Adoption Of Smart Grid Technologies Research Articles

Effects of trust and perceived benefits on consumer adoption of smart grid technologies: a mediation analysis

Effects of trust and perceived benefits on consumer adoption of smart grid technologies: a mediation analysis

THE IMPACT OF SMART GRIDS ON ENERGY EFFICIENCY: A COMPREHENSIVE REVIEW

Smart grids have emerged as a key technology in the quest for energy efficiency and sustainability. This review provides a comprehensive analysis of the impact of smart grids on energy efficiency, highlighting key findings, challenges, and future directions. Smart grids leverage advanced sensing, communication, and control technologies to optimize the generation, distribution, and consumption of electricity. By enabling two-way communication between utilities and consumers, smart grids improve grid reliability, reduce energy losses, and facilitate the integration of renewable energy sources. One of the key findings of this review is the significant impact of smart grids on energy efficiency. Studies have shown that smart grids can reduce energy consumption by enabling real-time monitoring and control of energy usage. This not only helps consumers reduce their electricity bills but also reduces the overall carbon footprint of the electricity sector. However, the deployment of smart grids faces several challenges, including high upfront costs, interoperability issues, and data privacy concerns. Addressing these challenges will be crucial for the widespread adoption of smart grid technologies. Looking ahead, the future of smart grids holds great promise for further improving energy efficiency. Emerging trends such as the Internet of Things (IoT) integration, artificial intelligence (AI) algorithms, and distributed energy resources (DERs) management are expected to drive further innovation in smart grid technologies. In conclusion, smart grids have a transformative impact on energy efficiency, offering significant benefits for consumers, utilities, and the environment. However, addressing challenges such as cost and interoperability will be crucial for realizing the full potential of smart grids. By embracing emerging trends and technologies, stakeholders can further enhance the efficiency and sustainability of the electricity sector.
 Keywords: Impact, Smart Grid, Energy, Efficiency, Review.

An attack-resilient distributed energy management strategy for integrated energy systems

The increasing integration of various energy sources and the adoption of smart grid technologies have revolutionized the way we generate, distribute, and consume energy. While these advancements offer numerous benefits, they also introduce vulnerabilities to cyber–physical attacks. To this aim, this paper investigates a resilient energy management strategy for integrated energy systems. By adopting a switched control approach and incorporating a local estimation mechanism, we have developed a resilient distributed energy management strategy to tackle the energy management problem (EMP) in integrated energy systems (IESs). This methodology demonstrates strong robustness and resilience, successfully detecting data integrity attacks and denial-of-service (DoS) attacks. Finally, we provide a case study to demonstrate the effectiveness of our proposed strategy in a real-world scenario.

A distributed data‐driven modelling framework for power flow estimation in power distribution systems

The power distribution system has increasing importance and complexity as a result of the exponential growth in the adoption of smart grid technologies. The ability to model the power distribution system is critical to ensure a smooth transition to a sustainable power system. This study presents a distributed data-driven framework based on Cellular Computational Networks (CCN) for power distribution system modelling where the CCN framework facilitates for system decomposition. The learning in CCN is distributed and asynchronous, thus adaptive models can be developed. The computational engine of the CCN cells is based on data-driven, physics-driven, or a hybrid approach. The CCN-based distribution system modelling secures the privacy and security of the sensitive utility information, thus allowing third-party application providers access to system models and behaviours. The application of a CCN-based power flow model is illustrated on a modified IEEE 34 test system. Typical results show the suitability of the new approach in modelling the sample distribution system, as well as its enhanced performance when compared with the centralised modelling approach.

Towards the Establishment of a Robust Load Forecasting Mechanism in Tanzania Grid: A Study to Investigate Effect of Air Temperature and Daytime on Electricity Consumption in Residential Buildings

- The current phenomenon of electric power management is towards the adoption of smart grid technologies to achieve efficient utility management processes such as transmission and distribution. Electric load forecasting has become an important aspect of smart grid technologies due to its capability of anticipating the power demand of a particular domain. The effective design of any load forecasting mechanism requires a critical investigation of electricity consumption determinants, following the diversification of social, economic, meteorological, and demographic grounds. Many research works have attempted to investigate the effect of temperature and daytime on electricity usage, targeting a specific country. The works have reported the existence of differing degrees of causality based on a particular country under investigation. The variation in findings from different research works has been a motivation to establish the study to examine the impact of air temperature and day time on load consumption in Tanzania. Four-years load and weather data have been collected from Tanzania Electric Supply Company (TANESCO) and Tanzania Meteorological Agency (TMA) respectively. The k-mean algorithm is used to detect outliers and missing values in the load dataset before further processing. Furthermore, the Shapiro-Wilk normality test method is applied to identify data distribution patterns which in turn leads us to the correct choice for Spearman’s rank correlation method. Results indicate that there is no linear relationship between electricity consumption and air temperature in residential buildings. Finally, findings indicate the existence of a strong causality degree between electricity consumption and day time in Tanzania.

Electricity sector in Ecuador: An overview of the 2007–2017 decade

The Ecuadorian electricity sector has undergone several changes during the past decade. The objective of this paper is twofold: a) to show how the Ecuadorian electricity sector has evolved from 2007 to 2017, and b) to discuss the relationship between energy policies and their impacts on electricity supply, management, tariffs, and the country's economy. Although oil remains as the main energy source and the leading driver for economic revenue, several hydropower projects have been built or are under construction intending in part to reduce the country's dependence on oil. The installed hydropower capacity in the country in 2017 is approximately 81% higher than in 2007 and it is expected that, by 2018, approximately 93% of the electricity will be produced from hydropower. Currently, biomass and biogas contribute with 1.8% of the total electricity generation, but only 0.6% of the electricity is produced in wind and solar farms. Adoption of smart grid technologies is key to transform the Ecuadorian electricity network and to positively impact the quality of the electricity supply. The future of the Ecuadorian electricity sector relies on the successful implementation of the new Organic Law of Public Service of Electricity and on external financing for new energy projects.

Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands

Building energy flexibility might play a crucial role in demand side management for integrating intermittent renewables into smart grids. The potential of building energy flexibility depends not only on the physical characteristics of a building but also on occupant behaviour in the building. Building users will have to adopt smart technologies and to change their daily energy use behaviours or routines, if energy flexibility is to be achieved. The willingness of users to make changes will determine how much demand flexibility can be achieved in buildings and whether energy flexible buildings can be realized. This will have a considerable impact on the transition to smart grids. This study is thus to assess the perception of smart grids and energy flexible buildings by building users, and their readiness for them on a large scale. We attempted to identify the key characteristics of the ideal user of flexible buildings. A questionnaire was designed and administered as an online survey in the Netherlands. The questionnaire consisted of questions about the sociodemographic characteristics of the current users, house type, household composition, current energy use behaviour, willingness to use smart technologies, and willingness to change energy use behaviour. The survey was completed by 835 respondents, of which 785 (94%) were considered to have provided a genuine response. Our analysis showed that the concept of smart grids is an unfamiliar one, as more than 60% of the respondents had never heard of smart grids. However, unfamiliarity with smart grids increased with age, and half of the respondents aged 20–29years old were aware of the concept. Monetary incentives were identified as the biggest motivating factor for adoption of smart grid technologies. It was also found that people would be most in favour of acquiring smart dishwashers (65% of the respondents) and refrigerator/freezers (60%). Statistical analysis shows that people who are willing to use smart technologies are also willing to change their behaviour, and can thus be categorised as potentially flexible building users. Given certain assumptions, 11% of the respondents were found to be potentially flexible building users. To encourage people to be prepared for energy flexible buildings, awareness of smart grids will have to be increased, and the adoption of smart technologies may have to be promoted by providing incentives such as financial rewards.

Erratum to: Adoption of smart grid technologies by electric utilities: factors influencing organizational innovation in a regulated environment

Erratum to: Adoption of smart grid technologies by electric utilities: factors influencing organizational innovation in a regulated environment

Adoption of smart grid technologies by electric utilities: factors influencing organizational innovation in a regulated environment

Incorporation of information and communications technologies has the potential to reduce the environmental impacts of electricity generation and distribution while improving the quality, reliability and efficiency of electricity supply. However, integrating smart grid technologies presents major organizational challenges to utilities, and adoption rates are still low. New knowledge is needed on organizational innovation in response to this potentially disruptive technology in the context of a regulated monopoly. This study examines factors influencing the adoption of smart grid technologies using data from 15 interviews with 12 U.S. utilities. The study provides useful insights and implications for utilities and regulators.

Adoption of smart grid technologies: An analysis of interactions among barriers

Abstract Global electricity demands are increasing at rapid pace. Energy supply, their usage and technologies involved need to be more economical, environment friendly and socially sustainable. Efforts are being done all over the globe to reduce this greenhouse effect; and renewable energy technologies to combat climate changes, which require extensive changes to the current electricity generation and distribution systems. To meet this goal, it is required to optimize the grid operations and available resources to meet the ever increasing energy demands in an efficient, effective and environment sustainable way. It has been found that smart grid technologies have not been so popular due to some obstacles that are hindering its maturation and rapid deployment. An attempt has been made to identify and analyze the barriers to implement smart grid technologies adoption. Twelve relevant barriers towards implementation of smart grid technologies have been identified from extensive literature review and duly validated with experts׳ (from academia and industry) opinions. Also, valuable experts׳ opinions have been utilized to identify contextual relationships among these important barriers and a hierarchical model has been developed based on Interpretive Structural Modeling methodology. Matrice d׳Impacts Croises-Multipication Applique´ an Classment (MICMAC) analysis has also been used to: classify the barriers based upon dependence and driving power; and validate developed ISM based model. “Lack of Regulatory Framework” barrier has been identified as driver or independent level barrier i.e. most important bottom level barrier hindering adoption of smart grid technologies. The developed structured model will help to understand interrelationships and interdependencies among the identified barriers to implement smart grid technologies. Different solutions for handling these identified barriers have also been suggested in the paper. Organizations involved in power generation and distribution may be benefited by understanding of these barriers, their interactions and suggested mitigation solutions towards effective adoption of smart grid technologies.

Towards Privacy Protection in Smart Grid

The smart grid is an electronically controlled electrical grid that connects power generation, transmission, distribution, and consumers using information communication technologies. One of the key characteristics of the smart grid is its support for bi-directional information flow between the consumer of electricity and the utility provider. This two-way interaction allows electricity to be generated in real-time based on consumers' demands and power requests. As a result, consumer privacy becomes an important concern when collecting energy usage data with the deployment and adoption of smart grid technologies. To protect such sensitive information it is imperative that privacy protection mechanisms be used to protect the privacy of smart grid users. We present an analysis of recently proposed smart grid privacy solutions and identify their strengths and weaknesses in terms of their implementation complexity, efficiency, robustness, and simplicity.