Optimization method of power source and power flow structure based on multi-stage stochastic programming theory

Abstract

Power supply and power flow structure optimization method based on multi-stage stochastic programming theory, use Monte Carlo simulation method to generate scenes; use fast forward scene tree reduction algorithm to reduce the scene, obtain the scene tree, and obtain the power consumption prediction error of each node in the scene tree The value and probability of value; establish a multi-stage stochastic optimization model of power supply and power flow structure; bring the value and probability of power consumption prediction error of each node in the scene tree into the multi-stage stochastic optimization model of power supply and power flow structure In , the multi-stage stochastic optimization model of power supply and power flow structure is solved to obtain a set of optimization schemes. The present invention can reasonably consider the impact of renewable energy access on power grid peak regulation under the environment of rapid development of renewable energy and uncertainties in power consumption growth in the future, and provides a set of solutions when facing uncertainties , compared with other existing methods, it has higher flexibility and applicability, and has good performance in long-term optimization.

Description

technical field [0001] The invention belongs to the field of power system planning, and relates to a method for optimizing the structure of a power supply and power flow, in particular to a method for optimizing the structure of a power supply and power flow based on a multi-stage stochastic programming theory. Background technique [0002] The optimization of power supply and power flow structure will provide decision-making reference for the power supply structure and power flow pattern of the future power grid, and provide data support for the structural transformation of the energy system. important meaning. Since the optimization of power supply and power flow structure is affected by many factors such as the prediction accuracy of electricity consumption, the development of the national economy, the progress of energy technology, and the cost of new energy investment and construction, the long-term uncertainty of these factors makes the single deterministic optimizatio...

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Problems solved by technology

From the perspective of long-term optimization, the optimization results are not suitable for the rapid development of renewable energy in the future

[0004] (2) The single optimization result obtained by the deterministic optimization method lacks flexibility and applicability in the face of future uncertainties

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