The research, conducted by a team at the Electric Power Research Institute of Guangxi Power Grid in Nanning, China, tackles a problem that has grown acute as transport electrification accelerates. Large-scale electric vehicle integration can worsen nodal voltage deviations, overload lines and create local congestion in distribution networks. Yet the same flexible charging loads, if managed well, can support peak-valley regulation and help accommodate renewable energy. The challenge, the authors argue, is that most existing studies focus on individual operational goals such as voltage security, charging cost or station planning, without a unified framework for assessing how the various participants actually coordinate with one another.

To fill this gap, the team proposed representing the entire vehicle-charger-grid system as a service-oriented collaborative network. Rather than treating electric vehicles merely as adjustable loads, the method maps distribution network nodes, aggregators, charging stations and electric vehicle user groups into a multi-agent network in which participants are characterized by the coordination services they provide or receive. Each interaction between two participants is modeled as a directed edge carrying three distinct types of coupling: information, energy and value-price. The information dimension captures state sharing rates, data update frequencies, control-command arrival rates and prediction consistency. The energy dimension measures interactive power intensity, local energy matching, peak-valley complementarity and aggregation execution rates. The value-price dimension tracks how consistently participants respond to price incentives, how well benefits are shared and how reliably contracts are fulfilled.

A key technical innovation lies in how these three dimensions are weighted. The researchers use a state-driven softmax formulation in which a normalized state-feature vector reflects system security pressure, economic operation pressure and service quality pressure. Under heavy-load or abnormal conditions, security-related weights increase, emphasizing the coupling dimensions most relevant to keeping the grid stable. During normal economic operation, energy and value-price coupling receive greater emphasis. This dynamic weighting allows the assessment to shift its focus as operating conditions change, rather than applying a fixed set of priorities regardless of circumstances.

The framework also introduces a coordination-consistency correction mechanism. Even when two nodes exchange large amounts of information and power, their coordination can fail if control actions suffer from time mismatch, delays or execution deviations. The consistency coefficient penalizes response-delay deviations and execution deviations exponentially while rewarding a high action-direction consistency rate. The corrected edge-level coordination strengths are then assembled into a weighted adjacency matrix, which serves as the basis for computing node importance through weighted strength, betweenness and closeness centrality, and for deriving network-level indicators including network density, weighted clustering coefficient, network efficiency, average coordination response delay and operational risk.

Because the vehicle-charger-grid system is highly time-varying, the method applies exponential smoothing to filter short-term noise and uses exponential decay time weighting so that coordination behaviors closer to the end of an evaluation period carry more weight. The edge-level, node-level and network-layer results are aggregated into a periodic comprehensive coordination score, which is then classified into weak, medium and strong coordination levels using an adaptive quantile-driven threshold method rather than manually set empirical cutoffs.

To validate the approach, the team tested it on the IEEE 33-bus distribution system, a standard benchmark in power systems research, under three scenarios. In the non-collaborative scenario, charging stations operate independently based on local demand. The basic collaborative scenario introduces a coordination mechanism between aggregators and stations. The enhanced collaborative scenario adds the joint effects of information, energy and value-price coupling across all participants. The results were striking. The non-collaborative scenario scored 0.4355 on the comprehensive coordination index, with voltage deviation of 0.0405 and operational risk of 0.0045. The basic collaborative scenario improved the score to 0.4955 and reduced voltage deviation to 0.0280. The enhanced collaborative scenario achieved the highest score of 0.5031 while cutting voltage deviation to 0.0203 and operational risk to 5.55 times ten to the negative fifth, roughly eighty times lower than the non-collaborative case.

The physical benefits were equally clear. The evening peak-valley difference in main-grid power supply dropped from 0.2132 in the non-collaborative case to 0.1862 under enhanced coordination. Minimum voltages at weak buses rose from 0.9070 per unit to 0.9555 per unit at Bus33, providing stronger voltage support in terminal and heavily loaded areas. Maximum line loading fell from 0.4413 to 0.4090 per unit, well below the 1.0 per unit limit, indicating a better security margin. The time-weighted coordination strength matrices revealed that enhanced collaboration did not merely reinforce local links between charging stations and nearby users but significantly strengthened cross-layer interactions, with the aggregator-to-charging-station link increasing from 0.3146 to 0.3770 and the distribution-network-to-user-group link rising from 0.1731 to 0.2037.

What makes this framework particularly valuable is that it links coordination structure to physical operating performance rather than simply ranking scenarios in the abstract. The agreement between the assessment results and the observed changes in nodal voltage and line loading demonstrates that the method can explain why a coordination mode performs better and where the critical links lie. This addresses a persistent gap in the literature, where studies on coordinated charging and infrastructure planning have largely focused on peak-valley regulation, cost reduction or service capability without explicitly characterizing the multi-agent coordination structures that drive those outcomes.

The researchers suggest that their interpretable, multi-level tool can support the comparison of alternative coordination modes and provide a quantitative basis for coordination optimization, active distribution network regulation and multi-agent interaction mechanism design. As electric vehicle fleets grow and renewable generation deepens its penetration into distribution systems, the ability to measure, diagnose and improve the strength of collaboration among vehicles, chargers and the grid may prove essential to keeping power flowing reliably while the transportation sector completes its electrification transition.

Subject of Research: Coordination strength assessment for vehicle-charger-grid interaction in electric vehicle-integrated distribution networks

Article Title: A collaboration-strength-aware assessment method for vehicle-charger-grid coordination in distribution systems via multi-dimensional business coupling

Article References: Wu, N., Yu, X., Luo, Y., & Huang, S. (2026). A collaboration-strength-aware assessment method for vehicle-charger-grid coordination in distribution systems via multi-dimensional business coupling. Frontiers in Energy Research, 14, Article 1947122. https://doi.org/10.3389/fenrg.2026.1947122

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1947122

Keywords: electric vehicles, smart charging, distribution networks, coordination assessment, vehicle-to-grid, aggregators, charging stations, power grid, voltage deviation, collaborative network, IEEE 33-bus, energy coupling

The setting is Phongsaly Province, a rugged region where Laos has built its reputation as the so-called Battery of Southeast Asia on the strength of its abundant hydropower. National installed generation capacity nearly doubled between 2015 and 2023, rising from 6,273 megawatts to 12,757 megawatts. Yet that headline growth conceals a persistent problem at the provincial level. Long radial feeders in mountainous terrain accumulate significant electrical impedance over distance, so customers at the far ends of the network experience chronic voltage sags, frequent outages, and little redundancy when something goes wrong. The evening peak, between 18:00 and 22:00, when households switch on cooking appliances, lighting, and entertainment, places acute stress on infrastructure that has little margin to spare.

The reliability statistics for the study area are striking. Muang Khua Substation Feeder 6, one of the feeders examined in the research, recorded an average of 151.33 interruptions per customer per year between 2021 and 2023, according to records from Electricité du Laos North. The average total interruption duration reached 1,145.67 minutes per customer per year, with an average restoration time of 7.57 minutes per interruption. Weather-related and environmental factors appear among the reported causes of outages, reflecting the network’s exposure to mountain conditions. These figures describe a system operating under sustained pressure, where even modest additional demand at the wrong moment can push marginal buses toward unacceptable voltage levels.

The research team, led by Chan Thavysak with colleagues Panlop Sintuya and Worajit Setthapun, built an aggregated equivalent model of the Bounneua–Muang Khua 22 kilovolt distribution network in PowerWorld Simulator, a widely used professional power-system analysis package. Their approach deliberately constrained the vehicle-to-grid scenario in an unusual and scientifically rigorous way. Rather than assuming the bidirectional inverters on electric vehicles could supply both active and reactive power, the researchers modeled active-power-only injection, fixing reactive power at zero because the reactive capability of their experimental inverter had not been validated over the modeled operating range. This conservative choice isolates the pure effect of local real-power injection on feeder voltage, a contribution that is often entangled with reactive-power support in less careful analyses.

The fleet scenario was grounded in local data. The studied feeder serves 5,225 electricity customers, and a 15 percent participation rate yields 783.75 potential participants, rounded to 784 electric two-wheelers. Each vehicle was assigned a rated alternating-current discharge output of 1.5 kilowatts, giving an aggregated injection of 1.176 megawatts delivered directly into the network model. The team was careful to note that this figure represents power on the grid side of the inverter; battery-side energy consumption would be higher once conversion losses are accounted for. Laboratory measurements at the adiCET research facility in Thailand, conducted under vehicle-to-home conditions, showed inverter efficiencies rising from 64.83 percent at low loads to 93.87 percent at high loads, with a peak of 97.47 percent, though the researchers emphasized that these measurements did not extend to the full 1.5 kilowatt operating point assumed in the simulation.

The central result comes from P-V continuation analysis, a technique that progressively ramps load on the network while tracking how bus voltages decline, revealing how much additional stress a feeder can absorb before reaching a critical threshold. Using 0.95 per-unit voltage as the operational comparison threshold, the base case without vehicle-to-grid support reached that limit at a nominal continuation shift of 16.184 megawatts. With the 1.176 megawatt vehicle fleet injecting power at the terminal bus, the same threshold was not reached until 17.157 megawatts, an increase of 0.973 megawatts, or roughly 6.01 percent in relative terms. At the initial operating point, the terminal bus voltage rose from approximately 0.9899 to 0.9922 per unit, a modest but meaningful gain achieved entirely without reactive-power compensation or direct voltage regulation.

Recognizing that assuming every participating scooter is plugged in and fully charged simultaneously is an optimistic upper bound, the researchers ran additional sensitivity cases at 25, 50, 75, and 100 percent fleet availability, corresponding to 196, 392, 588, and 784 available vehicles. The continuation-margin gains scaled almost linearly with available power: 1.50 percent at 25 percent availability, 3.05 percent at 50 percent, 4.52 percent at 75 percent, and the full 6.01 percent at complete availability. This monotonic relationship demonstrates that the voltage-support benefit is approximately proportional to the aggregated active-power injection, giving grid planners a transparent rule of thumb for estimating what partial participation might achieve. It also underscores that the headline figure depends on a synchronized, fully available fleet, an assumption that real-world mobility patterns, battery states of charge, and user behavior would erode.

The study is notable as much for what it declines to claim as for what it demonstrates. The authors explicitly state that the 6.01 percent margin improvement is a comparative sensitivity metric under a specified ramping procedure, not a 6 percent increase in physical feeder capacity or a validated voltage-collapse improvement. They also decline to convert historical outage statistics into projected reliability gains, noting that the available records cannot establish which interruptions would have been prevented by voltage support. The simulation itself is a balanced, steady-state, positive-sequence assessment that does not capture phase unbalance, harmonics, transients, communication delays, or protection behavior. The laboratory inverter tests were performed under vehicle-to-home conditions at power levels well below the modeled 1.5 kilowatt per-vehicle output, so they serve as supporting context rather than validation of grid-connected operation.

These caveats point toward a clear research trajectory. The authors identify several priorities for future work: experimentally mapping the full active-reactive power capability of bidirectional inverters, calibrating the feeder model section by section with field measurements, incorporating stochastic models of vehicle availability and battery state of charge, extending the analysis to unbalanced three-phase operation, and eventually conducting hardware-in-the-loop testing and a controlled grid-connected pilot. A practical deployment would also need to satisfy interconnection requirements such as anti-islanding protection, fault response, and communication performance. The fixed-injection scenario modeled here represents what the fleet could contribute if fully dispatched, whereas a real controller would activate discharge only when measured voltage actually fell below the operational threshold, a distinction the study handles with unusual transparency.

For Laos and other countries with long, weak rural feeders, the significance of this work lies in its reproducible framework rather than any single number. It shows that small electric two-wheelers, vehicles far more common than passenger cars across much of Asia, can be quantified as distributed grid-support resources using standard power-system tools, and that even active-power-only operation yields a measurable steady-state benefit at a voltage-sensitive terminal bus. As electric two-wheeler adoption accelerates across Southeast Asia, fleets that today are seen purely as transport could double as a decentralized buffer against voltage stress, buying time for utilities that cannot afford immediate network reinforcement. The Phongsaly analysis suggests that the value of that buffer, while moderate, is real, predictable, and ready to be tested in the field.

Subject of Research: Vehicle-to-grid voltage support assessment for a weak mountainous distribution feeder in Laos

Article Title: PowerWorld-Based Assessment of Active-Power-Only Vehicle-to-Grid Voltage Support for a Weak 22 kV Mountainous Feeder in Phongsaly, Laos

Article References: Thavysak, C., Sintuya, P., & Setthapun, W. (2026). PowerWorld-Based Assessment of Active-Power-Only Vehicle-to-Grid Voltage Support for a Weak 22 kV Mountainous Feeder in Phongsaly, Laos. Results in Engineering, 32, Article 113381. https://doi.org/10.1016/j.rineng.2026.113381

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113381

Keywords: vehicle-to-grid, electric two-wheelers, voltage stability, distribution network, PowerWorld Simulator, P-V continuation analysis, Laos, Phongsaly, rural electrification, bidirectional inverter, grid reliability, distributed energy resources

The research, led by Kunhao Niu and colleagues, tackles a problem that has grown increasingly urgent as the world electrifies transport. Photovoltaic-energy storage-charging stations, which combine rooftop or canopy solar arrays with on-site batteries and charging hardware, are a cornerstone of the renewable transition. Yet they face a stubborn trio of challenges: solar output is inherently volatile, stationary storage is expensive to cycle, and the charging behavior of thousands of individual drivers is chaotic and hard to predict. Left unmanaged, these factors produce stations that lean heavily on the grid, waste solar energy they cannot absorb, and operate at razor-thin economic margins.

The core innovation of the study lies in how it tames that chaos. Rather than treating every electric vehicle as an individual decision-maker, the researchers introduce an intermediary called an EV aggregator, which bundles vehicles into clusters based on shared characteristics such as arrival times, departure times, and state-of-charge needs. To sort vehicles into these groups, the team deployed a hybrid algorithm combining particle swarm optimization with fuzzy C-means clustering. The fuzzy clustering approach allows each vehicle to belong partially to multiple groups, capturing the fuzzy reality of driver behavior, while the swarm-based global search prevents the algorithm from getting trapped in poor solutions, a known weakness of conventional fuzzy clustering alone.

Once vehicles are clustered, the framework faces a second mathematical hurdle: how to describe the collective flexibility of dozens or hundreds of cars without tracking each one separately. Here the authors borrow an elegant tool from control theory called the zonotope, a convex geometric object that can represent the full range of feasible charging and discharging schedules for a cluster. Because zonotopes have a remarkable property, their combination under the Minkowski sum is simply the sum of their centers and generators, aggregating many clusters becomes a matter of basic linear algebra rather than a computationally explosive high-dimensional optimization. The zonotope representations are then converted into half-space inequality constraints that standard solvers can handle directly, preserving the essential operational features of the fleet while dramatically reducing the computational burden of dispatch.

With the fleet flexibility captured, the study then structures the economics as a bi-level, leader-follower game. At the upper level, the station acts as the leader, setting dynamic charging prices and vehicle-to-grid discharge subsidies to minimize its total operating cost, which includes grid electricity purchases, solar maintenance, penalties for curtailed solar power, storage usage charges, and compensation for battery degradation in customers’ vehicles. At the lower level, the EV aggregator acts as the follower, responding to those prices by scheduling its clusters to minimize the combined charging and discharging expenses of users. The two levels never merge into a single objective; instead, they are coupled purely through the prices the station announces and the load responses the aggregator returns.

Solving such a nested problem requires care, because a commercial solver cannot simply minimize both objectives at once. The researchers employed an iterative best-response procedure implemented with the CPLEX solver in MATLAB: the station announces prices, the aggregator computes its optimal response, the station re-optimizes given that response, and the loop repeats. The procedure converges when prices and costs stop changing between iterations, with a price tolerance of one thousandth of a yuan per kilowatt-hour and a cost tolerance of 0.1 percent. In the case study, the process reached a fixed point, a Stackelberg equilibrium where neither side can improve its own outcome by unilaterally changing its strategy, by the fourth iteration, demonstrating that the framework is not just theoretically sound but computationally practical.

To test the model, the team simulated a real station with 1,500 kilowatts of installed solar capacity, a 500 kilowatt-hour battery system, and two aggregators each managing five vehicle clusters with distinct grid-connection windows and power limits. The battery, with charging and discharging efficiencies of 0.92, was required to return to its initial state of charge by the end of each 24-hour cycle, a constraint that ensures fair economic comparisons between scenarios and prevents the optimizer from artificially draining the battery to flatter the day’s numbers. Time-of-use tariffs ranged from 0.295 yuan per kilowatt-hour off-peak to 0.804 yuan at peak, with the station selling surplus power back at 75 percent of the purchase price.

The comparison between two operating scenarios delivers the study’s headline finding. In the first scenario, vehicles charge in an orderly, unidirectional fashion alongside storage and solar. In the second, vehicles gain bidirectional capability, discharging back to the station when prices and demand warrant. The difference is striking: during the evening peak from 18:00 to 24:00, the station’s maximum grid purchase drops from 200 kilowatts to 150 kilowatts, a 25 percent reduction, while the solar consumption rate climbs from 90.1 percent to 90.8 percent. Those percentages may look modest, but at scale they translate into substantially less solar energy wasted and materially lower operating costs, all while drivers still get their cars charged on schedule.

The cluster-level dispatch profiles reveal how this coordination actually works. Under uncontrolled charging, sharp demand spikes appear at mid-morning, mid-afternoon, and early evening, worsening the peak-valley disparity that plagues power systems. Under the optimized regime, clusters flip their behavior fluidly: they charge deeply during overnight and midday windows when solar generation is abundant or tariffs are low, then discharge during the evening peak to provide peak regulation. Individual clusters adapt to their own constraints, with one cluster absorbing solar energy in the late afternoon while another charges overnight to meet its demand, showing that the aggregation method preserves the granular realities of driver schedules rather than flattening them into an unrealistic average.

Beyond the immediate numbers, the study points toward a broader reimagining of electric vehicles as distributed energy assets. Millions of car batteries sitting in parking lots represent an enormous, largely untapped reservoir of storage capacity, one that could buffer renewable generation without the capital cost of building new grid-scale batteries. The bi-level pricing mechanism at the heart of this framework offers a market-compatible way to unlock that reservoir, compensating drivers for battery wear while giving stations a dispatchable resource. The authors note that future work could extend the model to formal electricity market participation, allowing aggregators to bid their clustered flexibility into energy and reserve markets. If the gap between solar abundance at noon and demand peaks at dusk is the central problem of the renewable era, this research suggests the solution may already be parked in the garage, waiting for the right price signal to plug in.

Subject of Research: Bi-level optimal scheduling of photovoltaic-energy storage-charging stations with electric vehicle aggregators and vehicle-to-grid dispatch

Article Title: Optimal bi-level scheduling of a PV – energy storage – charging station with consideration for EV aggregators

Article References: Niu, K., Yang, C., Yang, Y., Cheng, Z., & Tang, M. (2026). Optimal bi-level scheduling of a PV – energy storage – charging station with consideration for EV aggregators. Energy Reports, 16, Article 109775. https://doi.org/10.1016/j.egyr.2026.109775

Image Credits: AI Generated

DOI: 10.1016/j.egyr.2026.109775

Keywords: electric vehicles, vehicle-to-grid, photovoltaics, energy storage, bi-level optimization, EV aggregator, fuzzy C-means clustering, particle swarm optimization, zonotope, renewable energy, smart charging, Stackelberg equilibrium

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