The business of Zhongkang Electric Power subordinate to Akcome involves new energy asset investment, intelligent operation of power station, and energy asset agency operations. Zhongkang Electric Power is committed to growing into the industry leading green energy and energy Internet asset investment and operation service provider.
Energy Investment: Following energy development and reform trends, Zhongkang Electric Power will fully transfer to distributed power station development and investment business in 2017. Now, Zhongkang Electric Power has completed the layout in Suzhou, Changzhou, Zhangjiakou, Zhangjiagang and Yangquan. On the generation side, Zhongkang Electric Power will build complementary and synergetic advantages of light, wind and air energy to provide the users with the energy solution of integrated power generation, distribution and marketing and joint supply of cold and hot electricity so as to offer sustainable and steady energy services for industrial and commercial parks.
Intelligent Operation of Power Station: The company is committed to providing professional energy asset operation, maintenance and custody business to reduce full life circle operation cost of energy asset, so that the energy asset can maintain and add value to the maximum extent. Akcome imports the Internet of Things technology on the generation side of solar photovoltaic power station by means of technical cooperation with Internet leading enterprises to make accurate judgment on and rapid response to all kinds of situations of the big data system accumulated by big data mining and analysis via knowledge base platform, and carry out intelligent operation and management on power station through wireless transmission, cloud services and cloud computing functions. The company has very rich and professional photovoltaic power station management experience, and possesses photovoltaic above-ground power stations with the scale of more than 1GW. At present, the company sets up Xinjiang, Qinghai, Gansu and Shandong regional centers, and possesses distributed power stations with the scale of more than 50MW which are distributed in Anhui, Jiangxi, Jiangsu and Zhejiang. Moreover, the scale of the power stations under construction reaches about 400MW. As of the end of 2017, Zhongkang Electric Power will realize the operation and management of power station with the scale of above 1.5GW.
Operation and maintenance experts and technical experts shall be deployed in the headquarters to take charge of the formulation of the operation and maintenance system standards, the remote instruction area and the work of the power station. Analyze big data of the power station, connect with the latest operation and management philosophy of the industry, and develop the latest operation and maintenance technology.
Regional operation and maintenance management team is responsible for managing power stations in the region, sharing the operation and maintenance test equipment, establishing the library of spare parts, thus ensuring a uniform management in the power station region.
The operation and maintenance team of the power station is responsible for executing the standard operation and maintenance manual, and performing differential management of the power station according to the features of the power station, thus ensuring the safe and efficient operation of the power station.
Escorted by scientific system
Power Station Operation and Maintenance System
Systems executed throughout the entire operation and maintenance life of the power station
Power station patrol
Power station inspection
Equipment overhaul and maintenance
Akcome PV power stations will cover five major regions in China, with such forms of ground, distributed, agriculture-light complementary and hill-based PV power stations put in place. Through constant improvement of the PV power generation database of Akcome, it provides strong support to the project development, post-evaluation of power station, data benchmarking, financial service, power station consultation, preliminary design and equipment model selection.