CN111563637A - Multi-target probability optimal power flow calculation method and device based on demand response - Google Patents

Multi-target probability optimal power flow calculation method and device based on demand response Download PDF

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CN111563637A
CN111563637A CN201910113024.0A CN201910113024A CN111563637A CN 111563637 A CN111563637 A CN 111563637A CN 201910113024 A CN201910113024 A CN 201910113024A CN 111563637 A CN111563637 A CN 111563637A
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曹佳
陈艺峰
廖资阳
刘永丰
郭积晶
赵香桂
黄敏
唐海燕
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Zhuzhou CRRC Times Electric Co Ltd
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Abstract

The invention provides a multi-target probability optimal power flow calculation method based on demand response, which comprises the following steps: based on the determined power system network topology, considering the uncertainty and the correlation of random variables, and establishing a multi-target probability optimal power flow model taking fuel cost and carbon tax cost as targets; for any node, solving a multi-target probability optimal power flow model by adopting a Monte Carlo simulation method to obtain a node comprehensive electricity price before demand response; the node comprehensive electricity price before the demand response is brought into the constructed demand response model to obtain the node load after the demand response; and substituting the node loads into a multi-target probability optimal load flow model, and performing conventional optimal load flow calculation on each load sample to obtain the node comprehensive electricity price after demand response. According to the invention, the multi-target probability optimal power flow model and the demand response model are combined, so that the problems that large-scale new energy grid connection and load fluctuation are not easy to track only by a conventional method for adjusting the output of a generator are solved.

Description

Multi-target probability optimal power flow calculation method and device based on demand response
Technical Field
The invention relates to the field of intelligent power grids, in particular to a multi-target probability optimal power flow calculation method and device based on demand response.
Background
The probability optimal power flow calculation (POPF) can take the economy, safety, uncertainty and relevance in the operation process of the power system into consideration, but does not consider a plurality of optimization targets and user-side demand response to participate in the scheduling operation of the power grid. Due to large-scale new energy grid connection and load fluctuation, the uncertainty variables are tracked only by a conventional method for adjusting the output of a generator, and the requirement of modern power grid dispatching operation is difficult to meet. The demand side response means that a user changes or optimizes the inherent habit power utilization mode according to the response made by the incentive mechanism given by the power department or the current power price information, so that the current power utilization load is reduced or pushed to other time periods to respond to the demand of the power grid. Therefore, it is necessary to calculate the multi-objective probability optimal power flow considering the participation of the user side in the demand response.
Therefore, the invention provides a multi-target probability optimal power flow calculation method and device based on demand response.
Disclosure of Invention
In order to solve the above problems, the present invention provides a demand response-based multi-objective probabilistic optimal power flow calculation method, including the following steps:
based on the determined power system network topology, considering uncertainty and correlation of random variables, and establishing a multi-target probability optimal power flow model of the power system with the fuel cost and the carbon tax cost as targets, wherein the random variables comprise wind speed and load;
for any node, solving a multi-target probability optimal power flow problem in the multi-target probability optimal power flow model by adopting a Monte Carlo simulation method to obtain a node comprehensive electricity price before demand response;
the node comprehensive electricity price before the demand response is brought into the constructed demand response model to obtain the node load after the demand response;
and for the current node, substituting the node load into the multi-target probability optimal power flow model, and performing conventional optimal power flow calculation on each load sample in the Monte Carlo simulation method to obtain the node comprehensive electricity price after demand response.
According to an embodiment of the invention, the probability nonlinear optimization function corresponding to the multi-objective probability optimal power flow model is as follows:
Figure BDA0001968959550000021
wherein x represents a vector composed of control variables and state variables of the power system, f (x) represents a comprehensive function composed of fuel cost and carbon tax cost of the power system, g (x) represents a power flow equation, and h (x) represents a safety constraint equation.
According to one embodiment of the invention, the active power provided by the wind generators of the power system in the multi-objective probabilistic optimal power flow model is determined based on wind speed.
According to one embodiment of the invention, the active power is determined by the following formula:
Figure BDA0001968959550000022
wherein, PwiRepresenting said active power, V representing wind speed, Vci、Vr、VcoRespectively representing cut-in wind speed, rated wind speed, cut-out wind speed, PrRepresenting the rated active power supplied by the wind turbine.
According to one embodiment of the invention, the demand response model is constructed as follows:
Figure BDA0001968959550000023
wherein λ is*Indicating the node's integrated electricity price, P, before the demand responseDiIndicates the load corresponding to the ith node, PloadRepresents the total load of the power system,
Figure BDA0001968959550000024
and the upper limit value and the lower limit value represent the participation of the ith node in the demand response.
According to one embodiment of the invention, the node load after the demand response is obtained, the operating state of the power system corresponding to the node load after the demand response is brought into the multi-target probability optimal power flow model, and whether the operating state meets the safety constraint condition or not is judged.
According to one embodiment of the invention, the method further comprises the steps of:
if the operation state meets the safety constraint condition, saving the current total operation cost F of the power systemcostAnd node comprehensive electricity price lambda after demand response**Node voltage UiNode injection active PiNode injection reactive QiBranch current Iij；
And if the running state does not meet the safety constraint condition, locally adjusting the node load after the demand response.
According to another aspect of the present invention, there is also provided a demand response-based multi-objective probabilistic optimal power flow calculation apparatus, including:
the model building module is used for building a multi-target probability optimal power flow model of the power system by taking the fuel cost and the carbon tax cost as targets based on the determined power system network topology and considering the uncertainty and the correlation of random variables, wherein the random variables comprise wind speed and load;
the first comprehensive electricity price module is used for solving a multi-target probability optimal power flow problem in the multi-target probability optimal power flow model by adopting a Monte Carlo simulation method for any node to obtain the node comprehensive electricity price before demand response;
the demand response module is used for bringing the node comprehensive electricity price before demand response into the constructed demand response model to obtain the node load after demand response;
and the second comprehensive electricity price module is used for substituting the node load into the multi-target probability optimal power flow model for the current node, and performing conventional optimal power flow calculation on each load sample in the Monte Carlo simulation method to obtain the node comprehensive electricity price after demand response.
According to one embodiment of the invention, the apparatus further comprises:
and the active power module is used for determining active power provided by a wind driven generator of the power system in the multi-target probability optimal power flow model based on wind speed.
According to one embodiment of the invention, the apparatus further comprises:
and the safety check module is used for obtaining the node load after the demand response, bringing the operating state of the power system corresponding to the node load after the demand response into the multi-target probability optimal power flow model, and judging whether the operating state meets a safety constraint condition.
The multi-target probability optimal power flow calculation method and device based on demand response respectively consider the uncertainty and the relevance of the wind speed and the load, establish a multi-target probability optimal power flow model taking fuel cost and carbon tax cost as targets, and solve by adopting a simulated Monte Carlo simulation method. In addition, the multi-target probability optimal power flow model and the demand response model are combined, so that the problems that large-scale new energy grid connection and load fluctuation are not easy to track only by a conventional method for adjusting the output of a generator are successfully solved, and the requirement of modern power grid dispatching operation is met.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for calculating a multi-objective probabilistic optimal power flow based on demand response according to an embodiment of the invention;
FIG. 2 shows a system diagram of an IEEE-30 node including a wind farm;
FIG. 3 is a flow chart of a method for calculating a multi-objective probabilistic optimal power flow based on demand response according to another embodiment of the invention; and
fig. 4 shows a block diagram of a multi-objective probabilistic optimal power flow calculation device based on demand response according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
Fig. 1 shows a flow chart of a demand response-based multi-objective probabilistic optimal power flow calculation method according to an embodiment of the invention.
First, in step S101, a multi-objective probabilistic optimal power flow model of the power system is established based on the determined network topology of the power system, taking uncertainty and correlation of random variables into account, wherein the random variables include wind speed and load.
Preferably, the probability nonlinear optimization function corresponding to the multi-objective probability optimal power flow model is as follows:
Figure BDA0001968959550000041
wherein x represents a vector composed of control variables and state variables of the power system, F (x) represents a comprehensive function composed of fuel cost and carbon tax cost of the power system, g (x) represents a power flow equation, and h (x) represents a safety constraint equation.
Wherein, the total fuel cost f of the conventional unit of the system1Carbon tax fee f2The minimum is the objective function. Due to system fuel cost f1And carbon tax f2Have the same dimension, and therefore are combined into a single objective function by a linear weighting method.
Further, active power provided by a wind driven generator of the power system in the multi-target probability optimal power flow model is determined based on the wind speed.
Preferably, the active power is determined by the following formula:
Figure BDA0001968959550000051
wherein, PwiRepresenting active power, V representing wind speed, Vci、Vr、VcoRespectively representing cut-in wind speed, rated wind speed, cut-out wind speed, PrRepresenting the rated active power supplied by the wind turbine.
Then, in step S102, for any node, a multi-target probability optimal power flow problem in the multi-target probability optimal power flow model is solved by using a quasi-monte carlo simulation method, so as to obtain a node comprehensive electricity price before demand response.
Next, in step S103, the node integrated electricity prices before the demand response are brought into the constructed demand response model, so as to obtain the node loads after the demand response.
Preferably, the constructed demand response model is as follows:
Figure BDA0001968959550000052
wherein λ is*Indicating the node's integrated electricity price, P, before the demand responseDiIndicates the load corresponding to the ith node, PloadRepresents the total load of the power system,
Figure BDA0001968959550000053
and the upper limit value and the lower limit value represent the participation of the ith node in the demand response.
Finally, in step S104, for the current node, the node load is brought into the multi-objective probability optimal power flow model, and the conventional optimal power flow calculation is performed for each load sample in the quasi-monte carlo simulation method, so as to obtain the node comprehensive electricity price after the demand response.
Further, the node comprehensive load after the demand response is obtained, the operation state of the power system corresponding to the node load after the demand response is brought into the multi-objective probability optimal power flow model, and whether the operation state meets the safety constraint condition or not is judged.
Specifically, if the operation state meets the safety constraint condition, the current total operation cost F of the power system is savedcostAnd node comprehensive electricity price lambda after demand response**Node voltage UiNode injection active PiNode injection reactive QiBranch current Iij。
In addition, if the operation state does not meet the safety constraint condition, the node load after the demand response is locally adjusted.
As shown in fig. 1, on the basis of considering the MPOPF problem of uncertainty and correlation of random variables such as load, wind speed of a wind power plant, and the like, the present invention obtains a lagrange multiplier corresponding to a power flow equation as a node comprehensive electricity price index, further considers that a user side participates in demand response based on the node comprehensive electricity price, finally obtains an MPOPF model considering demand response, and solves the problem by using a quasi-monte carlo simulation method. The problem that large-scale new energy grid connection and load fluctuation are not easy to track only by a conventional method for adjusting the output of the generator is solved, and therefore the requirement of modern power grid dispatching operation is met. Meanwhile, the user participates in demand response, the comprehensive electricity price of the node is reduced, the load size is changed, the possibility of occurrence of the power transmission resistor plug of the line can be further reduced, and a certain promotion effect on safe, stable and economic operation of the power grid is achieved.
FIG. 2 shows a system diagram of an IEEE-30 node including a wind farm. In one embodiment, the testing may be performed based on the system shown in FIG. 2, with the flow shown in FIG. 3. Fig. 3 shows a flow chart of a multi-objective probabilistic optimal power flow calculation method based on demand response according to another embodiment of the invention.
Firstly, in the embodiment, wind power plants are all composed of double-fed asynchronous wind power generators of uniform models, the consumed reactive power of the wind power plants is compensated by a controller, and a control mode with a constant power factor of 1.0 is adopted. Active power P provided by wind farmwiCan be loaded to the corresponding PQ node of the system.
And calculating the power factor according to the basic data of the load of each node, and assuming that the power factor of the load in the system is kept unchanged, such as the power factor is kept at 1.0. Thus, the load reactive power can be determined as long as the load active power is known. Meanwhile, linear correlation among all loads is assumed to exist, and is obtained by a Cholesky factorization method. Then, the linear correlation coefficient for all the loads was set to 0.9, and the standard deviation σ was set to the desired 5%. And the correlation between the wind speeds of the wind power plants is constructed by adopting a Pair-Copula method.
Then, the size N of the sample required by the Monte Carlo simulation method is givenxWind speed samples considering high dimensional correlation were obtained according to the Pair-Copula method. From wind power PwiConverting the wind speed sample data into active power P provided by the wind turbine generator set in a conversion relation with the wind speed Vwi。
Preferably, wind power PwiWith wind speedThe conversion relationship of V is specifically as follows:
Figure BDA0001968959550000071
wherein, PwiRepresenting active power, V representing wind speed, Vci、Vr、VcoRespectively representing cut-in wind speed, rated wind speed, cut-out wind speed, PrRepresenting the rated active power supplied by the wind turbine.
Then, the size N of the sample required based on the Monte Carlo simulation methodxAnd determining a low deviation point sequence of the load active power so as to replace the random number sequence sampled by the Monte Carlo simulation method. And then, based on the obtained low deviation point sequence corresponding to the random variable load, obtaining a load active power sample with a specified correlation coefficient according to a Cholesky factorization method.
And then, converting the load active power sample with the correlation coefficient from a standard normal space to a non-standard normal space according to the mean value and the standard deviation of the active load of the input node, wherein the mean value mu is basic data of the system, and the standard deviation sigma is 5% of the mean value. And determining a reactive load sample according to the active load sample because the node load power factor is kept unchanged.
Then, according to the data obtained above, in step S301, the ith load and wind power sample data is obtained. In step S302, the conventional OPF problem which takes the system fuel cost and the carbon tax cost as the target simultaneously is solved by adopting an interior point method to obtain the corresponding node comprehensive electricity price lambda*. After the conventional OPF calculation is finished, taking a Lagrange multiplier corresponding to the power flow equation as a node comprehensive electricity price lambda*. The Optimal Power Flow (OPF) refers to adjusting available control variables (such as generator output Power, adjustable transformer taps, etc.) to find a Power Flow distribution that can satisfy all operation constraints and make a certain performance index of a system reach an Optimal value when structural parameters and load conditions of the system are given.
In step S303, demand response calculation is performed. Computing demand response model correspondencesTo obtain a demand response optimized problem, and obtaining a load P 'after demand response'Di。
Preferably, the demand response model is as follows:
Figure BDA0001968959550000072
wherein λ is*Indicating the node's integrated electricity price, P, before the demand responseDiIndicates the load corresponding to the ith node, PloadRepresents the total load of the power system,
Figure BDA0001968959550000073
and the upper limit value and the lower limit value represent the participation of the ith node in the demand response. Preferably, for the demand response model shown above, a linear programming lingprog function in Matlab software may be called to solve, so as to obtain the load P 'after demand response'Di。
Next, in step S304, the obtained demand-responded load P'DiSubstituting into a conventional OPF model targeted at the total cost F of system operation. Solving by adopting an interior point method to obtain the node comprehensive electricity price lambda after demand response**。
Then, in step S305, security verification is performed. Check load P 'after demand response'DiWhether the running state of the corresponding system meets all the safety constraint conditions of the system.
If not, in step S306, the load P 'is checked'DiLocal adjustment is performed.
If yes, the process proceeds to step S307, where the relevant parameters are saved. The parameters needing to be stored have the current total operating cost F of the power systemcostAnd node comprehensive electricity price lambda after demand response**Node voltage UiNode injection active PiNode injection reactive QiBranch current Iij。
Next, in step S308, it is determined whether i is equal to Nx. I.e. whether the N required for the Monte Carlo simulation is completedxAnd (5) performing sub-routine optimal power flow calculation.
If the number of variables is equal to the predetermined value, in step S3010, the results stored in step S307 are counted and output as expected values, standard deviations, mean values, variances, higher moments, quantiles, and the like corresponding to the variables.
If not, the process proceeds to step S309, where i is i + 1. I.e. the value of i is increased by 1. The routine OPF calculation is performed next time in step S301.
As shown in fig. 3, the correlation between wind speed and load is considered by the Pair-Copula method and the cholesky factorization method, respectively, and an MPOPF model is established with the system fuel cost and the carbon tax cost as targets, and is solved by the quasi-monte carlo simulation method. And after the conventional OPF calculation is finished each time, taking the Lagrange multiplier corresponding to the power flow equation as the node comprehensive electricity price LCP, and finally establishing a demand response model by using the LCP index.
Based on the OPF problem, the influence of uncertainty of a random variable in the operation process of the Power system on the operation of the system is further considered, and conventional OPF calculation needs to be performed for each load and wind speed sample. The POPF calculation formed in conjunction with probability theory can adequately account for these uncertainty factors. Based on the POPF problem, the Multi-Objective Probabilistic optimal power Flow (MPOPF) further considers the power Flow distribution when a plurality of performance indexes reach the optimal value.
Besides, MPOPF and demand response can be organically combined well, and the problems that large-scale new energy grid connection and load fluctuation are not easy to track only by a conventional method for adjusting the output of a generator are successfully solved, so that the requirement of modern power grid dispatching operation is met. Demand Response (DR) means that when the power wholesale market price increases or the system reliability is threatened, after receiving a direct compensation notification of an inductive reduction load or a power price increase signal sent by a power supplier, a power consumer changes its inherent usual power mode to reduce or shift the power consumption load for a certain period of time to respond to power supply, thereby ensuring the stability of a power grid and inhibiting the short-term behavior of power price increase.
In addition, the user participates in demand response based on LCP (node comprehensive electricity price information) indexes, reduces LCP, changes the load size, and meanwhile can reduce the possibility of generating a resistance plug on a line, and plays a certain role in promoting the safe, stable and economic operation of a power grid.
Fig. 4 shows a block diagram of a multi-objective probabilistic optimal power flow calculation device based on demand response according to an embodiment of the invention.
As shown in fig. 4, the multi-objective probabilistic optimal power flow calculation device 400 includes a model construction module 401, a first integrated power rate module 402, a demand response module 403, and a second integrated power rate module 404.
The model building module 401 is configured to build a multi-objective probabilistic optimal power flow model of the power system, which takes fuel cost and carbon tax cost as targets, based on the determined network topology of the power system and considering uncertainty and correlation of random variables, where the random variables include wind speed and load.
The first comprehensive electricity price module 402 is configured to solve the multi-target probability optimal power flow problem in the multi-target probability optimal power flow model by using a quasi-monte carlo simulation method for any node, so as to obtain a node comprehensive electricity price before demand response.
The demand response module 403 is configured to bring the node comprehensive electricity price before demand response into the constructed demand response model, so as to obtain the node load after demand response.
The second comprehensive electricity price module 404 is configured to bring the node load into the multi-objective probability optimal power flow model for the current node, and perform conventional optimal power flow calculation for each load sample in the quasi-monte carlo simulation method to obtain the node comprehensive electricity price after the demand response.
Further, the multi-objective probabilistic optimal power flow calculation device 400 further includes an active power module for determining active power provided by the wind power generator of the power system in the multi-objective probabilistic optimal power flow model based on the wind speed.
Further, the multi-objective probabilistic optimal power flow calculation device 400 further includes a safety check module, configured to obtain the node load after the demand response, bring the operating state of the power system corresponding to the node load after the demand response into the multi-objective probabilistic optimal power flow model, and determine whether the operating state meets the safety constraint condition.
In summary, the multi-target probability optimal power flow calculation method and device based on demand response provided by the invention respectively consider the uncertainty and the relevance of the wind speed and the load, establish a multi-target probability optimal power flow model taking the fuel cost and the carbon tax cost as the targets, and adopt a quasi-Monte Carlo simulation method to solve. In addition, the multi-target probability optimal power flow model and the demand response model are combined, so that the problems that large-scale new energy grid connection and load fluctuation are not easy to track only by a conventional method for adjusting the output of a generator are successfully solved, and the requirement of modern power grid dispatching operation is met.
It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A multi-target probability optimal power flow calculation method based on demand response is characterized by comprising the following steps:
based on the determined power system network topology, considering uncertainty and correlation of random variables, and establishing a multi-target probability optimal power flow model of the power system with the fuel cost and the carbon tax cost as targets, wherein the random variables comprise wind speed and load;
for any node, solving a multi-target probability optimal power flow problem in the multi-target probability optimal power flow model by adopting a Monte Carlo simulation method to obtain a node comprehensive electricity price before demand response;
the node comprehensive electricity price before the demand response is brought into the constructed demand response model to obtain the node load after the demand response;
and for the current node, substituting the node load into the multi-target probability optimal power flow model, and performing conventional optimal power flow calculation on each load sample in the Monte Carlo simulation method to obtain the node comprehensive electricity price after demand response.
2. The method of claim 1, wherein the probability nonlinear optimization function corresponding to the multi-objective probabilistic optimal power flow model is as follows:
Figure FDA0001968959540000011
wherein x represents a vector composed of control variables and state variables of the power system, f (x) represents a comprehensive function composed of fuel cost and carbon tax cost of the power system, g (x) represents a power flow equation, and h (x) represents a safety constraint equation.
3. The method according to any of claims 1-2, characterized by determining the active power provided by the wind generators of the power system in the multi-objective probabilistic optimal power flow model based on wind speed.
4. The method of claim 3, wherein the active power is determined by the formula:
Figure FDA0001968959540000021
wherein, PwiRepresenting said active power, V representing wind speed, Vci、Vr、VcoRespectively representing cut-in wind speed, rated wind speed, cut-out wind speed, PrRepresenting the rated active power supplied by the wind turbine.
5. The method of any of claims 1-4, wherein the demand response model is constructed as follows:
Figure FDA0001968959540000022
wherein λ is*Indicating the node's integrated electricity price, P, before the demand responseDiIndicates the load corresponding to the ith node, PloadRepresents the total load of the power system,
Figure FDA0001968959540000023
and the upper limit value and the lower limit value represent the participation of the ith node in the demand response.
6. The method according to any one of claims 1 to 5, wherein a node load after demand response is obtained, the power system operating state corresponding to the node load after demand response is brought into the multi-objective probability optimal power flow model, and whether the operating state meets a safety constraint condition or not is judged.
7. The method of claim 6, further comprising the steps of:
if the operation state meets the safety constraint condition, saving the current total operation cost F of the power systemcostAnd node comprehensive electricity price lambda after demand response**Node voltage UiNode injection active PiNode injection reactive QiBranch current Iij；
And if the running state does not meet the safety constraint condition, locally adjusting the node load after the demand response.
8. A demand response-based multi-objective probabilistic optimal power flow calculation apparatus, comprising:
the model building module is used for building a multi-target probability optimal power flow model of the power system by taking the fuel cost and the carbon tax cost as targets based on the determined power system network topology and considering the uncertainty and the correlation of random variables, wherein the random variables comprise wind speed and load;
the first comprehensive electricity price module is used for solving a multi-target probability optimal power flow problem in the multi-target probability optimal power flow model by adopting a Monte Carlo simulation method for any node to obtain the node comprehensive electricity price before demand response;
the demand response module is used for bringing the node comprehensive electricity price before demand response into the constructed demand response model to obtain the node load after demand response;
and the second comprehensive electricity price module is used for substituting the node load into the multi-target probability optimal power flow model for the current node, and performing conventional optimal power flow calculation on each load sample in the Monte Carlo simulation method to obtain the node comprehensive electricity price after demand response.
9. The apparatus of claim 8, wherein the apparatus further comprises:
and the active power module is used for determining active power provided by a wind driven generator of the power system in the multi-target probability optimal power flow model based on wind speed.
10. The apparatus of claim 8, wherein the apparatus further comprises:
and the safety check module is used for obtaining the node load after the demand response, bringing the operating state of the power system corresponding to the node load after the demand response into the multi-target probability optimal power flow model, and judging whether the operating state meets a safety constraint condition.
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