CN117829451A - Method and system for generating comprehensive energy planning scheme of grid-connected low-carbon park - Google Patents
Method and system for generating comprehensive energy planning scheme of grid-connected low-carbon park Download PDFInfo
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Abstract
The invention belongs to the technical field of energy optimization scheduling, and provides a method and a system for generating a comprehensive energy planning scheme of a grid-connected low-carbon park, wherein the method and the system have the following technical scheme: based on the load data and the resource data, carrying out working condition division according to a preset working condition division rule, and carrying out typical day recognition based on feature clusters extracted under different working conditions; the situation that the day is typical and atypical is solved; and constructing an inner-layer optimization model and an outer-layer optimization model according to the related system parameters and the typical day recognition result, solving an optimal energy storage scheduling strategy by taking the lowest electric quantity of the typical day power grid as a target, constructing a multi-target optimization function according to the typical day recognition result under the optimal energy storage scheduling strategy by the outer-layer optimization model, and solving the multi-target function to obtain an optimal solution set as an optimal equipment capacity configuration planning scheme, thereby solving the problem that a single optimization target cannot meet the current service scene.
Description
Technical Field
The invention belongs to the technical field of energy optimization scheduling, and particularly relates to a method and a system for generating a comprehensive energy planning scheme of a grid-connected low-carbon park.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
At present, the establishment of comprehensive energy resource allocation schemes for parks or large bases mainly depends on expert experience, and wind, light resources, cold and heat load data are required to be collected manually in the process, so that the problems of long time consumption, large human resource investment, limited scheme quality and the like in the scheme establishment process are caused. Meanwhile, with the characteristics of different policies in various places, complex market environment and the like, higher challenges and requirements are brought to the configuration scheme.
The traditional energy planning mainly adopts an optimization algorithm to solve an optimal planning configuration scheme under the conditions of multi-energy complementation and load balance. However, there are some disadvantages in terms of scheme design and algorithm selection, mainly including:
(1) In the aspect of optimizing scheme design, most research results only consider a single optimization target, and the current external policy environment changes faster and more complicated, so that the single optimization target cannot meet the current service scene.
(2) In the simulation calculation process of most research achievements, a clustering algorithm is adopted to extract typical daily load and output curves in four seasons, and the method is extremely easy to generate typical daily but atypical conditions, so that the output and load have overlarge deviation from an actual scene.
Disclosure of Invention
In order to solve at least one technical problem in the background technology, the invention provides a method and a system for generating a comprehensive energy planning scheme of a grid-connected low-carbon park, which comprehensively consider a plurality of factors such as economy, construction specifications, external environment and the like to construct a double-layer multi-objective optimization method, thereby realizing the design of the comprehensive energy planning scheme of the grid-connected low-carbon park and the application of the system.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a method for generating a comprehensive energy planning scheme of a grid-connected low-carbon park, which comprises the following steps:
acquiring load data, resource data and related system parameters;
based on the load data and the resource data, carrying out working condition division according to a preset working condition division rule, and carrying out typical day recognition based on feature clusters extracted under different working conditions;
and constructing an inner-layer optimization model and an outer-layer optimization model according to the related system parameters and the typical day recognition result, solving an optimal energy storage scheduling strategy by taking the lowest electric quantity of the typical day power grid as a target in the inner-layer optimization model, constructing a multi-target optimization function according to the typical day recognition result under the optimal energy storage scheduling strategy in the outer-layer optimization model, and solving the multi-target function to obtain an optimal solution set as an optimal equipment capacity configuration planning scheme.
Further, the working condition division is performed according to a preset working condition division rule, which specifically includes:
if the output is smaller than the load within any hour of the day, the first working condition is adopted;
if the output force is larger than the sum of the load and the energy storage discharge power within a certain hour on the same day, the second working condition is adopted;
and if the conditions are other conditions, the working condition is taken as a third working condition.
Further, the characteristic clustering extracted under different working conditions is used for typical day recognition, and the method comprises the following steps:
extracting feature vectors under different working conditions, and carrying out cluster analysis by adopting a k-means clustering algorithm according to the number of clusters to obtain the category of each working condition and the number of days contained in each category;
and traversing different types in each working condition in sequence, and calculating load data and resource data of wind power and photovoltaic in the same working condition and the same type to obtain typical daily output and load.
Further, the inner layer optimization model solves an optimal energy storage scheduling strategy by taking the lowest electric quantity under a typical daily power grid as a target, and specifically comprises the following steps:
determining a variable to be optimized and an objective function with the lowest electric quantity under a typical daily power grid;
determining output and load balance constraint, energy storage charge-discharge power constraint and energy storage capacity constraint;
and (3) carrying out random initialization population, target calculation, constraint calculation, crossover and mutation processes, and iterating until the iteration times are larger than the maximum iteration times, so as to obtain the energy storage optimal scheduling strategy under each equipment capacity ratio.
Further, the multi-objective optimization function comprises the lowest static investment, the lowest carbon discharge capacity and the lowest electricity rejection rate, wherein the static investment is the sum of investment costs of each energy equipment configuration, the carbon discharge capacity is the product of the electric quantity of a power grid and a carbon discharge coefficient matched with the electric quantity of the power grid, and the electricity rejection rate is the ratio of the electric quantity of the power rejection to the theoretical new energy power generation.
Further, a non-dominant ordered genetic algorithm is used to solve the multiple objective functions.
Further, after the load data and the resource data are acquired, the load data and the resource data are evaluated, including:
measuring and calculating the wind power and photovoltaic output per hour in a model year of unit installation conditions based on wind and light resource data;
based on the electric load data, estimating the electric load of the whole park, so as to form annual hourly electric load data;
based on the cold load data, counting the cold supply area of each area every hour of the whole year, and converting the cold supply area into cold load based on the cold supply area;
for the heat load data, the annual hourly heat supply volume and indoor and outdoor temperature difference are counted and converted into heat load based on the heat supply volume and the indoor and outdoor temperature difference.
The second aspect of the present invention provides a system for generating a grid-connected low-carbon park comprehensive energy planning scheme, comprising:
the data acquisition module is used for acquiring load data, resource data and related system parameters;
the typical day identification module is used for carrying out working condition division according to a preset working condition division rule based on load data and resource data and carrying out typical day identification based on feature clusters extracted under different working conditions;
and constructing an inner-layer optimization model and an outer-layer optimization model according to the related system parameters and the typical day recognition result, solving an optimal energy storage scheduling strategy by taking the lowest electric quantity of the typical day power grid as a target in the inner-layer optimization model, constructing a multi-target optimization function according to the typical day recognition result under the optimal energy storage scheduling strategy in the outer-layer optimization model, and solving the multi-target function to obtain an optimal solution set as an optimal equipment capacity configuration planning scheme.
A third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of generating a grid-tied low-carbon campus integrated energy planning scheme as described above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the processor when executing the program implementing the steps in a grid-connected low-carbon park comprehensive energy planning scheme generation method as described above.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, related system parameters are combined, an inner-layer optimization model and an outer-layer optimization model are built according to typical day recognition results, an optimal energy storage scheduling strategy is solved by taking the lowest electric quantity of a typical day power grid as a target, a multi-target optimization function is built under the optimal energy storage scheduling strategy by combining with typical day recognition results in an outer-layer optimization model, and the multi-target function is solved to obtain an optimal solution set as an optimal equipment capacity configuration planning scheme, so that the problem that a single optimization target cannot meet the current service scene is solved.
2. According to the invention, based on load data and resource data, the working condition division is carried out according to the preset working condition division rule, and the typical day identification is carried out based on the feature clusters extracted under different working conditions, so that the problem of overlarge deviation between output and load and an actual scene caused by typical days and atypical conditions is solved.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a method for generating a grid-connected low-carbon park comprehensive energy planning scheme provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a comprehensive energy planning model for a double-layer low-carbon park according to an embodiment of the invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
As shown in fig. 1, the embodiment provides a method for generating a comprehensive energy planning scheme of a grid-connected low-carbon park, which includes the following steps:
step 1: wind resource, light resource, electrical load, thermal load and cold load data are obtained and the data are evaluated.
And (3) for wind and light resource collection and evaluation, collecting annual light irradiation amount data and wind speed data of the project location, and averaging the annual wind and light resource data according to the hours to obtain model year data. Based on the wind-solar energy resource data, measuring and calculating the wind power and photovoltaic output O of 8760 hours of model year of unit installation situation WG And O PV . For electric load collection and evaluation, the electric load of the whole park is estimated based on the power of all enterprises in the park, so as to form 8760-hour electric load data O of the whole year PW . For cold load data collection and evaluation, the cold supply area of each region in 8760 hours of the whole year needs to be counted, and the cold supply area is converted into cold load based on the cold supply area, and the conversion formula is as follows:
wherein O is CL The term "t" represents time, 8760 hours in this embodiment, i represents a cooling area, and a t,i Indicating the ith zone cold load index at hour t from the local air conditioning refrigeration guidelines, k t,i Indicating the index coefficient of the cooling load of the ith area at the t-th hour, s t,i The ith area cooling area at the t-th hour is shown.
For heat load data collection and evaluation, the heat supply volume and indoor and outdoor temperature difference of 8760 hours in the whole year need to be counted, and the heat supply volume and the indoor and outdoor temperature difference are converted into heat load based on the heat supply volume and the indoor and outdoor temperature difference, and the conversion formula is as follows:
wherein O is HL Represents a heat load, t represents a time, t is 8760 hours, i represents a heating area, q t,i Indicating the i-th zone heat load index at t-th hour, deltat t,i Indicating the indoor and outdoor temperature difference of the ith area at t hours, v t,i Represents the ith district heating volume at the t hour.
Step 2: the comprehensive energy planning design of the low-carbon park is surrounded, and system parameters are obtained;
the system parameters include: fan single watt investment c WG Investment c of photovoltaic single watt PV Single watt investment c for heating equipment HL Unit cell/w, energy storage single watt investment c SE Unit cell/wh, single watt investment c for refrigeration equipment CL Unit cell/w. Loss rate r in energy storage discharge process SE Energy storage charge-discharge multiplying power e SE Electric carbon emission factor f of electric network c The new energy occupies the lowest proportion ng of the load.
Step 3: a typical day identification method based on a k-means clustering algorithm.
In step 3, the typical day identification method based on the k-means clustering algorithm specifically comprises the following steps:
step 301: combining the data acquired in the step 1 and the step 2, and identifying the working condition of 8760 hours by day based on the corresponding working condition dividing rule and attributing the working condition to different working conditions, wherein the method specifically comprises the following steps:
the output is smaller than the load within any hour of the day, and the first working condition is adopted;
namely:
the output force is larger than the sum of the load and the energy storage discharge power within a certain hour in the present day, and the sum is used as a second working condition;
namely:
wherein i represents the ith day, j represents the jth hour,wind power output at j hours on day i, < > j->Represents the j-th hour photovoltaic output on day i, +.>Represents the electrical load at the j-th hour on the i-th day,>represents the j-th hour heat load on the i-th day,/->Represents the j-th hour cold load on the i-th day, p SE Representing the stored power.
And if the conditions are other conditions, the working condition is taken as a third working condition.
Step 302: by applying an output redundancy amount O LS As a feature vector, and calculate the output redundancy per dayThe specific calculation method is as follows:
step 303: typical day identification
Based on the feature vectors extracted from the first working condition, the second working condition and the third working condition, carrying out cluster analysis on the feature vectors by adopting a k-means clustering algorithm according to the clustering number k, and further obtaining the category of each working condition and the number n of days contained in each category;
on the basis, traversing different types in each working condition in turn and traversing the same working condition and the same typeCalculating the average value, and finally taking the average value calculation result as a typical sunrise forceAnd load data->Wherein t represents the t-th typical day and t.epsilon.1, 2, …,3 Xk]。
Step 4: and constructing a double-layer low-carbon park comprehensive energy planning model, and solving the double-layer low-carbon park comprehensive energy planning model by adopting a non-dominant ordering genetic algorithm to obtain an optimal planning scheme.
The double-layer low-carbon park comprehensive energy planning model is characterized by comprising an inner layer optimization model and an outer layer optimization model, wherein the inner layer optimization model and the outer layer optimization model are respectively a low-carbon park comprehensive energy planning model based on a multi-objective optimization algorithm and an energy storage scheduling optimization model based on a genetic algorithm.
The inner layer model aims at solving an optimal energy storage scheduling strategy by taking the lowest electric quantity of a typical daily power grid as a target, so that data is provided for the minimum carbon discharge capacity and the lowest electricity rejection rate of an objective function in a low-carbon park comprehensive energy planning model based on a multi-target optimization algorithm of the outer layer model, and an optimal planning scheme is obtained.
Step 401: determining a variable to be optimized, an objective function and an objective function, and constructing a double-layer low-carbon park comprehensive energy planning model based on the variable to be optimized, the objective function and the objective function, wherein the method specifically comprises the following steps:
step 4011: determining variables to be optimized
The variables to be optimized and determined include capacities of fans, photovoltaics, energy storage, refrigeration and heating equipment, namely: p is p WG ,p PV ,p SE ,p HL ,p CL ；
Step 4012: construction of objective functions
The objectives include 3, respectively, minimum static investment, minimum carbon displacement, and minimum electrical reject rate.
Wherein, the static investment is the sum of investment costs of each energy equipment configuration, namely:
f cost (p WG ,p PV ,p SE ,p HL ,p CL )＝∑ i∈[WG,PV,SE,HL,CL] c i ×p i ，
in the cost i ，p WG 、p PV 、p SE 、p HL 、p CL Respectively representing the installed capacity of wind power, photovoltaic, energy storage, electric refrigerator and electric heating equipment, x i Representing the capacity of i. c i Representing the investment price of the equipment i.
Carbon displacement is the electricity quantity O under the power grid PG Carbon number f matched with it c Is the product of (1), namely:
the electricity rejection rate is the ratio of the electricity rejection amount to the theoretical new energy generating capacity, namely:
step 4013: determining constraint conditions, including output and load balance constraint, power rejection rate constraint, new energy power generation duty ratio constraint and upper and lower limits of capacities of wind, light, energy storage, heating equipment and refrigeration equipment;
wherein the force and load balancing constraints are:
in the method, in the process of the invention,indicating that power is being drawn from the grid.
The rejection rate constraint is as follows:
f loss (p WG ,p PV ,p SE ,p HL ,p CL )<and loss, wherein loss represents the upper limit value of the power rejection rate.
The new energy power generation duty ratio constraint is as follows:
the upper and lower limits of the capacities of the wind, light, energy storage, heating equipment and refrigeration equipment are as follows:
step 4014: based on step 4011-step 4013, constructing and obtaining a double-layer low-carbon park comprehensive energy planning model:
minf(x)＝(f cost (x),f carbon (x),f loss (x)),x＝[p WG ,p PV ,p SE ,p HL ,p CL ]，
for the solution of the definition, a Non-dominant ranking genetic algorithm (Non-dominated Sorting Genetic Algorithm-II, NSGA-II for short) is adopted, so that an optimal solution set is obtained.
Step 402: determining a variable to be optimized, an objective function and an objective function, and constructing a double-layer low-carbon park comprehensive energy planning model based on the variable to be optimized, the objective function and the objective function, wherein the method specifically comprises the following steps:
step 4021: determining variables to be optimized
A typical daily energy storage of 24 hours of charge and discharge capacity, namely
Step 4022: the goal is that the power supply quantity of the typical daily power grid is the lowest;
i.e.
Step 4023: determining constraint conditions, including output and load balance constraint, energy storage charge and discharge power constraint and energy storage capacity constraint;
the output and load balance constraint is as follows:
wherein the typical amount of electricity is discardedThe calculation method is as follows:
the energy storage charge-discharge power constraint is as follows:
the energy storage capacity constraint is:
step 4024: based on the steps 4021-4023, constructing and obtaining a double-layer low-carbon park comprehensive energy planning model:
as shown in fig. 2, step 403: and solving a comprehensive energy planning model of the double-layer low-carbon park by adopting a non-dominant sorting genetic algorithm to obtain an optimal planning scheme, wherein the specific solving process is as follows:
in step 4031, load data (electric/cold/heat load), resource data (wind resource/light resource), parameters (algorithm parameters/constraint parameters/business parameters) are prepared and input into the low-carbon park comprehensive energy planning model based on the multi-objective optimization algorithm.
Step 4032, randomly initializing and generating wind, light, cold, heat and storage equipment capacity populations in the low-carbon park comprehensive energy planning model based on the multi-objective optimization algorithm, and inputting the wind, light, cold, heat and storage equipment capacity populations into objective calculation and constraint calculation of the multi-objective optimization algorithm.
Step 4033, the transmitted equipment capacity, load and resource data are subjected to a typical day identification method to obtain typical solar photovoltaic output, wind power output, electric load, thermal load and cold load curves, and the curves are returned to a multi-objective optimization algorithm and are transmitted to an energy storage scheduling optimization model as input.
Step 4034, the energy storage dispatching optimization model is based on the equipment capacity, typical daily output and load curve, and the processes of random initialization population, target calculation, constraint calculation, intersection and variation are continuously iterated until the iteration times are larger than the maximum iteration times, finally, the energy storage optimal dispatching strategy under each equipment capacity proportion is obtained, the strategy is returned to the multi-target optimization algorithm, the typical daily output and load curve returned in step 4033 is fused to perform target calculation and constraint calculation on different capacity schemes, and then intersection/variation and non-dominant sequencing are performed. Thereby obtaining a new device capacity scheme set with better current
Step 4035, repeating the steps 4033 and 4034 until the number of iterations is greater than the maximum number of iterations, and outputting the new optimal device capacity scheme output in the next iteration as the optimal capacity solution set.
Example two
The embodiment provides a grid-connected low-carbon park comprehensive energy planning scheme generation system, which comprises the following steps:
the data acquisition module is used for acquiring load data, resource data and related system parameters;
the typical day identification module is used for carrying out working condition division according to a preset working condition division rule based on load data and resource data and carrying out typical day identification based on feature clusters extracted under different working conditions;
and constructing an inner-layer optimization model and an outer-layer optimization model according to the related system parameters and the typical day recognition result, solving an optimal energy storage scheduling strategy by taking the lowest electric quantity of the typical day power grid as a target in the inner-layer optimization model, constructing a multi-target optimization function according to the typical day recognition result under the optimal energy storage scheduling strategy in the outer-layer optimization model, and solving the multi-target function to obtain an optimal solution set as an optimal equipment capacity configuration planning scheme.
Example III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a grid-connected low-carbon park comprehensive energy planning scheme generation method as described above.
Example IV
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in the method for generating the grid-connected type low-carbon park comprehensive energy planning scheme when executing the program.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for generating the comprehensive energy planning scheme of the grid-connected low-carbon park is characterized by comprising the following steps of:
acquiring load data, resource data and related system parameters;
based on the load data and the resource data, carrying out working condition division according to a preset working condition division rule, and carrying out typical day recognition based on feature clusters extracted under different working conditions;
and constructing an inner-layer optimization model and an outer-layer optimization model according to the related system parameters and the typical day recognition result, solving an optimal energy storage scheduling strategy by taking the lowest electric quantity of the typical day power grid as a target in the inner-layer optimization model, constructing a multi-target optimization function according to the typical day recognition result under the optimal energy storage scheduling strategy in the outer-layer optimization model, and solving the multi-target function to obtain an optimal solution set as an optimal equipment capacity configuration planning scheme.
2. The method for generating the grid-connected low-carbon park comprehensive energy planning scheme according to claim 1, wherein the working condition division is performed according to a preset working condition division rule, specifically comprising:
if the output is smaller than the load within any hour of the day, the first working condition is adopted;
if the output force is larger than the sum of the load and the energy storage discharge power within a certain hour on the same day, the second working condition is adopted;
and if the conditions are other conditions, the working condition is taken as a third working condition.
3. The method for generating the grid-connected low-carbon park comprehensive energy planning scheme according to claim 1, wherein the characteristic clustering extracted under different working conditions is based on typical day recognition, and the method comprises the following steps:
extracting feature vectors under different working conditions, and carrying out cluster analysis by adopting a k-means clustering algorithm according to the number of clusters to obtain the category of each working condition and the number of days contained in each category;
and traversing different types in each working condition in sequence, and calculating load data and resource data of wind power and photovoltaic in the same working condition and the same type to obtain typical daily output and load.
4. The method for generating the grid-connected low-carbon park comprehensive energy planning scheme according to claim 1, wherein the inner layer optimization model solves an optimal energy storage scheduling strategy by taking the lowest electric quantity of a typical daily power grid as a target, and specifically comprises the following steps:
determining a variable to be optimized and an objective function with the lowest electric quantity under a typical daily power grid;
determining output and load balance constraint, energy storage charge-discharge power constraint and energy storage capacity constraint;
and (3) carrying out random initialization population, target calculation, constraint calculation, crossover and mutation processes, and iterating until the iteration times are larger than the maximum iteration times, so as to obtain the energy storage optimal scheduling strategy under each equipment capacity ratio.
5. The method for generating the comprehensive energy planning scheme for the grid-connected low-carbon park according to claim 1, wherein the multi-objective optimization function comprises minimum static investment, minimum carbon discharge and minimum electricity rejection rate, the static investment is the sum of investment costs of each energy equipment configuration, the carbon discharge is the product of the electric quantity under a power grid and a carbon discharge coefficient matched with the electric quantity under the power grid, and the electricity rejection rate is the ratio of the electric quantity to the theoretical new energy generating capacity.
6. The method for generating a grid-connected low-carbon park comprehensive energy planning scheme as claimed in claim 1, wherein a non-dominant ranking genetic algorithm is adopted to solve the multiple objective functions.
7. The method for generating a grid-connected low-carbon park comprehensive energy planning scheme according to claim 1, wherein the method for evaluating load data and resource data after obtaining the load data and the resource data comprises the following steps:
measuring and calculating the wind power and photovoltaic output per hour in a model year of unit installation conditions based on wind and light resource data;
based on the electric load data, estimating the electric load of the whole park, so as to form annual hourly electric load data;
based on the cold load data, counting the cold supply area of each area every hour of the whole year, and converting the cold supply area into cold load based on the cold supply area;
for the heat load data, the annual hourly heat supply volume and indoor and outdoor temperature difference are counted and converted into heat load based on the heat supply volume and the indoor and outdoor temperature difference.
8. The utility model provides a grid-connected type low carbon garden comprehensive energy planning scheme generation system which characterized in that includes:
the data acquisition module is used for acquiring load data, resource data and related system parameters;
the typical day identification module is used for carrying out working condition division according to a preset working condition division rule based on load data and resource data and carrying out typical day identification based on feature clusters extracted under different working conditions;
and constructing an inner-layer optimization model and an outer-layer optimization model according to the related system parameters and the typical day recognition result, solving an optimal energy storage scheduling strategy by taking the lowest electric quantity of the typical day power grid as a target in the inner-layer optimization model, constructing a multi-target optimization function according to the typical day recognition result under the optimal energy storage scheduling strategy in the outer-layer optimization model, and solving the multi-target function to obtain an optimal solution set as an optimal equipment capacity configuration planning scheme.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of a method for generating a grid-tied low-carbon park comprehensive energy planning scheme according to any of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, performs the steps of a method of generating a grid-tied low-carbon campus integrated energy planning scheme as claimed in any one of claims 1 to 7.
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