Search Results (620)

This paper presents a comprehensive analysis of different energy system configurations for Energy Communities (ECs) supplied by multiple renewable-based technologies, with a specific focus on solar solutions in the Mediterranean region. The authors have studied and then proposed the optimal aggregation of different end-user loads within possible energy system configurations (identifying the most adequate combination of prosumers, i.e., households, municipality offices, commercial activity, and others) in order to narrow the gap between peak/off-peak demand and renewable energy availability by also integrating energy storage technologies, and in order to pursue a sustainable energy transition in urban contexts proposing smart cities at low CO₂ emissions. The study demonstrates that increasing the complexity of the generation mix involves a tangible influence on self-sufficiency and self-consumption, as well as on the mitigation of CO₂ emissions. In fact, a more complex system configuration, including heat pumps and energy storage, allows for up to five months of 100% self-sufficiency and almost 100% self-consumption for the entire year. In terms of greenhouse gas emissions, relevant CO₂ reduction potential is possible, with up to 50% of CO₂ emission reduction, when heat pumps, solar cooling, and energy storage are installed. Full article

Opportunities for citizens to become prosumers have grown rapidly with renewable energy (RE) technologies reaching grid parity. The European Union’s ability to harness this potential depends on empowering energy citizens, fostering active engagement, and overcoming resistance to RE deployment. European energy law introduced “renewable self-consumers” and “active customers” with rights to consume, sell, store, and share RE, alongside rights for citizens collectively organised in energy communities. This article explores conditions for inclusive citizen engagement and empowerment within the RE system. Building on an ownership- and governance-oriented approach, we further develop the concept of energy citizenship, focusing on three elements: conditions for successful engagement, individual versus collective (financial) participation, and the role of public (co-)ownership in fostering inclusion. The analysis is supported by 82 semi-structured interviews, corroborating our theoretical lens. Findings show that participation, especially of vulnerable consumers, relies on an intact “engagement chain,” while energy communities remain an underused instrument for inclusion. Institutional environments enabling municipalities and public entities to act as pace-making (co-)owners are identified as key. Complementing the market and the State, civil society holds important potential to enhance engagement. Inspired by the 2017 European Pillar of Social Rights, we propose a corresponding “European Pillar of Energy Rights.” 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

Local energy systems (LESs) are becoming larger and more heterogeneous as distributed energy resources, electrified loads, and active prosumers proliferate, increasing the need for reliable coordination of operation, markets, and community governance. This Perspective synthesizes recent literature to map how artificial intelligence (AI) supports forecasting and situational awareness, optimization, and real-time control of distributed assets, and community-oriented markets and engagement, while arguing that adoption is limited by system-level credibility rather than model accuracy alone. The analysis highlights interlocking deployment barriers, such as governance-integrated explainability, distributional equity, privacy and data governance, robustness under non-stationarity, and the computational footprint of AI. Building on this diagnosis, the paper proposes principles-as-constraints for sustainable, trustworthy LES AI and a deployment-oriented validation and reporting framework. It recommends evaluating LES AI with deployment-ready evidence, including stress testing under shift and rare events, calibrated uncertainty, constraint-violation and safe-fallback behavior, distributional impact metrics, audit-ready documentation, edge feasibility, and transparent energy/carbon accounting. Progress should be judged by measurable system benefits delivered under verifiable safeguards. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

This study examines the economic performance of residential photovoltaic systems combined with battery storage (PV-BESS) under Poland’s net-billing regime for a single-family household without subsidy support in 10-year operational horizon. These insights extend existing European evidence by demonstrating how net-billing fundamentally alters investment incentives. The analysis incorporates real production data from selected locations and realistic household consumption profiles. Results demonstrate that optimal system configuration (6 kWp PV with 15 kWh storage) achieves 64.3% reduction in grid electricity consumption and positive economic performance with NPV of EUR 599, IRR of 5.32%, B/C ratio of 1.124 and discounted payback period of 9.0 years. The optimized system can cover electricity demand in the summer half-year by over 90% and reduce local network stress by shifting surplus solar generation away from midday peaks. Residential PV-BESS systems can achieve economic efficiency in Polish conditions when properly optimized, though marginal profitability requires careful risk assessment regarding component costs, durability and electricity market conditions. For Polish energy policy, the findings indicate that net-billing creates strong incentives for regulatory instruments that promote higher self-consumption, which would enhance the economic role of residential storage. Full article

The heating and cooling sector accounts for nearly half of Europe’s energy consumption and remains heavily dependent on fossil fuels, emphasizing the urgent need for decarbonization. Simultaneously, the global shift toward renewable energy is accelerating, alongside growing interest in decentralized energy systems where prosumers play a significant role. In this context, district heating and cooling networks, serving nearly 100 million people, are strategically important. In next-generation systems, thermal prosumers can feed-in locally produced or industrial waste heat into the network via bidirectional substations, allowing energy flows in both directions and enhancing system efficiency. The complexity of these networks, with numerous users and interacting heat flows, requires advanced predictive models to manage large volumes of data and multiple variables. This work presents the development of a predictive model based on artificial neural networks (ANNs) for forecasting excess thermal renewable energy from a bidirectional substation. The numerical model of a substation prototype designed by ENEA provided the physical data for the ANN training. Thirteen years of simulation results, combined with extensive meteorological data from ECMWF, were used to train and to test a multi-step ANN capable of forecasting the six-hour thermal power feed-in horizon using data from the preceding 24 h, improving operational planning and control strategies. The ANN model demonstrates high predictive capability and robustness in replicating thermal power dynamics. Accuracy remains high for horizons up to six hours, with MAE ranging from 279 W to 1196 W, RMSE from 662 W to 3096 W, and R² from 0.992 to 0.823. Overall, the ANN satisfactorily reproduces the behavior of the bidirectional substation even over extended forecasting horizons. Full article

The main objective of this article is to model, simulate, and analyze the interaction of energy storage systems with BIPV installations. Currently, due to the instability of energy generation, the economic challenges of integrating PV installations into the electricity grid, and the desire to increase self-consumption, energy storage facilities are becoming increasingly popular. Subsidy programs most often favor PV installations, including BIPV, that work with energy storage devices. Therefore, there is a justified need to model energy storage devices for use with BIPV. The article describes the rationale for the benefits of using energy storage systems within current billing models, using Poland as an example. The introduction also provides an overview of the most popular energy storage technologies compatible with renewable energy installations. To achieve these objectives, appropriate system solutions were designed in the MATLAB environment and used to perform simulations, taking into account variable energy demand. An economic analysis of the system’s operation was conducted using a prosumer net-billing model, and adjustments were made to the system configuration. It has been shown that the use of appropriate energy storage solutions, cooperating with photovoltaic installations, allows for increased self-consumption and more efficient management of electricity obtained in BIPV, which has a positive impact on the payback time and economic profits. The analysis method used and the results obtained are true for the assumed known load profile; however, the method can be successfully applied to various load profiles. Full article

With the rapid increase in photovoltaic (PV) micro-installations in Europe, ensuring the stability and safety of the power grid has become a critical challenge. A key aspect in this context is the reliable detection of unintentional islanding by distributed energy resources. This paper presents the results of metrological tests on seven commercially available three-phase and single-phase PV inverters, conducted in accordance with the requirements of the EN 50549-1 and EN 62116 standards. A dedicated test setup was developed to enable measurements following standardized procedures. The tests assessed both the response time and the effectiveness of islanding detection mechanisms under various fault scenarios, including simulations of autonomous operation of multiple inverters. The main findings indicate that while all inverters with active islanding protection successfully detected islanding within the mandated 2-s limit, their individual response times varied significantly. Parallel operation further influenced this behavior: when one inverter operated with its islanding protection intentionally disabled, the remaining units exhibited notably increased detection times, though still within regulatory thresholds. Moreover, the inverter with disabled protection was capable of sustaining stable islanded operation indefinitely under balanced load conditions. Repeated multi-inverter tests also revealed significant variability in detection time within the same scenario, demonstrating that detection dynamics are sensitive to subtle changes in operating conditions. These findings highlight important limitations of existing certification procedures, which focus primarily on single-inverter testing. Real-world interactions between simultaneously operating inverters can substantially affect detection performance. The results therefore support the need to revise and extend test standards to include controlled multi-inverter parallel-operation conditions, ensuring the safe integration of prosumer PV systems into distribution networks. Full article

Local United Arab Emirates (UAE) inhabitants have shown heightened awareness and interest in renewable energy (RE), resulting in a rise in the installation of solar photovoltaic (PV) systems in their residences; however, electric utility earnings have decreased due to this tendency. Energy decision-makers are concerned about discriminatory resident access to incentives and publicly funded solar PV frameworks. To reduce solar PV installations, utilities and energy players have adjusted RE initiatives. Utility companies provide solar PV-assisted installations. Nonetheless, adopting such frameworks requires a comprehensive feasibility study of all elements to achieve a win–win condition for all stakeholders, namely energy consumers, grid operators, solar PV company owners, regulators, and financiers. This article predicts the success of numerous local UAE solar PV models using agent-based modeling (ABM) to assess stakeholders’ measurements and objectives. Agents represent prosumers who choose solar PV. The effects of their installation choices on stakeholder performance measures are studied over time. ABM results show that suitable solar community pricing policies can benefit all stakeholders. Therefore, enhanced RE implementation rates can grow equitably. Also, electric utility companies can recoup profit losses from solar PV installations, and solar PV firms can thrive. The proposed modeling technique provides a viable policy design that supports all parties, preventing injustice to any stakeholder. Full article

The main objective of this article is to conduct a literature review on the use of open innovation (OI) for green transition to identify tools and methods that can make green transition more effective, efficient, and socially acceptable. This review is accompanied by an attempt to answer the following research questions: R1. How can open innovation be used in the economy and by individual entities to achieve the goals of the green transition? R2. How can individual stakeholders be activated and motivated to participate in the process of creating open innovation for the green transition? and R3. What are the real effects of using open innovation on a macroeconomic, social, and individual scale? The results allow concluding that OI is used by enterprises, cities, regions, and entire economies. Among the methods of activating and motivating individual stakeholders to engage in the process of creating OI for green transition, the following can be selected: (1) internal resources and competencies (knowledge management, internal programs, open leadership, trust, complementarity of resources); (2) partnership characteristics (modern business models, involvement of partnership intermediaries, strengthening relationships with suppliers and customers, involvement of prosumers, cooperation with universities and research institutions); (3) external legal and regulatory conditions (protection of intellectual property rights, pro-innovation and pro-environmental education systems, creation of a legal framework for cooperation between science and business); and (4) external technical and organizational solutions (online platforms, social media, Living Labs, external sources of knowledge). The most frequently mentioned individual effects of open innovation in the energy sector include: improved efficiency, effectiveness and competitiveness in environmental management and the implementation of sustainable development, as well as the use of modern technologies. At the economic level, OI supports investment and economic growth. It can also have a positive impact on reducing energy poverty and developing renewable energy sources, including in emerging economies. This form of innovation also promotes social integration and the creation of social values. The findings of this review can be utilized by scholars to identify current and future research directions. They may also prove valuable for practitioners as both an incentive to engage in open innovation and guidance for its design and implementation. Furthermore, the results can contribute to disseminating knowledge about open innovation and its role in the green transformation. Full article

Energy management in smart microgrids is critical to achieving sustainable and efficient energy utilization. This study introduces a hybrid optimization framework combining neural networks (NNs) and multi-objective genetic algorithms (MOGAs) (hybrid NN-MOGA) to address the dual objectives of minimizing total energy cost and maximizing customer satisfaction. The hybrid NN-MOGA approach leverages NNs for predictive modeling of load and renewable energy generation, feeding accurate inputs to the MOGA for enhanced Pareto-optimal solutions. The performance of the proposed method is benchmarked against traditional optimization techniques, including MOGA, multi-objective particle swarm optimization (MOPSO), and the multi-objective firefly algorithm (MOFA). The simulation results demonstrate that hybrid NN-MOGA outperforms the alternative model. The proposed method produces uniformly distributed and highly convergent Pareto frontiers, ensuring robust trade-offs of USD 48.2817 and 81.7898 for total cost and customer satisfaction, respectively. Convexity analysis and the satisfaction of Karush–Kuhn–Tucker (KKT) conditions further validate the optimization model. Full article

In accordance with the European common rules for the internal electricity market, suppliers offer end users contracts with dynamic energy prices. To reduce energy costs, prosumers must manage their installations with energy storage devices (ESSs). The authors propose a novel control strategy with a relatively simple simulation-based algorithm that effectively reduces daily energy costs by managing the ESS charging and discharging schedule under different types of dynamic energy tariffs. The algorithm operates in a running window mode to ensure ongoing control updates in response to the changing conditions of the prosumer’s installation operation and dynamically changing energy prices. A feature of the control system is its ability to regulate the power exchanged with the supply network in response to an external signal from a superior control system or a network operator. This feature allows the control system to participate in regulatory services provided by the prosumer to the DSO. The effectiveness of the proposed control algorithm was verified in the PSCAD V4 Professional environment and with the MS Excel SOLVER for Office 365 optimisation tool. The results showed good accuracy with respect to the cost reduction algorithm and confirmed that the additional regulatory service can be effectively implemented within the same prosumer ESS control system. Full article

Free trading of distributed energy resources (DERs) is an effective way to enhance local renewable consumption and user-side economic efficiency. Yet unrestricted sharing may threaten operational security. To address this, this paper proposes a voltage-constrained, differentiated resource-sharing framework for active distribution networks (ADNs). The framework maximizes users’ economic benefits and renewable absorption while keeping system voltages within safe limits. A local energy market with prosumers and the distribution network operator (DNO) is established. Prosumers optimize trading decisions considering transaction costs, wheeling charges, and operational costs. Based on this, a generalized Nash bargaining model is developed with two sub-problems: cost optimization under voltage constraints and payment negotiation. The DNO verifies prosumer decisions to ensure system constraints are satisfied. This paper quantifies prosumer heterogeneity by integrating market participation and voltage regulation contributions, and proposes a differentiated bargaining model to improve fairness and efficiency in DER trading. Finally, an ADMM-based distributed algorithm achieves market clearing under AC power flow constraints. Case studies on modified IEEE 33-bus and 123-bus systems validate the method’s effectiveness, the allocation of benefits between producers and consumers is more equitable, and the costs for highly engaged producers and consumers can be reduced by 46.75%. Full article