CN117273317A - Method and device for optimizing demand response of park load - Google Patents

Method and device for optimizing demand response of park load Download PDF

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CN117273317A
CN117273317A CN202311168304.4A CN202311168304A CN117273317A CN 117273317 A CN117273317 A CN 117273317A CN 202311168304 A CN202311168304 A CN 202311168304A CN 117273317 A CN117273317 A CN 117273317A
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hawk
optimization model
demand response
park
load demand
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温鑫
郑茵
黄力宇
郭斌
蔡妙妆
陈少梁
李慧
刘常
黎艺炜
彭正阳
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The application discloses a method and a device for optimizing demand response of park load, wherein the method comprises the following steps: and calculating the electric power carbon emission coefficient of each user node in the park, constructing a load demand response optimization model of the park based on the electric power carbon emission coefficient of each user node, solving the load demand response optimization model through a fish eagle optimization algorithm, and determining an optimal solution of the load demand response optimization model so that park load of the park is powered under the optimal solution. Therefore, the optimization algorithm based on the littoral behavior introduces the dynamic electric carbon factor into the low-carbon demand response of the load of the park, so that the dynamic electric carbon factor is used as one element of the objective function, and the system is ensured to run economically and stably and is lower in carbon and environment-friendly.

Description

Method and device for optimizing demand response of park load
Technical Field
The application relates to the technical field of electric power, in particular to a method and a device for optimizing demand response of park load.
Background
With the continuous increase in electricity consumption, various industries are devoted to exploring lower carbon development modes. The campus is an important component of the city. The whole energy planning of the park has a vital role in realizing green low-carbon energy production, energy supply and utilization. Some scholars in the past aim at the load demands of certain specific users in the park, through the optimization integration of an energy system, the renewable energy utilization rate is improved, a green low-carbon park comprehensive energy solution is explored, and a related reference method is provided for park-like energy planning.
Traditional park demand responses such as price type demand response, incentive type demand response and substitution type demand response can realize coordination and optimization of multiple energy sources, ensure stability and economy of electricity consumption in the park, but lack dynamic consideration of carbon emission.
How to optimize the demand response process of the park load by adding a mode for measuring the dynamic carbon emission factor, so as to realize the diversification of the low carbon and park energy utilization modes, and the method is a problem needing attention.
Disclosure of Invention
In view of the above problems, the present application is proposed to provide a method and apparatus for optimizing demand response of park load, so as to realize low carbon and park energy utilization mode diversification, which is a problem that needs attention.
In order to achieve the above object, the following specific solutions are proposed:
a method of demand response optimization for campus load, comprising:
the power carbon emission coefficient for each customer node in the campus was calculated using the following:
wherein delta s Electric carbon emission coefficient for the s-th user node, P g For the active power, delta, output by the power plant in the park g Omega, the electric carbon emission coefficient of the power plant s Branch connection node set for the s-th user node, P so For branch active power, delta, flowing from the s-th user node to its o-th branch connection node o The electric power carbon emission coefficient of the user node o;
constructing a load demand response optimization model of the park based on the electric power carbon emission coefficient of each user node;
and solving the load demand response optimization model through a preset hawk optimization algorithm, and determining an optimal solution of the load demand response optimization model so as to enable the park load of the park to be powered up under the optimal solution.
Optionally, the building the load demand response optimization model of the campus based on the electric power carbon emission coefficient of each user node includes:
when all user nodes in the park do not need to respond to a power grid power instruction and the overall power consumption of all user nodes before and after responding to the power grid power instruction is unchanged, building a load demand response optimization model of the park as a first optimization model based on the power carbon emission coefficient of each user node;
when all user nodes in the park do not need to respond to the power grid power command and the overall power consumption of all user nodes before and after responding to the power grid power command is changed, building a load demand response optimization model of the park as a second optimization model based on the power carbon emission coefficient of each user node;
When all user nodes in the park need to respond to a power grid power instruction and the overall power consumption of all user nodes before and after responding to the power grid power instruction is unchanged, building a load demand response optimization model of the park as a third optimization model based on the power carbon emission coefficient of each user node;
when all user nodes in the park need to respond to the power grid power command, and the overall power consumption of all user nodes before and after responding to the power grid power command is changed, a load demand response optimization model of the park is built to be a fourth optimization model based on the power carbon emission coefficient of each user node.
Optionally, the first optimization model is:
wherein F is an objective function, T is the total number of time periods for park load electricity consumption of the park,for the park load, responding to the power consumption after the preset load demand in the t period lambda E (t) is the electricity price of the t period, O is the total number of user nodes in the park, lambda C (t) carbon emission price at the t-th period, P L (t) is the power usage of the park load before the t period responds to the preset load demand, delta (t) is the maximum reduction proportion of the park load in the t period,/- >Maximum power for the park load;
the second optimization model is as follows:
wherein F is E (t) is a price penalty coefficient of a t period, and beta is a maximum reduction proportion of the electricity consumption of the park load;
the third optimization model is as follows:
wherein T is c A set of preset response time periods, the set of response time periods comprising a plurality of time periods,a power value corresponding to the power command of the power grid;
the fourth optimization model is:
optionally, the solving the load demand response optimization model through a preset hawk optimization algorithm, to determine an optimal solution of the load demand response optimization model includes:
in a preset hawk optimization algorithm, each user node is used as a hawk, each user node is initialized, a hawk group is obtained, the optimal hawks in the hawk group are prey objects, and the position of the optimal hawks in the hawk group in a preset search space is the position provided with the hawk;
updating the position of a first part of the hawks in the hawk group in the preset search space based on the load demand response optimization model in the exploration stage of the hawk optimization algorithm, wherein the first part of the hawks are half of the hawk group;
In the exploration stage of the hawk optimization algorithm, based on the load demand response optimization model, updating the position of a second part of hawks in the hawk group in the preset search space by setting the position of the exendin in the preset search space, wherein the second part of hawks are the complement of the first part of hawks in the hawk group;
in the development stage of the hawk optimization algorithm, performing one position update iteration on each hawk in the hawk group based on the load demand response optimization model;
and when the current iteration number of the position updating iteration reaches the preset maximum iteration number, determining the optimal solution of the current hawk group relative to the load demand response optimization model.
Optionally, in an exploration phase of the hawk optimization algorithm, updating a position of a first part of the hawks in the hawk group in the preset search space based on the load demand response optimization model, including:
in the exploration phase of the hawk optimization algorithm, for each of the first portion of hawks in the population of hawks, a target position of the hawks in a preset search space is calculated using the following formula:
Wherein,for the target position of the ith osprey in the preset search space during the exploration phase,/>To the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j Iguana, the value of the j decision variable in the current position of the i-th osprey j For the j-th dimension of the current position of the hyena, I is a number randomly selected from the set {1,2}, N is the total number of the hawks in the hawk group, m is the number of decision variables in the position of each hawk, and r is a number randomly selected from the set {0,1 };
in an exploration phase of the hawk optimization algorithm, for each of the first partial hawks, based on the load demand response optimization model, updating an updated position of the hawk in the preset search space using:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,f for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, in an exploration phase of the hawk optimization algorithm, based on the load demand response optimization model, updating the position of the second part of the hawks in the hawk group in the preset search space by setting the position of the exendin in the preset search space, including:
setting the position of the hyb in the preset search space by using the following formula:
wherein Iguana G For the location of the hyena in the preset search space,for the j-th dimension, lb, of the position of the hyena in the preset search space j Being the lower bound of the jth decision variable, ub j For the upper bound of the jth decision variable, m is the number of decision variables in the position of each osprey, and r is one number randomly selected from the set {0,1 };
in the exploration phase of the hawk optimization algorithm, for each of the second partial hawks in the hawk population, the hawk is driven to move using the following formula:
wherein,for the target position of the ith hawk in the exploration phase in the preset search space,/th>For the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number from the set {0,1}, F IguanaG An objective function value of the load demand response optimization model for the position of the hyena in the preset search space, F i Objective function value for the current position of the ith osprey with respect to the load demand response optimization model,/>Rounding up the function symbols;
in the exploration phase of the hawk optimization algorithm, for each of the second partial hawks, updating the updated position of the hawk in the preset search space using:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,the target position for the ith osprey in the exploration phase is related to the loadObjective function value of demand response optimization model, F i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, in a development stage of the hawk optimization algorithm, performing a location update iteration on each hawk in the hawk group based on the load demand response optimization model, including:
in the development phase of the hawk optimization algorithm, the safe position of each hawk in the hawk population in one position update iteration is calculated using the following formula:
Wherein,for the safe position of the ith hawk in the preset search space during the development phase +.>To the value of the j-th decision variable in the position of the i-th hawk in the development stage, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number in the set {0,1}, k is the current iteration number, lb j Being the lower bound of the jth decision variable, ub j Is the upper bound of the j-th decision variable, < ->The lower bound of the current iteration for the jth decision variable,for the upper bound of the current iteration of the jth decision variable, N is the total number of hawks in the hawk population, m is the number of decision variables in the location of each hawk;
a second optimization model; in the development stage of the algorithm, updating the updating position of each hawk in the hawk group in the preset search space by using the following formula:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,f for the objective function value of the safety position of the ith osprey in the development stage with respect to the load demand response optimization model i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, in a development stage of the hawk optimization algorithm, after performing a location update iteration on each hawk in the hawk group based on the load demand response optimization model, the method further includes:
and when the current iteration number of the position updating iteration does not reach the preset maximum iteration number, returning to the step of executing the first part of the hawks in the preset search space in the exploration phase of the hawk optimization algorithm based on the load demand response optimization model.
A demand response optimizing apparatus for a campus load, comprising:
an electric power carbon emission coefficient calculation unit for calculating an electric power carbon emission coefficient of each user node in the campus using:
wherein delta s Electric carbon emission coefficient for the s-th user node, P g For the active power, delta, output by the power plant in the park g Omega, the electric carbon emission coefficient of the power plant s Branch connection node set for the s-th user node, P so For branches flowing from the s-th user node to its o-th branch connection nodeActive power, delta o The electric power carbon emission coefficient of the user node o;
the optimization model construction unit is used for constructing a load demand response optimization model of the park based on the electric power carbon emission coefficient of each user node;
And the algorithm solving unit is used for solving the load demand response optimizing model through a preset hawk optimizing algorithm, and determining an optimal solution of the load demand response optimizing model so as to enable the park load of the park to use electricity under the optimal solution.
Optionally, the optimizing model building unit includes:
the system comprises a first optimization model construction subunit, a second optimization model construction subunit and a load demand response optimization model, wherein the first optimization model construction subunit is used for constructing a load demand response optimization model of the park as a first optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park does not need to respond to an electric power command and the overall power consumption of each user node before and after responding to the electric power command is unchanged;
the second optimization model construction subunit is used for constructing a load demand response optimization model of the park into a second optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park does not need to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is changed;
the third optimization model construction subunit is used for constructing a load demand response optimization model of the park as a third optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park needs to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is unchanged;
And the fourth optimization model construction subunit is used for constructing a load demand response optimization model of the park into a fourth optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park needs to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is changed.
Optionally, the algorithm solving unit includes:
the hawk group initializing unit is used for initializing each user node by taking each user node as a hawk in a preset hawk optimizing algorithm to obtain a hawk group, wherein the optimal hawk in the hawk group is a prey, and the position of the optimal hawk in the hawk group in a preset search space is a position provided with a zaenan;
the first updating unit of the exploration phase is used for updating the position of a first part of the hawks in the hawk group in the preset search space based on the load demand response optimization model in the exploration phase of the hawk optimization algorithm, wherein the first part of the hawks is half of the hawk group;
the second updating unit in the exploration stage is used for updating the position of a second part of the hawks in the hawk group in the preset search space by setting the position of the hawks in the preset search space based on the load demand response optimization model, wherein the second part of the hawks are the complement of the first part of the hawks in the hawk group;
A development stage position updating unit, configured to perform a position updating iteration on each hawk in the hawk group based on the load demand response optimization model in a development stage of the hawk optimization algorithm;
and the optimal solution determining unit is used for determining the optimal solution of the current hawk group relative to the load demand response optimization model when the current iteration number of the position updating iteration reaches the preset maximum iteration number.
Optionally, the first updating unit of the exploration phase includes:
a first target position calculating unit, configured to calculate, for each of the first part of the eagles in the eagle population, a target position of the eagle in a preset search space using the following formula:
wherein,for the target position of the ith osprey in the preset search space during the exploration phase,/>To the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j Iguana, the value of the j decision variable in the current position of the i-th osprey j For the j-th dimension of the current position of the hyena, I is a number randomly selected from the set {1,2}, N is the total number of the hawks in the hawk group, m is the number of decision variables in the position of each hawk, and r is a number randomly selected from the set {0,1 };
A first updating unit, configured to update, in an exploration phase of the hawk optimization algorithm, for each hawk in the first part, based on the load demand response optimization model, an updated position of the hawk in the preset search space using:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey, F i P1 F for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, the second updating unit of the exploration phase includes:
an exendin location setting unit for setting the location of the exendin in the preset search space using the following formula:
wherein Iguana G For the location of the hyena in the preset search space,for the j-th dimension, lb, of the position of the hyena in the preset search space j Being the lower bound of the jth decision variable, ub j For the upper bound of the jth decision variable, m is the number of decision variables in the position of each osprey, and r is one number randomly selected from the set {0,1 };
A second target location updating unit for driving the movement of each of the second partial osprey in the osprey population during the exploration phase of the osprey optimization algorithm, using the following formula:
wherein,for the target position of the ith hawk in the exploration phase in the preset search space,/th>For the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number from the set {0,1}, F IguanaG An objective function value of the load demand response optimization model for the position of the hyena in the preset search space, F i Objective function value for the current position of the ith osprey with respect to the load demand response optimization model,/>Rounding up the function symbols;
a second updating unit, configured to update, for each of the second partial osprey, an updated position of the osprey in the preset search space using the following formula:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey, F i P1 F for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, the development stage location updating unit includes:
a safe position calculation unit, configured to calculate, in a development stage of the hawk optimization algorithm, a safe position of each hawk in the hawk population in one position update iteration using the following formula:
wherein,for the safe position of the ith hawk in the preset search space during the development phase +.>To the value of the j-th decision variable in the position of the i-th hawk in the development stage, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number in the set {0,1}, k is the current iteration number, lb j Being the lower bound of the jth decision variable, ub j As the j-th decision variableUpper bound (I)>The lower bound of the current iteration for the jth decision variable,for the upper bound of the current iteration of the jth decision variable, N is the total number of hawks in the hawk population, m is the number of decision variables in the location of each hawk;
A third updating unit, configured to update, in a development stage of the hawk optimization algorithm, an updated position of each hawk in the hawk population in the preset search space by using the following formula:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,f for the objective function value of the safety position of the ith osprey in the development stage with respect to the load demand response optimization model i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, the apparatus further comprises:
in the development stage of the hawk optimization algorithm, after carrying out one position update iteration on each hawk in the hawk group based on the load demand response optimization model, when the current iteration number of the position update iteration does not reach the preset maximum iteration number, returning to the first updating unit for executing the exploration stage.
By means of the technical scheme, the load demand response optimization model of the park is built based on the electric power carbon emission coefficient of each user node in the park by calculating the electric power carbon emission coefficient of each user node, the load demand response optimization model is solved through a preset hawk optimization algorithm, and the optimal solution of the load demand response optimization model is determined, so that park load of the park is powered down by the optimal solution. Therefore, the optimization algorithm based on the littoral behavior introduces the dynamic electric carbon factor into the low-carbon demand response of the load of the park, so that the dynamic electric carbon factor is used as one element of the objective function, and the system is ensured to run economically and stably and is lower in carbon and environment-friendly.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a schematic flow chart of a method for optimizing demand response of a campus load according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a hawk optimization algorithm for solving a load demand response optimization model according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an apparatus for implementing demand response optimization of campus load according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The scheme can be realized based on the terminal with the data processing capability, and the terminal can be a computer, a server, a cloud end and the like.
Next, as described in connection with fig. 1, the method for optimizing demand response of a campus load of the present application may include the following steps:
step S110, calculating the electric power carbon emission coefficient of each user node in the park by using the first formula.
It will be appreciated that the carbon emissions measurement of the consumer depends on the grid and the corresponding type of generating end. Specifically, the first formula is:
wherein delta s Electric carbon emission coefficient for the s-th user node, P g For the active power, delta, output by the power plant in the park g Omega, the electric carbon emission coefficient of the power plant s Branch connection node set for the s-th user node, P so For branch active power, delta, flowing from the s-th user node to its o-th branch connection node o Is the electrical carbon emission coefficient of the user node o.
And step 120, constructing a load demand response optimization model of the park based on the electric power carbon emission coefficient of each user node.
And step 130, solving the load demand response optimization model through a preset hawk optimization algorithm, and determining an optimal solution of the load demand response optimization model so as to enable the park load of the park to use electricity under the optimal solution.
Specifically, the hawk optimization algorithm is an optimization algorithm based on the hawk behavior, and the algorithm performs optimization by simulating the hawk falciparum behavior, and has the characteristics of strong optimizing capability, high convergence rate, capability of providing proper values for decision variables of low-carbon demand response of the load of a park and the like when applied to a load demand response optimization model based on a dynamic electric carbon factor.
According to the method for optimizing the demand response of the park load, the power carbon emission coefficient of each user node in the park is calculated, the load demand response optimizing model of the park is built based on the power carbon emission coefficient of each user node, the load demand response optimizing model is solved through a preset hawk optimizing algorithm, and the optimal solution of the load demand response optimizing model is determined, so that the park load of the park is powered down in the optimal solution. Therefore, the optimization algorithm based on the littoral behavior introduces the dynamic electric carbon factor into the low-carbon demand response of the load of the park, so that the dynamic electric carbon factor is used as one element of the objective function, and the system is ensured to run economically and stably and is lower in carbon and environment-friendly.
In some embodiments of the present application, the process of constructing the load demand response optimization model of the campus based on the electric carbon emission coefficient of each user node in the step S120 is described, where the process may include the following four cases:
First, when all user nodes in the park do not need to respond to a power grid power instruction, and the overall power consumption of all user nodes before and after responding to the power grid power instruction is unchanged, a load demand response optimization model of the park is built to be a first optimization model based on the power carbon emission coefficient of each user node.
Specifically, the first optimization model may be:
wherein F is an objective function, T is the total number of time periods for park load electricity consumption of the park,for the park load, responding to the power consumption after the preset load demand in the t period lambda E (t) is the electricity price of the t period, O is the total number of user nodes in the park, lambda C (t) carbon emission price at the t-th period, P L (t) is the power usage of the park load before the t period responds to the preset load demand, delta (t) is the maximum reduction proportion of the park load in the t period,/->Maximum power for the park load.
It will be appreciated that to obtain a response in this case, one should input: the corresponding electricity price curve of the park, the electricity consumption peak Gu Qi time period, the normal electricity consumption curve of the load of the park (24 hours, one point per hour), the maximum reduction proportion of each time period and the maximum electricity consumption power of the load of the park. The corresponding response output that can be obtained is: response to park load electricity usage profile (24 hours, one point per hour).
Secondly, when all user nodes in the park do not need to respond to the power grid power command, and the overall power consumption of all user nodes before and after responding to the power grid power command is changed, the load demand response optimization model of the park is built to be a second optimization model based on the power carbon emission coefficient of each user node.
Specifically, the second optimization model may be:
wherein F is E And (t) is a price penalty coefficient of the t period, and beta is the maximum reduction proportion of the electricity consumption of the park load.
It will be appreciated that to obtain a response in this case, one should input: the corresponding electricity price curve of the park, electricity consumption peak Gu Qi time period, the normal electricity consumption curve of the load of the park, the maximum reduction proportion of each time period, the maximum electricity consumption power of the load of the park, and the electricity consumption reduction price penalty coefficient and the maximum reduction proportion of the electricity consumption for the region where the park is located. The corresponding response output that can be obtained is: response power usage profile for park load.
Thirdly, when all user nodes in the park need to respond to the power grid power command, and the overall power consumption of all user nodes before and after responding to the power grid power command is unchanged, building a load demand response optimization model of the park to be a third optimization model based on the power carbon emission coefficient of each user node.
Specifically, the third optimization model may be:
wherein T is c A set of preset response time periods, the set of response time periods comprising a plurality of time periods,and commanding a corresponding power value for the power grid power.
It will be appreciated that to obtain a response in this case, one should input: the power price curve corresponding to the park, the electricity consumption peak Gu Qi time period, the normal electricity consumption curve of the load of the park, the maximum reduction proportion of each time period, the maximum electricity consumption power of the load of the park, the power grid electricity consumption command corresponding to the park and the command response time period set. The corresponding response output that can be obtained is: response power usage profile for park load.
Fourth, when each user node in the park needs to respond to the power command of the power grid, and the overall power consumption of each user node before and after responding to the power command of the power grid is changed, a load demand response optimization model of the park is built to be a fourth optimization model based on the power carbon emission coefficient of each user node.
Specifically, the fourth optimization model may be:
it will be appreciated that to obtain a response in this case, one should input: the corresponding electricity price curve of the park, electricity consumption peak Gu Qi time intervals, the normal electricity consumption curve of the load of the park, the maximum reduction proportion of each time interval, the maximum electricity consumption power of the load of the park, the reduction price penalty coefficient, the maximum reduction proportion of the electricity consumption, the electricity consumption command of the power grid and the command response time interval set. The corresponding response output that can be obtained is: response power usage profile for park load.
In some embodiments of the present application, a process for solving the load demand response optimization model by using a preset hawk optimization algorithm mentioned in the foregoing embodiments to determine an optimal solution of the load demand response optimization model is described, and as shown in fig. 2, the process may include:
step S210, in a preset hawk optimization algorithm, each user node is used as a hawk, and each user node is initialized to obtain a hawk group.
The optimal hawk in the hawk group is a prey, and the position of the optimal hawk in the hawk group in the preset search space is a position provided with the hyena.
Step S220, in the exploration stage of the hawk optimization algorithm, updating the position of the first part of hawks in the hawk group in the preset search space based on the load demand response optimization model.
Wherein the first portion of the hawks is half of the population of hawks.
Specifically, in the exploring stage of the osprey optimization algorithm, based on the load demand response optimization model, the process of updating the position of the first part of osprey in the osprey group in the preset search space may include:
S2201, in the exploration stage of the hawk optimization algorithm, calculating a target position of each hawk in a preset search space by using a second formula aiming at each hawk in a first part of the hawks in the hawk group.
It will be appreciated that positioning and fishing are performed during the exploration phase of the hawk optimization algorithm. The hawk has acute vision, and can accurately detect the position of underwater fish. After the fish hawks have been located, they attack it and predate under water. For better description, the location of the prey is modeled as a tree, and the prey falls to the ground after being attacked, and the hawk predates. The algorithm models population updates by simulating the natural behavior of the hawk. Modeling fish attacks by hawks can result in significant changes in the location of the hawks in the search space, which increases the ability of the algorithm to explore in identifying optimal regions and escaping local optima. For each hawk, the location of other hawks in the search space that have closer objective function values are considered underwater fish, i.e., hunting, that falls to the ground.
Specifically, the second formula is:
wherein,for the target position of the ith osprey in the preset search space during the exploration phase,/ >To the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j Iguana, the value of the j decision variable in the current position of the i-th osprey j And I is one number selected randomly from a set {1,2} for the j-th dimension of the current position of the hyena, N is the total number of the hawks in the hawk group, m is the number of decision variables in the position of each hawk, and r is one number selected randomly from the set {0,1 }.
S2202, in the exploration stage of the hawk optimization algorithm, updating the updating position of each hawk in the preset search space by using a third formula based on the load demand response optimization model aiming at each hawk in the first part of hawks.
Specifically, the third formula is:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,f for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Step S230, in the exploration stage of the hawk optimization algorithm, based on the load demand response optimization model, updating the position of the second part of hawks in the hawk group in the preset search space by setting the position of the exendin in the preset search space.
Wherein the second portion of the hawks is a complement of the first portion of the hawks in the population of hawks.
Specifically, in the exploring stage of the hawk optimization algorithm, based on the load demand response optimization model, by setting the position of the exendin in the preset search space, the process of updating the position of the second part of hawks in the hawk group in the preset search space may include:
s2301, setting the position of the hyodendi in the preset search space by using a fourth formula.
Specifically, the fourth formula is:
wherein Iguana G For the location of the hyena in the preset search space,for the j-th dimension, lb, of the position of the hyena in the preset search space j Being the lower bound of the jth decision variable, ub j For the upper bound of the jth decision variable, m is the number of decision variables in the location of each osprey, and r is a randomly selected number in the set {0,1 }.
S2302, in the exploring stage of the hawk optimization algorithm, driving the hawks to move by using a fifth formula for each hawk of the second part of the hawks in the hawk group.
Specifically, the fifth formula is:
wherein,for the target position of the ith hawk in the exploration phase in the preset search space,/th >For the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number in the set {0,1}, +.>An objective function value of the load demand response optimization model for the position of the hyena in the preset search space, F i Objective function value for the current position of the ith osprey with respect to the load demand response optimization model,/>To round the function symbols up.
It will be appreciated that when the queen falls to the ground, it will be placed at a random location in the search space. Based on this random position, the other osprey moves in its search space on the ground, so simulations can be performed using the fourth and fifth formulas.
S2303, in the exploration stage of the hawk optimization algorithm, updating the updating position of the hawk in the preset search space by using a sixth formula for each hawk in the second part of hawks.
Specifically, the sixth formula is:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,f for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Step S240, in the development stage of the hawk optimization algorithm, performing one position update iteration on each hawk in the hawk group based on the load demand response optimization model.
Specifically, in the development stage of the hawk optimization algorithm, based on the load demand response optimization model, a process of performing a location update iteration on each hawk in the hawk group may include:
s2401, in the development stage of the hawk optimization algorithm, calculating the safe position of each hawk in the hawk group in one position updating iteration by using a seventh formula.
It will be appreciated that the stage of development of the hawk optimization algorithm is the process of escaping predators. This process simulates the natural behavior of a hawk to escape a predator when encountering the predator, to update the search space. When a predator attacks a hawk, the hawk escapes from its present location. The hawk brings itself in a safe position close to the current position during this process.
Specifically, the seventh formula is:
Wherein,for the safe position of the ith hawk in the preset search space during the development phase +.>To the value of the j-th decision variable in the position of the i-th hawk in the development stage, x i,j For the ith fishThe value of the jth decision variable in the current position of the hawk, r is a number randomly selected from the set {0,1}, k is the current iteration number, lb j Being the lower bound of the jth decision variable, ub j Is the upper bound of the j-th decision variable, < ->The lower bound of the current iteration for the jth decision variable,for the upper bound of the current iteration of the jth decision variable, N is the total number of hawks in the hawk population, and m is the number of decision variables in the location of each hawk.
S2402, in the development stage of the hawk optimization algorithm, updating the updating position of each hawk in the hawk group in the preset search space by using an eighth formula.
Specifically, the eighth formula is:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,f for the objective function value of the safety position of the ith osprey in the development stage with respect to the load demand response optimization model i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Step S250, judging whether the current iteration number reaches the preset maximum iteration number, if so, executing step S260, and if not, executing step S220.
Step S260, determining the optimal solution of the current hawk group relative to the load demand response optimization model.
The device for realizing the demand response optimization of the campus load, which is provided by the embodiment of the application, is described below, and the device for realizing the demand response optimization of the campus load and the method for realizing the demand response optimization of the campus load, which are described below, can be referred to correspondingly.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an apparatus for optimizing demand response of a campus load according to an embodiment of the present application.
As shown in fig. 3, the apparatus may include:
an electric power carbon emission coefficient calculation unit 11 for calculating an electric power carbon emission coefficient of each user node in the campus using:
wherein delta s Electric carbon emission coefficient for the s-th user node, P g For the active power, delta, output by the power plant in the park g Omega, the electric carbon emission coefficient of the power plant s Branch connection node set for the s-th user node, P so For branch active power, delta, flowing from the s-th user node to its o-th branch connection node o The electric power carbon emission coefficient of the user node o;
an optimization model construction unit 12 for constructing a load demand response optimization model of the campus based on the electric power carbon emission coefficient of each user node;
and the algorithm solving unit 13 is used for solving the load demand response optimizing model through a preset hawk optimizing algorithm, and determining an optimal solution of the load demand response optimizing model so as to enable the park load of the park to use electricity under the optimal solution.
Optionally, the optimizing model building unit includes:
the system comprises a first optimization model construction subunit, a second optimization model construction subunit and a load demand response optimization model, wherein the first optimization model construction subunit is used for constructing a load demand response optimization model of the park as a first optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park does not need to respond to an electric power command and the overall power consumption of each user node before and after responding to the electric power command is unchanged;
the second optimization model construction subunit is used for constructing a load demand response optimization model of the park into a second optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park does not need to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is changed;
The third optimization model construction subunit is used for constructing a load demand response optimization model of the park as a third optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park needs to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is unchanged;
and the fourth optimization model construction subunit is used for constructing a load demand response optimization model of the park into a fourth optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park needs to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is changed.
Optionally, the algorithm solving unit includes:
the hawk group initializing unit is used for initializing each user node by taking each user node as a hawk in a preset hawk optimizing algorithm to obtain a hawk group, wherein the optimal hawk in the hawk group is a prey, and the position of the optimal hawk in the hawk group in a preset search space is a position provided with a zaenan;
The first updating unit of the exploration phase is used for updating the position of a first part of the hawks in the hawk group in the preset search space based on the load demand response optimization model in the exploration phase of the hawk optimization algorithm, wherein the first part of the hawks is half of the hawk group;
the second updating unit in the exploration stage is used for updating the position of a second part of the hawks in the hawk group in the preset search space by setting the position of the hawks in the preset search space based on the load demand response optimization model, wherein the second part of the hawks are the complement of the first part of the hawks in the hawk group;
a development stage position updating unit, configured to perform a position updating iteration on each hawk in the hawk group based on the load demand response optimization model in a development stage of the hawk optimization algorithm;
and the optimal solution determining unit is used for determining the optimal solution of the current hawk group relative to the load demand response optimization model when the current iteration number of the position updating iteration reaches the preset maximum iteration number.
Optionally, the first updating unit of the exploration phase includes:
a first target position calculating unit, configured to calculate, for each of the first part of the eagles in the eagle population, a target position of the eagle in a preset search space using the following formula:
wherein,for the target position of the ith osprey in the preset search space during the exploration phase,/>To the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j Iguana, the value of the j decision variable in the current position of the i-th osprey j For the j-th dimension of the current position of the hyena, I is a number randomly selected from the set {1,2}, N is the total number of the hawks in the hawk group, m is the number of decision variables in the position of each hawk, and r is a number randomly selected from the set {0,1 };
a first updating unit, configured to update, in an exploration phase of the hawk optimization algorithm, for each hawk in the first part, based on the load demand response optimization model, an updated position of the hawk in the preset search space using:
Wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey,f for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, the second updating unit of the exploration phase includes:
an exendin location setting unit for setting the location of the exendin in the preset search space using the following formula:
wherein Iguana G For the location of the hyena in the preset search space,for the j-th dimension, lb, of the position of the hyena in the preset search space j Being the lower bound of the jth decision variable, ub j For the upper bound of the jth decision variable, m is the number of decision variables in the position of each osprey, and r is one number randomly selected from the set {0,1 };
a second target location updating unit for driving the movement of each of the second partial osprey in the osprey population during the exploration phase of the osprey optimization algorithm, using the following formula:
wherein,for the target position of the ith hawk in the exploration phase in the preset search space,/th >For the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number in the set {0,1}, +.>An objective function value of the load demand response optimization model for the position of the hyena in the preset search space, F i Objective function value for the current position of the ith osprey with respect to the load demand response optimization model,/>Rounding up the function symbols;
a second updating unit, configured to update, for each of the second partial osprey, an updated position of the osprey in the preset search space using the following formula:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey, F i P1 For the objective function value of the load demand response optimization model in relation to the objective position of the ith osprey in the exploration phase,F i and (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, the development stage location updating unit includes:
A safe position calculation unit, configured to calculate, in a development stage of the hawk optimization algorithm, a safe position of each hawk in the hawk population in one position update iteration using the following formula:
wherein,for the safe position of the ith hawk in the preset search space during the development phase +.>To the value of the j-th decision variable in the position of the i-th hawk in the development stage, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number in the set {0,1}, k is the current iteration number, lb j Being the lower bound of the jth decision variable, ub j Is the upper bound of the j-th decision variable, < ->The lower bound of the current iteration for the jth decision variable,for the upper bound of the current iteration of the jth decision variable, N is the total number of hawks in the hawk population, m is the number of decision variables in the location of each hawk;
a third updating unit, configured to update, in a development stage of the hawk optimization algorithm, an updated position of each hawk in the hawk population in the preset search space by using the following formula:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey, F i P2 F for the objective function value of the safety position of the ith osprey in the development stage with respect to the load demand response optimization model i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
Optionally, the apparatus further comprises:
in the development stage of the hawk optimization algorithm, after carrying out one position update iteration on each hawk in the hawk group based on the load demand response optimization model, when the current iteration number of the position update iteration does not reach the preset maximum iteration number, returning to the first updating unit for executing the exploration stage.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and may be combined according to needs, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method of demand response optimization for campus load, comprising:
the power carbon emission coefficient for each customer node in the campus was calculated using the following:
wherein delta s Electric carbon emission coefficient for the s-th user node, P g For the active power, delta, output by the power plant in the park g Omega, the electric carbon emission coefficient of the power plant s Branch connection node set for the s-th user node, P so For branch active power, delta, flowing from the s-th user node to its o-th branch connection node o The electric power carbon emission coefficient of the user node o;
constructing a load demand response optimization model of the park based on the electric power carbon emission coefficient of each user node;
and solving the load demand response optimization model through a preset hawk optimization algorithm, and determining an optimal solution of the load demand response optimization model so as to enable the park load of the park to be powered up under the optimal solution.
2. The method of claim 1, wherein constructing a load demand response optimization model for the campus based on the power carbon emission coefficient for each user node comprises:
when all user nodes in the park do not need to respond to a power grid power instruction and the overall power consumption of all user nodes before and after responding to the power grid power instruction is unchanged, building a load demand response optimization model of the park as a first optimization model based on the power carbon emission coefficient of each user node;
when all user nodes in the park do not need to respond to the power grid power command and the overall power consumption of all user nodes before and after responding to the power grid power command is changed, building a load demand response optimization model of the park as a second optimization model based on the power carbon emission coefficient of each user node;
When all user nodes in the park need to respond to a power grid power instruction and the overall power consumption of all user nodes before and after responding to the power grid power instruction is unchanged, building a load demand response optimization model of the park as a third optimization model based on the power carbon emission coefficient of each user node;
when all user nodes in the park need to respond to the power grid power command, and the overall power consumption of all user nodes before and after responding to the power grid power command is changed, a load demand response optimization model of the park is built to be a fourth optimization model based on the power carbon emission coefficient of each user node.
3. The method of claim 2, wherein the first optimization model is:
wherein F is an objective function, T is the total number of time periods for park load electricity consumption of the park,for the park load to use electric power after responding to the preset load demand in the t period,λ E (t) is the electricity price of the t period, O is the total number of user nodes in the park, lambda C (t) carbon emission price at the t-th period, P L (t) is the power usage of the park load before the t period responds to the preset load demand, delta (t) is the maximum reduction proportion of the park load in the t period,/- >Maximum power for the park load;
the second optimization model is as follows:
wherein F is E (t) is a price penalty coefficient of a t period, and beta is a maximum reduction proportion of the electricity consumption of the park load;
the third optimization model is as follows:
wherein T is c A set of preset response time periods, the set of response time periods comprising a plurality of time periods,a power value corresponding to the power command of the power grid;
the fourth optimization model is:
4. the method of claim 1, wherein the solving the load demand response optimization model by a preset hawk optimization algorithm to determine an optimal solution for the load demand response optimization model comprises:
in a preset hawk optimization algorithm, each user node is used as a hawk, each user node is initialized, a hawk group is obtained, the optimal hawks in the hawk group are prey objects, and the position of the optimal hawks in the hawk group in a preset search space is the position provided with the hawk;
updating the position of a first part of the hawks in the hawk group in the preset search space based on the load demand response optimization model in the exploration stage of the hawk optimization algorithm, wherein the first part of the hawks are half of the hawk group;
In the exploration stage of the hawk optimization algorithm, based on the load demand response optimization model, updating the position of a second part of hawks in the hawk group in the preset search space by setting the position of the exendin in the preset search space, wherein the second part of hawks are the complement of the first part of hawks in the hawk group;
in the development stage of the hawk optimization algorithm, performing one position update iteration on each hawk in the hawk group based on the load demand response optimization model;
and when the current iteration number of the position updating iteration reaches the preset maximum iteration number, determining the optimal solution of the current hawk group relative to the load demand response optimization model.
5. The method of claim 4, wherein updating the location of the first portion of the eagle in the group of eagles in the preset search space based on the load demand response optimization model in an exploration phase of the eagle optimization algorithm comprises:
in the exploration phase of the hawk optimization algorithm, for each of the first portion of hawks in the population of hawks, a target position of the hawks in a preset search space is calculated using the following formula:
Wherein,for the target position of the ith osprey in the preset search space during the exploration phase,/>To the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j Iguana, the value of the j decision variable in the current position of the i-th osprey j For the j-th dimension of the current position of the hyena, I is a number randomly selected from the set {1,2}, N is the total number of the hawks in the hawk group, m is the number of decision variables in the position of each hawk, and r is a number randomly selected from the set {0,1 };
in an exploration phase of the hawk optimization algorithm, for each of the first partial hawks, based on the load demand response optimization model, updating an updated position of the hawk in the preset search space using:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey, F i P1 F for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
6. The method of claim 4, wherein in the exploration phase of the hawk optimization algorithm, updating the location of the second portion of the population of hawks in the preset search space by setting the location of the exendin in the preset search space based on the load demand response optimization model, comprises:
setting the position of the hyb in the preset search space by using the following formula:
wherein Iguana G For the location of the hyena in the preset search space,for the j-th dimension, lb, of the position of the hyena in the preset search space j Being the lower bound of the jth decision variable, ub j For the upper bound of the jth decision variable, m is the number of decision variables in the position of each osprey, and r is one number randomly selected from the set {0,1 };
in the exploration phase of the hawk optimization algorithm, for each of the second partial hawks in the hawk population, the hawk is driven to move using the following formula:
wherein,for the target position of the ith hawk in the exploration phase in the preset search space,/th>For the value of the j-th decision variable in the position of the i-th osprey in the exploration phase, x i,j The value of the j decision variable in the current position of the ith osprey, r is the following in the set {0,1}Mechanically selected number,/->An objective function value of the load demand response optimization model for the position of the hyena in the preset search space, F i Objective function value for the current position of the ith osprey with respect to the load demand response optimization model,/>Rounding up the function symbols;
in the exploration phase of the hawk optimization algorithm, for each of the second partial hawks, updating the updated position of the hawk in the preset search space using:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey, F i P1 F for the objective function value of the objective position of the ith osprey in relation to the load demand response optimization model in the exploration phase i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
7. The method of claim 4, wherein, in a development phase of the hawk optimization algorithm, performing one location update iteration for each hawk in the hawk population based on the load demand response optimization model, comprising:
In the development phase of the hawk optimization algorithm, the safe position of each hawk in the hawk population in one position update iteration is calculated using the following formula:
wherein,for the safe position of the ith hawk in the preset search space during the development phase +.>To the value of the j-th decision variable in the position of the i-th hawk in the development stage, x i,j For the value of the jth decision variable in the current position of the ith osprey, r is a randomly selected number in the set {0,1}, k is the current iteration number, lb j Being the lower bound of the jth decision variable, ub j Is the upper bound of the j-th decision variable, < ->The lower bound of the current iteration for the jth decision variable,/-for>For the upper bound of the current iteration of the jth decision variable, N is the total number of hawks in the hawk population, m is the number of decision variables in the location of each hawk;
in the development stage of the hawk optimization algorithm, updating the updated position of each hawk in the hawk group in the preset search space by using the following formula:
wherein X is new_i For the update position of the ith osprey in the preset search space, X i For the current position of the ith osprey, F i P2 For the safe position of the ith euonymus in the development phase with respect to the objective function value of the load demand response optimization model, F i And (3) optimizing an objective function value of the model for the current position of the ith hawk according to the load demand response.
8. The method of any one of claims 4-7, further comprising, during a development phase of the eagle optimization algorithm, after performing one location update iteration for each eagle in the eagle population based on the load demand response optimization model:
and when the current iteration number of the position updating iteration does not reach the preset maximum iteration number, returning to the step of executing the first part of the hawks in the preset search space in the exploration phase of the hawk optimization algorithm based on the load demand response optimization model.
9. A demand response optimizing apparatus for a campus load, comprising:
an electric power carbon emission coefficient calculation unit for calculating an electric power carbon emission coefficient of each user node in the campus using:
wherein delta s Electric carbon emission coefficient for the s-th user node, P g For the active power, delta, output by the power plant in the park g Omega, the electric carbon emission coefficient of the power plant s Branch connection node set for the s-th user node, P so For branch active power, delta, flowing from the s-th user node to its o-th branch connection node o The electric power carbon emission coefficient of the user node o;
the optimization model construction unit is used for constructing a load demand response optimization model of the park based on the electric power carbon emission coefficient of each user node;
and the algorithm solving unit is used for solving the load demand response optimizing model through a preset hawk optimizing algorithm, and determining an optimal solution of the load demand response optimizing model so as to enable the park load of the park to use electricity under the optimal solution.
10. The apparatus according to claim 9, wherein the optimization model construction unit includes:
the system comprises a first optimization model construction subunit, a second optimization model construction subunit and a load demand response optimization model, wherein the first optimization model construction subunit is used for constructing a load demand response optimization model of the park as a first optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park does not need to respond to an electric power command and the overall power consumption of each user node before and after responding to the electric power command is unchanged;
the second optimization model construction subunit is used for constructing a load demand response optimization model of the park into a second optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park does not need to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is changed;
The third optimization model construction subunit is used for constructing a load demand response optimization model of the park as a third optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park needs to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is unchanged;
and the fourth optimization model construction subunit is used for constructing a load demand response optimization model of the park into a fourth optimization model based on the electric power carbon emission coefficient of each user node when each user node in the park needs to respond to the electric power command and the overall power consumption of each user node before and after responding to the electric power command is changed.
CN202311168304.4A 2023-09-11 2023-09-11 Method and device for optimizing demand response of park load Pending CN117273317A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117647706A (en) * 2024-01-30 2024-03-05 山东昊能电力建设有限公司 Intelligent power grid operation fault diagnosis system and method based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117647706A (en) * 2024-01-30 2024-03-05 山东昊能电力建设有限公司 Intelligent power grid operation fault diagnosis system and method based on big data
CN117647706B (en) * 2024-01-30 2024-04-05 山东昊能电力建设有限公司 Intelligent power grid operation fault diagnosis system and method based on big data

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