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Distributed optimization method and system for dynamic economic dispatching of smart grid

A technology of economic dispatch and smart grid, applied in the direction of power network operating system integration, system integration technology, AC network voltage adjustment, etc., can solve problems such as large computational burden, no consideration of dynamic constraints, and poor robustness of centralized optimization strategies

Active Publication Date: 2022-06-03
SHANDONG UNIV
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Problems solved by technology

[0005] In order to overcome the deficiencies of the above-mentioned existing technologies, the present disclosure provides a distributed optimization method for dynamic economic dispatching of smart grids, which solves the shortcomings of centralized optimization strategies such as poor robustness and large computational burden, and makes up for the fact that static optimization methods do not consider dynamic constraints. insufficient

Method used

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  • Distributed optimization method and system for dynamic economic dispatching of smart grid
  • Distributed optimization method and system for dynamic economic dispatching of smart grid
  • Distributed optimization method and system for dynamic economic dispatching of smart grid

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Experimental program
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Embodiment 1

[0034] FIG. 1 is a system structure diagram, the solid line represents the energy transmission line, and the dashed line represents the communication topology of the system. Figure 2 is

[0036] The initialization time t=0, the scheduling time τ∈R, the original residual r, the dual residual d, the alternate direction multiplier method optimization iteration

[0038]

[0040]

[0041]

[0043]

[0045]

[0046]

[0048]

[0049] Wherein is the charging power, is the discharge power. Satisfy the constraints:

[0050]

[0051]

[0052] Wherein and respectively charge, discharge power upper limit.

[0053]

[0054] The optimization variable is the power generation of each node or the power of charging and discharging.

[0057]

[0058]

[0060] where X=[P, S, W]

[0064] Step 3.3.1: set the rated frequency to be f

[0066] Step 3.3.2: Input Δf in the neural network controller

[0067] O

[0070] Step 3.4: The power generation node and the energy storage system node receive the Lagrang...

Embodiment 2

Embodiment 3

[0122] The purpose of this embodiment is to provide a computer-readable storage medium.

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Abstract

This disclosure proposes a distributed optimization method and system for dynamic economic scheduling of smart grids, measures the frequency of each node, and calculates the Lagrangian multiplier deviation; Sub-deviation, update the Lagrangian multiplier at the current moment; update the power generation and charge and discharge power in this X update and make a judgment, and if the conditions are met, update Y for all nodes. Based on the neural network controller, the variation of Lagrangian multipliers is calculated by the frequency deviation to realize the power balance in the power grid, which provides a new idea for the realization of power balance constraints in economic dispatching problems.

Description

Distributed optimization method and system for dynamic economic dispatch of smart grid technical field The present disclosure belongs to the technical field of smart grid energy dispatching, and in particular relates to the dynamic economic dispatching distribution of smart grids method and system for optimization. Background technique The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art information technique. [0003] With the development of society and the progress of science and technology, the power generation and operation modes of traditional power grids have been unable to meet people's demand for electricity. Various needs and requirements of energy conservation, emission reduction, and environmental protection. Therefore, research on smart grids incorporating renewable energy has become A major trend for the development of the power grid. Wind energy is one of the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/00H02J3/38H02J3/32H02J3/46H02J3/14G06Q50/06G06Q10/06G06F17/10
CPCH02J3/008H02J3/381H02J3/388H02J3/32H02J3/466H02J3/144G06Q10/06315G06Q50/06G06F17/10H02J2203/20H02J2300/28H02J2300/40Y04S20/222Y02B70/3225
Inventor 刘帅王祎帆宇文成杨伟明
Owner SHANDONG UNIV
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