Transmission and distribution integrated network low-carbon optimization scheduling strategy considering carbon transaction and demand response

Abstract

The invention discloses a transmission and distribution integrated network low-carbon optimization scheduling strategy considering carbon transaction and demand response. The transmission and distribution integrated network low-carbon optimization scheduling strategy considering the carbon transaction and the demand response comprises the following steps: constructing a transmission and distribution collaborative optimization scheduling framework based on a typical distributed structure of ITDNs; analyzing and calculating the transmission and distribution collaborative optimization scheduling model to obtain a transmission and distribution collaborative low-carbon optimization scheduling model under coordination of a demand side management strategy and a carbon transaction mechanism; and carrying out distributed solution on the transmission and distribution collaborative low-carbon optimization scheduling model under coordination of a demand side management strategy and a carbon transaction mechanism by adopting an alternating current optimal power flow to obtain a convex non-convex planning sub-problem of a power transmission network and a power distribution network. According to the invention, through a transmission and distribution cooperation double-layer low-carbon optimization scheduling model giving consideration to "source-load" interaction coordination, a problem of insufficient coordination between a transmission network and an active power distribution network easily caused by a traditional transmission and distribution split scheduling mode in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon optimization scheduling of integrated transmission and distribution networks, and in particular to a low-carbon optimization scheduling strategy for integrated transmission and distribution networks taking into account carbon trading and demand response. Background Art

[0002] With the transformation of energy Internet and the proposal of carbon neutrality goals, the power system structure is undergoing new changes. The access of various renewable energy sources and active loads has significantly enhanced the coupling between the transmission network and the distribution network. The traditional passive distribution network has gradually transformed into an active distribution network, and the energy flow relationship between the transmission network has also changed from one-way flow to two-way flow. These changes have brought new challenges to the dispatching and management of the transmission and distribution networks.

[0003] In traditional dispatching, the transmission network is independently controlled by the transmission system operator, and the distribution network is regarded as a fixed load. The distribution network is independently controlled by the distribution system operator, and the transmission network is equivalent to a fixed power source. This fragmented dispatching method cannot fully utilize the potential of dispatchable resources in each system, and it is easy to cause power imbalance at the common nodes of the transmission and distribution network, transmission line congestion, load loss and other safety issues. It is an inevitable trend for future dispatching to tap the synergy potential between the transmission and distribution networks, formulate reasonable synergy dispatching strategies, and realize two-way interaction between the transmission and distribution networks.

[0004] In the existing technology, the traditional scheduling mode of separated transmission and distribution easily leads to insufficient coordination between the transmission network and the active distribution network, making it difficult to fully absorb renewable energy and affecting the clean and low-carbon operation of the integrated transmission and distribution network. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a low-carbon optimization scheduling strategy for an integrated transmission and distribution network taking into account carbon trading and demand response, through a transmission and distribution collaborative two-layer low-carbon optimization scheduling model that takes into account the interactive coordination of "source-load", so as to solve the problem that the traditional transmission and distribution separation scheduling mode in the prior art easily leads to insufficient coordination between the transmission network and the active distribution network.

[0006] To achieve the above object, the present invention provides the following technical solutions: A low-carbon optimization scheduling strategy for an integrated transmission and distribution network taking into account carbon trading and demand response comprises the following steps: constructing a transmission and distribution coordinated optimization scheduling framework based on a typical distributed structure of ITDNs; analyzing and calculating the transmission and distribution coordinated optimization scheduling model to obtain a transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism; using AC optimal power flow to perform distributed solution on the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism to obtain non-convex planning subproblems of transmission network and distribution network in convex form.

[0007] In a preferred embodiment, the transmission and distribution coordinated optimization scheduling framework is obtained by the following method: obtaining the load status at different times, and according to the load status at different times, deciding the optimal unit power generation plan with the goal of optimizing the total operating cost; based on the optimal unit power generation plan, calculating the node marginal electricity price, and obtaining the transaction price of the transmission and distribution network at the boundary node; constructing a flexible load demand response model and a carbon trading model with the goal of optimizing the economic efficiency of the distribution network, and combining the transaction price of the transmission and distribution network at the boundary node to obtain the plan of the distribution network purchasing electricity from the transmission network; feeding back the plan of the distribution network purchasing electricity from the transmission network to the transmission network, iteratively calculating, and obtaining the global optimal solution.

[0008] In a preferred embodiment, the flexible load demand response model is divided into a transferable load demand response model and a curtailable load demand response model.

[0009] In a preferred embodiment, the carbon trading model is obtained by the following method: obtaining an initial carbon emission quota; obtaining the actual total carbon emissions of the system when renewable energy does not generate carbon emissions during the power generation process; and combining the initial carbon emission quota and the actual total carbon emissions of the system to obtain a carbon trading model.

[0010] In a preferred embodiment, the AC optimal power flow is used to decouple and distribute the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism, specifically: the SDP relaxation of the transmission network and the SOCP relaxation of the distribution network are used to relax the non-convex planning problem of the transmission and distribution network into a convex problem; the non-convex planning problem of the transmission and distribution network relaxed to a convex problem is solved to obtain the non-convex planning sub-problems of the transmission network and the distribution network in convex form.

[0011] In a preferred embodiment, the ATC algorithm is combined to decouple the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism, specifically: assign initial values ​​to the penalty function's first-order multiplier, second-order multiplier, and coupling variables obtained by the transmission network layer from the distribution network layer, and set the number of inner and outer layer loop iterations; after receiving the data transmitted by each active distribution network, the transmission network solves the power generation plan and calculates the calculated power generation plan. The node marginal electricity price of the transmission and distribution boundary node is transmitted to the lower distribution network as the known quantity for parallel optimization of the distribution network layer; each active distribution network solves the optimization scheduling sub-problem and calculates the It is passed to the upper transmission network as a known quantity for transmission network layer optimization; it is determined whether the inner loop and the outer loop converge at the same time. When the inner loop and the outer loop converge at the same time, the optimization ends and the calculation results are output.

[0012] In a preferred embodiment, the determination of whether the inner loop and the outer loop converge simultaneously includes the following situations: when the inner loop does not converge, the inner loop is re-performed; when the inner loop converges but the outer loop does not converge, the penalty function multiplier is updated, and the inner loop is re-performed after the update; when the inner loop and the outer loop converge simultaneously, the optimization ends and the calculation result is output.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through a transmission and distribution coordinated double-layer low-carbon optimization scheduling model that takes into account the interactive coordination of "source-load", the effect of fully absorbing renewable energy is achieved, thereby realizing the efficient coordination of energy and controllable resources in the transmission and distribution network, and effectively solving the problem that the traditional transmission and distribution separation scheduling mode in the existing technology easily leads to insufficient coordination between the transmission network and the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a low-carbon optimization scheduling strategy for an integrated transmission and distribution network taking into account carbon trading and demand response provided in an embodiment of the present application; Figure 2 The structure diagram of the ITDNs low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response provided in the embodiment of the present application; Figure 3 A transmission and distribution coordinated optimization scheduling framework diagram of a low-carbon optimization scheduling strategy system for an integrated transmission and distribution network taking into account carbon trading and demand response provided in an embodiment of the present application; DETAILED DESCRIPTION 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.

[0015] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0016] Embodiment 1, Figure 1 The present invention provides a low-carbon optimization scheduling strategy for an integrated transmission and distribution network taking into account carbon trading and demand response, comprising the following steps: S1 builds a transmission and distribution collaborative optimization scheduling framework based on the typical distributed structure of ITDNs; The typical distributed structure of ITDNs consists of three parts: transmission network, distribution network and border area. Each distribution network is connected to the transmission network through a substation.

[0017] For ITDNs, a global optimization model can be used for analysis and calculation. However, in actual power systems, the transmission and distribution networks belong to different dispatching departments. The privacy of information among various stakeholders in the market environment makes centralized optimization difficult to achieve. The implementation of a distributed collaborative dispatching model is the inevitable choice for the current power system dispatching model.

[0018] The typical distributed structure of ITDNs includes two layers: the transmission network dispatching layer and the distribution network dispatching layer. The transmission network and the distribution network are relatively independent in terms of the affiliation of the stakeholders, and have different dispatching targets. They can make autonomous decisions within the permitted range. At the same time, the power and information interaction on the interconnection lines makes the dispatching results of the transmission and distribution networks influence each other.

[0019] The transmission and distribution coordinated optimization scheduling framework is obtained as follows: Obtaining the load status at different times, and according to the load status at different times, making decisions on the optimal unit power generation plan with the goal of optimizing the total operating cost, and obtaining the power transmitted from the transmission network to the distribution network; Based on the optimal unit power generation plan, the node marginal electricity price is calculated to obtain the transaction price of the transmission and distribution network at the boundary node; With the goal of optimizing the economic efficiency of the distribution network, the node marginal electricity price is used as a guiding signal, and the flexible load demand response model and the carbon trading model are used as means to optimize the state of the internal power source and the load. The optimal scheduling plan is determined by combining the power transmitted from the transmission network to the distribution network and the transaction price of the transmission and distribution network at the boundary node, and the plan for the distribution network to purchase electricity from the transmission network is obtained; The plan of the distribution network purchasing electricity from the transmission network is fed back to the transmission network, and iterative calculation is performed to obtain a global optimal solution.

[0020] It should be noted that since the transmission and distribution networks are connected through substations, there are coupling variables. Based on the idea of ​​distributed decoupling, the transmission network and the distribution network complete power interaction, marginal electricity price transmission and other processes at the contact point, iterate the calculation, and gradually obtain the global optimal solution.

[0021] It should be noted that when dispatching units, the node marginal electricity price will be calculated, that is, the cost of adding one unit of electricity at a specific node (such as a substation, exchange point, etc.), which reflects the electricity supply cost of each node and provides a basis for setting electricity prices in the power market. Based on these dispatch results, the transmission network decides how much electricity to transmit to the distribution network, forms a transaction price, and obtains the node marginal electricity price.

[0022] Demand response is a power system management strategy that makes part of the load a flexible and adjustable resource through incentives or dynamic adjustment of electricity prices, and participates in the operation and regulation of the system. It can better balance the relationship between power supply and demand, improve the utilization efficiency of electric energy, and reduce the overall carbon emissions of the power system. It is an important means that cannot be ignored in the low-carbon development of the power industry.

[0023] The flexible load demand response model is divided into a transferable load demand response model and a curtailable load demand response model, and economic incentives are used to encourage users to adjust their electricity consumption behaviors.

[0024] Transferable load refers to the load whose power and time period can be flexibly adjusted under the premise that the total power consumption remains unchanged, such as electric vehicles. The transferable load demand response model is as follows:

[0025] Where: represents the power after load transfer at time t, represents the power before load transfer at time t, represents the power transferred at time t, represents the upper limit of the transferable power, Represents the lower limit of transferable power.

[0026] It should be noted that the transferable load demand response model ensures that the total power consumption before and after load transfer remains unchanged within the scheduling period.

[0027] The curtailable electric load refers to the load whose electric power consumption can be curtailed within a certain range. The curtailable electric load demand response model is as follows:

[0028] Where: represents the power after load reduction at time t, represents the power before load reduction at time t, represents the maximum power that can be cut at time t, Indicates that the elastic coefficient can be reduced at time t.

[0029] It should be noted that the elastic coefficient can be reduced to a value between 0 and 1 at time t.

[0030] The carbon trading market is an environmental policy tool based on market mechanisms, which is used to control and reduce greenhouse gas emissions. In the carbon trading market of the power industry, the government or regulatory authorities will issue free initial carbon emission quotas based on the system's power generation, and the remaining or insufficient part will be bought and sold in the carbon trading market.

[0031] The carbon trading model is obtained as follows: Obtain initial carbon emission quotas; Obtain the actual total carbon emissions of the system when renewable energy does not produce carbon emissions during the power generation process; Combining the initial carbon emission quota and the actual total carbon emissions of the system, a carbon trading model is obtained.

[0032] According to the positive correlation between the initial carbon emission quota and the total power generation of the system, the initial carbon emission quota is obtained, and the initial carbon emission quota can be expressed as:

[0033] Where: Expressed as carbon emission quota per unit of electricity generation, Indicates the total power generation of the system.

[0034] It should be noted that this paper considers the output of gas turbines and photovoltaics in the distribution network, and the output of thermal power units in the transmission network.

[0035] Assuming that renewable energy does not generate carbon emissions during the power generation process, the actual total carbon emissions of the system are obtained. The actual total carbon emissions of the system can be expressed as:

[0036] Where: Indicates the active output of conventional generator sets. Represents the carbon emission intensity of conventional power generation units, Represents the active output of the controllable distributed power source, Represents the carbon emission intensity of controllable distributed power sources.

[0037] According to the initial carbon emission quota and the total actual carbon emission of the system, the carbon trading cost of the system is calculated to obtain a carbon trading model. The carbon trading model is as follows:

[0038] Where: is the current carbon trading price in the market, It is the penalty price to be paid for excess emissions.

[0039] It should be noted that when carbon emissions are less than the allocated carbon quota, the excess carbon quota can be sold on the market for profit, and the carbon trading cost is negative. When carbon emissions are greater than the allocated carbon quota, a certain fine is required, or the enterprise must purchase the corresponding carbon quota in the market in advance to compensate. This paper assumes that

[0040] The node marginal electricity price is defined as the minimum additional cost of the system when a node increases its unit load while ensuring the safe operation of the power grid.

[0041] The node marginal electricity price is calculated as follows:

[0042] Where: represents the transaction price of the transmission and distribution network at the boundary node, represents an equality constraint, represents an inequality constraint, is the lower limit of the inequality constraint, is the upper limit of the inequality constraint.

[0043] S2 analyzes and calculates the transmission and distribution coordinated optimization scheduling model to obtain a transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism. The transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism includes an optimal output model of units at the transmission network layer and a comprehensive optimization scheduling model at the distribution network layer; The optimization goal of the transmission network layer is to reduce the carbon emissions of the units and improve the overall economy and low carbon performance while meeting the load demand. Therefore, this paper proposes an optimal output model for the units based on AC optimal power flow at the transmission network level, and considers the fuel cost and carbon trading cost of the thermal power units in the objective function.

[0044] The optimal output model of the transmission network layer units includes a minimum transmission network total operating cost objective function, and the constraints of the minimum transmission network total operating cost objective function include power balance constraints, node voltage amplitude constraints, branch transmission capacity constraints, and thermal power unit output power constraints.

[0045] It should be noted that the constraint conditions of the objective function of the minimum total operation cost of the transmission network must be met at every moment.

[0046] The objective function of the minimum total operation cost of the transmission network is as follows:

[0047] Where: is the total operating cost of the transmission network, is the power generation cost of thermal power units, is the carbon trading cost, T represents the scheduling period, represents the collection of thermal power units, , , represents the coal consumption cost coefficient of thermal power unit n, represents the active output of thermal power unit n at time t, represents the carbon emission intensity of thermal power unit n, is the current carbon trading price in the market, Expressed as carbon emission quota per unit of electricity generation.

[0048] The power balance constraints are as follows:

[0049]

[0050]

[0051] Where: represents the set of all nodes in the transmission network, Represents the set of branches containing the i-node, represents the active power output of the thermal power unit at node i at time t, represents the reactive power output of the thermal power unit at node i at time t, represents the active load of node i at time t, represents the reactive load of node i at time t, represents the active power of branch ij at time t, represents the reactive power of branch ij at time t, represents the node i at time t, represents the voltage of node j at time t, represents the admittance of branch ij, represents the admittance of node i to the ground branch, is the conjugation operator.

[0052] It should be noted that the power flows from node i to node j.

[0053] The node voltage amplitude constraint is as follows:

[0054] Where: represents the upper limit of the voltage amplitude at node i, Indicates the lower limit of the voltage amplitude at node i.

[0055] The branch transmission capacity constraints are as follows:

[0056] Where: Expressed as the maximum allowable apparent power of branch ij.

[0057] The output power constraints of the thermal power units are as follows:

[0058]

[0059] Where: It represents the upper limit of the active output of thermal power unit n. Indicates the lower limit of the active output of thermal power unit n, It represents the upper limit of reactive power output of thermal power unit n. Indicates the lower limit of reactive power output of thermal power unit n, Represents a collection of thermal power units.

[0060] The distribution network layer comprehensive optimization scheduling model includes a distribution network objective function, and the constraints of the distribution network objective function include power balance constraints, branch transmission capacity constraints, node voltage amplitude constraints, gas turbine output power constraints, abandoned photovoltaic power constraints, and demand response constraints.

[0061] The distribution network objective function is specifically:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] Where: represents the gas turbine in the distribution network k, represents the photovoltaic unit in the distribution network k, represents the transferable load in distribution network k, represents the set of loads that can be reduced in the distribution network k, , , represents the power generation cost coefficient of gas turbine m, represents the active power output of gas turbine m at time t, represents the power generation cost coefficient of photovoltaic unit w, represents the actual active output of the photovoltaic unit w at time t, represents the power that the distribution network k purchases from the transmission network through the boundary node at time t, represents the electricity transaction price between the transmission network and the distribution network k at time t, represents the carbon emission intensity of gas turbine m, They represent the compensation price for flexible load transfer unit power and reduction unit power, express Distribution network at all times The power transferred by the transferable load (TL) and the power cut by the curtailable load (CL) are Photovoltaic unit The penalty price for abandoning light, Represents photovoltaic unit exist Time prediction output.

[0069] The power balance constraint is specifically:

[0070]

[0071]

[0072]

[0073] Where: Represents the distribution network The set of all nodes in ; Represents the distribution network Include The set of branches of a node; express Distribution network at all times The gas turbine in the Active output and reactive output at express Time Node Active load and reactive load; express Time Branch The active power, reactive power and current amplitude of Node to node; Indicates branch resistance and reactance; express Time Node and The voltage amplitude.

[0074] The branch transmission capacity constraint is specifically:

[0075] Where: Indicates branch The upper limit of the current amplitude allowed to flow The node voltage amplitude constraint is specifically:

[0076] Where: Representation Node The upper and lower limits of the voltage amplitude.

[0077] The gas turbine output power constraint is specifically:

[0078]

[0079] Where: Gas Turbine The upper and lower limits of active and reactive output.

[0080] The constraints on abandoned photovoltaic power are specifically:

[0081] S3 uses AC optimal power flow to perform distributed solution on the transmission and distribution coordinated low-carbon optimization dispatch model under the coordination of demand-side management strategy and carbon trading mechanism, and obtains the non-convex planning sub-problems of transmission network and distribution network in convex form; The AC optimal power flow is used to decouple and distribute the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism, specifically: The non-convex planning problem of transmission and distribution network is relaxed into a convex problem by using SDP relaxation of transmission network and SOCP relaxation of distribution network. The non-convex planning problem of the transmission and distribution network, which is relaxed into a convex problem, is solved, and the non-convex planning sub-problems of the transmission and distribution networks in convex form are obtained.

[0082] The non-convex planning problem of the transmission and distribution network that is relaxed into a convex problem is solved as follows: A distributed solution method for transmission and distribution collaborative optimization based on ATC technology is used to independently optimize the non-convex planning problems of the transmission and distribution network in each region; By transferring boundary coupling information, the non-convex planning problems of the transmission and distribution networks after independent optimization are coordinated to obtain the non-convex planning sub-problems of the transmission and distribution networks in convex form.

[0083] The SOCP relaxation of the distribution network is specifically: Two new variables are introduced and applied to the comprehensive optimization dispatching model of the distribution network layer to obtain a non-convex nonlinear equation; The non-convex nonlinear equation is relaxed to obtain a convex second-order cone feasible region.

[0084] It should be noted that through SDP relaxation, the non-convex feasible domain of the comprehensive optimization scheduling model at the distribution network layer can be relaxed into a convex second-order cone feasible domain, and common commercial solvers can handle this problem efficiently.

[0085] The non-convex nonlinear equations include the following:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] Where: and are the two new variables introduced.

[0092] It should be noted that , .

[0093] The relaxing process of the non-convex nonlinear equation includes the following steps:

[0094] The SDP relaxation of the transmission network is specifically: Two new variables are introduced and applied to the optimal unit output model at the transmission network layer, and the non-convex original problem of the transmission network is obtained; The non-convex original problem of the transmission network is transformed into an equivalent form by adding semi-positive definite constraints; By adding a rank constraint, the non-convex original optimization problem is transformed into a standard SDP problem.

[0095] S4 combines the ATC algorithm to decouple the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism.

[0096] The basic idea of ​​the ATC algorithm is as follows: ATC is a distributed optimization method that aims to solve the optimization problems of complex systems with hierarchical structures and multiple agents. Its core idea is to decompose a global optimization problem layer by layer, transforming the original complex problem into several interconnected sub-problems, adding the associated variables between the upper and lower systems into the optimization objectives in the form of penalty terms, so that the sub-problems at the same level can be solved independently and in parallel.

[0097] The upper and lower systems perform iterative calculations by continuously passing related variables to each other until the optimization problem of the entire system is solved optimally. ATC has the advantages of unlimited number of levels, different optimization forms for sub-problems of the same level, and easy parameter selection

[21] . This idea is consistent with the distributed structure of ITDNs, so ATC can be applied to solving ITDNs scheduling problems.

[0098] The ATC algorithm is combined to decouple the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism, specifically: Assign initial values ​​to the penalty function's first-order multiplier, second-order multiplier, and coupling variables obtained by the transmission network layer from the distribution network layer, and set the number of inner and outer layer loop iterations; After receiving the data transmitted by each active distribution network, the transmission network solves the power generation plan and converts the calculated and the node marginal electricity price of the transmission and distribution boundary nodes is transmitted to the lower distribution network as a known quantity for parallel optimization at the distribution network level; Each active distribution network solves the optimal dispatch sub-problem and calculates the It is transmitted to the upper transmission grid as a known quantity for optimization at the transmission grid level; Determine whether the inner loop and the outer loop converge at the same time. When the inner loop and the outer loop converge at the same time, the optimization ends and the calculation results are output.

[0099] The determination of whether the inner loop and the outer loop converge simultaneously includes the following situations: When the inner loop does not converge, re-do the inner loop; When the inner loop converges but the outer loop does not converge, the penalty function multiplier is updated, and the inner loop is re-performed after the update; When the inner loop and the outer loop converge at the same time, the optimization ends and the calculation results are output.

[0100] The updated penalty function multiplier is specifically:

[0101]

[0102] ,

[0103] Where: is a constant, and its value is generally .

[0104] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0105] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0106] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0107] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0109] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A low-carbon optimization dispatching strategy for the integrated transmission and distribution network taking into account carbon trading and demand response, characterized in that: The following steps are involved: Based on the typical distributed structure of ITDNs, a transmission and distribution coordinated optimization scheduling framework is constructed; The transmission and distribution coordinated optimization scheduling model is analyzed and calculated to obtain a transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism; The AC optimal power flow is used to perform distributed solution on the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism, and the non-convex planning sub-problems of the transmission network and distribution network in convex form are obtained.

2. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 1, characterized in that: The transmission and distribution coordinated optimization scheduling framework is obtained as follows: Obtaining the load status at different times, and deciding the optimal unit power generation plan based on the load status at different times with the goal of optimizing the total operating cost; Based on the optimal unit power generation plan, the node marginal electricity price is calculated to obtain the transaction price of the transmission and distribution network at the boundary node; A flexible load demand response model and a carbon trading model are constructed with the goal of optimizing the economic efficiency of the distribution network. Combined with the transaction price at the boundary node of the transmission and distribution network, a plan for the distribution network to purchase electricity from the transmission network is obtained. The plan of the distribution network purchasing electricity from the transmission network is fed back to the transmission network, and iterative calculation is performed to obtain a global optimal solution.

3. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 2, characterized in that: The flexible load demand response model is divided into a transferable load demand response model and a curtailable load demand response model, specifically: The transferable load demand response model is as follows: Where: represents the power after load transfer at time t, represents the power before load transfer at time t, represents the power transferred at time t, represents the upper limit of the transferable power, represents the lower limit of transferable power; The demand response model for curtailable electric load is as follows: Where: represents the power after load reduction at time t, represents the power before load reduction at time t, represents the maximum power that can be cut at time t, Indicates that the elastic coefficient can be reduced at time t.

4. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 3 is characterized in that: The carbon trading model is obtained as follows: Obtain initial carbon emission quotas; Obtain the actual total carbon emissions of the system when renewable energy does not produce carbon emissions during the power generation process; Combining the initial carbon emission quota and the actual total carbon emissions of the system, a carbon trading model is obtained.

5. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 4, characterized in that: The carbon trading model is as follows: Where: is the current carbon trading price in the market, It is the penalty price to be paid for excess emissions.

6. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 5, characterized in that: The node marginal electricity price is calculated as follows: Where: represents the transaction price of the transmission and distribution network at the boundary node, represents an equality constraint, represents an inequality constraint, is the lower limit of the inequality constraint, is the upper limit of the inequality constraint.

7. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 6, characterized in that: The AC optimal power flow is used to decouple and distribute the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism, specifically: The non-convex planning problem of transmission and distribution network is relaxed into a convex problem by using SDP relaxation of transmission network and SOCP relaxation of distribution network. The non-convex planning problem of the transmission and distribution network, which is relaxed into a convex problem, is solved, and the non-convex planning sub-problems of the transmission and distribution networks in convex form are obtained.

8. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 7, characterized in that: The ATC algorithm is combined to decouple the transmission and distribution coordinated low-carbon optimization scheduling model under the coordination of demand-side management strategy and carbon trading mechanism, specifically: Assign initial values ​​to the penalty function's first-order multiplier, second-order multiplier, and coupling variables obtained by the transmission network layer from the distribution network layer, and set the number of inner and outer layer loop iterations; After receiving the data transmitted by each active distribution network, the transmission network solves the power generation plan and converts the calculated and the node marginal electricity price of the transmission and distribution boundary nodes is transmitted to the lower distribution network as a known quantity for parallel optimization at the distribution network level; Each active distribution network solves the optimal dispatch sub-problem and calculates the It is transmitted to the upper transmission grid as a known quantity for optimization at the transmission grid level; Determine whether the inner loop and the outer loop converge at the same time. When the inner loop and the outer loop converge at the same time, the optimization ends and the calculation results are output.

9. The low-carbon optimization scheduling strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 8, characterized in that: The determination of whether the inner loop and the outer loop converge simultaneously includes the following situations: When the inner loop does not converge, re-do the inner loop; When the inner loop converges but the outer loop does not converge, the penalty function multiplier is updated, and the inner loop is re-performed after the update; When the inner loop and the outer loop converge at the same time, the optimization ends and the calculation results are output.

10. The low-carbon optimization dispatching strategy for the integrated transmission and distribution network taking into account carbon trading and demand response as claimed in claim 9, characterized in that: The updated penalty function multiplier is specifically: ， Where: is a constant, and its value is generally .

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