Electricity Consumers Research Articles

ENERGY POTENTIAL OF DISTRIBUTED GENERATION AT POWERFUL BOILER HOUSES OF UKRAINE IN CONDITIONS OF MILITARY AGGRESSION

The Integrated Power System of Ukraine (IPS) is purposefully destroyed by the russian aggressor and suffers significant damage, which has caused long-term emergency interruptions in the power supply of critical infrastructure facilities, enterprises and the population. One of the ways to increase the stability of the IPS of Ukraine is to increase the share of distributed generation in its composition. The conceptual idea of solving this problem is the transformation of existing critical infrastructure objects from electricity consumers into electricity producers/suppliers who will provide their own electricity needs and the needs of other electricity consumers. Boiler houses of centralized heat supply systems (DHS) of Ukrainian cities with their existing electric power and fuel infrastructure can become such objects in wartime conditions. The author proposes the widespread introduction of energy-efficient cogeneration gas-piston units (CGPUs) at boiler houses of the DHS. The container-type CGPU is a typical mass-produced equipment that is manufactured in Ukraine and abroad and has a short implementation period (up to 5 days from the date of delivery to the industrial site of the boiler house). The high maneuverability of the CGPUs allows for an increase the flexibility of the IPS of Ukraine. The article presents the main technical and economic indicators of distributed generation with the use of CGPU at existing boiler houses for further national planning of the development of distributed generation during the martial law and post-war reconstruction of Ukraine. The application of CGPU at boiler houses of the DHS of Ukrainian cities has the potential to provide additional distributed generation with a total capacity of up to 2365.7 MW, in particular, 1224.8 MW at 158 boiler houses with a thermal capacity of 100 Gcal/h and more (612 CGPUs with an average unit electric power of 2.0 MW each); 561.9 MW at 409 boiler houses from 20 to 100 Gcal/h (803 CGPUs of 0.7 MW each); 561.9 MW in 2101 boiler houses from 3 to 20 Gcal/h (4136 CGPUs of 0.14 MW each). Under the conditions of 8 hours a day of use of the installed capacity of CGPUs, 567.8 thousand MWh of electricity will be produced every month at the boiler houses of the DHS (taking into account the consumption of the boiler houses for their own needs and release to the network to other consumers) and 136–142 million cubic meters of natural gas will be consumed. According to the average specific cost of modular CGPU (turnkey supply) from 800 to 950 USD/kW of installed electric capacity, capital costs for implementing the CGPUs with a total capacity of 2,365.7 MW will amount to USD 1.89–2.25 billion. Bibl. 31, Fig. 3.

Valuing flexible resources in the Korean electricity market based on stated preference methods

This study quantifies the value of lost load (VoLL) for the manufacturing sector in South Korea using stated preference methods. VoLL represents the cost of a stable electricity supply, which serves as a public good without a market price to compensate for flexible resources in the electricity market. The contingent valuation and choice experiment showed VoLL ranges from 0.35 to 0.80 USD per kWh unserved, below the industrial VoLL estimates in the existing literature. This finding suggests that outages may have lower costs for electricity consumers than investing in flexible resources. Using the correct estimate of outage costs is required to ensure capacity adequacy.

Low-Cost System with Transient Reduction for Automatic Power Factor Controller in Three-Phase Low-Voltage Installations

In power engineering, the importance of maintaining a high power factor in low-voltage electrical installations is known. In power substations for industry, the usual method of coupling is to use an automatic power factor controller which connects capacitors banks (with electromagnetic contactors). Sometimes, AC reactors are connected to the phases of the capacitors banks (to reduce transient phenomena and the deforming regime), depending on the desired value of the power factor. This paper presents an analysis (more focused on experimentation) of a low-cost system for automatic regulation of the power factor with a reduction in transients and an increase in the life of contactors (eliminating the electric arc during switching on), with capacitors banks for low-voltage three-phase installations that connect the capacitors banks by means of one three-phase solid-state relay (an expensive device for a quality device; one is used for all capacitors banks) and using several electromagnetic contactors. The automatic power factor adjustment system has a controller with a microprocessor with six outputs, controlled by the phase shift between the current (measured with a current transformer proportional to the current in a bar) and the phase voltage, which is part of a system of distribution bars (L1,2,3, N) from which electrical consumers (e.g., induction motors) are supplied. To reduce transients when connecting capacitors banks, a three-phase solid-state relay and two related electromagnetic contactors are used for each capacitors bank. The automatic power factor controller is connected to two low-capacity PLCs that control the logic of connecting the capacitors banks to reduce transients. By using the proposed regulation system, a cheaper control solution is obtained compared to the use of one solid-state relay for each capacitors banks, under the conditions in which the power factor adjustment is made as in the classic solution. If twelve capacitors banks are used, the proposed installation is 22.57% cheaper than the classical power factor regulation installation.

Smartgrid-based hybrid digital twins framework for demand side recommendation service provision in distributed power systems

Electricity consumers face challenges in selecting an optimal energy-saving plan, and this is a sustainability problem. To set consumers focus on sustainable energy management, developments around ”Industry 4.0” are needed to achieve an optimal balance between cost and energy consumption with a focus on cutting-edge machine-learning models and smart services introduction. Energy modelling is crucial for confronting the challenges introduced by the energy transition. This research is to promote digital twin (DT) technology in smart energy grids. The hybrid digital twin model is developed based on smart grid end-users’ electrical components. This approach emulates the component specification of the reference smart grid system and encourages a reduction in net energy consumption. Additionally, the application of AI connects smart meters, IoT devices, and the assets of the smart grids to the DT recreation of demand-side end-users for energy efficiency recommendation provision; this setup improves energy management, energy efficiency, and the usage of renewable energy resources. The novelty of this study is that the recent scope of demand-side recommendation schemes has been extended in the direction of cutting-edge Industry 4.0 hybrid DTs. Additionally, a prototype system and its functional characteristics have been proposed with the potential to pave the path for utilization in microgrid environments. Simulation results show a unique level of parallelism between the reference system and the developed model, along with a 36.8% reduction in net energy consumption following implementation of the recommendation advice.

The potential of residential load flexibility: An approach for assessing operational flexibility

This study presents a novel methodology for evaluating the flexibility of residential electricity consumers under diverse operational conditions introducing innovative flexibility indicators. The increasing demand for energy flexibility from the demand side is crucial for ensuring the power grid’s economic stability and resilience. Buildings offer a range of potential flexibility sources that can be effectively leveraged for this purpose. Accordingly, this paper formulates energy flow management in a Smart House using a Mixed-Integer Linear Programming (MILP) framework, incorporating the scheduling of shiftable appliances and an Energy Storage System (ESS). By defining different consumption strategies, the feasible region for Energy-Time Profiles (ETPs) is explored through the establishment of the corresponding optimization problems. A series of case studies are presented to evaluate the impact of operational constraints on the feasible region. Finally, by meticulously analyzing the characteristics of ETPs extracted from these case studies, the proposed flexibility indicators are introduced, quantifying the Smart House’s energy flexibility potential. Furthermore, a sensitivity analysis is conducted to scrutinize the influence of key parameters on the new flexibility indicator, encompassing ESS characteristics. The findings substantiate the efficacy of the proposed indicators in providing comprehensive insights into the potential energy flexibility under a range of operational conditions.

Electricity-carbon coupling retail package optimization considering low-carbon benefits of distributed renewable energy

Distributed renewable energy has developed rapidly in recent years, but its low-carbon value is usually ignored due to its limited individual capacity. It will be an important business for electricity retailers to aggregate these distributed renewable resources and make use of their carbon emission reduction benefits. Therefore, this paper proposes a business model for electricity retailers based on electricity-carbon coupling retail packages. Firstly, a framework of the business model is proposed to support distributed renewable energy transaction among the electricity retailer, prosumers, and consumers. Secondly, dynamic carbon evaluation mechanism-based electricity-carbon coupling retail packages are designed, which contain three types of generation packages for prosumers (e.g., fixed subsidy package, dynamic subsidy package and daytime generation package) and three types of consumption packages for consumers (e.g., daytime consumption package, nighttime consumption package and minimum consumption package). In addition, a single-leader and multi-followers Stackelberg game model is constructed to optimize the designed packages. Especially, the electricity retailer aims at maximizing its profit considering conditional value at risk (CVaR) as the leader. Meanwhile, prosumers and consumers respectively aim at maximizing their profit satisfaction and comprehensive satisfaction as followers. Finally, the particle swarm optimization (PSO) algorithm and CPLEX commercial solver are both used to obtain the model's Nash equilibrium (NE). Example analysis verifies the designed electricity-carbon coupling retail packages can utilize the low-carbon value of distributed renewable resources and improve the benefits and satisfaction of prosumers and consumers.

Toward a Blockchain-Based, Reputation-Aware Secure Transactive Energy Market

The rapid expansion of transactive energy has transformed traditional electricity consumers into producers, engaging in local energy trading. In the context of distributed energy transactions, blockchain technology has been increasingly applied to facilitate transaction transparency and reliability. However, due to the challenges in collecting accurate energy transmission data from power lines, most existing studies on the blockchain-based transactive energy market are still vulnerable to security attacks, such as malicious users misreporting energy prices, refusing to pay or refusing to transmit energy. Therefore, based on the co-simulation platform PEMT-CoSim and a blockchain, we establish a blockchain-based, reputation-aware secure transactive energy market (STEM) by introducing a reputation scheme to evaluate the trustworthiness of all prosumers and designing reputation-aware, multi-round double auction and energy transmission algorithms to detect and penalize malicious attacks. Furthermore, we run comprehensive experiments for different use cases. The results show that even with malicious participants, the proposed system can guarantee the interests of the honest participants and improve the robustness and effectiveness of the energy market.

Would manufacturing go for renewable energy? Manufacturers' preference towards sustainability

Malaysia needs to fully utilize its renewable energy resources to meet its goal of installed capacity of 31% of renewable energy in 2025 and 40% in 2035. In order to empower renewable energy sources, the government has established a fund known as the renewable energy fund (RE FUND). In Malaysia, most manufacturing sectors contribute to the RE FUND through their monthly electricity bills due to their electricity consumption exceeding 300kwh per month. As Malaysia's highest electricity consumer, the manufacturing sector needs government investment incentives to switch to renewable energy sources to generate electricity. Therefore, this study was conducted to identify attribute preferences of the manufacturing sector due to investing in renewable energy sources. The Choice Experiment method was employed where the Mixed Logit model was chosen to identify the willingness to pay for the manufacturing sectors based on their preferences among the four attributes: types of renewable energy, project location, annual reduction in GHG emissions, and RE FUND. The study results found that the manufacturing sector places the highest value on the project location, where they prefer to improve the project location from current condition to far location. This study can also help to achieve the Goal 7 in the Sustainable Development Goal (SDG), where investment in renewable energy sources can guarantee that all individuals obtain affordable, reliable, sustainable, and modern electricity in 2030.

Energy poverty and social justice in Mexico: The rights of electricity consumers

This article analyzes energy poverty in Latin America and the Caribbean and the importance of energy efficiency to address this issue. The paper also touches upon energy subsidies in Mexico and their negative environmental and social consequences. Then, the article studies the potentially detrimental billing practices of Mexico’s state electricity company, which are provoked by the enterprise’s growing past-due portfolio and losses, and the possible infringement of electricity consumers’ rights. The authors propose three courses of action for consumers to protest the company's possibly perjudicial billing. Finally, the authors suggest that a distributed electricity system in Mexico could combat energy poverty and strengthen electricity users’ rights.

Retail competition among multi-type retail electric providers in social networks

Liberalization of the electricity retail market has intensified the competition among different types of retail electricity providers (REPs), leading REPs to pay more attention to electricity consumers' (ECs') preferences for electricity service quality, such as reliability and flexibility. In addition, social learning also greatly influences consumers' electricity purchase strategies. Therefore, this paper models the price and service quality competition among multi-type REPs in a two-layer electricity retail market network that integrates electricity trading and consumer social learning. Firstly, we construct a two-layer network that transforms the dynamic process of ECs switching REPs and changing neighbors into the reconfiguration of the network structure. The upper layer is a bipartite network to express the dynamic transaction between REPs and ECs, while the lower layer is a random network to represent the learning interaction between ECs. Secondly, considering network externalities, we model a retail competition based on price and ECs' preferences for electricity service quality in the network above and get the equilibrium solutions in different scenarios by simulating the electricity consumption data of 600 London households. The results demonstrate that network externalities hinder the transmission of price signals, and the density of the EC network affects the market game result, while the update speed of the EC network has little effect. This study provides theoretical support for multi-agent decision-making in the electricity retail market.

The Influence of Subsequent Harmonics of the Load Current on Errors of Electronic Energy Meters

In the electrical installations of buildings, there is an increasing number of non-linear energy receivers, which introduce strong distortions of the load current of electronic energy meters. Since the readings of these meters are the basis for financial settlements of electricity consumers, it is very important to determine how much the distortion of the receiver current affects the correct operation of commonly used electronic energy meters. The article will present exemplary results and analyses of research work on the impact of individual current harmonics on the readings and errors of selected energy meters.

New markets for local, renewable, or affordable energy? A segmentation analysis to identify consumer preferences for decentralized energy markets

Local Energy Markets (LEMs) allow direct citizen involvement in energy production and trading. A study surveyed 636 Swiss electricity consumers using choice-based conjoint analysis to explore preferences for various LEM characteristics. Three consumer segments emerged: 'Renewable energy enthusiasts' (42 %), interested in energy mix and community PV investment; 'Price sensitives' (30 %), driven mainly by costs; and 'Uninvolved' (28 %), focused on energy mix but hesitant about PV investment. Demographic and psychological variables helped characterize these segments, suggesting tailored communication strategies to boost participation in future energy markets. Communication strategies should focus on ‘Renewable energy enthusiasts’ segment and should emphasize environmental benefits and community engagement to leverage their high motivation for involvement.

Nuclear microreactors: Status review and potential applications; the Mexican case

Microreactors are a class of small modular reactor with a typical power output up to 10 MWe. Their factory fabricated, transportable, and self-regulating design makes the microreactors suitable for supplying electricity to off-grid communities, emergency situations, and space and naval applications. Likewise, microreactors are appropriate for non-power applications such as hydrogen and synthetic fuels production, high and low temperature process heat, various industries (mining, cement, etc.), district heating, and water desalination. Many microreactor concepts are under development around the world with different nuclear technologies, such as the Micro Modular Reactor, a high temperature gas-cooled reactor; Aurora, a liquid metal-cooled fast reactor; and eVinci, a heat pipe-cooled reactor, among others. A recent market assessment of microreactors, conducted by the Idaho National Laboratory, identifies five potential markets for microreactors: Isolated Operations, Distributed Energy, Resilient Urban, Disaster Relief, and Marine Propulsion. In the case of Mexico, which remains dependent on fossil fuels for its energy supply (oil covers 44% of the energy source, while natural gas covers 38% and coal 3%), all potential markets are attractive to consider deploying microreactors, particularly Isolated Operations and Distributed Energy, due to its industrial sector and remote/isolated communities. Mexico’s mining cement, and iron and steel industries are the major consumers of electricity, which is generated primarily with fossil fuels. Similarly, oil accounts for 99.7% of the energy mix of the national transport sector. Furthermore, isolated regions in Mexico, such as the state of Baja California Sur, which generates its own electricity mainly with natural gas, fuel oil, and diesel, could implement microreactors, not only as a potential solution for their electricity supply, but also to implement cogeneration application as water desalination.

Pattern-driven behaviour for demand-side management: An analysis of appliance use

Energy communities play a key role in the transition to sustainable energy, helping to inform and engage end-users so that they can become active energy consumers. In practice, trials and pilots often risk failure due to misplaced expectations and unforeseen behaviours when it comes to achieving flexible energy demand resources. In order to tackle these challenges, residential electricity load profile datasets and consumer survey results emerge as powerful tools for identifying controllable loads, energy consumption models, and tailored understanding of communities' energy contexts. This paper first outlines and analyses these datasets' capabilities to leverage data-driven decision-making for more efficient deployments of demand-side management (DSM) systems. A number of appliance behaviour patterns are extracted, based on high and flexible loads for shifting, being validated over three different use cases to support turn-key DSM in the presence and absence of renewable supply and bill saving. A genetic algorithm optimization is applied to underpin flexible demand reallocation and optimal community load profiles by combining time-variable tariff of use. Experiments demonstrate that controllable and shiftable appliances can reduce average peak load by up to 29% by increasing renewable self-consumption, leading to a valuable energy bill saving of 9%. Our findings also point to the current limitations of existing load/consumption datasets, which are hindering more efficient DSM design of flexibility and demand response programmes in energy communities.

Classification of intrusion cyber‐attacks in smart power grids using deep ensemble learning with metaheuristic‐based optimization

AbstractThe most advanced power grid design, known as a ‘smart power grid’, integrates information and communication technology (ICT) with a conventional grid system to enable remote management of electricity distribution. The intelligent cyber‐physical architecture enables bidirectional, real‐time data sharing between electricity suppliers and consumers through smart meters and advanced metering infrastructure (AMI). Data protection issues, such as data tampering, firmware exploitation, and the leakage of sensitive information arise due to the smart power grid's substantial reliance on ICT. To maintain reliable and efficient power distribution, these issues must be identified and resolved quickly. Intrusion detection is essential for providing secure services and alerting system administrators in the case of adversary attacks. This paper proposes an intrusion classification scheme that identifies several types of cyber attacks on modern smart power grids. Grey‐Wolf metaheuristic optimization‐based feature selection is used to learn non‐linear, overlapping, and complex electrical grid properties. An extended deep‐stacked ensemble technique is advanced by putting predictions from weak learners (CNNs) into a meta‐learner (MLP). The outcomes of this approach are explained and confirmed using explainable AI (XAI). The publicly available dataset from Mississippi State University and Oak Ridge National Laboratory (MSU‐ORNL) is used to conduct experiments. The experimental results show that the proposed method achieved a peak accuracy of 96.6% while scrutinizing the original MSU‐ORNL data feature set and a maximum accuracy of 99% when analysing the selected feature set. Therefore, the proposed intrusion classification scheme may protect smart power grid systems against cyber security attacks.

The Impact of Public Policies and Civil Society on the Sustainable Behavior of Romanian Consumers of Electrical and Electronic Products

The negative impact of the waste generated by long-lasting products is a pressing issue for humanity. As such, socio-economic activities worldwide must be carried out according to sustainability principles, and governments have been increasingly pressured to develop laws and policies that protect the environment. The influence of civil society can boost the impact of public policies. For Romania, an emerging country, a well-developed legislation is needed, as well as the involvement of the administrative and civil sectors, not only in raising consumers’ awareness regarding the importance of recovering and recycling waste, but also in changing their purchasing behavior in the direction of ecological products. An empirical investigation was conducted to identify the direct and mediation effects of external factors on the behavior of electrical and electronic product consumers. Data were collected from 421 respondents and evaluated with latent variable analyses in the R statistical program. The originality of the study resides in articulating a novel relationship between recovery and recycling intentions and the preference for ecological products, enhancing understanding about sustainability in relation to electrical and electronic products. The link between recovering and recycling intentions and consumers’ preferences for ecological products was validated for the first time in our investigation. The study concludes that external factors have a meaningful direct and mediating effect on Romanian consumers’ green behavior.

A Three-Stage Optimal Operation Strategy of Interconnected Microgrids With Rule-Based Deep Deterministic Policy Gradient Algorithm.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

Impact of electrical equipment on the power factor

A large proportion of electricity consumers alter the electricity quality through the equipment they use. The alteration is caused by a drop in the power factor, which leads to additional losses in the power grid and additional reactive power costs. In order to identify the consumers’ electrical equipment that alter the power factor, we monitor their electricity use in a production hall looking at the variation of the effective average values of voltages, currents, powers and power factor. The analysis of these records gives us the difference between the power factor and cos φ (the phase difference between phase voltage and phase current in single-phase operation). Our monitoring shows, at the same time, the necessity of an individual analysis of each electrical equipment in the production hall under observation, as well the necessity to locally compensate the power factor.

КОНЦЕПЦІЯ КООПЕРАТИВУ-ЛОКАЛЬНОЇ МІКРОМЕРЕЖІ ВДЕ ЯК ІНСТРУМЕНТ ІНКЛЮЗИВНОГО РОЗВИТКУ І ПІДТРИМКИ ЕНЕРГЕТИЧНОЇ БЕЗПЕКИ УКРАЇНИ

The article analyzes the prerequisites and presents the motivational grounds and concepts of creating digital platform cooperatives (DPs) as the basis for the formation of local microgrids of renewable energy generation (LMREG). The cooperation of small local RES producers, "Platform Cooperativeism", is considered as a tool of inclusive development, an alternative to "Platform Capitalism" as a result of the monopolization of markets by platforms - digital intermediaries.An overview of research on the development of cooperation of small REG producers is presented. The reasons hindering its development in Ukraine have been revealed. Attention is focused on the potential advantages that can be created by the use of the cross-subsidization effect inherent in the DP for the development of LMREG based on DP cooperatives. The structural and functional scheme of the cooperative DP of LMREG is proposed. Its structural components include the CPU core, the local self-government body, groups of legal entities and individuals - prosumers, who combine the roles of producers and consumers of electricity, as well as investors who invest in the development of the cooperative but do not have their power-generating equipment. The functional component provides generation, accumulation, and distribution of energy flows, their monitoring, data accumulation and analysis, algorithmic control and balancing of the state of the microgrid, provision of data for mutual calculations between its participants, and interaction with the unified energy system of Ukraine. The motivational reasons for participation in the Cooperative-DP of various groups of power generation equipment owners are analyzed. The possible consequences of the formation of the DP core - LMREG on a commercial basis are characterized.

Analysis of Factors Driving Purchase Intention of Electric Cars: Perspective of Theory of Planned Behavior, Norm Activation Model, and Technology Acceptance Model

Objective: The purpose of this study is to empirically investigate the factors that drive the purchase intention of electric cars from the perspective of the Theory of Planned Behavior (TPB), Norm Activation Model (NAM), and Technology Acceptance Model (TAM). Design/Methods/Approach: This study used quantitative research methods with purposive sampling. The data collection method used in this research is the survey method, conducted using a questionnaire distributed online using Google Forms to 253 respondents who do not own an electric car, have a driver’s license, belong to Socioeconomic Status A group, and have knowledge related to electric cars.. A 49-item questionnaire was developed, with a five-point Likert scale on each item. This study used partial least square structural equation modeling (PLS-SEM) for data analysis. Data were analyzed using Smart-PLS 3.2.9 application. Findings: Perceived Usefulness (PU) and Perceived Behavioral Control (PBC) positively and significantly impact the intention to purchase electric cars. Originality/Value: This study contributes to the existing literature on the purchase intention of electric cars by combining three theories namely TPB, NAM, and TAM. Practical/Policy implication: Given the results, government and manufacturers of electric cars in Indonesia should focus the strategies in the area of the usefulness of electric cars and consumers’ perceived behavioral control to increase the purchase intention of electric cars.