Search Results (48)

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 purpose of this paper is to discuss the optimal operation of ice-storage air-conditioning systems by considering thermal comfort and demand response (DR) in order to obtain the maximum benefit. This paper first collects the indoor environment parameters and human body parameters to calculate the Predicted Mean Vote (PMV). By considering the DR strategy, the cooling load requirements, thermal comfort, and the various operation constraints, the dispatch model of the ice-storage air-conditioning systems is formulated to minimize the total bill. This paper takes an office building as a case study to analyze the cooling capacity in ice-melting mode and ice-storage mode. A dynamic programming model is used to solve the dispatch model of ice-storage air-conditioning systems, and analyzes the optimal operation cost of ice-storage air-conditioning systems under a two-section and three-section Time-of-Use (TOU) price. The ice-storage mode and ice-melting mode of the ice-storage air-conditioning system are used as the analysis benchmark, and then the energy-saving strategy, thermal comfort, and the demand response (DR) strategy are added for analysis and comparison. It is shown that the total electricity cost of the two-section TOU and three-section TOU was reduced by 18.67% and 333%, respectively, if the DR is considered in our study. This study analyzes the optimal operation of the ice-storage air-conditioning system from an overall perspective under various conditions such as different seasons, time schedules, ice storage and melting, etc. Through the implementation of this paper, the ability for enterprise operation and management control is improved for the participants to reduce peak demand, save on an electricity bill, and raise the ability of the market’s competition. Full article

The integration of renewable energy sources, particularly solar photovoltaics, into household power supply has become increasingly popular due to its potential to reduce energy costs and environmental impact. However, solar power variability and new regulative changes concerning excess solar energy compensation schemes call for effective energy storage management and sizing to ensure a stable and profitable electricity supply. This paper focuses on optimizing residential battery storage systems under different electricity pricing schemes such as time-of-use tariffs, dynamic pricing, and different excess solar energy compensation schemes. The central question addressed is how different pricing mechanisms and compensation strategies for excess solar energy, as well as varying battery storage investment costs, determine the optimal sizing of battery storage systems. A comprehensive mixed-integer linear programming model is developed to analyze these factors, incorporating various financial and operational parameters. The model is applied to a residential case study in Croatia, examining the impact of monthly net metering/billing, 15 min net billing, and dynamic pricing on optimal battery storage sizing and economic viability. 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

The accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Forecasting methods, both traditional and modern, often face challenges in achieving the desired level of accuracy. To address these shortcomings, this research presents a novel hybrid approach that combines a robust forecaster with an advanced optimization technique. Specifically, the FS-FCRBM-GWDO model has been developed to enhance the performance of short-term load forecasting (STLF), aiming to improve prediction accuracy and reliability. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective was to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and utility companies by lowering bills and enhancing power system stability. The simulation results validate the proposed model’s efficacy and efficiency compared to existing benchmark models. 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 European Union, increasing attention is already being paid not only to the development of renewable energy sources, but also to the establishment of solutions to achieve local energy self-sufficiency while increasing the role of citizens in managing the energy they generate. This approach is expected both to have a positive impact on the environment and the reduction of greenhouse gas emissions, and to enhance energy security—both in economic and civic terms by, i.a., combating energy poverty. The development of local energy communities promoted in the EU is supported i.a. by energy cooperatives. These contribute to the efficient harnessing of renewable energy potential in rural and urban-rural areas, and have been developing in Poland for several years now. In their previous studies, the authors of this research paper attempted to verify the generation (number, type and capacity of installed sources) and consumption (energy demand) configurations in which an energy cooperative would be a viable solution for prosumers who might establish it. However, over the past few years, the conditions for prosumers and the method of their accounting with the electricity seller have changed radically in Poland (shift from net metering to net billing). This situation has opened up space for further research and encouraged the authors to revisit the problem of analyzing the viability of establishing energy cooperatives in relation to the rules of operation of individual prosumers. This research was carried out for three scenarios, and the horizon of the analyses conducted and described extends to 2045. The comparative analysis included energy consumers without their own generation sources, prosumers with a photovoltaic generation installation covered by the net billing model, as well as a scenario involving prosumers’ cooperation within an energy cooperative, which by law is settled in the net metering model. Conclusions from the research and simulations made it possible to confirm the claim that, despite changes in the rules of prosumer billing, developing energy independence in the energy community formula results in a significant reduction in the cost of purchasing electricity (even several times lower purchase costs in the timeframe analyzed) and can lead to a reduction in the payback time of investments in generation sources even by a factor of two. The results presented in this research paper open up space for further research. The outcomes allow us to assume that energy cooperatives—in the organizational and institutional model in Poland—are a good tool for reducing the phenomenon of energy poverty on a local scale. Full article

In order to improve the utilization of user-side power resources in the distribution network and promote energy conservation, this paper designs a distributed system suitable for power demand response (DR), considering multi-agent system (MAS) technology and consistency algorithms. Due to the frequent changes in the power system structure caused by changes in the load of a large number of users, this paper proposes using cluster partitioning indicators as communication weights between agents, enabling agents to utilize the distribution network for collaborative optimization. In order to achieve the integration of multiple load-side power resources and improve the refinement level of demand-side management (DSM), two types of agents with load aggregator (LA) functions are provided, which adopt the demand response strategies of Time-of-Use (TOU) or Direct Load Control (DLC) and model the uncertainty of individual device states using Monte Carlo method, so that the two typical flexible loads can achieve the target load-reduction requirements under the MAS framework. The research results demonstrate that this method achieves complementary advantages of the two types of loads participating in DR on a time scale, reducing the costs of power companies and saving customers’ electricity bills while peak shaving. Full article

Increasing electricity demand and the emergence of smart grids have given home energy management systems new potential. This research investigates the use of an artificial neural network algorithm for a home energy management system. The system keeps track of and organizes the use of electrical appliances in a typical home with the objective of lowering consumer electricity bills. An artificial-neural-network-based maximum-power-point-tracking scheme is applied to maximize power generation from photovoltaic sources. The proposed neural network senses solar energy and calculates load requirements to switch between solar and grid sources effectively. The implementation of improved source utility does not require numerical calculations. Traditional relational operator techniques and fuzzy logic controllers are compared with the suggested neural network. The model is simulated in MATLAB, and the results show that the artificial neural network performs better in terms of source switching following load demand, with an operating time of less than 2 s and a reduced error of 0.05%. The suggested strategy reduces electricity costs without affecting consumer satisfaction and contributes to environmental friendliness by reducing CO₂ emissions. Full article

Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well. Full article

The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR. Full article

In this paper, a pricing alternative based on the scheme of Time of Use Pricing (TOUP) is proposed for customers connected to the Cooperativa Eléctrica San Pedro de Atacama (CESPA) microgrid, located in the far north of Chile. With this proposal, the promotion of Demand-Side Management, DSM, aims at optimizing both the use of electric energy and the available infrastructure. The pricing proposal replaces the current tariff scheme based on sections of energy consumption and does not give an incentive for customers to efficiently manage their energy consumption. The proposal considers the creation of time bands and their corresponding tariff formulas, to obtain economic benefits both for customers and the electric company. Study cases consider the operation of a photovoltaic plant of 2 MWp in the electric system of CESPA, which is currently underway. The obtained results report benefits for all parts of the electric market. For customers, favorable pricing up to a 19.1% monthly reduction in electric bills is shown, whereas the company presents an increment in their average monthly income of about 7.7%. Full article

To ensure the flexibility and reliability of the power system demand and supply, demand side management with distributed generation is a tool to manage the high demand. In the present work, the demand side residential energy management for household equipment is investigated. Loads of units are divided according to priority. Every individual load is smart enough to switch on/off itself with respect to the power generated from the distributed source. Experiments are performed using MATLAB Simulink and the results showed improvements in electricity bills. Real-time pricing in the present system of billing is more economical than a flat rate system. When distributed resources are connected with the units, then flat rate billing is also more economical than real-time pricing. Full article