| 引用本文: | 王金叶,马良,刘勇,等.智能电网实时定价的自适应光学优化算法[J].电力系统保护与控制,2017,45(24):29-35.[点击复制] |
| WANG Jinye,MA Liang,LIU Yong,et al.Self-adaptive optics inspired optimization for real-time pricing of smart grid[J].Power System Protection and Control,2017,45(24):29-35[点击复制] |
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| 摘要: |
| 为了解决传统优化算法在求解智能电网的实时电价模型过程中易陷入局部最优的不足，引入光学优化算法对实时电价模型进行求解。采用可变适应度的方法对光学优化算法求解模型过程中的不变适应度进行改进，提出自适应光学优化(Self-adaptive Optics Inspired Optimization, SAOIO)算法。根据迭代次数的变动，自适应地变动适应度从而提升收敛速度，提高求解精度。针对智能电网实时电价模型，对拉格朗日对偶算法和自适应光学优化算法进行同步仿真。结果表明自适应光学优化算法能够更好地寻得全局最优电价，提高结果精度，具有更好的实用性。 |
| 关键词: 智能电网 实时电价 自适应光学优化算法 拉格朗日 仿真 |
| DOI：10.7667/PSPC161988 |
| 投稿时间：2016-12-01修订日期：2017-02-07 |
| 基金项目:国家自然科学基金项目(71401106);教育部人文社科规划基金项目(16YJA630037);上海高校青年教师培养资助计划项目(ZZsl15018);上海理工大学博士科研启动经费项目(1D-15-303-005) |
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| Self-adaptive optics inspired optimization for real-time pricing of smart grid |
| WANG Jinye,MA Liang,LIU Yong,YANG Weiyuan |
| (School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;Second High School of Huaibin, Huaibin 464400, China) |
| Abstract: |
| In order to solve the shortage of local optimum in the process of solving the real time price model of smart grid by using traditional optimization algorithm, the Optics Inspired Optimization (OIO) is introduced to solve it. The variable fitness method is used to improve the constant fitness of the optics inspired optimization in the process of solving the model. The new algorithm is named Self-adaptive Optics Inspired Optimization (SAOIO) algorithm. According to the changes of the number of iterations, the adaptive degree can be changed by itself to improve the convergence speed and improve the accuracy of the solution. In the real-time pricing model of smart grid, the Lagrange dual algorithm and SAOIO are synchronously simulated. The results show that the SAOIO can better find the global optimal price, improve the accuracy, and have better practicability. This work is supported by National Natural Science Foundation of China (No. 71401106), and Humanities and Social Sciences of Ministry of Education Planning Fund (No. 16YJA630037), and Shanghai College Young Teachers Training Program (No. ZZsl15018) and Doctoral Scientific Research Foundation of USST (No. 1D-15-303-005). |
| Key words: smart grid real-time pricing self-adaptive optics inspired optimization algorithm Lagrange simulation |
