Source network load storage smart park low-carbon optimization operation method based on load identification data

Abstract

The invention discloses a source network load storage smart park low-carbon optimization operation method based on load identification data, and the method comprises the steps: obtaining an equipment operation state in a smart park based on a load identification technology, and building a multi-target formula of a distributed energy system of the smart park; establishing a constraint condition of smart park operation based on the equipment operation state; determining power distribution network parameters in the smart park according to a multi-target formula and constraint conditions; the power distribution network parameters and the algorithm constants are input into an improved fireworks algorithm for iterative calculation, and a multi-objective optimized Pareto solution is obtained; and performing decision analysis on the Pareto solution of multi-objective optimization by adopting a grey correlation multi-attribute decision based on intuitionistic fuzzy, and determining an optimal strategy for low-carbon optimization operation of the smart park.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy consumption and high-reliability operation of smart parks, and more specifically, to a low-carbon optimized operation method for a source-grid-load-storage smart park based on load identification data. Background Art

[0002] At present, distributed energy sources such as wind power and solar energy are developing rapidly, which brings about a series of distributed energy consumption problems. At the same time, in order to promote the sustainable development of cities, various smart parks have been vigorously built, bringing opportunities for distributed energy consumption. Compared with traditional parks, smart parks have outstanding characteristics such as green, low-carbon, and intelligent. However, the low-carbon operation of smart parks faces more complex elements, more intensive interactions, and more flexible transactions. As an emerging technology, reports on smart parks currently only involve planning and design, operation and maintenance management, technical platforms and architectures, and operation control, and existing research is still in its infancy. In addition, the number and types of equipment in the park are also increasing, which brings new challenges to the low-carbon operation of the park. It is urgent to propose a high-reliability source-grid-load-storage low-carbon optimization operation method for smart parks based on load identification data to achieve low-carbon and high-reliability operation of smart parks. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a low-carbon optimized operation method for a source-grid-load-storage smart park based on load identification data.

[0004] According to one aspect of the present invention, a method for low-carbon optimization operation of a source-grid-load-storage smart park based on load identification data is provided, comprising:

[0005] Based on load identification technology, the equipment operation status inside the smart park is obtained, and a multi-objective formula for the distributed energy system of the smart park is established;

[0006] Establish constraints for smart park operation based on equipment operating status;

[0007] Determine the distribution network parameters within the smart park based on multi-objective formulas and constraints;

[0008] The distribution network parameters and algorithm constants are input into the improved fireworks algorithm for iterative calculation to obtain the Pareto solution of multi-objective optimization;

[0009] The intuitionistic fuzzy-based grey relational multi-attribute decision-making method is used to analyze the Pareto solution of multi-objective optimization and determine the optimal strategy for low-carbon optimization operation of the smart park.

[0010] Optionally, the multi-objective formulation includes maximum similarity between distributed energy resources and loads, minimum operating cost, and maximum reliability.

[0011] Optionally, the similarity S between DERs and loads d The expression is:

[0012]

[0013] Among them, N T is the number of reconfiguration cycles in a day, N p is the length of the scattered data of the tth reconfiguration cycle is the power of distributed energy and load at the jth time point in the tth period, and E() is the Euclidean distance.

[0014] Optionally, the operating cost C O The expression is:

[0015] C O =C om +C RC +C LOSS

[0016] in,

[0017]

[0018]

[0019]

[0020] In the formula, C om is the operation and maintenance cost, C RC is the reconstruction cost, C LOSS is the network loss cost; C WT , C PV , C ESS and C MT is the cost of orderly operation and maintenance of wind turbines, photovoltaic cells, energy storage equipment and gas turbines; C pure,t , C MT,t is the unit energy price and fuel cost of the gas turbine in period t; △T t is the duration of the tth period; P pure,t N is the electricity purchased from the upper power grid; b , N s is the branch number and switch number; γ is a binary variable, 0 means closed, 1 means open; C SW,t , C Shi,t and C Cur,t is the switching cost, load transfer cost and load abandonment cost of the tth cycle; C SW,t is the action cost of the jth switch.

[0021] Optionally, reliability C ReliThe expression is:

[0022]

[0023] In the formula, λ t,i is the interruption time of the ith load in the tth cycle; P t,i is the interrupted power of the ith load in the tth cycle; N l is the load number; C gc,t is the average interruption cost; Ct is the unit interruption cost of the i-th load in the t-th cycle; P EENs,t It is a power outage; P t,I is the interrupted power of the ith load in the tth cycle.

[0024] Optionally, the constraints include power balance constraints, node voltage and branch power constraints, switching action time constraints, distribution line connection constraints, demand response constraints, and storage device constraints.

[0025] Optionally, the power balance constraint expression is:

[0026]

[0027] Where P Gi , Q Gi is the active power and reactive power of the ith node; U i is the voltage of the ith node; Gij, Bij, δij are the ordered branch conductance, branch susceptance, and node voltage phase angle difference, respectively;

[0028] The expressions of node voltage and branch power constraints are:

[0029] U i min ≤U i ≤U i max ,i∈Ω

[0030]

[0031] Where U i min , U i max are the lower and upper limits of the node voltage respectively; S ij , S ji is the forward and reverse apparent power of branch ij; Ω is the node set;

[0032] The expression of switching action time constraint is:

[0033]

[0034] In the formula, γs max is the upper limit of the switch action time;

[0035] The expression of the distribution line connection constraint is:

[0036]

[0037] Where N Node , L Subs max is the node number and substation number; Ω g ,Ω c To disconnect the branch group and the normally closed branch;

[0038] The expression of demand response constraint is:

[0039]

[0040] In the formula, ξ Lmin , Lmax is the lower and upper limits of the transfer load rate; P Load,t is the load value at time t; P △L,t is the total power transmitted to the load;

[0041] The expression for the storage device constraint is:

[0042]

[0043] SOC i (t-1) = SOC i (t)

[0044] In the formula, SOC i(t) is the state of charge (SOC) of the i-th storage device at time t; SOC max,I , SOC min,I are the upper and lower limits of the SOC of the i-th storage device respectively; P min,i EES , P max,i EES are the upper and lower limits of the power of the i-th storage device respectively.

[0045] Optionally, the distribution network parameters and algorithm constants are input into the improved fireworks algorithm for iterative calculation to obtain the Pareto solution of multi-objective optimization, including:

[0046] Step 1: Input the distribution network parameters and algorithm constants into the improved fireworks algorithm to determine the initial population;

[0047] Step 2: Calculate the number of explosion sparks, explosion amplitude and fitness of the explosion fireworks of the initial population;

[0048] Step 3: optimizing the explosion sparks of the explosion fireworks by using gene mutation operation to determine the optimized explosion fireworks;

[0049] Step 4: Use a repulsion operator to optimize the overlapping explosion sparks in the optimized explosion fireworks, update the positions of the explosion sparks, and determine the updated explosion fireworks;

[0050] Step 5: Use out-of-bounds detection to detect out-of-bounds fireworks in the updated exploding fireworks to obtain valid exploding fireworks;

[0051] Step 6: Calculate the fitness value of the effective explosion fireworks and select the best explosion spark according to the fitness value;

[0052] Step 7: Iterate steps 2 to 6 and output the Pareto solution of multi-objective optimization.

[0053] Optionally, the determination expression of m mutation points in the gene mutation operation is:

[0054]

[0055] Where N r is the number of fireworks, f(X j ) is the fitness value of the best, worst and median fireworks, δ is the minimum amount of machines;

[0056] The expression of the repulsion operator is:

[0057]

[0058] Where ΔR is the Euclidean distance; X j is the position vector of the jth spark generated by firework i.

[0059] The out-of-bounds detection expression is:

[0060]

[0061] Where, X i k It is a substitute for fireworks that cross the border, X max k , X min k are the lower and upper limits of the value of fireworks, and D is the variable dimension.

[0062] Optionally, the decision analysis of the Pareto solution of multi-objective optimization is performed using the intuitionistic fuzzy grey correlation multi-attribute decision-making method to determine the optimal strategy for low-carbon optimization operation of the smart park, including:

[0063] Construct decision matrix based on intuitionistic fuzzy coefficients of Pareto solution of multi-objective optimization;

[0064] Calculate the grey correlation coefficient between the decision matrix of each Pareto solution and the ideal operating state;

[0065] According to the grey relational coefficient, calculate the grey relational degree of each Pareto solution;

[0066] Determine the optimal solution of the Pareto solution of multi-objective optimization according to the grey correlation degree of each Pareto solution;

[0067] Determine the optimal strategy for low-carbon optimized operation of the smart park based on the optimal solution.

[0068] Optionally, the expression for constructing the decision matrix is:

[0069] π ij =1-μ ij -v ij

[0070] In the formula, μ ij , ν ij is the non-membership degree and non-membership dissatisfaction of the j-th target on the i-th solution; π ij It is uncertainty;

[0071] The calculation formula of grey correlation coefficient is:

[0072]

[0073] Where ρ is the resolution coefficient, x j - 、x j + is a negative ideal solution and a positive ideal solution; d j - d j + are the grey correlation coefficients between each solution and the negative ideal solution and the positive ideal solution; x ij - 、x ij + are the lower and upper limits of the interval number;

[0074] The calculation formula of grey relational degree is:

[0075]

[0076] In the formula, w i Yes th The weight of the i-th objective of the solution, CC j is the grey relational degree.

[0077] According to another aspect of the present invention, a source-grid-load-storage smart park low-carbon optimization operation device based on load identification data is provided, comprising:

[0078] The first establishment module is used to obtain the equipment operation status inside the smart park based on the load identification technology and establish a multi-objective formula for the distributed energy system of the smart park;

[0079] The second establishment module is used to establish constraints for the operation of the smart park based on the equipment operation status;

[0080] A determination module is used to determine the distribution network parameters in the smart park based on multi-objective formulas and constraints;

[0081] A calculation module is used to input the distribution network parameters and algorithm constants into the improved fireworks algorithm for iterative calculation to obtain the Pareto solution of multi-objective optimization;

[0082] The analysis module is used to conduct decision analysis on the Pareto solution of multi-objective optimization by using grey correlation multi-attribute decision-making based on intuitionistic fuzzy, and determine the optimal strategy for low-carbon optimization operation of the smart park.

[0083] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0084] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0085] Therefore, the present invention proposes a high-reliability source-grid-load-storage low-carbon optimization operation method for a smart park based on load identification data. Based on the load identification technology, the equipment operation status inside the smart park is obtained, and a multi-objective formula for the distributed energy system of the smart park is established; based on the obtained load identification data, constraints for the operation of the smart park are established, including power balance constraints, node voltage and branch power constraints, switching action time constraints, distribution line connection constraints, demand response constraints, storage device constraints, etc. The optimization efficiency is improved by improving the fireworks algorithm, and the Pareto solution of multi-objective optimization is obtained to provide a reference for decision makers. The final decision analysis is carried out using grey correlation multi-attribute decision-making based on intuitionistic fuzzy to cope with the influence of uncertainty and ambiguity of distributed energy systems, and finally achieve high-reliability and low-carbon operation of the smart park. The operation efficiency of the source-grid-load-storage smart park is effectively improved, and low-carbon and high-reliability operation of the smart park can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0087] Figure 1It is a flow chart of a low-carbon optimization operation method of a source-grid-load-storage smart park based on load identification data provided by an exemplary embodiment of the present invention;

[0088] Figure 2 is a flow chart based on an improved fireworks algorithm provided by an exemplary embodiment of the present invention;

[0089] Figure 3 It is a Pareto solution decision flow chart based on intuitionistic fuzzy grey association provided by an exemplary embodiment of the present invention;

[0090] Figure 4 It is a structural schematic diagram of a source-grid-load-storage smart park low-carbon optimization operation device based on load identification data provided by an exemplary embodiment of the present invention;

[0091] Figure 5 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0092] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0093] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0094] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0095] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0096] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0097] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0098] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0099] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0100] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0101] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0102] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0103] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0104] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0105] Exemplary Methods

[0106] Figure 11 is a flow chart of a method for low-carbon optimization operation of a source-grid-load-storage smart park based on load identification data provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the source-grid-load-storage smart park low-carbon optimization operation method 100 based on load identification data includes the following steps:

[0107] Step 101, the district obtains the equipment operation status inside the smart park based on the load identification technology, and establishes a multi-objective formula for the distributed energy system of the smart park;

[0108] Step 102, establishing constraints for the operation of the smart park based on the equipment operation status;

[0109] Step 103, determining the distribution network parameters in the smart park according to the multi-objective formula and constraint conditions;

[0110] Step 104, inputting the distribution network parameters and algorithm constants into the improved fireworks algorithm for iterative calculation to obtain a Pareto solution for multi-objective optimization;

[0111] Step 105, using the grey correlation multi-attribute decision-making based on intuitionistic fuzzy to perform decision analysis on the Pareto solution of multi-objective optimization, and determine the optimal strategy for low-carbon optimization operation of the smart park.

[0112] Specifically, the present invention relates to a low-carbon optimization operation method for a smart park with high reliability, source-grid-load-storage based on load identification data, which can realize low-carbon and high-reliability operation of the smart park. Specifically, the method includes the following steps:

[0113] Step 1: Based on the load identification technology, the equipment operation status inside the smart park is obtained, and a multi-objective formula for the distributed energy system of the smart park is established, including the maximum similarity between distributed energy and loads, the lowest operating cost, and the highest reliability.

[0114] (1) Similarities between distributed energy and loads

[0115] The similarity between distributed energy resources and loads refers to the average of the time series similarity within a day, as shown below

[0116]

[0117] Among them, N T is the number of reconfiguration cycles in a day. N p is the length of the scattered data of the tth reconfiguration cycle is the power of distributed energy and load at the jth time point in the tth period. E() is the Euclidean distance.

[0118] (2) Operating costs

[0119] Economy has always been one of the main concerns of park operation. The operating cost of a smart park is the sum of operation and maintenance costs, reconstruction costs, and network loss costs.

[0120] C O =C om +C RC +C LOSS (2)

[0121]

[0122]

[0123]

[0124] Among them, C WT , C PV , C ESS and C MT is the cost of operating and maintaining wind turbines, photovoltaic cells, energy storage devices, and gas turbines. pure,t , C MT,t is the unit energy price and fuel cost of the gas turbine in period t. t is the duration of the tth period. pure,t It is the electricity purchased from the upper power grid. b , N s is the branch number and switch number. γ is a binary variable, 0 means closed and 1 means open. C SW,t , C Shi,t and C Cur,t is the switching cost, load transfer cost and load abandonment cost of the tth cycle. SW,t is the action cost of the jth switch.

[0125] (3) Reliability

[0126] Reliability reflects the interruption characteristics of the load.

[0127]

[0128] where λ t,i is the interruption time of the ith load in the tth cycle. t,i is the interrupted power of the ith load in the tth cycle. N l is the load number. C gc,t is the average interruption cost. Ct is the unit interruption cost of the i-th load in the t-th cycle. P EENs,t There is a power outage. t,I is the interrupted power of the ith load in the tth cycle.

[0129] Step 2: Establish constraints for the operation of the smart park based on the obtained equipment operating status, including power balance constraints, node voltage and branch power constraints, switching action time constraints, distribution line connection constraints, demand response constraints, storage device constraints, etc.

[0130] (1) Power balance constraints

[0131]

[0132] Where P Gi , Q Gi is the active power and reactive power of the ith node. i is the voltage of the ith node. Gij, Bij, δij are the ordered branch conductance, branch susceptance, and node voltage phase angle difference.

[0133] (2) Node voltage and branch power constraints

[0134] U i min ≤U i ≤U i max ,i∈Ω (8)

[0135]

[0136] Among them U i min , U i max are the lower and upper limits of the node voltage respectively. ij , S ji is the forward and reverse apparent power of branch ij. Ω is the node set.

[0137] (3) Switching action time constraints

[0138]

[0139] where γ s max It is the upper limit of the switch action time.

[0140] (4) Distribution line connection constraints

[0141] Distribution network operation must meet connectivity and radiation constraints.

[0142]

[0143] Where N Node , L Subs max is the node number and substation number. Ω g ,Ω cTo disconnect the branch group and the normally closed branch.

[0144] (5) Demand response constraints

[0145] For the benefit of users, the demand response value should be within a certain range. Here is the transfer load rate.

[0146]

[0147] where ξ Lmin , Lmax are the lower and upper limits of the transfer load rate. Load,t is the load value at time t. △L,t is the total power transmitted to the load.

[0148] (6) Storage device constraints

[0149] For storage devices, both storage capacity and storage power should be met. During dynamic reconstruction, the energy storage adjacency of adjacent reconstruction cycles should remain consistent.

[0150]

[0151] Among them, SOC i(t) is the state of charge (SOC) of the i-th storage device at time t. SOC max,I , SOC min,I is the upper and lower limits of the SOC of the i-th storage device. min,i EES , P max,i EES are the upper and lower power limits of the i-th storage device.

[0152] Step 3: Improve the optimization efficiency by improving the fireworks algorithm, obtain the Pareto solution of multi-objective optimization, and provide a reference for decision makers. The algorithm flow is as follows: Figure 2 shown.

[0153] (1) Gene mutation operation

[0154] In order to solve the problem of overlapping fireworks and unsatisfactory mutation operation, a gene-localized mutation operation is proposed to improve population diversity and convergence performance of the algorithm. That is, a certain number of fireworks are selected for mutation under certain probability conditions, and the mutation points are updated. Here, the best, worst and median fireworks are selected for mutation, and then m mutation points are selected for mutation.

[0155]

[0156] Where N r is the number of fireworks, f(X j ) are the fitness values ​​of the best, worst, and median fireworks. Assume X ki is the kth mutation point, then:

[0157]

[0158] (2) Repulsion operator

[0159] Here, the explosion range of fireworks is increased to avoid fireworks overlapping. When the distance between fireworks and explosion sparks is too close, a repulsion operator is performed. The Euclidean distance ΔR is used to measure the distance. Once ΔR is less than a certain range, repulsion is performed to update the spark position.

[0160]

[0161] Among them, X j is the position vector of the jth spark generated by firework i. As long as the distance between the spark and the firework is less than ΔR, it will be repelled to a position within an acceptable distance from firework i. Assuming the repulsion operator is Δs, the repulsion method is as follows

[0162]

[0163] (3) Cross-border detection

[0164] The random mapping rule is utilized to handle sparks that are not within the specified range.

[0165]

[0166] Among them, X i k An alternative to out-of-bounds fireworks. max k , X min k It is the lower and upper limit of the value of fireworks.

[0167] Step 4: Use the grey correlation multi-attribute decision-making based on intuitionistic fuzzy to conduct the final decision analysis to cope with the influence of uncertainty and ambiguity of distributed energy systems, and ultimately achieve high-reliability and low-carbon operation of the source-grid-load-storage smart park.

[0168] Uncertainty and fuzziness are typical characteristics of distributed energy systems and have a significant impact on the operation of smart parks. Therefore, intuitionistic fuzzy grey correlation is used here to perform decision analysis on Pareto solutions to cope with the impact of uncertainty. In other words, through intuitionistic fuzzy judgment, the multi-objective values ​​corresponding to the Pareto solution are converted into intuitionistic fuzzy numbers. The decision flow chart based on intuitionistic fuzzy grey correlation is shown in Figure 1. Figure 3 shown.

[0169] (1) Construction of intuitionistic fuzzy decision matrix based on Pareto solution

[0170] The intuitionistic fuzzy decision matrix is ​​an n×m matrix. m represents the number of multi-objectives, while n represents the number of Pareto solutions. The elements in the matrix are the multi-objective intuitive fuzzy values ​​corresponding to the Pareto solutions. The formation of the intuitionistic fuzzy matrix is ​​that the decision maker converts each determined Pareto solution into an intuitive fuzzy number according to the fuzzy judgment principle, which represents the satisfaction and dissatisfaction of the decision maker. Here, membership degree, non-membership degree and uncertainty are used to represent them.

[0171] π ij =1-μ ij -v ij (20)

[0172] Among them, μ ij , ν ij is the non-membership degree and non-membership dissatisfaction degree of the j-th target on the i-th solution. ij It's uncertainty.

[0173] (2) Calculation of grey correlation coefficient between each solution and the ideal solution

[0174] Select positive and negative ideal solutions according to the normalized decision matrix. This is the optimal solution and the worst solution of the interval decision matrix. Calculate the grey correlation coefficient between each solution and the positive and negative ideal solutions.

[0175]

[0176]

[0177] Among them, x j - 、x j + is a negative ideal solution and a positive ideal solution. j - d j + are negative ideal solutions and positive ideal solutions respectively. ij - 、x ij + are the lower and upper bounds of the interval number.

[0178] (3) Grey correlation

[0179] The grey relational degree of each solution is:

[0180]

[0181] Among them, w i Yes th The weight of the i-th objective of the solution, CC j is the grey relational degree.

[0182] Finally, after calculating the grey correlation of all solutions, the optimal solution of the Pareto formula is obtained and output, realizing high reliability and low-carbon operation of the source-grid-load-storage smart park.

[0183] Therefore, the present invention proposes a high-reliability source-grid-load-storage low-carbon optimization operation method for a smart park based on load identification data. Based on the load identification technology, the equipment operation status inside the smart park is obtained, and a multi-objective formula for the distributed energy system of the smart park is established; based on the obtained load identification data, constraints for the operation of the smart park are established, including power balance constraints, node voltage and branch power constraints, switching action time constraints, distribution line connection constraints, demand response constraints, storage device constraints, etc. The optimization efficiency is improved by improving the fireworks algorithm, and the Pareto solution of multi-objective optimization is obtained to provide a reference for decision makers. The final decision analysis is carried out using grey correlation multi-attribute decision-making based on intuitionistic fuzzy to cope with the influence of uncertainty and ambiguity of distributed energy systems, and finally achieve high-reliability and low-carbon operation of the smart park. The operation efficiency of the source-grid-load-storage smart park is effectively improved, and low-carbon and high-reliability operation of the smart park can be achieved.

[0184] Exemplary Devices

[0185] Figure 4 1 is a schematic diagram of a low-carbon optimization operation device for a source-grid-load-storage smart park based on load identification data provided by an exemplary embodiment of the present invention. Figure 4 As shown, the device 400 includes:

[0186] The first establishment module 410 is used to obtain the equipment operation status inside the smart park based on the load identification technology and establish a multi-objective formula for the distributed energy system of the smart park;

[0187] The second establishing module 420 is used to establish the constraint conditions of the operation of the smart park based on the equipment operation status;

[0188] A determination module 430 is used to determine the distribution network parameters in the smart park according to the multi-objective formula and the constraint conditions;

[0189] A calculation module 440 is used to input the distribution network parameters and algorithm constants into the improved fireworks algorithm for iterative calculation to obtain a Pareto solution for multi-objective optimization;

[0190] The analysis module 450 is used to perform decision analysis on the Pareto solution of multi-objective optimization by using grey correlation multi-attribute decision-making based on intuitionistic fuzzy to determine the optimal strategy for low-carbon optimized operation of the smart park.

[0191] Optionally, the multi-objective formulation includes maximum similarity between distributed energy resources and loads, minimum operating cost, and maximum reliability.

[0192] Optionally, the similarity S between DERs and loads d The expression is:

[0193]

[0194] Among them, N T is the number of reconfiguration cycles in a day, N p is the length of the scattered data of the tth reconfiguration cycle is the power of distributed energy and load at the jth time point in the tth period, and E() is the Euclidean distance.

[0195] Optionally, the operating cost C O The expression is:

[0196] C O =C om +C RC +C LOSS

[0197] in,

[0198]

[0199]

[0200]

[0201] In the formula, C om is the operation and maintenance cost, C RC is the reconstruction cost, C LOSS is the network loss cost; C WT , C PV , C ESS and C MT is the cost of orderly operation and maintenance of wind turbines, photovoltaic cells, energy storage equipment and gas turbines; C pure,t , C MT,t is the unit energy price and fuel cost of the gas turbine in period t; △T t is the duration of the tth period; P pure,t N is the electricity purchased from the upper power grid; b , N s is the branch number and switch number; γ is a binary variable, 0 means closed, 1 means open; C SW,t , C Shi,t and C Cur,t is the switching cost, load transfer cost and load abandonment cost of the tth cycle; C SW,t is the action cost of the jth switch.

[0202] Optionally, reliability C ReliThe expression is:

[0203]

[0204] In the formula, λ t,i is the interruption time of the ith load in the tth cycle; P t,i is the interrupted power of the ith load in the tth cycle; N l is the load number; C gc,t is the average interruption cost; Ct is the unit interruption cost of the i-th load in the t-th cycle; P EENs,t It is a power outage; P t,I is the interrupted power of the ith load in the tth cycle.

[0205] Optionally, the constraints include power balance constraints, node voltage and branch power constraints, switching action time constraints, distribution line connection constraints, demand response constraints, and storage device constraints.

[0206] Optionally, the power balance constraint expression is:

[0207]

[0208] Where P Gi , Q Gi is the active power and reactive power of the ith node; U i is the voltage of the ith node; Gij, Bij, δij are the ordered branch conductance, branch susceptance, and node voltage phase angle difference, respectively;

[0209] The expressions of node voltage and branch power constraints are:

[0210] U i min ≤U i ≤U i max ,i∈Ω

[0211]

[0212] Where U i min , U i max are the lower and upper limits of the node voltage respectively; S ij , S ji is the forward and reverse apparent power of branch ij; Ω is the node set;

[0213] The expression of switching action time constraint is:

[0214]

[0215] In the formula, γs max is the upper limit of the switch action time;

[0216] The expression of the distribution line connection constraint is:

[0217]

[0218] Where N Node , L Subs max is the node number and substation number; Ω g ,Ω c To disconnect the branch group and the normally closed branch;

[0219] The expression of demand response constraint is:

[0220]

[0221] In the formula, ξ Lmin , Lmax is the lower and upper limits of the transfer load rate; P Load,t is the load value at time t; P △L,t is the total power transmitted to the load;

[0222] The expression for the storage device constraint is:

[0223]

[0224] SOC i (t-1) = SOC i (t)

[0225] In the formula, SOC i(t) is the state of charge (SOC) of the i-th storage device at time t; SOC max,I , SOC min,I are the upper and lower limits of the SOC of the i-th storage device respectively; P min,i EES , P max,i EES are the upper and lower limits of the power of the i-th storage device respectively.

[0226] Optionally, the calculation module 440 includes:

[0227] Step 1: Input the distribution network parameters and algorithm constants into the improved fireworks algorithm to determine the initial population;

[0228] Step 2: Calculate the number of explosion sparks, explosion amplitude and fitness of the explosion fireworks of the initial population;

[0229] Step 3: optimizing the explosion sparks of the explosion fireworks by using gene mutation operation to determine the optimized explosion fireworks;

[0230] Step 4: Use a repulsion operator to optimize the overlapping explosion sparks in the optimized explosion fireworks, update the positions of the explosion sparks, and determine the updated explosion fireworks;

[0231] Step 5: Use out-of-bounds detection to detect out-of-bounds fireworks in the updated exploding fireworks to obtain valid exploding fireworks;

[0232] Step 6: Calculate the fitness value of the effective explosion fireworks and select the best explosion spark according to the fitness value;

[0233] Step 7: Iterate steps 2 to 6 and output the Pareto solution of multi-objective optimization.

[0234] Optionally, the determination expression of m mutation points in the gene mutation operation is:

[0235]

[0236] Where N r is the number of fireworks, f(X j ) is the fitness value of the best, worst and median fireworks, δ is the minimum amount of machines;

[0237] The expression of the repulsion operator is:

[0238]

[0239] Where ΔR is the Euclidean distance; X j is the position vector of the jth spark generated by firework i.

[0240] The out-of-bounds detection expression is:

[0241]

[0242] Where, X i k It is a substitute for fireworks that cross the border, X max k , X min k are the lower and upper limits of the value of fireworks, and D is the variable dimension.

[0243] Optionally, the analysis module 450 includes:

[0244] Construct decision matrix based on intuitionistic fuzzy coefficients of Pareto solution of multi-objective optimization;

[0245] Calculate the grey correlation coefficient between the decision matrix of each Pareto solution and the ideal operating state;

[0246] According to the grey relational coefficient, calculate the grey relational degree of each Pareto solution;

[0247] Determine the optimal solution of the Pareto solution of multi-objective optimization according to the grey correlation degree of each Pareto solution;

[0248] Determine the optimal strategy for low-carbon optimized operation of the smart park based on the optimal solution.

[0249] Optionally, the expression for constructing the decision matrix is:

[0250] π ij =1-μ ij -v ij

[0251] In the formula, μ ij , ν ij is the non-membership degree and non-membership dissatisfaction of the j-th target on the i-th solution; π ij It is uncertainty;

[0252] The calculation formula of grey correlation coefficient is:

[0253]

[0254] Where ρ is the resolution coefficient, x j - 、x j + is a negative ideal solution and a positive ideal solution; d j - ,d j + are the grey correlation coefficients between each solution and the negative ideal solution and the positive ideal solution; x ij - 、x ij + are the lower and upper limits of the interval number;

[0255] The calculation formula of grey relational degree is:

[0256]

[0257] In the formula, w i Yes th The weight of the i-th objective of the solution, CC j is the grey relational degree.

[0258] Exemplary Electronic Devices

[0259] Figure 5 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 5 As shown, the electronic device 50 includes one or more processors 51 and a memory 52 .

[0260] The processor 51 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0261] The memory 52 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 51 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 53 and an output device 54, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0262] In addition, the input device 53 may also include, for example, a keyboard, a mouse, etc.

[0263] The output device 54 can output various information to the outside. The output device 54 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0264] Of course, to simplify, Figure 5 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0265] Exemplary computer program products and computer-readable storage media

[0266] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0267] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0268] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0269] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0270] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0271] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0272] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0273] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0274] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0275] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A low-carbon optimization operation method for a source-grid-load-storage smart park based on load identification data, characterized in that: include: Based on the load identification technology, the equipment operation status inside the smart park is obtained, and the multi-objective formula of the distributed energy system of the smart park is established; Establishing constraint conditions for the operation of the smart park based on the operating status of the equipment; Determining the distribution network parameters within the smart park according to the multi-objective formula and the constraint conditions; Inputting the distribution network parameters and algorithm constants into the improved fireworks algorithm for iterative calculation to obtain a Pareto solution for multi-objective optimization; The intuitionistic fuzzy-based grey correlation multi-attribute decision-making is used to analyze the Pareto solution of multi-objective optimization and determine the optimal strategy for the low-carbon optimized operation of the smart park.

2. The method according to claim 1, characterized in that The multi-objective formulation includes maximum similarity between distributed energy resources and loads, minimum operating cost, and maximum reliability.

3. The method according to claim 2, characterized in that The similarity S between the distributed energy and the load d The expression is: Among them, N T is the number of reconfiguration cycles in a day, N p is the length of the scattered data of the tth reconfiguration cycle is the power of distributed energy and load at the jth time point in the tth period, and E() is the Euclidean distance.

4. The method according to claim 2, characterized in that: The operating cost C O The expression is: C O =C om +C RC +C LOSS in, In the formula, C om is the operation and maintenance cost, C RC is the reconstruction cost, C LOSS is the network loss cost; C WT , C PV , C ESS and C MT is the cost of orderly operation and maintenance of wind turbines, photovoltaic cells, energy storage equipment and gas turbines; C pure,t , C MT,t is the unit energy price and fuel cost of the gas turbine in period t; △T t is the duration of the tth period; P pure,t N is the electricity purchased from the upper power grid; b , N s is the branch number and switch number; γ is a binary variable, 0 means closed, 1 means open; C SW,t , C Shi,t and C Cur,t is the switching cost, load transfer cost and load abandonment cost of the tth cycle; C SW,t is the action cost of the jth switch.

5. The method according to claim 2, characterized in that: The reliability C Reli The expression is: In the formula, λ t,i is the interruption time of the ith load in the tth cycle; P t,i is the interrupted power of the ith load in the tth cycle; N l is the load number; C gc,t is the average interruption cost; Ct is the unit interruption cost of the i-th load in the t-th cycle; P EENs,t It was a power outage; P t,I is the interrupted power of the ith load in the tth cycle.

6. The method according to claim 1, characterized in that The constraints include power balance constraints, node voltage and branch power constraints, switching action time constraints, distribution line connection constraints, demand response constraints, and storage device constraints.

7. The method according to claim 6, characterized in that The power balance constraint expression is: Where P Gi , Q Gi is the active power and reactive power of the ith node; U i is the voltage of the ith node; Gij, Bij, δij are the ordered branch conductance, branch susceptance, and node voltage phase angle difference, respectively; The expressions of the node voltage and branch power constraints are: In the formula, are the lower and upper limits of the node voltage respectively; S ij , S ji is the forward and reverse apparent power of branch ij; Ω is the node set; The expression of the switching action time constraint is: In the formula, is the upper limit of the switch action time; The expression of the distribution line connection constraint is: Where N Node , L Subs max is the node number and substation number; Ω g ,Ω c To disconnect the branch group and the normally closed branch; The expression of the demand response constraint is: In the formula, ξ Lmin , Lmax are the lower and upper limits of the transfer load rate; P Load,t is the load value at time t; P △L,t is the total power transmitted to the load; The expression of the storage device constraint is: SOC i (t-1)=SOC i (t) In the formula, SOC i(t) is the state of charge (SOC) of the i-th storage device at time t; SOC max,I , SOC min,I are the upper and lower limits of the SOC of the i-th storage device respectively; P min,i EES , P max,i EES are the upper and lower limits of the power of the i-th storage device respectively.

8. The method according to claim 1, characterized in that The distribution network parameters and algorithm constants are input into the improved fireworks algorithm for iterative calculation to obtain the Pareto solution of multi-objective optimization, including: Step 1: Input the distribution network parameters and algorithm constants into the improved fireworks algorithm to determine the initial population; Step 2: Calculate the number of explosion sparks, explosion amplitude and fitness of the explosion fireworks of the initial population; Step 3: optimizing the explosion sparks of the explosion fireworks by using a gene mutation operation to determine the optimized explosion fireworks; Step 4: Use a repulsion operator to optimize the overlapping explosion sparks in the optimized explosion fireworks, update the positions of the explosion sparks, and determine the updated explosion fireworks; Step 5: Use out-of-bounds detection to detect out-of-bounds fireworks in the updated exploding fireworks to obtain valid exploding fireworks; Step 6: Calculate the fitness value of the effective explosion fireworks, and select the best explosion spark according to the fitness value; Step 7: Iterate steps 2 to 6 and output the Pareto solution of multi-objective optimization.

9. The method according to claim 8, characterized in that The determination expression of the m mutation points of the gene mutation operation is: Where N r is the number of fireworks, f(X j ) is the fitness value of the best, worst and median fireworks, δ is the minimum amount of machines; The expression of the repulsion operator is: Where ΔR is the Euclidean distance; X j is the position vector of the jth spark generated by firework i. The cross-border detection expression is: In the formula, It is a substitute for fireworks that cross the border, X max k , X min k are the lower and upper limits of the value of fireworks, and D is the variable dimension.

10. The method according to claim 1, characterized in that The decision analysis of the Pareto solution of multi-objective optimization is carried out by using the grey correlation multi-attribute decision-making based on intuitionistic fuzzy to determine the optimal strategy for the low-carbon optimization operation of the smart park, including: Construct decision matrix based on intuitionistic fuzzy coefficients of Pareto solution of multi-objective optimization; Calculate the grey correlation coefficient between the decision matrix of each Pareto solution and the ideal operating state; According to the grey relational coefficient, the grey relational degree of each Pareto solution is calculated; Determine the optimal solution of the Pareto solution of multi-objective optimization according to the grey correlation degree of each Pareto solution; The optimal strategy for low-carbon optimized operation of the smart park is determined based on the optimal solution.

11. The method according to claim 10, characterized in that The expression of the decision matrix construction is: p ij =1-m ij -v ij In the formula, μ ij , ν ij is the non-membership degree and non-membership dissatisfaction of the j-th target on the i-th solution; π ij It is uncertainty; The calculation formula of the grey correlation coefficient is: Where ρ is the resolution coefficient, x j - 、x j + are negative ideal solutions and positive ideal solutions; are the grey correlation coefficients between each solution and the negative ideal solution and the positive ideal solution; x ij - 、x ij + are the lower and upper limits of the interval number; The calculation formula of the grey relational degree is: In the formula, w i Yes th The weight of the i-th objective of the solution, CC j is the grey relational degree.

12. A low-carbon optimization operation device for a source-grid-load-storage smart park based on load identification data, characterized in that: include: The first establishment module is used to obtain the equipment operation status inside the smart park based on the load identification technology and establish a multi-objective formula for the distributed energy system of the smart park; A second establishing module, used to establish constraint conditions for the operation of the smart park based on the operating status of the equipment; A determination module, used to determine the distribution network parameters in the smart park according to the multi-objective formula and the constraint conditions; A calculation module, used for inputting the distribution network parameters and algorithm constants into the improved fireworks algorithm for iterative calculation to obtain a Pareto solution for multi-objective optimization; The analysis module is used to perform decision analysis on the Pareto solution of multi-objective optimization by using grey correlation multi-attribute decision-making based on intuitionistic fuzzy to determine the optimal strategy for low-carbon optimized operation of the smart park.

13. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 11.

14. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-11.

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