Energy Utility Companies Research Articles

Organisational ethics context factors in a public energy utility company: A millennial view

Orientation: Millennials have become the largest age cohort in the modern world of work. Understanding the phenomenological experiences of millennials may be the key to facilitating more ethical organisational cultures.Research purpose: The aim of this article is to explore the lived, working experiences of millennials’ organisational ethics contexts factors as predisposed by ethical leadership in an organisation in the energy sector.Motivation for the study: An extensive body of research exists concerning the statistical relationship between ethical leadership, ethical culture, and workplace ethics climates. However, less explored is qualitative studies regarding millennials’ work experiences of organisational ethics context factors.Research design, approach and method: A qualitative research design was selected. Purposive and convenience sampling were utilised. The sample included eight millennials, and face-to-face interviews were used for data collection. Data were analysed by means of content analysis.Main findings: The findings suggest an ambivalent and paradoxical split in how millennials experience the ethical context factors in the participating organisation.Practical and managerial implications: The study has implications for the type of leadership styles that should be considered when engaging with millennials. Top human resource management could use the results to create a working environment that actively fosters psychological attachment, satisfaction, and boosts employee commitment.Contribution or value added: Expanding and enhancing the comprehension of how ethical leadership contributes to the theory of ethical leadership in the work context.

Financialization, dependence, and state regulation: The accumulation strategies of German energy companies in Hungary

In recent years, geographical scholarship has paid an increasing amount of attention to the accumulation strategies of energy and public utility companies. This article contributes to these debates by emphasizing the multiscalar nature of profit extraction by transnational energy companies through financialization, dependent power relations, and state regulation regarding transnational energy companies. Empirically, this article analyzes German energy companies’ activities in Hungary. These energy giants bought Hungarian gas and electricity providers, networks, and power plants during the privatization waves of the 1990s, and largely left the Hungarian market after the 2010s re-nationalization process. Previous literature emphasized nation-state agency in the Hungarian energy sector’s development. This study—based on an analysis of companies’ annual reports, media coverage, and financial statements—draws attention to firm-level strategies in understanding techniques and strategies of accumulation by German energy companies in Hungary.

Automatic update strategy for real-time discovery of hidden customer intents in chatbot systems

Model degradation is still a challenge in real-time applications such as chatbot systems. This work refers to a webchat service of a Brazilian energy utility company, whose central part is composed of a supervised model and a question-and-answer list. User queries not met by it go to an NLP-based clustering model, responsible for identifying unknown customer intents. Manual labeling is impractical in this case due to the large volume of data. This work proposes an automatic update strategy for this clustering model, necessary due to changes in customer behavior from time to time. A series of experiments showed considerable temporal variation in the number of user queries per customer intent, the allocation of queries from unknown intents to few clusters, and larger relative variations in cluster sizes for unknown rather than known intents over time. Based on these findings, a monitoring metric, together with a cut-off point, was proposed to be used as a trigger for updating the clustering model. This update task was demonstrated in a real situation, from which the discovery of new customer intents was confirmed by experts. This resulted in a significant recovery rate of 13.9% (2251 messages), as 85.1% of the queries are already answered promptly by the central part of the chatbot system. These findings are valuable for the company to improve service quality and, ultimately, customer satisfaction.

Energy price modeling in sub-Saharan Africa: a systematic literature review

Abstract Researchers have found that despite a wide range of renewable energy sources in sub-Saharan Africa (SSA), renewable energy pricing policies have focused extensively on metered electricity energy, an early source of renewable energy. Supply, access, and regulation of price for metered electricity energy is mostly controlled by the governments across SSA. There is an increasing use of other renewable energy sources including portable electricity, solar power, and wind power. However, in SSA, the pricing for domestic renewable domestic renewable power such as portable electricity, rechargeable cookstoves, and portable solar power sources are left to the market to legislate, with energy prices dependent on forces of demand and supply and seldom on clear scientific models. This commercially focused energy market means businesses operating in the energy industry are more interested in profits and set prices relative to their market perceptions. The main problem with the energy market in SSA is the lack of a participatory approach where customers, businesses, the government, and other stakeholders are involved in the pricing for energy. We further note that lack of a participatory approach in energy pricing is a major challenge in uptake and demand for the domestic renewable energy sources. Through a systematic literature review, including a review of peer-reviewed journals, documents from energy utility companies, and published information on the websites for energy companies, this review analyzes the current application of energy price modeling and hypothesizes that mobile technology and a participatory pricing approach can improve pricing for domestic renewable power. Our initial literature review showed that energy price modeling had received little attention in SSA, especially for domestic renewable power energy sources. This paper, therefore, fills this gap by using a systematic literature review to consolidate knowledge on how energy price modeling has been applied in the SSA context. The systematic literature review results reveal four commonly used models: time series, artificial neural network, hybrid iterative reactive adaptive, and hybrid models. These energy pricing models are mainly applied to metered electricity power, the predominant source of energy in SSA. The literature hypothesizes that applying mobile technology to energy pricing and a participatory approach involving the consumers and energy supply businesses can move SSA closer to transitioning to renewable energy. Although other factors have hindered this transition, a participatory energy pricing approach incorporating relevant pricing models and market information creates potential solutions to these challenges. In the discussion, we hypothesize that a participatory approach to price modeling with the incorporation of mobile technology can be used at the household level to improve energy decision-making. For this to work, energy price modeling for domestic renewable sources should be simplified, user-friendly, and accessible to households. In conclusion, we recommend that SSA governments develop a more holistic view of energy price modeling to better harness the potential for domestic renewable energy sources.

Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers’ interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology has enabled accurate forecasting of electricity consumption. However, the impact of computation cost afforded by these ML models is often ignored in favour of accuracy. This study considers both accuracy and computation cost as concurrently significant factors because together they shape the technology environment as well as create economic impact. Thus, a three-fold optimized load forecasting model is proposed which includes (1) application specific parameters selection, (2) impact of different dataset granularities and (3) implementation of specific data preparation. It deploys and compares the widely used back-propagation Artificial Neural Network (ANN) and Random Forest (RF) models for the prediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operational calendars resulting in improved performance. The outcomes are indicative that the granularity i.e. the scale of details in data, and set of reduced and full input parameters impact performance accuracies differently for ANN and RF models. Experimental results show that overall RF model performed better both in terms of accuracy as well as computational time for a 1-min, 15-min and 1-h dataset granularities with the mean absolute percentage error (MAPE) of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suited for a real-time energy monitoring application.

Impacts of the COVID-19 pandemic on electric energy load and pricing in the Iberian electricity market

The ongoing COVID-19 pandemic has established itself has one of the biggest health crises facing humanity. Countries all around the world were forced to adopt unprecedented restrictive measures in order to halt the spread of the virus and safeguard public health. These measures have profoundly changed the way of life and severely affected practically all sectors of activity, with major demand and supply shocks, leading to one of the largest recessions in world history. An essential pillar to the proper functioning of modern societies is energy security, particularly electricity security, which guarantees a reliable and efficient supply of electricity. Energy distributors and utility companies remained operational during mandatory stay-at-home orders, to ensure an uninterrupted power supply. Given the relevant role of energy in society, this work will study the consequences of the economic shutdown on the Iberian electricity market, and discuss the timeline of events, the macroeconomic outlook, the financial status of the major electric utility companies (prior to being hit by the COVID-19 health pandemic), the changes in load profile, the generation mix and, finally, the electricity market spot prices.

The Price of Wind: An Empirical Analysis of the Relationship between Wind Energy and Electricity Price across the Residential, Commercial, and Industrial Sectors

This paper quantifies the long-term impact of wind energy development on electricity prices across the residential, commercial, and industrial sectors in the United States. Our data set is made up of state level panel data from 2000 through 2018. This time period covers the vast majority of total wind energy capacity installed in the history of the USA. Our econometric model accounts for the primary factors that influence electricity prices, incorporating both fixed effects and general method of moments in order to more precisely isolate the effect of wind energy. The empirical results conclude that wind energy is positively and significantly related to electricity prices across all sectors, as indicated by the higher average electricity prices in states with higher percentages of wind energy. The price increase is largest in the industrial sector, followed by commercial, then residential. Wind turbine technology has become significantly more efficient, but the technical gains have been offset by the increased indirect costs of incorporating wind energy into the grid. Transmission and balancing costs have increased the final price to consumers. Our results highlight the need to view wind energy development from a more holistic perspective that accounts for structural and systemic costs. This will ensure the continued growth of wind energy. These results provide relevant insight to help wind energy developers, policy makers, and utility companies build a more sustainable energy future.

Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches

In this paper, a novel deep neural network-based energy prediction algorithm for accurately forecasting the day-ahead hourly energy consumption profile of a residential building considering occupancy rate is proposed. Accurate estimation of residential load profiles helps energy providers and utility companies develop an optimal generation schedule to address the demand. Initially, a comprehensive multi-criteria analysis of different machine learning approaches used in energy consumption predictions was carried out. Later, a predictive micro-grid model was formulated to synthetically generate the stochastic load profiles considering occupancy rate as the critical input. Finally, the synthetically generated data were used to train the proposed eight-layer deep neural network-based model and evaluated using root mean square error and coefficient of determination as metrics. Observations from the results indicated that the proposed energy prediction algorithm yielded a coefficient of determination of 97.5% and a significantly low root mean square error of 111 Watts, thereby outperforming the other baseline approaches, such as extreme gradient boost, multiple linear regression, and simple/shallow artificial neural network.

Implementation of Value Engineering in Optimizing Project Cost for Sustainable Energy Infrastructure Asset Development

The purpose of this study is to optimize costs by analyzing a case study of the Energy Utility Company (EUC) project in Malaysia and building the main electric distribution station (MEDS) project specifically. In order to achieve this objective, Value Engineering (VE) technique as one of the proven tools was selected, and three alternatives were applied. The first alternative was to reduce the room sizes of the building. The second alternative was to replace the plaster painting with normal painting, and the last one was to replace painting up to 1.5 meters with 1 meter of the high of walls from the floor. Results approved that the implementation of VE has successfully reduced the cost of the project without compromising the quality of the materials. For the first alternative, 17.1% of the cost was saved. Regarding the second alternative, 69.8% of the total cost for ceiling painting was saved, and for the third alternative, 41.6% of the total cost of walls from the floor was saved. The findings of this research may serve as a guide for engineers, scholars, and constructors to reduce the cost of the building project.

Understanding the impact of financial sustainability on South African municipalities

This article acknowledges the lack of skills and poor financial governance within South African municipalities but argues that these factors are an insufficient explanation of their increasing fiscal distress, which was found at over 90% by 2016. It employs the concept of financial condition to construct an econometric function for South African local government. This financial condition function confirms that the fiscal framework of South African local government is distressed. The article further concludes that as service delivery improves, so does the fiscal distress. This applies even to municipalities that receive audit outcomes without material findings. As an illustration, the article uses the collective debt incurred by municipalities to Eskom – the energy utility company. The article argues that it is mathematically impossible to settle this debt within the current local government fiscal framework. The conclusion confirms the importance of constructing the financial condition function as an enabler for improved local government financial health.

Debt and Asset Maturity - Do Companies Match, and If So, Why?

This study re-examines the impact of asset maturity on debt maturity choices. The pertinent literature suffers from the fact that asset maturity typically is hard to observe. In this paper we exploit the fact that for energy utilities asset maturity can directly be measured. Using a sample of 193 energy utility companies in the period 2002 to 2009 we first show that asset maturity proxies used in the literature do a fairly good job. Second, we present evidence that asset maturity is not only a statistically, but also economically significant driver for debt maturity. Third, we find that firms do not determine the maturity of newly issued debt instruments with respect to overall asset maturity, but in a way that their maturity gap is reduced. We estimate that an increase in maturity gap by one year causes the maturity of the new debt instrument to be almost four years longer than average debt maturity.

Energy forecasting tools and services

The increasing complexity of the power grid and the continuous integration of volatile renewable energy systems on all aspects of it have made more precise forecasts of both energy supply and demand necessary for the future Smart Grid. Yet, the ever increasing volume of tools and services makes it difficult for users (e.g., energy utility companies) and researchers to obtain even a general sense of what each tool or service offers. The present contribution provides an overview and categorization of several energy‐related forecasting tools and services (specifically for load and volatile renewable power), as well as general information regarding principles of time series, load, and volatile renewable power forecasting. WIREs Data Mining Knowl Discov 2018, 8:e1235. doi: 10.1002/widm.1235This article is categorized under: Application Areas > Business and Industry Application Areas > Data Mining Software Tools Technologies > Prediction

Can supplier innovations substitute for internal R&D? A multiple case study from an absorptive capacity perspective

Prior literature suggests that significant internal R&D resources are needed to leverage suppliers for innovation and that external knowledge sources can be used to complement the internal knowledge base. Based on the analysis of four inbound open innovation projects at Fortum, a multinational energy utility company, we argue that companies with low R&D intensity may adopt an alternative approach which aims at substituting – not merely complementing – internal R&D with external innovations. We adopt the absorptive capacity perspective while investigating the cases and focus on four distinct capabilities: acquisition, assimilation, transformation, and exploitation. We find that the substitution approach consists of short-term research on new technological areas in order to gain the ability to identify and evaluate alternative technologies, as well as joint business models and operations based on complementary capabilities between the parties. The cases also suggest that the innovation process requires significant collaboration and the buying company's supplier management capabilities may improve the success of inbound open innovation projects of this type.

Predictive segmentation of energy consumers

This paper proposes a predictive segmentation technique for identifying sub-groups in a large population that are both homogeneous with respect to certain patterns in customer attributes, and predictive with respect to a desired outcome. Our motivation is creating a highly-interpretable and intuitive segmentation and targeting process for customers of energy utility companies that is also optimal in some sense. In this setting, the energy utility wants to design a small number of message types to be sent to appropriately-chosen customers who are most likely to respond to different types of communications. The proposed method uses consumption, demographics, and program enrollment data to extract basic predictive patterns using standard machine learning techniques. We next define a feasible potential assignment of patterns to a small number of segments described by expert guidelines and hypotheses about consumer characteristics, which are available from prior behavioral research. The algorithm then identifies an optimal allocation of patterns to segments that is feasible and maximizes predictive power. This is formulated as maximizing the minimum enrollment rate from across the segments, which is then expressed as solving a mixed-integer linear-fractional program. We propose a bisection-based method to quickly solve this program by means of identifying feasible sets. We exemplify the methodology on a large-scale dataset from a leading U.S. energy utility, and obtain segments of customers whose likelihood of enrollment is more than twice larger than that of the average population, and that are described by a small number of simple, intuitive rules. The segments designed this way achieve a 2–3× improvement in the probability of enrollment over the overall population.

Co-benefits of primary energy conservation, reduced emissions and costs through biomass and waste incineration CHP in district heating

Energy utility companies face trade-offs in navigating through today’s environmental challenges. On the one hand, they face intense political, social and environmental pressures to move toward adopting energy systems that incorporate the use of renewable energy resources. By making this transition, they would contribute to carbon reduction and mitigate climate change. On the other hand, they need to coordinate their resources and become efficient when investing in new plants or upgrading existing production systems. This paper seeks to address the gains that utility companies can make when replacing older fossil-fuel-based plants with efficient combined heat and power (CHP) plants. We discuss the system effects from the changes in production of other units when new plants are constructed. Using one of the largest energy utility companies in Sweden, Fortum, as empirical point of departure, we analyzed the company’s transition from using coal and hydrocarbons to an increased use of renewables and waste incineration CHP. Our analysis was based on comprehensive production data on CO 2 , SO x and NO x emissions. Our findings suggest that primary energy consumption drops when older, less efficient fossil plants are substituted for new efficient CHP plants; this drop includes the effect on remaining production. The benefits in terms of primary energy savings might even be greater than what is achieved in meeting the goal of climate change abatement through reduced CO 2 emissions; NO x and SO x emissions are decreased with new biomass CHPs. Waste incineration CHP increases NO x and SO x emissions, when there is less fossil fuel to replace after the use of biomass is extended. In both cases, economic efficiency increase as costs are reduced.

Cost-of-Service Segmentation of Energy Consumers

Uncertainty in consumption is a key challenge at energy utility companies, which are faced with balancing highly stochastic demand with increasingly volatile supply characterized by significant penetration rates of intermittent renewable sources. This paper proposes a methodology to quantify uncertainty in consumption that highlights the dependence of the cost-of-service with volatility in demand. We use a large and rich dataset of consumption time series to provide evidence that there is a substantial degree of high-level structure in the statistics of consumption across users which may be partially explained by certain characteristics of the users. To uncover this structure, we propose a new technique for extracting typical statistical signatures of consumption-energy demand distributions (EDDs)-that is based on clustering distributions using a fast, approximated algorithm. We next study the factors influencing the choice of consumption signature and identify certain types of appliances and behaviors related to appliance operation that are most predictive. Finally, we comment on how structure in consumption statistics may be used to target residential energy efficiency programs to achieve greatest impact in curtailing cost of service.

Optimal Energy Scheduling for a Smart Entity

Real-time availability of electricity prices via a smart power grid has a potential bilateral benefit to electricity users and suppliers. The users can reduce their costs by consuming energy during low-price hours and balancing their energy usage during other hours. This in turn benefits energy utility companies by reducing their peak power demand. This article describes an optimal shrinking horizon model for electricity-consuming units based on user preferences. The proposed model optimizes the end user's electricity cost while meeting preferred comfort levels. The user can set preferences in the model using a tristate flexibility parameter for each electric-power-consuming unit. The electricity price model used in the optimization model is general and covers all pricing schemes in the electricity market today. The model derived can be described by a simple mixed integer linear program and solved by most optimization software in a short time. The most distinguishing characteristics of our proposed model are its simplicity, generality, comprehensibility, and ease of implementation. Simulation results are used to verify the model's performance in reducing consumer electricity costs and satisfying comfort preferences.

Smart meter adoption and deployment strategy for residential buildings in Indonesia

For countries pursuing sustainable development and energy efficiency, the use of smart meters is considered a first step in allowing residential consumers to remotely control their energy consumption, and a promising technology for conserving limited energy resources. However, despite the growing interest in smart meters, limited consumer awareness, knowledge, and understanding of these devices contributes to skepticism. This study thus developed an index to measure consumer propensity to adopt smart meters in residential buildings. Data obtained from a survey of energy use by Indonesian households were analyzed using structural equation modeling to determine the interacting factors in consumer acceptance of smart meters. Consumer perceptions, expectations, and intentions regarding the potential use of smart meters in Indonesia were also discussed. The findings of this study enhance understanding of consumer perceptions and behaviors, and can help decision makers and energy utility companies develop policies and strategies for a “one-size-fits-all” program related to smart meter applications in future residential buildings.

The co-evolutionary relationship between Energy Service Companies and the UK energy system: Implications for a low-carbon transition

The Energy Service Company (ESCo) business model is designed to reward businesses by satisfying consumers’ energy needs at less cost and with fewer carbon emissions via energy demand management and/or sustainable supply measures. In contrast, the revenue of the incumbent Energy Utility Company (EUCo) model is coupled with the sale of units of energy, which are predominantly sourced from fossil fuels. The latter is currently dominant in the UK. This paper addresses two questions. First, why has the ESCo model traditionally been confined to niche applications? Second, what role is the ESCo model likely to play in the transition to a low-carbon UK energy system? To answer these, the paper examines the core characteristics of the ESCo model, relative to the EUCo model. The paper then examines how ESCos have co-evolved with the various dimensions of the energy system (i.e. ecosystems, institutions, user practices, technologies and business models) to provide insight into how ESCos might help to shape the future UK energy system. We suggest that institutional and technological changes within the UK energy system could result in a more favourable selection environment for ESCos, consequently enabling the ESCo model to proliferate at the expense of the EUCo model.

Urban energy consumption mapping for energy management

The amount of energy consumption of individual buildings is a widely discussed theme. However, there are few studies that have analyzed the consumption of energy by groups of buildings. This work aims to develop a model able to estimate the energy consumption by residential sectors, in different areas inside a city, through the adoption of an energy planning methodology using the city of Belo Horizonte, Brazil as a case study. In this model, the most important variables of consumption behavior are related to the economy. The methodology coupled different approaches. Its first step was to survey the ownership of appliances and the socioeconomic profile of households in each region inside of the city, through the statistical analysis of census data. The second step was to characterize the average energy consumption by typical residential equipment. Then, it was possible to compute the energy consumption according to equipment and region. Finally, the modeled municipal energy consumption was compared with that recorded by the energy utility company. As a result, maps of energy consumed by end use are presented for the years 1991, 2000, and 2007. The conclusions of this work can be applied in energy planning projections and in local urban planning.