Search Results (50)

In the context of urban decarbonization, multi-energy microgrids (MEMGs) are gaining increasing relevance due to their ability to enhance synergies across multiple energy vectors. This study presents a block-based MILP framework developed to optimize the operations of a real MEMG, with a particular focus on accurately modeling the structure of electricity and natural gas bills. The objective is to assess the added economic value of integrating a battery energy storage system (BESS) under the assumption it is employed to provide implicit flexibility—namely, bill management, energy arbitrage, and peak shaving. Results show that under assumed market conditions, tariff schemes, and BESS costs, none of the analyzed BESS configurations achieve a positive net present value. However, a 2 MW/4 MWh BESS yields a 3.8% reduction in annual operating costs compared to the base case without storage, driven by increased self-consumption (+2.8%), reduced thermal energy waste (–6.4%), and a substantial decrease in power-based electricity charges (–77.9%). The performed sensitivity analyses indicate that even with a significantly higher day-ahead market price spread, the BESS is not sufficiently incentivized to perform pure energy arbitrage and that the effectiveness of a time-of-use power-based tariff depends not only on the level of price differentiation but also on the BESS size. Overall, this study provides insights into the role of BESS in MEMGs and highlights the need for electricity bill designs that better reward the provision of implicit flexibility by storage systems. Full article

The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R² of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure. Full article

Increasingly, renewable energy resources, energy storage systems (ESSs), and demand response programs (DRPs) are being discussed due to environmental concerns and smart grid developments. An innovative home appliance scheduling scheme is presented in this paper, which incorporates a local energy grid with wind turbines (WTs), photovoltaic (PV), and ESS, which is connected to an upstream grid, to schedule household appliances while considering various constraints and DRP. Firstly, the household appliances are specified as non-shiftable and shiftable (interruptible, and uninterruptible) loads, respectively. Secondly, an enhanced mathematical formulation is presented for smart home energy management which considers the real-time price of upstream grids, the price of WT, and PV, and also the sold energy from the smart home to the microgrid. Three objective functions are considered in the proposed energy management: electricity bill, peak-to-average ratio (PAR), and pollution emissions. To solve the optimization problem, a novel modification-based grey wolf optimizer (GWO) is proposed. When the wolves hunt prey, other wild animals try to steal the prey or some part of the prey, hence they should protect the prey; therefore, this modification mimics the battle between the grey wolves and other wild animals for the hunted prey. This modification improves the performance of the GWO in finding the best solution. Simulations are examined and compared under different conditions to explore the effectiveness and efficiency of the suggested scheme for simultaneously optimizing all three objective functions. Also, both GWO and improved GWO (IGWO) are compared under different scenarios, which shows that IGWO improvement has better performance and is more robust. It has been seen in the results that the suggested framework can significantly diminish the energy costs, PAR, and emissions simultaneously. Full article

Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems. Full article

The rise in electricity costs for households over the past year has driven significant changes in energy usage patterns, with many residents adopting smarter energy-efficient practices, such as improved indoor insulation and advanced home energy management systems powered by IoT and Digital Twin technologies. These measures not only mitigate rising bills but also ensure optimized thermal comfort and sustainability in typical residential settings. This paper proposes an innovative framework to facilitate the adoption of energy-efficient practices in households by leveraging the integration of Internet of Things technologies with Digital Twins. It introduces a novel approach that exploits standardized parametric 3D models, enabling the efficient simulation and optimization of home energy systems. This design significantly reduces deployment complexity, enhances scalability, and empowers users with real-time insights into energy consumption, indoor conditions, and actionable strategies for sustainable energy management. The results showcase that the proposed method significantly outperforms traditional approaches, achieving a 94% reduction in deployment time and a 98% decrease in memory usage through the use of standardized parametric models and plug-and-play IoT integration. Full article

This research investigates the integration of blockchain technology into smart grids, focusing on optimizing both electrical billing and peer-to-peer energy trading between producers and consumers. Using blockchain smart contracts, the system automates and secures energy consumption recording, bill calculation, payment processing, and energy transactions. In the electrical billing framework, a blockchain-based approach was developed to model these functionalities, utilizing an EnergyBilling smart contract to calculate bills and an EnergyPayment smart contract to ensure payment accuracy. Validation using actual consumption data from Sinoma Handan’s project site confirmed the system’s accuracy and reliability when cross-verified with mathematical models. Simultaneously, the study explores peer-to-peer energy trading, where producers (represented by Askari Cement Plant.Nizampur, Pakistan) and consumers (Sinoma Handan Ltd, Handan, China.) conduct automated, transparent transactions. Blockchain’s decentralized nature ensures transparency, data immutability, and a secure, tamper-proof record of transactions. The system eliminates intermediaries, enhancing operational efficiency and reducing costs. Key outcomes demonstrate successful transaction execution with detailed settlements, ensuring financial accountability. Our research highlights blockchain’s transformative potential in revolutionizing electrical billing and energy trading. It offers a secure, transparent, and efficient solution while acknowledging scalability, transaction costs, and regulatory hurdles. Future work could focus on real-world implementation, integration with IoT devices for real-time data collection, and scaling these technologies for broader industrial applications in global energy markets. Full article

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform. Full article

This work presents a production management platform developed to minimize the costs of the capacity charge, part of the electricity bill associated with the cost of maintaining grid capacity during periods of high, fluctuating loads. After a summary of the regulatory solutions on the capacity market in Poland, a capacity charge management system is presented, specifically designed for production facilities within the Energy-Intensive Industry sector. The proposed platform combines hardware data collection, a simulation tool analyzing the electrical energy demand profile to predict the future impact on the capacity charge, and a cloud-based user interface providing real-time recommendations to the plant operators regarding the corrective actions needed to minimize the cost of operation. It was pilot tested in collaboration with a large production facility in Poland, for which the capacity charge was among the main components of the electricity distribution costs. Pilot tests were conducted in the period from January 2022 to September 2023. The tested platform allowed us to shorten the time span of elevated capacity charges from 33% in the year 2022 to only 7% in the year 2023. It also reduced the benchmark capacity charge indicator by more than 11%, from 4.02% to −7.56%, over the duration of the experiments. This improvement was achieved without major changes to the organization and planning of the work. Full article

In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency. Full article

Recently, with the increasing application of the Internet of Things (IoT), various IoT environments such as smart factories, smart homes, and smart grids are being generated. In the IoT environment, a lot of data are generated in real time, and the generated IoT data can be used as source data for various services such as artificial intelligence, remote medical care, and finance, and can also be used for purposes such as electricity bill generation. Therefore, data access control is required to grant access rights to various data users in the IoT environment who need such IoT data. In addition, IoT data contain sensitive information such as personal information, so privacy protection is also essential. Ciphertext-policy attribute-based encryption (CP-ABE) technology has been utilized to address these requirements. Furthermore, system structures applying blockchains with CP-ABE are being studied to prevent bottlenecks and single failures of cloud servers, as well as to support data auditing. However, these systems do not stipulate authentication and key agreement to ensure the security of the data transmission process and data outsourcing. Accordingly, we propose a data access control and key agreement scheme using CP-ABE to ensure data security in a blockchain-based system. In addition, we propose a system that can provide data nonrepudiation, data accountability, and data verification functions by utilizing blockchains. Both formal and informal security verifications are performed to demonstrate the security of the proposed system. We also compare the security, functional aspects, and computational and communication costs of previous systems. Furthermore, we perform cryptographic calculations to analyze the system in practical terms. As a result, our proposed protocol is safer against attacks such as guessing attacks and tracing attacks than other protocols, and can provide mutual authentication and key agreement functions. In addition, the proposed protocol is more efficient than other protocols, so it can be applied to practical IoT environments. Full article

The momentous deployment of photovoltaic (PV) installations in modern times converted schemes utilised to support behind-the-meter systems to compensation mechanisms promoting self-consumption for all prosumer types. Moreover, their incorporation with battery storage systems (BSS) is expected to remove technical counter effects and assist in more self-sufficient prosumer sites. As electricity prices are continuously rising, negatively impacting consumers, we intend for this study to serve as a guideline for residential PV-BSS sizing. Additionally, its objective is to provide an operational and economic evaluation of PV-BSS by considering relevant schemes and concentrating on the most effective parameters. This study contributes to the literature with a holistic methodology for sizing and techno-economically evaluating residential systems in the Mediterranean area that is replicable for any state or consumption class. Simulations addressing PV-BSS performance were exploited with the use of real (high-resolution) data, estimating particular sizing, operational, and techno-economic indicators during the entire system lifetime within the framework of a techno-economic analysis. The simulations calculated the initial expenditure, the yearly revenues from the PV-BSS operation, and the corresponding expenses, contrasting them on a year-to-year basis. The results demonstrate that for the five countries addressed as case studies, PV-BSS sizing is significantly impacted by the supporting scheme regarding maximum financial gains. A likeness amid the ideal power-to-energy ratio (PER) indicator of every addressed state for the examined parameters (electricity price and consumption class) was demonstrated for the full self-consumption scheme, whereas for net billing, intercountry discrepancies and generally higher optimal PER values were observed. Finally, an increase in electricity prices or consumption generally decreases optimal PER; therefore, a recommendation is provided for the avoidance of inessential expenditures in surplus system component sizes. Full article

Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity—industrial or commercial—and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted. Full article

Power quality problems exist in every power system; the more advanced the system is, the higher the probability for issues to occur is, and the smart grid is no exception. In this paper, a study of a residential area is implemented using the LABVIEW simulation software, in which the power of each house with its own appliances is monitored in real time, meaning the power of the whole system is also monitored, including instantaneous and accumulative power, current, power factor—all elements required for system assessment. In addition, a billing policy of each individual house to see the total price to be paid for the power supplied is also devised. All these factors contribute to the analysis and overview of the effect of power quality problems on the grid, both electrically and economically. The results of the simulation indicate that the pricing policy proposed is indeed effective for both sides of the grid, the supplier and the consumer. Full article

As the next-generation grid, the smart grid (SG) can significantly enhance the reliability, flexibility as well as efficiency of electricity services. To address latency and bandwidth issues during data analysis, there have been attempts to introduce fog computing (FC) in SG. However, fog computing-based smart grid (FCSG) face serious challenges in security and privacy. In this paper, we propose a privacy-preserving data aggregation scheme that supports dynamic billing and arbitration, named PPDB. Specifically, we design a four-layer data aggregation framework which uses fog nodes (FNs) to collect and aggregate electricity consumption data encrypted under the ElGamal cryptosystem and employ distributed decryption to achieve fine-grained access and bills generation based on real-time prices. In addition, we introduce a trusted third party to arbitrate disputed bills. Detailed security analysis proves that the proposed PPDB can guarantee the confidentiality, authentication and integrity of data. Compared with related schemes, the experimental results show that the communication overhead of our scheme is reduced by at least $38\%$, and the computational efficiency in the billing phase is improved by at least 40 times. Full article

This study proposes a community solar operation strategy for smart energy communities (SECs), which comprise members of an energy consumption group, to minimize the electricity bill of its members. When sharing resources within a group, resource distribution is a critical problem, and fairness in resource sharing is the main constraint for operation. The proposed community solar operation is formulated as a mixed-integer liner problem that can be optimally solved using centralized control and future time information. However, obtaining information of a future time is not causal. By decomposing the problem into individual problems that are solved by each member at each decision time, the proposed strategy operates the community solar in a distributed manner with partial information. The simulation results using the real dataset recorded in Korea show that the use of the proposed operation strategy results in a fair distribution of electricity bill savings with a marginal benefit reduction of 10% compared to the optimal operation that requires a centralized control and information on the future time. Moreover, a discussion on the tradeoff between the benefits of electricity bill savings and guarantee of fairness is provided. Based on the results, this study can serve as a reference for the design of community solar operations for SECs. Full article