The Power Purchase Optimization Model in China considering the Renewable Energy Risks under Different Risk Preferences

To achieve the strategic target of energy conservation and emission reduction, China is vigorously developing its large-scale and distributed renewable energy power generation industry, dominated by wind power and photovoltaic power generation. In the new situation of renewable energy power interconnection, the grid company in China must fully consider the risks caused by renewable energy when making power purchase optimization decisions. This paper sets up a power purchasing model considering the risks of the renewable energy power interconnection and testifies to the effectiveness of the model through a case study. On this basis, this paper puts forward several reasonable suggestions to help the grid company make power purchase decisions under different risk preferences.

Download Full-text

The Influence and Countermeasures Research of Large-Scale Distributed Photovoltaic Access to the Distribution Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.953-954.61 ◽

2014 ◽

Vol 953-954 ◽

pp. 61-65

Author(s):

Jing Chao Zhang ◽

Zheng Gang Wang ◽

Feng Zhen Zhou ◽

Ning Xi Song ◽

Qian Wang

Keyword(s):

Power Generation ◽

Renewable Energy ◽

Power Flow ◽

Large Scale ◽

Industrial Revolution ◽

Simulation Analysis ◽

Negative Impact ◽

Rapid Development ◽

Distribution Network ◽

Photovoltaic Power

In recent years, with the gradual depletion of traditional energy, as renewable energy representatives, new energy has developed rapidly. We know that distributed photovoltaic power generation with clean, pollution-free, easy installation, and therefore has been rapid development. However, the large number of distributed photovoltaic power generation connected to the distribution network would have a negative impact on the grid with a safe and reliable operation because of its randomness and volatility intrinsic properties. In this paper, in terms of power flow, voltage distribution, load characteristics, power quality, system protection and reliability departure, through MATLAB simulation analysis, the distribution network transformation strategies of primary and secondary devices has been proposed. It laid an important foundation for renewable energy development and the Third Industrial Revolution.

Download Full-text

Low-current Charge Properties of Large-scale Nickel Metal Hydride Batteries and Application to Photovoltaic Power Generation

ECS Transactions ◽

10.1149/1.3110084 ◽

2019 ◽

Vol 16 (34) ◽

pp. 71-78

Author(s):

Akira Yamashita ◽

Riichi Kitano ◽

Akihiro Miyasaka ◽

Takahisa Shodai

Keyword(s):

Power Generation ◽

Metal Hydride ◽

Large Scale ◽

Nickel Metal ◽

Nickel Metal Hydride ◽

Photovoltaic Power

Download Full-text

Power Generation Expansion Planning Including Large Scale Wind Integration: A Case Study of Oman

Wind Resources and Future Energy Security ◽

10.1201/b18529-5 ◽

2015 ◽

pp. 51-68

Author(s):

Arif Malik ◽

Cornelius Kuba

Keyword(s):

Power Generation ◽

Large Scale ◽

Generation Expansion Planning ◽

Wind Integration ◽

Expansion Planning ◽

Generation Expansion ◽

Power Generation Expansion Planning

Download Full-text

Solar thermal power generation technology research

E3S Web of Conferences ◽

10.1051/e3sconf/201913602016 ◽

2019 ◽

Vol 136 ◽

pp. 02016

Author(s):

Yudong Liu ◽

Fangqin Li ◽

Jianxing Ren ◽

Guizhou Ren ◽

Honghong Shen ◽

...

Keyword(s):

Power Generation ◽

Renewable Energy ◽

Renewable Resources ◽

Solar Power ◽

Thermal Power ◽

Solar Photovoltaic ◽

Photovoltaic Power ◽

Power Generation Technology ◽

Generation Technology ◽

Solar Thermal Power Generation

China is a big consumer of energy resources. With the gradual decrease of non-renewable resources such as oil and coal, it is very important to adopt renewable energy for economic development. As a kind of abundant renewable energy, solar power has been widely used. This paper introduces the development status of solar power generation technology, mainly introduces solar photovoltaic power generation technology, briefly describes the principle of solar photovoltaic power generation, and compares and analyzes four kinds of solar photovoltaic power generation technology, among which photovoltaic power generation technology is the most mature solar photovoltaic power utilization technology at present.

Download Full-text

Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study

Applied Soft Computing ◽

10.1016/j.asoc.2020.106389 ◽

2020 ◽

Vol 93 ◽

pp. 106389 ◽

Cited By ~ 5

Author(s):

Dongxiao Niu ◽

Keke Wang ◽

Lijie Sun ◽

Jing Wu ◽

Xiaomin Xu

Keyword(s):

Power Generation ◽

Feature Selection ◽

Random Forest ◽

Short Term ◽

Photovoltaic Power

Download Full-text

Solar Irradiance Fluctuation Prediction Methodology Using Artificial Neural Networks

Journal of Solar Energy Engineering ◽

10.1115/1.4045315 ◽

2019 ◽

Vol 142 (3) ◽

Cited By ~ 2

Author(s):

Jane Oktavia Kamadinata ◽

Tan Lit Ken ◽

Tohru Suwa

Keyword(s):

Neural Networks ◽

Power Generation ◽

Artificial Neural Networks ◽

Renewable Energy ◽

Solar Irradiance ◽

Movement Direction ◽

Simple Method ◽

Photovoltaic Power ◽

Sampling Points ◽

Artificial Neural

Abstract Renewable energy is an attractive alternative source of energy to fossil fuels, as it can help prevent global warming and air pollution. Solar energy, one of the most promising renewable energy sources, can be converted into electricity using photovoltaic power generation systems. Anywhere on the Earth, solar irradiance generally fluctuates during the day but depends on atmospheric conditions. Thus, when a photovoltaic power generation system is connected to a conventional electricity network, predicting near-future global solar irradiance, especially its drastic increases and decreases, is critical to stabilize the network. In this research, a simple method utilizing artificial neural networks to predict large increases and decreases in global solar irradiance is developed. The red–blue ratio (RBR) values, which are extracted from a set of sampling points in images of the sky, as well as the corresponding global solar irradiance values, are used as the artificial neural network inputs. The direction of the movement of clouds is predicted using RBR data at the sampling points. Then, solar irradiance is predicted using the RBR values along the axis closest to the predicted cloud movement direction and the corresponding solar irradiance measurements. The proposed methodology is able to predict both large increases and decreases in solar irradiance greater than 50 through 100 W/m2 1 min in advance with a 40% prediction error. A significant reduction in computational effort is achieved compared to existing sky image-based methodologies using limited sky image data.

Download Full-text