CN112784439A - Energy internet planning method and device based on discretization model - Google Patents
Energy internet planning method and device based on discretization model Download PDFInfo
- Publication number
- CN112784439A CN112784439A CN202110180964.9A CN202110180964A CN112784439A CN 112784439 A CN112784439 A CN 112784439A CN 202110180964 A CN202110180964 A CN 202110180964A CN 112784439 A CN112784439 A CN 112784439A
- Authority
- CN
- China
- Prior art keywords
- equipment
- energy
- capacity
- load
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000005457 optimization Methods 0.000 claims abstract description 35
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 150000001875 compounds Chemical class 0.000 claims description 5
- 230000005477 standard model Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/02—CAD in a network environment, e.g. collaborative CAD or distributed simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses an energy internet planning method and device based on a discretization model, wherein the method comprises the following steps: establishing an energy Internet system equipment capacity standard optimization model; discretizing continuous variables in the equipment capacity standard optimization model, and establishing an equipment capacity discretization model; and solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value. The invention provides an energy internet planning method and device based on a discretization model, which solve the problem of complex modeling and solving of an energy internet system, avoid the influence of the subjectivity of a planner and greatly reduce the difficulty of planning work.
Description
Technical Field
The invention belongs to the field of energy Internet, and particularly relates to a discretization model-based energy Internet planning method and a discretization model-based energy Internet planning device.
Background
The energy internet is an energy system taking renewable power as a core, and by applying internet concepts and technologies to the fields of energy infrastructure, information communication, energy markets and the like, the energy system is optimal under multiple targets of safety, economy, environmental protection and the like.
The traditional energy planning is mainly based on special planning, different energy planning is independently carried out, and the problems of repeated load calculation, floor area conflict, unreasonable energy structure and the like are caused. The energy internet planning matches demands and resources from the top layer of an energy system by uniformly planning different energy sources, can avoid the problems, and has the characteristics of cleanness, low carbon, safety, high efficiency and the like.
Because the energy internet covers different types of energy (such as electricity, heat, cold, gas and the like) and different links (such as source, network, load, storage and the like), a large number of devices can be selected for different energy sources in different links, and great difficulty is brought to planning work. Previous planning methods are mainly divided into two categories:
the method is a scene analysis method. According to the energy consumption requirement, resources and infrastructure conditions of the project, selecting a plurality of typical planning scenes for comparative analysis. The method has the advantages of strong operability and strong subjectivity, and may not obtain an optimal scheme depending on experience and accumulation of planning personnel.
The second is a modeling analysis method. The method needs to consider all constraint conditions of the system as much as possible, and the model has the characteristics of multiple targets, multiple scales and nonlinearity. The method has the advantages that the optimal scheme can be obtained theoretically, and the defects that modeling and solving are difficult.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides an energy internet planning method and device based on a discretization model, which can overcome the defects in the prior art.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
an energy internet planning method based on a discretization model comprises the following steps:
establishing an energy Internet system equipment capacity standard optimization model;
discretizing continuous variables in the equipment capacity standard optimization model, and establishing an equipment capacity discretization model;
and solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value.
Further, the equipment capacity standard optimization model comprises: the system comprises an objective function and a constraint condition, wherein the objective function is a system economic indicator, and the constraint condition is a system power balance equation.
Further, the system economic index is a total return on investment of the system, wherein the total return on investment of the system is calculated by the following formula:
wherein, ROI is total return on investment of the system in a period of time, f (i), g (i), h (i) are respectively the income, the operation cost and the equipment investment of the ith set of equipment in the period of time, and n is the quantity of the energy Internet equipment.
Further, the system power balance equation is:
wherein m is the system energy type, n is the energy Internet equipment quantity, T is the total number of time with equal interval in a period of time,
load of the system m-type energy at the Tth moment;
capacity of equipment for producing m types of energy for the nth equipment;
and (4) producing the utilization coefficient of the m types of energy at the Tth moment for the nth equipment.
Further, the optimization model of the equipment capacity standard is as follows:
i＝1，2，…，n，
j＝1，2，…，m，
t＝1，2，…，T，
wherein, ROI is total return on investment of the system in a period of time, and f (i), g (i), h (i) are respectively the income, the operation cost and the equipment investment of the ith set of equipment in the period of time; n is the number of energy Internet devices, m is the type of system energy, T is the number of time intervals equal in a period of time, and the duration of each interval is TN；
The load of the system m-type energy source at the Tth moment,
the utilization coefficient of j-type energy sources produced for the ith equipment at the t moment, namely the ratio of the actual output power to the equipment capacity;
capacity of equipment producing a j-type energy source for an ith plant;
the income of j types of energy per unit is produced for the t moment;
the operating cost of producing j types of energy per unit for the ith equipment at the t moment;
investment per unit volume for producing j-type energy for ith equipment。
Further, the decision variable of the equipment capacity standard optimization model is the equipment capacity
And use the fastener
The number of the decision variables is (mn + Tmn), the decision variables are continuous variables, wherein,
the capacity of the equipment for producing j-type energy for the ith equipment,
and the utilization coefficient of the ith equipment at the T moment for producing j types of energy is represented, m is the type of system energy, n is the quantity of energy Internet equipment, and T is the total number of moments with equal intervals in a period of time.
Further, the equipment capacity discretization model comprises: the system comprises an objective function and a constraint condition, wherein the objective function is a system economic indicator, and the constraint condition is a system power balance equation of continuous variable discretization.
Further, the discretization method of the system power balance equation comprises the following steps: for a certain j-type energy load of the system, a period of time is averagely divided into T moments with equal intervals, and the duration of each interval is TNThe maximum load is divided into K load sections with equal intervals, and the power value of each section of load is
Further, the load curve of the system can be represented by T × K discrete grids, each grid having coordinates (T, K), T ∈ [1, T ]]，k∈[1，K]The load value and the energy value represented by each grid are respectively Lj(t, k) and Lj(t，k)TNAnd L isj(t, k) satisfies:
when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
when in use
When L isj(t，k)＝0，
In the formula, t is the horizontal axis of the load curve and represents the time; k is the vertical axis of the load curve and represents a load section;
the load value of the t moment of the j-type energy source is obtained.
Further, when the load curve is used for each section of the load power value
Load value L represented by each grid when the load is far less than the maximum load of the systemj(t, k) will approach 0 or
Energy value L of each grid representationj(t，k)TNWill approach 0 or
And each grid is powered by only one device.
Further, the discretization model of the equipment capacity is as follows:
i＝1，2，…，n，
j＝1，2，…，m，
t＝1，2，…，T，
in the formula (I), the compound is shown in the specification,
expressing the rounding-up, wherein ROI is the total return on investment of the system in a period of time, and f (i), g (i) and h (i) are the income, the operation cost and the equipment investment of the ith set of equipment respectively; n is the number of energy Internet devices, m is the type of system energy, T is the number of time intervals equal in a period of time, and the duration of each interval is TN，
The power value of each load section of the j-type energy of the system;
the load of the system j type energy source at the time t,
the grid number of the ith time point for producing the j-type energy sources by the ith equipment,
for the revenue per unit of j-type energy produced at time t,
for the operation cost of producing each unit of j-type energy sources at the t moment of the ith equipment,
investment per unit volume for producing j-type energy for the ith equipment.
Further, the decision variable of the equipment capacity discretization model is a load section power value
And device mesh number
The number of the decision variables is (m + Tmn), the decision variables are discrete variables, wherein,
for the power value of each load segment of the system class j energy source,
the grid number of the ith time when j types of energy are produced for the ith equipment, m is the type of system energy, n is the quantity of energy Internet equipment, and T is the total number of times with equal intervals in a period of time.
Further, the device capacity discretization model is solved to obtain the device capacity and the operation scheme with the optimal objective function value, and the following steps are specifically executed:
calculating the grid number combination of all the devices at each moment;
taking out one equipment grid number combination from each moment to form an equipment operation scheme;
and calculating the optimization index of each equipment operation scheme to obtain the optimal equipment capacity and operation scheme.
On the other hand, the invention provides an energy internet planning device based on a discretization model, which comprises:
the standard model unit is used for establishing a capacity standard optimization model of each device of the energy Internet;
the discretization model unit is used for discretizing continuous variables in the equipment capacity standard optimization model and establishing an equipment capacity discretization model;
and the calculating unit is used for solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value.
Further, the computing unit specifically executes the following steps:
calculating the grid number combination of all the devices at each moment;
taking out one equipment grid number combination from each moment to form an equipment operation scheme;
and calculating the optimization index of each equipment operation scheme to obtain the optimal equipment capacity and operation scheme.
The invention provides an energy internet planning method and device based on a discretization model, which solve the problem of complex modeling and solving of an energy internet system, avoid the influence of the subjectivity of a planner and greatly reduce the difficulty of planning work.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 shows a flow diagram of a discretized model-based energy internet planning method in accordance with an embodiment of the invention;
FIG. 2 illustrates a load graph of a system type of energy source according to an embodiment of the present invention;
FIG. 3 shows a discretized load graph in accordance with embodiments of the invention;
FIG. 4 shows a block flow diagram for solving a discretized model of the capacity of a device in accordance with an embodiment of the invention;
FIG. 5 illustrates a typical summer solar electrical load graph for a business user, according to an embodiment of the present invention;
FIG. 6 shows a discretized electrical load graph in accordance with embodiments of the invention;
FIG. 7 is a block diagram illustrating a flow diagram for calculating the combination of the grid numbers of all devices at each time instant according to an embodiment of the present invention;
fig. 8 shows a schematic structural diagram of an energy internet planning apparatus based on a discretization model according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an energy internet planning method based on a discretization model includes:
step S1: establishing an equipment capacity standard optimization model of the energy Internet system, wherein the equipment capacity standard optimization model comprises the following steps: an objective function and a constraint; the target function is a system economic index, and the constraint condition is a system power balance equation;
for example, the optimization goal of the energy internet system equipment capacity standard optimization model is to optimize the system economy.
In some embodiments of the present invention, the objective function for measuring the system economy may be defined as the total return on investment of the system, i.e. the system economy index is the total return on investment of the system. Assuming that the system has m types of energy loads and n sets of energy Internet equipment, the total return on investment of the system can be expressed as follows:
wherein, ROI is total return on investment of the system in a period of time, and f (i), g (i), h (i) are respectively the income, the operation cost and the equipment investment of the ith equipment in the period of time.
And the constraint condition of the energy Internet system equipment capacity standard optimization model is a system power balance equation. Assuming that there are T equally spaced times in a period of time, the system power balance equation can be expressed as:
wherein m is the system energy type, n is the energy Internet equipment quantity, T is the total number of time with equal interval in a period of time,
the load of the system m-type energy source at the Tth moment,
the power of the equipment for producing the m types of energy sources for the ith equipment at the Tth moment,
the capacity of the equipment for producing m types of energy for the nth equipment,
and (3) producing the utilization coefficient (the ratio of the actual output power to the equipment capacity) of the m types of energy sources for the nth equipment at the Tth moment.
In some embodiments of the invention, the energy internet system device capacity criteria optimization model may be expressed as:
i＝1，2，…，n，
j＝1，2，…，m，
t＝1，2，…，T，
wherein, ROI is total return on investment of the system in a period of time, and f (i), g (i), h (i) are respectively the income, the operation cost and the equipment investment of the ith set of equipment in the period of time; n is the number of energy Internet devices, m is the type of system energy, T is the number of time intervals equal in a period of time, and the duration of each interval is TN；
Is system m class energyThe load at the time T of the source,
the utilization coefficient of j-type energy sources produced for the ith equipment at the t moment, namely the ratio of the actual output power to the equipment capacity;
capacity of equipment producing a j-type energy source for an ith plant;
the income of j types of energy per unit is produced for the t moment;
the operating cost of producing j types of energy per unit for the ith equipment at the t moment;
investment per unit volume for producing j-type energy for the ith equipment.
The decision variable of the equipment capacity standard optimization model is the equipment capacity
And coefficient of utilization
There are (mn + Tmn), all continuous variables, wherein,
the capacity of the equipment for producing j-type energy for the ith equipment,
and the utilization coefficient of the ith equipment at the T moment for producing j types of energy is represented, m is the type of system energy, n is the quantity of energy Internet equipment, and T is the total number of moments with equal intervals in a period of time.
Step S2: discretizing continuous variables in the equipment capacity standard optimization model, and establishing an equipment capacity discretization model, wherein the equipment capacity discretization model comprises the following steps: an objective function and a constraint; the target function is a system economic index, and the constraint condition is a system power balance equation of continuous variable discretization;
in some embodiments of the invention, a method of discretizing a system power balance equation comprises: for a certain j-type energy load of the system, a period of time is averagely divided into T moments with equal intervals, and the duration of each interval is TNThe maximum load is divided into K load sections with equal intervals, and the power value of each section of load is
For example, as shown in FIG. 2, suppose that the load data of a certain j-type energy source in the system has load values equal in T time intervals
Is formed by the time length of each interval being TN. The maximum load is divided into K load sections with equal load interval, and the power value of each section of load is
In some embodiments of the invention, as shown in FIG. 3, the load curve for this type of energy source may be represented by T K discrete grids, each grid having coordinates of (T, K) (T ∈ [1, T [ ], and]，k∈[1，K]) The load value and the energy value represented by each grid are respectively Lj(t, k) and Lj(t，k)TNAnd L isj(t, k) satisfies:
when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
when in use
When L isj(t，k)＝0，
In the formula, t is the horizontal axis of the load curve and represents the time; k is the vertical axis of the load curve and represents a load section;
the load value of the t moment of the j-type energy source is obtained.
When the power value of each section of load of the load curve
Load value L represented by each grid when the load is far less than the maximum load of the systemj(t, k) will approach 0 or
Energy value L of each grid representationj(t，k)TNWill approach 0 or
And each grid is powered by only one device.
The system's certain class j energy load can be expressed as:
in the formula (I), the compound is shown in the specification,
indicating rounding up.
The power balance equation of a certain j-type energy source of the system can be expressed as follows:
in the formula (I), the compound is shown in the specification,
the grid number of the nth equipment at the Tth moment for producing the j-type energy.
In some embodiments of the invention, the energy internet system device capacity discretization model may be expressed as:
i＝1，2，…，n，
j＝1，2，…，m，
t＝1，2，…，T，
in the formula (I), the compound is shown in the specification,
showing the rounding-up, ROI is the total return on investment of the system in a period of time, f (i), g (i), h (i) are the income and running cost of the ith equipment respectivelyAnd equipment investment; n is the number of energy Internet devices, m is the type of system energy, T is the number of time intervals equal in a period of time, and the duration of each interval is TN，
The power value of each load section of the j-type energy of the system;
the load of the system j type energy source at the time t,
the grid number of the ith time point for producing the j-type energy sources by the ith equipment,
for the revenue per unit of j-type energy produced at time t,
for the operation cost of producing each unit of j-type energy sources at the t moment of the ith equipment,
investment per unit volume for producing j-type energy for the ith equipment.
In some embodiments of the invention, the decision variable of the equipment capacity discretization model is a load section power value
And device mesh number
There are (m + Tmn) in total, all discrete variables, wherein,
for the power value of each load segment of the system class j energy source,
the grid number of the ith time when j types of energy are produced for the ith equipment, m is the type of system energy, n is the quantity of energy Internet equipment, and T is the total number of times with equal intervals in a period of time.
The smaller the value is, the larger the calculation amount of the solved model is, and the more accurate the calculation result is. When in use
After the determination is made, the user may,
is a finite discrete variable.
Step S3: and solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value.
As shown in fig. 4, solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value, and specifically executing the following steps:
step S31: calculating the grid number combination of all the devices at each moment;
step S32: taking out an equipment grid number combination from each moment to form an equipment operation scheme;
step S33: and calculating the optimization index of each equipment operation scheme to obtain the optimal equipment capacity and operation scheme.
The optimal solution of the equipment capacity discretization model is as follows:
i.e. the operating scheme of the system equipment. Optimal solution for certain class j energy sources
Each row represents the power situation (number of grids) of different devices at the same time, and each column represents the power situation (number of grids) of different devices at the same time. The capacity of the i-th device is equal toMaximum value of grid number of equipment in different moments
And load section power value
Product of, i.e.
As shown in fig. 5, the planning method will be described by taking an energy system in which the energy type m is 1 as an example. The typical daily electric load curve of a certain commercial user in summer is known (the time number T is 24, the interval duration TN1), the maximum load of the user's typical day in summer occurs at the 12 th and 13 th moments (L)12＝L13200 kW). Assuming that the user has three optional power supply devices (device type n is 3), the optimization model of the capacity standard of the power supply system device is expressed as:
h(i)＝PN(i)IN(i)，
i＝1，2，3，
t＝1，2，…，24，
in the formula, L1～L24Is the electrical load from the 1 st to the 24 th time of the system, etat(i) Is the ith equipmentUtilization factor of time, PN(i) Is the capacity of the ith plant, RtFor the unit gain of power generation at time t, Ct(i) Unit running cost for generating power at the t moment of the ith equipment, IN(i) Is the unit capacity investment of the ith equipment. As shown in fig. 6, the maximum load is divided into 10 load segments (the number of load segments K is 10, and the power value K of each load segment is the same as that of the maximum loadN20kW), the user's typical daily electrical load curve in summer can be represented by 24 × 10 discrete grids. The power supply system equipment capacity discretization model can be expressed as follows:
h(i)＝20IN(i)max(kt(i))，
i＝1，2，3，
t＝1，2，…，24，
in the formula, kt(i) And the grid number of the ith equipment for generating power at the t moment is shown.
First, the grid number combinations of all the devices at each time are calculated. Suppose that three kinds of power supply apparatuses in total x at time ttThe combination of the number of the grids (i.e. the power combination) satisfies the system power balance equation, plant(xt) Denotes the xth time point xtSeed gridNumber combination:
plant(xt)＝(kt(1) kt(2) kt(3))，
t＝1，2，…，24，
plan1(1)～plan1(x1) And x1The solution can be obtained by a flow as shown in fig. 7, which is looped until taking pass t equal to 1, 2, 3, …, 24, resulting in all combinations of the grid numbers at each time instant 1 to 24.
And then taking out a device grid number combination from each moment to form a device operation scheme. The system has common equipment operation scheme
Capacity P of the ith equipment in each schemeN(i) Multiplying the maximum value of the grid number in the 1 st to 24 th time moments by KN：
PN(i)＝20max(kt(i))，
i＝1，2，3，
t＝1，2，…，24，
And finally, calculating the optimization index of each equipment operation scheme to obtain the optimal equipment capacity and operation scheme. And calculating and comparing the optimization index ROI of each scheme to obtain the equipment capacity and the running scheme with optimal economy.
In another aspect, as shown in fig. 8, the present invention provides an energy internet planning apparatus based on a discretization model, including:
the standard model unit is used for establishing a capacity standard optimization model of each device of the energy Internet system;
the discretization model unit is used for discretizing continuous variables in the equipment capacity standard optimization model and establishing an equipment capacity discretization model;
and the calculating unit is used for solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value.
The calculation unit specifically executes the following steps:
calculating the grid number combination of all the devices at each moment;
taking out one equipment grid number combination from each moment to form an equipment operation scheme;
and calculating the optimization index of each equipment operation scheme to obtain the optimal equipment capacity and operation scheme.
The invention provides an energy internet planning method and device based on a discretization model, which solve the problem of complex modeling and solving of an energy internet system, avoid the influence of the subjectivity of a planner and greatly reduce the difficulty of planning work.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (15)
1. An energy internet planning method based on a discretization model is characterized by comprising the following steps:
establishing an energy Internet system equipment capacity standard optimization model;
discretizing continuous variables in the equipment capacity standard optimization model, and establishing an equipment capacity discretization model;
and solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value.
2. The discretization model-based energy internet planning method of claim 1, wherein the equipment capacity criteria optimization model comprises: the system comprises an objective function and a constraint condition, wherein the objective function is a system economic indicator, and the constraint condition is a system power balance equation.
3. The discretization model-based energy internet planning method of claim 2, wherein the system economic indicator is a total return on investment of the system, and the total return on investment of the system is calculated by the following formula:
wherein, ROI is total return on investment of the system in a period of time, f (i), g (i), h (i) are respectively the income, the operation cost and the equipment investment of the ith set of equipment in the period of time, and n is the quantity of the energy Internet equipment.
4. The discretization model-based energy internet planning method of claim 2, wherein the system power balance equation is:
wherein m is the system energy type, n is the energy Internet equipment quantity, T is the total number of time with equal interval in a period of time,
load of the system m-type energy at the Tth moment;
capacity of equipment for producing m types of energy for the nth equipment;
and (4) producing the utilization coefficient of the m types of energy at the Tth moment for the nth equipment.
5. The discretization model-based energy internet planning method of claim 1, wherein the optimization model for the equipment capacity criteria is:
i＝1，2，…，n，
j＝1，2，…，m，
t＝1，2，…，T，
wherein, ROI is total return on investment of the system in a period of time, and f (i), g (i), h (i) are respectively the income, the operation cost and the equipment investment of the ith set of equipment in the period of time; n is the number of energy Internet devices, m is the type of system energy, T is the number of time intervals equal in a period of time, and the duration of each interval is TN；
The load of the system m-type energy source at the Tth moment,
is the ith set of equipmentThe utilization coefficient of j-type energy at the t moment is the ratio of the actual output power to the equipment capacity;
capacity of equipment producing a j-type energy source for an ith plant;
the income of j types of energy per unit is produced for the t moment;
the operating cost of producing j types of energy per unit for the ith equipment at the t moment;
investment per unit volume for producing j-type energy for the ith equipment.
6. The discretization model-based energy internet planning method of claim 1, wherein the decision variable of the plant capacity criteria optimization model is plant capacity
And coefficient of utilization
The number of the decision variables is (mn + Tmn), the decision variables are continuous variables, wherein,
the capacity of the equipment for producing j-type energy for the ith equipment,
the utilization coefficient of the ith equipment at the T moment for producing j types of energy is shown, m is the type of system energy, n is the quantity of energy Internet equipment, and T is the moment with equal intervals in a period of timeAnd (4) total number.
7. The discretization model-based energy internet planning method according to claim 1, wherein the discretization model of the equipment capacity comprises: the system comprises an objective function and a constraint condition, wherein the objective function is a system economic indicator, and the constraint condition is a system power balance equation of continuous variable discretization.
8. The discretization model-based energy internet planning method according to claim 7, wherein the discretization method of the system power balance equation comprises: for a certain j-type energy load of the system, a period of time is averagely divided into T moments with equal intervals, and the duration of each interval is TNThe maximum load is divided into K load sections with equal intervals, and the power value of each section of load is
9. The discretization model-based energy internet planning method of claim 8, wherein the load curve of the system is represented by T x K discrete grids, each grid has coordinates (T, K), te [1, T ∈ [1 ], T]，k∈[1，K]The load value and the energy value represented by each grid are respectively Lj(t, k) and Lj(t，k)TNAnd L isj(t, k) satisfies:
when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
when in use
When L isj(t，k)＝0，
In the formula, t is the horizontal axis of the load curve and represents the time; k is the vertical axis of the load curve and represents a load section;
the load value of the t moment of the j-type energy source is obtained.
10. The method of claim 9, wherein the power value of each load in the load curve is determined according to the discretization model
Load value L represented by each grid when the load is far less than the maximum load of the systemj(t, k) will approach 0 or
Energy value L of each grid representationj(t，k)TNWill approach 0 or
And each grid is powered by only one device.
11. The discretization model-based energy internet planning method of claim 1, wherein the discretization model of the equipment capacity is:
i＝1，2，…，n，
j＝1，2，…，m，
t＝1，2，…，T，
in the formula (I), the compound is shown in the specification,
expressing the rounding-up, wherein ROI is the total return on investment of the system in a period of time, and f (i), g (i) and h (i) are the income, the operation cost and the equipment investment of the ith set of equipment respectively; n is the number of energy Internet devices, m is the type of system energy, T is the number of time intervals equal in a period of time, and the duration of each interval is TN，
The power value of each load section of the j-type energy of the system;
the load of the system j type energy source at the time t,
the grid number of the ith time point for producing the j-type energy sources by the ith equipment,
for the revenue per unit of j-type energy produced at time t,
for the operation cost of producing each unit of j-type energy sources at the t moment of the ith equipment,
investment per unit volume for producing j-type energy for the ith equipment.
12. The discretization model-based energy internet planning method of claim 1, wherein the decision variable of the discretization model of the equipment capacity is a load segment power value
And device mesh number
The number of the decision variables is (m + Tmn), the decision variables are discrete variables, wherein,
for the power value of each load segment of the system class j energy source,
the grid number of the ith time when j types of energy are produced for the ith equipment, m is the type of system energy, n is the quantity of energy Internet equipment, and T is the total number of times with equal intervals in a period of time.
13. The discretization model-based energy internet planning method of claim 1, wherein the discretization model of the device capacity is solved to obtain the device capacity and the operation scheme with the optimal objective function value, and the following steps are specifically performed:
calculating the grid number combination of all the devices at each moment;
and taking out one equipment grid number combination from each moment to form an equipment operation scheme:
and calculating the optimization index of each equipment operation scheme to obtain the optimal equipment capacity and operation scheme.
14. An energy internet planning device based on a discretization model is characterized by comprising:
the standard model unit is used for establishing a capacity standard optimization model of each device of the energy Internet;
the discretization model unit is used for discretizing continuous variables in the equipment capacity standard optimization model and establishing an equipment capacity discretization model;
and the calculating unit is used for solving the equipment capacity discretization model to obtain the equipment capacity and the operation scheme with the optimal objective function value.
15. The discretization model-based energy internet planning apparatus of claim 14, wherein the computing unit is further configured to perform the following steps:
calculating the grid number combination of all the devices at each moment;
taking out one equipment grid number combination from each moment to form an equipment operation scheme;
and calculating the optimization index of each equipment operation scheme to obtain the optimal equipment capacity and operation scheme.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110180964.9A CN112784439B (en) | 2021-02-09 | 2021-02-09 | Energy internet planning method and device based on discretization model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110180964.9A CN112784439B (en) | 2021-02-09 | 2021-02-09 | Energy internet planning method and device based on discretization model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112784439A true CN112784439A (en) | 2021-05-11 |
| CN112784439B CN112784439B (en) | 2024-06-14 |
Family
ID=75761466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110180964.9A Active CN112784439B (en) | 2021-02-09 | 2021-02-09 | Energy internet planning method and device based on discretization model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112784439B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113899036A (en) * | 2021-09-30 | 2022-01-07 | 港华能源投资有限公司 | Method and device for planning cold accumulation project |
| CN114357700A (en) * | 2021-11-24 | 2022-04-15 | 港华能源投资有限公司 | Equipment model selection method of hot water supply system and related device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080046387A1 (en) * | 2006-07-23 | 2008-02-21 | Rajeev Gopal | System and method for policy based control of local electrical energy generation and use |
| CN106845775A (en) * | 2016-12-21 | 2017-06-13 | 国网浙江省电力公司 | Energy internet evolution alignment analysis method based on Luo Teka-Wo Tai draw models |
| CN108365608A (en) * | 2018-01-05 | 2018-08-03 | 中国电力科学研究院有限公司 | A kind of Regional Energy internet uncertain optimization dispatching method and system |
| CN108764519A (en) * | 2018-04-11 | 2018-11-06 | 华南理工大学 | A kind of garden energy internet energy device capacity configuration optimizing method |
| CN109636056A (en) * | 2018-12-24 | 2019-04-16 | 浙江工业大学 | A kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology |
| CN110598894A (en) * | 2019-07-22 | 2019-12-20 | 新奥数能科技有限公司 | Data processing method and device for energy Internet and electronic equipment |
-
2021
- 2021-02-09 CN CN202110180964.9A patent/CN112784439B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080046387A1 (en) * | 2006-07-23 | 2008-02-21 | Rajeev Gopal | System and method for policy based control of local electrical energy generation and use |
| CN106845775A (en) * | 2016-12-21 | 2017-06-13 | 国网浙江省电力公司 | Energy internet evolution alignment analysis method based on Luo Teka-Wo Tai draw models |
| CN108365608A (en) * | 2018-01-05 | 2018-08-03 | 中国电力科学研究院有限公司 | A kind of Regional Energy internet uncertain optimization dispatching method and system |
| CN108764519A (en) * | 2018-04-11 | 2018-11-06 | 华南理工大学 | A kind of garden energy internet energy device capacity configuration optimizing method |
| CN109636056A (en) * | 2018-12-24 | 2019-04-16 | 浙江工业大学 | A kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology |
| CN110598894A (en) * | 2019-07-22 | 2019-12-20 | 新奥数能科技有限公司 | Data processing method and device for energy Internet and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| 别朝红等: "能源互联网规划研究综述及展望", 《中国电机工程学报》, vol. 37, no. 22, 20 November 2017 (2017-11-20), pages 6445 - 6462 * |
| 黄子硕等: "园区级综合能源系统优化模型功能综述及展望", 《电力自动化设备》, vol. 40, no. 01, 31 January 2020 (2020-01-31), pages 10 - 18 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113899036A (en) * | 2021-09-30 | 2022-01-07 | 港华能源投资有限公司 | Method and device for planning cold accumulation project |
| CN114357700A (en) * | 2021-11-24 | 2022-04-15 | 港华能源投资有限公司 | Equipment model selection method of hot water supply system and related device |
| CN114357700B (en) * | 2021-11-24 | 2024-05-24 | 港华能源投资有限公司 | Equipment selection method of hot water supply system and related device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112784439B (en) | 2024-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109508857B (en) | Multi-stage planning method for active power distribution network | |
| Zhou et al. | Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers | |
| CN111353656B (en) | Steel enterprise oxygen load prediction method based on production plan | |
| Cadenas et al. | Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks | |
| CN103400209B (en) | Power distribution network maintenance embodiment optimization method | |
| CN111082451B (en) | Incremental distribution network multi-objective optimization scheduling model based on scene method | |
| CN104181900B (en) | Layered dynamic regulation method for multiple energy media | |
| CN103326353A (en) | Environmental economic power generation dispatching calculation method based on improved multi-objective particle swarm optimization algorithm | |
| CN112784439A (en) | Energy internet planning method and device based on discretization model | |
| CN105631528A (en) | NSGA-II and approximate dynamic programming-based multi-objective dynamic optimal power flow solving method | |
| CN112952807B (en) | Multi-objective optimization scheduling method considering wind power uncertainty and demand response | |
| CN113673738B (en) | Comprehensive energy system optimal configuration method based on supply and demand response and adjustable scene | |
| CN111079972A (en) | Method, device and medium for planning reliability of active power distribution network | |
| Dieu et al. | Improved merit order and augmented Lagrange Hopfield network for short term hydrothermal scheduling | |
| CN116599151A (en) | Source network storage safety management method based on multi-source data | |
| CN107145968A (en) | Photovoltaic apparatus life cycle cost Forecasting Methodology and system based on BP neural network | |
| CN112712281A (en) | Cloud model-based energy storage working condition adaptability comprehensive evaluation method and system | |
| Zhang et al. | Ultra-short term wind power prediction model based on modified grey model method for power control in wind farm | |
| CN115860205A (en) | Two-stage distribution robust hydrogen storage equipment optimal configuration method considering cross-season scheduling | |
| CN112330089B (en) | Comprehensive energy efficiency monitoring method and monitoring system for equipment manufacturing enterprises | |
| CN110298456A (en) | Plant maintenance scheduling method and device in group system | |
| CN115758763A (en) | Multi-energy flow system optimal configuration method and system considering source load uncertainty | |
| CN107480917A (en) | A kind of probability load flow calculation method based on quasi-Monte Carlo simulation | |
| CN112686555A (en) | Comprehensive analysis method, device and system for regional power distribution network | |
| CN112561115A (en) | Active power distribution network user power consumption behavior variable weight combination prediction model and prediction method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |