Real-time economic dispatch method for smart grid based on distributed consensus algorithm

Abstract

The present invention belongs to the field of renewable energy power generation, and specifically relates to a real-time economic dispatch method for smart grids based on a distributed consensus algorithm. The method uses distributed computing technology to achieve seamless communication and coordination between generator nodes in a power grid, while taking transmission losses into account, thereby increasing the complexity and practicability of the solution. The present invention can monitor and respond to mismatches between power demand and output in real time, and achieve real-time updates and scalability of generators. In addition, the present invention has plug-and-play capabilities, and can quickly adapt to rapid changes in smart grids, effectively optimizing the operating costs and reliability of the power system, and improving the management and operation efficiency of smart grids. The present invention has high real-time economic dispatch efficiency and the ability to quickly adapt to dynamic grid changes, thereby improving the overall management and operation efficiency of smart grids.

Description

Technical Field

[0001] The present invention belongs to the field of renewable energy power generation, and in particular to a real-time economic dispatching method for a smart grid based on a distributed consensus algorithm. Background Art

[0002] In the context of economic dispatch, smart grid can be defined as a power supply system that uses advanced information and communication technologies and intelligent control methods, with the goal of achieving real-time monitoring, precise control and intelligent dispatch of all aspects of the power system. The development of smart grid aims to improve the efficiency, reliability and sustainability of the power system, meet the growing power demand through optimized resource allocation and flexible regulation, and promote large-scale access and utilization of clean energy.

[0003] Generators in smart grids generate different amounts of power to meet the needs of the entire load, and the economic dispatch problem is derived from this. The economic dispatch problem is one of the basic energy management problems in power systems. Its goal is to minimize the operating cost while meeting the load demand and system constraints by dispatching the output power of all generators.

[0004] Centralized economic dispatch algorithms, such as genetic algorithms, particle swarm optimization, evolutionary programming, differential evolution, and simulated annealing, were initially used to solve the economic dispatch problem. These centralized economic dispatch algorithms rely on a powerful central controller to collect global information in order to process large amounts of data and require large amounts of storage capacity and fast running speeds. However, this results in centralized economic dispatch algorithms being costly and prone to single point failures. In addition, they lack robustness, and when generators and loads need to be added or removed from the smart grid, the entire system needs to be rebuilt.

[0005] In recent years, distributed economic dispatch has received extensive attention. Distributed economic dispatch emphasizes the autonomous decision-making and individual intelligence of each unit. Each unit communicates with its neighbors through an information exchange protocol, makes independent decisions after information integration to adjust its own output, and thus achieves the goal of economic dispatch. Compared with centralized economic dispatch, distributed economic dispatch has stronger robustness, scalability, uniform computing and communication burden, and stronger information confidentiality and security. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a real-time economic dispatching method for a smart grid based on a distributed consensus algorithm in view of the defects of the above-mentioned prior art.

[0007] The present invention adopts the following scheme to implement a real-time economic dispatch method for a smart grid based on a distributed consensus algorithm, comprising the following steps:

[0008] Step 1: Construct a smart grid economic dispatch problem model based on transmission loss conditions. The model includes: determining the type of generators in the smart grid and their power generation costs; setting the objective function of the economic dispatch problem; defining the supply and demand balance constraints of the generator output power and the limit constraints of the generator output power;

[0009] Step 2: Determine the communication topology and Laplace matrix of the smart grid based on the established smart grid economic dispatch model, and then normalize the Laplace matrix to ensure that it is suitable for subsequent algorithm calculations;

[0010] Step 3: Initialize the relevant parameters required by the algorithm;

[0011] Step 4: Realize communication and coordination between generator nodes in the smart grid through the communication topology diagram of the smart grid, and realize dynamic update of generator node information;

[0012] Step 5: Design a distributed algorithm for the economic dispatch problem under transmission loss conditions, taking into account the actual line transmission loss conditions without the need for global information, and dynamically calculate the incremental cost, output power and mismatch of each generator;

[0013] Step 6: Real-time monitoring and judging whether the output power of each generator meets the predetermined generator output power constraint condition; if so, further considering whether the supply and demand balance constraint condition is met; if not, processing the generator output power that does not meet the constraint condition, and further considering whether the supply and demand balance constraint condition of the generator output power is met;

[0014] Step 7: Determine whether the supply and demand balance constraint of the generator output power is met; if so, output the incremental cost of the generator node and the generator output power; if not, return to step 6 and recalculate until the generator output power limit constraint and the supply and demand balance constraint are met.

[0015] Furthermore, in the step 1, the types of distributed generators in the smart grid include photovoltaic power generation and wind power generation. The output power cost of the i-th distributed generator in the smart grid under the condition of considering transmission loss is specifically:

[0016]

[0017] Wherein, i = 1, 2, ..., n; n is the number of generators in the smart grid; k = 1, 2, ..., m; m is the number of iterations; is the power generation cost of the i-th generator at the k-th iteration based on transmission loss; p i(k) is the output power of the i-th generator at the k-th iteration under the condition of transmission loss; α i , βi and γ i is the cost coefficient of the i-th generator and satisfies α i >0, β i >0 and γ i ≥ 0. The economic dispatch problem can be modeled as:

[0018]

[0019] p i_min ≤p i(k) ≤p i_max

[0020] in, is the objective function of the economic dispatch problem; p i_sh is the transmission loss of the output power of the i-th generator, which is specifically p i_sh =σ×p i(k) , σ is the transmission loss coefficient of the generator output power, σ∈[5%,10%]; is the output power supply and demand balance constraint of the generator; p i_min ≤p i(k) ≤p i_max is the output power limit constraint of the generator; p d is the total required power; p i_min is the minimum output power of the i-th generator; p i_max is the maximum output power of the i-th generator.

[0021] Furthermore, the step 2 is specifically as follows:

[0022] Determine the smart grid communication topology and obtain the Laplace matrix C, where c ij is the i-th row and j-th column element of the Laplace matrix C; the Laplace matrix C is normalized and represented as the matrix θ, θ ij It is represented as the element of the i-th row and j-th column of the matrix θ. The specific normalization operation is as follows:

[0023]

[0024] Furthermore, the step three is specifically as follows:

[0025] The initial value of the output power of the i-th generator is set to p based on the transmission loss condition. i(0) ; The initial mismatch value of the i-th generator is D pi(0) ; The initial value of the incremental cost of the i-th generator is At the same time, the initial value is required to meet the following requirements:

[0026]

[0027] Furthermore, the step 4 is specifically as follows:

[0028] The communication topology of the smart grid is used to realize the communication and coordination between the generator nodes in the smart grid, and the dynamic update of the generator node information is realized: the update information of the i-th generator node includes the number of known generator nodes n of the j-th generator at the k-th iteration j , incremental cost and mismatch D pj(k) , where j∈N i , j = 1, 2, ..., n, N i represents the set of generators that communicate with the i-th generator.

[0029] Furthermore, the step five is specifically as follows:

[0030] A distributed algorithm for the economic dispatch problem under transmission loss conditions is designed to calculate the incremental cost, output power and mismatch of the generator at the current moment. The information update rule of the algorithm can be summarized as follows:

[0031]

[0032] in and p i(k) denote the estimated values ​​of the incremental cost and power generation of the i-th generator at the k-th iteration respectively; ε is the convergence coefficient, which determines the speed at which the incremental cost converges to consistency; D pi(k) represents the mismatch of the i-th generator at the k-th iteration.

[0033] Furthermore, the step six is ​​specifically as follows:

[0034] Monitor and judge in real time whether the output power of each generator meets the preset generator output power constraint condition; if so, further consider whether it meets the supply and demand balance constraint condition; if not, process the generator output power that does not meet the constraint condition, and further consider whether it meets the generator output power supply and demand balance constraint condition; when the generator output power does not meet the generator output power constraint condition, the processing method for the generator that does not meet the generator output power constraint condition is as follows:

[0035]

[0036] Furthermore, the step seven is specifically as follows:

[0037] Determine whether the supply and demand balance constraint of the generator output power is met; if it is met, the mismatch D of the i-th generator at the k+1th iteration is pi(k+1)If both are less than the preset threshold, then the incremental cost of the generator node and the output power of the generator are output; if not, step six is ​​re-executed for calculation until the output power restriction constraint and the supply and demand balance constraint of the generator are met.

[0038] The present invention provides a real-time economic dispatching method for a smart grid based on a distributed consensus algorithm. Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention designs and implements a smart grid framework through a distributed algorithm for economic dispatch problems under transmission loss conditions, and dynamically updates the local information of each generator node. The update process of each generator node only depends on the information received from its neighboring generator nodes, and autonomously adjusts and updates the local information according to its algorithm. This process belongs to a fully distributed method.

[0040] 2. The present invention combines the advantages of distributed consensus algorithms. When each generator communicates through a smart grid framework, there is no need for a central controller. Only by communicating through adjacent generator nodes can the number of generator nodes in the entire smart grid be independently estimated, which greatly reduces the communication cost and makes economic dispatch more economical and safer. The generator nodes can eventually independently estimate the number of generator nodes in the entire smart grid.

[0041] 3. Since the method of the present invention does not rely on a central controller, the smart grid can also achieve the optimal solution to the economic dispatch problem when expanding new generators or adjusting the existing structure. This means that the smart grid can seamlessly connect new generators at any time without large-scale modifications to the existing dispatch strategy, making the expansion of the smart grid easier. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of the real-time economic dispatching method of smart grid based on distributed consensus algorithm of the present invention;

[0043] Figure 2 It is an IEEE14-node standard power network;

[0044] Figure 3 A communication topology diagram of the smart grid of the present invention;

[0045] Figure 4 This is a smart grid framework diagram of the present invention;

[0046] Figure 5 A diagram of a process for updating the number of generator nodes of the smart grid of the present invention;

[0047] Figure 6 This is the incremental cost change diagram of the present invention without adding a generator;

[0048] Figure 7 This is a diagram showing the variation of the output power of a generator without adding a generator in the middle of the present invention;

[0049] Figure 8 This is a mismatch variation diagram without adding a generator in the middle of the present invention;

[0050] Fig. 9 This is a graph showing the incremental cost change after adding a generator midway in the present invention;

[0051] Fig.10 This is a diagram showing the change in generator output power after a generator is added midway in the present invention;

[0052] Fig.11 This is a diagram showing the mismatch change after a generator is added midway in the present invention; DETAILED DESCRIPTION

[0053] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0054] Embodiment 1:

[0055] The process of the smart grid real-time economic dispatch method based on the distributed consensus algorithm provided in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0056] Step 1: Construct a smart grid economic dispatch problem model based on transmission loss conditions. The model includes: determining the type of generators in the smart grid and their power generation costs; setting the objective function of the economic dispatch problem; defining the supply and demand balance constraints of the generator output power and the limit constraints of the generator output power;

[0057] Step 2: Determine the communication topology and Laplace matrix of the smart grid based on the established smart grid economic dispatch model, and then normalize the Laplace matrix to ensure that it is suitable for subsequent algorithm calculations;

[0058] Step 3: Initialize the relevant parameters required by the algorithm;

[0059] Step 4: Realize communication and coordination between generator nodes in the smart grid through the communication topology diagram of the smart grid, and realize dynamic update of generator node information;

[0060] Step 5: Design a distributed algorithm for the economic dispatch problem under transmission loss conditions, taking into account the actual line transmission loss conditions without the need for global information, and dynamically calculate the incremental cost, output power and mismatch of each generator;

[0061] Step 6: Real-time monitoring and judging whether the output power of each generator meets the predetermined generator output power constraint condition; if so, further considering whether the supply and demand balance constraint condition is met; if not, processing the generator output power that does not meet the constraint condition, and further considering whether the supply and demand balance constraint condition of the generator output power is met;

[0062] Step 7: Determine whether the supply and demand balance constraint of the generator output power is met; if so, output the incremental cost of the generator node and the generator output power; if not, return to step 6 and recalculate until the generator output power limit constraint and the supply and demand balance constraint are met.

[0063] In this embodiment, the types of distributed generators in the smart grid in step 1 include photovoltaic power generation and wind power generation. The output power cost of the i-th distributed generator in the smart grid under the condition of considering transmission loss is specifically:

[0064]

[0065] Wherein, i = 1, 2, ..., n; n is the number of generators in the smart grid; k = 1, 2, ..., m; m is the number of iterations; is the power generation cost of the i-th generator at the k-th iteration based on transmission loss; p i(k) is the output power of the i-th generator at the k-th iteration under the condition of transmission loss; α i , β i and γ i is the cost coefficient of the i-th generator and satisfies α i >0, β i >0 and γ i ≥ 0. The economic dispatch problem can be modeled as:

[0066]

[0067] p i_min ≤p i(k) ≤p i_max

[0068] in, is the objective function of the economic dispatch problem; p i_sh is the transmission loss of the output power of the i-th generator, which is specifically p i_sh =σ×p i(k), σ is the transmission loss coefficient of the generator output power, σ∈[5%,10%]; is the output power supply and demand balance constraint of the generator; p i_min ≤p i(k) ≤p i_max is the output power limit constraint of the generator; p d is the total required power; p i_min is the minimum output power of the i-th generator; p i_max is the maximum output power of the i-th generator.

[0069] In this embodiment, the step 2 is specifically as follows:

[0070] Determine the smart grid communication topology and obtain the Laplace matrix C, where c ij is the i-th row and j-th column element of the Laplace matrix C; the Laplace matrix C is normalized and represented as the matrix θ, θ ij It is represented as the element of the i-th row and j-th column of the matrix θ. The specific normalization operation is as follows:

[0071]

[0072] In this embodiment, the step three is specifically as follows:

[0073] The initial value of the output power of the i-th generator is set to p based on the transmission loss condition. i(0) ; The initial mismatch value of the i-th generator is D pi(0) ; The initial value of the incremental cost of the i-th generator is At the same time, the initial value is required to meet the following requirements:

[0074]

[0075] In this embodiment, the step 4 is specifically as follows:

[0076] The communication topology of the smart grid is used to realize the communication and coordination between the generator nodes in the smart grid, and the dynamic update of the generator node information is realized: the update information of the i-th generator node includes the number of known generator nodes n of the j-th generator at the k-th iteration j , incremental cost and mismatch D pj(k) , where j∈N i , j = 1, 2, ..., n, N i represents the set of generators that communicate with the i-th generator.

[0077] In this embodiment, the step five is specifically as follows:

[0078] A distributed algorithm for the economic dispatch problem under transmission loss conditions is designed to calculate the incremental cost, output power and mismatch of the generator at the current moment. The information update rule of the algorithm can be summarized as follows:

[0079]

[0080] in and p i(k) denote the estimated values ​​of the incremental cost and power generation of the i-th generator at the k-th iteration respectively; ε is the convergence coefficient, which determines the speed at which the incremental cost converges to consistency; D pi(k) represents the mismatch of the i-th generator at the k-th iteration.

[0081] In this embodiment, the step six is ​​specifically as follows:

[0082] Monitor and judge in real time whether the output power of each generator meets the preset generator output power constraint condition; if so, further consider whether it meets the supply and demand balance constraint condition; if not, process the generator output power that does not meet the constraint condition, and further consider whether it meets the generator output power supply and demand balance constraint condition; when the generator output power does not meet the generator output power constraint condition, the processing method for the generator that does not meet the generator output power constraint condition is as follows:

[0083]

[0084] In this embodiment, the step seven is specifically as follows:

[0085] Determine whether the supply and demand balance constraint of the generator output power is met; if it is met, the mismatch D of the i-th generator at the k+1th iteration is pi(k+1) If both are less than the preset threshold, then the incremental cost of the generator node and the output power of the generator are output; if not, step six is ​​re-executed for calculation until the output power restriction constraint and the supply and demand balance constraint of the generator are met.

[0086] Embodiment 2:

[0087] Example 2 uses the proposed smart grid real-time economic dispatch method based on distributed consensus algorithm to perform numerical simulation on the IEEE 14-node standard power network with a total of 5 generators. Figure 2 It is an IEEE14-node standard power network; Figure 3 is the communication topology of the smart grid, and the cost function of the i-th generator is set to

[0088] The specific process is described as follows:

[0089] The types of distributed generators in smart grids include photovoltaic power generation and wind power generation. The output power cost of the i-th distributed generator in the smart grid under the condition of considering transmission loss is:

[0090]

[0091] Wherein, i = 1, 2, ..., n; n is the number of generators in the smart grid; k = 1, 2, ..., m; m is the number of iterations; is the power generation cost of the i-th generator at the k-th iteration based on transmission loss; p i(k) is the output power of the i-th generator at the k-th iteration under the condition of transmission loss; α i , β i and γ i is the cost coefficient of the i-th generator and satisfies α i >0, β i >0 and γ i ≥ 0. The economic dispatch problem can be modeled as:

[0092]

[0093] p i_min ≤p i(k) ≤p i_max (2c)

[0094] Where (2a) is the objective function of the economic dispatch problem; p i_sh is the transmission loss of the output power of the i-th generator, which is specifically p i_sh =σ×p i(k) , σ is the transmission loss coefficient of the generator output power, σ∈[5%,10%]; (2b) is the generator output power supply and demand balance constraint; (2c) is the generator output power limit constraint; p d is the total required power; p i_min is the minimum output power of the i-th generator; p i_max is the maximum output power of the i-th generator.

[0095] Determine the smart grid communication topology and obtain the Laplace matrix C, where c ij is the i-th row and j-th column element of the Laplace matrix C; the Laplace matrix C is normalized and represented as the matrix θ, θ ij It is represented as the element of the i-th row and j-th column of the matrix θ. The specific normalization operation is as follows:

[0096]

[0097] The initial value of the output power of the i-th generator is set to p based on the transmission loss condition.i(0) ; The initial mismatch value of the i-th generator is D pi(0) ; The initial value of the incremental cost of the i-th generator is At the same time, the initial value is required to meet the following requirements:

[0098]

[0099] The communication topology of the smart grid is used to realize the communication and coordination between the generator nodes in the smart grid, and the dynamic update of the generator node information is realized: the update information of the i-th generator node includes the number of known generator nodes n of the j-th generator at the k-th iteration j , incremental cost and mismatch D pj(k) , where j∈N i , j = 1, 2, ..., n, N i represents the set of generators that communicate with the i-th generator.

[0100] First, each generator node can independently estimate the number of generator nodes in the entire smart grid through continuous communication and coordination with the generator nodes directly connected to it. Figure 4 This is the framework diagram of the smart grid of the present invention, where n i is the number of generator nodes known to the i-th generator, n j is the number of generator nodes known to the jth generator; there are 4 generators in the figure. At the initial moment, each generator node has no information about other nodes, so the number of generators known to each generator is 1, which is itself; the initial node information of the 4 generators is represented by {ξ 1(1)}、{ξ 2(1)}、{ξ 3(1)} and {ξ 4(1) During the first generator node information communication, each generator node will receive information from the generator node it is directly connected to and update the number of known generators. For example, node 1 only receives information n from node 2. j And update the number of known generator nodes n i , at this time, the number of known generators in node 1 is 2, namely node 1 and node 2. The updated node 1 information is expressed as {ξ 1(2)}; Node 2 receives information n from node 1 and node 3 respectively j And update the number of known generator nodes n i , at this time, the number of known generators in node 2 is 3, namely node 1, node 2 and node 3. The updated node 2 information is expressed as {ξ 2(2)}; Node 3 and node 4 are similar, and are represented by {ξ 3(2)} and {ξ 4(2)}. Similarly, in the third generator node information communication, all generator nodes know that the number of generators on the field is 4, that is, the estimation of the number of generators is completed. The specific process of updating the number of generator nodes in the smart grid of the present invention is as follows Figure 5 shown.

[0101] After obtaining the estimated number of generators n, the i-th generator continuously updates the local incremental cost, output power and mismatch of the generator node by transmitting information to the generator node j it communicates with. The specific implementation is:

[0102] The jth generator node adds its incremental cost of the kth iteration and mismatch D pj(k) The information is transmitted to generator i, and the incremental cost of k+1 iterations is calculated using a distributed algorithm based on the economic dispatch problem under transmission loss conditions. As shown below:

[0103]

[0104] By incremental cost The output power of the generator is updated as follows:

[0105]

[0106] Monitor and judge in real time whether the output power of each generator meets the preset generator output power constraint condition; if so, further consider whether it meets the supply and demand balance constraint condition; if not, process the generator output power that does not meet the constraint condition, and further consider whether it meets the generator output power supply and demand balance constraint condition; when the generator output power does not meet the generator output power constraint condition, the processing method for the generator that does not meet the generator output power constraint condition is as follows:

[0107]

[0108] Determine whether the supply and demand balance constraint of the generator output power is met; if it is met, the mismatch D of the i-th generator at the k+1th iteration is pi(k+1) If both are less than the preset threshold, then the incremental cost of the generator node and the output power of the generator are output; if not, step six is ​​re-executed for calculation until the output power restriction constraint and the supply and demand balance constraint of the generator are met.

[0109]

[0110] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0111] Input the initial system parameters, total required power pd =120MW; the relevant parameters of the generator are shown in Table 1:

[0112] Table 1

[0113] Generator <![CDATA[α i ]]> <![CDATA[β i ]]> <![CDATA[γ i ]]> <![CDATA[p i_min ]]> <![CDATA[p i_max ]]> 1 0.094 1.22 51 10 80 2 0.078 3.41 31 8 60 3 0.055 1.53 78 20 40 4 0.052 2.80 42 20 45 5 0.084 3.15 62 5 14

[0114] The initial generator output power based on transmission loss conditions is p 1(0) =35MW, p 2(0) =20MW, p 3(0) =25MW, p 4(0) =30MW, p 5(0) =10MW, the transmission loss coefficient σ of the generator output power is 5%; the initial values ​​of the local power mismatch are D p1(0) =0MW, D p2(0) =0MW, D p3(0) =0MW, D p4(0) =0MW and D p5(0) =0MW; feedback gain ε = 0.02; the adjacency matrix obtained based on the communication topology of the smart grid is shown in (9). Based on the actual situation, this numerical simulation will be repeated 50 times, and it is assumed that each iteration takes 0.2 seconds. Based on the given data, this numerical simulation shows that the optimal incremental cost converges to the same value of 5.49$ / MW. In the transient response, it is noted that generator 5 reaches the upper limit of the generator constraint at 0.6 seconds and becomes saturated, maintaining the maximum output power of 14MW. According to the results, the final output power of the generator is 24.20MW, 15.03MW, 38.26MW, 28.50MW and 14.00MW. The output power of all generators is within the generator constraint range. Even in the transient response, the generator output power will not exceed the constraint range.

[0115]

[0116] In order to verify the plug-and-play characteristics of the algorithm in the economic dispatch of smart grids, the present invention adopts a dynamic adjustment strategy to adapt to the instant addition of generator sets in the smart grid. The simulation of the present invention adopts MATLAB2022a, and the computer configuration is COREi7, 2.80GHz; Figure 6 This is the incremental cost change diagram of the present invention without adding a generator; Figure 7 This is a diagram showing the variation of the output power of a generator without adding a generator in the middle of the present invention; Figure 8 This is a mismatch variation diagram without adding a generator in the middle of the present invention; Fig. 9 This is a graph showing the incremental cost change after adding a generator midway in the present invention; Fig.10 This is a diagram showing the change in generator output power after a generator is added midway in the present invention; Fig.11This is a graph showing the mismatch change after a generator is added midway in the present invention. Through numerical simulation, the present invention demonstrates the adjustment capability and output power change of the smart grid when the number and demand of generators change dynamically. This reflects that after the addition of new generator sets, the smart grid continuously adjusts the output of each generator set to achieve the goal of minimizing costs. The change in mismatch power further illustrates that in the plug-and-play operation mode, the smart grid can effectively maintain the supply and demand balance of the generator output power despite the fluctuations in demand and changes in power generation capacity. This can clearly verify the effectiveness of the smart grid in ensuring the stable operation of the power grid after the addition of a new generator at the 10th second.

[0117] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A real-time economic dispatching method for smart grid based on distributed consensus algorithm, characterized in that: The following steps are involved: Step 1: Construct a smart grid economic dispatch problem model based on transmission loss conditions. The model includes: determining the type of generators in the smart grid and their power generation costs; setting the objective function of the economic dispatch problem; defining the supply and demand balance constraints of the generator output power and the limit constraints of the generator output power; Step 2: Determine the communication topology and Laplace matrix of the smart grid based on the established smart grid economic dispatch model, and then normalize the Laplace matrix to ensure that it is suitable for subsequent algorithm calculations; Step 3: Initialize the relevant parameters required by the algorithm; Step 4: Realize communication and coordination between generator nodes in the smart grid through the communication topology diagram of the smart grid, and realize dynamic update of generator node information; Step 5: Design a distributed algorithm for the economic dispatch problem under transmission loss conditions, taking into account the actual line transmission loss conditions without the need for global information, and dynamically calculate the incremental cost, output power and mismatch of each generator; Step 6: Real-time monitoring and judging whether the output power of each generator meets the predetermined generator output power constraint condition; if so, further considering whether the supply and demand balance constraint condition is met; if not, processing the generator output power that does not meet the constraint condition, and further considering whether the supply and demand balance constraint condition of the generator output power is met; Step 7: Determine whether the supply and demand balance constraint of the generator output power is met; if so, output the incremental cost of the generator node and the generator output power; if not, return to step 6 and recalculate until the generator output power limit constraint and the supply and demand balance constraint are met; In step 1, the types of distributed generators in the smart grid include photovoltaic power generation and wind power generation. The output power cost of the i-th distributed generator in the smart grid under the condition of considering transmission loss is specifically: Wherein, i = 1, 2, ..., n; n is the number of generators in the smart grid; k is the number of iterations; is the power generation cost of the i-th generator at the k-th iteration based on transmission loss; p i(k) is the output power of the i-th generator at the k-th iteration under the condition of transmission loss; α i , β i and γ i is the cost coefficient of the i-th generator and satisfies α i >0, β i >0 and γ i ≥0; the economic dispatch problem can be modeled as: p i_min ≤P i(k) ≤P i_max in, is the objective function of the economic dispatch problem; p i_sh is the transmission loss of the output power of the i-th generator, which is specifically p i_sh =σ×p i(k) , σ is the transmission loss coefficient of the generator output power, σ∈[5%,10%]; is the output power supply and demand balance constraint of the generator; p i_min ≤p i(k) ≤p i_max is the output power limit constraint of the generator; p d is the total required power; p i_min is the minimum output power of the i-th generator; p i_max is the maximum output power of the i-th generator; The step five is specifically as follows: A distributed algorithm for the economic dispatch problem under transmission loss conditions is designed to calculate the incremental cost, output power and mismatch of the generator at the current moment; the information update rule of the algorithm can be summarized as follows: in and p i(k) denote the estimated values ​​of the incremental cost and power generation of the i-th generator at the k-th iteration respectively; ε is the convergence coefficient, which determines the speed at which the incremental cost converges to consistency; D pi(k) represents the mismatch of the i-th generator at the k-th iteration.

2. The real-time economic dispatching method for smart grid based on distributed consensus algorithm according to claim 1 is characterized in that: The step 2 is specifically as follows: Determine the smart grid communication topology and obtain the Laplace matrix C, c ij is the i-th row and j-th column element of the Laplace matrix C; The Laplace matrix C is normalized and expressed as matrix θ, θ ij It is represented as the element of the i-th row and j-th column of the matrix θ. The specific normalization operation is as follows:

3. The real-time economic dispatching method for smart grid based on distributed consensus algorithm according to claim 2 is characterized in that: The step three is specifically as follows: The initial value of the output power of the i-th generator is set to p based on the transmission loss condition. i(0) ; The initial mismatch value of the i-th generator is D pi(0) ; The initial value of the incremental cost of the i-th generator is At the same time, the initial value is required to meet the following requirements:

4. The real-time economic dispatching method for smart grid based on distributed consensus algorithm according to claim 3 is characterized in that: The step 4 is specifically as follows: The communication topology of the smart grid is used to realize the communication and coordination between the generator nodes in the smart grid, and the dynamic update of the generator node information is realized: the update information of the i-th generator node includes the number of known generator nodes n of the j-th generator at the k-th iteration j , incremental cost and mismatch D pj(k) , where j∈N i , j = 1, 2, ..., n, N i represents the set of generators that communicate with the i-th generator.

5. The real-time economic dispatching method for smart grid based on distributed consensus algorithm according to claim 4 is characterized in that: The step six is ​​specifically as follows: Monitor and judge in real time whether the output power of each generator meets the preset generator output power constraint condition; if so, further consider whether it meets the supply and demand balance constraint condition; if not, process the generator output power that does not meet the constraint condition, and further consider whether it meets the generator output power supply and demand balance constraint condition; when the generator output power does not meet the generator output power constraint condition, the processing method for the generator that does not meet the generator output power constraint condition is as follows:

6. The real-time economic dispatching method for smart grid based on distributed consensus algorithm according to claim 5 is characterized in that: The step seven is specifically as follows: Determine whether the supply and demand balance constraint of the generator output power is met; if it is met, the mismatch D of the i-th generator at the k+1th iteration is pi(k+1) If both are less than the preset threshold, then the incremental cost of the generator node and the output power of the generator are output; if not, step six is ​​re-executed for calculation until the output power restriction constraint and the supply and demand balance constraint of the generator are met.

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