Day-ahead scheduling decision-making method considering source-load uncertainty under limited energy storage

Abstract

The invention relates to a day-ahead scheduling decision-making method considering source load uncertainty under limited energy storage, and the method comprises the steps: 1, building a system load uncertainty model considering demand side management according to the load characteristics of a user; 2, predicting next-day data according to historical data and a system load uncertainty model basedon electricity price, wherein the next-day data comprises wind speed, wind power plant output, outdoor temperature, photovoltaic power station output, system load and electricity price fluctuation; 3,establishing a day-ahead low-carbon economic dispatching model by taking low-carbon economy as a target and considering a deep peak regulation working condition and a normal operation working condition of the thermal power generating unit; and 4, carrying out random sampling by using a Monte Carlo method. A day-ahead low-carbon economic dispatching model is solved based on an improved bat algorithm, and the unit output, the electricity price of each time period and the price type demand response quantity are determined. According to the method, the low-carbon economic dispatch of the power system is realized, the local optimum under the high-dimensional condition is avoided, and the globally optimal solution is quickly obtained.

Description

technical field [0001] The invention belongs to the technical field of electric power systems and automation thereof, relates to low-carbon economic dispatching of electric power systems, and specifically relates to a day-ahead dispatching decision-making method considering source-load uncertainty under limited energy storage. Background technique [0002] At this stage, with the vigorous promotion of the construction of the strong smart grid and the ubiquitous power Internet of Things, a high proportion of renewable energy is connected to the grid, and artificial intelligence, sensors, and advanced communication technologies obtain comprehensive new energy, energy storage and load. The data is applied to the optimal dispatching of the power system. At the same time, the impact of the global greenhouse effect on the ecosystem is increasing, and CO2 emission reduction in the power industry plays a key role in reducing greenhouse gas emissions. Under the strategy of "three ty...

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Problems solved by technology

[0004] The research in the above literature mainly focuses on considering the impact of volatility and intermittency of large-scale renewable energy generation on power dispatching, but less attention is paid to the uncertainty of demand response on the user side and the joint dispatching of multiple energy sources

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