teams can increase risk of FCOs due to missing scopes, errors in design, lack of existing field condition evaluation, constraints on the project schedule, or unexpected field conditions, among other causes. This study involved development of a quality control process that enables members of the FEP team to vote their confidence levels about risk control of FCOs before proceeding to final design. The proposed process utilized Analytical Hierarchy Process (AHP) methodologies to distribute the weights of stakeholder votes based on responsibilities for each category of FCOs to obtain an integrated metric of FEP team confidence. Data from an operational electric power utility was used to provide a case scenario approach and to illustrate the method. Three actual projects were analyzed to assess how well the process would have worked for them. The novelty of the proposed model is to enhance the effectiveness of collaborative working relationships across teams during the FEP process and to provide a quality control metric to capture risk of FCOs in the early phase to minimize cost overruns in the project execution phase.