Electric Vehicles and Waste-to-Energy Boost Regional Energy Efficiency in New Study
In a significant leap toward smarter, more sustainable urban energy ecosystems, researchers from Guangxi University have unveiled a groundbreaking model that seamlessly integrates electric vehicle (EV) load management with waste-to-energy conversion within regional comprehensive energy systems (RIES). The study, led by Associate Professor Li Jiyong, graduate student Song Zihan, and Associate Professor Liu Bin from the School of Electrical Engineering, presents a holistic approach to optimizing energy distribution, reducing reliance on external power grids, and enhancing overall system economics through intelligent coordination of diverse energy sources.
As cities grow and energy demands rise, traditional energy infrastructures—operating in silos of electricity, heating, and cooling—face mounting inefficiencies and environmental strain. These fragmented systems often result in energy waste, suboptimal load balancing, and missed opportunities for resource recovery. Against this backdrop, the concept of integrated energy systems has gained momentum, promising a future where energy flows are not only interconnected but also dynamically responsive to real-time demand, supply fluctuations, and environmental conditions.
The research team’s work, published in a leading energy journal, introduces an advanced RIES framework that incorporates two transformative elements: municipal solid waste incineration for power generation and bidirectional energy exchange with electric vehicles. This dual integration marks a pivotal shift from passive energy consumption to active, participatory energy ecosystems where waste is no longer a liability but a valuable energy asset, and EVs evolve from mere transportation tools into mobile energy storage units.
At the heart of the model is a sophisticated optimization strategy designed to minimize operational costs while maximizing energy efficiency. The researchers developed a multi-objective function that accounts for electricity procurement from the main grid, natural gas consumption for thermal and electrical generation, and the economic implications of EV charging and discharging cycles. By factoring in time-of-use electricity pricing, thermal demand patterns, and the availability of renewable sources such as photovoltaic generation, the model enables dynamic decision-making that aligns energy use with economic and environmental priorities.
One of the most innovative aspects of the study is the inclusion of waste-to-energy plants as a formal component of the RIES. Traditionally, waste management and energy production have been treated as separate domains. However, the team’s analysis demonstrates that leveraging the thermal energy from garbage incineration not only mitigates landfill dependency and reduces greenhouse gas emissions but also provides a stable, baseload-compatible power source. The heat generated from burning waste is converted into electricity through specialized turbines, which then feed into the local energy network. This process effectively transforms a public health and environmental challenge into a reliable energy stream.
The integration of electric vehicles adds another layer of flexibility. Rather than viewing EVs solely as consumers of electricity, the model treats them as distributed energy resources capable of both drawing power and injecting it back into the grid. This vehicle-to-grid (V2G) capability allows the system to harness EV batteries for peak shaving, load leveling, and frequency regulation. During off-peak hours—typically late at night when electricity demand and prices are low—the system prioritizes charging EVs. Conversely, during peak demand periods in the late afternoon and early evening, EVs can discharge stored energy back into the grid, reducing the need for costly and carbon-intensive peaking power plants.
The researchers conducted extensive simulations using Python-based optimization algorithms to validate their model. The case study focused on a typical urban district during summer months, where cooling demands are high, and solar generation is abundant. The simulation incorporated real-world load profiles, including residential, commercial, and industrial electricity use, heating and cooling requirements, and PV output curves. The results were compelling: the coordinated operation of waste-to-energy plants and EV fleets led to a substantial reduction in grid dependency and operational costs.
Notably, the model revealed that during low-demand periods, the system preferentially charged EVs using surplus power from photovoltaic panels and waste incineration plants. This not only maximized the utilization of renewable and recovered energy but also minimized the need to purchase electricity from the main grid at higher rates. As demand surged in the late morning and evening, the system shifted to self-generation and EV discharge, effectively flattening the load curve and avoiding price spikes.
The thermal side of the system was equally optimized. Excess heat from the combined heat and power (CHP) units—driven by both natural gas and waste incineration—was utilized in absorption chillers to meet cooling demands. This cascading use of energy, where high-grade heat is first used for power generation and the residual heat for cooling, exemplifies the principle of energy cascading and significantly improves overall system efficiency. The model showed that during midday hours, when solar radiation is strongest and cooling needs peak, the absorption chillers supplied a large portion of the required cooling load, reducing the burden on electrically driven air conditioning systems.
To solve the complex optimization problem involving multiple energy carriers, time-varying prices, and interdependent constraints, the team employed an enhanced particle swarm optimization (PSO) algorithm. This metaheuristic approach, inspired by the collective behavior of bird flocks, efficiently navigates the solution space to identify the most cost-effective operational strategy. The algorithm dynamically adjusts the charging and discharging schedules of EVs, the dispatch of CHP units, and the operation of electric boilers and chillers to minimize total system cost while respecting technical and operational limits.
A critical feature of the PSO implementation was the incorporation of realistic constraints for EV behavior. The model assumes that EVs cannot charge and discharge simultaneously, reflecting current technological limitations. It also accounts for battery degradation by limiting the total daily energy throughput and enforcing minimum and maximum state-of-charge levels. These constraints ensure that the optimization results are not only theoretically sound but also practically implementable.
The simulation results underscored the economic and operational benefits of the integrated approach. When EV charging capacity was increased from 5 MW to 25 MW per hour, the total system cost dropped from 45.53 million yuan to 34.01 million yuan over the simulated period. This reduction was primarily driven by decreased reliance on natural gas for power generation during peak hours, as EVs and waste-to-energy plants provided alternative sources of electricity. The findings suggest that scaling up EV adoption and waste-to-energy infrastructure can yield substantial cost savings for urban energy systems.
Moreover, the study highlights the environmental co-benefits of the proposed model. By diverting waste from landfills and converting it into useful energy, the system reduces methane emissions—a potent greenhouse gas. Simultaneously, the integration of EVs supports the decarbonization of the transportation sector, especially when charged with electricity from low-carbon sources. The combined effect is a dual reduction in both waste-related pollution and carbon emissions from fossil fuel combustion.
The researchers also explored the impact of policy incentives on system performance. In their model, EV owners receive a financial compensation of 0.4 yuan per kilowatt-hour for participating in V2G programs. This incentive not only encourages user participation but also enhances the economic viability of the entire system. The study suggests that well-designed tariff structures and subsidy mechanisms can accelerate the adoption of smart energy technologies and foster a more resilient and sustainable urban energy landscape.
Another key insight from the research is the importance of temporal coordination in multi-energy systems. The model demonstrates that the timing of energy conversion and storage processes is as crucial as their magnitude. For instance, charging EVs during off-peak hours not only reduces costs but also helps absorb excess renewable generation that might otherwise be curtailed. Similarly, using waste heat for cooling during the day aligns energy supply with demand patterns, maximizing the value of recovered energy.
The study also addresses the challenges of system scalability and adaptability. While the current model focuses on a single urban district, the underlying principles can be extended to larger regional networks or replicated across multiple communities. The modular nature of the RIES framework allows for incremental integration of new energy sources, storage technologies, and demand-side resources. This scalability is essential for cities aiming to transition toward low-carbon, high-efficiency energy systems in a phased and cost-effective manner.
From a governance perspective, the research underscores the need for cross-sectoral collaboration. Effective implementation of such integrated systems requires coordination among energy providers, waste management authorities, transportation agencies, and urban planners. The success of the model depends not only on technological innovation but also on institutional frameworks that support data sharing, joint planning, and aligned regulatory incentives.
The implications of this research extend beyond technical optimization. It offers a vision of cities where energy, transportation, and waste management are no longer isolated domains but interconnected systems working in harmony. In this future, a resident’s electric car is not just a means of getting to work—it is a node in a vast, intelligent energy network. A garbage truck is not merely a collector of refuse—it is a supplier of fuel for power generation. And the energy bill is not just a monthly expense—it is a reflection of a community’s collective effort to use resources wisely and sustainably.
The work of Li Jiyong, Song Zihan, and Liu Bin represents a significant contribution to the evolving field of smart energy systems. By demonstrating the tangible benefits of integrating waste-to-energy and electric vehicles into regional energy networks, they provide a practical blueprint for urban sustainability. Their model not only improves energy efficiency and reduces costs but also advances the broader goals of environmental protection and climate resilience.
As cities around the world grapple with the dual challenges of rapid urbanization and climate change, innovative solutions like the one proposed in this study will be essential. The integration of diverse energy sources, the empowerment of consumers as prosumers, and the transformation of waste into value are not just technical achievements—they are steps toward a more circular, equitable, and sustainable energy future.
The research was conducted at Guangxi University and published in a peer-reviewed journal, reflecting rigorous academic standards and methodological integrity. The findings are expected to influence both policy and practice, offering actionable insights for energy planners, municipal authorities, and technology developers. With further refinement and real-world deployment, the model could serve as a benchmark for next-generation urban energy systems.
In conclusion, the study exemplifies how interdisciplinary thinking and systems-level optimization can unlock new possibilities in energy management. By bridging the gap between waste management and energy production, and between transportation and power systems, the researchers have opened a pathway to smarter, cleaner, and more resilient cities. As the world moves toward a low-carbon future, such integrative approaches will be indispensable in building energy systems that are not only efficient but also equitable and sustainable for generations to come.
Li Jiyong, Song Zihan, Liu Bin, School of Electrical Engineering, Guangxi University, published in a leading energy journal, DOI: 10.19753/j.issn1001-1390.2024.04.016