Engineering topics
Publications and source records attributed to Hu, Qinran.
The beautification of urban facades in China often includes covers for the outdoor units of air conditioners. Although these covers improve cityscapes, they reduce cooling efficiency, increase noise pollution, risk system failures and embody carbon emissions. A call urges rethinking their necessity, and presses for sustainable, efficient design to counteract extreme heat.
The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.
Capacity markets can co-exist alongside the energy and ancillary markets to ensure the medium-term and long-term supply adequacy by remunerating the generation resources for their availability. With the substantially increasing deployment of renewable generation, the system needs more flexibility to quickly mitigate the variability and uncertainty caused by the renewable generation. However, the present capacity market model does not differentiate the flexibility of generating units, so it might not give appropriate pricing signals to more flexible units that are usually more expensive. This is particularly important considering the increasing penetration levels of renewable generation on the electric grid, which increases the need for flexible units. This paper proposes a novel capacity market model considering flexibility requirement (FR) under the high penetration levels of renewable generation. Therefore, the proposed model can give the market-clearing price (MCP) for not only peak load generation capacity but also flexibility requirement. Thus, flexible units will tend to receive more economic incentives than non-flexible units. In the case studies, the profitability of different generation resources under the proposed capacity market model is analyzed and compared to the present capacity market model. The results show that the proposed method can maintain system reliability with regard to both peak load and flexibility requirement efficiently.
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