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              考慮電網線路傳輸安全的分布式電力市場交易模式研究

              李遠征 張虎 劉江平 趙勇 連義成

              李遠征, 張虎, 劉江平, 趙勇, 連義成. 考慮電網線路傳輸安全的分布式電力市場交易模式研究. 自動化學報, 2022, 48(x): 1?15 doi: 10.16383/j.aas.c211244
              引用本文: 李遠征, 張虎, 劉江平, 趙勇, 連義成. 考慮電網線路傳輸安全的分布式電力市場交易模式研究. 自動化學報, 2022, 48(x): 1?15 doi: 10.16383/j.aas.c211244
              Li Yuan-Zheng, Zhang Hu, Liu Jiang-Ping, Zhao Yong, Lian Yi-Cheng. Research on distributed power market trading model considering grid transmission security. Acta Automatica Sinica, 2022, 48(x): 1?15 doi: 10.16383/j.aas.c211244
              Citation: Li Yuan-Zheng, Zhang Hu, Liu Jiang-Ping, Zhao Yong, Lian Yi-Cheng. Research on distributed power market trading model considering grid transmission security. Acta Automatica Sinica, 2022, 48(x): 1?15 doi: 10.16383/j.aas.c211244

              考慮電網線路傳輸安全的分布式電力市場交易模式研究

              doi: 10.16383/j.aas.c211244
              基金項目: 國家電網總部科技項目(1400-202099523A-0-0-00)資助
              詳細信息
                作者簡介:

                李遠征:華中科技大學人工智能與自動化學院副教授.主要研究方向為人工智能及其在智能電網中的應用, 深度學習, 強化學習和大數據分析. E-mail: Yuanzheng_Li@hust.edu.cn

                張虎:華中科技大學人工智能與自動化學院碩士研究生.主要研究方向為電力市場交易和電力系統優化. E-mail: dugujjiujian@gmail.com

                劉江平:湖北電力交易中心有限公司高級工程師.主要研究方向為電力市場和電力調度. E-mail: hzxjj@foxmail.com

                趙勇:華中科技大學人工智能與自動化學院教授. 主要研究方向為決策理論, 大型工程項目管理, 社會經濟系統的建模與仿真和系統分析與集成. 本文通信作者. E-mail: zhiwei98530@hust.edu.cn

                連義成:華中科技大學人工智能與自動化學院博士研究生.主要研究方向為考慮新能源接入的電力系統機組組合與經濟調度等. E-mail: hust2017l@163.com

              Research on Distributed Power Market Trading Model Considering Grid Transmission Security

              Funds: Supported by Science and Technology Project of State Grid Headquarters(1400-202099523A-0-0-00)
              More Information
                Author Bio:

                LI Yuan-Zheng Associate professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers artificial intelligence and its application in smart grid, deep learning, reinforcement learning, and big data analysis

                ZHANG Hu Master student at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers power market and power system optimization

                LIU Jiang-Ping Senior Engineer of Hubei Electric Power Exchange Center Limited Company. His research interest covers power market and power dispatching

                ZHAO Yong Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers decision-making theories, large-scale engineering project management, modeling and simulation of social economic systems, and system analysis and integration. Corresponding author of this paper

                LIAN Yi-Cheng Ph.D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers unit commitment and economic dispatch considering renewable energy uncertainty

              • 摘要: 分布式電力市場交易模式可以有效緩解傳統集中模式下市場主體的隱私安全等問題, 但難以在保障市場主體收益和電力系統安全穩定運行的同時實現社會福利最大化. 因此, 考慮電網線路傳輸約束, 首先以社會福利最大化為目標構建了集中式交易模型, 并采用拉格朗日乘子法和對偶理論將其等價分解為各市場主體自身利益最大化的分布式交易模型. 在此基礎上, 設計了兩種適用于不同場景的分布式交易方法, 并構造電網安全成本影響市場主體的決策, 從而保證電網線路傳輸安全. 最后, 基于算例分析驗證了兩種方法的有效性.
              • 圖  1  電力市場分布式交易示意圖

                Fig.  1  Distributed trading in the power market

                圖  2  發電成本和購電費用曲線

                Fig.  2  Power generation and power purchase cost

                圖  3  IEEE 9節點電力系統拓撲結構

                Fig.  3  IEEE 9 bus power system topology

                圖  4  兩種方法求得的社會福利對比

                Fig.  4  Comparison of social welfare obtained by the two methods

                圖  5  兩種方法求解結果的殘差

                Fig.  5  Residuals of the solution results of the two methods

                圖  6  兩個案例中各線路潮流的對比

                Fig.  6  Comparison of the power flow of each line in the two cases

                圖  7  場景1下兩種方法所求得的各發電商的出力

                Fig.  7  Power generation of each generator obtained by the two methods in Scenario 1

                圖  8  場景2下兩種方法所求得的各發電商的出力

                Fig.  8  Power generation of each generator obtained by the two methods in Scenario 2

                圖  9  IEEE 33節點配電網系統拓撲結構

                Fig.  9  IEEE 33 bus distribution network topology

                圖  10  集中式和分布式交易模式求解結果的殘差

                Fig.  10  Residuals of the solution results of centralized and distributed trading models

                圖  11  場景1下兩種方法所求得的各發電商的出力

                Fig.  11  Power generation of each generator obtained by the two methods in Scenario 1

                圖  12  場景2下兩種方法所求得的各發電商的出力

                Fig.  12  Power generation of each generator obtained by the two methods in Scenario 2

                圖  13  場景1下三種方法的迭代求解結果

                Fig.  13  The iterative solution results of the three methods in Scenario 1

                圖  14  場景2下三種方法的迭代求解結果

                Fig.  14  The iterative solution results of the three methods in Scenario 2

                表  1  兩種分布式交易場景下IEEE 9節點電力系統的發電商出力上下限(MW)

                Table  1  Upper and lower limits on generator output for IEEE 9 bus power systems in two distributed trading scenarios(MW)

                發電商 $G_1$ $G_2$ $G_3$
                場景1 $p_{G,i}^{\max }$ 350 290 400
                $p_{G,i}^{\min }$ 10 20 15
                場景2 $p_{G,i}^{\max }$ 120 100 140
                $p_{G,i}^{\min }$ 10 20 15
                下載: 導出CSV

                表  2  兩種分布式交易場景下IEEE 9節點電力系統的負荷商需求上下限(MW)

                Table  2  Upper and lower limits on loaders' demand for IEEE 9 bus power systems in two distributed trading scenarios(MW)

                柔性負荷商 ${{D}_{4}}$ ${{D}_{5}}$ ${{D}_{6}}$ ${{D}_{7}}$ ${{D}_{8}}$ ${{D}_{9}}$
                場景1 $p_{D,j}^{\max }$ 150 100 145 140 150 170
                $p_{D,j}^{\min }$ 60 50 90 60 50 70
                場景2 $p_{D,j}^{\max }$ 150 90 100 140 150 150
                $p_{D,j}^{\min }$ 20 15 30 30 15 20
                下載: 導出CSV

                表  3  IEEE 9節點電力系統線路潮流上限(MW)

                Table  3  Upper limit of line power flow for IEEE 9 bus power system(MW)

                線路 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
                下載: 導出CSV

                表  4  兩種方法下市場主體的交易量對比 (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
                下載: 導出CSV

                表  5  IEEE 9節點系統下兩種方法迭代次數和計算時間對比

                Table  5  Comparison of iterations and computation time of the two methods in IEEE 9 bus system

                對比結果 本文方法1 本文方法2
                場景1 248次, 71.5s 265次, 79.8s
                場景2 216次, 60.2s 52次, 15.7s
                下載: 導出CSV

                表  6  IEEE 33節點典型中壓配電網系統下兩個案例的潮流對比(MW)

                Table  6  Comparison of the power flow of each line in the two cases of the IEEE 33 bus system (MW)

                線路 $P_{l}^{PF,Case1}$ $P_{l}^{PF,Case2}$ $P_{l}^{PF\max}$
                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
                下載: 導出CSV

                表  7  IEEE 33節點系統下兩種方法迭代次數和計算時間對比

                Table  7  Comparison of iterations and computation time of the two methods in IEEE 33 bus system

                對比結果 本文方法1 本文方法2
                場景1 402次, 158.3s 433次, 165.9s
                場景2 374次, 143.3s 86次, 32.6s
                下載: 導出CSV

                表  8  場景1下三種方法的迭代次數和計算時間對比

                Table  8  Comparison of iterations and computation time of the three methods in Scenario 1

                方法 原始對偶方法 F-ADMM方法 本文方法1
                迭代次數 458 262 402
                計算時間 162.2s 96.5s 158.3s
                下載: 導出CSV

                表  9  場景2下三種方法的迭代次數和計算時間對比

                Table  9  Comparison of iterations and computation time of the three methods in Scenario 2

                方法 原始對偶方法 F-ADMM方法 本文方法2
                迭代次數 395 218 86
                計算時間 149.8s 80.4s 32.6s
                下載: 導出CSV
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                        • 收稿日期:  2021-12-28
                        • 錄用日期:  2022-04-28
                        • 網絡出版日期:  2022-07-21

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