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A method and system for collaborative optimization of temperature-controlled load comfort and frequency regulation

A frequency regulation and collaborative optimization technology, applied in information technology support systems, power network operating system integration, reducing/preventing power oscillations, etc. The relationship between the internal constraints and user comfort, and the inability to achieve compromise optimization between power supply quality cost and user comfort, etc.

Active Publication Date: 2020-08-21
YANSHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the process of using temperature-controlled loads to provide frequency regulation, the existing research work does not take into account the internal constraints of frequency regulation deviation, service cost and user comfort, and cannot achieve the compromise optimization of power supply quality cost and user comfort
In addition, the existing temperature-controlled load control methods ignore the problem of unbalanced comfort of different types of temperature-controlled load users in the frequency adjustment process, which affects the fairness of users participating in frequency adjustment services.

Method used

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  • A method and system for collaborative optimization of temperature-controlled load comfort and frequency regulation
  • A method and system for collaborative optimization of temperature-controlled load comfort and frequency regulation
  • A method and system for collaborative optimization of temperature-controlled load comfort and frequency regulation

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Embodiment 1

[0101] Such as figure 1 As shown, this embodiment provides a method for collaborative optimization of temperature-controlled load comfort and frequency adjustment, which specifically includes:

[0102] Step 101: Determine initial parameters; the initial parameters include the total number of iterations, the temperature setting value range, the maximum tracking error, the first setting threshold and the initial temperature setting value.

[0103] Step 102 : taking the minimum total discomfort of the user as the goal, and taking the temperature setting range and the maximum tracking error as constraints, a first optimization target model is established.

[0104] Step 103: Using the active target particle swarm optimization algorithm to calculate the first optimization target model, obtain the current target solution, and record the current iteration number.

[0105] Step 104: Judging whether the current target solution is smaller than a first set threshold, and obtaining a firs...

Embodiment 2

[0119] Such as figure 2 As shown, this embodiment provides a temperature control load comfort and frequency adjustment collaborative optimization system, specifically including:

[0120] The initial parameter determination module 201 is configured to determine the initial parameters; the initial parameters include the total number of iterations, the range of the temperature setting value, the maximum tracking error, the first setting threshold and the initial temperature setting value.

[0121] The first optimization target model establishment module 202 is used to establish a first optimization target model with the minimum total discomfort of the user as the target and with the temperature setting range and the maximum tracking error as constraints.

[0122] The current target solution obtaining module 203 is configured to use the active target particle swarm optimization algorithm to calculate the first optimization target model, obtain the current target solution, and rec...

Embodiment 3

[0128] Such as image 3 As shown, this embodiment provides a method for collaborative optimization of temperature-controlled load comfort and frequency adjustment, which specifically includes:

[0129] Step 301: Determine initial parameters; the initial parameters include the total number of iterations, the temperature setting value range, the maximum tracking error, the second setting threshold and the initial temperature setting value.

[0130] Step 302: Taking the minimum discomfort degree of users in the largest group as the goal, and taking the temperature setting range and the maximum tracking error as constraints, a second optimization objective model is established.

[0131] Step 303: Using the active target particle swarm optimization algorithm to calculate the second optimization target model, obtain the current target solution, and record the current iteration number.

[0132] Step 304: Judging whether the current target solution is smaller than the second set thre...

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Abstract

The invention discloses a coordinated optimization method and system for temperature control load comfort and frequency regulation, and relates to the technical field of smart grid frequency regulation. The invention mainly establishes an optimization model considering user discomfort and tracking errors and proposes three schemes. When a temperature set value satisfies constraints and an objective function converges, an intelligent algorithm is adopted to solve the optimal temperature set value, so that the deviation and cost of the temperature control load participating in the grid frequencyregulation service are reduced, the power utilization comfort of users is guaranteed, and the coordinated optimization for the frequency regulation deviation, the auxiliary service cost and the usercomfort is achieved, thereby providing a feasible technical scheme for the grid frequency regulation service based on the temperature control load, and establishing a quantitative index system for thegrid and users to evaluate the cost of participating in the auxiliary service.

Description

technical field [0001] The invention relates to the technical field of smart grid frequency regulation, in particular to a method and system for collaborative optimization of temperature-controlled load comfort and frequency regulation. Background technique [0002] In traditional grid control, the power system adjusts the output of each generator on the generating side through automatic generation control (AGC) to keep the frequency deviation within the allowable range. In the process of building a smart grid, frequency adjustment services have become an important part of auxiliary services in the power market in research at home and abroad. As part of the smart grid, demand side management (DSM) plays an important role in ancillary grid services. In recent years, the direct load control scheme based on air-conditioning load has attracted widespread attention in the study of the demand-side sales market. There have been in-depth studies on the use of temperature-controlle...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/00H02J3/24H02J3/48
CPCH02J3/00H02J3/24H02J3/48H02J2203/20H02J2310/14Y02B70/3225Y04S20/222
Inventor 杨婕刘桐语马锴田振华王伟强袁亚洲
Owner YANSHAN UNIV
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