Optimal proportion of wind and PV capacity in provincial power systems based on bilevel optimization algorithm under low-carbon economy
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- 作者：Yue Yuan (1)
Yang CAO (1)
Xinsong ZHANG (2)
Chun LIU (3)
Yuehui HUANG (3)
Peng LI (3)
Kejun QIAN (4)
1. College of Energy and Electrical Engineering ; Hohai University ; Nanjing ; 211100 ; China
2. College of Electrical Engineering ; Nantong University ; Nantong ; 226019 ; China
3. China Electrical Power Research Institute ; Beijing ; 100192 ; China
4. Suzhou Power Supply Company ; State Grid Corporation of China ; Suzhou ; 215004 ; China - 关键词：Time ; sequence production simulation ; Optimal proportion of renewable energy ; Pattern search method ; General algebraic modeling system
- 刊名：Journal of Modern Power Systems and Clean Energy
- 出版年：2015
- 出版时间：March 2015
- 年：2015
- 卷：3
- 期：1
- 页码：33-40
- 全文大小：789 KB
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- 出版者：Springer Berlin Heidelberg
- ISSN：2196-5420
文摘
In order to boost contributions of power systems to a low-carbon economy, the installed capacity of renewable power generation, such as wind and photovoltaic (PV) power generation should be well planned. A bilevel formulation is presented to optimize the proportion of wind and PV capacity in provincial power systems, in which, carbon emissions of generator units and features of renewable resources are taken into account. In the lower-level formulation, a time-sequence production simulation (TSPS) model that is suitable for actual power system has been adopted. In order to maximize benefits of energy conservation and emissions reduction resulting from renewable power generation, the commercial software called General Algebraic Modeling System (GAMS) is employed to optimize the annual operation of the power system. In the upper-level formulation, the optimal pattern search (OPS) algorithm is utilized to optimize the proportion of wind and PV capacity. The objective of the upper-level formulation is to maximize benefits of energy conservation and carbon emissions reductions optimized in the lower-level problem. Simulation results in practical provincial power systems validate the proposed model and corresponding solving algorithms. The optimization results can provide support to policy makers to make the polices related to renewable energy.