Low-carbon benefit analysis on DG penetration distribution system

• DOI: 10.1007/s40565-015-0097-z
• Publication Date: 2015-01-21T02:54:13Z
• Authors: Shaoyun GE; Li XU; Hong LIU; Jian FANG
• Keywords: TK1001-1841; Distribution generation penetration; Monte Carlo simulation method; TJ807-830; 02 engineering and technology; 7. Clean energy; Renewable energy sources; Production of electric energy or power. Powerplants. Central stations; Distribution system; 13. Climate action; 0202 electrical engineering, electronic engineering, information engineering; Low-carbon

In response to the severe energy and environmental issues, CO2 emission reduction and low-carbon development are inevitable. China has become the biggest CO2 emitter in the world since 2006. As a major CO2 emission source in China, the power industry is facing greater pressure for carbon emission abatement. By applying various low-carbon power technologies and mechanisms, the potential for CO2 emission reduction in power systems is considerable. This paper proposes a pseudo-sequential Monte Carlo simulation method for the low-carbon benefit evaluation of distribution system including distributed wind turbines, solar array and battery energy storage systems. The hourly sequential models and state variation models for wind turbines, solar array and battery are established. The non-power components are sequentially sampled and the components of the wind turbine, the solar array and the battery are non-sequentially sampled. The failure modes and effect analysis (FMEA) procedure for the system are discussed and the heuristic load shedding approach is used, then the low-carbon benefit evaluation procedure is illustrated. Based on this, the system state transition sampling method for calculating the loss of expected energy index with high DG penetration is proposed. The state transition models for non-active elements and DGs are established. The validity of the proposed method is demonstrated by a study case. The method can be used for the low-carbon benefit analysis on DG penetration distribution networks.