CN111952978A - Demand response excitation method and system considering user response characteristics - Google Patents

Demand response excitation method and system considering user response characteristics Download PDF

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Publication number
CN111952978A
CN111952978A CN202010654357.7A CN202010654357A CN111952978A CN 111952978 A CN111952978 A CN 111952978A CN 202010654357 A CN202010654357 A CN 202010654357A CN 111952978 A CN111952978 A CN 111952978A
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user
response
model
incentive
excitation
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石坤
田世明
陈宋宋
郑顺林
李源非
龚桃荣
周颖
韩凝晖
宫飞翔
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention provides a demand response incentive method and a demand response incentive system considering user response characteristics, wherein the demand response incentive method comprises the following steps: based on the response quantity of the user and the incentive provided by the aggregator to the user in each time period, solving a pre-constructed equivalent model by using a primary-dual interior point algorithm to obtain an optimal incentive strategy in each time period; optimizing the demand response quantity of each user with maximum benefit according to the optimal incentive strategy of each time interval; the equivalent model is constructed by combining a demand response model of a user and an optimal excitation model of an aggregator with a relation function of the response quantity and the excitation of each user; the invention considers the differentiated response characteristics of different users and realizes the global optimization of the incentive strategy.

Description

Demand response excitation method and system considering user response characteristics
Technical Field
The present invention relates to an incentive response mechanism, and more particularly, to a demand response incentive method and system considering user response characteristics.
Background
With the rapid development and the technological progress of social economy, controllable resources of a demand side distributed power supply, an electric automobile, distributed energy storage, an air conditioner, an electric boiler and the like on a user side are rapidly increased, and the types and the capacities of demand response resources of the user side are greatly enriched. In addition, with the new energy grid connection with a large amount of output uncertainty and the further expansion of peak-to-valley difference, the power demand response, especially the incentive type demand response, has become an important means for solving the supply and demand balance, and the existing research on the incentive type demand response does not take the response characteristics of different users into consideration. Therefore, a demand response incentive strategy considering user response characteristics is a difficult problem to be solved urgently at present, and it is particularly important to coordinate supply and demand balance of energy and guarantee satisfaction of users based on incentive strategies at different time periods.
The prior art electric power demand response methods mainly include a price-based demand response method and an incentive-based demand response method. Wherein the price-based demand-side response strategy is divided into time-of-use electricity prices, peak electricity prices, and real-time electricity prices. The time-of-use electricity price is a common electricity price strategy in China, can effectively reflect an electricity price mechanism of power supply cost difference of a power grid in different periods, and mainly takes measures of properly increasing the electricity price in a peak period, properly reducing the electricity price in a valley period, reducing load peak-valley difference, improving electricity consumption of users and achieving the effects of peak clipping and valley filling. However, the time division of the time-of-use electricity price is fixed, and the electricity price ratio of each time period is also fixed, so that the time-of-use electricity price can only reflect the statistical rule of daily load and power supply cost in a period of time, and the change of the daily time-of-use load and the power supply cost cannot be accurately reflected. The incentive type demand response is that a market participant releases a subsidy signal to a user to guide the user to increase or decrease electricity for a certain period of time to achieve balance of supply and demand. However, in the existing incentive subsidy research, the differentiated response characteristics of different users are not considered, so that the incentive strategy cannot achieve global optimization.
Disclosure of Invention
In order to solve the problems that differential response characteristics of different users are not considered in the existing incentive subsidy research, and an incentive strategy cannot achieve global optimization, the invention provides a demand response incentive method considering user response characteristics, which comprises the following steps:
based on the response quantity of the user and the incentive provided by the aggregator to the user in each time period, solving a pre-constructed equivalent model by using a primary-dual interior point algorithm to obtain an optimal incentive strategy in each time period;
optimizing the demand response quantity of each user with maximum benefit according to the optimal incentive strategy of each time interval;
the equivalent model is constructed by combining a demand response model of the user and an optimal excitation model of the aggregator with the relationship function between the response quantity and the excitation of each user.
Preferably, the constructing of the equivalent model comprises:
constructing a double-layer interaction model by taking the optimal excitation model of the aggregator as an upper layer and the demand response model of the user as a lower layer;
obtaining a relation function between the response quantity and the excitation of each user according to the excitation and the energy storage state of each time period;
and substituting the relation function of the response and the excitation of the user into the optimal excitation model of the aggregator on the upper layer of the initial double-layer interaction model to obtain an equivalent model.
Preferably, the demand response model of the user is as follows:
Figure BDA0002574746480000021
Figure BDA0002574746480000022
Figure BDA0002574746480000023
wherein: u shapei,k(xi,k): a benefit function representing participation demand response of the user i in the k period; s.t. is a constraint condition;
Figure BDA0002574746480000024
a comfort loss function representing user i's participation in the demand response over a period k; x is the number ofi,kThe response quantity of the user i in the k period; pii,kAn incentive provided to user i for the aggregator for period k; thetaeq,i,λeq,iEquivalent parameters for the user to participate in demand response;
Figure BDA0002574746480000025
the upper and lower limits of the response quantity of the user i in the k period.
Preferably, the optimal excitation model of the aggregator is as follows:
Figure BDA0002574746480000026
Figure BDA0002574746480000027
in the formula, pii,kAn incentive provided to user i for the aggregator for period k; x is the number ofi,kResponse for user i during period kAn amount; Δ LkTask response for the aggregator over period k.
Preferably, the response quantity of each user is a function of the stimulus, as shown in the following formula:
Figure BDA0002574746480000031
preferably, the equivalent model is represented by the following formula:
Figure BDA0002574746480000032
preferably, the optimal incentive strategy according to each time interval optimizes the response quantity of the user with the maximum benefit according to the following formula:
Figure BDA0002574746480000033
in the formula (I), the compound is shown in the specification,
Figure BDA0002574746480000034
the user response quantity after the benefit maximum optimization.
A demand response incentive system considering user response characteristics, comprising:
the excitation strategy calculation module is used for solving a pre-constructed equivalent model by utilizing a primary-dual interior point algorithm based on the response quantity of the user and the excitation provided by the aggregation quotient for the user at each time interval to obtain an optimal excitation strategy at each time interval;
the response quantity calculation module optimizes the response quantity of each user demand with the maximum benefit according to the optimal excitation strategy of each time interval;
the double-layer interaction model is constructed by combining a demand response model of a user and an optimal excitation model of an aggregator with the relationship function of the response quantity and the excitation of each user.
Preferably, the incentive policy calculation module includes: a model construction submodule and an excitation calculation submodule;
the model construction submodule is used for constructing an equivalent model;
the excitation calculation submodule is used for solving a pre-constructed equivalent model by utilizing a primary-dual interior point algorithm based on the response characteristics of the user to obtain an optimal excitation strategy of each period;
preferably, the model construction sub-module includes:
the model building unit is used for building a double-layer interaction model by taking the optimal excitation model of the aggregator as an upper layer and the demand response model of the user as a lower layer;
the relation function building unit is used for obtaining a relation function between the response quantity and the excitation of each user according to the excitation and the energy storage state of each time interval;
and the interaction module construction unit is used for bringing the relation function of the response and the excitation of the user into the optimal excitation model of the aggregator on the upper layer of the initial double-layer interaction model to obtain an equivalent model.
Compared with the prior art, the invention has the beneficial effects that:
in the process of implementing the demand response, incentive strategies can be accurately formulated for different users according to the response characteristics of the users, so that the uncertainty of the user response is reduced, the complexity of the user participating in the demand response is reduced as much as possible, and more users are guided to participate in the demand response. In addition, on the basis of promoting the user to participate in the demand response, the invention also reduces the total incentive of the aggregator in implementing the demand response, and finally realizes the win-win between the user side and the aggregator.
The invention provides a demand response incentive method considering user response characteristics, which comprises the following steps: based on the response quantity of the user and the incentive provided by the aggregator to the user in each time period, solving a pre-constructed equivalent model by using a primary-dual interior point algorithm to obtain an optimal incentive strategy in each time period; optimizing the demand response quantity of each user with maximum benefit according to the optimal incentive strategy of each time interval; the equivalent model is constructed by combining a demand response model of a user and an optimal excitation model of an aggregator with a relation function of the response quantity and the excitation of each user; the invention considers the differentiated response characteristics of different users and realizes the global optimization of the incentive strategy.
Drawings
FIG. 1 is a flow chart of a demand response incentive method of the present invention that takes into account user response characteristics;
FIG. 2 is a diagram illustrating an application of the demand response incentive method according to the present invention in consideration of user response characteristics;
FIG. 3 is a flow chart of the present invention for a user to optimize the response with maximum benefit according to the optimal incentive policy.
Detailed Description
The invention discloses a demand response incentive method considering user response characteristics, which realizes the establishment of a response model of a user and an optimal incentive model of an aggregator. On the basis, a double-layer interaction model of the aggregator and the user in the demand response is established, the upper layer is an optimal layer model of the aggregator, and the lower layer is a response model of the user. In the solving process, the incentive strategies in each time interval are regarded as parameter numbers, the benefit models of the users are optimized, and the relation function of the response quantity of each user and the incentive strategies is obtained. And then, the function is brought into an excitation electricity price model with the minimum cost of the upper aggregator, so that an equivalent model of the double-layer planning is obtained. And thirdly, solving the equivalent model by using a primary-dual interior point algorithm, and optimizing to obtain the optimal excitation strategy of each period. Finally, each user optimizes the response quantity of each user according to the incentive of each time interval with the maximum benefit.
The demand response incentive method considering the user response characteristics, as shown in fig. 1, includes:
step I: based on the response quantity of the user and the incentive provided by the aggregator to the user in each time period, solving a pre-constructed equivalent model by using a primary-dual interior point algorithm to obtain an optimal incentive strategy in each time period;
step II: optimizing the demand response quantity of each user with maximum benefit according to the optimal incentive strategy of each time interval;
the equivalent model is constructed by combining a demand response model of the user and an optimal excitation model of the aggregator with the relationship function between the response quantity and the excitation of each user.
The invention solves the problem that the existing incentive subsidy research does not consider the differentiated response characteristics of different users and the incentive strategy cannot realize the global optimization, and realizes the global optimization of the incentive strategy.
Step I: based on the response quantity of the user and the incentive provided by the aggregator to the user in each time period, solving the pre-constructed equivalent model by using a primal-dual interior point algorithm to obtain the optimal incentive strategy in each time period, which can be realized by adopting the steps shown in but not limited to fig. 2:
step 1: a demand response model of the user and an optimal incentive model of the aggregator;
step 2: establishing a double-layer interaction model of the aggregator and the user in the demand response, wherein the upper layer is an optimal layer model of the aggregator, and the lower layer is a response model of the user;
and step 3: taking the excitation and the energy storage state of each time period as parameters, optimizing the benefit model of each user, and obtaining a relation function between the response quantity and the excitation of each user;
and 4, step 4: the relation function obtained by the solution in the step 3 is brought into an excitation electricity price model with the minimum cost of the upper aggregator, and an equivalent model of the double-layer planning is obtained;
and 5: solving the equivalent model by using a primary-dual interior point algorithm, and optimizing to obtain an optimal excitation strategy of each time period;
step II: optimizing the demand response quantity of each user with maximum benefit according to the optimal incentive strategy of each time interval, which is specifically as follows:
step 6: and (5) optimizing the optimal excitation strategy obtained in the step (5) to obtain the optimal response quantity of each user.
Further, the algorithm comprises the following specific steps:
step 1: a demand response model of the user and an optimal incentive model of the aggregator;
(1) a demand response model of a user
Figure BDA0002574746480000061
Figure BDA0002574746480000062
Figure BDA0002574746480000063
Wherein: u shapei,k(xi,k): a benefit function representing participation demand response of the user i in the k period; s.t. is called totally subject to, i.e. constraint;
Figure BDA0002574746480000064
a comfort loss function representing user i's participation in the demand response over a period k; x is the number ofi,kThe response quantity of the user i in the k period; pii,kAn incentive provided to user i for the aggregator for period k; thetaeq,i,λeq,iEquivalent parameters for the user to participate in demand response;
Figure BDA0002574746480000065
the upper and lower limits of the response quantity of the user i in the k period.
(2) The optimal excitation model of the aggregators.
Figure BDA0002574746480000066
Figure BDA0002574746480000067
Wherein: pii,kAn incentive provided to user i for the aggregator for period k; x is the number ofi,kThe response quantity of the user i in the k period; Δ LkTask response for the aggregator over period k.
Step 2: establishing a double-layer interaction model of the aggregator and the user in the demand response, wherein the upper layer is an optimal layer model of the aggregator, and the lower layer is a response model of the user;
the two-layer interaction model of the aggregator-user known from step 1 can be expressed as follows
Figure BDA0002574746480000068
Figure BDA0002574746480000069
Figure BDA00025747464800000610
And step 3: taking the excitation and the energy storage state of each time period as parameters, optimizing the benefit model of each user, and obtaining a relation function between the response quantity and the excitation of each user;
Figure BDA0002574746480000071
Figure BDA0002574746480000072
and 4, step 4: the relation function obtained by the solution in the step 3 is brought into an excitation electricity price model with the minimum cost of the upper aggregator, and an equivalent model of the double-layer planning is obtained;
Figure BDA0002574746480000073
Figure BDA0002574746480000074
Figure BDA0002574746480000075
Figure BDA0002574746480000076
and 5: solving the equivalent model by using a primary-dual interior point algorithm, and optimizing to obtain an optimal excitation strategy of each time period;
step 6: and (5) stimulating the strategy in each time period obtained in the step 5, and optimizing the response quantity of each user according to the stimulation to the maximum benefit.
Figure BDA0002574746480000077
According to the method, the aggregator optimal incentive strategy and the user participation demand response optimal response strategy are comprehensively considered by establishing a double-layer interaction model of the aggregator and the user in the demand response. The demand response excitation strategy research method realizes a response mechanism of demand response considering the user response characteristics, perfects the excitation strategy of further refinement of the demand response, and provides reference for the demand response excitation strategy research of the demand response excitation strategy considering the user response characteristics.
Example 2
The demand response incentive method considering the user response characteristics provided by the invention is as shown in FIG. 3:
step 1: a demand response model of the user and an optimal incentive model of the aggregator;
(1) a demand response model of a user
Figure BDA0002574746480000078
Figure BDA0002574746480000079
Figure BDA00025747464800000710
Wherein: x is the number ofi,kThe response quantity of the user i in the k period; pii,kAn incentive provided to user i for the aggregator for period k; thetaeq,i,λeq,iEquivalent parameters for the user to participate in demand response;
Figure BDA0002574746480000081
the upper and lower limits of the response quantity of the user i in the k period.
(2) The optimal excitation model of the aggregators.
Figure BDA0002574746480000082
Figure BDA0002574746480000083
Wherein: pii,kAn incentive provided to user i for the aggregator for period k; x is the number ofi,kThe response quantity of the user i in the k period; Δ LkTask response for the aggregator over period k.
Step 2: establishing a double-layer interaction model of the aggregator and the user in the demand response, wherein the upper layer is an optimal layer model of the aggregator, and the lower layer is a response model of the user;
the two-layer interaction model of the aggregator-user known from step 1 can be expressed as follows
Figure BDA0002574746480000084
Figure BDA0002574746480000085
Figure BDA0002574746480000086
And step 3: taking the excitation and the energy storage state of each time period as parameters, optimizing the benefit model of each user, and obtaining a relation function between the response quantity and the excitation of each user;
Figure BDA0002574746480000087
Figure BDA0002574746480000088
and 4, step 4: the relation function obtained by the solution in the step 3 is brought into an excitation electricity price model with the minimum cost of the upper aggregator, and an equivalent model of the double-layer planning is obtained;
Figure BDA0002574746480000089
Figure BDA00025747464800000810
Figure BDA00025747464800000811
Figure BDA00025747464800000812
and 5: solving the equivalent model by using a primary-dual interior point algorithm, and optimizing to obtain an optimal excitation strategy of each time period;
step 6: and (5) stimulating the strategy in each time period obtained in the step 5, and optimizing the response quantity of each user according to the stimulation to the maximum benefit.
Figure BDA0002574746480000091
The demand response incentive strategy considering the user response characteristics provided by the patent is improved by further refining the incentive strategy of demand response, provides reference for research on the demand response incentive strategy considering the user response characteristics, and provides a good and feasible method for making the aggregator incentive strategy.
Example 3
The present invention based on the same inventive concept also provides a demand response incentive system considering user response characteristics, including:
the excitation strategy calculation module is used for solving a pre-constructed equivalent model by utilizing a primary-dual interior point algorithm based on the response quantity of the user and the excitation provided by the aggregation quotient for the user at each time interval to obtain an optimal excitation strategy at each time interval;
the response quantity calculation module optimizes the response quantity of each user demand with the maximum benefit according to the optimal excitation strategy of each time interval;
the double-layer interaction model is constructed by combining a demand response model of a user and an optimal excitation model of an aggregator with the relationship function of the response quantity and the excitation of each user.
Preferably, the incentive policy calculation module includes: a model construction submodule and an excitation calculation submodule;
the model construction submodule is used for constructing an equivalent model;
the excitation calculation submodule is used for solving a pre-constructed equivalent model by utilizing a primary-dual interior point algorithm based on the response characteristics of the user to obtain an optimal excitation strategy of each period;
preferably, the model construction sub-module includes:
the model building unit is used for building a double-layer interaction model by taking the optimal excitation model of the aggregator as an upper layer and the demand response model of the user as a lower layer;
the relation function building unit is used for obtaining a relation function between the response quantity and the excitation of each user according to the excitation and the energy storage state of each time interval;
and the interaction module construction unit is used for bringing the relation function of the response and the excitation of the user into the optimal excitation model of the aggregator on the upper layer of the initial double-layer interaction model to obtain an equivalent model.
The demand response model of the user is shown as follows:
Figure BDA0002574746480000101
Figure BDA0002574746480000102
Figure BDA0002574746480000103
wherein: u shapei,k(xi,k): a benefit function representing participation demand response of the user i in the k period; s.t. is called totally subject to, i.e. constraint;
Figure BDA0002574746480000104
a comfort loss function representing user i's participation in the demand response over a period k; x is the number ofi,kThe response quantity of the user i in the k period; pii,kAn incentive provided to user i for the aggregator for period k; thetaeq,i,λeq,iEquivalent parameters for the user to participate in demand response;
Figure BDA0002574746480000105
the upper and lower limits of the response quantity of the user i in the k period.
Preferably, the optimal excitation model of the aggregator is as follows:
Figure BDA0002574746480000106
Figure BDA0002574746480000107
in the formula, pii,kAn incentive provided to user i for the aggregator for period k; x is the number ofi,kThe response quantity of the user i in the k period; Δ LkTask response for the aggregator over period k.
Preferably, the response quantity of each user is a function of the stimulus, as shown in the following formula:
Figure BDA0002574746480000108
preferably, the equivalent model is represented by the following formula:
Figure BDA0002574746480000109
preferably, the optimal incentive strategy according to each time interval optimizes the response quantity of the user with the maximum benefit according to the following formula:
Figure BDA0002574746480000111
in the formula (I), the compound is shown in the specification,
Figure BDA0002574746480000112
user response optimized to maximize benefit
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (10)

1. A demand response incentive method considering user response characteristics, comprising:
based on the response quantity of the user and the incentive provided by the aggregator to the user in each time period, solving a pre-constructed equivalent model by using a primary-dual interior point algorithm to obtain an optimal incentive strategy in each time period;
optimizing the demand response quantity of each user with maximum benefit according to the optimal incentive strategy of each time interval;
the equivalent model is constructed by combining a demand response model of the user and an optimal excitation model of the aggregator with the relationship function between the response quantity and the excitation of each user.
2. The demand response incentive method of claim 1, wherein the construction of the equivalence model comprises:
constructing a double-layer interaction model by taking the optimal excitation model of the aggregator as an upper layer and the demand response model of the user as a lower layer;
obtaining a relation function between the response quantity and the excitation of each user according to the excitation and the energy storage state of each time period;
and substituting the relation function of the response and the excitation of the user into the optimal excitation model of the aggregator on the upper layer of the initial double-layer interaction model to obtain an equivalent model.
3. A demand response incentive method according to claim 2 wherein the demand response model of the user is as follows:
Figure FDA0002574746470000011
Figure FDA0002574746470000012
Figure FDA0002574746470000013
wherein: u shapei,k(xi,k): a benefit function representing participation demand response of the user i in the k period; s.t.: a constraint condition;
Figure FDA0002574746470000014
a comfort loss function representing user i's participation in the demand response over a period k; x is the number ofi,kThe response quantity of the user i in the k period; pii,kAn incentive provided to user i for the aggregator for period k; thetaeq,i,λeq,iEquivalent parameters for the user to participate in demand response;
Figure FDA0002574746470000015
the upper and lower limits of the response quantity of the user i in the k period.
4. The demand response incentive method of claim 3, wherein the optimal incentive model of the aggregators is as follows:
Figure FDA0002574746470000016
Figure FDA0002574746470000017
in the formula, pii,kAn incentive provided to user i for the aggregator for period k; xi,kThe response quantity of the user i in the k period; Δ LkTask response for the aggregator over period k.
5. The demand response incentive method according to claim 4, wherein the response quantity of each user is a function of the incentive as follows:
Figure FDA0002574746470000021
6. the demand response incentive method of claim 5, wherein the equivalence model is represented by the following equation:
Figure FDA0002574746470000022
7. the demand response incentive method according to claim 6, wherein the optimal incentive strategies according to the respective periods optimize the user's response quantity with maximum benefit, calculated as follows:
Figure FDA0002574746470000023
in the formula (I), the compound is shown in the specification,
Figure FDA0002574746470000024
the user response quantity after the benefit maximum optimization.
8. A demand response incentive system considering user response characteristics, comprising:
the excitation strategy calculation module is used for solving a pre-constructed equivalent model by utilizing a primary-dual interior point algorithm based on the response quantity of the user and the excitation provided by the aggregation quotient for the user at each time interval to obtain an optimal excitation strategy at each time interval;
the response quantity calculation module optimizes the response quantity of each user demand with the maximum benefit according to the optimal excitation strategy of each time interval;
the double-layer interaction model is constructed by combining a demand response model of a user and an optimal excitation model of an aggregator with the relationship function of the response quantity and the excitation of each user.
9. The demand response incentive system of claim 8, wherein the incentive policy calculation module comprises: a model construction submodule and an excitation calculation submodule;
the model construction submodule is used for constructing an equivalent model;
and the excitation calculation submodule is used for solving a pre-constructed equivalent model by utilizing a primary-dual interior point algorithm based on the response characteristics of the user to obtain the optimal excitation strategy of each period.
10. The demand response incentive system of claim 9, wherein the model building submodule comprises:
the model building unit is used for building a double-layer interaction model by taking the optimal excitation model of the aggregator as an upper layer and the demand response model of the user as a lower layer;
the relation function building unit is used for obtaining a relation function between the response quantity and the excitation of each user according to the excitation and the energy storage state of each time interval;
and the interaction module construction unit is used for bringing the relation function of the response and the excitation of the user into the optimal excitation model of the aggregator on the upper layer of the initial double-layer interaction model to obtain an equivalent model.
CN202010654357.7A 2020-07-08 2020-07-08 Demand response excitation method and system considering user response characteristics Pending CN111952978A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113159982A (en) * 2021-03-05 2021-07-23 国网山东省电力公司潍坊供电公司 Power dispatching method and system based on online demand response
CN116523273A (en) * 2023-07-04 2023-08-01 广东电网有限责任公司广州供电局 Demand response characteristic analysis method for industrial users

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113159982A (en) * 2021-03-05 2021-07-23 国网山东省电力公司潍坊供电公司 Power dispatching method and system based on online demand response
CN116523273A (en) * 2023-07-04 2023-08-01 广东电网有限责任公司广州供电局 Demand response characteristic analysis method for industrial users
CN116523273B (en) * 2023-07-04 2023-09-22 广东电网有限责任公司广州供电局 Demand response characteristic analysis method for industrial users

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