| 引用本文: | 汤晓青,刘辉,范宇,等.基于改进多目标遗传算法的实时发电市场优化调度研究[J].电力系统保护与控制,2017,45(17):65-71.[点击复制] |
| TANG Xiaoqing,LIU Hui,FAN Yu,et al.Analysis of the optimal dispatch in real-time generation market using an improved multi-objective genetic algorithm[J].Power System Protection and Control,2017,45(17):65-71[点击复制] |
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| 摘要: |
| 实时发电市场需在短时间内完成投标与出清，与中长期市场相比时间更紧、价格波动更明显且更容易受到参与者行为的影响。在交易过程中，若单纯采用总购电成本最低进行出清，可能导致少量参与者占有大部分市场份额或剩余资源，造成市场集中度偏大。少量机组可利用其市场力操纵市场并获得超额回报，从而影响市场效率及稳定性。为了防止该类情形，提出一种兼顾市场集中度的实时发电市场多目标优化模型，通过监控HHI静态指标以及DHHI动态指标将市场集中度控制在合理水平。在模型求解过程中，通过改进的多目标遗传算法(Multi-objective Generic Algorithm, MOGA)实现了快速收敛及有限方案筛选。最后采用IEEE30节点标准系统进行仿真，仿真结果证实了模型的有效性及其算法的高效性。 |
| 关键词: 优化调度 实时发电市场 市场力 多目标遗传算法 |
| DOI：10.7667/PSPC161432 |
| 投稿时间：2016-09-02修订日期：2016-11-30 |
| 基金项目:国家自然科学基金(61472136);湖南省自然科学基金项目(2016JJ2069);湖南省教育厅优秀青年基金(16B143) |
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| Analysis of the optimal dispatch in real-time generation market using an improved multi-objective genetic algorithm |
| TANG Xiaoqing,LIU Hui,FAN Yu,YANG Shengjie |
| (State Grid Sichuan Electric Power Company Skills Training Center, Chengdu 610072, China;NARI Group Corporation National Electricity Science Research Institute, Nanjing 211000, China;Hunan University of Commerce, Changsha 410205, China) |
| Abstract: |
| Real-time generation market has to complete the bidding and clearing in short time. It is more urgent in time, more fluctuating in price and more likely to be affected by participants’ behavior. In the trading process, if the market clears merely according to the total purchasing cost, it may result in a minority of participants seize most of the market share or residual capacity where the market is highly concentrated. A few units can gain abnormal return through market manipulation and market efficiency and stability will be reduced. To avoid the above situation, this paper proposes a multi-objective model for the real-time generation market considering market concentration, limits the market concentration into appropriate level through monitoring the HHI static index and the DHHI dynamic index. When solving the model, the improved multi-objective generation algorithm (MOGA) achieves the quick convergence and finite schemes choosing. At last, the IEEE standard system of 30 nodes is applied to the simulation. Simulation result confirms the model’s availability and the algorithm’s efficiency. This work is supported by National Natural Science Foundation of China (No.61472136). |
| Key words: optimal dispatch real-time generation market market power multi-objective genetic algorithm |
