Research on Distributed Power Market Trading Model Considering Grid Transmission Security
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摘要: 分布式电力市场交易模式可以有效缓解传统集中模式下市场主体的隐私安全等问题, 但难以在保障市场主体收益和电力系统安全稳定运行的同时实现社会福利最大化. 因此, 考虑电网线路传输约束, 首先以社会福利最大化为目标构建了集中式交易模型, 并采用拉格朗日乘子法和对偶理论将其等价分解为各市场主体自身利益最大化的分布式交易模型. 在此基础上, 设计了两种适用于不同场景的分布式交易方法, 并构造电网安全成本影响市场主体的决策, 从而保证电网线路传输安全. 最后, 基于算例分析验证了两种方法的有效性.Abstract: The distributed power market trading model can effectively alleviate the problems among market entities such as privacy problem in the traditional centralized trading model. However, there is still a lack of distributed trading models that can maximize social welfare while ensuring the benefits of market entities and the safe and stable operation of the power system. Therefore, a centralized trading model with the objective of maximizing social welfare is constructed, which considers the power grid line transmission constraint. Then, it is equivalently decomposed into a distributed trading model that maximizes the interests of each market agent using the Lagrange multiplier method and dual theory. On this basis, two distributed transaction methods suitable for different scenarios are designed, and the grid security cost is used to influence the decision-making of market entities, thereby ensuring grid line security. Finally, the effectiveness of the two methods are verified based on simulation analysis.
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图 7 场景1下两种方法所求得的各发电商的出力
Fig. 7 Power generation of each generator obtained by the two methods in Scenario 1
图 8 场景2下两种方法所求得的各发电商的出力
Fig. 8 Power generation of each generator obtained by the two methods in Scenario 2
图 10 集中式和分布式交易模式求解结果的残差
Fig. 10 Residuals of the solution results of centralized and distributed trading models
图 11 场景1下两种方法所求得的各发电商的出力
Fig. 11 Power generation of each generator obtained by the two methods in Scenario 1
图 12 场景2下两种方法所求得的各发电商的出力
Fig. 12 Power generation of each generator obtained by the two methods in Scenario 2
图 13 场景1下三种方法的迭代求解结果
Fig. 13 The iterative solution results of the three methods in Scenario 1
图 14 场景2下三种方法的迭代求解结果
Fig. 14 The iterative solution results of the three methods in Scenario 2
表 1 两种分布式交易场景下IEEE 9节点电力系统的发电商出力上下限(MW)
Table 1 Upper and lower limits on generator output for IEEE 9 bus power systems in two distributed trading scenarios(MW)
发电商 $G_1$ $G_2$ $G_3$ 场景1 $p_{G,i}^{\max }$ 350 290 400 $p_{G,i}^{\min }$ 10 20 15 场景2 $p_{G,i}^{\max }$ 120 100 140 $p_{G,i}^{\min }$ 10 20 15 
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表 2 两种分布式交易场景下IEEE 9节点电力系统的负荷商需求上下限(MW)
Table 2 Upper and lower limits on loaders' demand for IEEE 9 bus power systems in two distributed trading scenarios(MW)
柔性负荷商 ${{D}_{4}}$ ${{D}_{5}}$ ${{D}_{6}}$ ${{D}_{7}}$ ${{D}_{8}}$ ${{D}_{9}}$ 场景1 $p_{D,j}^{\max }$ 150 100 145 140 150 170 $p_{D,j}^{\min }$ 60 50 90 60 50 70 场景2 $p_{D,j}^{\max }$ 150 90 100 140 150 150 $p_{D,j}^{\min }$ 20 15 30 30 15 20
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表 3 IEEE 9节点电力系统线路潮流上限(MW)
Table 3 Upper limit of line power flow for IEEE 9 bus power system(MW)
线路 1-4 4-6 6-9 3-9 9-8 8-7 7-2 7-5 5-4 $P_{l}^{PF\max}$ 160 100 100 150 100 100 120 100 100
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表 4 两种方法下市场主体的交易量对比 (MW)
Table 4 Comparison of the trading volume of market entities obtained by the two methods (MW)
交易量 集中式 分布式 ${{G}_{1}}$ 155.374 155.376 ${{G}_{2}}$ 97.747 97.747 ${{G}_{3}}$ 126.912 126.918 ${{D}_{4}}$ 59.998 60.002 ${{D}_{5}}$ 50.01 50.007 ${{D}_{6}}$ 90.007 90.007 ${{D}_{7}}$ 60.006 60.006 ${{D}_{8}}$ 50.009 50.006 ${{D}_{9}}$ 70.012 70.006
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表 5 IEEE 9节点系统下两种方法迭代次数和计算时间对比
Table 5 Comparison of iterations and computation time of the two methods in IEEE 9 bus system
对比结果 本文方法1 本文方法2 场景1 248次, 71.5s 265次, 79.8s 场景2 216次, 60.2s 52次, 15.7s
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表 6 IEEE 33节点典型中压配电网系统下两个案例的潮流对比(MW)
Table 6 Comparison of the power flow of each line in the two cases of the IEEE 33 bus system (MW)
线路 $P_{l}^{PF,Case1}$ $P_{l}^{PF,Case2}$ $P_{l}^{PF\max}$ 1-2 189.22 190.36 250 2-3 145.40 78.52 250 3-4 136.56 98.60 150 4-5 58.56 56.30 250 5-6 258.55 168.56 200 6-7 59.87 43.69 250 7-8 25.21 8.96 250 8-9 62.17 56.18 250 9-10 32.74 32.80 250 10-11 62.15 57.71 150 11-12 16.12 15.23 150 12-13 30.06 34.89 200 13-14 51.10 40.55 250 14-15 218.53 188.37 200 15-16 39.89 22.97 150 16-17 101.01 59.95 150 17-18 165.36 137.69 150 2-19 74.80 60.84 150 19-20 95.37 87.90 250 20-21 212.80 183.99 200 21-22 61.00 68.73 150 3-23 58.97 60.04 150 23-24 45.51 37.73 200 24-25 75.72 43.37 250 6-26 145.70 155.01 250 26-27 169.74 125.79 150 27-28 243.25 188.26 200 28-29 135.98 97.89 150 29-30 14.31 32.59 150 30-31 34.64 44.72 250 31-32 43.94 44.88 150 32-33 140.00 122.20 150 21-8 122.87 99.63 150 9-15 87.32 65.97 150 12-22 120.66 156.98 200 18-33 35.62 40.33 200 25-29 158.77 142.65 200
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表 7 IEEE 33节点系统下两种方法迭代次数和计算时间对比
Table 7 Comparison of iterations and computation time of the two methods in IEEE 33 bus system
对比结果 本文方法1 本文方法2 场景1 402次, 158.3s 433次, 165.9s 场景2 374次, 143.3s 86次, 32.6s
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表 8 场景1下三种方法的迭代次数和计算时间对比
Table 8 Comparison of iterations and computation time of the three methods in Scenario 1
方法 原始对偶方法 F-ADMM方法 本文方法1 迭代次数 458 262 402 计算时间 162.2s 96.5s 158.3s
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表 9 场景2下三种方法的迭代次数和计算时间对比
Table 9 Comparison of iterations and computation time of the three methods in Scenario 2
方法 原始对偶方法 F-ADMM方法 本文方法2 迭代次数 395 218 86 计算时间 149.8s 80.4s 32.6s
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