Energy system transformation scheduling optimization method and system considering energy quality

Abstract

The present invention relates to the technical field of electric power systems, and in particular to an energy system transformation scheduling optimization method and system considering energy quality, which calculates all node sets, a set of equipment types containing energy quality information, and a set of all energy forms containing energy quality information of each node; calculates a system connection matrix and a power correction matrix to establish a collaborative optimization model with the purpose of optimizing equipment capacity and equipment power; solves the collaborative optimization model using an adaptive solution algorithm to obtain an optimal equipment capacity matrix and an optimal equipment power matrix; determines a transformation optimization result and a scheduling optimization result based on the optimal equipment capacity matrix and the optimal equipment power matrix, and considers the energy quality in the energy system and analyzes all energy system structures.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an energy system transformation scheduling optimization method and system taking energy quality into consideration. Background Art

[0002] At present, the methods for energy system planning and scheduling optimization are relatively comprehensive and in-depth, but there are still two outstanding problems. First, the existing energy system transformation and scheduling methods ignore the quality of old and new energy, and abstract all similar energy into one form of energy. However, in actual industrial production, the energy quality required by different processes varies greatly. For example, one process requires 60-80℃ hot water, while another process requires 180-220℃ dry hot air. There are huge differences between these two energy sources in production and transmission. It is inappropriate to simply abstract them into one form of energy in the analysis. Second, the existing energy system planning analysis often only analyzes a few established system structures, which is obviously subjective and one-sided, and easily leads to the direct exclusion of feasible and cost-effective solutions.

[0003] Therefore, it is of great significance to study an energy system transformation scheduling optimization method and system that analyzes all energy system structures and considers the energy quality within the energy system. Summary of the invention

[0004] The purpose of the present invention is to provide an energy system transformation scheduling optimization method and system that takes energy quality into consideration. By establishing detailed energy quality intervals and dividing energy forms, and constructing a system connection matrix that includes all connection possibilities, an energy system transformation scheduling optimization method that takes energy quality within the energy system and analyzes all energy system structures is realized.

[0005] In order to solve the above technical problems, the first technical solution of the present invention provides an energy system transformation scheduling optimization method considering energy quality, comprising the following steps:

[0006] S1, construct an existing equipment parameter set EX, an unassembled new equipment parameter set AVA and a node set NODE, and perform energy quality classification processing on the existing equipment parameter set EX and the unassembled new equipment parameter set AVA based on the node set NODE to obtain all node sets NODE′, a set D of equipment types after classification containing energy quality information, and a set Ω of all energy forms containing energy quality information of each node;

[0007] S2, based on the set of all nodes NODE′, traverse all connection relationships of the unassembled new equipment in the energy system to obtain the system connection matrix R and the power correction matrix K;

[0008] S3, based on the device type classification set D and the power correction matrix K, establishing a collaborative optimization model for the purpose of optimizing device capacity and device power;

[0009] S4, based on the set of all energy forms Ω, the set of equipment types D and the system connection matrix R, the collaborative optimization model is solved using an adaptive solution algorithm to obtain the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt ;

[0010] S5, based on the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt , determine the transformation optimization results and scheduling optimization results.

[0011] The second technical solution, the present invention provides an energy system transformation scheduling optimization system considering energy quality, which applies the energy system transformation scheduling optimization method considering energy quality described in the first technical solution, including:

[0012] A construction module is used to construct an existing device parameter set EX, an unassembled new device parameter set AVA and a node set NODE, and based on the node set NODE, perform energy quality classification processing on the existing device parameter set EX and the unassembled new device parameter set AVA to obtain all node sets NODE′, a set D of device types after classification containing energy quality information, and a set Ω of all energy forms containing energy quality information of each node;

[0013] A matrix generation module, for traversing all connection relationships of unassembled new equipment in the energy system based on the set of all nodes NODE′, to obtain a system connection matrix R and a power correction matrix K;

[0014] A model building module, used to establish a collaborative optimization model for optimizing device capacity and device power based on the device type classification set D and the power correction matrix K;

[0015] A solution module is used to solve the collaborative optimization model using an adaptive solution algorithm based on the set of all energy forms Ω, the set of equipment types D and the system connection matrix R to obtain the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt ;

[0016] An optimization module is used to optimize the equipment capacity matrix Q based on the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt , determine the transformation optimization results and scheduling optimization results.

[0017] The beneficial effects of the present invention are as follows:

[0018] 1. In step S1, this solution performs energy quality classification processing on the existing equipment parameter set EX and the unassembled new equipment parameter set AVA based on the node set NODE to obtain all node sets NODE′, the divided set D of equipment types containing energy quality information, and the set Ω of all energy forms containing energy quality information of each node. Therefore, when applying, the node set NODE is combined to perform detailed energy quality interval and energy form classification on the existing equipment parameter set EX and the unassembled new equipment parameter set AVA, and each type of energy is classified by quality, fully considering the quality of old energy and new energy, so that the supply and demand matching of the entire energy system transformation and scheduling is more accurate.

[0019] 2. Since this solution, in step S2, traverses all connection relationships of unassembled new equipment in the energy system based on the set of all nodes NODE′ to obtain the system connection matrix R and the power correction matrix K, when applied, it combines the network graph theory method to construct a system connection matrix that includes all connection possibilities, and uses it in the subsequent collaborative optimization model, achieving objectivity and comprehensiveness at the transformation planning analysis level.

[0020] 3. Since in steps S4 to S6, the improved adaptive solution algorithm based on the heuristic optimization principle is used to solve the collaborative optimization model, the optimal equipment capacity matrix Q is obtained opt and the optimal device power matrix Γ opt , and based on the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt , determine the transformation optimization results and scheduling optimization results, and through a comprehensive and objective analysis of all potential solutions, be able to form energy system transformation and scheduling control solutions with more practical application value, reduce the impact of subjective factors on the results, and improve the reliability, practicality and benefits of transformation optimization and scheduling optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a schematic diagram of the overall steps provided by the preferred embodiment of the present invention;

[0023] Figure 2It is a structural diagram of the energy system before transformation constructed according to the connection status of the original system equipment provided by the preferred embodiment of the present invention;

[0024] Figure 3 It is a schematic diagram of a method for constructing a maximum redundant equipment combination mode provided by a preferred embodiment of the present invention;

[0025] Figure 4 is a flow chart of an adaptive solution algorithm provided in a preferred embodiment of the present invention;

[0026] Figure 5 It is a schematic diagram of the overall system structure provided by the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0028] In order to solve the above problems, this scheme proposes an energy system transformation scheduling optimization method considering energy quality. Please refer to Figure 1 , including the following steps:

[0029] S1, construct the existing equipment parameter set EX, the unassembled new equipment parameter set AVA and the node set NODE, and perform energy quality classification on the existing equipment parameter set EX and the unassembled new equipment parameter set AVA based on the node set NODE, and obtain the set of all nodes NODE′, the set D of equipment types after classification containing energy quality information, and the set Ω of all energy forms containing the energy quality information of each node.

[0030] Preferably, S1 specifically includes the following steps:

[0031] S10, collecting parameter information of existing equipment in the energy system and new equipment that can be provided by suppliers, and constructing an existing equipment parameter set EX, an unassembled new equipment parameter set AVA, and a node set NODE.

[0032] For example, Figure 2 It is a structural diagram of the energy system before transformation, which is constructed based on the connection status of the original system equipment. It collects the equipment model, equipment capacity, energy conversion coefficient, self-loss coefficient, climbing rate constraint, input and output energy form and its upper and lower power limits and other parameter information of the existing energy supply facilities, energy conversion equipment, energy storage equipment and loads in the energy system, and counts the equipment model, equipment capacity, energy conversion coefficient, self-loss coefficient, climbing rate constraint, input and output energy form and its upper and lower power limits and other parameter information of the energy supply facilities, energy conversion equipment and energy storage equipment that can be provided by the supplier. At the same time, according to the models of the above-mentioned equipment, the existing equipment parameter set EX, the unassembled new equipment parameter set AVA and the node set NODE are constructed.

[0033] S11, calculate the index scores of the existing equipment parameter set EX and the unassembled new equipment parameter set AVA based on the preset attribute index system, and establish the energy quality range of the input and output energy of all equipment in the existing equipment parameter set EX and the energy quality range of the input and output energy of all equipment in the unassembled new equipment parameter set AVA according to the index scores.

[0034] Exemplarily, regarding the energy quality intervals of the input and output energies of all devices in the existing device parameter set EX, the attribute index scores of the input and output energies of all devices in the existing device parameter set EX are quantitatively calculated, and according to the differences in the four energy types of electricity, heat and hydrogen, the score values ​​of the five power quality indicators of the input and output electric energy of the equipment are quantitatively calculated to form an electric energy score upper limit matrix [eu1, ..., eu5] and an electric energy score lower limit matrix [el1, ..., el5]; the score values ​​of the five natural gas attribute indicators of the natural gas input and output are quantitatively calculated to form a natural gas score upper limit matrix [gu1, ..., gu5] and a natural gas score lower limit matrix [gl1, ..., gl5]; the score values ​​of the two thermal energy attribute indicators of the thermal energy input and output are quantitatively calculated to form a thermal energy score upper limit matrix [tu1, tu2] and a thermal energy score lower limit matrix [tl1, tl2]; the score values ​​of the 14 hydrogen attribute indicators of the hydrogen input and output are quantitatively calculated to form a hydrogen score upper limit matrix [hu1, ..., hu 14 ] and the hydrogen score lower limit matrix [hl1,…,hl 14 ]; All element values ​​of the above matrix need to be converted into positive real numbers, and then the energy quality ranges of the input and output energies of all devices in the set EX are established in turn.

[0035] Exemplarily, regarding the energy quality range of the input and output energy of all devices that are not equipped with the new device parameter set AVA, the attribute indicator scores of the input and output energy of all devices in the set AVA are quantified and measured, and the energy quality range of the input and output energy of all devices that are not equipped with the new device parameter set AVA is established in turn. The quantitative measurement process is the same as the measurement process of the energy quality range of the input and output energy of all devices in the existing device parameter set EX, and will not be repeated here.

[0036] Among them, the expression of the energy quality interval Φωζ of the energy ζ input / output of a certain device ω is as follows:

[0037]

[0038] In the above formula, is the yth element of the set in the energy quality interval, ωζl y and ωζu yare the values ​​of the yth element in the lower limit score matrix and the upper limit score matrix of energy ζ respectively, and the value of n is equal to the number of attribute indicators of energy ζ.

[0039] It should be noted that the preset attribute indicator system includes 5 power quality indicators, 5 natural gas attribute indicators, 2 thermal energy attribute indicators and 14 hydrogen attribute indicators, among which the power quality indicators specifically include voltage deviation value, voltage fluctuation value, voltage harmonic distortion rate, three-phase unbalance and frequency deviation value; the natural gas attribute indicators specifically include hydrogen sulfide content, total sulfur content, carbon dioxide content, oxygen content and calorific value; the thermal energy attribute indicators specifically include heat medium type and temperature; the hydrogen attribute indicators specifically include purity, moisture content, total nitrogen and argon content, oxygen content, helium content, ammonia content, carbon monoxide content, carbon dioxide content, total hydrocarbon content, total sulfur content, total halogen compound content, formaldehyde content, formic acid content and maximum particulate matter concentration.

[0040] S12, based on the preset energy system structure diagram before transformation (refer to Figure 2 ) and the energy quality interval of all equipment input and output energy of the existing equipment parameter set EX, and calculate the energy quality interval Φ of each node of the energy system before transformation node .

[0041] For example, the intersection of the energy quality intervals of the input and output energies of the devices connected to each element of the node set NODE is calculated in turn. Each node is recorded as the same energy form, and the intersection is the energy quality interval of this energy form. The energy quality interval of the energy form of node b is Φ b According to the following formula, the node energy quality interval set Φ corresponding to the set NODE is obtained: node ={Φ 1 , Φ 2 ,…,Φ m}:

[0042]

[0043] Where m is the number of branches connected to node b. and Respectively represent the yth element l of the energy quality interval of m branches vy and u vy The maximum and minimum values ​​in l vy and u vy They respectively represent the lower limit and upper limit of the yth element in the energy quality interval of the vth branch.

[0044] S13, based on the parameter set AVA of all devices without new equipment installed, the energy quality interval of the input and output energy of all devices and the energy quality interval of each node of the energy system before transformation Φ nodePerform device matching to obtain a parameter set of new devices to be added, intermediate nodes to be added, and energy quality intervals to be added.

[0045] Preferably, S13 specifically includes the following steps:

[0046] S130, calculating the energy quality interval of the input energy of a certain energy conversion device in the parameter set AVA of the unassembled new device and the energy quality interval Φ of each node of the energy system before the transformation node The first intersection number n1 between them is calculated, and the second intersection number n2 of the energy quality intervals of the input energy of the same energy conversion device in the unassembled new device parameter set AVA is calculated. If n1n2≠0, based on the product n1*n2 of the first intersection number and the second intersection number, the models of the corresponding number of energy conversion devices are constructed into the new device parameter set to be added, that is, n1*n2 models of this energy conversion device are constructed into the new device parameter set to be added; otherwise, this device is skipped and the next energy conversion device is judged until the judgment of all energy conversion devices in the unassembled new device parameter set AVA is completed.

[0047] For example, a dual-input, three-output device is taken as an example: the energy conversion device is denoted as A, the input energy of device A is denoted as IN1 and IN2, and the output energy of device A is denoted as OUT1, OUT2 and OUT3; it is assumed that the energy quality interval of IN1 intersects with the energy quality interval of node 1 (that is, the input IN1 of device A can be connected to node 1 and draw power from node 1), and similarly, IN2 intersects with nodes 3 and 5; at this time, the two input terminals IN1 and IN2 of device A can be connected to nodes 1 and 3, respectively, or can be connected to nodes 1 and 5, respectively, so there are two connection situations for the input terminals, so the first intersection number n1=2; at the same time, OUT1 intersects with node 6, OUT2 intersects with node 7, and OUT3 intersects with node 8. Intersection; at this time, the three output terminals OUT1, OUT2 and OUT3 of device A can be connected to nodes 6, 7 and 8 respectively, so the output terminal has only one connection situation, so the second intersection number n2=1; at this time n1*n2=2, so device A can be regarded as having 2 models. The two input terminals IN1 and IN2 of the first model can be connected to nodes 1 and 3 respectively, and the three output terminals OUT1, OUT2 and OUT3 can be connected to nodes 6, 7 and 8 respectively; the two input terminals IN1 and IN2 of the second model can be connected to nodes 1 and 5 respectively, and the three output terminals OUT1, OUT2 and OUT3 can be connected to nodes 6, 7 and 8 respectively; therefore, at this time, device A will be regarded as two device models as independent new devices to be added.

[0048] S131, calculate the energy quality interval of the output energy of a certain energy supply facility in the parameter set AVA before the new equipment is installed and the energy quality interval Φ of each node of the energy system before the transformation node If n3≠0, based on the third intersection number n3, the models of the corresponding number of energy supply facilities are added to the new equipment parameter set to be added in sequence, that is, the third intersection number n3 models of this energy supply facility are added to the new equipment parameter set to be added in sequence; otherwise, this type of equipment is skipped and the next type of energy supply facility is judged, until the judgment of all energy supply facilities in the unequipped new equipment parameter set AVA is completed.

[0049] Exemplarily, there are n3 types of models, that is, this type of energy supply facility is regarded as having n3 types of models, and each model is regarded as a separate device; for example, an air conditioner with heating and cooling functions, its input energy is electrical energy, which can be connected to node 1; its output energy can be two different temperatures of thermal energy, when it operates in the heating condition, it can be connected to node 2, when it operates in the cooling condition, it can be connected to node 3; therefore, the device can be regarded as having two models, the first device is an air conditioning device with only heating function, connected between node 1 and node 2; the second device is an air conditioning device with only cooling function, connected between node 1 and node 3; although they are essentially the same air conditioner, they can be connected to different nodes in the energy system and operate under different conditions, so they are regarded as having two models, which is convenient for distinction and comprehensive analysis.

[0050] S132, based on the matching relationship between the device and the node set NODE, calculate the corresponding multi-device combination method, and perform maximum redundancy processing on the device combination method, traverse all device combinations, obtain all combinations of devices with maximum redundancy, intermediate nodes to be added, and energy quality intervals to be added, and add all combinations of devices with maximum redundancy to the set of new device parameters to be added.

[0051] It should be noted that the specific method of combining multiple devices is: the devices are connected in a trunk-like manner through preset combination conditions to form a whole, wherein, as a necessary condition, the whole contains at least one new node that is different from any element in the current set NODE, and the new node is also the intermediate node to be added. The energy quality interval of the intermediate node is the energy quality interval to be added, which is equal to the intersection of the energy quality intervals of all branches connected to the node.

[0052] The preset combination condition is: the energy quality intervals of the branches connected to the same node have a non-empty intersection, and the energy quality interval of the branch connected to a node a in the node set NODE must satisfy Φ a ∩Φ nb =Φ a, where Φ a is the energy quality interval of one of the nodes a in the node set NODE, Φ nb The energy quality interval of the input / output energy nb of the device connected to one of the nodes a in the node set NODE, and all the devices in the whole have at least one input or output energy quality interval that is different from the energy quality interval of each node in the energy system before the transformation Φ node There is no intersection between the energy quality intervals of any nodes.

[0053] It should be noted that please refer to Figure 3 ,The maximum redundancy processing is specifically as follows: based on the preset redundancy condition, the devices in the unassembled new device parameter set AVA are added to the multi-device combination mode in a non-repetitive manner.

[0054] The preset redundancy condition is: when the input / output energy of the device is connected to the new node, the energy quality interval of the input / output energy of the device and the energy quality interval of the new node have a non-empty intersection; when the input / output energy of the device is connected to one of the nodes b in the node set NODE, the energy quality interval of the input / output energy of the device satisfies Φ b ∩Φ ne =Φ b , where Φ b is the energy quality interval of one of the nodes b in the node set NODE, Φ ne The energy quality interval of the input / output energy ne for a device connected to a node b belonging to the node set NODE.

[0055] The multi-device combination after maximum redundancy needs to meet the following two conditions: (1) All intermediate nodes are connected to at least one input branch and one output branch; (2) At least one input branch and one output branch are connected to the nodes in the current set NODE.

[0056] S133, calculating the energy quality interval of the input / output energy of a certain energy storage device in the parameter set AVA before the new device is installed and the energy quality interval Φ of each node of the energy system before the transformation node The number of nodes n4 that meet the preset conditions is n4, and based on the number of nodes n4, the models of the corresponding number of energy storage devices are added to the parameter set of new devices to be added, that is, the number of nodes n4 types of such energy storage devices are added to the parameter set of new devices to be added; otherwise, such devices are skipped and the next energy storage device is judged until the judgment of all energy storage devices in the parameter set AVA of new devices that are not equipped is completed.

[0057] It should be noted that the principle of the n4 types of models is the same as that of step S131.

[0058] Among them, the preset satisfying condition is how many nodes c satisfy Φ c ∩Φ s =Φ c .

[0059] S14, based on the existing equipment parameter set EX and the new equipment parameter set to be added, construct the equipment type pre-division set; add the intermediate node to be added to the node set NODE to obtain the set of all nodes NODE′; add the energy quality interval to be added to the energy quality interval Φ of each node in the energy system before transformation node , get all energy quality intervals Φ node′ .

[0060] S15, performing equipment type classification on the equipment type pre-classification set to obtain the equipment type post-classification set D, wherein the equipment type post-classification set D includes an energy supply facility set, an energy conversion equipment set, an energy storage equipment set and a load set, and constructs an equipment parameter vector.

[0061] For example, regarding the division of sets, when constructing and solving the subsequent optimization model, it is necessary to perform differentiated operations based on the characteristics of the equipment. Therefore, the set is further divided before the equipment type division. Equipment with only output ports is classified into the energy supply facility set Sup, equipment with both input and output ports is classified into the energy conversion equipment set B, equipment that realizes input and output through the same port is classified into the energy storage equipment set C, and equipment with only input ports is classified into the load set L.

[0062] Exemplarily, the relevant equipment parameter vectors, combined with the equipment parameter information collected in step S1, are used to construct the equipment parameter vectors of each equipment in the set D after the equipment type division, including: equipment rated capacity vector, energy conversion coefficient vector, self-loss coefficient vector, climbing rate upper limit vector and power upper and lower limit vectors.

[0063] Among them, the above equipment parameter vectors are all row vectors, and the number of columns is equal to the total number of elements in the set. For the original energy system equipment, the data in the above vectors are the actual installed data, and for the unassembled equipment, the data in the above vectors are the data of a single device.

[0064] S16, based on all node sets NODE′ and all energy quality intervals Φ node′ , construct the set Ω of all energy forms.

[0065] Specifically, the set of all energy forms Ω, the set of all nodes NODE′ and the set of all energy quality intervals Φ node′There is a one-to-one correspondence between the elements in . Based on the characteristics of the node energy quality interval, the energy forms of all nodes in the node set NODE′ are named in turn, and the energy form names are integrated in turn to construct the set of all energy forms Ω, that is, the elements in the set of all energy forms Ω are the names of the energy forms of all nodes in the node set NODE′.

[0066] To summarize the above step S1, in step S1, firstly, the energy quality ranges of the input and output energy of all devices in the existing equipment parameter set EX and the energy quality ranges of the input and output energy of all devices in the unequipped new equipment parameter set AVA are established. Then, based on these energy quality ranges, the energy quality ranges of each node in the energy system before the transformation and the conditions of each device, node and energy form in the unequipped new equipment parameter set AVA are calculated. The old and new equipment are divided into detailed energy quality ranges and energy forms, and each type of energy is divided into quality, fully considering the quality of the old and new energy, so that the supply and demand matching in the transformation and scheduling optimization of the entire energy system is more accurate.

[0067] S2, based on the set of all nodes NODE′, traverses all connection relationships of the unassembled new equipment in the energy system to obtain the system connection matrix R and the power correction matrix K, that is, analyzes all possible connection situations of the new equipment in the energy system, and according to the principle of matching the supply and demand energy quality, constructs the system connection matrix R and the power correction matrix K that include all possible device connections when all new equipment are connected to the energy system.

[0068] Regarding the system connection matrix R, the system connection matrix R is used to abstractly describe the device connection status of the real physical world in the form of a matrix. It is the basic information for building mathematical models and solving them. Each row represents a device branch, and each column represents an energy node. The number of rows is equal to the number of elements in the set D after the device type is divided, and the number of columns is equal to the number of elements in the set of all nodes NODE′. Different values ​​are used to characterize the energy input and output relationship between branches and nodes. Each connection status in the real physical world can uniquely determine a system connection matrix R. The elements R of the system connection matrix R are ij The value of is:

[0069] When the i-th row device of the system connection matrix R takes the j-th column energy as input energy, R ij The value of is 1;

[0070] When the i-th row device of the system connection matrix R outputs energy in the form of j-column energy, R ij The value of is -1;

[0071] When the i-th row device of the system connection matrix R stores the j-th column energy form as input / output, R ijThe value of is 2;

[0072] When the i-th row device of the system connection matrix R has no connection with the j-th column energy form, R ij The value of is 0;

[0073] Among them, R ij is the element in the i-th row and j-th column of the system connection matrix R;

[0074] Regarding the power correction matrix K, the flow of energy in the energy system needs to pay attention to its magnitude and direction at the same time. Through the power correction model, the direction of energy flow is marked. If the input power is specified as a positive value and the output power is a negative value, the power correction matrix K can be calculated according to the system connection matrix R. The specific expression of the power correction matrix K is:

[0075]

[0076] In the above formula, K ij is the element in the i-th row and j-th column of the power correction matrix K, R ij The element in the i-th row and j-th column of the system connection matrix R.

[0077] To summarize step S2, in step S2, based on the set of all nodes NODE′, all connection relationships of the unassembled new equipment in the energy system are traversed to obtain the system connection matrix R and the power correction matrix K. The system connection matrix that includes all connection possibilities is constructed by combining the network graph theory method, and is used in the subsequent collaborative optimization model, achieving objectivity and comprehensiveness at the transformation planning analysis level.

[0078] S3, based on the set D after equipment type classification and the power correction matrix K, a collaborative optimization model is established with the purpose of optimizing equipment capacity and equipment power. The collaborative optimization model is a model constructed by using a three-dimensional matrix to represent the energy system equipment power matrix, using the equipment capacity matrix as the optimization independent variable at the planning level and a group of independent equipment power vector groups as the independent variables at the operation level.

[0079] Among them, the energy system equipment power matrix Γ=[P ijt ], where P ijt is the power of the energy form of the jth column of the device in the i-th row of the system connection matrix in the tth period, which corresponds to the index of the three dimensions. ij = 0, there must be P ijt =0, the matrix consists of 24 two-dimensional matrix layers, each of which is accurately mapped to a unique time segment in a typical day, covering the device power data from the 1st time interval to the 24th time interval in a typical day.

[0080] Preferably, S3 is specifically:

[0081] The collaborative optimization model includes a main optimization model and a sub-optimization model. The main optimization model solves the transformation planning optimization problem, and the sub-optimization model solves the operation scheduling optimization problem. The independent variables of the main optimization model provide optimization boundary conditions for the sub-optimization model, which are implemented through the sub-optimization model and the main optimization model.

[0082] The specific expression of the main optimization model is:

[0083]

[0084] In the above formula, min means to find the minimum value of the objective function, Const means the constraint condition of the main optimization model, F is the objective function of the main optimization model, λ is the cost matrix, and λ ij represents the element in the i-th row and j-th column of the cost matrix, Q is the equipment capacity matrix, Q i represents the row vector of the i-th row of the equipment capacity matrix, Q ij represents the element in the i-th row and j-th column of the equipment capacity matrix, f opt is the optimal sub-optimization objective function value with Q as the optimization boundary condition, Sup is the energy supply facility set, μ s Indicates the weight coefficient of the sth harmful substance, δ ijs It represents the unit emission of the sth harmful substance by the energy form of the jth column of the equipment in the i-th row of the system connection matrix, It means that the device in the i-th row of the system connection matrix outputs the power in the j-th column of energy in the t-th period under the optimal operation state. represents the rated capacity vector of the equipment in the i-th row of the single system connection matrix, Indicates the upper limit of the number of devices that can be built in the i-th row of the system connection matrix.

[0085] The sub-optimization model includes objective function, energy supply facility transmission constraints, energy conversion equipment operation constraints, energy storage equipment operation constraints and energy supply and demand balance constraints.

[0086] The specific expression of the objective function is:

[0087]

[0088] In the above formula, f is the objective function of the sub-optimization model, P ijt represents the power of the energy form of the jth column of the device in the i-th row of the system connection matrix in the tth period, μ s Indicates the weight coefficient of the sth harmful substance, δ ijs It represents the unit emission of the sth harmful substance by the energy form in the jth column of the equipment in the i-th row of the system connection matrix.

[0089] The specific expression of the transmission constraint of energy supply facilities is:

[0090]

[0091] In the above formula, P sup，min is the lower limit of the output power of the energy supply facility, P sup，max is the upper limit of the output power of the energy supply facility, Represents the output power of the energy supply facility in the tth time period.

[0092] The specific expression of the energy conversion equipment operation constraint is:

[0093]

[0094] In the above formula, represents the input power of the energy conversion device in the tth period, represents the output power of the energy conversion device in the tth period, represents the output power of the energy conversion device in the t+1th period, H is the conversion efficiency matrix of the energy conversion device, Ramp is the upper limit of the ramp rate of the energy conversion device, P out，min is the lower limit of the output power of the energy conversion device, P out，max The upper limit of the output power of the energy conversion device.

[0095] The specific expression of the energy storage device operation constraint is:

[0096]

[0097] In the above formula, W t+1 represents the energy storage capacity of the energy storage device in the t+1th period, W t represents the energy storage capacity of the energy storage device in the tth period, W1 represents the energy storage capacity of the energy storage device in the first period, and W 24 represents the energy storage capacity of the energy storage device in the 24th period, σ ES is the self-loss coefficient of the energy storage device, η ch is the charging efficiency coefficient of the energy storage device, η dis is the discharge efficiency coefficient of the energy storage device, Δt represents the interval length of each time period, represents the power of the energy storage device in the tth period, P ES，min is the minimum power of the energy storage device, P ES，max is the maximum power of the energy storage device, W min is the lower limit of energy storage of energy storage equipment, W max It is the upper limit of energy storage capacity of energy storage device.

[0098] Energy supply and demand balance constraints include surplus energy supply and demand constraints and other energy supply and demand constraints;

[0099] The specific expression of surplus energy supply and demand constraints is:

[0100]

[0101] In the above formula, j represents the j-th column element of the corresponding matrix, t represents the t-th time period of a day, and K ij is the element in the i-th row and j-th column of the power correction matrix K, P ijt It is the power of the energy form of the jth column of the device in the i-th row of the system connection matrix in the t-th time period.

[0102] The specific expressions of other energy supply and demand constraints are:

[0103]

[0104] In the above formula, j represents the j-th column element of the corresponding matrix, t represents the t-th time period of a day, and K ij is the element in the i-th row and j-th column of the power correction matrix K, P ijt It is the power of the energy form of the jth column of the device in the i-th row of the system connection matrix in the t-th time period.

[0105] S4, based on the set of all energy forms Ω, the set of equipment types D and the system connection matrix R, the collaborative optimization model is solved using an adaptive solution algorithm to obtain the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt .

[0106] Preferably, step S4 specifically includes the following steps, please refer to Figure 4 :

[0107] S41, according to the preset value interval, generate the Q corresponding to each element in the set D after the device type is divided by a preset generation method. i , and form the equipment capacity matrix Q.

[0108] For example, from the device capacity vector Q i In the value range of , the Q corresponding to each element in the set D after the device type division is randomly generated by evenly distributing random numbers. i , and form the equipment capacity matrix Q.

[0109] S42, performing independent variable division on the elements in the set Ω of all energy forms one by one to obtain a plurality of first independent variable vector groups.

[0110] The independent variables are:

[0111] For each energy form in the set of all energy forms Ω, among the output power parameters of various devices in the set of energy conversion devices, one of the powers with the largest adjustable power range is used as a reference quantity, and the output power of the remaining devices in the set of energy conversion devices excluding the reference quantity is used as an independent variable;

[0112] For each energy form in the set of all energy forms Ω, the charging and discharging power of each type of equipment in the energy storage device set is taken as the independent variable.

[0113] S43, in the upper and lower limit ranges of the device power corresponding to each of the first independent variable vector groups, generate a device power vector and a device state vector through a preset generation method, multiply the preset device power vector by the device state vector to obtain a single second independent variable vector, traverse all first independent variable vector groups, and obtain all second independent variable vector groups.

[0114] Exemplarily, in the upper and lower limit intervals of device power corresponding to each first variable group, a device power vector is randomly generated by extracting evenly distributed random numbers, and a device state vector with element value ranges of {-1,0,1} or {0,1} is randomly generated with a given probability using a rounding function. The preset device power vector is dot-multiplied by the device state vector to obtain a single second independent variable vector, and the operation is repeated to complete the generation of all second independent variable vector groups.

[0115] S44, construct a first matrix according to the row and column information of the system connection matrix R and the value of the second independent variable vector group, determine whether the first matrix satisfies various constraints of the sub-optimization model, obtain the first device power matrix, and calculate the corresponding sub-optimization objective function value.

[0116] Exemplarily, a three-dimensional all-zero matrix is ​​generated, and the values ​​of the second independent variable vector group are assigned to the corresponding elements of the all-zero matrix according to the row and column information of the system connection matrix R, and then other missing power information is calculated according to the constraints of the sub-optimization model and it is checked whether all constraints are met. If so, the matrix is ​​used as the first device power matrix and the sub-optimization objective function value is calculated. Otherwise, return to S43 to regenerate the independent variable vector group.

[0117] S45, arbitrarily select any continuous segment of the two second independent variable vector groups, and exchange the selected continuous segments of the two second independent variable vector groups to obtain a third independent variable vector group, and then arbitrarily select some elements in the third independent variable vector group, regenerate the selected elements, and obtain a fourth independent variable vector group.

[0118] The second independent variable vector group selected in the first execution process is the second independent variable vector group obtained by processing in step S43.

[0119] It should be understood that S43 and S45 are both common operations of the heuristic algorithm. Step S43 generates an initial solution. Random generation in a preset manner can ensure that the initial solution is approximately evenly distributed in the solution space, and the rate and accuracy of subsequent evolutionary optimization will be improved; S45 implements crossover and mutation operations. Crossover and mutation are both necessary operations for the heuristic algorithm. Through crossover and mutation, the solution set will continue to evolve and eventually form the optimal solution.

[0120] S46, construct a second matrix based on the row and column information of the system connection matrix R and the value of the fourth independent variable vector group, determine whether the second matrix satisfies various constraints of the sub-optimization model, obtain the second device power matrix, and calculate the corresponding sub-optimization objective function value.

[0121] Exemplarily, a three-dimensional all-zero matrix is ​​generated, and the values ​​of the fourth independent variable vector group are assigned to the corresponding elements of the all-zero matrix according to the row and column information of the system connection matrix R, and then the other missing power information is calculated according to the constraints of the sub-optimization model and it is checked whether all constraints are met. If so, the matrix is ​​used as the second device power matrix to calculate the sub-optimization objective function value. Otherwise, the independent variable vector group is directly skipped and deleted.

[0122] S47, replace the original second independent variable vector group with the newly generated fourth independent variable vector group to form an updated second independent variable vector group, and loop through steps S45 to S46 with the updated second independent variable vector group until the current optimal sub-optimization objective function value remains unchanged, and output the optimal device power matrix Γ of the last loop opt , and calculate the value of the main optimization objective function.

[0123] S48, arbitrarily select any continuous segment of the device capacity matrix Q, exchange the segments of two device capacity matrices Q, and then arbitrarily select some elements in the device capacity matrix Q, regenerate the selected elements, and obtain an updated device capacity matrix Q. Each time S48 is executed, the optimized device capacity matrix of the previous iteration will be updated through crossover and mutation operations to obtain a new generation of device capacity matrix as the basis for the next iteration evolution or as the output of the last iteration result.

[0124] Among them, when this step is executed for the first time, the selected device capacity matrix Q is the device capacity matrix Q generated in step S41, and in the subsequent loop process, the selected device capacity matrix Q is the device capacity matrix Q updated in the previous generation step S48.

[0125] S49, looping steps S42 to S48, terminating when the number of repetitions reaches a predetermined value, and obtaining the optimal device power matrix Γ opt , optimal equipment capacity matrix Q opt and the value of the main optimization objective function.

[0126] S5, based on the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt , determine the transformation optimization results and scheduling optimization results, that is, according to Q opt Determine the transformation plan of the energy system and opt Carry out dispatching and control of the transformed energy system.

[0127] Preferably, step S5 specifically includes the following steps:

[0128] S50, calculating the quantity value of each device configuration based on the configuration formula, wherein the configuration formula is expressed as:

[0129]

[0130] In the above, num i The number of devices configured for the i-th row of the system connection matrix, Denotes the optimal equipment capacity matrix Q opt The row vector of the i-th row of represents the rated capacity vector of the equipment in the i-th row of the single system connection matrix, and ||·||2 represents the binary norm of the vector.

[0131] S51, connecting a corresponding number of devices to the energy system according to the number values ​​configured for each device.

[0132] S52, extracting the power control matrix of the energy system equipment in each time period, wherein the expression of the power control matrix in each time period is:

[0133]

[0134] In the formula, Γ t is the power control matrix of the tth period, Γ opt (:,:,t) is a three-dimensional matrix Γ opt The two-dimensional matrix layer corresponding to the t-th time period in .

[0135] S53, controlling the operating status of the energy system equipment in each time period respectively according to the power control matrix of each time period, and scheduling and controlling the input and output power of the corresponding equipment in the system one by one according to the value of the i-th row and j-th column of the optimal equipment power matrix.

[0136] Summarizing steps S4 to S6, the collaborative optimization model is solved using the improved adaptive solution algorithm based on the heuristic optimization principle to obtain the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt , and based on the optimal equipment capacity matrix Q opt and the optimal device power matrix Γopt , determine the transformation optimization results and scheduling optimization results, and through a comprehensive and objective analysis of all potential solutions, be able to form energy system transformation and scheduling control solutions with more practical application value, reduce the impact of subjective factors on the results, and improve the reliability, practicality and benefits of transformation optimization and scheduling optimization results.

[0137] From the above, we can know the basic process and steps of this scheme. The following will give an energy system transformation scheduling optimization system considering energy quality, which applies the above energy system transformation scheduling optimization method considering energy quality. Please refer to Figure 5 ,include:

[0138] The parameter import module 600 uses a unified xlsx file format to input data, and is used to input all constant coefficient values ​​in the objective function and constraint conditions, as well as the system connection matrix R according to the actual situation of the application scenario.

[0139] The construction module 601 is used to construct the existing equipment parameter set EX, the unassembled new equipment parameter set AVA and the node set NODE, and perform energy quality classification processing on the existing equipment parameter set EX and the unassembled new equipment parameter set AVA based on the node set NODE to obtain all node sets NODE′, the equipment type classification set D containing energy quality information, and the set Ω of all energy forms containing energy quality information of each node.

[0140] The matrix generation module 602 is used to traverse all connection relationships of unassembled new equipment in the energy system based on all node sets NODE′ to obtain the system connection matrix R and the power correction matrix K.

[0141] The model building module 603 is used to establish a collaborative optimization model for optimizing device capacity and device power based on the device type classification set D and the power correction matrix K.

[0142] Solving module 604 is used to solve the collaborative optimization model using an adaptive solving algorithm based on the set of all energy forms Ω, the set of equipment types D and the system connection matrix R to obtain the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt .

[0143] Optimization module 605, for optimizing the equipment capacity matrix Q opt and the optimal device power matrix Γ opt , determine the transformation optimization results and scheduling optimization results.

[0144] The output execution module 606 is used to convert the equipment capacity matrix Q, the optimal power matrix Γ and the main optimization objective function value into a practical transformation and dispatching control plan of the energy system that is visualized and can be directly executed, and the results can be saved using a memory.

[0145] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing energy system transformation and scheduling considering energy quality, characterized in that: The following steps are involved: S1, constructing an existing equipment parameter set EX, an unassembled new equipment parameter set AVA and a node set NODE, performing energy quality classification processing on the existing equipment parameter set EX and the unassembled new equipment parameter set AVA based on the node set NODE, and obtaining all node sets NODE′, a set D of equipment types after classification containing energy quality information, and a set Ω of all energy forms containing energy quality information of each node; specifically comprising the following steps: S10, collecting parameter information of existing equipment in the energy system and new equipment that can be provided by suppliers, and constructing the existing equipment parameter set EX, the unassembled new equipment parameter set AVA and the node set NODE; S11, calculating the index scores of the existing device parameter set EX and the unassembled new device parameter set AVA based on a preset attribute index system, and establishing energy quality intervals of all device input and output energies of the existing device parameter set EX and energy quality intervals of all device input and output energies of the unassembled new device parameter set AVA according to the index scores; S12, based on the preset energy system structure diagram before transformation and the energy quality intervals of the input and output energy of all devices in the existing device parameter set EX, calculate the energy quality interval Φ of each node in the energy system before transformation node ; S13, based on the energy quality intervals of the input and output energies of all devices without the new device parameter set AVA and the energy quality intervals Φ of each node of the energy system before the transformation node Perform device matching to obtain the parameter set of new devices to be added, the intermediate nodes to be added, and the energy quality range to be added; S14, based on the existing equipment parameter set EX and the new equipment parameter set to be added, construct a pre-set of equipment type classification; add the intermediate node to be added to the node set NODE to obtain the set of all nodes NODE′; add the energy quality interval to be added to the energy quality interval Φ of each node of the energy system before transformation node , get all energy quality intervals Φ node ′; S15, performing device type classification on the device type pre-classification set to obtain the device type post-classification set D, wherein the device type post-classification set D includes an energy supply facility set, an energy conversion device set, an energy storage device set, and a load set; S16, based on the set of all nodes NODE′ and all the energy quality intervals Φ node ′, construct the set of all energy forms Ω; S2, based on the set of all nodes NODE′, traverse all connection relationships of the unassembled new equipment in the energy system to obtain the system connection matrix R and the power correction matrix K; S3, based on the device type classification set D and the power correction matrix K, establishing a collaborative optimization model for the purpose of optimizing device capacity and device power; the collaborative optimization model includes a main optimization model and a sub-optimization model; The specific expression of the main optimization model is: In the above formula, min means to find the minimum value of the objective function, Const means the constraint condition of the main optimization model, F is the objective function of the main optimization model, λ is the cost matrix, and λ ij represents the element in the i-th row and j-th column of the cost matrix, Q is the equipment capacity matrix, Q i represents the row vector of the i-th row of the equipment capacity matrix, Q ij represents the element in the i-th row and j-th column of the equipment capacity matrix, f opt is the optimal sub-optimization objective function value with Q as the optimization boundary condition, Sup is the energy supply facility set, μ s Indicates the weight coefficient of the sth harmful substance, δ ijs It represents the unit emission of the sth harmful substance by the energy form of the jth column of the equipment in the i-th row of the system connection matrix, It means that the device in the i-th row of the system connection matrix outputs the power in the j-th column of energy in the t-th period under the optimal operation state. represents the rated capacity vector of the equipment in the i-th row of the single system connection matrix, It represents the upper limit of the number of devices that can be built in the i-th row of the system connection matrix; The sub-optimization model includes an objective function, energy supply facility transmission constraints, energy conversion equipment operation constraints, energy storage equipment operation constraints, and energy supply and demand balance constraints; The specific expression of the objective function is: In the above formula, f is the objective function of the sub-optimization model, P ijt represents the power of the energy form of the jth column of the device in the i-th row of the system connection matrix in the tth period, μ s Indicates the weight coefficient of the sth harmful substance, δ ijs It represents the unit emission of the sth harmful substance by the energy form in the jth column of the equipment in the i-th row of the system connection matrix; The specific expression of the energy supply facility transmission constraint is: In the above formula, P sup,min is the lower limit of the output power of the energy supply facility, P sup,max is the upper limit of the output power of the energy supply facility, represents the output power of the energy supply facility in the tth period; The specific expression of the energy conversion equipment operation constraint is: In the above formula, represents the input power of the energy conversion device in the tth period, represents the output power of the energy conversion device in the tth period, represents the output power of the energy conversion device in the t+1th period, Η is the conversion efficiency matrix of the energy conversion device, Ramp is the upper limit of the ramp rate of the energy conversion device, P out,min is the lower limit of the output power of the energy conversion device, P out,max The upper limit of the output power of the energy conversion device; The specific expression of the energy storage device operation constraint is: In the above formula, W t+1 represents the energy storage capacity of the energy storage device in the t+1th period, W t represents the energy storage capacity of the energy storage device in the tth period, W1 represents the energy storage capacity of the energy storage device in the first period, and W 24 represents the energy storage capacity of the energy storage device in the 24th period, σ ES is the self-loss coefficient of the energy storage device, η ch is the charging efficiency coefficient of the energy storage device, η dis is the discharge efficiency coefficient of the energy storage device, Δt represents the interval length of each time period, represents the power of the energy storage device in the tth period, P ES,min is the minimum power of the energy storage device, P ES,max is the maximum power of the energy storage device, W min is the lower limit of energy storage of energy storage equipment, W max The energy storage upper limit of the energy storage device; The energy supply and demand balance constraints include surplus energy supply and demand constraints and other energy supply and demand constraints; The specific expression of the surplus energy supply and demand constraint is: In the above formula, t represents the tth time period of a day, K ij is the element in the i-th row and j-th column of the power correction matrix K, P ijt The power of the energy form in the jth column of the device in the i-th row of the system connection matrix in the t-th time period; The specific expressions of the other energy supply and demand constraints are: In the above formula, t represents the tth time period of a day, K ij is the element in the i-th row and j-th column of the power correction matrix K, P ijt The power of the energy form in the jth column of the device in the i-th row of the system connection matrix in the t-th time period; S4, based on the set of all energy forms Ω, the set of equipment types D and the system connection matrix R, the collaborative optimization model is solved using an adaptive solution algorithm to obtain the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt ; Specifically: S41, according to the preset value interval, generate the Q corresponding to each element in the set D after the device type is divided by a preset generation method. i , and form the equipment capacity matrix Q, that is, from the equipment capacity vector Q i In the value range of , the Q corresponding to each element in the set D after the device type division is randomly generated by evenly distributing random numbers. i , and form the equipment capacity matrix Q; S42, performing independent variable division on the elements in the set Ω of all energy forms one by one to obtain a plurality of first independent variable vector groups; Among them, the independent variables are specifically: For each energy form in the set of all energy forms Ω, among the output power parameters of various types of equipment in the energy conversion equipment set, one of the powers with the largest power adjustable range is used as a reference quantity, and the output powers of the remaining devices in the energy conversion equipment set excluding the reference quantity are used as independent variables; For each energy form in the set of all energy forms Ω, the charging and discharging power of each type of equipment in the energy storage device set is used as an independent variable; S43, in the upper and lower limit intervals of the device power corresponding to each of the first independent variable vector groups, generate a device power vector and a device state vector by a preset generation method, multiply the device power vector by the device state vector to obtain a single second independent variable vector, traverse all the first independent variable vector groups, and obtain all the second independent variable vector groups; that is: In the upper and lower limit intervals of the device power corresponding to each first variable group, a device power vector is randomly generated by extracting random numbers with average distribution, and a device state vector with an element value range of {-1, 0, 1} or {0, 1} is randomly generated according to a given probability using a rounding function, and a preset device power vector is multiplied by the device state vector to obtain a single second independent variable vector, and the operation is repeated to complete the generation of all second independent variable vector groups; S44, constructing a first matrix according to the row and column information of the system connection matrix R and the value of the second independent variable vector group, determining whether the first matrix satisfies various constraints of the sub-optimization model, obtaining a first device power matrix, and calculating the corresponding sub-optimization objective function value; that is: According to the row and column information of the system connection matrix R, the values ​​of the second independent variable vector group are assigned to the corresponding elements of the all-zero matrix, and then other missing power information is calculated according to the constraints of the sub-optimization model and it is checked whether all constraints are satisfied. If so, the matrix is ​​used as the first device power matrix to calculate the sub-optimization objective function value, otherwise, the process returns to S43 to regenerate the independent variable vector group; S45, arbitrarily selecting any continuous segments of two of the second independent variable vector groups, and exchanging the selected continuous segments of the two second independent variable vector groups to obtain a third independent variable vector group, and then arbitrarily selecting some elements in the third independent variable vector group, regenerating the selected elements, and obtaining a fourth independent variable vector group; S46, constructing a second matrix according to the row and column information of the system connection matrix R and the value of the fourth independent variable vector group, determining whether the second matrix satisfies various constraints of the sub-optimization model, obtaining a second device power matrix, and calculating the corresponding sub-optimization objective function value; that is: According to the row and column information of the system connection matrix R, the value of the fourth independent variable vector group is assigned to the corresponding element of the all-zero matrix, and then other missing power information is calculated according to the constraint conditions of the sub-optimization model and it is checked whether all the constraint conditions are met. If they are met, the matrix is ​​used as the second device power matrix to calculate the sub-optimization objective function value. Otherwise, the independent variable vector group is directly skipped and deleted; S47, using the fourth independent variable vector group as the second independent variable vector group, and looping through steps S45 to S46 with the updated second independent variable vector group until the current optimal sub-optimization objective function value remains unchanged, and outputting the optimal device power matrix Γ opt ; S48, arbitrarily selecting any continuous segment of the device capacity matrix Q, exchanging the segments of the two device capacity matrices Q, and then arbitrarily selecting some elements in the device capacity matrix Q, regenerating the selected elements, and obtaining an updated device capacity matrix; S49, looping steps S42 to S48, terminating when the number of repetitions reaches a predetermined value, and obtaining the optimal device power matrix Γ opt and the optimal equipment capacity matrix Q opt ; S5, based on the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt , determine the transformation optimization results and scheduling optimization results.

2. The energy system transformation scheduling optimization method considering energy quality according to claim 1 is characterized in that: In step S13, specifically: S130, calculating the energy quality interval of the input energy of a certain energy conversion device in the unassembled new device parameter set AVA and the energy quality interval Φ of each node of the energy system before transformation node The first intersection number n1 between them is calculated, and the energy quality interval of the output energy of the same energy conversion equipment in the parameter set AVA without the new equipment is calculated, and the energy quality interval of each node of the energy system before the transformation is calculated. node A second intersection number n2 between the two; based on the product of the first intersection number and the second intersection number, constructing the parameter set of the new device to be added with the models of the corresponding number of energy conversion devices; S131, calculating the energy quality interval of the output energy of a certain energy supply facility in the parameter set AVA of the unequipped new equipment and the energy quality interval Φ of each node of the energy system before the transformation node A third intersection number n3 between the two, based on the third intersection number, adding the models of the corresponding number of energy supply facilities to the set of new equipment parameters to be added in sequence; S132, based on the matching relationship between the device and the node set NODE, calculate the corresponding multi-device combination mode, perform maximum redundancy processing on the device combination mode, traverse all device combinations, obtain all combinations of maximum redundant devices, the intermediate nodes to be added and the energy quality intervals to be added, and add all combinations of maximum redundant devices to the parameter set of new devices to be added; S133, calculating the energy quality interval of the input / output energy of a certain energy storage device in the unassembled new device parameter set AVA and the energy quality interval Φ of each node of the energy system before transformation node The number of nodes n4 that meet the preset conditions is determined, and based on the number of nodes, the models of the corresponding number of energy storage devices are added to the parameter set of new devices to be added.

3. The energy system transformation scheduling optimization method considering energy quality according to claim 2 is characterized in that: In step S132, The multi-device combination method is specifically: the devices are connected in a tree-trunk manner through preset combination conditions to form a whole, wherein the whole contains at least one new node that is different from any element in the current set NODE; The preset combination condition is: the energy quality intervals of the branches connected to the same node all have a non-empty intersection, and the energy quality interval of the branch connected to a node a in the node set NODE must satisfy Φ a ∩Φ nb =Φ a , where Φ a is the energy quality interval of one of the nodes a in the node set NODE, Φ nb The energy quality interval of the input / output energy nb of the device connected to one of the nodes a in the node set NODE, and all the devices in the whole have at least one input or output energy quality interval that is different from the energy quality interval Φ of each node of the energy system before transformation node There is no intersection between the energy quality intervals of any nodes; The maximum redundancy processing is specifically as follows: based on a preset redundancy condition, adding the devices in the unassembled new device parameter set AVA to the multi-device combination mode in a non-repetitive manner; The preset redundancy condition is: when the input / output energy of the device is connected to the new node, the energy quality interval of the input / output energy of the device and the energy quality interval of the new node have a non-empty intersection; when the input / output energy of the device is connected to one of the nodes b in the node set NODE, the energy quality interval of the input / output energy of the device satisfies Φ b ∩Φ ne =Φ b , where Φ b is the energy quality interval of one of the nodes b in the node set NODE, Φ ne An energy quality interval of input / output energy ne for a device connected to one of the nodes b in the node set NODE.

4. The energy system transformation scheduling optimization method considering energy quality according to claim 1 is characterized in that: In step S16, specifically: The set of all energy forms Ω, the set of all nodes NODE′ and the set of all energy quality intervals Φ node There is a corresponding relationship between the elements in ′, and the elements in the set of all energy forms Ω are the names of the energy forms of each node in the set of all nodes NODE′.

5. The energy system transformation scheduling optimization method considering energy quality according to claim 1 is characterized in that: In step S2, The elements R of the system connection matrix R ij The value of is: When the i-th row device of the system connection matrix R takes the j-th column energy as input energy, R ij The value of is 1; When the i-th row device of the system connection matrix R outputs energy in the form of j-column energy, R ij The value of is -1; When the i-th row device of the system connection matrix R stores the j-th column energy form as input / output, R ij The value of is 2; When the i-th row device of the system connection matrix R has no connection with the j-th column energy form, R ij The value of is 0; Among them, R ij is the element in the i-th row and j-th column of the system connection matrix R; The specific expression of the power correction matrix K is: In the above formula, K ij is the element in the i-th row and j-th column of the power correction matrix K, R ij The element in the i-th row and j-th column of the system connection matrix R.

6. The energy system transformation scheduling optimization method considering energy quality according to claim 1 is characterized in that: In step S5, specifically: S50, calculating the quantity value of each device configuration based on a configuration formula, wherein the configuration formula is expressed as: In the above, num i The number of devices configured for the i-th row of the system connection matrix, Denotes the optimal equipment capacity matrix Q opt The row vector of the i-th row of represents the rated capacity vector of the equipment in the i-th row of the single system connection matrix, and ∥·∥2 represents the two-norm; S51, connecting a corresponding number of devices to the energy system according to the number values ​​configured for each device; S52, extracting the power control matrix of the energy system equipment in each time period, wherein the expression of the power control matrix in each time period is: In the formula, Γ t is the power control matrix of the tth period, Γ opt (:,:,t) is the three-dimensional matrix Γ opt The two-dimensional matrix layer corresponding to the t-th time period in ; S53, controlling the operating status of the energy system equipment in each time period respectively according to the power control matrix of each time period, and scheduling and controlling the input and output power of the corresponding equipment in the system one by one according to the value of the i-th row and j-th column of the optimal equipment power matrix.

7. An energy system transformation scheduling optimization system considering energy quality, characterized in that: The energy system transformation scheduling optimization method considering energy quality as described in any one of claims 1 to 6 is applied, comprising: A construction module is used to construct an existing device parameter set EX, an unassembled new device parameter set AVA and a node set NODE, and based on the node set NODE, perform energy quality classification processing on the existing device parameter set EX and the unassembled new device parameter set AVA to obtain all node sets NODE′, a set D of device types after classification containing energy quality information, and a set Ω of all energy forms containing energy quality information of each node; Specifically, the following steps are included: S10, collecting parameter information of existing equipment in the energy system and new equipment that can be provided by suppliers, and constructing the existing equipment parameter set EX, the unassembled new equipment parameter set AVA and the node set NODE; S11, calculating the index scores of the existing device parameter set EX and the unassembled new device parameter set AVA based on a preset attribute index system, and establishing energy quality intervals of all device input and output energies of the existing device parameter set EX and energy quality intervals of all device input and output energies of the unassembled new device parameter set AVA according to the index scores; S12, based on the preset energy system structure diagram before transformation and the energy quality intervals of the input and output energy of all devices in the existing device parameter set EX, calculate the energy quality interval Φ of each node in the energy system before transformation node ; S13, based on the energy quality intervals of the input and output energies of all devices without the new device parameter set AVA and the energy quality intervals Φ of each node of the energy system before the transformation node Perform device matching to obtain the parameter set of new devices to be added, the intermediate nodes to be added, and the energy quality range to be added; S14, based on the existing equipment parameter set EX and the new equipment parameter set to be added, construct a pre-set of equipment type classification; add the intermediate node to be added to the node set NODE to obtain the set of all nodes NODE′; add the energy quality interval to be added to the energy quality interval Φ of each node of the energy system before transformation node , get all energy quality intervals Φ node ′; S15, performing device type classification on the device type pre-classification set to obtain the device type post-classification set D, wherein the device type post-classification set D includes an energy supply facility set, an energy conversion device set, an energy storage device set, and a load set; S16, based on the set of all nodes NODE′ and all the energy quality intervals Φ node ′, construct the set of all energy forms Ω; A matrix generation module, for traversing all connection relationships of unassembled new equipment in the energy system based on the set of all nodes NODE′, to obtain a system connection matrix R and a power correction matrix K; A model building module, used to establish a collaborative optimization model for optimizing device capacity and device power based on the device type classification set D and the power correction matrix K; Wherein, the collaborative optimization model includes a main optimization model and a sub-optimization model; The specific expression of the main optimization model is: In the above formula, min means to find the minimum value of the objective function, Const means the constraint condition of the main optimization model, F is the objective function of the main optimization model, λ is the cost matrix, and λ ij represents the element in the i-th row and j-th column of the cost matrix, Q is the equipment capacity matrix, Q i represents the row vector of the i-th row of the equipment capacity matrix, Q ij represents the element in the i-th row and j-th column of the equipment capacity matrix, f opt is the optimal sub-optimization objective function value with Q as the optimization boundary condition, Sup is the energy supply facility set, μ s Indicates the weight coefficient of the sth harmful substance, δ ijs It represents the unit emission of the sth harmful substance by the energy form of the jth column of the equipment in the i-th row of the system connection matrix, It means that the device in the i-th row of the system connection matrix outputs the power in the j-th column of energy in the t-th period under the optimal operation state. represents the rated capacity vector of the equipment in the i-th row of the single system connection matrix, It represents the upper limit of the number of devices that can be built in the i-th row of the system connection matrix; The sub-optimization model includes an objective function, energy supply facility transmission constraints, energy conversion equipment operation constraints, energy storage equipment operation constraints, and energy supply and demand balance constraints; The specific expression of the objective function is: In the above formula, f is the objective function of the sub-optimization model, P ijt represents the power of the energy form of the jth column of the device in the i-th row of the system connection matrix in the tth period, μ s Indicates the weight coefficient of the sth harmful substance, δ ijs It represents the unit emission of the sth harmful substance by the energy form in the jth column of the equipment in the i-th row of the system connection matrix; The specific expression of the energy supply facility transmission constraint is: In the above formula, P sup,min is the lower limit of the output power of the energy supply facility, P sup,max is the upper limit of the output power of the energy supply facility, represents the output power of the energy supply facility in the tth period; The specific expression of the energy conversion equipment operation constraint is: In the above formula, represents the input power of the energy conversion device in the tth period, represents the output power of the energy conversion device in the tth period, represents the output power of the energy conversion device in the t+1th period, Η is the conversion efficiency matrix of the energy conversion device, Ramp is the upper limit of the ramp rate of the energy conversion device, P out,min is the lower limit of the output power of the energy conversion device, P out,max The upper limit of the output power of the energy conversion device; The specific expression of the energy storage device operation constraint is: In the above formula, W t+1 represents the energy storage capacity of the energy storage device in the t+1th period, W t represents the energy storage capacity of the energy storage device in the tth period, W1 represents the energy storage capacity of the energy storage device in the first period, and W 24 represents the energy storage capacity of the energy storage device in the 24th period, σ ES is the self-loss coefficient of the energy storage device, η ch is the charging efficiency coefficient of the energy storage device, η dis is the discharge efficiency coefficient of the energy storage device, Δt represents the interval length of each time period, represents the power of the energy storage device in the tth period, P ES,min is the minimum power of the energy storage device, P ES,max is the maximum power of the energy storage device, W min is the lower limit of energy storage of energy storage equipment, W max The energy storage upper limit of the energy storage device; The energy supply and demand balance constraints include surplus energy supply and demand constraints and other energy supply and demand constraints; The specific expression of the surplus energy supply and demand constraint is: In the above formula, t represents the tth time period of a day, K ij is the element in the i-th row and j-th column of the power correction matrix K, P ijt The power of the energy form in the jth column of the device in the i-th row of the system connection matrix in the t-th time period; The specific expressions of the other energy supply and demand constraints are: In the above formula, t represents the tth time period of a day, K ij is the element in the i-th row and j-th column of the power correction matrix K, P ijt The power of the energy form in the jth column of the device in the i-th row of the system connection matrix in the t-th time period; A solution module is used to solve the collaborative optimization model using an adaptive solution algorithm based on the set of all energy forms Ω, the set of equipment types D and the system connection matrix R to obtain the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt ; Specifically: S41, according to the preset value interval, generate the Q corresponding to each element in the set D after the device type is divided by a preset generation method. i , and form the equipment capacity matrix Q, that is, from the equipment capacity vector Q i In the value range of , the Q corresponding to each element in the set D after the device type division is randomly generated by evenly distributing random numbers. i , and form the equipment capacity matrix Q; S42, performing independent variable division on the elements in the set Ω of all energy forms one by one to obtain a plurality of first independent variable vector groups; Among them, the independent variables are specifically: For each energy form in the set of all energy forms Ω, among the output power parameters of various types of equipment in the energy conversion equipment set, one of the powers with the largest power adjustable range is used as a reference quantity, and the output powers of the remaining devices in the energy conversion equipment set excluding the reference quantity are used as independent variables; For each energy form in the set of all energy forms Ω, the charging and discharging power of each type of equipment in the energy storage device set is used as an independent variable; S43, in the upper and lower limit intervals of the device power corresponding to each of the first independent variable vector groups, generate a device power vector and a device state vector by a preset generation method, multiply the device power vector by the device state vector to obtain a single second independent variable vector, traverse all the first independent variable vector groups, and obtain all the second independent variable vector groups; that is: In the upper and lower limit intervals of the device power corresponding to each first variable group, a device power vector is randomly generated by extracting random numbers with average distribution, and a device state vector with an element value range of {-1, 0, 1} or {0, 1} is randomly generated according to a given probability using a rounding function, and a preset device power vector is multiplied by the device state vector to obtain a single second independent variable vector, and the operation is repeated to complete the generation of all second independent variable vector groups; S44, constructing a first matrix according to the row and column information of the system connection matrix R and the value of the second independent variable vector group, determining whether the first matrix satisfies various constraints of the sub-optimization model, obtaining a first device power matrix, and calculating the corresponding sub-optimization objective function value; that is: According to the row and column information of the system connection matrix R, the values ​​of the second independent variable vector group are assigned to the corresponding elements of the all-zero matrix, and then other missing power information is calculated according to the constraints of the sub-optimization model and it is checked whether all constraints are satisfied. If so, the matrix is ​​used as the first device power matrix to calculate the sub-optimization objective function value, otherwise, the process returns to S43 to regenerate the independent variable vector group; S45, arbitrarily selecting any continuous segments of two of the second independent variable vector groups, and exchanging the selected continuous segments of the two second independent variable vector groups to obtain a third independent variable vector group, and then arbitrarily selecting some elements in the third independent variable vector group, regenerating the selected elements, and obtaining a fourth independent variable vector group; S46, constructing a second matrix according to the row and column information of the system connection matrix R and the value of the fourth independent variable vector group, determining whether the second matrix satisfies various constraints of the sub-optimization model, obtaining a second device power matrix, and calculating the corresponding sub-optimization objective function value; that is: According to the row and column information of the system connection matrix R, the value of the fourth independent variable vector group is assigned to the corresponding element of the all-zero matrix, and then other missing power information is calculated according to the constraint conditions of the sub-optimization model and it is checked whether all the constraint conditions are met. If they are met, the matrix is ​​used as the second device power matrix to calculate the sub-optimization objective function value. Otherwise, the independent variable vector group is directly skipped and deleted; S47, using the fourth independent variable vector group as the second independent variable vector group, and looping through steps S45 to S46 with the updated second independent variable vector group until the current optimal sub-optimization objective function value remains unchanged, and outputting the optimal device power matrix Γ opt ; S48, arbitrarily selecting any continuous segment of the device capacity matrix Q, exchanging the segments of the two device capacity matrices Q, and then arbitrarily selecting some elements in the device capacity matrix Q, regenerating the selected elements, and obtaining an updated device capacity matrix; S49, looping steps S42 to S48, terminating when the number of repetitions reaches a predetermined value, and obtaining the optimal device power matrix Γ opt and the optimal equipment capacity matrix Q opt ; An optimization module is used to optimize the equipment capacity matrix Q based on the optimal equipment capacity matrix Q opt and the optimal device power matrix Γ opt , determine the transformation optimization results and scheduling optimization results.

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