Power transmission and distribution network coordinated scheduling method based on DSO bidirectional quotation curve

Abstract

The invention discloses a power transmission and distribution network coordinated scheduling method based on a DSO bidirectional quotation curve, and the method comprises the steps that a power distribution system operator constructs a bidirectional quotation curve which reflects the marginal operation cost of a power distribution network through a parameterization optimization method based on the quotation information of a distributed energy aggregator and a load service entity in the power distribution network; an independent system operator takes a power distribution system operator as a special market participant, wholesale market clearing is carried out based on a bidirectional quotation curve, and node marginal prices and bid-winning power are determined; and the power distribution system operator optimizes the internal resource scheduling of the power distribution network under the condition of meeting the bid-winning power constraint according to the market clearing result. According to the invention, by constructing the complete bidirectional quotation curve, the economic characteristics of the power distribution network in different operation states are accurately reflected; the quotation curve is calculated in advance, so that the problem of complex double-layer optimization is avoided, the calculation efficiency is improved, and a feasible technical scheme is provided for power market operation under high-proportion distributed energy access.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatching operation, and particularly relates to a power transmission and distribution network coordinated dispatching method based on a DSO bidirectional quotation curve. BACKGROUND

[0002] With large-scale access of distributed energy to the power distribution network, the traditional power distribution network has been transformed from a pure power consumption network into an active network with bidirectional power flow characteristics. However, in the existing power market mechanism, the power distribution network is usually simply regarded as a load participating in the market, and the value and flexibility of the internal distributed resources cannot be fully reflected.

[0003] The prior art mainly has the following deficiencies: firstly, the distributed energy and flexible load in the power distribution network cannot effectively participate in the wholesale market, resulting in that the resource value cannot be fully reflected; secondly, the dispatching decisions of the power transmission network and the power distribution network are independent of each other, and there is a lack of coordinated optimization mechanism, and the overall system efficiency is low; thirdly, the calculation complexity of some existing coordinated methods is high, and it is difficult to meet the time requirements of actual market operation.

[0004] Therefore, there is a need for a power transmission and distribution coordination dispatching method which can accurately reflect the bidirectional characteristics of the power distribution network, has high calculation efficiency and is easy to implement. SUMMARY

[0005] The purpose of the present application is to provide a power transmission and distribution network coordinated dispatching method based on a DSO bidirectional quotation curve, so as to solve the problems in the prior art that the distributed resources in the power distribution network cannot effectively participate in the power wholesale market, and the lack of coordinated optimization mechanism between the power transmission network and the power distribution network leads to low overall system operation efficiency.

[0006] The technical scheme of the present application is a power transmission and distribution network coordinated dispatching method based on a DSO bidirectional quotation curve, comprising the following steps:

[0007] Constructing a bidirectional quotation curve of a distribution system operator DSO: collecting the quotations and operation parameters of the distributed resources in the power distribution network, constructing a parameterized economic dispatching optimization model of the power distribution network, selecting discrete points in the feasible region of exchanged power and solving the optimization problem, calculating the marginal cost and constructing the bidirectional quotation curve;

[0008] Market clearing of an independent system operator ISO: the ISO collects the quotation information of market participants, and the bidirectional quotation curve submitted by the distribution system operator and the quotation information of other market participants on the power transmission network side are included in the wholesale market clearing optimization model, the market clearing optimization problem is solved to determine and issue the node marginal price and the winning electricity quantity of each market subject;

[0009] Distribution system operator dispatch decomposition: Distribution system operator performs internal optimization resource allocation based on nodal marginal price and total dispatch power issued by independent system operator, and generates dispatch instructions for distributed energy aggregators and load serving entities.

[0010] Further, the collection of the bid and operating parameters of the distributed resources in the distribution network is specifically:

[0011] The distribution system operator collects information of all distributed energy aggregators and load serving entities in the distribution network, including: generation cost function and technical parameters of distributed energy aggregator g; demand response cost function and adjustable capacity of load serving entity d; network topology and electrical parameters of the distribution network, including line impedance, node voltage limit, line capacity limit.

[0012] Further, the construction of the parameterized distribution network economic dispatch optimization model is specifically:

[0013] The exchange power P dso between the distribution network and the transmission network is taken as a parameter variable, where P dso < 0 indicates that the distribution network purchases power from the transmission network, and P dso > 0 indicates that the distribution network sells power to the transmission network; the objective function of the distribution network economic dispatch optimization model is to minimize the total operation cost of the distribution network:

[0014]

[0015] Where G represents the set of distributed energy aggregators; D represents the set of load serving entities LSE; N g represents the set of nodes that aggregator g owns resources; N d represents the set of nodes that LSE d owns loads; p g,n and p d,n represent the power injected and flowed out of the distribution network node n by the aggregator g and the load serving entity d, respectively; π g,n and π d,n represent the low-voltage side distributed generation resources and demand response resource bids, which are simplified as single-section bids.

[0016] The constraint conditions include:

[0017] 1) Node power balance constraint, to ensure that the active and reactive power injection of each node is equal to the sum of power flow and corresponding network loss, where the exchange power P dso between the distribution network and the transmission network is taken as the power injection of the boundary node of the distribution network; including:

[0018] Active power balance:

[0019]

[0020] Reactive power balance:

[0021]

[0022] where G n denotes the set of aggregators owning resources at node n; D n denotes the set of LSEs owning loads at node n; N denotes the set of distribution network nodes, including the transmission-distribution network interface, and L denotes the set of distribution network lines; q g,n is the reactive power of aggregator g at distribution network node n; is the fixed active and reactive net load at node n; is an indicator variable, which is equal to 1 when node n is a transmission-distribution network connection point, and 0 otherwise; is the power factor angle tangent value for the load; Pl l and Ql l denote the active and reactive power flow of line l, respectively; A is the line-node incidence matrix, whose elements A(l,n) are defined as follows: A(l,n) = 1 when node n is the line l power flow inflow point; A(l,n) = -1 when node n is the line l power flow outflow point; otherwise, A(l,n) = 0;

[0023] 2) Node voltage relationship constraint, using linearized node voltage relationship constraint:

[0024]

[0025] where U m and U n denote the voltage magnitude square of nodes m and n, respectively; r l and x l are the resistance and reactance of line l, respectively;

[0026] 3) Node voltage constraint:

[0027]

[0028] where, U and denote the square values of the lower and upper voltage limits, respectively;

[0029] 4) Distributed resource operation constraint:

[0030] Active power output constraint:

[0031]

[0032] Reactive power output constraint:

[0033]

[0034] 5) Demand response constraints:

[0035]

[0036] 6) Line capacity constraints:

[0037] Active power flow constraints:

[0038]

[0039] Reactive power flow constraints:

[0040]

[0041] 7) Power factor constraints, limit the DSO's reactive power input, avoid the burden of reactive power to the main grid:

[0042]

[0043] wherein, represents the maximum allowed ratio of reactive power to active power.

[0044] Further, the process of selecting discrete points within the exchange power feasible region and solving the optimization problem is as follows:

[0045] determining the value range of P dso : selecting a series of discrete points P dso (1), P dso (2),..., P dso (K) within the range; the density of the selected points needs to be adjusted according to the characteristics of the cost function; in the area where the cost changes gently, sparse sampling is used; in the area where the cost changes dramatically, the sampling density is increased to accurately capture the characteristics of the cost change; for each discrete point, it is used as a fixed parameter in the node power balance constraint, and the economic dispatch optimization model of the distribution network is solved to obtain the corresponding minimum operating cost c dso (P dso (i)).

[0046] Further, the process of calculating the marginal cost and constructing the bidirectional offer curve is as follows:

[0047] Based on the cost values of the discrete points, the backward difference method is used to calculate the marginal cost:

[0048]

[0049] For the last point, forward difference is used:

[0050]

[0051] Finally, the discrete marginal cost points are connected to form continuous bid curves by piecewise linear interpolation method.

[0052] Further, the market participant's bid information includes the traditional generator's bid curve, i.e. each generator i provides its cost function DSO's submitted bid curve, i.e. DSOj provides its cost function

[0053] Further, the objective of the wholesale market clearing optimization model is to find a market clearing point that minimizes the operation cost, and the objective function is:

[0054]

[0055] where G is the set of traditional generators, J dso is the set of DSOs;

[0056] The constraints include:

[0057] 1) Power balance and network constraints of the transmission grid:

[0058]

[0059] where N Tra , L Tra represent the set of nodes and lines in the transmission system respectively, and the decision variable vector x Tra contains the traditional generator outputs DSOj's exchange power node load L n , node voltage phase angle θ n and line power flow Pl l ; the constraint set X Tra contains all the technical constraints of the transmission grid, including the power balance equations of each node, DC power flow equations, line transmission capacity limits;

[0060] 2) Generator unit output constraints:

[0061]

[0062] where defines the feasible output range of generator unit i, which usually includes minimum and maximum output limits;

[0063] 3) DSO's power exchange constraints:

[0064]

[0065] This constraint ensures that DSOj's power exchange is within its declared feasible range In.

[0066] Further, the solving market clearing optimization problem is specifically:

[0067] In the solving process, the ISO simultaneously solves the winning amount of each market participant and the location marginal price of each node; the winning amount of each market participant includes the output of each traditional generator The exchange power of each DSO And the electricity consumption of the load; the location marginal price of each node is obtained by the Lagrange multiplier of the optimization problem, and the marginal electricity price of each node reflects the marginal influence of increasing unit load on the total cost of the system;

[0068] The independent system operator regards the distribution system operator as a special market participant, and the bidding curve of the distribution system operator includes the electricity purchase price as a load and the electricity sale price as a power source; the market clearing model of the independent system operator minimizes the system operation cost as the target, and determines the market clearing result under the conditions of meeting the system power balance, the transmission network constraint and the declaration constraint of each market subject.

[0069] Further, the market clearing result includes:

[0070] The marginal electricity price of each transmission node, reflecting the influence of increasing unit load or power generation on the system cost; the winning power of the distribution system operator at the connection point of the transmission and distribution network, determining the actual power exchange amount of the distribution network and the transmission network; the dispatching period, clearly defining the execution time window of the above dispatching instruction.

[0071] Further, the specific implementation process of the distribution system operator dispatching decomposition is as follows:

[0072] Based on the winning power and settlement price determined by the independent system operator, the distribution system operator re-optimizes the internal resource dispatching of the distribution network; under the constraint that the power exchange amount with the transmission network is equal to the winning value, the sum of the internal operation cost of the distribution network and the wholesale market electricity purchase cost is minimized; according to the optimization result, the power generation dispatching instruction is issued to each distributed energy aggregator, and the demand response instruction is issued to each load service entity.

[0073] Beneficial effects: compared with the prior art, the beneficial effects of the present application are:

[0074] 1. Accurately express the bidirectional characteristics of the distribution network: by constructing a complete bidirectional bidding curve, the economic characteristics of the distribution network under different operating states are accurately reflected, including the two cases of being a net load and a net power source, which is not realized by the traditional method;

[0075] 2. Significantly improve computational efficiency: By pre-calculating the bid curve, the complex two-level optimization problem is transformed into two independent single-level optimization problems, which greatly reduces the computational complexity; ISO only needs to process the DSO's bid curve during market clearing, without needing to know the detailed information of the distribution network, thus meeting the strict requirements of the actual electricity market for computation time.

[0076] 3. Full realization of resource value: Distributed photovoltaic, wind power, energy storage, demand response and other resources in the distribution network can participate in wholesale market competition through DSO, and their value can be fully realized; market price signals can be effectively transmitted to the distribution network, incentivizing resources to optimize operation according to system demand;

[0077] 4. Maintaining compatibility with the existing market framework: This invention does not require fundamental modifications to existing electricity market rules. The ISO continues to operate according to the existing market clearing mechanism, only adding DSO as a special market participant; this greatly reduces the difficulty of implementing the method.

[0078] In summary, this invention, through a two-way bidding curve mechanism, achieves optimized allocation of distribution network resources in the electricity market while ensuring computational feasibility, providing an effective technical solution for the market-oriented operation of the power system under a high proportion of distributed energy access. Attached Figure Description

[0079] Figure 1 This is a flowchart of the present invention;

[0080] Figure 2 A schematic diagram of a transmission and distribution coordination system based on a DSO bidirectional pricing curve;

[0081] Figure 3 This is a flowchart of the method for constructing a two-way bidding curve for power distribution system operators proposed in this invention;

[0082] Figure 4 This is an example diagram of a two-way quotation curve provided for an embodiment of the present invention. Detailed Implementation

[0083] The present invention will now be described in further detail with reference to the accompanying drawings:

[0084] like Figure 1 As shown, this invention proposes a transmission and distribution network coordinated dispatch method based on the DSO bidirectional bidding curve, establishing a three-stage market coordination mechanism that enables the distribution network to participate efficiently in the wholesale electricity market as a whole. Figure 2As shown, the DSO-bilateral bidding curve based transmission-distribution coordination system includes two main levels: transmission grid level and distribution grid level. At the transmission grid level, the ISO, as the core of market operation, is responsible for coordinating market participants such as traditional power plants and large users. At the distribution grid level, the DSO, as the operator of the distribution grid, manages a plurality of distributed energy aggregators and load service entities. The distributed energy aggregators manage distributed resources such as photovoltaic, energy storage and wind power, and the load service entities manage conventional loads and demand response (DR) resources. The DSO submits a bilateral bidding curve to the ISO, which can express the economic characteristics of the DSO as a power purchaser and a power seller. After the market clearing based on the received bidding information, the ISO issues the results (including the winning power P dso and the nodal marginal price LMP) to the DSO. This bilateral interaction mechanism enables the distribution grid to participate in the wholesale market competition as a whole and fully realizes the value of its internal resources.

[0085] The first stage is the construction of a bilateral bidding curve for the distribution system operator. In this stage, the distribution system operator (DSO) plays the role of an agent for all distributed resources in the distribution grid. The DSO first collects the generation cost information and technical parameters of all distributed energy resource (DER) aggregators in the distribution grid, as well as the demand response capability and cost information of all load serving entities (LSEs). Based on this information, the DSO constructs a parameterized optimization model.

[0086] The key of the optimization model is to treat the exchange power P dso between the DSO and the transmission grid as a parameter variable. When P dso is negative, it means that the distribution grid purchases power from the transmission grid; when P dso is positive, it means that the distribution grid sells power to the transmission grid. By selecting a series of discrete points within the feasible region of P dso and solving the economic dispatch problem of the distribution grid for each point, the minimum operating cost of the distribution grid under different exchange power levels can be obtained.

[0087] Based on these discrete cost data, the present application uses numerical analysis method to calculate the marginal cost. Specifically, by calculating the cost change rate between adjacent power points, the marginal cost value of each point is obtained. In implementation, the backward difference method can be used to ensure that the marginal cost can reflect the actual incremental cost. Finally, by piecewise linearization or other interpolation methods, the discrete marginal cost points are connected into a continuous bidding curve. This curve completely expresses the economic characteristics of the distribution grid under different operating states.

[0088] As Figure 3 shown, the specific implementation process is as follows:

[0089] S1, collect the bids and operating parameters of the distributed resources in the distribution network.

[0090] The distribution system operator collects information of all distributed energy aggregators and load service entities in the distribution network, including: the generation cost function and technical parameters of the distributed energy aggregator g; the demand response cost function and adjustable capacity of the load service entity d; the network topology and electrical parameters of the distribution network, including line impedance, node voltage limit, line capacity limit, etc.

[0091] S2, establish a parameterized economic dispatch optimization model of the distribution network. The exchange power P dso as a parameter, the following optimization model is established.

[0092] The objective function is to minimize the total operating cost of the distribution network:

[0093]

[0094] Where G represents the set of distributed energy aggregators; D represents the set of load service entities LSE; N g represents the set of nodes that the aggregator g owns resources; N d represents the set of nodes that the LSE d owns loads; p g,n and p d,n represent the power injected and flowed at the distribution network node n by the aggregator g and the load service entity d, respectively; π g,n and π d,n represent the low-voltage side distributed generation resources and demand response resource bids, which are simplified as single-section bids.

[0095] The constraint conditions include:

[0096] (1) Node power balance constraint:

[0097] Active power balance:

[0098]

[0099] Reactive power balance:

[0100]

[0101] Where N represents the set of distribution network nodes (including the transmission and distribution network interface), L represents the set of distribution network lines; G n represents the set of aggregators that own resources at node n; D n represents the set of LSEs that own loads at node n; q g,nQg(n) is the reactive power supplied by the aggregator g at the distribution network node n; Qn is the fixed active and reactive net load at node n; δn is an indicator variable, which is equal to 1 when node n is a transmission-distribution network connection point, and 0 otherwise; tanφl is the power factor angle tangent value of load l; l Ql l P and Ql represent the active and reactive power flow of line l, respectively; A is the line-node incidence matrix, whose element A(l, n) is defined as follows: A(l, n) = 1 when node n is the line l power flow inflow point; A(l, n) = -1 when node n is the line l power flow outflow point; otherwise, A(l, n) = 0.

[0102] (2) Node voltage relationship constraint:

[0103] The linearized node voltage relationship constraint is adopted:

[0104]

[0105] where U m and U n are the voltage magnitude squares of nodes m and n, respectively; r l and x l are the resistance and reactance of line l, respectively.

[0106] (3) Node voltage constraint:

[0107]

[0108] where, U and are the square values of the lower and upper voltage limits, respectively.

[0109] (4) Distributed resource operation constraint:

[0110] Active power output constraint:

[0111]

[0112] Reactive power output constraint:

[0113]

[0114] (5) Demand response constraint:

[0115]

[0116] (6) Line capacity constraint:

[0117] Active power flow constraint:

[0118]

[0119] Reactive power flow constraints:

[0120]

[0121] (7) Power factor constraints:

[0122] Limit the reactive power input of DSO to avoid the burden of reactive power to the main grid:

[0123]

[0124] where, represents the maximum allowed ratio of reactive power to active power.

[0125] S3, select discrete points within the feasible region of exchanged power and solve the optimization problem.

[0126] First determine the value range of P dso : Select a series of discrete points P dso (1), P dso (2),..., P dso (K) within this range. The density of the selected points needs to be adjusted according to the characteristics of the cost function. In the area where the cost changes gently, sparse sampling can be used; while in the area where the cost changes sharply, the sampling density can be increased to accurately capture the characteristics of the cost change.

[0127] For each discrete point, take it as a fixed parameter in the node power balance constraint, solve the economic dispatch optimization model of the distribution network, and get the corresponding minimum operating cost c dso (P dso (i)).

[0128] S4, calculate the marginal cost and construct the bidirectional pricing curve.

[0129] Based on the cost values of discrete points, the backward difference method is used to calculate the marginal cost:

[0130]

[0131] For the last point, forward difference can be used:

[0132]

[0133] Finally, the discrete marginal cost points are connected into a continuous bidirectional pricing curve by piecewise linear interpolation method, as shown in Figure 4 .

[0134] The second stage is the market clearing of the independent system operator (ISO). The ISO incorporates the bid curve submitted by the DSO together with the bid information from other market participants (e.g. large power plants, large customers directly connected to the transmission grid, etc.) into the optimization model of the wholesale market. In this model, the DSO is treated as a special market participant - its bid curve contains both the buying segment (negative power interval) and the selling segment (positive power interval).

[0135] The market clearing of the ISO aims to maximize the social welfare or minimize the system operation cost, subject to the conditions of satisfying the system power balance, transmission grid security constraints, etc. The results of the market clearing include the locational marginal price (LMP) at each node, the winning power amount for each market participant including the DSO, and the dispatch time period. These results provide the boundary conditions for the internal optimization of the distribution grid in the third stage.

[0136] The second stage is implemented as follows:

[0137] The independent system operator performs the market clearing of the wholesale market. The ISO needs to find the optimal matching scheme among a large number of buyers (loads) and sellers (generators), and the DSO as a special participant can be both a buyer and a seller, whose bid curve contains both the buying bid (P dso <0) as a load and the selling bid (P dso >0) as a power source.

[0138] The ISO collects the bid information from all market participants. The independent system operator collects the bid information from all market participants, including: the bid curve of the traditional generator, i.e. each generator i provides its cost function The bid curve submitted by the DSO, i.e. the DSO j provides its cost function where the bid curve of the DSO is "bidirectional" - when P is negative, it represents the buying bid, and when it is positive, it represents the selling bid.

[0139] The ISO establishes the optimization model of the wholesale market clearing. The goal of this model is to find a market clearing point that minimizes the operation cost of the entire system. The objective function is:

[0140]

[0141] where G is the set of traditional generators, J dso is the set of DSOs.

[0142] The constraints include:

[0143] (1) Power balance constraints and network constraints of the transmission network:

[0144]

[0145] Where, N Tra L Tra Let x represent the set of nodes and the set of lines in the power transmission system, respectively, and let x be the decision variable vector. Tra Includes traditional generator output DSOj's switching power Node load L n Node voltage phase angle θ n

[0146] and line trend Pl l Constraint set X Tra It includes all the technical constraints of the transmission network, including: power balance equations for each node, DC power flow equations, line transmission capacity limits, and other physical constraints of the transmission network.

[0147] (2) Output constraints of generator sets:

[0148]

[0149] in The feasible output range of generator set i is defined, which typically includes minimum and maximum output limits.

[0150] (3) Power exchange constraints of DSO:

[0151]

[0152] This constraint ensures the power exchange of DSOj. Within the scope of its declaration Inside.

[0153] ISO employs a mature optimization algorithm to solve the aforementioned clearing optimization problem. During the solution process, ISO simultaneously determines the winning bid volume for each market participant and the location marginal price (LMP) for each node. The winning bid volume for each market participant includes the output of each conventional generator unit. Exchange power of each DSO And load power consumption. The location marginal price (LMP) of each node is obtained by using the Lagrange multipliers of the optimization problem to obtain the marginal price of each node. The LMP of each node reflects the marginal impact of adding a unit load at that node on the total system cost.

[0154] ISO will distribute the market clearing results to all market participants. For DSOs, key information includes: winning bid power. (DSO needs to exchange power with transmission grid according to this power value); nodal locational marginal price LMP (as the settlement price between DSO and ISO); dispatch period (explicitly indicates the execution time of the above dispatch instruction).

[0155] In this phase, the ISO implements market optimization clearing considering the bidirectional characteristics of the DSO. This market clearing mechanism is fully compatible with the existing power market framework. The ISO only needs to consider the bidirectional characteristics of the DSO when processing the bid of the DSO, without the need to fundamentally modify the market rules. This greatly reduces the difficulty of implementing the method of the application, and is conducive to the popularization and application in the actual power market.

[0156] The third phase is the dispatch decomposition of the distribution system operator. Based on the winning power and settlement price determined by the ISO, the DSO needs to decompose the overall dispatch result into each resource in the distribution grid. This is achieved by solving a new optimization problem: under the constraint that the power exchange amount with the transmission grid is equal to the winning value determined by the ISO, the resource dispatch in the distribution grid is re-optimized so that the sum of the internal operation cost of the distribution grid and the wholesale market purchase and sale electricity cost is minimized.

[0157] This optimization process not only determines the power generation dispatch amount of each DER aggregator and the demand response amount of each LSE, but also obtains the internal node price of the distribution grid through the dual variables of the optimization problem. These internal prices reflect the combined influence of the wholesale market price signal and the internal network constraints (such as line congestion, network loss, etc.) of the distribution grid, and provide correct economic incentives for the resources in the distribution grid.

[0158] The distribution system operator receives the dispatch instruction P dso* and the transmission-distribution interface nodal marginal price LMP from the independent system operator, and performs the following dispatch decomposition process.

[0159] The DSO establishes a dispatch decomposition optimization model as follows:

[0160] The objective function is:

[0161]

[0162] The constraint conditions include the distribution grid operation constraints (1)-(7) described in the first phase S2.

[0163] Transmission-distribution grid power exchange constraint: P dso = P dso* . Where P dso* is the winning power determined by the ISO, which is input as a fixed parameter.

[0164] By solving the above optimization problem, the DSO determines the output p g,nand the power consumption p of each load service entity d,n and issue corresponding dispatch instructions.

[0165] The DSO uses the node marginal price LMP of the transmission-distribution interface as the unified settlement price, and settles with each market subject in the distribution network. The specific settlement rules are: pay p g,n × LMP to the distributed energy aggregator; collect p d,n × LMP from the load service entity; and settle with the ISO: P dso* × LMP.

[0166] In this phase, based on the winning power and settlement price determined by the independent system operator, the distribution system operator re-optimizes the internal resource dispatch of the distribution network; under the constraint that the power exchange with the transmission network is equal to the winning value, the sum of the internal operating cost and the wholesale market purchase cost of the distribution network is minimized; according to the optimization result, the power generation dispatch instruction is issued to each distributed energy aggregator, and the demand response instruction is issued to each load service entity.

[0167] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power transmission and distribution network coordinated dispatching method based on DSO bidirectional offer curve, characterized in that, The implementation process is as follows: Constructing the bidirectional offer curve of the distribution system operator DSO: collecting the offers and operating parameters of the distributed resources in the distribution network, constructing a parameterized economic dispatch optimization model of the distribution network; selecting discrete points in the feasible region of power exchange and solving the optimization problem; calculating the marginal cost and constructing the bidirectional offer curve; Independent system operator ISO market clearing: the ISO collects the offer information of market participants, and incorporates the bidirectional offer curve submitted by the distribution system operator and the offer information of other market participants on the transmission network side into the wholesale market clearing optimization model, solves the market clearing optimization problem to determine and issue the nodal marginal price and the winning electricity quantity of each market participant; Distribution system operator dispatch decomposition: the distribution system operator executes the optimal resource allocation within the distribution network based on the nodal marginal price and the total dispatch electricity quantity issued by the independent system operator, and generates the dispatch instructions of the distributed energy aggregators and the load service entities.

2. The method of claim 1, wherein, The collection of the offers and operating parameters of the distributed resources in the distribution network is specifically: The distribution system operator collects the information of all distributed energy aggregators and load service entities in the distribution network, including: the generation cost function and technical parameters of the distributed energy aggregator g; the demand response cost function and adjustable capacity of the load service entity d; the network topology and electrical parameters of the distribution network, including line impedance, node voltage limit, line capacity limit.

3. The method of claim 1, wherein, The construction of the parameterized economic dispatch optimization model of the distribution network is specifically: The exchange power P between the distribution network and the transmission network dso P is a parameter variable, wherein P dso P < 0 indicates that the distribution network purchases power from the transmission network, and P dso P > 0 indicates that the distribution network sells power to the transmission network; and an objective function of an economic dispatch optimization model of the distribution network is to minimize total operation cost of the distribution network: where G denotes the set of distributed energy aggregators; D denotes the set of load serving entities (LSEs); N g denotes the set of nodes where aggregator g owns resources; N d denotes the set of nodes where LSE d owns loads; p g,n and p d,n denote the power injected and withdrawn by aggregator g and LSE d at distribution grid node n, respectively; π g,n and π d,n denote the low-voltage side distributed generation resources and demand response resources bids, simplified as single segment bids, respectively; The constraint conditions include: 1) Node power balance constraint, which ensures that the active and reactive power injection of each node is equal to the sum of power outflow and the corresponding network loss, where the power exchange P dso Power injection as a boundary node of the distribution network; comprising: Active power balance: Reactive power balance: where G n denotes the set of aggregators owning resources at node n; D n denotes the set of LSEs owning loads at node n; N denotes the set of distribution network nodes, including the transmission-distribution network interface, L denotes the set of distribution network lines; q g,n is the reactive power of aggregator g at distribution network node n; is the fixed active and reactive net load at node n; is an indicator variable, which is equal to 1 when node n is a transmission-distribution network connection point, and 0 otherwise; is the power factor angle tangent value for the load; P l and Q l denote the active and reactive power flow of line l, respectively; A is the line-node incidence matrix, whose elements A(l,n) are defined as follows: A(l,n) = 1 when node n is the flow-in point of line l; A(l,n) = -1 when node n is the flow-out point of line l; otherwise, A(l,n) = 0; 2) Node voltage relationship constraint, using linearized node voltage relationship constraint: where U m and U n denote the squared voltage magnitudes at nodes m and n; r l and x l are the resistance and reactance of line l, respectively. 3) Node voltage constraint: wherein U and Vmin and Vmax represent the square values of the lower and upper voltage limits, respectively; 4) Distributed resource operation constraint: Active power output constraint: Reactive power output constraint: 5) Demand response constraint: 6) Line capacity constraint: Active power flow constraint: Reactive power flow constraint: 7) Power factor constraint, limiting the reactive power input of the DSO to avoid the reactive burden on the main network: wherein, represents the maximum allowed ratio of reactive power to active power.

4. The method of claim 1, wherein, The specific implementation process of selecting discrete points in the feasible region of power exchange and solving the optimization problem is as follows: Determination of P dso Value range: A series of discrete points P dso (1), P dso (2),..., P dso (K) are selected within the range; the density of the selected points needs to be adjusted according to the characteristics of the cost function; in the region where the cost changes gently, sparse sampling is adopted; in the region where the cost changes sharply, the sampling density is increased to accurately capture the cost change characteristics; for each discrete point, it is taken as a fixed parameter in the node power balance constraint, and the economic dispatch optimization model of the power distribution network is solved to obtain the corresponding minimum operating cost c dso (P dso (i)).

5. The method of claim 1, wherein, The specific implementation process of calculating the marginal cost and constructing the bidirectional offer curve is as follows: Based on the cost values of the discrete points, the backward difference method is used to calculate the marginal cost: For the last point, forward difference is used: Finally, the discrete marginal cost points are connected into a continuous bidirectional offer curve by piecewise linear interpolation method.

6. The method of claim 1, wherein, The market participant's offer information comprises a conventional generator's offer curve, i.e. each generator i offers its cost function DSO's submitted bid-offer curve, i.e. DSOj offers its cost function 7. The method of claim 1, wherein, The objective of the wholesale market clearing optimization model is to find a market clearing point that minimizes the operating cost, and the objective function is: where G is a set of conventional generators, J dso is a set of DSOs; The constraint conditions include: 1) Power balance constraint and network constraint of the transmission network: where N Tra , L Tra are the sets of nodes and lines in the transmission system, respectively, and x Tra is the vector of decision variables i containing the traditional generator outputs P gen , the exchange power of DSOj, DSOj the node load L n , the node voltage phase angle θ n and the line power flow Pl l ; the constraint set X Tra contains all the technical constraints of the transmission network, including the power balance equations of each node, the DC power flow equations, and the line transmission capacity limits. 2) Output constraint of the generator set: wherein, defines the feasible power range of the generator set i, typically comprising minimum and maximum power limits; 3) Power exchange constraint of the DSO: This constraint ensures that the power exchange of DSOj within its declared feasible range of operation.

8. The method of claim 1, wherein, The solution of the market clearing optimization problem is specifically: In the solution process, ISO simultaneously solves the winning quantity of each market participant and the LMP of each node; the winning quantity of each market participant includes the output P i gen* of each traditional generator, the exchange power of each DSO and the power consumption of each load; the LMP of each node is obtained by the Lagrange multiplier of the optimization problem, and the LMP of each node reflects the marginal influence of increasing unit load at the node on the total cost of the system; The independent system operator regards the distribution system operator as a special market participant, and the offer curve includes the purchase offer as a load and the sale offer as a power source; the market clearing model of the independent system operator aims to minimize the system operating cost, and determines the market clearing result under the conditions of satisfying the system power balance, transmission network constraint and declaration constraint of each market participant.

9. The method of claim 8, wherein, The market clearing result includes: The marginal price of each power transmission node reflects the influence of increasing unit load or power generation on the system cost; the winning power of the distribution system operator at the power transmission and distribution network connection point determines the actual power exchange amount between the distribution network and the transmission network; and the dispatching period defines the execution time window of the above dispatching instructions.

10. The method of claim 1, wherein, The specific implementation process of the distribution system operator dispatching is as follows: Based on the winning power and settlement price determined by the independent system operator, the distribution system operator re-optimizes the internal resource dispatching of the distribution network; under the constraint that the power exchange amount with the transmission network is equal to the winning value, the sum of the internal operation cost and the wholesale market power purchase cost of the distribution network is minimized; According to the optimization result, the power generation dispatching instruction is issued to each distributed energy aggregator, and the demand response instruction is issued to each load service entity.

Images