CN115526381A - Zero-carbon park optimization planning method and device for energy Internet - Google Patents

Zero-carbon park optimization planning method and device for energy Internet Download PDF

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CN115526381A
CN115526381A CN202211061487.5A CN202211061487A CN115526381A CN 115526381 A CN115526381 A CN 115526381A CN 202211061487 A CN202211061487 A CN 202211061487A CN 115526381 A CN115526381 A CN 115526381A
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张明理
张娜
王春生
宋卓然
潘霄
赵琳
程孟增
韩震焘
高靖
胡旌伟
郭玥
赵娟
牛威
商文颖
王宗元
侯依昕
葛磊蛟
刘嘉恒
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
Tianjin University
State Grid Corp of China SGCC
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Tianjin University
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Abstract

The invention discloses an energy Internet-oriented zero-carbon park optimization planning method and device, which comprises the steps of constructing a zero-carbon park energy system based on the park and urban internal energy form and equipment composition; constructing a zero-carbon park energy system optimization planning mathematical model based on the zero-carbon park energy system; improving a mathematical model of air injection behaviors of a capsule group based on a traditional capsule group algorithm and weakening an operator
Figure DDA0003826417200000011
Establishing an improved quilt packet group algorithm for the influence of updating the individual positions of the quilt packet groups; scheme for realizing optimal configuration of mathematical model based on improved quilt group algorithmAnd optimally planning the zero-carbon park according to the optimal configuration scheme. The economy, reliability and environmental protection of an energy system are integrated, a capsule group algorithm is improved, average individuals of capsule group populations are introduced, the solving precision of a traditional capsule group algorithm is improved, the algorithm is prevented from falling into local optimization, the possibility of obtaining better zero-carbon park planning results is improved, and the investment of electric power energy enterprises and the construction of a zero-carbon park are guided.

Description

Zero-carbon park optimization planning method and device for energy Internet
Technical Field
The invention relates to the technical field of optimization planning, in particular to a zero-carbon park optimization planning method and device for energy Internet.
Background
Under the national energy strategy background of 'carbon peak, carbon neutralization', an energy development path of 'longitudinal through of source network load storage and transverse cooperation of electric cold and heat' becomes a necessary choice. The zero-carbon park planning design can effectively promote clean energy production, efficient energy utilization configuration, terminal consumption electrification and network charge interaction activation development of an energy system, and promote gradual evolution of cities from high carbon to low carbon and zero carbon, so that an original technical theory system of zero-carbon power grid gradual planning, wind-light-hydrogen nuclear storage full voltage level combined optimization carbon reduction and urban carbon transaction time sequence evolution for supporting urban carbon reduction of power grid enterprises is formed.
By developing optimization planning of the zero-carbon park, data characteristics obtained by an existing energy internet perception layer are fully mined, urban power grid planning, operation, maintenance and access to grid normalization management of a power supply unit at the local level are realized, a low-carbon implementation path of new energy such as wind, light and hydrogen centralized grid connection and local consumption, secondary regulation and control capability of a fully-mined system, intelligent linkage of grid source and storage users, cooperative optimization and green carbon right trading is formed, the optimal input and output are taken as starting points, and the strategic planning target of the zero-carbon park is gradually realized by applying the concept of sustainable development. However, since the zero carbon park is a new concept proposed in recent years, there is currently a lack of a corresponding economically reliable and rational planning method. Therefore, the invention provides an energy internet-oriented zero-carbon park optimization planning method, which is used for assisting in cleaning low-carbon parks and urban development.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides an energy internet-oriented zero-carbon park optimization planning method, which can solve the problem that the zero-carbon park optimization planning method is lacked in the prior art and the problem that the traditional capsule group algorithm is trapped in local optimization
In order to solve the technical problems, the invention provides the following technical scheme, and the energy internet-oriented zero-carbon park optimization planning method comprises the following steps:
constructing a zero-carbon park energy system based on the park and the urban internal energy form and equipment composition;
constructing a zero-carbon park energy system optimization planning mathematical model based on the zero-carbon park energy system;
weakening operator based on traditional capsule group algorithm
Figure BDA0003826417180000021
Establishing an improved quilt packet group algorithm for the influence of updating the individual positions of the quilt packet groups;
and obtaining the optimal configuration scheme of the mathematical model based on an improved tunicate group algorithm, and optimally planning the zero-carbon park according to the optimal configuration scheme.
The invention relates to a preferable scheme of an energy internet-oriented zero-carbon park optimization planning method, which comprises the following steps: the zero-carbon park energy system comprises a diesel generator, a photovoltaic cell panel, a wind power vortex machine, a hydrogen storage unit, an electric refrigerator, a cold air storage unit and the like.
The invention relates to a preferable scheme of an energy internet-oriented zero-carbon park optimization planning method, which comprises the following steps: the zero-carbon park energy system optimization planning mathematical model comprises an objective function and constraint conditions of zero-carbon park optimization planning.
As a preferred scheme of the zero-carbon park optimization planning method for the energy Internet, the method comprises the following steps: the objective function may include one or more of,
average cost of energy, the expression is as follows:
Figure BDA0003826417180000022
where CRF (i, n) is capital recovery, i is interest rate, n is life cycle life, T is the time period of interest, P load (t) is the sum of the loads at time t, and TNPC represents the total net present value cost.
The invention relates to a preferable scheme of an energy internet-oriented zero-carbon park optimization planning method, which comprises the following steps: the objective function may further include that,
capital recovery, the expression is as follows:
CRF(i,n)＝i(1+i) n /((1+i) n -1)
the total net present value cost is calculated as follows:
Figure BDA0003826417180000023
NPC C ＝I C +OM C +R C
I C ＝pr C ×N C
Figure BDA0003826417180000024
Figure BDA0003826417180000031
wherein C represents different component types of the zero-carbon park, NPC C For the current cost of a single component, N C Is the number of the component, pr C Represents a price for the component; i is C Is the investment cost of the component, om C Representing annual operating and maintenance costs of the component, OM C Is the cost of operation and maintenance, r C Represents the replacement cost of the component at one time, R C Is the replacement cost of the assembly.
The invention relates to a preferable scheme of an energy internet-oriented zero-carbon park optimization planning method, which comprises the following steps: the constraint conditions comprise constraint conditions on system reliability, constraint conditions on residual energy rate, constraint conditions on carbon dioxide emission and constraint conditions on component capacity and power.
The invention relates to a preferable scheme of an energy internet-oriented zero-carbon park optimization planning method, which comprises the following steps: the improved quilt group algorithm further comprises,
the mathematical model for improving the air injection behavior of the quilt bag group is as follows:
Figure BDA0003826417180000032
wherein the content of the first and second substances,
Figure BDA0003826417180000033
is a food source for the population of humans,
Figure BDA0003826417180000034
and
Figure BDA0003826417180000035
a collision avoidance position vector calculated by the individual and a distance, r, between the current individual and the food source, respectively 1 ,r 2 ,r 3 ,r 4 ,r 5 ,r 6 Are all [0,1]A random number uniformly distributed in between,
Figure BDA0003826417180000036
is the random position of the tunicate group population,
Figure BDA0003826417180000037
represents the p th individual of the tunicate group.
As a preferred scheme of the zero-carbon park optimization planning method for the energy Internet, the method comprises the following steps: the improved capsule group algorithm may further comprise,
individually computed collision avoidance position vector
Figure BDA0003826417180000038
And the distance between the current individual and the food source
Figure BDA0003826417180000039
The calculation method is as follows:
Figure BDA00038264171800000310
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00038264171800000311
is a food source of the human population, r and Is [0,1 ]]A random number in between, and a random number,
Figure BDA00038264171800000312
representing the p th individual of the tunic group in the tunic group.
As a preferred scheme of the zero-carbon park optimization planning method for the energy Internet, the method comprises the following steps: the improved quilt group algorithm further comprises,
random positions of the tunic group population, the expression is as follows:
Figure BDA00038264171800000313
wherein N is the total number of the tunicate groups,
Figure BDA0003826417180000041
representing the p th individual of the tunic group in the tunic group.
The invention also provides the following technical scheme, and the energy internet-oriented zero-carbon park optimization planning device is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the system construction module is used for forming energy forms and equipment in the garden and the city to construct a zero-carbon garden energy system;
the model building module is used for building a zero-carbon park energy system optimization planning mathematical model;
an algorithm improvement module for weakening operators of traditional capsule group algorithms
Figure BDA0003826417180000042
Establishing an improved quilt packet group algorithm for the influence of updating the individual positions of the quilt packet groups;
and the optimized configuration module is used for improving a capsule group algorithm to obtain an optimal configuration scheme of the mathematical model, and optimally planning the zero-carbon park according to the optimal configuration scheme.
The invention has the beneficial effects that: the invention provides an energy internet-oriented zero-carbon park optimization planning method, which is characterized in that an energy internet-oriented zero-carbon park mathematical model is designed, and besides the cost factor of a zero-carbon park, the model also introduces the carbon dioxide emission, the possibility of power supply loss and the energy surplus rate of the system, so that the zero-carbon park planning model considers the economy, the reliability and the environmental protection of a comprehensive energy system and guides the investment of power energy enterprises and the construction of the zero-carbon park. The capsule group algorithm is improved and used for processing a complex scale optimization model, average individuals of capsule group populations are introduced, the solving precision of the traditional capsule group algorithm is improved, the traditional capsule group algorithm is more suitable for the planning problem of the zero-carbon park, the algorithm is prevented from falling into local optimization, and the possibility of obtaining a better zero-carbon park planning result is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a flowchart of a method and an apparatus for optimizing and planning a zero-carbon park facing an energy internet according to an embodiment of the present invention;
fig. 2 is a detailed flowchart of a method and an apparatus for optimizing and planning a zero-carbon park facing an energy internet according to an embodiment of the present invention;
fig. 3 is a solar irradiance curve of a zero-carbon park optimization planning method and device for energy internet according to an embodiment of the present invention;
fig. 4 is a wind speed graph of a zero-carbon park optimization planning method and device for energy internet according to an embodiment of the present invention;
fig. 5 is a power load diagram of a zero-carbon park optimization planning method and apparatus for energy internet according to an embodiment of the present invention;
fig. 6 is a cold load curve diagram of a zero-carbon park optimization planning method and device for energy internet according to an embodiment of the present invention;
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described herein, and it will be appreciated by those skilled in the art that the present invention may be practiced without departing from the spirit and scope of the present invention and that the present invention is not limited by the specific embodiments disclosed below.
Furthermore, the references herein to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not necessarily enlarged to scale, and are merely exemplary, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected" and "connected" in the present invention are to be construed broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 5, a first embodiment of the present invention provides an energy internet-oriented zero-carbon park optimization planning method, including:
s1: constructing a zero-carbon park energy system based on the park and the urban internal energy form and equipment composition;
the zero-carbon park energy system comprises a diesel generator, a photovoltaic cell panel, a wind turbine, a hydrogen storage unit, an electric refrigerator, a cold air storage unit and other equipment;
further, the fuel consumption Q of the diesel generator DG (t) is described as follows:
Q DG (t)＝α DG ×P nDG +β DG ×P DG (t)
wherein alpha is DG And beta DG Respectively, coefficient of consumption curve, P nDG And P DG And (t) the rated power and the output power of the diesel generator respectively.
Further, the output P of the photovoltaic panel PV (t) is described as follows:
P PV (t)＝P s f PV (G(t)/G S )(1+α p [T c (t)-T S ])
Figure BDA0003826417180000061
wherein, P PV (t) is the output power, where P s Is the rated power of the photovoltaic panel, f PV Is the derating coefficient, G (t) is the radiation density, G S Is the radiation intensity in the standard state, alpha p Is the temperature coefficient, T c (T) is the real-time temperature of the photovoltaic panel, and T S Is the temperature of the photovoltaic panel in the standard state, (T) is the actual ambient temperature, T NOC,a Representative of the Nominal Operating Temperature (NOT), T, of the photovoltaic panel NOC,c Is rated ambient temperature, G NOC Is the rated radiation intensity, tau and beta are respectively the light transmittance and the radiation absorptivity, eta of the photovoltaic panel PV Is the energy conversion efficiency of the photovoltaic panel.
Further, the output P of the wind turbine WT (t) is described as follows:
Figure BDA0003826417180000062
wherein, P rp Represents the rated power of the wind turbine, and v (t) represents the actual wind speed at time t. v. of ci ,v co And v r Respectively a cut-in wind speed, a cut-out wind speed and a rated wind speed.
Further, the storage capacity of the hydrogen stored energy in the charged and discharged states is described as follows:
charging method and apparatus
Figure BDA0003826417180000071
Discharge of electricity
Figure BDA0003826417180000072
Wherein E is HT (t) and E HT (t-1) the capacities of the hydrogen gas tanks at times t and t-1, P EL (t) and P FC (t) the power of the electrolyzer and the fuel cell, respectively, at time t, P G (t) is the sum P of PV (t) and P WT (t)，P L (t) is the power load at time t, η inv Is the efficiency of the inverter, and η EL Is the charging efficiency of the electrolyzer, eta FC Is the discharge efficiency of the fuel cell and,
Figure BDA0003826417180000075
is the hydrogen production efficiency per kilowatt of electricity, and eta is the power produced per cubic meter of hydrogen.
It should be noted that, in order to simplify the optimization problem herein, the energy conversion efficiency is assumed to be constant.
Further, the storage capacity of ice storage in the charged and discharged states is described as follows:
charging of electricity
Figure BDA0003826417180000073
Discharge of electricity
Figure BDA0003826417180000074
Wherein E is IS (t) and E IS (t-1) the ice storage capacity at time t and t-1, respectively, P IS,ch (t) and P IS,dis (t) is the charging and discharging power of the ice storage at time t, P IL (t) is the refrigeration load at time t, η C Is the efficiency of the refrigerator, eta IT,chr And η IT,dis Respectively the charging and discharging efficiency of ice storage.
S2: constructing a zero-carbon park energy system optimization planning mathematical model based on the zero-carbon park energy system;
it should be noted that the objective function includes energy average cost, and the constraints include constraints on system reliability, constraints on energy remaining rate, constraints on carbon dioxide emission, constraints on component capacity and power.
Further, the energy cost of the zero carbon park (COE) of the system is minimized as an objective function, expressed as:
min[COE]＝f(x)
x＝[N PV ,N WT ,N CL ,N IS ,N HT ,N FC ,N EL, N DG ] T
wherein N is PV ,N WT ,N CL ,N IS N HT ,N FC ,N EL, And N DG Representing the number of photovoltaics, wind turbines, water chillers, ice storage, hydrogen tanks, fuel cells, electrolyzers and diesel generators, respectively.
It should be noted that the objective function of the zero carbon park is related to the dimensional configuration of each component, assuming that the nominal configuration of the individual components is known, the objective function is to find the best combination of x, and the energy Cost (COE) is considered as a major economic indicator of the zero carbon park, which is a widely recognized economic indicator, taking into account the investment costs of purchase and installation of stored energy, maintenance costs and replacement costs.
Figure BDA0003826417180000081
CRF(i,n)＝i(1+i) n /((1+i) n -1)
Where CRF (i, n) is capital recovery, i is interest rate, n is life cycle life, T is the time period of interest, P load (t) is the sum of the loads at time t, TNPC represents the total net present value cost.
Further, the total net present value cost calculation method is as follows:
Figure BDA0003826417180000082
NPC C ＝I C +OM C +R C
I C ＝pr C ×N C
Figure BDA0003826417180000083
R C ＝r C ×N C ×∑ n＝CK,2CK,…20 (1/1+i) n
wherein C represents different component types of the zero-carbon park, NPC C For the current cost of a single component, N C Is the number of the component, pr C Represents the price of the component, I C Is the investment cost of the component, om C Represents the annual operating and maintenance costs, OM, of the component C Is the cost of operation and maintenance, r C Represents the replacement cost of the component at one time, R C Is the replacement cost of the assembly.
Further, system reliability may translate into the possibility of power Loss (LPSP), which is described as follows:
Figure BDA0003826417180000084
LPSP≤LPSP max
wherein, P T (t) is the sum of the stored energy powers, LPSP max Is the maximum LPSP for a zero carbon park.
It should be noted that the possibility of loss of power is a proportion of the unmet load demand to the total load demand.
Furthermore, in order to consider the energy surplus ratio of the system, the following constraint is added to the optimization problem, which can be expressed as:
ESR＝∑P exc (t)/∑P load (t)
ESR≤ESR max
wherein, P exc (t) is the residual power, ESR, of time t max Is the microgrid of the maximum ESR of the microgrid.
It should be noted that the energy residual rate (ESR) of the system is defined as the ratio of the total residual energy of the system to the load demand power of the system, and the motivation for considering this constraint is to limit the renewable abandonment rate in the zero-carbon park planning, avoiding the waste of renewable energy generation in the zero-carbon park.
Further, the carbon dioxide emission limiting conditions are described below.
Figure BDA0003826417180000091
Figure BDA0003826417180000092
Wherein the content of the first and second substances,
Figure BDA0003826417180000093
is carbon dioxide emission, F E Is the fuel factor of the diesel generator,
Figure BDA0003826417180000094
is the maximum carbon dioxide emission.
It should be noted that carbon dioxide emissions from zero carbon parks are only considered to be caused by diesel generators.
Further, the limitations on the number of components and the capacity and power of a single stored energy are as follows:
N C ≤N C,max
P C ≤P C,max
E C,min ≤E C ≤E C,max
where C is the different component type of island microgrid, and N is C Is the number of components, N C,max Is the maximum number of components, P C Is the power of the component, P C,max Is the maximum and minimum power of the component, E C Is the capacity of the module, E C,max And E C,min Are the maximum and minimum capacities of the components.
S3: weakening operator based on traditional capsule group algorithm
Figure BDA0003826417180000095
Establishing an improved quilt group algorithm for the influence on the updating of individual positions of quilt groups;
it should be noted that the quilt group Algorithm (TSA) is a new optimization Algorithm proposed by Satnam Kaur et al in 2020, the motivation for choosing such an Algorithm is that TSA is a gradient-based optimization technique that can solve any model without considering complexity.
Further, population initialization of TSA is described as follows:
P p (x)＝lb(x)+R(ub(x)-lb(x))
wherein, P p (x) Is the individual of the bursa group, lb (x) and ub (x) are the lower and upper limits of the candidate solution, respectively. R is [0,1 ]]A random number in between.
Note that, in the initialization of the quilt group, each P p (x) In TSA, an alternative x is represented for optimizing the size of an island microgrid, i.e. the number of different components.
Further, the air-jet movement behavior of the quilt bag group is described as follows:
Figure BDA0003826417180000101
wherein the content of the first and second substances,
Figure BDA0003826417180000102
is a food source for the population. r is AND Is [0,1 ]]A random number in between.
Figure BDA0003826417180000103
And
Figure BDA0003826417180000104
respectively, a collision avoidance position vector calculated for the individual and the distance between the current individual and the food source.
Further, the result of the calculation is
Figure BDA0003826417180000105
And
Figure BDA0003826417180000106
the calculation method of (2) is as follows:
Figure BDA0003826417180000107
wherein r is and Is [0,1 ]]A random number in between.
In a still further aspect of the present invention,
Figure BDA0003826417180000108
wherein the content of the first and second substances,
Figure BDA0003826417180000109
which is representative of the force of gravity,
Figure BDA00038264171800001010
representing the social strength between individuals, is described as follows:
Figure BDA00038264171800001011
wherein, P min And P max Respectively representing the minimum and maximum speed of social interaction, and c 1 Is [0,1 ]]A random number in between.
Further, TSA mathematically mimics the population behavior of the cystic mass, as described below:
Figure BDA00038264171800001012
wherein, c 2 Is [0,1 ]]A random number in between.
It should be noted that the air injection behavior in the traditional bag swarm algorithm is to expand the search space, however, the air injection behavior adopts the optimal individual in the bag swarm
Figure BDA00038264171800001013
(food position) is guided and operator
Figure BDA0003826417180000111
Also are mixed with
Figure BDA0003826417180000112
In relation to the problem of progressive planning leading to the processing of low-carbon parks/zero-carbon cities, a local optimal solution is often obtained, and therefore, an enhanced improved capsule group algorithm is proposed to improve the possibility of obtaining a solution with higher precision.
Further, the mathematical model of the improved air injection behavior of the capsule group is as follows:
Figure BDA0003826417180000113
wherein r is 1 ,r 2 ,r 3 ,r 4 ,r 5 ,r 6 Are all [0,1]Are uniformly distributed with the random numbers in between,
Figure BDA0003826417180000114
is a random position of the group of the tunicate,
Figure BDA0003826417180000115
represents the p th individual of the tunicate group.
Figure BDA0003826417180000116
Wherein N is the total number of the tunicate groups.
S4: and obtaining the optimal configuration scheme of the mathematical model based on an improved tunicate group algorithm, and optimally planning the zero-carbon park according to the optimal configuration scheme.
It should be noted that the termination condition of the improved capsule group algorithm is that the iteration number T is larger than the initially set maximum iteration number T of the capsule group max The device is arranged according to the actual requirements of engineering personnel, the larger the scale of the general problem is, the larger the setting is, and the setting is generally 100.
Example 2
Referring to fig. 2-5, a zero-carbon park optimization planning method and device oriented to energy internet are provided as an embodiment of the present invention. The zero-carbon park optimization planning device oriented to the energy Internet is applied to Taizhou great Chen island of Zhejiang China. Radiation intensity, wind speed and temperature data were measured at a large old island weather station (longitude: 121.9, latitude 28.45) in 1 hour time steps. A representative portion of the annual hourly solar radiation density and wind speed profile is shown in figure 2. The annual average radiation intensity is 154.12W/m < 2 >, the average wind speed is 6.45m/s, and the average temperature is 18.38 ℃. The simulation of the power load data is based on the power consumption scale of the large old island. The simulation of the cooling load data is based on the local temperature and the characteristics of the large island cooling users. The present invention does not discuss the detailed load simulation method. Furthermore, to simplify the problem, we assume that ice storage is completely insulated from external heat. A representative portion of the hourly load curve for one year is shown in fig. 3. The time step for the load data is 1 hour. The average annual power load is 43.25KW, and the average cold load is 8.63KW. And (3) optimally planning the zero-carbon park by using the place meteorological information and the load information, wherein the information of each device is shown in table 1, and the obtained planning result and evaluation index of the zero-carbon park are respectively shown in tables 2 and 3:
TABLE 1 economic parameters of zero carbon park
Figure BDA0003826417180000121
Figure BDA0003826417180000131
TABLE 2 optimization planning index for zero carbon park
Figure BDA0003826417180000132
TABLE 3 results of optimization planning for zero carbon park
Figure BDA0003826417180000133
In some embodiments, the energy internet oriented zero carbon park optimization planning apparatus includes:
the system construction module is used for forming energy forms and equipment in the park and the city and constructing a zero-carbon park energy system;
the model building module is used for building a zero-carbon park energy system optimization planning mathematical model;
the algorithm improvement module is used for weakening operators of the traditional capsule group algorithm
Figure BDA0003826417180000134
Establishing an improved quilt packet group algorithm for the influence of updating the individual positions of the quilt packet groups;
and the optimized configuration module is used for improving the capsule group algorithm to realize the optimal configuration scheme for obtaining the mathematical model and optimally planning the zero-carbon park according to the optimal configuration scheme.
The objective function proposed by the model construction module comprises the average energy cost, and the expression is as follows:
Figure BDA0003826417180000135
where CRF (i, n) is capital recovery, i is interest rate, n is life cycle life, T is the time period of interest, P load (t) is the sum of the loads at time t, and TNPC represents the total net present value cost.
Capital recovery, expressed as follows:
CRF(i,n)＝i(1+i) n /((1+i) n -1)
the total net present value cost is calculated as follows:
Figure BDA0003826417180000141
NPC C ＝I C +OM C +R C
I C ＝pr C ×N C
Figure BDA0003826417180000142
R C ＝r C ×N C ×∑ n＝CK,2CK,…20 (1/1+i) n
whereinC represents a different component type of a zero-carbon park, NPC C As the current cost of a single component, N C Is the number of the component, pr C Representing a price for the component; i is C Is the investment cost of the component, om C Represents the annual operating and maintenance costs, OM, of the component C Is the cost of operation and maintenance, r C Represents the replacement cost of the component at one time, R C Is the replacement cost of the assembly.
The constraint conditions of the objective function proposed by the model construction module comprise a constraint condition on system reliability, a constraint condition on energy surplus rate, a constraint condition on carbon dioxide emission and a constraint condition on component capacity and power.
The algorithm improvement module improves the capsule group algorithm mode comprises,
the mathematical model for improving the air injection behavior of the quilt bag group is as follows:
Figure BDA0003826417180000143
wherein the content of the first and second substances,
Figure BDA0003826417180000144
is a food source for the human population,
Figure BDA0003826417180000145
and
Figure BDA0003826417180000146
a collision avoidance position vector calculated by the individual and a distance, r, between the current individual and the food source, respectively 1 ,r 2 ,r 3 ,r 4 ,r 5 ,r 6 Are all [0,1 ]]Are uniformly distributed with the random numbers in between,
Figure BDA0003826417180000147
is the random position of the tunicate group population,
Figure BDA0003826417180000148
representing the p-th quilt in the bag groupAnd (4) capsule group individuals.
Individually computed collision avoidance position vector
Figure BDA0003826417180000149
And the distance between the current individual and the food source
Figure BDA00038264171800001410
The calculation method is as follows:
Figure BDA00038264171800001411
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003826417180000151
is a food source of the human population, r and Is [0,1 ]]A random number in between, and a random number,
Figure BDA0003826417180000152
representing the p th individual of the tunic group in the tunic group.
Random positions of the tunic group population, the expression is as follows:
Figure BDA0003826417180000153
wherein N is the total number of the individual tunicates,
Figure BDA0003826417180000154
represents the p th individual of the tunicate group.
Furthermore, the objective function proposed by the model construction module comprises energy average cost, and the constraint conditions of the objective function proposed by the model construction module comprise constraint conditions on system reliability, constraint conditions on energy surplus rate, constraint conditions on carbon dioxide emission and constraint conditions on component capacity and power.
It should be noted that the objective function proposed by the model building block is related to the dimensional configuration of each component, assuming that the nominal configuration of the individual components is known, the objective function is to find the best combination of x, and the energy Cost (COE) is considered as a main economic indicator of the zero-carbon park, which is a widely recognized economic indicator, taking into account the investment costs of purchase and installation of stored energy, maintenance costs and replacement costs.
It should be noted that the improved passive bag group algorithm in the optimized configuration module is terminated with the condition that the iteration number T is greater than the initially set maximum iteration number T of the passive bag group max The method is set according to the actual requirements of engineering personnel, the larger the scale of the general problem is, the larger the setting is, the 100 is generally set.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (10)

1. An energy internet-oriented zero-carbon park optimization planning method is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
constructing a zero-carbon park energy system based on the park and the urban internal energy form and equipment composition;
constructing a zero-carbon park energy system optimization planning mathematical model based on the zero-carbon park energy system;
weakening operator based on traditional capsule group algorithm
Figure FDA0003826417170000014
Establishing an improved quilt packet group algorithm for the influence of updating the individual positions of the quilt packet groups;
and obtaining the optimal configuration scheme of the mathematical model based on an improved capsule group algorithm, and optimally planning the zero-carbon park according to the optimal configuration scheme.
2. The energy internet-oriented zero-carbon park optimization planning method of claim 1, wherein: the zero-carbon park energy system comprises at least one of the following: the system comprises a diesel generator, a photovoltaic cell panel, a wind power vortex machine, a hydrogen storage unit, an electric refrigerator and a cold air storage device unit.
3. The energy internet-oriented zero-carbon park optimization planning method of claim 2, wherein: the zero-carbon park energy system optimization planning mathematical model comprises an objective function and constraint conditions of zero-carbon park optimization planning.
4. The energy internet-oriented zero-carbon park optimization planning method of claim 3, wherein: the objective function may include one or more of,
average cost of energy, the expression is as follows:
Figure FDA0003826417170000011
where CRF (i, n) is capital recovery, i is interest rate, n is life cycle life, T is the time period of interest, P load (t) is negative for time tThe sum of loads, TNPC, represents the total net present value cost.
5. The energy internet-oriented zero-carbon park optimization planning method of claim 4, wherein: the objective function may further include that,
capital recovery, the expression is as follows:
CRF(i,n)＝i(1+i) n /((1+i) n -1)
the total net present value cost is calculated as follows:
Figure FDA0003826417170000012
NPC C ＝I C +OM C +R C
I C ＝pr C ×N C
Figure FDA0003826417170000013
R C ＝r C ×N C ×∑ n＝CK,2CK,…20 (1/1+i) n
wherein C represents different component types of the zero-carbon park, NPC C As the current cost of a single component, N C Is the number of the component, pr C Representing a price for the component; I.C. A C Is the investment cost of the component, om C Representing annual operating and maintenance costs of the component, OM C Is the cost of operation and maintenance, r C Represents the replacement cost of the component at one time, R C Is the replacement cost of the assembly.
6. The energy internet-oriented zero-carbon park optimization planning method of claim 4, wherein: the constraint conditions comprise constraint conditions on system reliability, constraint conditions on residual energy rate, constraint conditions on carbon dioxide emission and constraint conditions on component capacity and power.
7. The energy internet-oriented zero-carbon park optimization planning method of claim 6, wherein: the improved capsule group algorithm may further comprise,
the mathematical model for improving the air injection behavior of the quilt bag group is as follows:
Figure FDA0003826417170000021
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003826417170000022
is a food source for the population of humans,
Figure FDA0003826417170000023
and
Figure FDA0003826417170000024
a collision avoidance position vector calculated by the individual and a distance, r, between the current individual and the food source, respectively 1 ,r 2 ,r 3 ,r 4 ,r 5 ,r 6 Are all [0,1]Are uniformly distributed with the random numbers in between,
Figure FDA0003826417170000025
is a random position of the group of the tunicate,
Figure FDA0003826417170000026
representing the p th individual of the tunic group in the tunic group.
8. The energy internet-oriented zero-carbon park optimization planning method of claim 7, wherein: the improved capsule group algorithm may further comprise,
individually computed collision avoidance position vector
Figure FDA0003826417170000027
And the distance between the current individual and the food source
Figure FDA0003826417170000028
The calculation method is as follows:
Figure FDA0003826417170000029
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00038264171700000210
is a food source of the population r and Is [0,1 ]]A random number in between, and a random number,
Figure FDA00038264171700000211
representing the p th individual of the tunic group in the tunic group.
9. The energy internet-oriented zero-carbon park optimization planning method of claim 8, wherein: the improved capsule group algorithm may further comprise,
random positions of the tunic group population, the expression is as follows:
Figure FDA00038264171700000212
wherein N is the total number of the tunicate groups,
Figure FDA0003826417170000031
representing the p th individual of the tunic group in the tunic group.
10. The utility model provides a zero carbon garden optimizes planning device towards energy internet which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the system construction module is used for forming energy forms and equipment in the garden and the city to construct a zero-carbon garden energy system;
the model construction module is used for constructing a zero-carbon park energy system optimization planning mathematical model;
an algorithm improvement module for weakening operators of traditional capsule group algorithms
Figure FDA0003826417170000032
Establishing an improved quilt packet group algorithm for the influence of updating the individual positions of the quilt packet groups;
and the optimal configuration module is used for improving the capsule group algorithm to obtain the optimal configuration scheme of the mathematical model and optimally planning the zero-carbon park according to the optimal configuration scheme.
CN202211061487.5A 2022-08-31 2022-08-31 Zero-carbon park optimization planning method and device for energy Internet Pending CN115526381A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116579902A (en) * 2023-04-07 2023-08-11 南京电力设计研究院有限公司 Digital park electric carbon data mapping method, system, equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116579902A (en) * 2023-04-07 2023-08-11 南京电力设计研究院有限公司 Digital park electric carbon data mapping method, system, equipment and storage medium
CN116579902B (en) * 2023-04-07 2023-12-12 南京电力设计研究院有限公司 Digital park electric carbon data mapping method, system, equipment and storage medium

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