EV Fleets and CHP Systems Team Up to Boost Grid Flexibility and Profits
In the heart of tomorrow’s megacities, where urban density and energy demands collide, a new paradigm for sustainable infrastructure is emerging. A groundbreaking study led by Yang Liu and Tianyu Liu from Shanghai Dianji University introduces a visionary model of regional microgrids that seamlessly integrate plug-in electric vehicles (PEVs) and combined heat and power (CHP) systems to not only meet local energy needs but also actively participate in grid auxiliary services. Published in Computer Applications and Software, this research presents a transformative approach to urban energy management, demonstrating how coordinated fleets of electric vehicles can function as dynamic, fast-responding energy storage units, enhancing both grid stability and economic returns.
As global urbanization accelerates—with projections indicating that 70% of the world’s population will reside in cities by 2050—the strain on energy systems intensifies. Simultaneously, the transportation sector is undergoing a radical shift, with electric vehicles expected to capture 30% of the automotive market by 2030. This dual transformation presents both a challenge and an opportunity: how to manage the increased electricity demand from EVs without overburdening the grid, and how to leverage the growing number of EVs as a distributed energy resource.
The research by Liu and Liu offers a compelling answer. Their proposed regional microgrid framework combines conventional distributed energy resources—such as photovoltaic panels, wind turbines, micro gas turbines, and battery storage—with a novel aggregation of PEVs parked in urban lots. These parking facilities are no longer passive spaces but active nodes in a smart energy network. By clustering PEVs and linking them through power electronics, the system transforms idle vehicles into a virtual power plant capable of rapid response to grid signals.
At the core of this innovation is the integration of PEVs into the regulation market, a segment of the electricity market that requires fast-acting resources to balance supply and demand in real time. Traditional regulation services are typically provided by gas turbines or large-scale batteries, which are costly and have limited flexibility. In contrast, the aggregated PEV fleet offers a decentralized, scalable, and highly responsive alternative. When vehicles are parked and plugged in—particularly during standard work hours from 7 a.m. to 4 p.m.—their batteries can be tapped to deliver power back to the grid through vehicle-to-grid (V2G) technology, helping to stabilize frequency and voltage fluctuations.
The authors emphasize that the success of this model hinges on strategic coordination. PEV owners are assumed to participate voluntarily, motivated by financial incentives. The study models realistic driving patterns based on daily commutes, with data showing that over 85% of work-related trips begin between 6 a.m. and 9 a.m., and most vehicles remain parked for extended periods during the day. This predictable behavior allows for reliable forecasting of available battery capacity. The researchers further assume that drivers require their vehicles to be charged to at least 80% before departure, ensuring that mobility needs are never compromised.
To manage the complexity of coordinating multiple energy sources and fluctuating loads, the team developed an improved version of the Crow Search Algorithm (CSA), a metaheuristic optimization technique inspired by the intelligent foraging behavior of crows. Unlike traditional algorithms such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO), which require tuning of multiple parameters, the enhanced CSA is designed with a simpler structure, relying on only two adjustable parameters: flight length and awareness probability. This streamlined design not only reduces computational overhead but also accelerates convergence, making it highly suitable for real-time energy dispatch decisions.
The algorithm’s role is to maximize the microgrid’s profitability by optimizing the dispatch of energy across multiple markets. Revenue streams include the day-ahead (DA) market, where energy is sold based on forecasted prices; the district heating (DH) market, which leverages the thermal output of the CHP system; and the regulating up (RU) market, where the microgrid provides real-time balancing services. The total profit is calculated as the difference between revenue and operational costs, including fuel consumption, maintenance, and compensation paid to PEV owners for battery usage.
One of the most significant findings of the study is the dramatic economic uplift achieved when the microgrid participates in the RU market. Three distinct scenarios were simulated to evaluate performance. In the first, representing a conventional setup, the microgrid operates without engaging in auxiliary services, resulting in baseline profits. The second scenario introduces partial PEV participation, with two parking lots joining the CHP system to offer regulation capacity. The third scenario scales up the model, incorporating all available parking facilities into a unified storage cluster.
The results are striking. In the first case, with no market participation, daily profits remain static at approximately 141,120 units (currency unspecified). When partial PEV integration is introduced, profits rise to 172,021—a 22% increase. With full participation, profits soar to 208,872, representing a 48% gain over the baseline. This upward trend underscores the value of aggregation: the more vehicles and parking sites involved, the greater the collective energy capacity and the higher the potential earnings.
Beyond financial gains, the model delivers substantial technical benefits. The CHP system, already known for its high energy efficiency—reaching up to 93% compared to 50% for conventional power plants—gains additional operational flexibility through the inclusion of PEV storage. While CHP units are typically slow to respond due to thermal inertia, the fast-reacting PEV batteries compensate for this limitation, enabling the hybrid system to meet rapid power demands. This synergy allows the microgrid to operate more efficiently, reducing fuel consumption and emissions while maintaining reliability.
The study also highlights the importance of thermal storage in the form of hot water tanks. These tanks act as buffers, storing excess heat generated by the CHP system during periods of low thermal demand. This stored energy can then be dispatched when needed, decoupling heat and power production and expanding the feasible operating region of the CHP unit. However, the authors note that thermal storage alone is insufficient for fast grid response due to its slow dynamics. Hence, the integration of electrical storage via PEVs becomes essential for providing the agility required in modern power markets.
Another critical aspect of the research is its treatment of uncertainty. PEV availability, driver behavior, and renewable generation (from solar and wind) are inherently stochastic. To address this, the model incorporates probabilistic data on arrival times and trip distances, derived from real-world commuting patterns. For instance, 35% of workers arrive at their workplace between 8 a.m. and 9 a.m., and 53% of daily commutes are under 10 kilometers. These statistics inform the estimation of usable battery capacity, which is further adjusted for prediction errors and battery degradation.
The compensation mechanism for PEV owners is another key design feature. Participants are reimbursed based on the depth of battery discharge, with a coefficient reflecting the stress on the battery. This ensures fairness and encourages sustained participation. The researchers assume an average battery capacity of 85 kWh and a usable share of 70%, accounting for degradation and safety margins. Even with these conservative estimates, the aggregated storage potential is substantial, especially when scaled across multiple parking facilities.
From a policy perspective, the implications of this research are profound. It suggests that cities can turn their transportation infrastructure into a strategic energy asset. Municipalities could incentivize the development of smart parking systems equipped with bidirectional chargers, enabling seamless V2G integration. Utility companies, in turn, could contract with aggregators to access this distributed storage capacity, reducing reliance on peaker plants and enhancing grid resilience.
Moreover, the model aligns with broader sustainability goals. By increasing the utilization of renewable energy and improving the efficiency of fossil fuel use through CHP, the system reduces carbon emissions. The ability to store surplus solar and wind energy in PEV batteries also mitigates curtailment, a growing issue as renewable penetration increases. In this way, the microgrid acts as a bridge between the transportation and power sectors, fostering a truly integrated energy ecosystem.
The computational experiments further validate the robustness of the approach. Using MATLAB simulations, the team compared the performance of the improved CSA against GA and PSO. While all three algorithms achieved similar optimal solutions, the CSA required significantly less simulation time—nearly half that of GA and slightly less than PSO. This efficiency advantage makes it particularly suitable for practical deployment, where rapid decision-making is crucial.
One of the standout features of the improved CSA is the incorporation of a greedy selection mechanism. After generating a new candidate solution, the algorithm compares it with the current position and retains the better one. This simple yet effective enhancement prevents the loss of high-quality solutions during the search process, ensuring steady progress toward the global optimum.
The research also touches on practical implementation challenges. For example, the need for standardized communication protocols between vehicles, chargers, and grid operators is essential for large-scale adoption. Cybersecurity is another concern, as interconnected systems are vulnerable to malicious attacks. Additionally, regulatory frameworks must evolve to accommodate new market participants, such as PEV aggregators, and define clear rules for compensation and liability.
Despite these hurdles, the trajectory is clear: the future of urban energy lies in integration, intelligence, and interactivity. The work of Liu and Liu exemplifies this shift, demonstrating that electric vehicles are not just a means of zero-emission transportation but a cornerstone of a smarter, more resilient grid. As cities continue to grow and electrify, models like this will become increasingly vital in balancing sustainability, reliability, and economics.
The implications extend beyond individual microgrids. If replicated across metropolitan areas, such systems could collectively form a vast, distributed network of storage and generation, capable of providing system-wide ancillary services. This could fundamentally alter the dynamics of electricity markets, empowering consumers to become active producers and traders of energy.
In conclusion, the study presents a holistic vision of urban energy transformation. By combining the thermal efficiency of CHP systems with the electrical agility of PEV fleets, and optimizing their coordination through advanced algorithms, the researchers have created a blueprint for next-generation microgrids. The results are not just theoretical—they show tangible increases in profitability and grid support capability, paving the way for widespread adoption.
As the world moves toward decarbonization and digitalization, innovations like this will define the energy landscape of the 21st century. The message is clear: the cars parked in our cities are not just idle assets—they are untapped power plants, ready to drive the energy transition forward.
Yang Liu, Tianyu Liu, Shanghai Dianji University, Computer Applications and Software, DOI: 10.3969/j.issn.1000-386x.2024.04.010