基于電網(wǎng)線(xiàn)路傳輸安全的電力市場(chǎng)分布式交易模型研究
doi: 10.16383/j.aas.c211244
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1.
華中科技大學(xué)人工智能與自動(dòng)化學(xué)院 武漢 430074
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湖北電力交易中心有限公司 武漢 430077
Research on Distributed Power Market Trading Model Based on Grid Line Transmission Security
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1.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074
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Hubei Electric Power Exchange Center Limited Company, Wuhan 430077
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摘要: 電力市場(chǎng)分布式交易模型可有效緩解傳統集中模型下市場(chǎng)主體的隱私安全等問(wèn)題, 但難以在保障市場(chǎng)主體收益和電力系統安全穩定運行的同時(shí), 實(shí)現社會(huì )福利最大化. 因此, 基于電網(wǎng)線(xiàn)路傳輸安全, 首先以社會(huì )福利最大化為目標, 構建集中式交易模型, 并采用拉格朗日乘子法和對偶定理, 將其等價(jià)分解為各市場(chǎng)主體自身利益最大化的分布式交易模型. 在此基礎上, 設計2種適用于不同情形的分布式交易方法及其求解算法, 并構造電網(wǎng)安全成本影響市場(chǎng)主體的決策, 從而保證電網(wǎng)線(xiàn)路傳輸安全. 最后, 基于算例分析, 驗證了2種交易方法的有效性.
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關(guān)鍵詞:
- 區域配電網(wǎng) /
- 分布式電能交易 /
- 線(xiàn)路傳輸安全 /
- 市場(chǎng)交易模型
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 security. 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 duality theorem. On this basis, two distributed trading methods and the corresponding solution algorithms 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 transmission security. Finally, the effectiveness of the two trading methods are verified based on simulation analysis. -
圖 4 2種交易方法求得的社會(huì )福利對比
Fig. 4 Comparison of social welfare obtained by the 2 trading methods
圖 6 2個(gè)案例中, 各線(xiàn)路潮流的對比
Fig. 6 Comparison of the power flow of each grid line in 2 cases
圖 7 情形1下, 2種算法求得的各發(fā)電商的出力
Fig. 7 Power generation of each generator obtained by the 2 algorithms in scenario 1
圖 8 情形2下, 2種算法求得各發(fā)電商的出力
Fig. 8 Power generation of each generator obtained by the 2 algorithms in scenario 2
圖 10 在IEEE 33節點(diǎn)電力系統中, 2種交易方法求解結果的殘差
Fig. 10 Residuals of the solution results of the 2 trading models in IEEE 33 bus power system
圖 11 情形1下, 2種算法求得的各發(fā)電商的出力
Fig. 11 Power generation of each generator obtained by the 2 algorithms in scenario 1
圖 12 情形2下, 2種算法求得的各發(fā)電商的出力
Fig. 12 Power generation of each generator obtained by the 2 algorithms in scenario 2
圖 13 情形1下, 3種算法的迭代求解結果
Fig. 13 The iterative solution results of the 3 algorithms in scenario 1
圖 14 情形2下, 3種算法的迭代求解結果
Fig. 14 The iterative solution results of the 3 algorithms in scenario 2
表 1 2種分布式交易情形下, IEEE 9節點(diǎn)電力系統的發(fā)電商出力上限和下限(MW)
Table 1 Upper and lower limits on generator output for IEEE 9 bus power system in 2 distributed trading scenarios (MW)
發(fā)電商 $G_1$ $G_2$ $G_3$ 情形1 $p_{G,i}^{\max }$ 350 290 400 $p_{G,i}^{\min }$ 10 20 15 情形2 $p_{G,i}^{\max }$ 120 100 140 $p_{G,i}^{\min }$ 10 20 15 
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表 2 2種分布式交易情形下, IEEE 9節點(diǎn)電力系統的柔性負荷商需求上限和下限(MW)
Table 2 Upper and lower limits on flexible loaders＇demand for IEEE 9 bus power system in 2 distributed trading scenarios (MW)
柔性負荷商 ${{D}_{4}}$ ${{D}_{5}}$ ${{D}_{6}}$ ${{D}_{7}}$ ${{D}_{8}}$ ${{D}_{9}}$ 情形1 $p_{D,j}^{\max }$ 150 100 145 140 150 170 $p_{D,j}^{\min }$ 60 50 90 60 50 70 情形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節點(diǎn)電力系統線(xiàn)路潮流上限(MW)
Table 3 Upper limit of grid line power flow for IEEE 9 bus power system (MW)
線(xiàn)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 2種交易方法下, 各市場(chǎng)主體交易量對比 (MW)
Table 4 Comparison of the trading volume of market entities obtained by the 2 trading 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 ${{D}_{5}}$ 50.010 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節點(diǎn)電力系統下, 2種算法的迭代次數和 計算時(shí)間對比
Table 5 Comparison of iterations and computation time of the 2 algorithms in IEEE 9 bus system
情形 算法1 算法2 迭代次數 計算時(shí)間(s) 迭代次數 計算時(shí)間(s) 情形1 248 71.5 265 79.8 情形2 216 60.2 52 15.7
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表 6 IEEE 33節點(diǎn)電力系統中, 2個(gè)案例的 潮流對比(MW)
Table 6 Comparison of the power flow in the 2 cases of the IEEE 33 bus power system (MW)
線(xiàn)路 $P_{l}^{PF,\;{\rm{case} }\;1}$ $P_{l}^{PF,\;{\rm{case} }\;2}$ $P_{l}^{PF\max}$ 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節點(diǎn)系統下, 2種算法迭代次數和 計算時(shí)間對比
Table 7 Comparison of iterations and computation time of the 2 algorithms in IEEE 33 bus system
情形 算法1 算法2 迭代次數 計算時(shí)間(s) 迭代次數 計算時(shí)間(s) 情形1 402 158.3 433 165.9 情形2 374 143.3 86 32.6
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表 8 情形1下, 3種算法的迭代次數和計算時(shí)間對比
Table 8 Comparison of iterations and computation time of the 3 algorithms in scenario 1
算法名稱(chēng) 迭代次數 計算時(shí)間(s) 原始對偶法 458 162.2 F-ADMM 262 96.5 算法1 402 158.3
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表 9 情形2下, 3種算法的迭代次數和計算時(shí)間對比
Table 9 Comparison of iterations and computation time of the 3 algorithms in scenario 2
算法名稱(chēng) 迭代次數 計算時(shí)間(s) 原始對偶法 395 149.8 F-ADMM 218 80.4 算法2 86 32.6
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