Optimization of electricity generation and interprovincial trading strategies in Southern China

Abstract In today's China, the reverse distribution of electricity resources and electricity demand makes large-scale and long-distance transmission of electricity inevitable, resulting in the problem of electricity allocation optimization. To address this problem in the region covered by the China Southern Grid (CSG), the paper establishes an optimized model for electricity generation and interprovincial trading strategies in southern China. Based on data from 2015, this paper simultaneously optimizes electricity generation and interprovincial trading strategies in each of the five provinces. The result shows that interprovincial trading in the CSG-served area can yield significant energy, economic, environmental and interconnection benefits. It can reduce the power supply cost and environmental pressure in Guangdong province, help make Yunnan province a hydropower base in China and a thermal power base in southern China, facilitate the completion of the “West-to-East Power Transmission” project, and partly resolve the issues raised in each province's 13th Five-Year Plan as well as the problem of optimizing electricity allocation in the region covered by the CSG.

[1]  W. Short,et al.  Evaluating renewable portfolio standards and carbon cap scenarios in the U.S. electric sector , 2010 .

[2]  Brian Vad Mathiesen,et al.  Impact of Germany's energy transition on the Nordic power market – A market-based multi-region energy system model , 2016 .

[3]  Ram M. Shrestha,et al.  Effects of cross-border power trade between Laos and Thailand: Energy security and environmental implications , 2009 .

[4]  Jun Dong,et al.  Opportunity for inter-regional integration of electricity markets: the case of Shandong and Shanghai in East China , 2004 .

[5]  Margot Weijnen,et al.  The impact of inter-regional transmission grid expansion on China’s power sector decarbonization , 2016 .

[6]  Hailong Li,et al.  Multi-region optimal deployment of renewable energy considering different interregional transmission scenarios , 2016 .

[7]  Chuntian Cheng,et al.  Coordinated operations of large-scale UHVDC hydropower and conventional hydro energies about regional power grid , 2016 .

[8]  Youngho Chang,et al.  Infrastructure investments for power trade and transmission in ASEAN + 2: Costs, benefits, long-term contracts and prioritized developments , 2015 .

[9]  Hongguang Jin,et al.  The energy situation and its sustainable development strategy in China , 2011 .

[10]  Pei Liu,et al.  A multi-regional modelling and optimization approach to China's power generation and transmission planning , 2016 .

[11]  Zheng Li,et al.  A multi-region load dispatch model for the long-term optimum planning of China’s electricity sector , 2017 .

[12]  Boqiang Lin,et al.  Cost of long distance electricity transmission in China , 2017 .

[13]  Jin-Hua Xu,et al.  Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model , 2016 .

[14]  Efstratios N. Pistikopoulos,et al.  A spatial multi-period long-term energy planning model: A case study of the Greek power system , 2014 .

[15]  Ian Jones,et al.  A long-term multi-region load-dispatch model based on grid structures for the optimal planning of China's power sector , 2017, Comput. Chem. Eng..

[16]  Pei Liu,et al.  A multi-region optimization planning model for China's power sector , 2015 .

[17]  Can Wang,et al.  Analyzing the penetration barriers of clean generation technologies in China’s power sector using a multi-region optimization model , 2017 .

[18]  Tsan Sheng Ng,et al.  Energy import resilience with input???output linear programming models , 2015 .

[19]  Xiaoyu Yang,et al.  An overview of power transmission systems in China , 2010 .

[20]  Tieju Ma,et al.  A multi-regional energy transport and structure model for China’s electricity system , 2018, Energy.

[21]  Chuntian Cheng,et al.  Optimal allocation of hydropower and hybrid electricity injected from inter-regional transmission lines among multiple receiving-end power grids in China , 2018 .

[22]  Youngho Chang,et al.  Power generation and cross-border grid planning for the integrated ASEAN electricity market: A dynamic linear programming model , 2013 .

[23]  Tsan Sheng Ng,et al.  Energy-economic recovery resilience with Input-Output linear programming models , 2017 .

[24]  Jyoti K. Parikh,et al.  Transmission planning for Indian power grid: a mixed integer programming approach , 1999 .

[25]  Zheng Li,et al.  A multi-period modelling and optimization approach to the planning of China's power sector with consideration of carbon dioxide mitigation , 2012, Comput. Chem. Eng..

[26]  Arun Somani,et al.  A Long-Term Investment Planning Model for Mixed Energy Infrastructure Integrated with Renewable Energy , 2010, 2010 IEEE Green Technologies Conference.

[27]  Tobias Massier,et al.  Enhancing the integration of renewables by trans-border electricity trade in ASEAN , 2015, 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[28]  Zeng Ming,et al.  Trans-regional electricity transmission in China: Status, issues and strategies , 2016 .

[29]  Michael C. Georgiadis,et al.  Optimal scheduling of interconnected power systems , 2018, Comput. Chem. Eng..

[30]  Ali Almansoori,et al.  Design optimization model for the integration of renewable and nuclear energy in the United Arab Emirates’ power system , 2015 .

[31]  D. Chattopadhyay,et al.  A multi-area linear programming approach for analysis of economic operation of the Indian power system , 1996 .