A centralized Transformer trained with access to pooled data reached a higher F1 score of 0.967 ± 0.009 and was treated by the researchers as an upper-bound reference rather than a privacy-preserving practical alternative. The problem addressed is the vulnerability of state estimation in smart grids. A false data injection attack manipulates meter measurements so that an energy community’s manager can infer an incorrect electrical state.

The study explains that especially stealthy attacks can evade conventional residual-based bad-data detection: an attack vector can shift a measurement vector and the corresponding compromised state in a way that leaves the residual norm essentially unchanged. This creates risks for power flow, voltage security and operational decisions. The authors also place the electrical layer within broader electricity–hydrogen systems, where incorrect electrical estimates could affect electrolysers, hydrogen production, storage and conversion assets.

Their experiments, however, evaluate the detector at the electrical state-estimation level rather than in a coupled hydrogen system, so the reported results do not demonstrate protection of an operating multi-energy network. Energy communities are treated as independent federated-learning clients that retain their own measurement data and share only model updates. This arrangement avoids centralizing raw operational information, but it introduces the possibility that a community will upload a falsified, scaled, shifted, sign-reversed or otherwise poisoned gradient.

The study also addresses the single-point-of-failure problem associated with conventional federated learning, in which one aggregation server can become a bottleneck or a target. Each community trains a compact Transformer on private data, producing a local gradient that is sent to a committee elected for the current communication round. The committee evaluates gradient similarity, selects contributions according to the chosen strategy, applies an aggregation rule and proposes a new global model.

A subsequent election rotates committee membership, allowing the process to continue without a permanent central authority. The committee’s score for an energy community is based on the inverse squared Euclidean distance between that community’s gradient and the gradients of committee members. Under the assumption that honest updates form a comparatively compact cluster, an update resembling the committee receives a higher score.

Strategy I then selects the 40% of participants with the highest scores, placing priority on consensus and filtering updates that appear unusually distant. Strategy II selects the 40% with the lowest scores to preserve diversity among clients whose local data may represent different operating conditions. The researchers explicitly state that Strategy II is not a Byzantine-attack defense: under adversarial conditions, low-scored updates can be malicious.

The study instead presents Strategy I as the appropriate option when sabotage is anticipated, while Strategy II is intended to examine benign heterogeneity under non-IID data. This distinction is central to the framework because the same deviations that can be informative in a benign federation can also carry poisoned updates under attack. Three aggregation rules were evaluated.

FedAvg averages all supplied updates and is vulnerable to poisoning. TMedian performs coordinate-wise trimming, removing extreme upper and lower values before averaging the remainder. Krum ranks gradients by their distance from neighboring updates and selects the most central one, rather than combining information from multiple clients.

The committee-consensus protocol additionally requires an accepting majority of at least floor(C/2) + 1 members before a proposed aggregation is accepted. Committee membership is generated through a verifiable random function seeded with the hash of the previous global model, allowing authenticated participants to audit the election and limiting predictable targeting of committee members. A temporary coordinator may orchestrate a round, but it cannot unilaterally approve an update.

Under the stated security assumption that the committee majority remains honest, compromising the coordinator could delay or restart work but could not force acceptance of a malicious aggregation. Committee size is a trade-off: a larger committee makes majority capture less likely but increases scoring, verification and consensus traffic. The principal experiment used the IEEE 33-bus distribution system divided among nine energy communities, each covering three buses and 12 local features.

The generated dataset covered two years and contained 17,520 samples with 108 features in total, representing active and reactive power, voltage magnitude and voltage angle. Weak attacks altered mean active or reactive power injections by less than 10%, voltage magnitude by less than 5%, or voltage angle by less than 2 degrees; strong attacks exceeded 30%, 10% and 5 degrees, respectively. Training used 20 global rounds, a 40% participation rate, a two-head Transformer with two encoder blocks and the Adam optimizer with binary cross-entropy.

Model training was performed in Python with TensorFlow 2.18, while data generation used MATLAB 2022b and the MATPOWER toolbox. Because the weaker attacks are less conspicuous, they provided the more demanding test of the detector, and the distributed results broadly followed the same pattern for the easier strong-attack cases. The results reveal a sharp trade-off between benign diversity and adversarial resilience.

Under weak attacks without Byzantine manipulation, a typical federated-learning implementation performed poorly: one community recorded 0.84 accuracy but only 0.398 recall and a 0.304 F1 score, indicating many missed attacks. TMedian with both selection strategies improved on that baseline. During normal training, TMedian with Strategy II reached a final accuracy of 0.998, stabilized around round 13 and showed greater convergence than FedAvg, which ended at 0.85.

Strategy II generated frequent participant turnover, often admitting three or four new clients per round, whereas Strategy I repeatedly favored a stable core of three to four communities. In the larger 335-bus case, which represented 80 communities, four buses each and 35,040 samples over four years, Strategy II likewise produced stronger detection results for the first 20 communities. The authors associate this performance with access to a broader range of load patterns, voltage variations and power-flow dynamics, but the results remain based on generated measurements rather than live operational data.

Byzantine experiments reversed the ranking of the selection strategies. Strategy I achieved 0.973 accuracy with TMedian in the no-attack condition and remained between 0.701 and 0.782 across the five attack categories. Krum with Strategy I reached 0.821 without an attack and between 0.727 and 0.783 under attacks, while Strategy II fell to roughly 0.37–0.58 in adversarial settings.

Sign-flipping and random updates generally moved malicious gradients far from the honest cluster, allowing Strategy I to exclude them before aggregation. TMedian was particularly effective against those overt attacks, including gradient scaling. More subtle label flipping and constant shifts could remain close to honest gradient statistics, making selection alone less discriminating and leaving more influence to the aggregation rule.

Krum’s geometric treatment of whole updates sometimes gave it greater resilience to these structured attacks, while coordinate-wise TMedian could retain moderate coordinated perturbations. The comparison also found that prior federated detectors with Transformer or hybrid local models were more vulnerable to the tested manipulations. These measurements show performance under the study’s chosen attack models and case networks, not a universal ranking of all smart-grid defenses.

The approach introduces resource and deployment costs that the study does not fully resolve. The compact Transformer required approximately 6 × 10^5 floating-point operations per sample, 0.6 MB of model memory in 32-bit precision and nearly 1.0 GB of peak training memory, compared with lower requirements for XGBoost, CNN and LSTM. Committee-based participation reduced training computation relative to full-participation FedAvg by roughly 2.5 to 3 times, with reported total times ranging from 96 seconds for TMedian with Strategy II to 120 seconds for Krum with Strategy II.

These timings cover algorithmic and training work on an Intel i7-12700 system with an RTX 3090 GPU and 32 GB of RAM; they do not represent end-to-end network latency. Bandwidth, packet loss, queuing, synchronization and consensus-message exchange were not modeled. The authors also note that community numbers and boundaries were predefined, while real deployments may have variable sizes, communication systems, missing measurements, nonstationary loads and stronger imbalance.

Larger operational deployments and communication-aware studies are therefore needed before the framework’s scalability and resilience can be established outside controlled case studies.

Subject of Research: Evaluation of committee-based federated learning for detecting false data injection attacks in simulated energy-community distribution grids

Article Title: A novel resilient FDIA-aware federated learning framework for energy community-driven smart grids: A transformer-based committee consensus mechanism

Article References: Shabanian-Poodeh, M., Ameli, H., Kia, R., & Shafie-khah, M. (2026). A novel resilient FDIA-aware federated learning framework for energy community-driven smart grids: A transformer-based committee consensus mechanism. Results in Engineering, 32, Article 113556. https://doi.org/10.1016/j.rineng.2026.113556

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113556

Keywords: smart-grid security, federated learning, false data injection attacks, energy communities, Byzantine resilience, Transformers, state estimation, robust aggregation, distribution networks, decentralized learning, novel, resilient

The review is notable for its scale and rigor. The team screened the literature from 2015 to 2025 using structured searches across IEEE Xplore, Web of Science, and Scopus, with a targeted update in December 2025, and ultimately analyzed 130 peer-reviewed studies following the PRISMA 2020 reporting guidelines. Rather than treating forecasting, optimization, and energy storage management as separate silos—the way most previous surveys have done—the authors deliberately examined all three pillars together. Their central insight is that these components form a decision pipeline: predictions feed optimization, optimization feeds storage control, and storage control feeds back into the physical system whose behavior the forecasts must then capture. It is the joints between these layers, they find, where the field is weakest.

Forecasting emerges from the analysis as the foundational layer of the entire stack. Machine learning models that predict solar generation, wind output, electricity prices, and consumption profiles shape every downstream decision: a community that misjudges tomorrow’s solar peak will schedule its battery charging badly, trade at the wrong times, and potentially violate grid constraints. The review shows that optimization and energy storage control increasingly rely on learning-based and hybrid AI frameworks—combinations of reinforcement learning, deep learning, and classical mathematical programming—to manage uncertainty and juggle competing objectives such as cost, emissions, battery longevity, and fairness among members. Hybrid approaches are particularly attractive because pure learning methods can be brittle and hard to certify, while pure optimization struggles when the underlying models of the world are wrong.

Yet the authors’ cross-cutting synthesis reveals a sobering pattern: tight coupling between forecasting, optimization, and physical execution remains rare. Most studies build one layer in isolation and assume the others behave ideally. A forecasting paper rarely tests whether its predictions actually improve a battery schedule; an optimization paper rarely checks whether its assumptions match the forecast errors of a real prediction model. This fragmentation has direct consequences for robustness, scalability, and fairness—qualities that matter enormously when an algorithm is deciding whose heat pump gets curtailed during a price spike or whose electric vehicle charges first.

The review’s maturity assessment makes the deployment gap concrete. The authors coded each of the 130 studies on four practical dimensions: constraint realism, baseline comparison, reproducibility support, and validation setting. The good news is that most studies now model explicit technical, operational, market, or grid-related constraints, suggesting the literature has moved beyond purely abstract algorithmic demonstrations. Baseline comparison is common, though uneven—a substantial share of papers provides only limited comparative evidence, making it hard to judge whether claimed performance gains are real or artifacts of weak benchmarks.

Reproducibility, however, is the field’s most glaring weakness. Only a very small subset of studies provides clear access to code, open datasets, or implementation details sufficient for independent replication. In a domain where results can hinge on subtle data preprocessing, hyperparameter choices, or simulation assumptions, this lack of transparency undermines benchmarking and slows collective progress. It also makes it harder for utilities, cooperatives, and regulators to distinguish genuinely deployable methods from impressive-looking simulations.

Validation practice tells a similar story. The overwhelming majority of the reviewed studies were evaluated purely in simulation. Some used real-world datasets, and only a smaller number progressed to pilot projects, living labs, or field-oriented testing. The transition from methodological development to deployment-oriented evidence—demonstrating that an AI pipeline survives contact with noisy sensors, communication delays, hardware failures, and real human behavior—remains limited. This matters because energy communities are socio-technical systems: their success depends not just on algorithms but on the people who join them, the markets they participate in, and the grids they connect to.

The integration assessment goes deeper still. The authors coded each study on whether forecasts actually flowed into decisions, whether the system operated in closed loop, how storage execution was modeled, how uncertainty was represented—deterministic, probabilistic, scenario-based, or robust-set-based—whether downstream impacts were evaluated, and whether strategic interactions among agents were captured. The coding was deliberately conservative, classifying features only when explicitly documented, and the full study-level matrix is published in the paper’s appendices so that other researchers can inspect and reuse the analysis. This kind of transparent, mapping-oriented synthesis is itself a methodological contribution, giving the field its first unified picture of where intelligence actually connects to action.

From these findings, the review charts a research agenda built around three ideas: decision-centric forecasting, in which prediction models are designed and trained with their downstream use in mind rather than for standalone accuracy; asset-aware control, in which storage management respects the physical and lifecycle realities of batteries rather than treating them as idealized buffers; and market-compatible AI, in which community-level algorithms can interact credibly with electricity markets and grid operators. The authors argue that progress will require the field to stop optimizing components in isolation and start building, testing, and reporting integrated pipelines—ideally with open code and field validation.

For a world racing to electrify heating, transport, and industry while flooding the grid with variable renewables, the stakes of this research agenda are high. Energy communities promise cheaper, cleaner, more resilient power and a more active role for consumers, but only if the software that orchestrates them is robust, fair, and trustworthy. This review provides both a candid diagnosis of the current state of the art and a roadmap for closing the gap between clever algorithms and communities that actually run on them. The message for researchers, funders, and startups alike is clear: the next breakthrough in AI-driven energy systems will come not from a better forecaster or a smarter optimizer, but from making the pieces work together.

Subject of Research: Artificial intelligence methods for forecasting, optimization, and energy storage management in decentralized energy communities

Article Title: Artificial intelligence for forecasting, optimization, and energy storage in energy communities: a review

Article References: Lezama, F., Dias de Lima, T., Castro, F., Bairrao, D., & Vale, Z. (2026). Artificial intelligence for forecasting, optimization, and energy storage in energy communities: a review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11681-9

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11681-9

Keywords: artificial intelligence, energy communities, forecasting, optimization, energy storage systems, machine learning, reinforcement learning, smart grids, renewable energy, systematic review, energy management, decentralized energy

The study, published in Machine Learning with Applications, matters because energy communities are rapidly becoming a cornerstone of Europe’s decarbonization strategy. Energy production and use account for more than 75 percent of the European Union’s greenhouse gas emissions, and the EU’s Clean Energy for All Europeans package has created a legal framework that lets citizens generate, store, share, and even trade their own renewable electricity. For these communities to work, each household and facility needs accurate forecasts of its own electricity consumption, because those forecasts feed directly into scheduling decisions about when to charge batteries, when to import from the grid, and when to sell surplus solar power back.

What sets the new work apart is its insistence on statistical rigor. Most previous studies in this field have simply trained a model, tested it once on a held-out slice of data, and reported a single error metric such as root mean square error. The Asturias team instead used an expanding-window cross-validation protocol with four test folds spread across April, July, October, and December, so that every model was judged under four different seasonal regimes with growing amounts of training history. Model selection was then decided not by a single number but by a pairwise Wilcoxon signed-rank tournament, in which every model faced every other model in a formal statistical contest based on hourly forecast errors.

The results of that tournament were strikingly one-sided. Support vector regression won the maximum possible net wins against all twenty-three opponents in three of the four folds at the highest-consuming house, while Huber regression achieved near-perfect records at three other homes. Extra Trees took the honors at the rural technology center, and k-nearest neighbors at another house. All six deep learning configurations tested, including long short-term memory networks, gated recurrent units, and temporal convolutional networks, ranked in the lower half of the tournament at every single supply point. Even when the deep networks were retrained from scratch every day, a strategy that consistently reduced their error by 5 to 7 percent, they never closed the gap with the best classical models.

Perhaps the most eye-opening finding concerns how models are judged in the first place. When the researchers compared the model chosen by the statistical tournament with the model that simply achieved the lowest root mean square error, the two criteria agreed in only 8 percent of the fold-by-supply-point combinations. At one house, the error metric persistently favored a seasonal statistical model even though support vector regression had statistically dominated all twenty-three rivals in every fold. The authors are careful to note that neither criterion is wrong; they simply emphasize different aspects of forecast error. Intriguingly, when either selection was fed into the downstream energy scheduler, the realized operating costs differed by less than one euro over the entire 123-day evaluation period.

The forecasting framework was also designed to survive the realities of deployment on cheap hardware. Every model runs directly on edge devices at the point of consumption, and the team benchmarked inference on a Raspberry Pi 5 and an NVIDIA Jetson Orin Nano. All selected models produced a full 24-hour forecast in under 211 milliseconds, with Huber regression completing in under 3 milliseconds on the Raspberry Pi, comfortably inside the hours-long scheduling window that opens once the Spanish grid operator publishes day-ahead prices. Because the machine learning winners are computationally light, the inexpensive Raspberry Pi proved the most cost-effective choice, and retraining even the heaviest model took only seconds.

The forecasts then drive a linear programming optimizer that schedules battery charging and discharging, grid imports and exports, and photovoltaic self-consumption under real Spanish regulations, which restrict grid imports to instantaneous demand so the battery must be charged solely by local renewable energy. The community’s hardware includes one hundred solar panels with a peak output of 32 kilowatts and a battery system of just over 50 kilowatt-hours. Evaluated over 123 days spanning four seasons, the optimized scheduler saved 187.50 euros compared with the community’s unoptimized hardware baseline, with a bootstrap confidence interval confirming the saving was significantly positive. During spring and summer, the community actually became a net exporter of electricity, earning more from selling surplus solar power than it spent on procurement.

The study also quantified the cold-start problem that greets any new member joining an energy community. With only two weeks of consumption history, forecast errors were on average 30.5 percent higher than with a full training history, but that penalty shrank to under 5 percent after four weeks and essentially vanished after eight. In practical terms, a newly onboarded house reaches near-optimal forecasting accuracy within about a month, which is a valuable reassurance for communities that expect to grow. The team even tested whether combining multiple models into ensembles would beat the single tournament winner, and found the improvement was only about 1.2 percent on average, too small to justify the added complexity on edge hardware.

The authors are candid about the limits of their conclusions. The evidence comes from a single rural community with six heterogeneous supply points and one year of hourly data, so generalization to other communities, tariffs, and weather years remains unproven. Their machine learning models consumed only each supply point’s own consumption history, while the deep networks also received calendar and weather features, an asymmetry the team addressed with a feature-matched ablation. Restricting the networks to the same univariate input preserved the machine learning advantage in 21 of 24 comparisons, though at one house the best feature-matched network edged ahead on the four-fold mean. Notably, removing the weather and calendar channels actually improved deep learning accuracy in 15 of 18 cases, suggesting the extra inputs added variance faster than they added signal.

Beyond its specific findings, the study offers a template for how energy technology should be evaluated before deployment: seasonal test folds that mirror real operating conditions, formal hypothesis testing instead of single-metric rankings, explicit treatment of cold-start onboarding, and hardware benchmarks tied to the actual scheduling window. As energy communities proliferate across Europe under citizen-driven energy policy, the Asturias experiment suggests that the smartest grid may not require the deepest network, only the most disciplined statistics, a modest computer in the basement, and a well-scheduled battery.

Subject of Research: Electricity demand forecasting and energy cost optimization in a rural energy community using machine learning and linear programming

Article Title: A particularized forecasting and optimization framework for rural energy communities

Article References: Cosío, F., Molleda, J., Díez, F., & Usamentiaga, R. (2026). A particularized forecasting and optimization framework for rural energy communities. Machine Learning with Applications, 26, Article 101016. https://doi.org/10.1016/j.mlwa.2026.101016

Image Credits: AI Generated

DOI: 10.1016/j.mlwa.2026.101016

Keywords: energy communities, machine learning, deep learning, electricity demand forecasting, battery storage optimization, renewable energy, edge computing, linear programming, Wilcoxon signed-rank test, solar power, rural energy, Spain

The research, led by Amin Barati, Nicola Bianco, Marialaura Di Somma, and Francesco Scognamiglio, focuses on what the authors call integrated local energy communities, or ILECs. These are clusters of buildings that coordinate electricity, heating, cooling, and mobility at the neighborhood level, combining technologies such as combined heat and power units, photovoltaic panels, heat pumps, absorption chillers, batteries, and thermal storage into a single, intelligently managed ecosystem. The European Union has thrown its weight behind this model, with the REPowerEU agenda envisioning one renewable energy community per municipality, and studies suggesting that community membership can cut household energy bills by as much as 30 percent. What has been missing, the researchers argue, is a genuinely rigorous way to operate these communities once electric vehicles enter the picture.

Most previous studies, the team found, treat electric vehicles as simple, time-varying loads, blobs of extra demand that arrive and depart on schedule. Others compress entire fleets into aggregated flexibility proxies that obscure the very constraints that matter most in practice: when a specific car actually parks, how long it stays, what state its battery is in when it arrives, and what minimum charge its owner demands before leaving. The new framework takes a fundamentally different approach. Vehicles are grouped into clusters defined by battery capacity, arrival and departure times, and state-of-charge requirements, and each cluster becomes an explicit decision variable in a mixed-integer linear programming model that simultaneously optimizes the flow of electricity, heat, and cooling across the entire community.

The mathematical machinery behind the study is substantial. The model tracks the gas consumption and electrical output of a 250-kilowatt combined heat and power unit, the thermal contribution of a 600-kilowatt auxiliary boiler, the behavior of a 540-kilowatt heat pump operating in both heating and cooling modes, and the charge-discharge cycles of a 600-kilowatt-hour battery alongside thermal storage tanks. Photovoltaic generation is calculated from hourly solar irradiance data for Turin, and electricity prices are drawn from the Italian wholesale market for January 2025, with export prices conservatively assumed at half the purchase price. Crucially, the vehicles themselves can operate bidirectionally, charging from the community in grid-to-vehicle mode or discharging back in vehicle-to-grid mode, with their state of charge constrained between 20 and 80 percent of capacity and a guaranteed minimum of 80 percent at departure so that no driver is ever stranded.

To resolve the inherent conflict between saving money and saving carbon, the researchers employed a weighted-sum multi-objective approach, sweeping a weight parameter from zero to one to trace out a complete Pareto frontier of optimal trade-offs. At one extreme lies the cheapest possible operation; at the other, the lowest possible emissions; in between, a continuum of compromise strategies. The whole problem is solved with a branch-and-cut algorithm, and the results are benchmarked against a reference scenario in which heat comes entirely from conventional gas boilers and electricity entirely from the national grid, a baseline that costs 421.97 euros per day and emits 1,463.52 kilograms of carbon dioxide on a cold January day in Turin.

The headline findings are striking. Across three photovoltaic configurations ranging from 700 to 1,400 square meters of panels serving a community of 100 apartments and 15 vehicles, the optimized energy community cut operational costs by 45 to 52 percent and carbon dioxide emissions by 41 to 48 percent compared with the reference scenario. Doubling the photovoltaic area from the smallest to the largest configuration delivered a further 12 percent reduction in operating costs and an 11 percent reduction in emissions. An investment analysis confirmed that the transition pays for itself: the daily share of the capital cost of the additional panels, roughly 20.94 euros spread over a 30-year lifetime at a 2 percent discount rate, is more than offset by the operating savings in both economic and environmental optimization modes.

But the most revealing results concern the split personality of the parked electric car. Under pure economic optimization, the energy discharged from vehicle batteries is never used to power the community at all. Instead, every kilowatt-hour flows out to the main grid during high-price hours, generating revenue that drives the community’s daily operating cost down to 203 euros in the largest photovoltaic case. Under pure environmental optimization, the strategy inverts completely: vehicle energy is discharged exclusively for local self-consumption, the combined heat and power unit sits idle, the heat pump draws low-carbon grid electricity to cover the entire thermal load, and nothing is sold back to the grid. The optimization engine, in other words, discovers two fundamentally different roles for the same fleet of cars, and the choice between them depends entirely on what the community values most.

The researchers pushed the analysis further with a scaled-up scenario featuring 30 vehicles and 2,800 square meters of photovoltaics. Here, the economic optimum fell to just over 200 euros per day, with more than 100 euros of that achieved through the sale of vehicle flexibility alone, while the environmental optimum reached 814 kilograms of carbon dioxide. Normalized comparisons showed that doubling the fleet and the solar capacity reduced emission intensity by 3.14 percent, cut the specific energy cost by 11 percent, and lifted the community’s self-sufficiency from roughly 30 percent to over 40 percent. The volume of energy exported from vehicles during high-price hours rose 89 percent, from 280 to 530 kilowatt-hours, while energy discharged for local consumption doubled from 100 to 200 kilowatt-hours.

There is an important caveat, one the authors are careful to acknowledge. Very few electric vehicle models and alternating-current chargers on the market today are actually capable of bidirectional operation, as manufacturers have prioritized direct-current fast charging over vehicle-to-grid functionality. The framework is therefore best understood as a forward-looking assessment of what becomes possible as vehicle-to-grid-ready vehicles and chargers proliferate. It is a roadmap rather than a snapshot, quantifying the flexibility prize that awaits communities willing to invest in the enabling hardware.

The implications stretch well beyond a single neighborhood in Turin. As European cities race to decarbonize heating and transport simultaneously, the study demonstrates that sector coupling at the local level is not merely a theoretical convenience but a quantifiable economic and environmental advantage, and that the humble parked car may be the most underutilized asset in the entire energy transition. The researchers plan to extend the framework to handle uncertainty in solar generation and vehicle mobility patterns, to coordinate multiple communities, and to incorporate detailed battery degradation and maintenance costs. If their projections hold, the future of community energy may be sitting in the parking lot, fully charged and waiting to be asked for help.

Subject of Research: Multi-objective optimization of sector-coupled local energy communities using plug-in electric vehicle flexibility under varying photovoltaic installation scenarios

Article Title: Optimal operation of sector-coupled energy communities leveraging plug-in electric vehicle flexibility under different PV installation scenarios

Article References: Barati, A., Bianco, N., Di Somma, M., & Scognamiglio, F. (2026). Optimal operation of sector-coupled energy communities leveraging plug-in electric vehicle flexibility under different PV installation scenarios. Energy Reports, 16, Article 109701. https://doi.org/10.1016/j.egyr.2026.109701

Image Credits: AI Generated

DOI: 10.1016/j.egyr.2026.109701

Keywords: energy communities, vehicle-to-grid, electric vehicles, photovoltaics, sector coupling, mixed-integer linear programming, Pareto optimization, combined heat and power, heat pumps, decarbonization, self-consumption, demand flexibility