Modeling of Electric Vehicles as Mobile Energy Storage Systems Considering Multiple Congestions
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摘要:
为实现城市交通电力耦合系统在城市道路、充电设施、输电线路阻塞环境下的优化运行，提出了计及多重阻塞的动态交通电力流联合优化方法。首先，基于时空网络模型，提出了计及电动汽车移动、静止、充电、排队模式的队列时空网络模型，构建了适用于电动汽车的车辆调度模型，进而形成动态交通分配模型，以减少交通出行损失。其次，通过优化发电机组、储能等的出力和备用计划，计及城市电网安全、备用约束，构建了安全约束动态经济调度模型，以降低碳排放及发电成本。随后，形成多目标动态优化模型，并将其转换为混合整数凸二次规划问题。最后，在耦合IEEE-30、Sioux Falls系统中验证了所提模型的有效性。
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关键词:
- 阻塞 /
- 动态交通分配 /
- 队列时空网络 /
- 城市交通电力耦合系统
Abstract:To realize the optimal operation of urban coupled transportation power systems underthe road, charging facilities, and transmission line congestions, a dynamic optimal traffic power flow (DOTPF) model was formulated under congestions. Based on the time space network (TSN) approach, a novel TSN with queues was proposed, considering the moving, parking, charging, and queueing state transitions. A vehicle routing problem was formulated for electric vehicles (EVs) and further incorporated into the dynamic traffic assignment problem (DTAP), reducing the traffic demand losses. With security and reserve constraints, a dynamic security-constrained carbon dioxide-oriented optimal power flow (OPF) problem was formulated to reduce the carbon emission and generation cost, by optimizing the scheduling of thermal units and energy storage systems. A multi-objective DOTPF problem was formulated, and further reformulated into a convex mixed-integer quadratic programming problem. The effectiveness of the proposed DOTPF was verified based on the simulation results on coupled IEEE-30 and Sioux Falls system.
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图 3 不同情景下总充放电负荷曲线
Figure 3. Total charging/discharging load profile under different scenarios
表 1 仿真情景设计
Table 1. Simulation scenarios
scenario road congestion charging congestion line congestion 1 − − − 2 capacities of roads (1, 2) and (1, 4) are reduced to 25% during 2:00—16:00 − − 3 − capacities of charging stations are 1 fleet during 0:00—24:00 − 4 − − capacities of lines (2, 4) and (3, 4) are reduced to 20% during 7:00—15:00 5 capacities of roads (1, 2) and (1, 4) are reduced to 25% during 2:00—16:00 capacities of charging stations are 1 fleet during 0:00—24:00 − 6 capacity of roads (1, 2) and (1, 4) are reduced to 25% during 2:00—16:00 capacities of charging stations are 1 fleet during 0:00—24:00 capacities of lines (2, 4) and (3, 4) are reduced to 20% during 7:00—15:00 
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表 2 测试系统1各情景下计算结果
Table 2. Results under different scenarios for case 1
scenario total cost Ctotal/$ generation cost Cgeneration/$ carbon emission Mcarbon/t unmet traffic demand D 1 104 356.54 68 517.91 1 396.38 4 900 2 108 356.48 68 517.71 1 396.40 5 700 3 112 091.69 67 626.85 1 473.50 6 500 4 115 984.50 68 140.42 1 397.05 7 300 5 112 092.09 67 628.23 1 473.38 6 500 6 120 024.68 67 663.61 1 460.72 8 100
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表 3 测试系统2各情景下计算结果
Table 3. Results under different scenarios for case 2
scenario total cost Ctotal /$ generation cost Cgeneration /$ carbon emission Mcarbon /t unmet traffic demand D 1 261 404.13 175 428.59 10588.12 0 2 287 652.67 166 613.51 11581.17 5400 3 263 758.63 172 999.91 10869.30 500 4 271 298.96 169 484.63 12 292.40 400 5 287 657.34 167 446.14 11479.21 5400 6 287 713.96 167 626.16 11464.01 5400
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表 4 测试系统2情景1不同车队下的计算时间与差距
Table 4. CPU time and gap under different size of fleets in scenario 1 for case 2
fleet number computing time t/s gap δ/% 8 <20 0 12 <30 0.03 16 < 30 0.03 20 < 50 0.03 24 <100 0.3 28 < 600 0.6
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