Journal of Shanghai Jiao Tong University

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    Key Technologies and Applications of Shared Energy Storage
    SONG Meng, LIN Gujing, MENG Jing, GAO Ciwei, CHEN Tao, XIA Shiwei, BAN Mingfei
    Journal of Shanghai Jiao Tong University    2024, 58 (5): 585-599.   DOI: 10.16183/j.cnki.jsjtu.2022.360
    Abstract （1486）   HTML （27）    PDF（pc） （4173KB）（484）       Save

    Under the goal of “carbon peaking and carbon neutrality”, the penetration rate of renewable energy continues to rise, whose volatility, intermittency, and uncertainty pose significant challenges to the safe and stable operation of the power system. As a typical application of the sharing economy in the field of energy storage, shared energy storage (SES) can maximize the utilization of resources by separating the “ownership” and “usage” of energy storage resources, which provides a new solution to the problem of imbalance between supply and demand caused by the large-scale integration of renewable energy into the grid, and has broad development prospects. The business model of SES is explored based on value positioning, cost modeling, and profitability strategies, and a detailed summary of SES trading varieties, operational structure, and engineering applications is discussed. Finally, the future trend of shared energy storage is discussed and envisioned.

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    Dynamic Optimization of Carbon Reduction Pathways in Coastal Metropolises Considering Hidden Influence of Decarbonization on Energy Demand
    XIAO Yinjing, ZHANG Di, WEI Juan, GE Rui, CHEN Dawei, YANG Guixing, YE Zhiliang
    Journal of Shanghai Jiao Tong University    2024, 58 (5): 600-609.   DOI: 10.16183/j.cnki.jsjtu.2022.437
    Abstract （1147）   HTML （13）    PDF（pc） （1733KB）（237）       Save

    Setting a reasonable carbon reduction plan in coastal metropolises is the key part to reach the global carbon target. Carbon reduction will change urban climate and influence energy demand, both of which affect the optimization results of carbon reduction pathways. Current generation expansion optimization models consider direct abatement contribution and solve most problems of planning for long-term carbon emission reduction in energy systems. However, the construction of new type power systems also indirectly impacts carbon emissions by changing microclimate factors such as heat island intensity. By combining generation expansion with carbon emission prediction model, the proposed approach in this paper considers the hidden mechanism of carbon and heat emission change on air-conditioning loads and dynamically optimizes the carbon reduction pathways in coastal metropolises. Taking Pudong Area in Shanghai as an example, the estimated cost of carbon reduction is reduced by the proposed approach. Some suggestions for the carbon reduction in coastal metropolises are made according to the simulation results.

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    Bi-Level Optimization Operation Method of Multi-H2-IES Considering Dynamic Carbon Emission Factors
    FU Wenxi, DOU Zhenlan, ZHANG Chunyan, WANG Lingling, JIANG Chuanwen, XIONG Zhan
    Journal of Shanghai Jiao Tong University    2024, 58 (5): 610-623.   DOI: 10.16183/j.cnki.jsjtu.2022.225
    Abstract （1067）   HTML （9）    PDF（pc） （2901KB）（78）       Save

    In the context of achieving “carbon peaking and carbon neutrality”, the low-carbon transformation of the energy system is the development direction in the future. Hydrogen, known for its high calorific value and low pollution, has received extensive attention in recent years. Based on the carbon emission flow theory, a bi-level optimization operation model of multi-integrated energy system with hydrogen (H2-IES) is proposed considering dynamic carbon emission factors. At the upper level, an economic dispatch model is established by the main energy grid based on the principle of optimal benefit, and the energy prices and carbon emission factors of each park are determined and distributed to the lower level. At the lower level, a multi-park low-carbon cooperative operation model is established based on the Nash negotiation theory, and the adaptive alternating direction method of multipliers (A-ADMM) is used for distributed solution to determine the energy demand of each park and provide feedback to the upper level. The coordinated operation of both levels is realized in multiple iterative interactions. To equitably distribute the benefits of cooperation, a revenue distribution method based on comprehensive bargaining power is proposed. The analysis of a case study shows that the bi-level optimization method proposed in this paper can realize the coordinated operation between the upper and lower levels, and take into account the low-carbon and economical properties of multi-parks operation. Because the income is reasonably distributed, the enthusiasm of parks to participate in cooperation can be guaranteed.

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