| Author: | Liao, Wei |
| Title: | Optimization of capacity, electricity trading and operations of integrated community energy systems under building and transportation electrification |
| Advisors: | Xiao, Fu (BEEE) |
| Degree: | Ph.D. |
| Year: | 2026 |
| Department: | Department of Building Environment and Energy Engineering |
| Pages: | xxiv, 233 pages : color illustrations |
| Language: | English |
| Abstract: | The rapid electrification of building and transportation sectors is fundamentally transforming the landscape of energy demand, supply, and management in modern urban communities. As global cities strive to decarbonize their infrastructure and respond to ambitious climate targets, the integration of distributed energy resources (DERs) such as photovoltaics (PV), batteries, and electric vehicles (EVs) into local and district energy systems brings about unprecedented opportunities and challenges. These developments are further complicated by increasingly volatile loads, the need for flexible operation, and the imperative for resilience against both technical and environmental disruptions. Against this backdrop, the capacity design, energy trading, and operational strategies for community energy systems require rethinking and systematic optimization to ensure cost-effectiveness, grid reliability, and sustainability. This thesis focuses on optimizing capacity electricity trading and operational strategies for integrated community energy systems, specifically considering the tight coupling between building and transportation electrification and the diversity of multiple communities. Based on a unique, high-resolution dataset from the integrated energy system serving a real-world campus, the study establishes a robust empirical dataset, which are used to evaluate the proposed energy management and trading strategies, demonstrating the advantages of game-theory based peer-to-peer (P2P) strategy approaches in reducing costs, balancing supply and demand, and enhancing system flexibility. The work further addresses operational resilience under extreme events through short-term coordinated restoration strategies leveraging flexible resources such as DERs and vehicle-to-grid (V2G)-enabled EVs, and concludes with a multi-objective capacity planning framework that accounts for phased electrification and evolving long-term decarbonization targets. Firstly, the study utilizes a comprehensive, high-resolution dataset spanning two decades, which was collected from a real-world campus distributed energy system in Japan. This dataset encompasses hourly records of energy generation and consumption, covering a wide array of supply-side resources including combined heating and power units (fuel cells, gas engines), absorption chillers, gas boilers, solar PV, and grid imports, as well as detailed load profiles for electricity, heating, cooling, and hot water at the level of individual buildings. By spanning the full operational lifespan of on-site generators, this dataset provides an unparalleled empirical foundation for descriptive analytics, model validation, and scenario analysis. It enables the rigorous evaluation of system efficiency, flexibility, and sustainability under evolving technological, operational, and policy conditions, while also serving as a benchmark for future research and policy-making in distributed energy systems. Secondly, this study assesses conventional rule-based energy management strategies under both peer-to-grid (P2G) and P2P trading paradigms, to elucidate the differences in efficiency and techno-economic performance among different operational strategies within community-level energy systems. Four distinct energy management strategies are systematically compared, including the maximizing PV self-consumption (MSC) strategy, a cost-minimizing strategy based on time-of-use (TOU) tariffs, a P2P trading paradigm centered on the mid-market rate (MMR) principle, and a proposed demand response (DR)-based P2P strategy. Advanced stochastic simulation techniques, including Monte Carlo integrated Markov Chain methods, are employed to capture the variability and uncertainty in vehicles' travel among different buildings, allowing for the dynamic generation of P2P prices that reflect real-time supply-demand balances within and across communities. The comparative results show that, relative to traditional mainstream operation strategies, strategies under P2P paradigm (especially incorporating DR) can provide substantial benefits in terms of reducing grid dependency, minimizing electricity costs, smoothing peak demand, and improving the effective utilization of renewable generation. Notably, the study shows that the optimal strategy choice depends on the specific role and characteristics of each building community, demonstrating the importance of context-sensitive, adaptive management strategies for maximizing system-wide benefits. Thirdly, recognizing the limitations of rule-based and centralized trading strategies in increasingly decentralized, heterogeneous, and dynamic energy systems, the study develops a hybrid game theory-based P2P trading strategy for multi-microgrid systems to achieve optimal energy management and facilitate efficient, fair P2P electricity transactions. This proposed approach employs a non-cooperative game to determine optimal energy transactions and dynamic pricing among interconnected microgrids, while utilizing a cooperative game to fairly allocate revenues among buildings within each microgrid based on their individual contributions. The hybrid model accurately reflects both the competitive and collaborative dynamics that characterize real-world energy communities, capturing the complexities of decentralized decision-making, variable generation, and electricity demand. Through extensive simulation and case studies involving heterogeneous building communities and significant EV integration, this study demonstrates the effectiveness of the proposed approach. The results indicate that the hybrid game-theoretic P2P strategy significantly outperforms the TOU-based strategy under the P2G paradigm. Specifically, the proposed strategy reduces power imports from the utility grid for consumer-type microgrids by 30.4%, while also decreasing power exports for prosumer- and producer-type microgrids by 33.2% and 25.9%, respectively, compared to the TOU-based approach. Furthermore, the flexible utilization of EVs and other responsive loads as dispatchable assets further amplifies system responsiveness and resilience, supporting the transition toward more robust, decentralized, and interactive power systems. Fourthly, the study addresses the pressing challenge of enhancing operational resilience in power systems by facilitating rapid load restoration after outages caused by extreme weather events. Considering the effect of demand-side load rebound following outages, including building cold load pickup (CLPU) and the surge in EV charging load, the study develops a two-stage coordinated restoration strategy to ensure rapid restoration of demand-side load. This strategy is developed to optimize the post-outage restoration sequence of different loads across network nodes by coordinating all available generation and flexibility resources, including DERs, rooftop PV, and V2G-enabled EVs, based on the restoration weight of different load nodes. Meanwhile, the approach is evaluated across a range of grid architectures, especially hybrid AC/DC systems, which are emerging as a promising solution for next-generation grids. The results demonstrate that, under high-rebound scenarios, flexible resources such as rooftop PV and V2G-enabled EVs, can supply a majority share of the restoration power (up to 87.7%), significantly reducing dependency on traditional distributed generators (DGs) and enabling the restoration of critical loads up to 40% faster compared to conventional AC grid architectures. Voltage stability is also markedly improved, underscoring the value of distributed flexibility and advanced grid topologies in enhancing both the speed and robustness of power system restoration. These outcomes highlight the system's improved ability to withstand and recover from disruptions, reflecting a higher capability of operational resilience. Finally, long-term capacity planning for community energy systems is approached through the development of a multi-objective optimization method that explicitly incorporates the dynamic impacts of phased electrification and evolving carbon reduction targets. Drawing on empirical data for demand load, phased electrification, evolving carbon reduction targets, and heterogeneous power generation capacities and demand-load characteristics across diverse communities, the model evaluates optimal capacity planning decisions across a suite of decarbonization scenarios, including a 2010 benchmark and moderate and deep carbon reduction pathways for 2030 and 2050. The results reveal dramatic and transformative shifts in system configuration: the capacity of renewables such as PV and wind turbine are projected to increase by 300% from 2010 to 2030 and by an additional 230% by 2050, while installations of heat pumps for electrified heating and cooling are expected to rise by 1800% by 2030 and further by 182% by 2050. Deep decarbonization pathways yield a 43.4% higher share of renewables and a 17.3% reduction in fuel-dependent units compared to moderate scenarios, enabling technically feasible, seasonally carbon-neutral operation and dramatically expanding the scope for demand-side electrification. Under optimal configurations, the electrification ratio of buildings and transportation of community energy systems rises from 5–8% in 2010 to 20–28% by 2030, and reaches 53–77% by 2050, highlighting the transformative impact of integrated, forward-looking planning on decarbonization and sustainability outcomes. These findings reinforce the critical role of strategic planning that not only optimizes technical and economic performance but also supports the broader policy and societal objectives of deep electrification and sustainable development. In summary, this study delivers a comprehensive and empirically validated methodology for the optimization of community energy systems at the intersection of building and transportation electrification. The comprehensive methodology developed in this thesis can offer essential guidance for researchers, policymakers, and stakeholders to assess the technical, economic, and environmental feasibility of integrated energy solutions. Furthermore, this work provides actionable tools and strategies to facilitate energy demand management, supply integration, storage deployment, and coordinated grid interaction, thereby supporting the development and implementation of sustainable, decentralized, and resilient energy systems. Ultimately, the insights and frameworks presented contribute not only to the academic understanding of next-generation energy infrastructures, but also to practical policy formulation and strategic technology deployment, accelerating progress toward a low-carbon, electrified, and carbon-neutral future within urban environments. |
| Rights: | All rights reserved |
| Access: | open access |
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