Search Results (318)

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage at both whole-household and individual appliance levels. This granular forecasting enables the development of customized load-shifting schedules for controllable devices. These schedules are optimized using a metaheuristic genetic algorithm (GA) with the objectives of minimizing consumer energy costs and reducing peak demand. The iterative nature of GA allows for continuous fine-tuning, thereby adapting to dynamic energy market conditions. The implemented DSM technique yields significant results, successfully reducing the daily energy consumption cost for shiftable appliances. Overall, the proposed system decreases the per-day consumer electricity cost from 237 cents (without DSM) to 208 cents (with DSM), achieving a 12.23% cost saving. Furthermore, it effectively mitigates peak demand, reducing it from 3.4 kW to 1.2 kW, which represents a substantial 64.7% reduction. These promising outcomes demonstrate the potential for substantial consumer savings while concurrently enhancing the overall efficiency and reliability of the power grid. Full article

The aim of this paper is to present the transformation of the network tariff system in Slovenia using a comprehensive assessment methodology for the techno-economic evaluation of electricity costs for households. The novelty of the proposed approach lies in the combined assessment of the previous and new network tariff systems, explicitly accounting for power-based network tariff components, time-block-dependent charges, and different support schemes for household photovoltaic systems, including net metering and credit note-based schemes. The results show that the transition from an energy-based to a more power-based network tariff system, introduced primarily to mitigate congestion in distribution networks, is not inherently disadvantageous for consumers and prosumers. When tariff structures are appropriately designed, the new framework can support efficient grid utilization and maintain favorable conditions for prosumers, particularly those integrating battery storage systems. Overall, the proposed methodology provides a transparent and robust framework for evaluating the economic impacts of network tariff reforms on residential consumers and prosumers, offering relevant insights for tariff design and the development of future low-carbon household energy systems. Full article

This article analyzes the case of a typical modern residential area, which was built following current legal regulations in Poland. For the purposes of the calculations, a housing estate consisting of 32 houses was assumed, with a connection power of 36 kW each. The three variants evaluate power consumption and photovoltaic system operation: Variant I assumes no PV installations and fluctuating consumer power demands; Variant II involves PV installations in all estate buildings with a total capacity matching the building’s 36 kW connection power and minimal consumption; and Variant III increases installed PV capacity per building to 50 kW, aligning with apartment connection powers, also with minimal consumption. The simulations performed indicated that there may be problems with voltage levels and current overloads of network elements. Although in case I the transformer worked properly, after connecting the PV installation in an extreme case, it was overloaded by about 117% (Variant II) or even about 180% (Variant III). The described case illustrates the impact of changes in regulations on the stability of the electricity distribution network. A potential solution to this problem is to oversize the distribution network elements, introduce power restrictions for PV installations or to oblige prosumers to install energy storage facilities. Full article

Rapid decarbonization and decentralization of power systems are driving the integration of renewable generation, energy storage and digital technologies into unified energy ecosystems. In this context, photovoltaic (PV) systems combined with battery and hydrogen storage and blockchain-based platforms represent a promising pathway toward sustainable and transparent energy management. This study evaluates the techno-economic performance and operational feasibility of integrated PV systems combining battery and hydrogen storage with a blockchain-based peer-to-peer (P2P) energy trading platform. A simulation framework was developed for two representative consumer profiles: a scientific–educational institution and a residential household. Technical, economic and environmental indicators were assessed for PV systems integrated with battery and hydrogen storage. The results indicate substantial reductions in grid electricity demand and CO₂ emissions for both profiles, with hydrogen integration providing additional peak-load stabilization under current cost constraints. Blockchain functionality was validated through smart contracts and a decentralized application, confirming the feasibility of P2P energy exchange without central intermediaries. Grid electricity consumption is reduced by up to approximately 45–50% for residential users and 35–40% for institutional buildings, accompanied by CO₂ emission reductions of up to 70% and 38%, respectively, while hydrogen integration enables significant peak-load reduction. Overall, the results demonstrate the synergistic potential of integrating PV generation, battery and hydrogen storage and blockchain-based trading to enhance energy independence, reduce emissions and improve system resilience, providing a comprehensive basis for future pilot implementations and market optimization strategies. Full article

Accurate electrical load forecasting is fundamental to the efficient operation of energy systems and plays a decisive role in both generation planning and the prevention of supply interruptions. Anticipating demand with precision enables energy generation and distribution to be adjusted effectively, reducing risks for both industrial and residential consumers. However, forecasting is challenged by climatic variations, demographic changes, and evolving consumption patterns, which limit the effectiveness of traditional approaches. Advanced machine learning techniques such as artificial neural networks have demonstrated potential to address these challenges, although their performance depends strongly on hyperparameter optimization. This study applies a multinodal forecasting methodology based on the Fuzzy ARTMAP network to predict short-term electricity demand at nine substations in New Zealand. The method involves an exhaustive search for network parameters, particularly the vigilance parameters ${\rho }_a$ and ${\rho }_b$ and the learning rate $\beta$, which are critical to model performance. The input data were extended with statistical measures—maximum, minimum, mean, and standard deviation—to evaluate their contribution to forecast accuracy. The results showed that the standard deviation provided the most consistent improvements among the windowing techniques, reducing the Mean Absolute Percentage Error (MAPE) in most substations. Parameter analysis further indicated that specific combinations such as ${\rho }_a$ and $\beta$ strongly influence category formation within the network, and consequently the precision of the forecasts. Full article

Many electricity providers are offering their customers an array of tariff options intended to discourage electricity consumption at specific times of the day. The problem facing a customer is whether to switch from their existing tariff to a new tariff. The aim of this paper is twofold: first, to develop two analytical methods that help residential customers evaluate when switching from a flat-rate tariff to time-varying pricing options, specifically the Time-of-Use (TOU) tariff and an event-based tariff, becomes economically beneficial, and second, to review customers’ experiences with the tariffs. The methods identify the specific consumption distributions at which the TOU or event-based tariffs are in energy- and cost-equilibrium with the domestic service tariff for residential customers. For the TOU structure, the analysis shows that customers must maintain a non-winter-to-winter-peak consumption ratio exceeding 3.0756 for cost neutrality, a condition rarely met by households with winter-dominant loads. In contrast, event-based structures require only minimal behavioral adjustments to achieve savings, with as little as 1.75% of annual consumption needing to be avoided during event periods to match domestic-service costs. Additional savings are observed with partial or full load shifting away from peak events. The findings highlight that while TOU may benefit households with high summer usage, event-based tariffs present a more practical and economically favorable option for residential customers living in the Canadian province of Nova Scotia. The paper concludes with implications for tariff selection and consumer behavior. This research will be of value to anyone considering designing a time-varying rate or having to choose between an existing flat-rate tariff and a time-varying tariff. Full article

This study investigates the accelerating adoption of electric vehicles (EVs) integrated with residential rooftop solar and battery storage in Australia, employing Actor–Network Theory (ANT) to elucidate socio-technical dynamics. Through purposive sampling, semi-structured interviews with 15 EV industry stakeholders were conducted and analysed using NVivo 14. Findings revealed EV–solar–storage adoption as a negotiated process shaped by alignments among human and non-human actors, structured by three interdependent obligatory passage points. First, technological integration hinges on interoperability among inverters, smart chargers, EV supply equipment, batteries, and home energy management systems. These are constrained by factors like off-street parking availability. Second, policy and market frameworks require clear interconnection standards, bidirectional charging protocols, streamlined approvals, and targeted incentives. Third, consumer engagement depends on energy literacy, equitable access for renters, and daytime charging infrastructure. Smart and bidirectional charging positions EVs as flexible energy assets, yet gaps in standards and awareness destabilise networks. This ANT-framed study offers a practice-oriented model for clean mobility integration, proposing targeted interventions such as device compatibility standards, equitable policies, and education to maximise environmental and economic benefits at household and system levels. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article

The integration of residential solar photovoltaic (PV) systems with electric vehicle (EV) charging infrastructure offers significant potential for reducing carbon emissions and enhancing energy autonomy. This study presents a real-world case of a solar-powered EV charging system installed at a residential property in Dublin, Ireland. Unlike prior studies that rely solely on simulation, this work covers the complete process from digital design using OpenSolar to on-site installation and performance evaluation. The system includes 16 high-efficiency solar panels (435 W each), a 4 kW hybrid inverter, a 5.3 kWh lithium-ion battery, and a smart EV charger. Real-time monitoring tools were used to collect energy performance data post-installation. The results indicate that 67% of the household’s solar energy was self-consumed, leading to a 50% reduction in electricity costs. In summer 2024, the client achieved full grid independence and received a €90 credit through feed-in tariffs. The system also enabled free EV charging and generated environmental benefits equivalent to planting 315 trees. This study provides empirical evidence supporting the practical feasibility and economic–environmental advantages of integrated PV–EV systems in temperate climates. Full article

The transformation of energy systems towards low emission is one of the key assumptions of the climate and energy policy of the European Union and many countries around the world. These changes include not only the power and transport sectors but also the heating of residential buildings, which consume significant amounts of energy and emit large amounts of greenhouse gases. This article presents a detailed comparative analysis of the costs of heating using an air-to-water heat pump and a condensing gas boiler. The study concerned a retrofitted single-family building from the 1990s, located in southern Poland. The calculations were made taking into account daily meteorological data for two full heating seasons: 2022/2023 and 2023/2024. This approach made it possible to more precisely reproduce real operating conditions. The study was conducted for various configurations of the central heating system: surface and radiator. The following parameters were also taken into account: (1) variable heat pump parameters, such as supply temperature LWT and coefficient of performance COP; (2) current tariffs for electricity and natural gas; and (3) forecasted tariffs for electricity and natural gas in the conditions of market liberalization and phasing out of protective mechanisms. A comparison of the two heating seasons revealed lower costs with a heat pump. In some cases, the cost of heat generated by a gas boiler was over 100% higher than with a heat pump. This applies to both heating seasons. Under the current tariffs, the calculated gas cost for the first season was PLN 6856 (EUR 1605) (1 EUR = 4.27 PLN) compared to heat pump heating costs ranging from PLN 3191 to PLN 4576 (EUR 747 to 1072). For future gas and electricity tariffs, the costs were PLN 8227 (EUR 1926) for gas and PLN 3841 to PLN 5304 (EUR 899 to 1242) for a heat pump. Similarly, for the second heating season, these values were PLN 6055 (EUR 1418) for gas heating and PLN 2741–3917 (EUR 642–917) for a heat pump under the current tariffs, and PLN 7267 (EUR 1702) and PLN 3307–4540 (EUR 774–1064) under future tariffs. This means percentage savings of between approximately 33% and 55%, depending on the heating type and tariff. Therefore, the obtained results indicate the higher profitability of using an air heat pump compared to a gas boiler. This advantage was maintained in all the discussed scenarios, and its scale depended on the type of installation, supply temperature, and the selected electricity tariff. The highest economic profitability was noted for low-temperature systems. These results can provide a basis for making rational investment and design decisions in the context of the energy transformation of single-family housing. Full article

In many countries, most one-phase residential electricity consumers are supplied from three-phase, four-wire local networks operated in radial tree-like configurations. Uneven consumer placement on the wires of the three-phase circuit leads to unbalanced phase loads that break the voltage symmetry and increase the energy losses. One way to mitigate these problems is to balance the phase loads on the feeders by choosing the optimal phase of connection of the consumers. The authors proposed earlier a phase balancing algorithm based on metaheuristic optimization. For networks with a high number of supply nodes, this algorithm requires finding a solution for all the consumers simultaneously. Two alternative approaches are proposed in this paper that use the tree-like structure of the network to divide the optimization between a main distribution feeder and several branches, creating a multistage process, with the aim of minimizing energy losses. A case study is performed using a real low-voltage distribution network and a comparison is made between the three algorithms. The resulting losses have marginal variations between the proposed approaches, with a maximum of 1.3% difference. Full article

This study investigates residential electricity consumption behaviors in the Moldova region of Romania, with a focus on identifying consumption patterns through a non-invasive, survey-based approach. Unlike intrusive monitoring or smart metering methods, the survey collected detailed self-reported data on appliance use, time-of-use awareness, and household characteristics across 55 residential units. The analysis introduced an error-based metric comparing calculated and billed consumption, modeled under a normal distribution to assess estimation accuracy. Results reveal a stable dominance of mid-range consumption bands, alongside emerging stratification, with an increasing share of households transitioning to higher consumption levels. Appliance-level analyses highlight systematic underestimation of high-load devices, such as washing machines and HVAC systems, reflecting perceptual gaps in consumer awareness. Furthermore, demographic profiling indicates that in many households, high-duration and high-load consumers differ, with women more frequently assuming dual roles in energy-intensive tasks within the traditional Eastern European context. The findings demonstrate the potential of non-invasive survey methods to capture behavioral dimensions of energy use that remain underexplored in the absence of smart metering infrastructure, offering new insights into demand-side heterogeneity in peripheral EU regions. Full article

As the energy transition advances toward a low-carbon economy, small- and medium-sized consumers are increasingly becoming active prosumers, capable of generating, storing, and managing their own electricity. However, the intermittent nature of renewable sources poses significant challenges in matching generation with consumption, making energy storage a key element for prosumer participation in smart grids. This work assesses the performance of various energy storage technologies suitable for prosumer applications, focusing on parameters such as efficiency, lifecycle behavior, and system integration. Lithium-ion batteries, supercapacitors, and hydrogen-based technologies were tested under real-world operating conditions within residential, commercial, and industrial scenarios. The results confirm that hybrid configurations deliver the most balanced performance, with supercapacitors improving short-term stability in commercial contexts and hydrogen storage enabling long-duration autonomy in industrial settings. In terms of battery state of charge, the experimental tests showed clear differences across prosumer types: in the residential case, it dropped to about 20–25% in the morning, but recovered to nearly full capacity by midday and stabilized at around 70–75% by the end of the day; in the commercial case, it fluctuated more widely, between roughly 18% and 100%, evidencing the highest stress on batteries; while in the industrial case, it reached 25–30% at peak demand, with hydrogen sustaining autonomy under extended load and ensuring greater long-term reliability. Overall, the findings reinforce the importance of tailored storage strategies to unlock the full potential of prosumers in smart grids. Full article

In Pakistan and most other developing nations, the residential building sector is one of the highest energy-consuming domains. The residential sector has the highest share of 50% of final electricity use of the country. Though Energy Conservation Building Codes (ECBC-2023) provide structured energy guidelines, no work has been performed to quantify the actual energy-saving potential of code-compliant retrofits in residential buildings. This study investigates the performance of ECBC-compliant retrofitting strategies for residential buildings under Pakistan’s diverse climatic conditions. The Passive House Planning Package (PHPP), a validated simulation tool, was used to assess energy performance improvements through building envelope interventions such as thermal insulation, solar shading, window glazing, and optimal orientation. Field data were collected from three representative cities, Multan (hot desert), Taxila (humid subtropical), and Quetta (cold semi-arid), to simulate both conventional and energy-efficient building scenarios. The results showed substantial seasonal energy savings in all three climates. During the heating period, energy savings were 48%, 50%, and 60% for Taxila, Multan, and Quetta, respectively. Similarly, energy savings during the cooling season were 44%, 33%, and 16%. Life cycle economic analysis revealed that these retrofits yielded Net Present Values (NPVs) of USD 752 (Taxila), USD 1226 (Multan), and USD 1670 (Quetta) over a 30-year period, with discounted payback periods ranging from 6 to 10 years. Furthermore, a life cycle assessment demonstrated that retrofitted buildings yielded up to 26% reduction in overall carbon emissions, combining both embodied and operational sources. The findings highlight that ECBC-2023 is not only a technically viable solution for energy savings but also financially attractive in residential retrofitting. By incorporating localized climate responsiveness into ECBC-compliant building design, the study provides a practical roadmap for achieving Pakistan’s energy efficiency goals. Additionally, the outcomes serve as a basis for informing policy initiatives, supporting building code adaptation, and raising public awareness of sustainable housing practices. Full article

This research introduces a two-stage, low-carbon industrial heating process, leveraging advanced waste heat recovery (WHR) technologies and exploiting waste heat (WH) to drive decentralised hydrogen production. This study is supported by a data-driven analysis of individual technologies, followed by 0D modelling of the integrated system for technical and feasibility assessment. Within 10 years, the EU industry will be supported by two main strategies to transition to low-carbon energy: (a) shifting from grid-mix electricity towards fully renewable sources, and (b) expanding low-carbon hydrogen infrastructure within industrial clusters. On the demand side, process heating in the industrial sector accounts for 70% of total energy consumption in industry. Almost one-fifth of the energy consumed to fulfil the process heat demand is lost as waste. The proposed heating solution is tailored for process heat in industry and stands apart from the dual-mode residential heating system (i.e., heat pump and gas boiler), as it is based on integrated and simultaneous operation to meet industry-level reliability at higher temperatures, focusing on WHR and low-carbon hydrogen. The solution uses a cascaded heating approach. Low- and medium-temperature WH are exploited to drive high-temperature heat pumps (HTHPs), followed by hydrogen burners fuelled by hydrogen generated on-site by electrolysers, which are powered by advanced WHR technologies. The results revealed that the deployment of the solution at scale could fulfil ~14% of the process heat demand in EU/UK industries by 2035. Moreover, with further availability of renewable energy sources and clean hydrogen, it could have a higher contribution to the total process heat demand as a low-carbon solution. The economic analysis estimates that adopting the combined heating solution—benefiting from the full capacity of WHR for the HTHP and on-site hydrogen production—would result in a levelised cost of heat of ~EUR 84/MWh, which is lower than that of full electrification of industrial heating in 2035. Full article