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

This transition poses a crucial question: how can the control of millions of decentralized energy devices be effectively managed? Javad Khazaei, an assistant professor of electrical and computer engineering at Lehigh University, is pioneering research aimed at addressing this monumental challenge. His work focuses on developing innovative control frameworks that can capture the intricate dynamics of DERs and optimize their operation in real time. Recently awarded a prestigious National Science Foundation (NSF) CAREER grant, Khazaei is advancing a novel, geometry-based control paradigm that promises to reshape how smart grids manage their increasingly complex resources.

At the heart of traditional grid operation lies the centralized optimization approach, commonly known as optimal power flow. This method relies on aggregating data from every node in the power network—ranging from power plants to consumer loads—and sending it to a central controller. The controller, equipped with a model of the grid’s generation capabilities and demand requirements, calculates an optimal dispatch strategy to maintain the delicate balance of supply and demand. However, this model-centric, centralized approach faces fundamental limitations as grids grow exponentially in scale and heterogeneity. The sheer volume of data and computational power required rapidly becomes impractical for real-time application.

Khazaei’s research deviates from conventional methods by embracing a data-driven perspective that leverages modern behavioral and geometric modeling techniques. Instead of relying on exhaustive physical models that capture every nuance of device behavior, his team proposes to learn the dynamic characteristics of aggregated DER units directly from data streams. Using system identification and advanced machine learning principles, they extract the core behavioral “shape” of the system—identifying the boundaries within which the system operates reliably. This geometric representation enables the creation of simplified, reduced-order models that retain critical dynamics while significantly cutting down computational complexity.

The ability to represent complex nonlinear systems with compact mathematical frameworks is a game-changer. Reduced-order modeling translates into fewer differential equations that comprehensively describe DER behavior without overwhelming computational resources. By focusing control actions on these geometrically defined boundaries, the system can more reliably predict short-term grid responses to fluctuations caused by variable renewable generation or shifting demand. This predictive capability could transform operational practices, enabling proactive adjustments instead of reactive responses, thus enhancing grid resilience and stability.

Such innovation is especially critical given the increasing penetration of DERs across microgrids and distribution networks. Today’s grid does not just transmit power—it also must manage stability issues emerging from fluctuating inputs, nonlinear interactions, and the bidirectional nature of energy flow. Khazaei’s geometry-focused method promises to harmonize these complexities, enabling seamless integration of diverse energy sources while avoiding system-wide failures. The potential scalability of this approach—from local microgrids to national grid levels—points to a scalable future where control is decentralized, responsive, and resilient.

Integral to this effort is the deployment of artificial intelligence (AI) techniques, which further accelerate the data-driven modeling process. With the exponential growth of sensor networks and grid monitoring infrastructure, AI can digest vast datasets and extract meaningful patterns that traditional algorithms might overlook. Khazaei’s research harnesses AI to improve the precision and speed of control design, promising to shorten development cycles from months or years to days or weeks. This AI-enhanced paradigm elevates the intelligence embedded in grid controllers, enabling adaptive learning and continuous optimization in fluctuating operational contexts.

By incorporating system behavioral theory and geometric principles, this research represents a departure from the classical physics-based modeling standard in power system engineering. The focus on “behavioral shapes” rather than detailed state variables provides compelling proof that simplicity, when grounded in rigorous theory, can drive technological breakthrough. This approach not only reduces the burden on computational engines but also creates a transparent framework to understand and forecast grid behavior in a holistic yet manageable manner.

The impact of this research extends beyond control algorithms into the broader energy landscape, encompassing power distribution, hybrid systems, and energy storage integration. The simplified yet robust models can guide infrastructural decisions, optimize energy market operations, and inform policy frameworks that support sustainable transitions. As smart grids evolve into intelligent networks capable of self-diagnosis and autonomous recovery, Khazaei’s geometry-based control paradigm lays a foundational stone for next-generation energy infrastructure.

In summary, the transformation of electrical grids necessitates a fundamental rethinking of control strategies. The blending of data-driven geometric modeling with AI opens a promising pathway to orchestrate millions of DERs in real time, ensuring reliable, efficient, and sustainable energy delivery. Javad Khazaei’s pioneering work, supported by the NSF CAREER program, signals a paradigmatic shift where control systems not only react to today’s conditions but also anticipate tomorrow’s uncertainties—ushering in a new era of resilient, adaptive smart grids.

As we face the imperative to reduce carbon emissions and integrate variable renewable resources at unprecedented scales, innovations like Khazaei’s offer a vision of grids that are not just smarter but fundamentally transformative. By reimagining control through the lens of geometry and data, this research illuminates a future where electrical systems harness complexity as a strength, delivering robust performance amid uncertainty and change.

Subject of Research: Geometry-based control of nonlinear distributed energy resources in smart grids.

Article Title: Geometry-Based Control Paradigm for Distributed Energy Resources Promises a New Era in Smart Grid Management

News Publication Date: Not specified in original content.

Web References:
– https://engineering.lehigh.edu/faculty/javad-khazaei
– https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2538831
– https://sites.google.com/view/javadkhazaei

Image Credits: Lehigh University

Keywords

Distributed Energy Resources, Smart Grids, Geometry-based Control, Predictive Control, Reduced-order Modeling, Artificial Intelligence, Data-driven Modeling, Microgrids, Power Systems, Energy Storage, Electrical Engineering, Renewable Energy Integration

Zhong’s seminal contribution, known as the synchronized-and-democratized (SYNDEM) architecture, represents a paradigm shift in power system engineering. By embedding synchronization—traditionally studied in physics and biology—into the fundamental structure of power grids, and coupling it with democratic principles emphasizing local autonomy and distributed decision-making, Zhong has devised a system where the coordination and stability of the grid emerge inherently from the interactions of its components. Unlike conventional grids that rely heavily on centralized control and communications networks, the SYNDEM architecture facilitates self-organizing dynamics that promote robust, scalable, and adaptive power delivery.

Central to the SYNDEM framework is the concept of virtual synchronous machines (VSMs). These digital constructs emulate the inertial and dynamic properties of traditional synchronous generators, which are foundational to grid stability but are progressively being phased out in favor of renewable and inverter-based resources lacking inherent inertia. Zhong’s VSM technology injects synthetic inertia and damping into the grid, enabling inverter-connected sources to mimic physical synchronous machines. This emulation not only ensures dynamic stability but also orchestrates autonomous local interactions that maintain frequency and voltage synchrony across distributed energy resources, thereby forming the operational backbone of the SYNDEM vision.

The implications of Zhong’s work extend beyond theoretical constructs into tangible industry impact. Modern power systems face escalating challenges from the integration of intermittent renewable energy sources, distributed generation, and the growing demand for energy equity and freedom. The SYNDEM architecture and VSM technologies offer a resilient alternative to centralized control paradigms by empowering localized power generation units to autonomously coordinate their behavior, reducing vulnerabilities to cyberattacks, communication failures, and operational bottlenecks. Such resilience is critical for sustaining power delivery amidst increasingly complex grid conditions driven by climate change, urbanization, and electrification trends.

Zhong’s multidisciplinary vision bridges the gap between natural and social sciences through an integrated engineering framework. By applying principles of synchronization—originally from nonlinear dynamics and complex systems theory—to a democratically structured power system, he reimagines autonomy in terms of emergent coordination rather than imposed directives. This novel lens enables the creation of power grids that not only function efficiently but also uphold the values of democracy and equitable participation among diverse stakeholders, aligning technological innovation with socio-political ideals.

One of the most revolutionary aspects of Zhong’s work is the operationalization of decentralized control through physical laws governing synchronization. This intrinsic mechanism facilitates power components at various grid nodes to adjust their dynamics based on local information without the need for extensive supervisory commands or communication infrastructure. Consequently, the SYNDEM-based systems exhibit improved fault tolerance, scalability, and adaptability, allowing for the seamless accommodation of fluctuating generation sources and dynamic load profiles pervasive in contemporary electric grids.

The evolution of power systems has traditionally been constrained by legacy technological and control models centered around centralized management. Zhong’s technological innovations confront these limitations directly by introducing foundational changes in system architecture. His SYNDEM and VSM methodologies collectively enable the conceptualization and realization of next-generation power grids characterized by “autonomous coordination” — a self-organizing orchestration of grid functions that arises naturally from the physical interactions of system components. This radical approach not only enhances reliability but also creates pathways for democratizing energy access and participation.

Zhong’s recognition by AAAS, one of the world’s largest and most prestigious scientific societies, underscores the scientific and practical significance of these innovations. AAAS fellows are chosen for their extraordinary achievements that transcend disciplinary boundaries; Zhong’s election highlights the transformative potential of cross-pollinating principles from natural sciences and social philosophies for engineering robust and equitable technological infrastructures. His election signals a growing acknowledgment within the scientific community of the importance of interdisciplinary, socially conscious approaches to engineering critical infrastructure systems.

The convergent evolution of SYNDEM and VSM technologies signals a foundational shift in how power systems are designed and operated. By embedding democratically inspired principles into the synchronization and control mechanisms of power electronics and grid dynamics, Zhong’s work paves the way for an energy future characterized by autonomy, resilience, sustainability, and inclusivity. The broader adoption of these concepts could catalyze a global transition towards grids that are not only technologically advanced but also socially equitable, supporting the aspirations of communities worldwide.

Furthermore, the SYNDEM architecture’s intrinsic reliance on physical laws over centralized communication networks presents compelling cybersecurity benefits. The distributed, self-synchronized operation reduces the attack surface susceptible to malicious cyber activities that target communication and control infrastructures. As the threat landscape escalates with increasingly sophisticated cyber incursions, Zhong’s framework offers a proactive pathway to fortify grid resilience through its natural, physics-based control schemes, enhancing the overall security posture of future energy systems.

The academic and industrial impact of Sino-American scholar Qing-Chang Zhong’s work is evident in the growing research and development momentum around virtual synchronous machines and decentralized coordination principles. His methodologies empower engineers and researchers alike to rethink grid stability frameworks, integrating inverter-based energy resources seamlessly into existing infrastructure. By enabling such a technological evolution, Zhong’s contributions facilitate the long-term transition toward fully sustainable and democratically governed power systems, driving innovation that aligns with global decarbonization and energy democratization goals.

Ultimately, Zhong’s election as an AAAS fellow not only honors his exceptional individual achievements but also signals a broader shift within the energy research community toward embracing holistic and interdisciplinary frameworks. His work’s implications extend beyond academic theorization, influencing practical grid modernization strategies worldwide. As the energy sector grapples with the twin imperatives of climate change mitigation and equitable access, innovations rooted in synchronized-democratized architectures and virtual synchronous machines stand mature to become cornerstones of future power infrastructures.

Subject of Research: Autonomous, sustainable, and democratized power systems through synchronized-democratized (SYNDEM) architecture and virtual synchronous machines (VSM) technologies.

Article Title: Redefining Power Systems: How SYNDEM Architecture and Virtual Synchronous Machines Are Revolutionizing the Grid

News Publication Date: 2024

Web References:
– https://www.iit.edu/directory/people/qing-chang-zhong
– https://www.aaas.org/news/aaas-welcomes-449-scientists-and-engineers-honorary-fellows

Image Credits: Illinois Institute of Technology

Keywords: Electrical engineering, Power systems, Power distribution, Electrical power, Virtual synchronous machines, SYNDEM architecture, Grid resilience, Autonomous coordination, Sustainable energy, Energy equity, Energy freedom, Decentralized control