Method and device for applying low-carbon demand response strategy

Abstract

The invention discloses a low-carbon demand response strategy application method and device, and the method comprises the steps: obtaining the power historical data of renewable energy sources in a power system, obtaining moment information according to the power historical data, and constructing a corresponding fuzzy set; the method comprises the following steps: acquiring information data of a distributed generator, renewable energy sources, loads and carbon transactions in a power system, and constructing to obtain a system operation cost minimization problem considering uncertainty; obtaining a constraint condition according to the fuzzy set, and converting a system operation cost minimization problem considering uncertainty into a deterministic system operation cost minimization problem; and solving the deterministic system operation cost minimization problem to obtain a low-carbon demand response strategy, and implementing the low-carbon demand response strategy by the power system to realize low-carbon control and operation of the power system. The technical problems that in the prior art, prediction errors are large, practical practicability is not achieved, and accuracy is low are solved.

Description

Technical Field

[0001] The present invention relates to the field of carbon emissions in power systems, and in particular to a method and device for applying a low-carbon demand response strategy. Background Art

[0002] Regarding global warming and climate change issues, there is an urgent need to replace carbon-intensive and highly polluting fossil energy with low-carbon and clean renewable energy (RES) in the power system.

[0003] At present, demand response strategies are mainly used in power demand response plans, and the impact of system strategies on carbon emissions has not been considered for the time being. The uncertainty associated with distributed energy on the demand side is crucial to improving the adaptability of RES to the power system. The representative methods for dealing with uncertainty are stochastic optimization (SO) methods, chance constraint (CC) methods, and robust optimization (RO) methods. The SO method is usually very intuitive because it considers random scenarios of uncertainty. Due to the inherent volatility and randomness of DER on the demand side, prediction errors are inevitable; the CC method assumes that uncertainty follows certain known probability distributions and allows violation of uncertainty constraints with a predefined probability. Compared with the SO method, the CC method can obtain better computational efficiency. However, like the SO method, the effectiveness of the CC method also depends on accurate probability distributions, which is difficult to obtain in practical applications; the RO method does not require any prior information about uncertainty, and it considers the worst case in the fuzzy set to ensure robustness. However, the RO method often derives overly conservative strategies because it only considers the worst case, and in practical applications, the worst case is usually a low-probability case, which leads to the current inability to handle uncertainty in low-carbon demand response strategies for carbon emissions. At the same time, the handling of uncertainty also has the problem of large prediction errors and lack of practical application prospects.

[0004] Therefore, there is an urgent need for an application method that can reduce the prediction error of low-carbon demand response strategies and improve their practical applicability and accuracy. Summary of the invention

[0005] The present invention provides a method and device for applying a low-carbon demand response strategy to solve the technical problems in the prior art of large prediction error, lack of practical applicability and low accuracy.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for applying a low-carbon demand response strategy, including:

[0007] Acquire power history data of renewable energy in the power system, obtain moment information based on the power history data, and then construct corresponding fuzzy sets based on the moment information;

[0008] Obtaining information data of distributed generators, renewable energy, loads and carbon trading in the power system, and constructing a system operation cost minimization problem considering uncertainty based on the information data of distributed generators, renewable energy, loads and carbon trading;

[0009] According to the fuzzy set, the information data of distributed generators, renewable energy, loads and carbon trading are constrained to obtain constraint conditions, and according to the constraint conditions, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem; wherein the constraint conditions exist in all distributions of the fuzzy set;

[0010] The deterministic system operation cost minimization problem is solved to obtain a low-carbon demand response strategy, and the low-carbon control and operation of the power system is achieved by executing the low-carbon demand response strategy through the power system.

[0011] As a preferred solution, the method of acquiring power history data of renewable energy in the power system, obtaining moment information based on the power history data, and then constructing a corresponding fuzzy set based on the moment information specifically includes:

[0012] Acquire initial power history data of renewable energy in the power system, and preprocess the initial power history data to obtain power history data;

[0013] According to a preset moment type, moment information of a power prediction error of renewable energy is calculated by using the power history data;

[0014] The probability distribution corresponding to the moment information is calculated, and the corresponding fuzzy set is constructed and generated according to the moment information and its corresponding probability distribution.

[0015] As a preferred solution, the information data of distributed generators, renewable energy, loads and carbon trading in the power system is obtained, and based on the information data of distributed generators, renewable energy, loads and carbon trading, a system operation cost minimization problem considering uncertainty is constructed, which specifically includes:

[0016] Obtain information data of distributed generators, renewable energy, loads and carbon trading in the power system; wherein the information data of the distributed generators include: operating costs, power generation, power generation costs and standby costs of the distributed generators; the information data of the renewable energy include: power reduction and compensation costs of renewable energy; the data information of the loads include: load incompatibility costs and adjustment power; the information data of the carbon trading include: carbon trading prices and carbon trading volume ranges;

[0017] According to the operating cost, power generation, power generation cost and standby cost of the distributed generator, the operating cost of the distributed generator is constructed;

[0018] According to the reduced power and reduced compensation cost of the renewable energy, a reduced cost of the renewable energy is constructed;

[0019] Constructing a load adjustment cost of the load according to the load incompatibility cost and the adjustment power of the load;

[0020] According to the carbon trading price and carbon trading volume range of the carbon trading, the carbon trading cost is constructed;

[0021] According to the operating cost of the distributed generator, the reduction cost of the renewable energy, the load adjustment cost and the carbon trading cost, a system operating cost minimization function considering uncertainty is obtained; wherein, the system operating cost minimization function considering uncertainty is used to characterize the system operating cost minimization problem considering uncertainty.

[0022] As a preferred solution, the information data of distributed generators, renewable energy, loads and carbon trading are constrained according to the fuzzy sets to obtain constraint conditions, and according to the constraint conditions, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem, specifically including:

[0023] Based on the fuzzy set, according to the generated power, ramp power and reserve power of the distributed generator, the rescheduling constraint, the generated power constraint, the upward reserve power constraint, the downward reserve power constraint, the upward ramp power constraint, the downward ramp power constraint and the rescheduling coefficient constraint of the distributed generator are obtained;

[0024] Based on the fuzzy set, according to the limited power and predicted power of the renewable energy, the operation constraint of the renewable energy is obtained;

[0025] Based on the fuzzy set, according to the adjustment power of the distributed generator and its upper and lower bounds, as well as the power, total energy demand and maximum discomfort tolerance of the load, the load power constraint, the adjustment power limit constraint of the distributed generator corresponding to the load, the total energy demand constraint of the load and the load power regulation discomfort tolerance constraint are obtained;

[0026] Based on the fuzzy set, according to the initial free carbon emission quota of the carbon trading, the initial carbon emission quota coefficient, the predicted power of the load, and the power generation power of the distributed generator, the initial free carbon emission quota constraint, the actual carbon emission constraint and the carbon trading constraint are obtained;

[0027] Based on the fuzzy set, grid operation constraints are obtained according to the injected power of the distributed generators and renewable energy, as well as load power constraints, re-dispatching power, limited power of renewable energy and predicted power;

[0028] Based on the fuzzy set, according to the grid operation constraint, the line power lower limit and the upper limit, a branch power limit constraint, a reserved power constraint and a rescheduling power constraint are obtained, and a convex reconstruction is performed on the branch power limit constraint, the reserved power constraint and the rescheduling power constraint;

[0029] According to the rescheduling constraints, power generation constraints, upward reserve power constraints, downward reserve power constraints, upward ramp power constraints, downward ramp power constraints, rescheduling coefficient constraints, renewable energy operation constraints, load power constraints, load corresponding distributed generator adjustment power limit constraints, load total energy demand constraints, load power regulation inappropriateness tolerance constraints, initial free carbon emission quota constraints, actual carbon emission constraints, carbon trading constraints and grid operation constraints, as well as the branch power limit constraints, retained power constraints and rescheduling power constraints after convex reconstruction, the system operation cost minimization problem considering uncertainty is converted into a deterministic system operation cost minimization problem.

[0030] As a preferred solution, the convex reconstruction of the branch power limit constraint, the reserved power constraint and the rescheduling power constraint specifically includes:

[0031] Combining the branch power limit constraint, the reserved power constraint and the rescheduling power constraint in a compact form to obtain a constraint compact form;

[0032] According to the preset symmetry factor, the constraint compact form is rewritten to obtain the constraint symmetry form, and then the constraint symmetry form is convexly relaxed to obtain the convex reconstruction constraint, which serves as the branch power limit constraint, retention power constraint and rescheduling power constraint after convex reconstruction.

[0033] As a preferred solution, the deterministic system operation cost minimization problem is solved to obtain a low-carbon demand response strategy, and the low-carbon demand response strategy is executed by the power system to achieve low-carbon control and operation of the power system, specifically including:

[0034] Solving the deterministic system operation cost minimization problem, obtaining power execution data of distributed generators and renewable energy in the power system, and data corresponding to loads and carbon trading;

[0035] The power execution data of the distributed generators and renewable energy, as well as the data corresponding to the load and carbon trading are input into the power system, so that the power system can perform power control and execution on the current distributed generators and renewable energy according to the power execution data of the distributed generators and renewable energy, thereby realizing low-carbon control and operation of the power system.

[0036] Accordingly, the present invention also provides a device for applying a low-carbon demand response strategy, comprising: an acquisition module, a construction module, a constraint module and a solution module;

[0037] The acquisition module is used to acquire the power history data of the renewable energy in the power system, and obtain the moment information according to the power history data, and then construct the corresponding fuzzy set according to the moment information;

[0038] The construction module is used to obtain information data of distributed generators, renewable energy, loads and carbon trading in the power system, and construct a system operation cost minimization problem considering uncertainty based on the information data of distributed generators, renewable energy, loads and carbon trading;

[0039] The constraint module is used to constrain the information data of distributed generators, renewable energy, loads and carbon trading according to the fuzzy set to obtain constraint conditions, and according to the constraint conditions, transform the system operation cost minimization problem considering uncertainty into a deterministic system operation cost minimization problem; wherein the constraint conditions exist in all distributions of the fuzzy set;

[0040] The solving module is used to solve the deterministic system operation cost minimization problem to obtain a low-carbon demand response strategy, and implement low-carbon control and operation of the power system through the execution of the low-carbon demand response strategy by the power system.

[0041] As a preferred solution, the deterministic system operation cost minimization problem is solved to obtain a low-carbon demand response strategy, and the low-carbon demand response strategy is executed by the power system to achieve low-carbon control and operation of the power system, specifically including:

[0042] Solving the deterministic system operation cost minimization problem, obtaining power execution data of distributed generators and renewable energy in the power system, and data corresponding to loads and carbon trading;

[0043] The power execution data of the distributed generators and renewable energy, as well as the data corresponding to the load and carbon trading are input into the power system, so that the power system can perform power control and execution on the current distributed generators and renewable energy according to the power execution data of the distributed generators and renewable energy, thereby realizing low-carbon control and operation of the power system.

[0044] Correspondingly, the present invention also provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for applying a low-carbon demand response strategy as described in any one of the above items is implemented.

[0045] Accordingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method of applying a low-carbon demand response strategy as described in any one of the above.

[0046] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0047] The technical solution of the present invention obtains the power history data of renewable energy in the power system to obtain the corresponding moment information to construct the corresponding fuzzy set, and then obtains the information data of distributed generators, renewable energy, loads and carbon trading in the power system, and constructs the system operation cost minimization problem considering uncertainty, so that the corresponding constraint conditions can be generated based on the fuzzy set, and the system operation cost minimization problem considering uncertainty is converted into a deterministic system operation cost minimization problem, avoiding the prediction error existing in the processing of uncertainty problems, thereby improving the accuracy of the calculated low-carbon demand response strategy to be operated, and finally solving the deterministic system operation cost minimization problem to obtain the low-carbon demand response strategy, so that the power system executes the low-carbon demand response strategy, thereby realizing the low-carbon operation of the power system, and also avoiding the overly conservative strategy that considers the worst result in the fuzzy set, and improving the actual operation reliability and practicality of the low-carbon demand response strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 : A flowchart of a method for applying a low-carbon demand response strategy provided by an embodiment of the present invention;

[0049] Figure 2 : A structural diagram of a device for applying a low-carbon demand response strategy provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1

[0052] Please refer to Figure 1 , a method for applying a low-carbon demand response strategy provided by an embodiment of the present invention, comprising the following steps S101-S104:

[0053] Step S101: Obtain historical power data of renewable energy in the power system, obtain moment information based on the historical power data, and then construct a corresponding fuzzy set based on the moment information.

[0054] As a preferred solution of this embodiment, the step of acquiring historical power data of renewable energy in the power system, obtaining moment information according to the historical power data, and then constructing a corresponding fuzzy set according to the moment information specifically includes:

[0055] Acquire initial power history data of renewable energy in the power system, and preprocess the initial power history data to obtain power history data; calculate the moment information of the power prediction error of renewable energy through the power history data according to a preset moment type; calculate the probability distribution corresponding to the moment information, and construct and generate a corresponding fuzzy set based on the moment information and its corresponding probability distribution.

[0056] In this embodiment, the power history data of renewable energy in the power system includes historical wind power data, historical solar power data, historical hydropower power data, historical geothermal power data, historical ocean power data and historical biomass power data. By acquiring the power history data of renewable energy, data related to random variables are obtained, and then the power history data is pre-initialized, including but not limited to data cleaning, data removal and data interpolation, so that the moment information of the power prediction error of renewable energy can be calculated through the power history data according to the preset moment type, and the moment information can be mean, variance, high-order moment, etc. Among them, the preset moment types include central moment, origin moment, standard moment, cumulative moment, factorial moment and absolute moment, which can be selected according to actual needs and problems to be solved.

[0057] In this embodiment, since the moment information can provide a basic description of the distribution characteristics of the random variable, the probability distribution corresponding to the moment information can be obtained through the moment information of the power prediction error of the renewable energy, and combined with the estimated moment information, a fuzzy set defined by the first-order and second-order moment information can be defined. This fuzzy set contains all probability distributions that are consistent with the given moment information. It can be understood that the fuzzy set The potential possible probability distribution of RES power prediction errors can be bounded.

[0058] In this embodiment, the fuzzy set The expression is:

[0059]

[0060] Where t represents time, R represents renewable energy, is the probability distribution of the RES (renewable energy) power prediction error at time slot t, represents the expectation operator, is the RES power prediction error at time node t. and They are The mean vector and covariance matrix of .

[0061] Step S102: Obtain information data on distributed generators, renewable energy, loads and carbon trading in the power system, and construct a system operation cost minimization problem taking into account uncertainty based on the information data on distributed generators, renewable energy, loads and carbon trading.

[0062] As a preferred solution of this embodiment, the information data of distributed generators, renewable energy, loads and carbon trading in the power system is obtained, and based on the information data of distributed generators, renewable energy, loads and carbon trading, a system operation cost minimization problem considering uncertainty is constructed, which specifically includes:

[0063] The information data of the distributed generator, renewable energy, load and carbon trading in the power system are obtained; wherein the information data of the distributed generator includes: the operating cost, power generation, power generation cost and standby cost of the distributed generator; the information data of the renewable energy includes: the power reduction and compensation cost of the renewable energy; the data information of the load includes: load incompatibility cost and adjustment power; the information data of the carbon trading includes: carbon trading price and carbon trading volume range; the operating cost of the distributed generator is constructed according to the operating cost, power generation, power generation cost and standby cost of the distributed generator; the renewable energy reduction cost is constructed according to the power reduction and compensation cost of the renewable energy; the load adjustment cost of the load is constructed according to the load incompatibility cost and adjustment power of the load; the carbon trading cost is constructed according to the carbon trading price and carbon trading volume range of the carbon trading; the system operation cost minimization function considering uncertainty is obtained according to the operating cost of the distributed generator, the renewable energy reduction cost, the load adjustment cost and the carbon trading cost; wherein the system operation cost minimization function considering uncertainty is used to characterize the system operation cost minimization problem considering uncertainty.

[0064] In this embodiment, information data of distributed generators, renewable energy, loads and carbon trading in the power system are obtained, and then the system operation cost minimization problem considering uncertainty can be constructed. The expression of the system operation cost minimization problem considering uncertainty is:

[0065]

[0066] Among them, f Sys is the total cost of operating the system, is a set of times within the operating range, f t G is the operating cost of a conventional distributed generator (DG) at time t, f t R is RES cost reduction, f t L is the load adjustment cost, f t C is the carbon trading cost in period t. t G is the DG power generation, T is the transposed sign, is the coefficient matrix of linear and quadratic terms of DG power generation cost. R is the DG standby cost coefficient matrix, are the upward reserve power and downward reserve power vector of DG at time t respectively. R,cut , C L,disare the RES reduction cost coefficient matrix and the load incompatibility compensation cost coefficient matrix, P t R,cut is the RES power reduction vector at time t, ΔP t L is the adjusted power vector of the load at time t.

[0067]

[0068] in, is the carbon trading price at time t, δ is the carbon trading volume interval, λ and ρ are the carbon trading reward price coefficient and penalty price coefficient respectively, is the system carbon trading volume at time t.

[0069] Step S103: According to the fuzzy set, the information data of distributed generators, renewable energy, loads and carbon trading are constrained to obtain constraint conditions, and according to the constraint conditions, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem; wherein the constraint conditions exist in all distributions of the fuzzy set.

[0070] As a preferred solution of this embodiment, the information data of distributed generators, renewable energy, loads and carbon trading are constrained according to the fuzzy sets to obtain constraint conditions, and according to the constraint conditions, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem, which specifically includes:

[0071] Based on the fuzzy set, according to the generated power, ramp power and reserve power of the distributed generator, the rescheduling constraint, generated power constraint, upward reserve power constraint, downward reserve power constraint, upward ramp power constraint, downward ramp power constraint and rescheduling coefficient constraint of the distributed generator are obtained; based on the fuzzy set, according to the limited power and predicted power of the renewable energy, the operation constraint of the renewable energy is obtained; based on the fuzzy set, according to the adjusted power and its upper and lower bounds of the distributed generator, and the power, total energy demand and maximum discomfort tolerance of the load, the load power constraint, the adjusted power limit constraint of the load corresponding to the distributed generator, the total energy demand constraint of the load and the load power regulation discomfort tolerance constraint are obtained; based on the fuzzy set, according to the initial free carbon emission quota of the carbon trading, the initial carbon emission quota coefficient, the predicted power of the load and the generated power of the distributed generator, the initial free carbon emission quota constraint, the actual carbon emission constraint and the carbon trading constraint are obtained; based on the fuzzy set, according to the distributed generator and the renewable energy The injected power, as well as the load power constraint, the re-dispatching power, the limited power and the predicted power of renewable energy are obtained to obtain the grid operation constraint: based on the fuzzy set, according to the grid operation constraint, the line power lower limit and the upper limit, the branch power limit constraint, the reserved power constraint and the re-dispatching power constraint are obtained, and the branch power limit constraint, the reserved power constraint and the re-dispatching power constraint are convexly reconstructed; according to the re-dispatching constraint, the power generation constraint, the upward reserve power constraint, the downward reserve power constraint, the upward ramp power constraint, the downward ramp power constraint, the re-dispatching coefficient constraint, the operation constraint of renewable energy, the load power constraint, the load corresponding to the distributed generator adjustment power limit constraint, the total energy demand constraint of the load, the load power regulation inappropriate tolerance constraint, the initial free carbon emission quota constraint, the actual carbon emission constraint, the carbon trading constraint and the grid operation constraint, as well as the branch power limit constraint, the reserved power constraint and the re-dispatching power constraint after convex reconstruction, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem.

[0072] In this embodiment, the constraints include rescheduling constraints, power generation constraints, upward reserve power constraints, downward reserve power constraints, upward ramp power constraints, downward ramp power constraints, rescheduling coefficient constraints, renewable energy operation constraints, load power constraints, load corresponding distributed generator adjustment power limit constraints, total energy demand constraints of the load, load power regulation inappropriate tolerance constraints, initial free carbon emission quota constraints, actual carbon emission constraints, carbon trading constraints and grid operation constraints.

[0073] In this embodiment, the rescheduling constraint, generation power constraint, upward reserve power constraint, downward reserve power constraint, upward ramp power constraint, downward ramp power constraint and rescheduling coefficient constraint of the distributed generator can be obtained through the generation power, ramp power and reserve power of the distributed generator.

[0074] Among them, the operating constraints of distributed generators DG are as follows:

[0075]

[0076]

[0077]

[0078] Where, formula (3a) is the rescheduling constraint of DG, R t G is the rescheduling power vector of DG at time t, d t G is the DG rescheduling participation coefficient vector, and e is a column vector of 1. Formula (3b) is the DG power generation constraint at time t, P DG and are the lower bound matrix and upper bound matrix of DG power generation, respectively. Equations (3c) and (3d) are the upward ramp power constraint and downward ramp power constraint of DG at time slot t, respectively. Equations (3c) and (3d) are the upward reserve power constraint and downward reserve power constraint of DG, respectively. and are the upper bound matrix of DG upward reserve power and the upper bound matrix of downward reserve power, respectively. Equation (3e) and Equation (3f) are the upper ramp power constraint and the downward ramp power constraint of DG, respectively. and are the DG generation power, downward reserve power vector and upward reserve power vector at t-1, and are the DG upward ramp power upper bound matrix and the downward ramp power upper bound matrix respectively. Equation (3g) is the rescheduling coefficient constraint of DG at time t.

[0079] In this embodiment, the operation constraints of the renewable energy are obtained according to the limited power and predicted power of the renewable energy:

[0080]

[0081] Where, formula (4) is the power limit constraint of RES at time t, where P t R,cut is the RES power limit vector at time t. tR,fore is the predicted power of the RES node.

[0082] In this embodiment, according to the adjustment power of the distributed generator and its upper and lower bounds, as well as the power, total energy demand and maximum discomfort tolerance of the load, the load power constraint, the adjustment power limit constraint of the distributed generator corresponding to the load, the total energy demand constraint of the load and the load power regulation discomfort tolerance constraint are obtained:

[0083] P t FL =ΔP t L +P t L,fore (5a)

[0084]

[0085] Where, (5a) is the load power constraint after FL adjustment at time t, P t FL and ΔP t L They are FL (flexible load, both loads in this embodiment) power vector and DR adjustment power vector at time t, P t L,fore is the FL predicted power. (5b) is the DR adjusted power limit constraint of FL at time t, Δ P t FL and are the lower bound matrix and upper bound matrix of DR node power adjustment, respectively. (5c) can ensure that the total energy demand of FL can still be met after load power adjustment. (5d) is the load power adjustment incompatibility tolerance constraint of FL, where is the maximum discomfort tolerance vector of the load node.

[0086] In this embodiment, the initial free carbon emission quota constraint, the actual carbon emission constraint and the carbon trading constraint are obtained according to the initial free carbon emission quota of the carbon trading, the initial carbon emission quota coefficient, the predicted power of the load, and the power generation power of the distributed generator:

[0087]

[0088] Among them, the ladder-type carbon trading scheme describes the carbon trading constraints, (6a) is the initial free carbon emission quota of the system at time t Constraints, where is the system initial carbon emission quota coefficient, i is the bus i in the power system, It is the busbar group in the power system. is the predicted power of FL at bus i at time t. (6b) is the actual carbon emission of the system at time t constraint, is the carbon emission coefficient of the DG connected to bus i, is the DG power generation at bus i. (6c) is the carbon trading of the system at time t constraint.

[0089] In this embodiment, the grid operation constraints are obtained according to the injected power of the distributed generators and renewable energy, as well as the load power constraint, the re-dispatching power, the limited power of renewable energy and the predicted power:

[0090]

[0091] Where, (7a) is the node injection power constraint of the system at time t, P t inj is the node injection power, SF G , SF R and SF L They are the DG power offset factor matrix, RES power offset factor matrix and load power offset factor matrix respectively. (7b) is the branch power constraint of the system. (7c) is the system power balance constraint.

[0092] In this embodiment, based on the fuzzy set, according to the grid operation constraint, the line power lower limit and upper limit, the branch power limit constraint, the reserved power constraint and the rescheduling power constraint are obtained:

[0093]

[0094] Wherein, Equation (8a) is the DRCC (Distributionally Robust Chance-Constrained) branch power limit constraint of the system at time t, ε line is the system risk preference parameter for branch power constraints, P line and are the lower and upper limits of the line power respectively, A is the node injection power offset factor matrix, P t inj is the node injection power. Equation (8b) is the DRCC reserved power constraint of DG at time t, ε G,R is the system risk preference parameter of the DG's backup power constraint. Equation (8c) is the DRCC rescheduling power constraint of the DG at time t, ε G,P is the system risk preference parameter for DG rescheduling power limit. System risk preference parameter ε line , ε G,R and εG,P They represent the corresponding DRCC system operation constraints, i.e., the expected confidence levels (i.e., the allowed constraint violation probabilities) of equations (8a)-(8c).

[0095] As a preferred solution of this embodiment, the convex reconstruction of the branch power limit constraint, the reserved power constraint and the rescheduling power constraint specifically includes:

[0096] The branch power limit constraint, the reserved power constraint and the rescheduling power constraint are combined in a compact form to obtain a constraint compact form; according to a preset symmetry factor, the constraint compact form is rewritten to obtain a constraint symmetric form, and then the constraint symmetric form is convexly relaxed to obtain a convex reconstruction constraint, which serves as the branch power limit constraint, the reserved power constraint and the rescheduling power constraint after convex reconstruction.

[0097] In this embodiment, the DRCC operation constraints (8a)-(8c) can be reformulated as convex constraints by using the SOC-based convex reconstruction technique as follows:

[0098]

[0099] Wherein, Equation (9a) is a compact form of the DRCC operation constraints (8a) to (8c), c (x) and u c (x) are the affine images of the lower and upper bounds of the decision variable x of the cth DRCC operation constraint at time t, respectively. c (x) and b c (x) are the first affine image and the second affine image of the decision variable x bound of the cth DRCC operation constraint at time t, respectively. is the set of DRCC operation constraints, ε c is the risk parameter of the cth DRCC operation constraint. By introducing the definition in equation (9b) and Equation (9a) can be rewritten as the symmetric form in Equation (9c). Finally, the convex reconstruction constraint of Equation (9a) based on SOC is shown in Equation (9d), where z c / q c is an auxiliary variable.

[0100] In this embodiment, a two-sided DRCC (TS-DRCC) method is used to deal with the uncertainty problem, and in order to transform the intractable implicit distributional robust chance constraints into tractable and explicit chance constraints, a convex reconstruction method based on SOC (Second-Order Cone) is used to convexify and approximate these constraints, thereby improving the accuracy of the conversion uncertainty problem.

[0101] Step S104: Solve the deterministic system operation cost minimization problem to obtain a low-carbon demand response strategy, and implement low-carbon control and operation of the power system by executing the low-carbon demand response strategy through the power system.

[0102] As a preferred solution of this embodiment, the deterministic system operation cost minimization problem is solved to obtain a low-carbon demand response strategy, and the low-carbon demand response strategy is executed by the power system to achieve low-carbon control and operation of the power system, specifically including:

[0103] The deterministic system operation cost minimization problem is solved to obtain power execution data of distributed generators and renewable energy in the power system, as well as data corresponding to loads and carbon trading; the power execution data of distributed generators and renewable energy, as well as data corresponding to loads and carbon trading are input into the power system, so that the power system performs power control and execution on the current distributed generators and renewable energy according to the power execution data of distributed generators and renewable energy, thereby realizing low-carbon control and operation of the power system.

[0104] In this embodiment, the power execution data, load data and carbon trading data of distributed generators and renewable energy are input into the power system, and power control and execution are performed based on this, and the power execution data of distributed generators and renewable energy are adjusted according to real-time data to achieve low-carbon control and operation of the power system, thereby achieving efficient and accurate execution of low-carbon demand response strategies.

[0105] Implementing the above embodiments has the following effects:

[0106] The technical solution of the present invention obtains the power history data of renewable energy in the power system to obtain the corresponding moment information to construct the corresponding fuzzy set, and then obtains the information data of distributed generators, renewable energy, loads and carbon trading in the power system, and constructs the system operation cost minimization problem considering uncertainty, so that the corresponding constraint conditions can be generated based on the fuzzy set, and the system operation cost minimization problem considering uncertainty is converted into a deterministic system operation cost minimization problem, avoiding the prediction error existing in the processing of uncertainty problems, thereby improving the accuracy of the calculated low-carbon demand response strategy to be operated, and finally solving the deterministic system operation cost minimization problem to obtain the low-carbon demand response strategy, so that the power system executes the low-carbon demand response strategy, thereby realizing the low-carbon operation of the power system, and also avoiding the overly conservative strategy that considers the worst result in the fuzzy set, and improving the actual operation reliability and practicality of the low-carbon demand response strategy.

[0107] Embodiment 2

[0108] See also Figure 2 , the present invention also provides a device for applying a low-carbon demand response strategy, comprising: an acquisition module 201, a construction module 202, a constraint module 203 and a solution module 204;

[0109] The acquisition module 201 is used to acquire power history data of renewable energy in the power system, and obtain moment information according to the power history data, and then construct a corresponding fuzzy set according to the moment information;

[0110] The construction module 202 is used to obtain information data of distributed generators, renewable energy, loads and carbon trading in the power system, and construct a system operation cost minimization problem considering uncertainty based on the information data of distributed generators, renewable energy, loads and carbon trading;

[0111] The constraint module 203 is used to constrain the information data of distributed generators, renewable energy, loads and carbon trading according to the fuzzy set to obtain constraint conditions, and according to the constraint conditions, transform the system operation cost minimization problem considering uncertainty into a deterministic system operation cost minimization problem; wherein the constraint conditions exist in all distributions of the fuzzy set;

[0112] The solving module 204 is used to solve the deterministic system operation cost minimization problem to obtain a low-carbon demand response strategy, and implement low-carbon control and operation of the power system through the execution of the low-carbon demand response strategy by the power system.

[0113] As a preferred solution, the method of acquiring power history data of renewable energy in the power system, obtaining moment information based on the power history data, and then constructing a corresponding fuzzy set based on the moment information specifically includes:

[0114] Acquire initial power history data of renewable energy in the power system, and preprocess the initial power history data to obtain power history data;

[0115] According to a preset moment type, moment information of a power prediction error of renewable energy is calculated by using the power history data;

[0116] The probability distribution corresponding to the moment information is calculated, and the corresponding fuzzy set is constructed and generated according to the moment information and its corresponding probability distribution.

[0117] As a preferred solution, the information data of distributed generators, renewable energy, loads and carbon trading in the power system is obtained, and based on the information data of distributed generators, renewable energy, loads and carbon trading, a system operation cost minimization problem considering uncertainty is constructed, which specifically includes:

[0118] Obtain information data of distributed generators, renewable energy, loads and carbon trading in the power system; wherein the information data of the distributed generators include: operating costs, power generation, power generation costs and standby costs of the distributed generators; the information data of the renewable energy include: power reduction and compensation costs of renewable energy; the data information of the loads include: load incompatibility costs and adjustment power; the information data of the carbon trading include: carbon trading prices and carbon trading volume ranges;

[0119] According to the operating cost, power generation, power generation cost and standby cost of the distributed generator, the operating cost of the distributed generator is constructed;

[0120] According to the reduced power and reduced compensation cost of the renewable energy, a reduced cost of the renewable energy is constructed;

[0121] Constructing a load adjustment cost of the load according to the load incompatibility cost and the adjustment power of the load;

[0122] According to the carbon trading price and carbon trading volume range of the carbon trading, the carbon trading cost is constructed;

[0123] According to the operating cost of the distributed generator, the reduction cost of the renewable energy, the load adjustment cost and the carbon trading cost, a system operating cost minimization function considering uncertainty is obtained; wherein, the system operating cost minimization function considering uncertainty is used to characterize the system operating cost minimization problem considering uncertainty.

[0124] As a preferred solution, the information data of distributed generators, renewable energy, loads and carbon trading are constrained according to the fuzzy sets to obtain constraint conditions, and according to the constraint conditions, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem, specifically including:

[0125] Based on the fuzzy set, according to the generated power, ramp power and reserve power of the distributed generator, the rescheduling constraint, the generated power constraint, the upward reserve power constraint, the downward reserve power constraint, the upward ramp power constraint, the downward ramp power constraint and the rescheduling coefficient constraint of the distributed generator are obtained;

[0126] Based on the fuzzy set, according to the limited power and predicted power of the renewable energy, the operation constraint of the renewable energy is obtained;

[0127] Based on the fuzzy set, according to the adjustment power of the distributed generator and its upper and lower bounds, as well as the power, total energy demand and maximum discomfort tolerance of the load, the load power constraint, the adjustment power limit constraint of the distributed generator corresponding to the load, the total energy demand constraint of the load and the load power regulation discomfort tolerance constraint are obtained;

[0128] Based on the fuzzy set, according to the initial free carbon emission quota of the carbon trading, the initial carbon emission quota coefficient, the predicted power of the load, and the power generation power of the distributed generator, the initial free carbon emission quota constraint, the actual carbon emission constraint and the carbon trading constraint are obtained;

[0129] Based on the fuzzy set, the grid operation constraints are obtained according to the injected power of the distributed generators and renewable energy, as well as the load power constraints, the re-dispatching power, the limited power of renewable energy and the predicted power:

[0130] Based on the fuzzy set, according to the grid operation constraint, the line power lower limit and the upper limit, a branch power limit constraint, a reserved power constraint and a rescheduling power constraint are obtained, and a convex reconstruction is performed on the branch power limit constraint, the reserved power constraint and the rescheduling power constraint;

[0131] According to the rescheduling constraints, power generation constraints, upward reserve power constraints, downward reserve power constraints, upward ramp power constraints, downward ramp power constraints, rescheduling coefficient constraints, renewable energy operation constraints, load power constraints, load corresponding distributed generator adjustment power limit constraints, load total energy demand constraints, load power regulation inappropriateness tolerance constraints, initial free carbon emission quota constraints, actual carbon emission constraints, carbon trading constraints and grid operation constraints, as well as the branch power limit constraints, retained power constraints and rescheduling power constraints after convex reconstruction, the system operation cost minimization problem considering uncertainty is converted into a deterministic system operation cost minimization problem.

[0132] As a preferred solution, the convex reconstruction of the branch power limit constraint, the reserved power constraint and the rescheduling power constraint specifically includes:

[0133] Combining the branch power limit constraint, the reserved power constraint and the rescheduling power constraint in a compact form to obtain a constraint compact form;

[0134] According to the preset symmetry factor, the constraint compact form is rewritten to obtain the constraint symmetry form, and then the constraint symmetry form is convexly relaxed to obtain the convex reconstruction constraint, which serves as the branch power limit constraint, retention power constraint and rescheduling power constraint after convex reconstruction.

[0135] As a preferred solution, the deterministic system operation cost minimization problem is solved to obtain a low-carbon demand response strategy, and the low-carbon demand response strategy is executed by the power system to achieve low-carbon control and operation of the power system, specifically including:

[0136] Solving the deterministic system operation cost minimization problem, obtaining power execution data of distributed generators and renewable energy in the power system, and data corresponding to loads and carbon trading;

[0137] The power execution data of the distributed generators and renewable energy, as well as the data corresponding to the load and carbon trading are input into the power system, so that the power system can perform power control and execution on the current distributed generators and renewable energy according to the power execution data of the distributed generators and renewable energy, thereby realizing low-carbon control and operation of the power system.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0139] Implementing the above embodiments has the following results:

[0140] The technical solution of the present invention obtains the power history data of renewable energy in the power system to obtain the corresponding moment information to construct the corresponding fuzzy set, and then obtains the information data of distributed generators, renewable energy, loads and carbon trading in the power system, and constructs the system operation cost minimization problem considering uncertainty, so that the corresponding constraint conditions can be generated based on the fuzzy set, and the system operation cost minimization problem considering uncertainty is converted into a deterministic system operation cost minimization problem, avoiding the prediction error existing in the processing of uncertainty problems, thereby improving the accuracy of the calculated low-carbon demand response strategy to be operated, and finally solving the deterministic system operation cost minimization problem to obtain the low-carbon demand response strategy, so that the power system executes the low-carbon demand response strategy, thereby realizing the low-carbon operation of the power system, and also avoiding the overly conservative strategy that considers the worst result in the fuzzy set, thereby improving the actual operation reliability and practicality of the low-carbon demand response strategy.

[0141] Embodiment 3

[0142] Correspondingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for applying the low-carbon demand response strategy as described in any one of the above embodiments is implemented.

[0143] The terminal device of this embodiment includes: a processor, a memory, and a computer program and a computer instruction stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned embodiment 1 is implemented, for example: Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment are implemented, such as constructing module 202.

[0144] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the construction module 202 is used to obtain information data of distributed generators, renewable energy, loads and carbon trading in the power system, and construct a system operation cost minimization problem considering uncertainty based on the information data of the distributed generators, renewable energy, loads and carbon trading.

[0145] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0146] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0147] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0148] Wherein, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0149] Embodiment 4

[0150] Accordingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method of applying the low-carbon demand response strategy as described in any one of the above embodiments.

[0151] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for applying a low-carbon demand response strategy, It is characterized in that include: Acquire power history data of renewable energy in the power system, obtain moment information based on the power history data, and then construct corresponding fuzzy sets based on the moment information; Obtaining information data of distributed generators, renewable energy, loads and carbon trading in the power system, and constructing a system operation cost minimization problem considering uncertainty based on the information data of distributed generators, renewable energy, loads and carbon trading; According to the fuzzy set, the information data of distributed generators, renewable energy, loads and carbon trading are constrained to obtain constraint conditions, and according to the constraint conditions, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem; wherein the constraint conditions exist in all distributions of the fuzzy set; The deterministic system operation cost minimization problem is solved to obtain a low-carbon demand response strategy, and the low-carbon control and operation of the power system is achieved by executing the low-carbon demand response strategy through the power system.

2. A method for applying a low-carbon demand response strategy as claimed in claim 1, It is characterized in that The acquiring of power history data of renewable energy in the power system, obtaining moment information according to the power history data, and then constructing a corresponding fuzzy set according to the moment information specifically includes: Acquire initial power history data of renewable energy in the power system, and preprocess the initial power history data to obtain power history data; According to a preset moment type, moment information of a power prediction error of renewable energy is calculated by using the power history data; The probability distribution corresponding to the moment information is calculated, and the corresponding fuzzy set is constructed and generated according to the moment information and its corresponding probability distribution.

3. A method for applying a low-carbon demand response strategy as claimed in claim 2, It is characterized in that The obtaining of information data of distributed generators, renewable energy, loads and carbon trading in the power system, and constructing a system operation cost minimization problem taking into account uncertainty based on the information data of distributed generators, renewable energy, loads and carbon trading, specifically includes: Obtain information data of distributed generators, renewable energy, loads and carbon trading in the power system; wherein the information data of the distributed generators include: operating costs, power generation, power generation costs and standby costs of the distributed generators; the information data of the renewable energy include: power reduction and compensation costs of renewable energy; the data information of the loads include: load incompatibility costs and adjustment power; the information data of the carbon trading include: carbon trading prices and carbon trading volume ranges; According to the operating cost, power generation, power generation cost and standby cost of the distributed generator, the operating cost of the distributed generator is constructed; According to the reduced power and reduced compensation cost of the renewable energy, a reduced cost of the renewable energy is constructed; Constructing a load adjustment cost of the load according to the load incompatibility cost and the adjustment power of the load; According to the carbon trading price and carbon trading volume range of the carbon trading, the carbon trading cost is constructed; According to the operating cost of the distributed generator, the reduction cost of the renewable energy, the load adjustment cost and the carbon trading cost, a system operating cost minimization function considering uncertainty is obtained; wherein, the system operating cost minimization function considering uncertainty is used to characterize the system operating cost minimization problem considering uncertainty.

4. A method for applying a low-carbon demand response strategy as claimed in claim 3, It is characterized in that According to the fuzzy set, the information data of distributed generators, renewable energy, loads and carbon trading are constrained to obtain constraint conditions, and according to the constraint conditions, the system operation cost minimization problem considering uncertainty is transformed into a deterministic system operation cost minimization problem, specifically including: Based on the fuzzy set, according to the generated power, ramp power and reserve power of the distributed generator, the rescheduling constraint, the generated power constraint, the upward reserve power constraint, the downward reserve power constraint, the upward ramp power constraint, the downward ramp power constraint and the rescheduling coefficient constraint of the distributed generator are obtained; Based on the fuzzy set, according to the limited power and predicted power of the renewable energy, the operation constraint of the renewable energy is obtained; Based on the fuzzy set, according to the adjustment power of the distributed generator and its upper and lower bounds, as well as the power, total energy demand and maximum discomfort tolerance of the load, the load power constraint, the adjustment power limit constraint of the distributed generator corresponding to the load, the total energy demand constraint of the load and the load power regulation discomfort tolerance constraint are obtained; Based on the fuzzy set, according to the initial free carbon emission quota of the carbon trading, the initial carbon emission quota coefficient, the predicted power of the load, and the power generation power of the distributed generator, the initial free carbon emission quota constraint, the actual carbon emission constraint and the carbon trading constraint are obtained; Based on the fuzzy set, grid operation constraints are obtained according to the injected power of the distributed generators and renewable energy, as well as load power constraints, re-dispatching power, limited power of renewable energy and predicted power; Based on the fuzzy set, according to the grid operation constraint, the line power lower limit and the upper limit, a branch power limit constraint, a reserved power constraint and a rescheduling power constraint are obtained, and a convex reconstruction is performed on the branch power limit constraint, the reserved power constraint and the rescheduling power constraint; According to the rescheduling constraints, power generation constraints, upward reserve power constraints, downward reserve power constraints, upward ramp power constraints, downward ramp power constraints, rescheduling coefficient constraints, renewable energy operation constraints, load power constraints, load corresponding distributed generator adjustment power limit constraints, load total energy demand constraints, load power regulation inappropriateness tolerance constraints, initial free carbon emission quota constraints, actual carbon emission constraints, carbon trading constraints and grid operation constraints, as well as the branch power limit constraints, retained power constraints and rescheduling power constraints after convex reconstruction, the system operation cost minimization problem considering uncertainty is converted into a deterministic system operation cost minimization problem.

5. A method for applying a low-carbon demand response strategy as claimed in claim 4, It is characterized in that The convex reconstruction of the branch power limit constraint, the reserved power constraint and the rescheduling power constraint specifically includes: Combining the branch power limit constraint, the reserved power constraint and the rescheduling power constraint in a compact form to obtain a constraint compact form; According to the preset symmetry factor, the constraint compact form is rewritten to obtain the constraint symmetry form, and then the constraint symmetry form is convexly relaxed to obtain the convex reconstruction constraint, which serves as the branch power limit constraint, retention power constraint and rescheduling power constraint after convex reconstruction.

6. A method for applying a low-carbon demand response strategy as claimed in claim 1, It is characterized in that The method of solving the deterministic system operation cost minimization problem to obtain a low-carbon demand response strategy, and implementing the low-carbon demand response strategy by the power system to achieve low-carbon control and operation of the power system, specifically includes: Solving the deterministic system operation cost minimization problem, obtaining power execution data of distributed generators and renewable energy in the power system, and data corresponding to loads and carbon trading; The power execution data of the distributed generators and renewable energy, as well as the data corresponding to the load and carbon trading are input into the power system, so that the power system can perform power control and execution on the current distributed generators and renewable energy according to the power execution data of the distributed generators and renewable energy, thereby realizing low-carbon control and operation of the power system.

7. A device for applying a low-carbon demand response strategy, It is characterized in that include: Acquisition module, construction module, constraint module and solution module; The acquisition module is used to acquire the power history data of the renewable energy in the power system, and obtain the moment information according to the power history data, and then construct the corresponding fuzzy set according to the moment information; The construction module is used to obtain information data of distributed generators, renewable energy, loads and carbon trading in the power system, and construct a system operation cost minimization problem considering uncertainty based on the information data of distributed generators, renewable energy, loads and carbon trading; The constraint module is used to constrain the information data of distributed generators, renewable energy, loads and carbon trading according to the fuzzy set to obtain constraint conditions, and according to the constraint conditions, transform the system operation cost minimization problem considering uncertainty into a deterministic system operation cost minimization problem; wherein the constraint conditions exist in all distributions of the fuzzy set; The solving module is used to solve the deterministic system operation cost minimization problem to obtain a low-carbon demand response strategy, and implement low-carbon control and operation of the power system through the execution of the low-carbon demand response strategy by the power system.

8. A device for applying a low-carbon demand response strategy as claimed in claim 7, It is characterized in that The method of solving the deterministic system operation cost minimization problem to obtain a low-carbon demand response strategy, and implementing the low-carbon demand response strategy by the power system to achieve low-carbon control and operation of the power system, specifically includes: Solving the deterministic system operation cost minimization problem, obtaining power execution data of distributed generators and renewable energy in the power system, and data corresponding to loads and carbon trading; The power execution data of the distributed generators and renewable energy, as well as the data corresponding to the load and carbon trading are input into the power system, so that the power system can perform power control and execution on the current distributed generators and renewable energy according to the power execution data of the distributed generators and renewable energy, thereby realizing low-carbon control and operation of the power system.

9. A terminal device, It is characterized in that The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for applying a low-carbon demand response strategy as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for applying a low-carbon demand response strategy as described in any one of claims 1 to 6.

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