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Study on Consumers’ Perceived Benefits and Risks of Smart Energy System
Abstract
This study explores consumer perceptions of smart energy systems, delving into both the perceived benefits and risks associated with their adoption and usage. This study addresses a crucial gap in understanding the consumer side of smart energy system implementation. Through ordinal logistic regression analysis, the study examines the relationship between various independent variables and an ordinal dependent variable represented on a Likert scale. The findings highlight a significant consumer emphasis on 'Safe Energy System Construction' and 'Economic Benefits,' including 'Home Energy Saving' and 'New Profit Creation.' However, the perceived benefits and risks are influenced by these factors and individual propensities, such as sensitivity to environmental destruction and acceptance of new technology. The study uncovers new areas of concern, exceptionally high energy consumption and the 'Uncertainty of Electricity Rates,' which have not been extensively addressed in previous research. The conclusions drawn from this study suggest a need for balanced policy-making that fosters technological advancement while addressing consumer apprehensions about energy consumption, rate volatility, and privacy. This study contributes to the broader discourse on technology acceptance and the sustainable implementation of smart energy solutions by providing a nuanced understanding of consumer perceptions in the evolving landscape of smart energy systems.
... When individuals believe in their ability to understand and effectively use technology, they are more inclined to take actions that facilitate its adoption [69]. High self-efficacy can lead to greater exploration of renewable energy benefits, overcoming barriers, and sustained use of the technology [67]. Conversely, low self-efficacy may result in a reluctance to engage with the technology, fear of failure, and ultimately lower adoption rates. ...
This study explores the determinants of microgrid technology adoption, emphasizing enhanced security through the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. As microgrids become critical components of decentralized energy systems, the increasing reliance on information and communication technologies introduces substantial cybersecurity risks. To address these concerns, this research integrates key security constructs—Information Security Alignment (ISA) and Perceived Severity of Information Security Threats (PSIST)—into the UTAUT model to investigate their impact on adoption intentions. A quantitative study involving 302 respondents reveals that strong ISA practices and proactive cybersecurity measures positively influence microgrid adoption, while heightened perceptions of security threats deter adoption. Contrary to expectations, leadership commitment to cybersecurity policies does not have a direct effect on adoption intention. These findings provide critical insights for cybersecurity policymakers, technology developers, and IT security professionals by emphasizing the need for robust security frameworks, regulatory compliance, and enhanced risk communication strategies to foster trust and facilitate secure microgrid adoption. By addressing security vulnerabilities at multiple levels, this study contributes to advancing secure and resilient energy systems in the face of evolving cyber threats.
The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays.
In today's digital economy, digital wallets are becoming increasingly significant. Various factors influence digital wallet’s usage behaviour, e.g. consumer attitudes and views of digital wallets, the perceived benefits and hazards of using digital wallets, and the ease of use and reliability of digital wallet services. This study empirically examines the usage behaviour of digital wallets, with a particular focus on the role of perceived risk as a mediator and role of perceived trust and perceived service quality as moderators. This quantitative and primary data-based study used survey approach to collect data using structured questionnaires from 450 digital wallet users, and data were analyzed using PLS-SEM (Smart PLS 4.0). The findings show that behavioural intention positively and significantly impacts digital wallet usage behaviour, and perceived risk mediates the proposed relationship, while perceived trust and service quality showed strong moderating effects on the relationship between perceived risk and digital wallet usage behaviour. The study adds to the literature on digital wallet adoption and usage by emphasizing the complex relationship between behavioural intention, perceived risk, perceived trust, and perceived service quality. The findings can help digital wallet service providers to build successful strategies to increase its’ acceptance and usage.
The purpose of this study was to determine guest pro-environmental behavior on the application of energy efficiency in capsule hotels with case studies in Bobobox Indonesia and Digital Airport Hotel. The method used in this research is qualitative analysis. The samples used were two capsule hotel management informants in Indonesia, namely Bobobox Indonesia and Digital Airport Hotel. The method used is purposive sampling. Data collection techniques in this study were carried out by online interviews through Zoom meetings and observations. Based on the results of data analysis, it can be concluded that the implementation of energy efficiency through smart key technology in capsule hotel improves the pro-environmental behavior of the guests. It can be observed through the willingness of guests to use the smart key technology implemented by the company. This study contributes to the field by exploring the impact of energy-efficient measures, particularly through the implementation of smart key technology, on guest pro-environmental behavior in the hospitality industry. The findings offer valuable insights for both businesses aiming to enhance sustainability in accommodations and travelers seeking to make eco-conscious choices while on the move.
The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.
Energy is the foundation for human survival and socio-economic development, and electricity is a key form of energy. Electricity prices are a key factor affecting the interests of various stakeholders in the electricity market, playing a significant role in the sustainable development of energy and the environment. As the number of distributed energy resources (DERs) increases, today’s power systems no longer rely on a vertical market model and fixed electricity pricing scheme but instead depend on power dispatch and dynamic pricing to match supply and demand. This can help prevent significant fluctuations in supply–load imbalance and maintain system stability. Modern power grids have evolved by integrating information, communication, and intelligent control technologies with traditional power systems, giving rise to the concept of smart electric grids. Choosing an appropriate pricing scheme to manage large-scale DERs and controllable loads in today’s power grid become very important. However, the existing literature lacks a comprehensive review of electricity pricing in power systems and its transformative impact on shaping the energy landscape. To fill this void, this paper provides a survey on the developments, methods, and frameworks related to electricity pricing and energy trading. The review mainly considers the development of pricing in a centralized power grid, peer-to-peer (P2P) and microgrid-to-microgrid (M2M) energy trading and sharing, and various pricing methods. The review will cover the pricing schemes in modern power systems, particularly with respect to renewable energy sources (RESs) and batteries, as well as controllable load applications, and the impact of pricing schemes based on demand-side ancillary services (DSAS) for grid frequency support. Lastly, this review article describes the current frameworks and limitations of electricity pricing in the current energy market, as well as future research directions. This review should offer a great overview and deep insights into today’s electricity market and how pricing methods will drive and facilitate the future establishment of smart energy systems.
In recent years, most of the research in the field of smart grids integrating renewable energy sources assumed energy efficiency as a scheduling objective. However, the aspects of energy consumption or energy demand have not been described clearly, even though they have been proven to be an effective way of reducing energy consumption. In this context, this study aimed to cover a key research challenge in the field, such as the development of an intelligent strategy for solving energy consumption scheduling problems. The added value of our proposal consists of classifying individual consumption profiles assigned to each operation cycle phase, instead of considering an average of non-varying consumption of household appliances. Within this hybrid approach, the proposed explainable system, based on self-organizing maps of neural networks, fuzzy clustering algorithm, and scheduling technics, correlates the complex interrelation between power generated from renewable energy sources in a smart grid, prosumers’ load behaviors, and the consumption profile of controllable or uncontrollable appliances. The tests were made using green energy consumption and production from real monitored data sets. The load-shifting algorithm that was used to reduce energy consumption from the national energy grid proved its effectiveness. In fact, consumers paid 25% less for the energy they used from the national energy grid during the times when the amount of electricity produced from renewable sources was reduced as a result of weather conditions.
The incorporation of communication technology with Smart Grid (SG) is proposed as an optimal solution to fulfill the requirements of the modern power system. A smart grid integrates multiple energy sources or microgrids and is supported by an extensive control and communication network using the Internet of Things (IoT) for a carbon-free, more reliable, and intelligent energy system. Along with many benefits, the system faces novel security challenges, data management, integration, and interoperability challenges. The advanced control and communication network in the smart grid is susceptible to cyber and cyber-physical threats. A lot of research has been done to improve the cyber security of the smart grid. This review aims to provide an overview of the types of cyber security threats present for smart grids with an insight into strategies to overcome the challenges. As the selection of techniques and technologies may vary according to the threats faced, therefore the adoption of researched methods is compared and discussed. As cyber-security is the greatest challenge in smart grid implementation, this review is beneficial during the planning and operation of smart grids for enhanced security.
Smart meters (SMs) can play a key role in monitoring vital aspects of different applications such as smart grids (SG), alternative currents (AC) optimal power flows, adversarial training, time series data, etc. Several practical privacy implementations of SM have been made in the literature, but more studies and testing may be able to further improve efficiency and lower implementation costs. The major objectives of cyberattacks are the loss of data privacy on SM-based SG/power grid (PG) networks and threatening human life. As a result, losing data privacy is very expensive and gradually hurts the national economy. Consequently, employing an efficient trust model against cyberattacks is strictly desired. This paper presents a research pivot for researchers who are interested in security and privacy and shade light on the importance of the SM. We highlight the involved SMs’ features in several applications. Afterward, we focus on the SMs’ vulnerabilities. Then, we consider eleven trust models employed for SM security, which are among the common methodologies utilized for attaining and preserving the data privacy of the data observed by the SMs. Following that, we propose a comparison of the existing solutions for SMs’ data privacy. In addition, valuable recommendations are introduced for the interested scholars, taking into consideration the vital effect of SM protection on disaster management, whether on the level of human lives or the infrastructure level.
The explosive development of electrical engineering in the early 19th century marked the birth of the 2nd industrial revolution, with the use of electrical energy in place of steam power, as well as changing the history of human development. The versatility of electricity allows people to apply it to a multitude of fields such as transportation, heat applications, lighting, telecommunications, and computers. Nowadays, with the breakout development of science and technology, electric energy sources are formed by many different technologies such as hydroelectricity, solar power, wind power, coal power, etc. These energy sources are connected to form grid systems to transmit electricity to cities, businesses and homes for life and work. Electrical energy today has become the backbone of all modern technologies. To ensure the safe, reliable and energy-efficient operation of the grid, a wide range of grid management applications have been proposed. However, a significant challenge for monitoring and controlling grids is service response time. In recent times, to solve this problem, smart grid management applications based on IoT and edge computing have been proposed. In this work, we perform a comprehensive survey of edge computing for IoT-enabled smart grid systems. In addition, recent smart grid frameworks based on IoT and edge computing are discussed, important requirements are presented, and the open issues and challenges are indicated. We believe that in the Internet of Things era, the smart grid will be the future of energy. We hope that these study results will contribute important guidelines for in-depth research in the field of smart grids and green energy in the future.
The article's primary purpose is to determine the attitude of Czech entrepreneurs towards innovation by comparing data obtained before and during the pandemic. Moreover, to compare the perception of the risks associated with the pandemic among Czech and Polish entrepreneurs. The use of innovation helps transform and modernize products, thereby affecting a firm's competitiveness in the market. At the same time, the introduction of new technologies allows the company to gain an advantage in creating new products that can allow it to expand its activities abroad and, thus, open up new markets. It can play an essential role in the company's development during a difficult market situation in its own country. This article examines trends in SMEs' drive to innovate in a pandemic. It also compares the results of entrepreneurs from the Czech Republic and Poland. The data obtained revealed that despite the overall negative impact of the pandemic on the activities of SMEs, it had a positive effect on the attitude of entrepreneurs towards the introduction of innovations. The willingness of Czech SMEs to innovate in the near future has increased by almost 23%. Also, during the pandemic, the expectations of entrepreneurs for help and decisive steps from the state increased. In this way, entrepreneurs preferred to rely on government assistance in financing the company than on banks, and the percentage of expectations for advantageous conditions from banks decreased by 19%. A comparison of data between Polish and Czech SMEs indicated differences in the perception of the main risk of a pandemic, where Czech entrepreneurs have fewer problems selling products or services (32% against 48%).
Distributed generation presents the potential solution to the power deficit problem faced in developing counties by encouraging local generation with renewable energy resources that are abundantly available in some part of the world, especially in Ghana. Distributed generations on the other hand are closely linked to Smart Grid (SG) that uses information technology to manage electricity networks. A plethora of problems relating to safety, reliability and affordability of Smart grid solutions remains challenging today. In Africa, especially in Ghana, the adoption of smart grid has been very slow, poorly managed, and almost inexistent in some countries despite the crucial need for electricity provision. This slow adoption can be attributed to many limiting factors that demand a thorough investigation. This paper aims at investigating the barriers to the adoption of smart grids in Africa and also develops and evaluates countermeasures to hasten the adoption process. A stratified sampling approach was used to collect comprehensive data in Ghana that were handled with SmartPLS software. The evaluation of factors was done through inferential statistics. The impact of the proposed measures was tested using a partial least structural equation model analysis. Findings revealed some factors that strongly influence the adoption of SG including cost, education, government policies; other factors like culture, Grid stability, had slight influence on the adoption process, nevertheless, societal perception had a low impact on the adoption process and therefore attracts less attention in fostering the SG acceptance in Ghana. Regarding SG prospects, the study identified several factors that can foster the prospect of smart grid adoption in Ghana namely education on smart grid technology, government policies and consumers behaviour. It is believed that, if careful attention is given to the factors listed above, the adoption of SG in Ghana will move at a faster speed.
Smart grid technologies are considered an important enabler in the transition to more sustainable energy systems because they support the integration of rising shares of volatile renewable energy sources into electricity networks. To implement them in a large scale, broad acceptance in societies is crucial. However, a growing body of research has revealed societal concerns with these technologies. To achieve sustainable energy systems, such concerns should be taken into account in the development of smart grid technologies. In this paper, we show that many concerns are related to moral values such as privacy, justice, or trust. We explore the effect of moral values on the acceptance of smart grid technologies. The results of our systematic literature review indicate that moral values can be both driving forces and barriers for smart grid acceptance. We propose that future research striving to understand the role of moral values as factors for social acceptance can benefit from an interdisciplinary approach bridging literature in ethics of technology with technology acceptance models.
Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads toward production periods. Therefore, this article introduces a systematic approach to provide a preliminary evaluation of the thermoeconomic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an ϵ-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed-integer linear programming technique. To illustrate the contribution of MPC, different applications on the single- and multi-dwelling level are presented and analyzed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single-building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock.
Over the last decade, concerns have been raised about increases in the electricity used by information technologies, other consumer electronic devices, data centres, and to a much lesser degree, Internet distribution networks. At the same time, ‘smart’ innovations are widely anticipated to help reduce energy demand across diverse sectors of society. Yet such potential savings, as well as the increasing use of other digital services, are predicated upon continued expansion of digital infrastructures. This paper focuses on the phenomenal growth in Internet traffic, as a trend with important implications for energy demand. It outlines an agenda to better understand how data demand is changing. Drawing on findings from our own research in combination with secondary data analysis, we examine the alignment of peak demand for electricity and data. Peaks in data appear to fall later in the evening, reflecting the use of online entertainment, but this is far from fixed. Overall, the paper argues that a better understanding of how everyday practices are shifting, in concert with the provision and design of online services, could provide a basis for the policies and initiatives needed to mitigate the most problematic projections of Internet energy use.
Smart homes are a priority area of strategic energy planning and national policy. The market adoption of smart home technologies (SHTs) relies on prospective users perceiving clear benefits with acceptable levels of risk. This paper characterises the perceived benefits and risks of SHTs from multiple perspectives. A representative national survey of UK homeowners (n=1025) finds prospective users have positive perceptions of the multiple functionality of SHTs including energy management. Ceding autonomy and independence in the home for increased technological control are the main perceived risks. An additional survey of actual SHT users (n=42) participating in a smart home field trial identifies the key role of early adopters in lowering perceived SHT risks for the mass market. Content analysis of SHT marketing material (n=62) finds the SHT industry are insufficiently emphasising measures to build consumer confidence on data security and privacy. Policymakers can play an important role in mitigating perceived risks, and supporting the energy-management potential of a smart-home future. Policy measures to support SHT market development include design and operating standards, guidelines on data and privacy, quality control, and in situ research programmes. Policy experiences with domestic energy efficiency technologies and with national smart meter roll-outs offer useful precedents.
Power system restoration has attracted more attention and made great progress recently. Research progress of the power system restoration from 2006 to 2016 is reviewed in this paper, including black-start, network reconfiguration and load restoration. Some emerging methods and key techniques are also discussed in the context of the integration of variable renewable energy and development of the smart grid. There is a long way to go to achieve automatic self-healing in bulk power systems because of its extreme complexity. However, rapidly developing artificial intelligence technology will eventually enable the step-by-step dynamic decision-making based on the situation awareness of supervisory control and data acquisition systems (SCADA) and wide area measurement systems (WAMS) in the near future.
Policy makers and environmental agencies have echoed concerns brought forward by academics about the need to address the rebound effect for achieving absolute energy and environmental decoupling. However, such concerns have generally not been translated into tangible policy action. The reasons behind this inaction are not fully understood, and much remains unknown about the status of the rebound effect issue on the policy agenda and policy pathways available. Such knowledge gaps may hamper the development of effective policies to address this issue. In this paper, we examine the extent to and ways in which the rebound effect is considered in policy documents and analyse thirteen specific policy pathways for rebound mitigation. The effectiveness of the pathways is scrutinised and conclusions are offered to mitigate rebound effects. The main policy conclusions of the paper are that an appropriate policy design and policy mix are key to avoiding undesired outcomes, such as the creation of additional rebound effects and environmental trade-offs. From the discussion, economy-wide cap-and-trade systems as well as energy and carbon taxes, when designed appropriately, emerge as the most effective policies in setting a ceiling for emissions and addressing energy use across the economy.
Consumer acceptance of smart meters remains crucial in achieving the potential carbon emission reductions offered by advanced metering infrastructures. Given this, the present research used deliberative focus groups to examine what is needed to secure acceptance and engagement from domestic consumers with services, products and ‘offers’ in smarter power systems. Our findings suggest that consumers are able to identify not just threats relating to smart metering initiatives but opportunities as well. In particular, our focus group participants responded positively to the idea of an automated system that could be used to achieve energy savings in combination with time-of-use tariffs. We conclude by outlining suggestions for policy recommendations that may help consumer acceptance of smart meter enabled services be more readily achieved.
Energy management system for efficient load management is presented in this paper. Proposed method consists of the two main parts. One is the energy management center (EMC) consisting of graphical user interface. EMC shows the runtime data and also maintains the data log for the user along with control of the appliances. Second part of the method is load scheduling which is performed using the single knapsack problem. Results of the EMC are shown using LABVIEW while MATLAB simulations are used to show the results of load scheduling. Hardware model is implemented using human machine interface (HMI). HMI consists of PIC18f4520 of microchip family and zigbee transceiver of MC12311 by Freescale. The microcontroller interface with the zigbee transceiver is on standard RS232 interface. INDEX TERMS—Smart Grid, Energy Management, Zigbee.
It is important to ensure consumer acceptance in a smart grid since the ultimate deployment of the smart grid depends on the end users׳ acceptance of smart grid products and services such as smart meters and advanced metering services. We examine how residential consumers perceive the smart grid and what factors influence their acceptance of the smart grid through a survey for electricity consumers in Korea. In this study, consumers׳ smart grid acceptance factors, including the perceived risk, were examined with the existing technology acceptance model suggested by Davis. This study has an implication that it has provided theoretical and empirical ground, based on which the policies to promote consumer participation in the deployment of the smart grid can be developed. Since there are few studies on the policies from the perspective of the smart grid users, this study will contribute directly to the development of the strategy to ensure the acceptance of the smart grid.
In the current landscape, Smart Grids (SGs) grapple with critical challenges, notably their reliance on centralized networks and the absence of microgrid system integration. The concept of a multi-microgrid solution emerges as an approach that interconnects and orchestrates multiple microgrids, enhancing the electrical system’s resilience, scalability, and capacity to integrate distributed renewable energy sources, enabling Demand-Side Management (DSM) for a more efficient and secure network. This study introduces a novel hybrid Internet of Things (IoT) architecture employing LoRaWAN and LoRaMESH to address these challenges. This approach, operating within a decentralized multi-microgrid framework, reduces message transmission time by over 50%, demonstrating its efficacy and relevance while streamlining communication between Smart Meters (SMs) and the Electric Power Company (EPC), ultimately fostering the adoption of DSM services by consumers and representing a transformative solution for the future of Sgs.
In recent years, there has been a growing interest in academia and industry in the analysis of electrical consumption in residential buildings and the implementation of smart home energy management systems (HEMS) to reduce household energy usage and costs. HEMS have been designed to emulate the statistical and functional characteristics of real smart grids. However, a major challenge in this research area is the limited availability of publicly accessible datasets. To address this challenge and further leverage the potential of artificial HEMS applications, it is crucial to develop time series that accurately represent diverse operating conditions of synthetic systems. This paper introduces a novel approach based on the combination of variational auto-encoder-generative adversarial network (VAE-GAN) techniques to generate time-series data of energy consumption in smart homes. Additionally, we investigate the performance of the generative model when integrated with a Q-learning based HEMS. The effectiveness of the Q-learning based HEMS is assessed through online experiments using real-world smart home data. To evaluate the quality of the generated dataset, we employ various metrics including Kullback–Leibler (KL) divergence, maximum mean discrepancy (MMD), and the Wasserstein distance, which quantify the disparities between probability distributions of the real and synthetic data. Our experimental results demonstrate that the synthetic data generated by VAE-GAN closely aligns with the distribution of real data. Furthermore, we demonstrate that the utilization of the generated data facilitates the training of a more efficient Q-learning based HEMS, surpassing the performance achieved with datasets generated using baseline approaches.
Cognitive Autonomous Networks (CAN) promise to advance Self Organizing Networks (SON) by applying artificial intelligence to significantly raise the degree of automation in mobile networks. In CAN, Cognitive Functions (CFs) learn the optimal configuration parameter values to optimize specific network metrics, with the execution coordinated via a controller. In open, multi-vendor systems however, the CF’s learning ability may raise a new risk: a manipulative CF (MCF) may learn not only its objective, but also to manipulate the coordination system in pursuit of that objective. In this paper we propose and evaluate our proposed functionality, called CoDeRa, that neutralizes manipulative CF behavior. However, although CoDeRa is effective against MCFs, it is inadequate to resolving error propagation in CF coordination, caused by corrupted network data, for which we have proposed an alternate simpler and cost efficient network management architecture. Our evaluation shows that the proposed design is robust against the observed concerns, and in context of ongoing worldwide standardization efforts, we summarize the relevance and implications of our proposed architecture.
The high instantaneous discharging capability of battery energy storage systems (BESSs) make them ideal candidates for reducing peak loads in commercial buildings. An efficient online BESS control algorithm can be beneficial for reducing the monthly electricity bill of individual commercial buildings. Conventional model-based BESS control algorithms rely heavily on accurate long-horizon net load forecast, which is difficult to obtain. To address this problem, we develop a Lyapunov optimization-based online BESS control algorithm and derive its theoretical performance bound. Comprehensive numerical study results using real world commercial building smart meter data in southern California show that our proposed Lyapunov optimization-based online control algorithm with time-varying weighting parameters yields higher savings in electricity cost and requires less computation time in comparison to the state-of-the-art baseline algorithms.
Accurate electricity price forecasts is the common concern of market participants. With the integration of high penetration of wind and solar energy resources into the power system, the renewable energy sources will have a great impact on the electricity price volatility undoubtedly. In this regard, a novel attention mechanism (AM) based electricity price forecasting model for electricity market with high proportion of renewable energy is proposed in this paper. In order to investigate the effect of renewable energy on the electricity price prediction, the wind power generation, solar power generation, predicted load and the historical price series are simultaneously taken as the input features. In the data preprocessing stage, the empirical wavelet transform (EWT) is applied to decompose each of the input features into multiple components to avoid learning the autocorrelation of the original sequence. In the model training stage, a hybrid AM-based long short-term memory network (LSTM) is proposed as the forecasting model, aiming to make full use of the AM to dynamically evaluate the importance of different input feature. Furthermore, the crisscross optimization algorithm (CSO) is adopted to retrain the parameters of fully-connected layer so as to further enhance the generalization ability. The proposed method is validated on the datasets of Danish electricity market with a high proportion of renewable energy, and the experimental results show that the proposed model is superior to other hybrid models involved in this study.
Nowadays, multi-energy systems are receiving special attention from smart grid community owing to their high flexibility potentials integrating with multiple energy carriers. In this regard, energy hub is known as a flexible and efficient platform to supply energy demands with an acceptable range of affordability and reliability by relying on various energy production, storage and conversion facilities. Given the increasing penetration of renewable energy sources to promote a low-carbon energy transition, accurate economic and environmental assessment of energy hub, along with the real-time automatic energy management scheme has become a challenging task due to the high variability of renewable energy sources. Furthermore, the conventional model-based optimization approach requiring full knowledge of the employed mathematical operating models and accurate uncertainty distributions may become impractical for real-world applications. In this context, this paper proposes a model-free safe deep reinforcement learning method for the optimal control of a renewable-based energy hub operating in multiple energy carries while satisfying the physical constraints within the energy hub operation model. The main objective of this work is to minimize the system energy cost and carbon emission by considering various energy components. The proposed deep reinforcement learning method is trained and tested on a real-world dataset to validate its superior performance in reducing energy cost, carbon emission, and computational time with respect to the state-of-the-art deep reinforcement learning and optimized-based approaches. Moreover, the effectiveness of the proposed method in dealing with model operation constraints is evaluated on both training and test environments. Finally, the generalization performance for the learnt energy management scheme as well as the sensitivity analysis on storage flexibility and carbon price are also examined in the case studies.
The paper studies the consumer readiness for participation in new business models for demand response in electricity markets. The main premise is that consumers’ attitudes, if considered during the development, ensure that the business model meets the interests of the consumers, while still maintaining a focus on its original purpose. The goal of the paper is twofold: to develop a new business model suitable for a developing electricity market, that enables customer participation in the demand response services, and to analyze customer readiness to participate in the developed model. The proposed business model relies on internet of things, as a technology that provides customers with an easy way to participate in demand response services. The customers’ attitudes towards various incentives, such as green energy, environmental protection, profit and interest in new technologies, as well as trust in the market operator were taken into consideration. The results indicate that different service packages can be created for different types of customers depending on individual characteristics. Additionally, results show that consumers show interest in environmental conservation, while they prefer state institutions as service operators. Using the gathered results, new insights can be gained into demand response user acceptance, and further business strategies formulated.
The electrical grid in the United States comprises all of the power plants generating electricity, together with the transmission and distribution lines and systems that bring power to end-use customers. The "grid" also connects the many publicly and privately owned electric utility and power companies in different states and regions of the United States. However, with changes in federal law, regulatory changes, and the aging of the electric power infrastructure as drivers, the grid is changing from a largely patchwork system built to serve the needs of individual electric utility companies to essentially a national interconnected system, accommodating massive transfers of electrical energy among regions of the United States. The modernization of the grid to accommodate today's more complex power flows, serve reliability needs, and meet future projected uses is leading to the incorporation of electronic intelligence capabilities for power control purposes and operations monitoring. The "Smart Grid" is the name given to this evolving intelligent electric power network. The U.S. Department of Energy (DOE) describes the Smart Grid as "an intelligent electricity grid-one that uses digital communications technology, information systems, and automation to detect and react to local changes in usage, improve system operating efficiency, and, in turn, reduce operating costs while maintaining high system reliability." In 2007, Congress passed the Energy Independence and Security Act (P.L. 110-140). Title XIII of the act described characteristics of the Smart Grid and directed DOE to establish a Smart Grid Investment Matching Grant (SGIG) program to help support the modernization of the nation's electricity system. In 2014, DOE concluded that the adoption of Smart Grid technologies was accelerating but at varying rates "depending largely on decision-making at utility, state, and local levels." DOE noted that the nation's electricity system is in the midst of "potentially transformative change," with challenges for Smart Grid deployment remaining with respect to grid-connected renewable and distributed energy sources and adaptability to current and future consumer-oriented applications. Costs of deploying the Smart Grid remains an issue, and study estimates vary. While some DOE programs have supported grid modernization, Congress has not explicitly appropriated funding for deployment of the Smart Grid since the American Recovery and Reinvestment Act of 2009 (P.L. 111-5). In its 2014 study, DOE estimated historical and forecast investment in the Smart Grid as approximately 3.61 billion annually in the period. If this level of investment remains constant, it would put spending well below levels the Electric Power Research Institute (in 2011) and the Brattle Group (in 2008) estimated were needed to fully build the Smart Grid by approximately 2030. From 2010 to 2015, 8 billion in grid modernization. Congress could provide funding to help bridge the funding gap if it chooses to accelerate adoption of the Smart Grid. A number of near-term trends-including electric vehicles, environmental concerns, and the ability of customers to take advantage of real-time pricing programs to reduce consumer cost and energy demand-would benefit from investments in Smart Grid enabled technologies. While concerns such as cybersecurity and privacy exist, most electric utilities appear to view Smart Grid systems positively. Costs could be reduced and system resiliency improved by further integration of automated switches and sensors, even considering the cost of a more cybersecure environment. But with the potentially high costs of a formal transition, some see the deployment of the Smart Grid continuing much the same as it has, with a gradual modernization of the system as older components are replaced.
This study examines factors influencing the risk perception of Internet of Things (IoT) services in the home energy management sector. We focus on investigating the effects of the various types of perceived risks and individuals’ propensities on the overall risk perception of the IoT services and explore some demographic variables. The perceived risks include perceived financial risk, perceived performance risk, perceived security/privacy risk, and perceived electromagnetic radiation (EMR) risk. The individuals’ propensities include the sensitivity to electricity price changes, environmental destruction, and new technology acceptance. As a result, this study confirms the existence of various influencing factors in the risk perception of the IoT-based home energy management services. Perceived EMR risk is the most influential one among the various types of perceived risks of the services, followed by perceived performance risk and perceived security/privacy risk. In general, experts point out cybersecurity threats as a weakness of ICT services, but users are more likely to have fear of the EMR. Moreover, sensitivity to electricity price changes and sensitivity to new technology acceptance are significant factors. This study has a significance in that we explore various factors affecting the risk perception of IoT services in the energy management sector in detail and comprehensively. Particularly, we discuss not only perceptions about the IoT services, but also individuals’ propensities. This work may contribute not only to studies on perceived risk and technology acceptance but also to stakeholders trying to market the IoT services to consumers.
In a low-carbon and sustainable future, smart grids have a key role to play not only in saving energy with demand and supply-side management of energy systems, but also in optimizing the integration of a wider range of generation and storage options including renewable energy sources. Moreover, some challenges can follow the transition from a traditional grid into a smart grid. In this context, “public attitude” and “public acceptance” are major components to make more radical scenarios about the feasible implementation of smart grid technologies and successful integration of renewable energy sources.
This paper presents unique survey results of public attitudes towards smart grids, smart meters, renewable energy and environment in Qatar using a representative panel from Qatar University community. It contributes to the literature by providing a better understanding of electricity consumers’ perception and behavior about the possible deployment of smart grid applications. It also aims to create data about public attitudes to inform policy makers, the business community and other stakeholders for decision-making by introducing institutional and regulatory changes, and modifying the relationship between consumers, the government and utilities.
The main conclusions can be used as a basis for sustainable energy planning, and awareness campaigns formulation in order to enhance public acceptance regarding renewable energy projects and smart grid technologies. However, the results are a first attempt to elicit public views and will need to be extended to the larger community in Qatar and repeated at regular basis.
The Smart Grid (SG) is a technological transformation from conventional electric grid, electro-mechanically controlled system, to smart, intelligent, and electronically controlled system called the “Smart Grid” (SG). There are about 20–30% losses present in the conventional electric grid due to substandard operations at generation, transmission and distribution side. The major players in the transformation are: (a) increased electricity cost, (b) aging infrastructure, (c) carbon footprint, (d) Green House Gas emissions, (e) climate change, and (f) less efficient electrical network. The promising features of the Smart Grid are: (a) intelligent de-centralized control, (b) resilience, (c) flexibility, (d) sustainability, (e) digitalization, (f) intelligence, (g) consumer empowerment, (h) green energy, and (i) smart infrastructure. The fundamental issues and open challenges in the SG are lack of awareness, consumer acceptance, cyber terrorism, data collection management, energy metering, dynamic optimization and energy control. Considering above, in this paper, a comprehensive review exploring information of development, technologies, and techniques in the SG. The main goal is to investigate and reveal the key enabling technologies, to obtain better picture about the current status of SG development. The focus areas of this review study are Architectural Model focusing Consumer Empowerment (CE), Demand Response Program (DRP), and Demand Side Management (DSM). Our survey discusses in detail the Communication Technologies, such as Wireless Advance Metering Infrastructure (AMI), Phasor Measurement Unit (PMU), Supervisory Control and Data Acquisition (SCADA), and Machine to Machine Communication (M2M). The power systems such as Micro Grid, Nano Grid, Pico Grid, Inter Grid, Virtual Power Plants, and Distributed Generation are also elaborated in this review study. Renewable Energy Resources (RERs) Integration with the SG and Integration issues related to Distributed generation (DG) are presented in this survey. This survey also analyzes Architectural Model of the Smart Grid focusing consumer empowerment and prosumers interaction. The aim of this study is to provide deep understanding of technologies and their applications in the SG.
In this study, smart energy systems are investigated and comparatively assessed to solve major global energy-related issues in a sustainable manner. In order to be considered as smart and sustainable, the energy systems should use technologies and resources that are adequate, affordable, clean, and reliable. Therefore, selected smart energy systems are evaluated based on their efficiencies, environmental performance, and energy and material sources. Our results show that increasing the number of products from the same energy source decreases emissions per unit product and increases efficiencies. Also, among the identified sources, geothermal has the most potential in terms of using cleaner technologies with energy conservation, renewability and the possibility of multiple desired products from the same source. Solar, hydro, and biomass are also beneficial. Even with carbon capture technologies, fossil fuels are not very desirable in smart energy systems because of their emissions and non-renewability.
A deep transformation of the electrical grid system at the global level is expected in the near future, with new economic relations between energy suppliers and consumers. This is the smart grid framework, which can integrate the behavior of all connected users, who become prosumers, to ensure a sustainable energy supply in an efficient, economical and safe manner. Smart grid projects have a significant impact on electricity consumers through shifts in consumer behavior, culture and lifestyle. The aim of this paper is to explore the degree of investigation of the main socio-economic features, in terms of private and social costs, which are relevant for smart grids development and public acceptance. To this aim, a literature review of 148 peer-reviewed scientific journal articles on smart grids has been conducted, developing an original taxonomy for the socio-economic features in terms of private (direct) costs directly associated with the monetary costs paid by consumers, and social (indirect) costs constituted by consumers’ perception, privacy, cyber security and regulation. The importance of the analysis of social costs arising from externalities is that they may hinder the deployment of specific technologies, even if these technologies appear to be useful based on private costs. The results reveal that the explored literature mainly deals with private costs, although an emerging literature starts to address the social costs that may hamper smart grids deployment. The paper reviews new opportunities and challenges for further research, bridging the gap between engineering and socio-economic research areas, including business, organizational applications, policy and security issues. The resulting enrichment of interdisciplinary know-how can be the basis for the sustainable development of current and future generations.
Many medical and epidemiologic studies incorporate an ordinal response variable. In some cases an ordinal response Y represents levels of a standard measurement scale such as severity of pain (none, mild, moderate, severe). In other cases, ordinal responses are constructed by specifying a hierarchy of separate endpoints. For example, clinicians may specify an ordering of the severity of several component events and assign patients to the worst event present from among none, heart attack, disabling stroke, and death. Still another use of ordinal response methods is the application of rank-based methods to continuous responses so as to obtain robust inferences. For example, the proportional odds model described later allows for a continuous Y and is really a generalization of the Wilcoxon-Mann-Whitney rank test.
The term cyber security is often used interchangeably with the term information security. This paper argues that, although there is a substantial overlap between cyber security and information security, these two concepts are not totally analogous. Moreover, the paper posits that cyber security goes beyond the boundaries of traditional information security to include not only the protection of information resources, but also that of other assets, including the person him/herself. In information security, reference to the human factor usually relates to the role(s) of humans in the security process. In cyber security this factor has an additional dimension, namely, the humans as potential targets of cyber attacks or even unknowingly participating in a cyber attack. This additional dimension has ethical implications for society as a whole, since the protection of certain vulnerable groups, for example children, could be seen as a societal responsibility.
Prepublication version available at http://www.davidjhess.net. Wireless smart meters (WSMs) promise numerous environmental benefits, but they have been installed without full consideration of public acceptance issues. Although societal-implications research and regulatory policy have focused on privacy, security, and accuracy issues, our research indicates that health concerns have played an important role in the public policy debates that have emerged in California. Regulatory bodies do not recognize non-thermal health effects for non-ionizing electromagnetic radiation, but both homeowners and counter-experts have contested the official assurances that WSMs pose no health risks. Similarities and differences with the existing social science literature on mobile phone masts are discussed, as are the broader political implications of framing an alternative policy based on an opt-out choice. The research suggests conditions under which health-oriented precautionary politics can be particularly effective, namely, if there is a mandatory technology, a network of counter-experts, and a broader context of democratic contestation.
Purpose
The purpose of this paper is to analyse how consumer innovativeness can be used as a variable to positively influence internet banking adoption both directly and reducing consumer perceived risk.
Design/methodology/approach
The impact of innovativeness and risk on internet banking adoption has been tested through structural equation modelling techniques. The sample consists of 511 Spanish internet banking services users accessed through an internet survey. Risk has been measured as a formative construct.
Findings
Results reveals consumer innovativeness as a key construct to improve e‐banking adoption both directly and by its effective role in reducing consumer risk perception of using internet channel in the financial services context.
Practical implications
Practical guidelines are provided to bank managers on how to use consumer innovativeness level as a segmentation variable to increase the use of internet banking among actual customers who are non users or light users of the electronic channel.
Originality/value
There is a lack of studies which connect consumer innovativeness and perceived risk in the electronic commerce context and specially on e‐banking research. Formative configuration of risk is quite an innovative approach to measure this construct.
This study presents an extended technology acceptance model that integrates innovation diffusion theory to investigate what determine user mobile commerce acceptance. This paper models the factors relationships such as perceived usefulness, perceived ease of use, personal innovativeness, subjective norms, behavioral control and intention to adopt mobile commerce. The proposed model was empirically tested using data collected from a survey of mobile commerce consumers. Empirical data from regression analysis reflects users ease of use influence behavioral intention to adopt mobile commerce. The majority of positive relationships between perceived ease of use, subjective norms, behavioral control and intention to adopt are supported by empirical data. Results also reveal that behavioral control and subjective norms influence perceived ease of use which affects then their adoption intention. The paper concludes some important implications for the practitioners.