CPDS intra-day economic dispatching method and system based on fully distributed architecture

Abstract

The invention discloses a CPDS intraday economic dispatch method and system based on a fully distributed architecture, and the method comprises the steps: collecting data through an intelligent electric meter and a sensor, dividing a CPDS physical system into sub-regions, defining decision variables in the sub-regions, optimizing the intraday economic dispatch of each sub-region, constructing a sub-region objective function, and setting the internal constraints of the sub-regions. And sub-region coupling constraints are defined, and the DCADMM is used to solve the CPDS intra-day economic dispatching problem in a full-distributed manner. And the scheduling decision of each sub-region is calculated in parallel, so that the calculation load and complexity are remarkably reduced, and the calculation efficiency is improved. Under the distributed architecture, the failure of a single sub-region or a communication link does not cause the failure of the whole system, and the fault-tolerant capability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system and power engineering technology, and in particular to a CPDS intraday economic dispatch method and system based on a fully distributed architecture. Background Art

[0002] With the development of technologies such as the Internet of Things (IoT), big data, and artificial intelligence, distribution networks are gradually transforming into cyber-physical systems (CPDS). CPDS integrates information and communication technology (ICT) and distribution physical networks, enhancing the observability and controllability of the system. The application of micro-phase measurement units (μPMUs) and smart meters (SMs) has significantly improved the situational awareness and intelligence level of the distribution network.

[0003] The large-scale access to renewable energy (such as solar energy, wind energy) and distributed generation systems poses a huge challenge to traditional centralized dispatching. The volatility and uncertainty of distributed energy increase the complexity of power system dispatching. Traditional centralized dispatching methods rely on a single central control center to centrally manage massive data and widely distributed equipment. This approach has problems such as data transmission bottlenecks, heavy computing loads, and slow response speeds.

[0004] In a centralized scheduling architecture, once the central control center fails, the operation of the entire system will be seriously affected, lacking robustness and flexibility. The fully distributed scheduling method delegates decision-making power to each sub-region and achieves global optimal scheduling through inter-regional collaboration and information exchange. This can effectively solve the dimensionality disaster and privacy protection problems in the centralized method.

[0005] The introduction of advanced distributed optimization algorithms such as the Distributed Consistency Alternating Direction Method of Multipliers (DCADMM) provides a theoretical basis and implementation means for fully distributed scheduling methods. In a fully distributed architecture, the flexibility of the information system is crucial. It is necessary to establish a dynamic and flexible communication topology to adapt to the operational changes and sudden failures of the physical system. The flexibility and minimized configuration of the information system can improve the economy and operational robustness of the system and ensure stable operation in various complex operating scenarios. Summary of the invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is that the traditional centralized scheduling method requires global optimization of the entire power distribution system, which leads to a huge calculation scale, especially when the scale of the power distribution system continues to expand, it is easy to cause the problem of dimensionality disaster. It is difficult for traditional scheduling methods to effectively deal with the volatility and uncertainty of these energy sources. The centralized scheduling method needs to collect and process a large amount of user-side data, which may cause data privacy leakage and security risks. The centralized scheduling method relies on a single central control center. Once the central control center fails, the operation of the entire system will be affected, lacking robustness and flexibility.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a CPDS intraday economic dispatch method based on a fully distributed architecture, comprising:

[0009] The CPDS physical system is divided into sub-areas through data collection by smart meters and sensors;

[0010] Define decision variables within the sub-region, optimize the intra-day economic dispatch of each sub-region, construct the sub-region objective function, set the internal constraints of the sub-region, and define the coupling constraints of the sub-region;

[0011] Use DCADMM to achieve fully distributed solution of CPDS intraday economic dispatch problem.

[0012] As a preferred solution of the CPDS intraday economic dispatch method based on a fully distributed architecture described in the present invention, wherein: the data collected through smart meters and sensors include power system operation data, power load data, renewable energy data, equipment status data, communication system data, geographic information data and economic data;

[0013] The sensor includes a sensor network, a micro-phase measurement unit, a weather forecast system and a communication network.

[0014] As a preferred solution of the CPDS intraday economic dispatch method based on the fully distributed architecture described in the present invention, the CPDS physical system is divided into sub-areas, wherein under the fully distributed control architecture, the physical system part of the CPDS is divided into a number of interconnected sub-areas, and the definition and division criteria of the interconnected sub-areas adopted include:

[0015] Each sub-area of ​​the physical system is defined as a set consisting of a series of internal buses and internal distribution lines. For any sub-area α, its internal bus set is recorded as Its internal power distribution circuit is recorded as

[0016] Each sub-region is an acyclic connected graph;

[0017] There can only be 0 or 1 distribution line between any two sub-areas. If there is 1 distribution line, the two sub-areas are interconnected, and this distribution line is called the sub-area interconnection line.

[0018] If there are 0 distribution lines, the two sub-areas are not interconnected;

[0019] Each sub-region does not overlap with other sub-regions, that is, for any two sub-regions α and β, they all satisfy

[0020] The distribution system adopts the principle of closed-loop design and open-loop operation. The physical system topology of CPDS is abstracted as a tree diagram. Under the premise of following the above sub-area division criteria, the sub-area interconnection topology obtained by division still maintains a tree structure. After generating the sub-area interconnection topology, the boundary bus and injection / outflow interconnection lines of any sub-area α are obtained, and the formula is expressed as follows:

[0021]

[0022] Among them, F j represents the predecessor bus set of bus j, S j represents the set of successor buses of bus j, represents the set of injection boundary generatrixes of sub-region α, represents the outflow boundary generatrix set of sub-region α, represents the set of injection interconnection lines of sub-region α, Represents the set of outgoing interconnection lines of sub-area α.

[0023] As a preferred solution of the CPDS intraday economic dispatch method based on a fully distributed architecture described in the present invention, wherein: the definition of decision variables in the sub-region, optimizing the intraday economic dispatch of each sub-region, and constructing the sub-region objective function include that when optimizing the CPDS, the voltage and power flow constraints of the regional interconnection lines must be considered;

[0024] In order to reflect and consider the relevant constraints on regional interconnection lines in each sub-region model and facilitate the description of the subsequent optimization model, the augmented bus set and augmented line set of each sub-region are defined, and the formula is expressed as follows:

[0025]

[0026] in, represents the set of busbars inside the sub-region α, represents the injection boundary bus set of sub-region a, represents the outflow boundary generatrix set of sub-area a, represents the augmented generatrix set of sub-region a, ξ αrepresents the set of distribution lines inside sub-area α, represents the set of injection interconnection lines of sub-region α, represents the set of outgoing interconnection lines of sub-region α, represents the set of augmented lines of sub-region α;

[0027] For any sub-region α, the total number of decision variables y involved in its local decision is α , the formula is:

[0028]

[0029] The decision variables represent is the square of the bus voltage amplitude, the decision variable Represents the square of the current in the distribution line (i, j); the direction from i to j is the positive direction of the flow, p ij +jq ij represents the complex power flow of the distribution line (i, j) from the starting bus i, and the set represents the set of generator buses in region α, p g ,j+jq g ,j is its power generation complex power;

[0030] The optimization goal of DED in each sub-region is to minimize the total power generation cost of the sub-region. The convex quadratic cost function is used to construct the sub-region objective function, which is expressed as follows:

[0031]

[0032] Among them, c2, c1, c0 represent the power generation cost coefficient, minf α represents the objective function of the subregion α, which needs to be minimized; represents the summation of all generators j belonging to subregion α, represents the set of all generators in subregion α, c 2,j represents the quadratic cost coefficient of generator j, p g,j represents the power generated by generator j, c 1,j represents the primary cost coefficient of generator j, c 0,j represents the fixed cost coefficient of generator j.

[0033] As a preferred solution of the CPDS intraday economic dispatch method based on a fully distributed architecture described in the present invention, wherein: the sub-region internal constraints are set and the sub-region coupling constraints are defined, including sub-region internal constraints, sub-region coupling constraints, local DED model and global DED model;

[0034] The internal constraints of the sub-area include bus power balance constraints, bus voltage safety constraints, voltage drop equations, line power flow safety constraints, generator adjustable range constraints, and voltage, current and complex power equation constraints;

[0035] The bus power balance constraint formula is expressed as:

[0036] for

[0037]

[0038] Among them, p j represents the active power injection into bus j, q j represents the reactive power injection into bus j, represents the sum of all internal lines from bus i to bus j, represents the sum of all augmented lines from bus j to bus k, represents the set of distribution lines inside sub-area α, represents the set of distribution lines within sub-area α and connected to other sub-areas, p ij represents the active power flow from bus i to bus j, r ij represents the line resistance from busbar i to busbar j, l ij represents the square of the line current from busbar i to busbar j, g j represents the conductance of busbar j, v j represents the square of the voltage of bus j, q ij represents the reactive power flow from bus i to bus j, x ij represents the line reactance from busbar i to busbar j, b j represents the susceptance of bus j;

[0039] for The bus voltage safety lower limit and safety upper limit are defined, and the bus voltage constraint formula is expressed as:

[0040]

[0041] Among them, ν j represents the square of the voltage of bus j, represents the minimum value of the square of the voltage of bus j, represents the maximum value of the square voltage of bus j;

[0042] for The relationship between the bus voltage drop at the beginning and end of the distribution line and the line flow is described. The voltage drop equation formula is expressed as:

[0043]

[0044] Among them, νi represents the voltage square of bus i, r ij represents the resistance of line (i, j), x ij represents the reactance of line (i, j), l ij represents the square of the current in line (i, j), ν j represents the square of the voltage of bus j, p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j);

[0045] for The safe operation domain of the bidirectional power flow of the distribution line (i, j) is limited, the square of the current is introduced as a decision variable, and the ι ij ≥0 characterizes the decision variable ι ij The non-negative constraint of the line flow safety constraint formula is expressed as:

[0046]

[0047] Among them, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j), represents the maximum active power flow of line (i, j), represents the maximum reactive power flow of line (i, j);

[0048] for If the bus has a generator, the adjustable range of complex power generation is modeled as a closed convex set R by reasonably simplifying the operating characteristics of the generator. j , the generator adjustable range constraint formula is expressed as:

[0049]

[0050] Using the box constraint as this closed convex set, the formula is expressed as:

[0051]

[0052] Among them, p g,j represents the active power of generator j, q g,j represents the reactive power of generator j, R j represents the adjustable range of active and reactive power of generator j, R 2 represents the two-dimensional real number space, represents the minimum active power of generator j, represents the maximum active power of generator j, represents the minimum reactive power of generator j, represents the maximum reactive power of generator j;

[0053] The voltage, current and complex power equality constraints are expressed as follows:

[0054] ν i l ij =(p ij ) 2 +(q ij ) 2

[0055] Among them, ν i represents the voltage square of bus i, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j);

[0056] There is a product term ν of the decision variables i l ij and the square term p ij , the non-convex equality constraint is relaxed as follows and converted into an inequality constraint. The formula is expressed as follows:

[0057] ||2p ij ;2q ij ; l ij -v i ||2≤l ij +v i

[0058] Among them, ν i represents the square of the voltage of bus i, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j);

[0059] In summary, the coupling variable of sub-region α is denoted as x α , the formula is:

[0060]

[0061] in, represents the union of all coupling variables in subregion α, v i represents the square of the voltage of bus i, x α represents the set of coupled variables in subregion α, p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j), ν jrepresents the square voltage of bus j, m=(i,j) represents line (i,j), represents the set of augmented lines of subregion α, ξ α represents the internal line set of sub-region α, denotes the set of coupled lines representing the sub-region α;

[0062] x α represents the sub-region decision variable y α The local DED optimization model formula of the sub-region α is expressed as follows:

[0063]

[0064] In the first set of constraints, represents the feasible domain of decision variables formed by the internal constraint set of the sub-region; the second set of constraints corresponds to the coupling constraints of the interconnection lines between sub-region α and other sub-regions;

[0065] Among them, y α represents the set of decision variables for subregion α, f α (y α ) represents the objective function of subregion α, represents the feasible domain of decision variables formed by the internal constraint set of the sub-region, represents the coupling variable m of subregion α, represents the coupling variable m of the sub-region β, represents the set of interconnected lines of sub-region α, represents the set of interconnected lines of sub-region β, It indicates that there is a common coupling line m between sub-regions α and β;

[0066] The global DED model can be obtained by merging the sub-region models into a formula:

[0067]

[0068] Among them, Σ α f α (y α ) represents the sum of the objective functions of all sub-areas, represents the total cost of the entire system, and the constraints are the same as those of the sub-area DED model, including internal constraints and coupling constraints.

[0069] As a preferred solution of the CPDS intraday economic dispatch method based on a fully distributed architecture described in the present invention, wherein: using DCADMM to achieve fully distributed solution to the CPDS intraday economic dispatch problem includes proposing an improved consistent alternating direction multiplier method with fully distributed characteristics to implement fully distributed solution to the CPDS intraday economic dispatch optimization problem;

[0070] The DCADMM algorithm uses the following form of constraints to equivalently characterize the coupling constraints of each interconnected area:

[0071]

[0072] First, a mirror decision variable z is generated for each sub-region's coupling decision variable x, and then the sub-region coupling variable x is explicitly constrained to be the same as the mirror variable Z of its interconnected sub-region. The above constraints do not require the coupling variables of each sub-region to be globally consistent, but only require that the coupling variables of the interconnected lines of adjacent sub-regions maintain local consistency.

[0073] Among them, y α represents the set of decision variables for subregion α, f α (y α ) represents the objective function of subregion α, represents the set of constraints inside the subregion α, represents the coupling variable m of subregion α, represents the set of augmented lines of subregion α, ξ α represents the internal line set of sub-region α, represents the coupling line m between sub-regions α and β.

[0074] The problem is solved by improving the distributed iterative algorithm and convergence mechanism. First, the augmented Lagrange function of the DED optimization problem is established, and its coupling constraints are integrated into the objective function in the form of a penalty function:

[0075]

[0076] in, Represents the constraint corresponding to The Lagrange multiplier, Represents the corresponding constraint The Lagrange multiplier, L α represents the augmented Lagrange function of the subregion α, represents the penalty function coefficient, ∥·∥2 represents the two-norm;

[0077] Rearrange and define the following variables:

[0078]

[0079] in, represents the set of mirror coupling variables of subregion α, z α represents the set of mirror variables of subregion α, represents the set of mirror variables of subregion α, represents the set of Lagrange multipliers of the subregion α, Represents the set of mirrored Lagrange multipliers for the subregion α.

[0080] As a preferred solution of the CPDS intraday economic dispatch method based on a fully distributed architecture described in the present invention, the use of DCADMM to achieve fully distributed solution of the CPDS intraday economic dispatch problem also includes decomposing the DED optimization problem into each sub-region, and each sub-region can alternately iterate and update its own y according to the following steps a ,x a ,z a ,λ a variables to achieve a fully distributed solution to the DED optimization problem; the sub-region locally updates y α ,x α , fixed variables The value is the result of the previous iteration Optimize only y α (x α ), assuming that the sub-region α has obtained and sorted the mirror variable through communication and interaction with its adjacent sub-regions The formula for the known quantity of the model is expressed as:

[0081]

[0082] in, represents the coupling variable of sub-region α in the t+1th iteration, represents the set of independent constraints for subregion α, represents the Lagrange multiplier of subregion α in the tth iteration, represents the penalty coefficient in the tth iteration, represents the mirror decision variable of the adjacent subregion of subregion α in the tth iteration;

[0083] For sub-region α, the latest Each component of the value is sent to the corresponding adjacent sub-region, and the sub-region α can be sorted and obtained The variables serve as inputs for subsequent model solutions;

[0084] Sub-region local update z α , fixed variables The value is the latest iteration result Optimize z α The formula is:

[0085]

[0086] Directly apply the first-order optimality necessary conditions in the KKT conditions to obtain the analytical expression of the optimal solution:

[0087]

[0088] in, represents the mirror decision variable of subregion α in the t+1th iteration, represents the Lagrange multiplier of subregion α in the tth iteration, represents the Lagrange multiplier of the adjacent subregion of subregion α in the tth iteration, represents the coupling variable of the adjacent sub-region of sub-region α in the t+1th iteration, represents the penalty coefficient in the tth iteration;

[0089] For sub-region α, the latest Each component of the value is sent to the corresponding adjacent sub-region. After communication, the sub-region α can be sorted and obtained The variables serve as inputs for subsequent model solutions;

[0090] Fixed variable (x α ,z α ) is the value of the latest iteration result optimization According to the duality theory, the formula for maximizing the Lagrange dual function is expressed as:

[0091]

[0092] right The update follows the gradient ascent rule formula:

[0093]

[0094]

[0095] In summary, based on the process of solving the DED optimization problem using DCADMM, by executing the above DCADMM algorithm, the two coupling variables of the same sub-region interconnection line will gradually become consistent.

[0096] A CPDS intraday economic dispatch system based on a fully distributed architecture, in which:

[0097] The acquisition module collects data through smart meters and sensors and divides the CPDS physical system into sub-areas;

[0098] Define modules, define decision variables within sub-regions, optimize the intra-day economic dispatch of each sub-region, construct sub-region objective functions, set sub-region internal constraints, and define sub-region coupling constraints;

[0099] The solution module uses DCADMM to achieve fully distributed solution of CPDS intraday economic dispatch problem.

[0100] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0101] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0102] Beneficial effects of the present invention: The CPDS intraday economic dispatch method based on a fully distributed architecture provided by the present invention divides the distribution system into multiple sub-areas and calculates the dispatch decisions of each sub-area in parallel, which significantly reduces the computational load and complexity and improves the computational efficiency. Under the distributed architecture, the failure of a single sub-area or communication link will not cause the failure of the entire system, thereby improving the fault tolerance of the system. Each sub-area performs data processing and decision-making locally, and only needs to share the necessary coupling information, which reduces the amount of data transmission and protects the privacy and security of user data. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0104] Figure 1 An overall flow chart of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the first embodiment of the present invention;

[0105] Figure 2 A schematic diagram of sub-region division of a CPDS physical system based on a particle swarm genetic algorithm provided in the first embodiment of the present invention;

[0106] Figure 3 A DED optimization model variable and multiplier relationship diagram of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the first embodiment of the present invention;

[0107] Figure 4 A distributed DED optimization flow chart based on DCADMM of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the first embodiment of the present invention;

[0108] Figure 5A schematic diagram of a regional CPDS of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the first embodiment of the present invention;

[0109] Figure 6 An information flow process for fully distributed solution of ARDED, a CPDS intraday economic dispatch method based on a fully distributed architecture, provided in the first embodiment of the present invention;

[0110] Figure 7 A CPDS intraday economic dispatch method based on a fully distributed architecture is provided as a first embodiment of the present invention, and a regional CPDS distributed communication interaction process is deduced;

[0111] Figure 8 An improved IEEE33-node CPDS physical system topology of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in a second embodiment of the present invention;

[0112] Fig. 9 A CN11 coupling variable convergence curve diagram of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the second embodiment of the present invention;

[0113] Fig.10 A CN09 coupling variable convergence curve of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the second embodiment of the present invention;

[0114] Fig.11 A coupling variable residual and DED optimal value convergence curve diagram of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in a second embodiment of the present invention;

[0115] Fig.12 A CN11 coupling voltage variable convergence curve diagram under a communication interruption fault of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in a second embodiment of the present invention;

[0116] Fig.13 A topology change diagram of a physical network before and after reconstruction of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in a second embodiment of the present invention;

[0117] Fig.14 A diagram of information system topology changes before and after physical network reconstruction of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in a second embodiment of the present invention;

[0118] Fig.15 A CN01 coupling variable convergence curve of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the second embodiment of the present invention;

[0119] Fig.16 A convergence curve diagram of coupling variable residual and DED optimal value in a physical network reconstruction scenario of a CPDS intraday economic dispatch method based on a fully distributed architecture provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0120] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0121] Example 1

[0122] Reference Figure 1 , which is an embodiment of the present invention, provides a CPDS intraday economic dispatch method based on a fully distributed architecture, comprising:

[0123] S1: The CPDS physical system is divided into sub-areas by collecting data through smart meters and sensors.

[0124] The data collected through smart meters and sensors include power system operation data, power load data, renewable energy data, equipment status data, communication system data, geographic information data and economic data.

[0125] Sensors include sensor networks, micro-phase measurement units, weather forecast systems and communication networks.

[0126] The physical system of CPDS is divided into sub-areas. Under the fully distributed control architecture, the physical system part of CPDS is divided into several interconnected sub-areas. The definition and division criteria of the interconnected sub-areas used include:

[0127] Each sub-area of ​​the physical system is defined as a set consisting of a series of internal buses and internal distribution lines. For any sub-area α, its internal bus set is recorded as Its internal power distribution circuit is recorded as

[0128] Each sub-region is a connected graph without cycles.

[0129] There can only be 0 or 1 distribution lines between any two sub-areas. If there is 1 distribution line, the two sub-areas are interconnected, and this distribution line is called the sub-area interconnection line.

[0130] If there are 0 distribution lines, the two sub-areas are not interconnected.

[0131] Each sub-region does not overlap with other sub-regions, that is, for any two sub-regions α and β, they all satisfy

[0132] The distribution system adopts the principle of closed-loop design and open-loop operation. The physical system topology of CPDS is abstracted as a tree diagram. Under the premise of following the above sub-area division criteria, the sub-area interconnection topology obtained by division still maintains a tree structure. After generating the sub-area interconnection topology, the boundary bus and injection / outflow interconnection lines of any sub-area α are obtained, and the formula is expressed as follows:

[0133]

[0134]

[0135]

[0136] Among them, F j represents the predecessor bus set of bus j, S j represents the set of successor buses of bus j, represents the set of injection boundary generatrixes of sub-region α, represents the outflow boundary generatrix set of sub-region α, represents the set of injection interconnection lines of sub-region α, Represents the set of outgoing interconnection lines of sub-area α.

[0137] S2: Define decision variables within the sub-region, optimize the intraday economic scheduling of each sub-region, construct the sub-region objective function, set the internal constraints of the sub-region, and define the coupling constraints of the sub-region.

[0138] When optimizing CPDS, the voltage and power flow constraints of regional interconnection lines must be considered;

[0139] In order to reflect and consider the relevant constraints on regional interconnection lines in each sub-region model and facilitate the description of the subsequent optimization model, the augmented bus set and augmented line set of each sub-region are defined, and the formula is expressed as follows:

[0140]

[0141] in, represents the set of busbars inside the sub-region α, represents the injection boundary bus set of sub-region a, represents the outflow boundary generatrix set of sub-area a, represents the augmented generatrix set of sub-region a, ξ α represents the set of distribution lines inside sub-area α, represents the set of injection interconnection lines of sub-region α, represents the set of outgoing interconnection lines of sub-region α, represents the set of augmented lines of subregion α.

[0142] For any sub-region α, the total number of decision variables y involved in its local decision is α , the formula is:

[0143]

[0144] The decision variables represent is the square of the bus voltage amplitude, the decision variable Represents the square of the current in the distribution line (i, j); the direction from i to j is the positive direction of the flow, p ij +jq ij represents the complex power flow of the distribution line (i, j) from the starting bus i, and the set represents the set of generator buses in region α, p g ,j+jq g , j is its generated power.

[0145] The optimization goal of DED in each sub-region is to minimize the total power generation cost of the sub-region. The convex quadratic cost function is used to construct the sub-region objective function, which is expressed as follows:

[0146]

[0147] Among them, c2, c1, c0 represent the power generation cost coefficient, minf α represents the objective function of the subregion α, which needs to be minimized; represents the summation of all generators j belonging to subregion α, represents the set of all generators in subregion α, c 2,j represents the quadratic cost coefficient of generator j, p g,j represents the power generated by generator j, c 1,j represents the primary cost coefficient of generator j, c 0,j represents the fixed cost coefficient of generator j.

[0148] Sub-region internal constraints, sub-region coupling constraints, local DED model and global DED model;

[0149] The internal constraints of the sub-area include bus power balance constraints, bus voltage safety constraints, voltage drop equations, line flow safety constraints, generator adjustable range constraints, and voltage, current and complex power equation constraints.

[0150] The bus power balance constraint formula is expressed as:

[0151] for

[0152]

[0153] Among them, p j represents the active power injection into bus j, q j represents the reactive power injection into bus j, represents the sum of all internal lines from bus i to bus j, represents the sum of all augmented lines from bus j to bus k, represents the set of distribution lines inside sub-area α, represents the set of distribution lines within sub-area α and connected to other sub-areas, p ij represents the active power flow from bus i to bus j, r ij represents the line resistance from busbar i to busbar j, l ij represents the square of the line current from busbar i to busbar j, g j represents the conductance of busbar j, v j represents the square of the voltage of bus j, q ij represents the reactive power flow from bus i to bus j, x ij represents the line reactance from busbar i to busbar j, b j Represents the susceptance of bus j.

[0154] for The bus voltage safety lower limit and safety upper limit are defined, and the bus voltage constraint formula is expressed as:

[0155]

[0156] Among them, ν j represents the square of the voltage of bus j, represents the minimum value of the square of the voltage of bus j, Indicates the maximum value of the square of the voltage of bus j.

[0157] for The relationship between the bus voltage drop at the beginning and end of the distribution line and the line flow is described. The voltage drop equation formula is expressed as:

[0158]

[0159] Among them, ν i represents the voltage square of bus i, r ij represents the resistance of line (i, j), x ij represents the reactance of line (i, j), l ij represents the square of the current in line (i, j), ν j represents the square of the voltage of bus j, p ijrepresents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j).

[0160] for The safe operation domain of the bidirectional power flow of the distribution line (i, j) is limited, the square of the current is introduced as a decision variable, and the ι ij ≥0 characterizes the decision variable ι ij The non-negative constraint of the line flow safety constraint formula is expressed as:

[0161]

[0162] Among them, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j), represents the maximum active power flow of line (i, j), represents the maximum reactive power flow of line (i,j).

[0163] for If the bus has a generator, the adjustable range of complex power generation is modeled as a closed convex set R by reasonably simplifying the operating characteristics of the generator. j , the generator adjustable range constraint formula is expressed as:

[0164]

[0165] Using the box constraint as this closed convex set, the formula is expressed as:

[0166]

[0167] Among them, p g,j represents the active power of generator j, q g,j represents the reactive power of generator j, R j represents the adjustable range of active and reactive power of generator j, R 2 represents the two-dimensional real number space, represents the minimum active power of generator j, represents the maximum active power of generator j, represents the minimum reactive power of generator j, represents the maximum reactive power of generator j.

[0168] The voltage, current and complex power equality constraints are expressed as follows:

[0169] ν i l ij =(p ij )2 +(q ij ) 2

[0170] Among them, ν i represents the square of the voltage of bus i, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j).

[0171] There is a product term ν of the decision variables i l ij With the square term p ij , the non-convex equality constraint is relaxed as follows and converted into an inequality constraint. The formula is expressed as follows:

[0172] ||2p ij ;2q ij ; l ij -v i ||2≤l ij +v i

[0173] Among them, ν i represents the square of the voltage of bus i, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j).

[0174] In summary, the coupling variable of sub-region α is denoted as x α , the formula is:

[0175]

[0176] in, represents the union of all coupling variables in subregion α, v i represents the square of the voltage of bus i, x α represents the set of coupled variables in subregion α, p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j), ν j represents the square voltage of bus j, m=(i,j) represents line (i,j), represents the set of augmented lines of subregion α, ξ α represents the internal line set of sub-region α, Denotes the set of coupled lines representing the sub-region α.

[0177] x αrepresents the sub-region decision variable y α The local DED optimization model formula of the sub-region α is expressed as follows:

[0178]

[0179] In the first set of constraints, represents the feasible domain of decision variables formed by the internal constraint set of the sub-region. The second set of constraints corresponds to the coupling constraints between the sub-region α and the interconnection lines of other sub-regions;

[0180] Among them, y α represents the set of decision variables for subregion α, f α (y α ) represents the objective function of subregion α, represents the feasible domain of decision variables formed by the internal constraint set of the sub-region, represents the coupling variable m of subregion α, represents the coupling variable m of the sub-region β, represents the set of interconnected lines of sub-region α, represents the set of interconnected lines of sub-region β, It indicates that there is a common coupling line m between sub-regions α and β.

[0181] The global DED model can be obtained by merging the sub-region models into a formula:

[0182]

[0183] Among them, Σ α f α (y α ) represents the sum of the objective functions of all sub-areas, represents the total cost of the entire system, and the constraints are the same as those of the sub-area DED model, including internal constraints and coupling constraints.

[0184] S3: Use DCADMM to achieve fully distributed solution of CPDS intraday economic dispatch problem.

[0185] The use of DCADMM to achieve fully distributed solution to the CPDS intraday economic dispatch problem includes proposing an improved consistent alternating direction multiplier method with fully distributed characteristics to implement fully distributed solution to the CPDS intraday economic dispatch optimization problem.

[0186] The DCADMM algorithm uses the following form of constraints to equivalently characterize the coupling constraints of each interconnected area:

[0187]

[0188] First, a completely consistent mirror decision variable z is generated for each sub-region's coupling decision variable x, and then the sub-region coupling variable x is explicitly constrained to be the same as the mirror variable Z of its interconnected sub-region. The above constraints do not require the coupling variables of each sub-region to have global consistency, but only need to satisfy the local consistency of the coupling variables related to the interconnection lines of adjacent sub-regions.

[0189] Among them, y α represents the set of decision variables for subregion α, f α (y α ) represents the objective function of subregion α, represents the set of constraints inside the subregion α, represents the coupling variable m of subregion α, represents the set of augmented lines of subregion α, ξ α represents the internal line set of sub-region α, represents the coupling line m between sub-regions α and β.

[0190] The problem is solved by improving the distributed iterative algorithm and convergence mechanism. First, the augmented Lagrange function of the DED optimization problem is established, and its coupling constraints are integrated into the objective function in the form of a penalty function:

[0191]

[0192] in, Represents the corresponding constraint The Lagrange multiplier, Represents the corresponding constraint The Lagrange multiplier, L α represents the augmented Lagrange function of the subregion α, ρ represents the penalty function coefficient, and ∥·∥2 represents the second norm.

[0193] Rearrange and define the following variables:

[0194]

[0195] in, represents the set of mirror coupling variables of subregion α, z α represents the set of mirror variables of subregion α, represents the set of mirror variables of subregion α, represents the set of Lagrange multipliers of the subregion α, Represents the set of mirrored Lagrange multipliers for the subregion α.

[0196] After decomposing the DED optimization problem into sub-regions, each sub-region can iteratively update its own y in the following steps:a ,x a ,z a ,λ a variables to achieve a fully distributed solution to the DED optimization problem; the sub-region locally updates y α ,x α , fixed variables The value is the result of the previous iteration Optimize only y α (x α ), assuming that the sub-region α has obtained and sorted the mirror variable through communication and interaction with its adjacent sub-regions The formula for the known quantity of the model is expressed as:

[0197]

[0198] in, represents the coupling variable of sub-region α in the t+1th iteration, represents the set of independent constraints for subregion α, represents the Lagrange multiplier of subregion α in the tth iteration, represents the penalty coefficient in the tth iteration, represents the mirror decision variable of the adjacent sub-region of sub-region α in the tth iteration. For sub-region α, the latest Each component of the value is sent to the corresponding adjacent sub-region, and the sub-region α can be sorted and obtained The variables serve as input for subsequent model solving.

[0199] Sub-region local update z α , fixed variables The value is the latest iteration result Optimize z α The formula is:

[0200]

[0201] Directly apply the first-order optimality necessary conditions in the KKT conditions to obtain the analytical expression of the optimal solution:

[0202]

[0203] in, represents the mirror decision variable of subregion α in the t+1th iteration, represents the Lagrange multiplier of subregion α in the tth iteration, represents the Lagrange multiplier of the adjacent subregion of subregion α in the tth iteration, represents the coupling variable of the adjacent sub-region of sub-region α in the t+1th iteration, represents the penalty coefficient in the tth iteration.

[0204] For sub-region α, the latest Each component of the value is sent to the corresponding adjacent sub-region. After communication, the sub-region α can be sorted and obtained The variables serve as input for subsequent model solving.

[0205] Fixed variable (x α ,z α ) is the value of the latest iteration result optimization According to the duality theory, the formula for maximizing the Lagrange dual function is expressed as:

[0206]

[0207] right The update follows the gradient ascent rule formula:

[0208]

[0209]

[0210] In summary, based on the process of solving the DED optimization problem using DCADMM, by executing the above DCADMM algorithm, the two coupling variables of the same sub-region interconnection line will gradually become consistent.

[0211] On the other hand, this embodiment also provides a CPDS intraday economic dispatch system based on a fully distributed architecture, which includes:

[0212] The acquisition module collects data through smart meters and sensors and divides the CPDS physical system into sub-areas.

[0213] Define modules, define decision variables within sub-regions, optimize the intraday economic scheduling of each sub-region, construct sub-region objective functions, set sub-region internal constraints, and define sub-region coupling constraints.

[0214] The solution module uses DCADMM to achieve fully distributed solution of CPDS intraday economic dispatch problem.

[0215] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0216] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0217] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0218] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0219] Example 2

[0220] Select a distribution network of appropriate size as the test object, such as Figure 8 IEEE33 node power distribution system or IEEE69 node power distribution system.

[0221] Physical parameters: including the initial voltage of each bus, upper and lower voltage limits, impedance parameters of each line, output range of distributed power sources, etc.

[0222] Economic parameters: including the power generation cost function of each distributed power source (such as quadratic function coefficient), load electricity price information, etc.

[0223] Optimization parameters: including the initial value of the Lagrange multiplier, the initial value and decreasing rule of the penalty coefficient, the iteration step size, etc.

[0224] Initialize the decision variables of each sub-region; Initialize the coupling variables of each sub-region and mirror variables; initialize Lagrange multipliers.

[0225] Perform iterative calculations according to the following steps until all variables converge.

[0226] Fixed variables The value is the result of the previous iteration Optimize only y α (x α ).

[0227] The latest Each component of the value is sent to the corresponding adjacent sub-region, and the sub-region α is sorted to obtain As input for subsequent model solving, the fixed variables The value is the latest iteration result Optimize only z α .

[0228] The latest Each component of the value is sent to the corresponding adjacent sub-region, and the sub-region α is sorted to obtain As the input for subsequent model solving. Fixed variables (xα ,z α ) is the value of the latest iteration result According to the duality theory, the Lagrangian dual function is maximized. The update follows the gradient ascent rule.

[0229] Check the convergence of the algorithm to ensure that the coupling variables of each sub-region are consistent.

[0230] By monitoring the changes in the objective function value and the Lagrange multiplier, determine whether the algorithm has reached the convergence standard. Calculate the total scheduling cost to verify the economic feasibility of the scheduling plan. Simulate different failure scenarios (such as communication interruption, line failure) to verify the robustness and flexibility of the system. Count the number of iterations and calculation time of the algorithm to evaluate its calculation efficiency.

[0231] Analyze the dispatch results of each sub-area and the overall performance of the system under normal operating conditions. Simulate communication interruptions between some sub-areas to observe how the system continues to achieve economic dispatch by dynamically adjusting the communication topology. Simulate topological changes in the physical network to verify that the system achieves a new optimal dispatch by adaptively adjusting the information system.

[0232] The present invention has significant beneficial effects in improving computing efficiency, enhancing system robustness, ensuring data privacy, improving economy, enhancing intelligence level, reducing operating costs, and promoting new energy consumption, and provides strong support for the efficient, intelligent and safe operation of modern power systems. Figure 9-Figure 16 .

[0233] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A CPDS intraday economic dispatch method based on a fully distributed architecture, characterized in that: include: The CPDS physical system is divided into sub-areas through data collection by smart meters and sensors; Define decision variables within the sub-region, optimize the intra-day economic dispatch of each sub-region, construct the sub-region objective function, set the internal constraints of the sub-region, and define the coupling constraints of the sub-region; Use DCADMM to achieve fully distributed solution of CPDS intraday economic dispatch problem.

2. The CPDS intraday economic dispatch method based on a fully distributed architecture as claimed in claim 1, characterized in that: The data collected by smart meters and sensors include power system operation data, power load data, renewable energy data, equipment status data, communication system data, geographic information data and economic data; The sensor includes a sensor network, a micro-phase measurement unit, a weather forecast system and a communication network.

3. The CPDS intraday economic dispatch method based on a fully distributed architecture as claimed in claim 2, characterized in that: The physical system of CPDS is divided into sub-areas. Under the fully distributed control architecture, the physical system part of CPDS is divided into several interconnected sub-areas. The definition and division criteria of the interconnected sub-areas used include: Each sub-area of ​​the physical system is defined as a set consisting of a series of internal buses and internal distribution lines. For any sub-area α, its internal bus set is recorded as Its internal power distribution circuit is recorded as Each sub-region is an acyclic connected graph; There can only be 0 or 1 distribution line between any two sub-areas. If there is 1 distribution line, the two sub-areas are interconnected, and this distribution line is called the sub-area interconnection line. If there are 0 distribution lines, the two sub-areas are not interconnected; Each sub-region does not overlap with other sub-regions, that is, for any two sub-regions α and β, they all satisfy The distribution system adopts the principle of closed-loop design and open-loop operation. The physical system topology of CPDS is abstracted as a tree diagram. Under the premise of following the above sub-area division criteria, the sub-area interconnection topology obtained by division still maintains a tree structure. After generating the sub-area interconnection topology, the boundary bus and injection / outflow interconnection lines of any sub-area α are obtained, and the formula is expressed as follows: Among them, F j represents the predecessor bus set of bus j, S j represents the set of successor buses of bus j, represents the set of injection boundary generatrixes of sub-region α, represents the outflow boundary generatrix set of sub-region α, represents the set of injection interconnection lines of sub-region α, Represents the set of outgoing interconnection lines of sub-area α.

4. The CPDS intraday economic dispatch method based on a fully distributed architecture as claimed in claim 3, characterized in that: The definition of decision variables in the sub-region, optimization of the intra-day economic dispatch of each sub-region, and construction of the sub-region objective function include that when optimizing the CPDS, the voltage and power flow constraints of the regional interconnection lines must be considered; In order to reflect and consider the relevant constraints on regional interconnection lines in each sub-region model and facilitate the description of the subsequent optimization model, the augmented bus set and augmented line set of each sub-region are defined, and the formula is expressed as follows: in, represents the set of busbars inside the sub-region α, represents the injection boundary bus set of sub-region a, represents the outflow boundary generatrix set of sub-area a, represents the augmented generatrix set of sub-region a, ξ α represents the set of distribution lines inside sub-area α, represents the set of injection interconnection lines of sub-region α, represents the set of outgoing interconnection lines of sub-region α, represents the set of augmented lines of sub-region α; For any sub-region α, the total number of decision variables y involved in its local decision is α , the formula is: The decision variables represent is the square of the bus voltage amplitude, the decision variable Represents the square of the current in the distribution line (i, j); the direction from i to j is the positive direction of the flow, p ij +jq ij represents the complex power flow of the distribution line (i, j) from the starting bus i, and the set represents the generator bus set of region α, p g ,j+jq g ,j is its power generation complex power; The optimization goal of DED in each sub-region is to minimize the total power generation cost of the sub-region. The convex quadratic cost function is used to construct the sub-region objective function, which is expressed as follows: Among them, c2, c1, c0 represent the power generation cost coefficient, minf α represents the objective function of the subregion α, which needs to be minimized; represents the summation of all generators j belonging to subregion α, represents the set of all generators in subregion α, c 2,j represents the quadratic cost coefficient of generator j, p g,j represents the power generated by generator j, c 1,j represents the primary cost coefficient of generator j, c 0,j represents the fixed cost coefficient of generator j.

5. The CPDS intraday economic dispatch method based on a fully distributed architecture as claimed in claim 4, characterized in that: The sub-region internal constraints are set, and the sub-region coupling constraints are defined, including sub-region internal constraints, sub-region coupling constraints, local DED models, and global DED models; The internal constraints of the sub-area include bus power balance constraints, bus voltage safety constraints, voltage drop equations, line power flow safety constraints, generator adjustable range constraints, and voltage, current and complex power equation constraints; The bus power balance constraint formula is expressed as: for Among them, p j represents the active power injection into bus j, q j represents the reactive power injection into bus j, represents the sum of all internal lines from bus i to bus j, represents the sum of all augmented lines from bus j to bus k, represents the set of distribution lines inside sub-area α, represents the set of distribution lines within sub-area α and connected to other sub-areas, p ij represents the active power flow from bus i to bus j, r ij represents the line resistance from busbar i to busbar j, l ij represents the square of the line current from busbar i to busbar j, g j represents the conductance of busbar j, v j represents the square of the voltage of bus j, q ij represents the reactive power flow from bus i to bus j, x ij represents the line reactance from busbar i to busbar j, b j represents the susceptance of bus j; for The bus voltage safety lower limit and safety upper limit are defined, and the bus voltage constraint formula is expressed as: Among them, ν j represents the square of the voltage of bus j, represents the minimum value of the square of the voltage of bus j, represents the maximum value of the square voltage of bus j; for The relationship between the bus voltage drop at the beginning and end of the distribution line and the line flow is described. The voltage drop equation is expressed as: Among them, ν i represents the voltage square of bus i, r ij represents the resistance of line (i, j), x ij represents the reactance of line (i, j, l ij represents the square of the current in line (i, j), ν j represents the square of the voltage of bus j, p ij represents the active power flow of line i,j), q ij represents the reactive power flow of line (i, j); for The safe operation domain of the bidirectional power flow of the distribution line (i, j) is limited, the square of the current is introduced as a decision variable, and the ι ij ≥0 characterizes the decision variable ι ij The non-negative constraint of the line flow safety constraint formula is expressed as: l ij ≥0 Among them, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j), represents the maximum active power flow of line (i, j), represents the maximum reactive power flow of line i,j); for If the bus has a generator, the adjustable range of complex power generation is modeled as a closed convex set R by reasonably simplifying the operating characteristics of the generator. j , the generator adjustable range constraint formula is expressed as: Using the box constraint as this closed convex set, the formula is expressed as: Among them, p g,j represents the active power of generator j, q g,j represents the reactive power of generator j, R j represents the adjustable range of active and reactive power of generator j, R 2 represents the two-dimensional real number space, represents the minimum active power of generator j, represents the maximum active power of generator j, represents the minimum reactive power of generator j, represents the maximum reactive power of generator j; The voltage, current and complex power equality constraints are expressed as follows: n i l ij =(p ij ) 2 +(q ij ) 2 Among them, ν i represents the square of the voltage of bus i, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j); There is a product term ν of the decision variables i l ij and the square term p ij , the non-convex equality constraint is relaxed as follows and converted into an inequality constraint. The formula is expressed as follows: ||2p ij ;2q ij ;l ij -in i ||2≤l ij +v i Among them, ν i represents the square of the voltage of bus i, l ij represents the square of the current in line (i, j), p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j); In summary, the coupling variable of sub-region α is denoted as x α , the formula is: in, represents the union of all coupling variables in subregion α, v i represents the square of the voltage of bus i, x α represents the set of coupled variables in subregion α, p ij represents the active power flow of line (i, j), q ij represents the reactive power flow of line (i, j), ν j represents the square voltage of bus j, m=(i,j) represents line (i,j), represents the set of augmented lines of subregion α, ξ α represents the internal line set of sub-region α, denotes the set of coupled lines representing the sub-region α; x α represents the sub-region decision variable y α The local DED optimization model formula of the sub-region α is expressed as follows: In the first set of constraints, represents the feasible domain of decision variables formed by the internal constraint set of the sub-region; The second set of constraints corresponds to the coupling constraints between sub-region α and the interconnection lines of other sub-regions; Among them, y α represents the set of decision variables for subregion α, f α (y α ) represents the objective function of subregion α, represents the feasible domain of decision variables formed by the internal constraint set of the sub-region, represents the coupling variable m of subregion α, represents the coupling variable m of the sub-region β, represents the set of interconnected lines of sub-region α, represents the set of interconnected lines of sub-region β, It indicates that there is a common coupling line m between sub-regions α and β; The global DED model can be obtained by merging the sub-region models into a formula: Among them, ∑ α f α (y α ) represents the sum of the objective functions of all sub-areas, represents the total cost of the entire system, and the constraints are the same as those of the sub-area DED model, including internal constraints and coupling constraints.

6. The CPDS intraday economic dispatch method based on a fully distributed architecture as claimed in claim 5, characterized in that: Using DCADMM to achieve fully distributed solution to the intraday economic dispatch problem of CPDS includes proposing an improved consistent alternating direction multiplier method with fully distributed characteristics to implement fully distributed solution to the intraday economic dispatch optimization problem of CPDS; The DCADMM algorithm uses the following form of constraints to equivalently characterize the coupling constraints of each interconnected area: First, a mirror decision variable z is generated for each sub-region's coupling decision variable x, and then the sub-region coupling variable x is explicitly constrained to be the same as the mirror variable Z of its interconnected sub-region. The above constraints do not require the coupling variables of each sub-region to be globally consistent, but only require that the coupling variables of the interconnected lines of adjacent sub-regions maintain local consistency. Among them, y α represents the set of decision variables for subregion α, f α (y α ) represents the objective function of subregion α, represents the set of constraints inside the subregion α, represents the coupling variable m of subregion α, represents the set of augmented lines of subregion α, ξ α represents the internal line set of sub-region α, represents the coupling line m between sub-regions α and β; The problem is solved by improving the distributed iterative algorithm and convergence mechanism. First, the augmented Lagrange function of the DED optimization problem is established, and its coupling constraints are integrated into the objective function in the form of a penalty function: in, Represents the corresponding constraint The Lagrange multiplier, Represents the corresponding constraint The Lagrange multiplier, L α represents the augmented Lagrange function of the subregion α, ρ represents the penalty function coefficient, and ∥·∥2 represents the two-norm; Rearrange and define the following variables: in, represents the set of mirror coupling variables of subregion α, z α represents the set of mirror variables of subregion α, represents the set of mirror variables of subregion α, represents the set of Lagrange multipliers of the subregion α, Represents the set of mirrored Lagrange multipliers for the subregion α.

7. The CPDS intraday economic dispatch method based on a fully distributed architecture as claimed in claim 6, characterized in that Using DCADMM to achieve a fully distributed solution to the CPDS intraday economic dispatch problem also includes decomposing the DED optimization problem into sub-areas, and each sub-area can iteratively update its own y in the following steps: a ,x a ,z a ,λ a variables to achieve a fully distributed solution to the DED optimization problem; the sub-region locally updates y α ,x α , fixed variables The value is the result of the previous iteration Optimize only y α (x α ), assuming that the sub-region α has obtained and sorted the mirror variable through communication and interaction with its adjacent sub-regions The formula for the known quantity of the model is expressed as: in, represents the coupling variable of sub-region α in the t+1th iteration, represents the set of independent constraints for subregion α, represents the Lagrange multiplier of subregion α in the tth iteration, represents the penalty coefficient in the tth iteration, represents the mirror decision variable of the adjacent subregion of subregion α in the tth iteration; For sub-region α, the latest Each component of the value is sent to the corresponding adjacent sub-region, and the sub-region α can be sorted and obtained The variables serve as inputs for subsequent model solutions; Sub-region local update z α , fixed variables The value is the latest iteration result Optimize z α The formula is: Directly apply the first-order optimality necessary conditions in the KKT conditions to obtain the analytical expression of the optimal solution: in, represents the mirror decision variable of subregion α in the t+1th iteration, represents the Lagrange multiplier of subregion α in the tth iteration, represents the Lagrange multiplier of the adjacent subregion of subregion α in the tth iteration, represents the coupling variable of the adjacent sub-region of sub-region α in the t+1th iteration, represents the penalty coefficient in the tth iteration; For sub-region α, the latest Each component of the value is sent to the corresponding adjacent sub-region. After communication, the sub-region α can be sorted and obtained The variables serve as inputs for subsequent model solutions; Fixed variables The value is the latest iteration result optimization According to the duality theory, the formula for maximizing the Lagrange dual function is expressed as: right The update follows the gradient ascent rule formula: For sub-region α, the latest Each component of the value is sent to the corresponding adjacent sub-region. After communication, the sub-region α can be sorted and obtained The variables serve as inputs for subsequent model solving; in summary, in the process of solving the DED optimization problem based on DCADMM, by executing the above DCADMM algorithm, the two coupling variables of the same sub-region interconnection line will gradually converge to the same consistency.

8. A CPDS intraday economic dispatch system based on a fully distributed architecture using the method as described in any one of claims 1 to 7, characterized in that: The acquisition module collects data through smart meters and sensors and divides the CPDS physical system into sub-areas; Define modules, define decision variables within sub-regions, optimize the intra-day economic dispatch of each sub-region, construct sub-region objective functions, set sub-region internal constraints, and define sub-region coupling constraints; The solution module uses DCADMM to achieve fully distributed solution of CPDS intraday economic dispatch problem.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the CPDS intraday economic dispatch method based on a fully distributed architecture are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the CPDS intraday economic dispatch method based on a fully distributed architecture are implemented.

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