Electricity purchasing cost and power generation prediction
Hua Ke, Rong Cao, Xuedan Zhan, Gang Liu
Abstract
Hua Ke, Rong Cao, Xuedan Zhan, Gang Liu
Abstract
The achievement of optimizing electricity-purchase and balancing the planned and actual power generation is significant for local sustainable development and safely stable power supply. This study first constructs an electricity purchasing cost model for analyzing how to optimize electricity purchasing costs by using the example of the State Grid Heilongjiang Electric Power Co., LTD. We found that influenced by relevant regulations, the key to optimize electricity purchasing costs is the differences between the electricity purchasing quantity and the actual demand. Second, considering that the electricity purchasing quantity is affected by local power generation capacities, we build power generation prediction models using the actual power generation data of Heilongjiang Province to provide practical implications for electricity purchasing decisions.
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The achievement of optimizing electricity-purchase and balancing the planned and actual power generation is significant for local sustainable development and safely stable power supply. This study first constructs an electricity purchasing cost model for analyzing how to optimize electricity purchasing costs by using the example of the State Grid Heilongjiang Electric Power Co., LTD. We found that influenced by relevant regulations, the key to optimize electricity purchasing costs is the differences between the electricity purchasing quantity and the actual demand. Second, considering that the electricity purchasing quantity is affected by local power generation capacities, we build power generation prediction models using the actual power generation data of Heilongjiang Province to provide practical implications for electricity purchasing decisions.
Key concepts: Purchasing, Electricity, Electricity generation, Environmental economics, Electricity retailing, Stand-alone power system, Electricity market, Purchasing power