CN115081724A - Optimized scheduling method and device for regional energy supply microgrid - Google Patents

Optimized scheduling method and device for regional energy supply microgrid Download PDF

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CN115081724A
CN115081724A CN202210792427.4A CN202210792427A CN115081724A CN 115081724 A CN115081724 A CN 115081724A CN 202210792427 A CN202210792427 A CN 202210792427A CN 115081724 A CN115081724 A CN 115081724A
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孙亮
唐旭
张新鹤
岳国荣
钟鸣
杨泳
许皓
魏然
齐文凯
姚立国
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Beijing Electric Power Co Ltd
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China Electric Power Research Institute Co Ltd CEPRI
State Grid Beijing Electric Power Co Ltd
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Abstract

The invention relates to the technical field of optimal scheduling of power systems, and particularly provides an optimal scheduling method and device for a regional energy supply microgrid, which comprises the following steps: substituting the source load data of the regional energy supply microgrid into a pre-constructed multi-target optimization scheduling model facing the regional energy supply microgrid and solving to obtain an optimization result; obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result; wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output. The technical scheme provided by the invention can improve the economical efficiency, stability and comprehensive energy efficiency of system operation, and has reference value for scheduling problems of a low-carbon power supply station.

Description

Optimized scheduling method and device for regional energy supply microgrid
Technical Field
The invention relates to the technical field of optimal scheduling of power systems, in particular to an optimal scheduling method and device for a regional energy supply microgrid.
Background
At present, the important trend of energy industry development is to replace the traditional energy and reduce carbon emission. However, with the large access of new energy and the increase of energy demand of users, the efficient, stable and reliable operation of the regional power grid faces more serious challenges, and the research on the optimized operation method of the low-carbon power supply station is significant.
In recent years, domestic and foreign scholars carry out a series of researches on the optimized operation of the micro-grid, and ideas are provided for power supply station dispatching. The azolla wanshanensis and the like are researched aiming at the flexible resource response characteristics and uncertainty in the intelligent building microgrid system, and the system operation economy is optimized; the method is obvious in intelligence and the like, discusses the electric energy trading mode of the local microgrid, and establishes a local microgrid energy trading model; roping and the like research the combined cooling heating and power supply type micro-grid and provide a multi-energy coordination economic optimization scheduling strategy of the micro-grid; researching Huchenzhuang and the like aiming at a family microgrid system, and providing an energy management strategy of a wind-solar-storage integrated family; shah P and the like propose a microgrid optimization scheduling method considering an island operation state; farzin H and the like take the conflict problem between the stability and the economy of the microgrid into consideration, and provide a microgrid energy storage optimization operation strategy. The existing research has made a great progress on the economic optimization scheduling problem of the microgrid, however, as the energy demand of users increases, a large amount of cooling/heating equipment such as electric refrigeration, electric heating and heat pumps are connected into the microgrid, so that the energy supply structure of the microgrid is more complex, the energy utilization efficiency is reduced, and a novel optimization scheduling method for realizing efficient power supply of a power supply station is lacked.
Disclosure of Invention
In order to overcome the defects, the invention provides an optimal scheduling method and device for a regional energy supply microgrid.
In a first aspect, an optimized scheduling method for a regional energy supply microgrid is provided, and the optimized scheduling method for the regional energy supply microgrid comprises the following steps:
substituting the source load data of the regional energy supply microgrid into a pre-constructed multi-target optimization scheduling model facing the regional energy supply microgrid and solving to obtain an optimization result;
obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result;
wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output.
Preferably, the source load data includes at least one of: power grid supply data, cold/heat/electrical load data.
Preferably, the pre-constructed multi-objective optimization scheduling model for the area-oriented power supply microgrid comprises:
the method comprises a first objective function, a second objective function and a third objective function which respectively take daily comprehensive energy efficiency maximization, power supply benefit maximization and power fluctuation minimization as targets, and constraint conditions for optimizing and scheduling configuration of the regional energy supply microgrid.
Further, the first objective function is calculated as follows:
max E c
the second objective function is calculated as follows:
Figure BDA0003730822130000021
the third objective function is calculated as follows:
Figure BDA0003730822130000022
in the above formula, E c Daily integrated energy efficiency, F, for low-carbon microgrid 2 For power supply efficiency, p e,t In response to the pre-electricity rate at time t,
Figure BDA0003730822130000023
for the cold load at the time t,
Figure BDA0003730822130000024
for the thermal load at the time t,
Figure BDA0003730822130000025
for the electrical load at time t, F 3 For power fluctuation, P G,t For the grid supply at time t, P G,t-1 The power supply amount of the power grid at the moment T-1, and T is an optimized dispatching cycle.
Further, the daily comprehensive energy efficiency of the low-carbon microgrid is calculated according to the following formula:
Figure BDA0003730822130000026
in the above formula, P ES,loss,t For storing electrical losses, P, at time t E2H,loss,t For electric heating losses at time t, P E2C,loss,t For electric refrigeration losses at time t, P HP,loss,t For heat pump losses at time t, P TE,loss,t For the transmission loss at time t, P DG,t And supplying power to the distributed power supply at the time t.
Further, the constraint condition includes at least one of: price demand response constraint, supply and demand balance constraint, equipment output constraint, energy storage electric quantity constraint and interaction power constraint.
Further, the price demand response constraint is calculated as follows:
Figure BDA0003730822130000027
in the above formula, Delta P e,T The change of the electrical load before the response at time T, P e,T Predicted value of electric load before T time response, delta p e,T The change of electricity price before T time response, p e,T Electric price before response for time T, E e Is a price elasticity matrix.
Further, the supply and demand balance constraint is calculated as follows:
Figure BDA0003730822130000031
Figure BDA0003730822130000032
Figure BDA0003730822130000033
the calculation of the device output constraint is as follows:
P ES,min ≤P ES,t ≤P ES,max
Figure BDA0003730822130000034
the calculation formula of the energy storage electric quantity constraint is as follows:
S ES,min ≤S ES,t ≤S ES,max
the interactive power constraint is calculated as follows:
P G,min ≤P G,t ≤P G,max
in the above formula, P ES,t 、P E2H,t 、P E2C,t Respectively an energy storage output, an electric heating power and an electric cooling power at the moment t,
Figure BDA0003730822130000035
Figure BDA0003730822130000036
respectively the electric heating equipment and the electric refrigerating equipment at the time t and the electric power absorbed by the heat pump,
Figure BDA0003730822130000037
respectively the cold and hot power output of the heat pump, P ES,max 、P ES,min Respectively are the upper limit and the lower limit of the energy storage output range,
Figure BDA0003730822130000038
respectively the upper and lower limits of the absorbed electric power of the heat pump, S ES,t For storing energy at time t, S ES,max 、S ES,min Respectively an upper limit and a lower limit of the energy storage capacity, P G,max 、P G,min Respectively an upper limit and a lower limit of the interactive power.
Preferably, in the process of substituting the source load data of the regional energy supply microgrid into the pre-constructed multi-target optimization scheduling model for the regional energy supply microgrid and solving, the pre-constructed multi-target optimization scheduling model for the regional energy supply microgrid is solved by adopting a multi-target particle swarm optimization algorithm.
In a second aspect, an optimized dispatching device for a regional power supply microgrid is provided, and the optimized dispatching device for the regional power supply microgrid comprises:
the system comprises a first analysis module, a second analysis module and a third analysis module, wherein the first analysis module is used for substituting source load data of a regional energy supply micro-grid into a pre-constructed multi-objective optimization scheduling model facing the regional energy supply micro-grid and solving the model to obtain an optimization result;
the second analysis module is used for obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result;
wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output.
In a third aspect, a computer device is provided, comprising: one or more processors;
the processor to store one or more programs;
when the one or more programs are executed by the one or more processors, the optimized scheduling method for the area-oriented energy supply microgrid is realized.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the method for optimizing and scheduling a regional power supply micro grid is implemented.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
the invention provides an optimal scheduling method and device for a regional energy supply microgrid, which comprise the following steps: substituting the source load data of the regional energy supply microgrid into a pre-constructed multi-target optimization scheduling model facing the regional energy supply microgrid and solving to obtain an optimization result; obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result; wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output. The technical scheme provided by the invention can improve the economical efficiency, stability and comprehensive energy efficiency of system operation, and has reference value for scheduling problems of a low-carbon power supply station;
furthermore, comprehensive energy efficiency is introduced as one of control targets in the process of establishing the multi-target optimization scheduling model for the regional energy supply microgrid, and the method has a guiding effect on the operation planning of the low-carbon power supply station.
Drawings
Fig. 1 is a schematic flow chart of main steps of an optimal scheduling method for a regional power supply microgrid according to an embodiment of the present invention;
fig. 2 is a main structural block diagram of an optimized scheduling device for a regional power supply microgrid according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
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.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating main steps of a regional power supply microgrid-oriented optimal scheduling method according to an embodiment of the present invention. As shown in fig. 1, the optimal scheduling method for the area-oriented energy supply microgrid in the embodiment of the present invention mainly includes the following steps:
step S101: substituting the source load data of the regional energy supply microgrid into a pre-constructed multi-target optimization scheduling model facing the regional energy supply microgrid and solving to obtain an optimization result;
step S102: obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result;
wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output.
Wherein the source load data comprises at least one of: power grid supply data, cold/heat/electrical load data.
In this embodiment, the pre-constructed multi-objective optimization scheduling model for the area-oriented energy supply microgrid includes:
the method comprises a first objective function, a second objective function and a third objective function which respectively take daily comprehensive energy efficiency maximization, power supply benefit maximization and power fluctuation minimization as targets, and constraint conditions for optimizing and scheduling configuration of the regional energy supply microgrid.
In one embodiment, the first objective function is calculated as follows:
max E c
the second objective function is calculated as follows:
Figure BDA0003730822130000051
the third objective function is calculated as follows:
Figure BDA0003730822130000052
in the above formula, E c Daily integrated energy efficiency, F, for low-carbon microgrid 2 For power supply efficiency, p e,t In response to the pre-electricity rate at time t,
Figure BDA0003730822130000053
for the cold load at the time t,
Figure BDA0003730822130000054
for the thermal load at the time t,
Figure BDA0003730822130000055
for the time t the electrical load is,F 3 for power fluctuation, P G,t For the grid supply at time t, P G,t-1 The power supply amount of the power grid at the moment T-1, and T is an optimized dispatching cycle.
The calculation formula of the daily comprehensive energy efficiency of the low-carbon microgrid is as follows:
Figure BDA0003730822130000056
in the above formula, P ES,loss,t For storing electrical losses, P, at time t E2H,loss,t Electric heating losses at time t, P E2C,loss,t For electric refrigeration losses at time t, P HP,loss,t For heat pump losses at time t, P TE,loss,t For the transmission loss at time t, P DG,t And supplying power to the distributed power supply at the time t.
In one embodiment, the constraints include at least one of: price demand response constraint, supply and demand balance constraint, equipment output constraint, energy storage electric quantity constraint and interaction power constraint.
Wherein the price demand response constraint is calculated as follows:
Figure BDA0003730822130000061
in the above formula, Delta P e,T The change of the electrical load before the response at time T, P e,T Predicted value of electric load before T time response, delta p e,T The change of electricity price before T time response, p e,T Electric price before response for time T, E e Is a price elasticity matrix.
Wherein the supply and demand balance constraint is calculated by the following formula:
Figure BDA0003730822130000062
Figure BDA0003730822130000063
Figure BDA0003730822130000064
the calculation of the device output constraint is as follows:
P ES,min ≤P ES,t ≤P ES,max
Figure BDA0003730822130000065
the calculation formula of the energy storage electric quantity constraint is as follows:
S ES,min ≤S ES,t ≤S ES,max
the interactive power constraint is calculated as follows:
P G,min ≤P G,t ≤P G,max
in the above formula, P ES,t 、P E2H,t 、P E2C,t Respectively an energy storage output, an electric heating power and an electric cooling power at the moment t,
Figure BDA0003730822130000066
Figure BDA0003730822130000067
respectively the electric heating equipment and the electric refrigerating equipment at the time t and the electric power absorbed by the heat pump,
Figure BDA0003730822130000068
respectively the cold and hot power output of the heat pump, P ES,max 、P ES,min Respectively are the upper limit and the lower limit of the energy storage output range,
Figure BDA0003730822130000069
respectively the upper and lower limits of the absorbed electric power of the heat pump, S ES,t For storing energy at time t, S ES,max 、S ES,min Respectively an upper limit and a lower limit of the energy storage capacity, P G,max 、P G,min Respectively an upper limit and a lower limit of the interactive power.
Furthermore, in the process of substituting the source-load data of the regional energy supply microgrid into the pre-constructed multi-target optimization scheduling model facing the regional energy supply microgrid and solving, the pre-constructed multi-target optimization scheduling model facing the regional energy supply microgrid is solved by adopting a multi-target particle swarm algorithm.
Example 2
Based on the same inventive concept, the invention further provides an optimized scheduling device for the area-oriented energy supply microgrid, as shown in fig. 2, the optimized scheduling device for the area-oriented energy supply microgrid comprises:
the system comprises a first analysis module, a second analysis module and a third analysis module, wherein the first analysis module is used for substituting source load data of a regional energy supply micro-grid into a pre-constructed multi-objective optimization scheduling model facing the regional energy supply micro-grid and solving the model to obtain an optimization result;
the second analysis module is used for obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result;
wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output.
Preferably, the source load data includes at least one of: power grid supply data, cold/heat/electrical load data.
Preferably, the pre-constructed multi-objective optimization scheduling model for the area-oriented power supply microgrid comprises:
the method comprises a first objective function, a second objective function and a third objective function which respectively take daily comprehensive energy efficiency maximization, power supply benefit maximization and power fluctuation minimization as targets, and constraint conditions for optimizing and scheduling configuration of the regional energy supply microgrid.
Further, the first objective function is calculated as follows:
max E c
the second objective function is calculated as follows:
Figure BDA0003730822130000071
the third objective function is calculated as follows:
Figure BDA0003730822130000072
in the above formula, E c Daily integrated energy efficiency, F, for low-carbon microgrid 2 For power supply efficiency, p e,t In response to the pre-electricity rate at time t,
Figure BDA0003730822130000073
for the cold load at the time t,
Figure BDA0003730822130000074
for the thermal load at the time t,
Figure BDA0003730822130000075
for the electrical load at time t, F 3 For power fluctuation, P G,t For the grid supply at time t, P G,t-1 The power supply amount of the power grid at the moment T-1, and T is an optimized dispatching cycle.
Further, the daily comprehensive energy efficiency of the low-carbon microgrid is calculated according to the following formula:
Figure BDA0003730822130000076
in the above formula, P ES,loss,t For storing electrical losses, P, at time t E2H,loss,t For electric heating losses at time t, P E2C,loss,t For electric refrigeration losses at time t, P HP,loss,t For heat pump losses at time t, P TE,loss,t For the transmission loss at time t, P DG,t And supplying power to the distributed power supply at the time t.
Further, the constraint condition includes at least one of: price demand response constraint, supply and demand balance constraint, equipment output constraint, energy storage electric quantity constraint and interaction power constraint.
Further, the price demand response constraint is calculated as follows:
Figure BDA0003730822130000081
in the above formula, Delta P e,T The change of the electrical load before the response at time T, P e,T Predicted value of electric load before T time response, delta p e,T The change of electricity price before T time response, p e,T Electric price before response for time T, E e Is a price elasticity matrix.
Further, the supply-demand balance constraint is calculated as follows:
Figure BDA0003730822130000082
Figure BDA0003730822130000083
Figure BDA0003730822130000084
the calculation of the device output constraint is as follows:
P ES,min ≤P ES,t ≤P ES,max
Figure BDA0003730822130000085
the calculation formula of the energy storage electric quantity constraint is as follows:
S ES,min ≤S ES,t ≤S ES,max
the interactive power constraint is calculated as follows:
P G,min ≤P G,t ≤P G,max
in the above formula, P ES,t 、P E2H,t 、P E2C,t Respectively an energy storage output, an electric heating power and an electric cooling power at the moment t,
Figure BDA0003730822130000086
Figure BDA0003730822130000087
respectively the electric heating equipment and the electric refrigerating equipment at the time t and the electric power absorbed by the heat pump,
Figure BDA0003730822130000088
respectively the cold and hot power output of the heat pump, P ES,max 、P ES,min Respectively are the upper limit and the lower limit of the energy storage output range,
Figure BDA0003730822130000089
respectively the upper and lower limits of the absorbed electric power of the heat pump, S ES,t For storing energy at time t, S ES,max 、S ES,min Respectively an upper limit and a lower limit of the energy storage capacity, P G,max 、P G,min Respectively an upper limit and a lower limit of the interactive power.
Preferably, in the process of substituting the source load data of the regional energy supply microgrid into the pre-constructed multi-target optimization scheduling model for the regional energy supply microgrid and solving, the pre-constructed multi-target optimization scheduling model for the regional energy supply microgrid is solved by adopting a multi-target particle swarm optimization algorithm.
Example 3
Based on the same inventive concept, the present invention also provides a computer apparatus comprising a processor and a memory, the memory being configured to store a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to implement one or more instructions, and to specifically load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement the step of the optimized scheduling method for the local power supply microgrid in the foregoing embodiments.
Example 4
Based on the same inventive concept, the present invention further provides a storage medium, in particular, a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium may be loaded and executed by a processor to implement the steps of the method for optimizing and scheduling for the area-oriented power supply microgrid in the above embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (12)

1. An optimal scheduling method for a regional energy supply microgrid is characterized by comprising the following steps:
substituting the source load data of the regional energy supply microgrid into a pre-constructed multi-target optimization scheduling model facing the regional energy supply microgrid and solving to obtain an optimization result;
obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result;
wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output.
2. The method of claim 1, wherein the source charge data comprises at least one of: power grid supply data, cold/heat/electrical load data.
3. The method of claim 1, wherein the pre-constructed multi-objective optimization scheduling model for the area-powered microgrid comprises:
the method comprises a first objective function, a second objective function and a third objective function which respectively take daily comprehensive energy efficiency maximization, power supply benefit maximization and power fluctuation minimization as targets, and constraint conditions for optimizing and scheduling configuration of the regional energy supply microgrid.
4. The method of claim 3, wherein the first objective function is calculated as follows:
max E c
the second objective function is calculated as follows:
Figure FDA0003730822120000011
the third objective function is calculated as follows:
Figure FDA0003730822120000012
in the above formula, E c Daily integrated energy efficiency, F, for low-carbon microgrid 2 For power supply efficiency, p e,t In response to the front electricity prices at time t,
Figure FDA0003730822120000013
for the cold load at the time t,
Figure FDA0003730822120000014
for the purpose of the thermal load at the time t,
Figure FDA0003730822120000015
for the electrical load at time t, F 3 For power fluctuation, P G,t For the grid supply at time t, P G,t-1 The power supply amount of the power grid at the moment T-1, and T is an optimized dispatching cycle.
5. The method of claim 4, wherein the daily integrated energy efficiency of the low-carbon microgrid is calculated as follows:
Figure FDA0003730822120000016
in the above formula, P ES,loss,t For storing electrical losses, P, at time t E2H,loss,t For electric heating losses at time t, P E2C,loss,t For electric refrigeration losses at time t, P HP,loss,t For heat pump losses at time t, P TE,loss,t For the transmission loss at time t, P DG,t And supplying power to the distributed power supply at the time t.
6. The method of claim 5, wherein the constraints comprise at least one of: price demand response constraint, supply and demand balance constraint, equipment output constraint, energy storage electric quantity constraint and interaction power constraint.
7. The method of claim 6, wherein the price demand response constraint is calculated as follows:
Figure FDA0003730822120000021
in the above formula, Delta P e,T The change of the electrical load before the response at time T, P e,T Predicted value of electric load before T time response, delta p e,T The change of electricity price before T time response, p e,T Electric price before response for time T, E e Is a price elasticity matrix.
8. The method of claim 7, wherein the supply and demand balance constraint is calculated as follows:
Figure FDA0003730822120000022
Figure FDA0003730822120000023
Figure FDA0003730822120000024
the calculation of the device output constraint is as follows:
P ES,min ≤P ES,t ≤P ES,max
Figure FDA0003730822120000025
the calculation formula of the energy storage electric quantity constraint is as follows:
S ES,min ≤S ES,t ≤S ES,max
the interactive power constraint is calculated as follows:
P G,min ≤P G,t ≤P G,max
in the above formula, P ES,t 、P E2H,t 、P E2C,t Respectively an energy storage output, an electric heating power and an electric cooling power at the moment t,
Figure FDA0003730822120000026
Figure FDA0003730822120000027
respectively the electric heating equipment and the electric refrigerating equipment at the time t and the electric power absorbed by the heat pump,
Figure FDA0003730822120000028
respectively the cold and hot power output of the heat pump, P ES,max 、P ES,min Respectively are the upper limit and the lower limit of the energy storage output range,
Figure FDA0003730822120000029
respectively an upper limit and a lower limit of the absorbed electric power of the heat pump, S ES,t For storing energy at time t, S ES,max 、S ES,min Respectively an upper limit and a lower limit of the energy storage capacity, P G,max 、P G,min Respectively an upper limit and a lower limit of the interactive power.
9. The method of claim 1, wherein in the process of substituting and solving the source-to-load data of the regional energy supply microgrid into the pre-constructed regional energy supply microgrid-oriented multi-objective optimization scheduling model, a multi-objective particle swarm optimization is adopted to solve the pre-constructed regional energy supply microgrid-oriented multi-objective optimization scheduling model.
10. An optimized dispatching device for a regional energy supply micro-grid, which is characterized by comprising:
the system comprises a first analysis module, a second analysis module and a third analysis module, wherein the first analysis module is used for substituting source load data of a regional energy supply micro-grid into a pre-constructed multi-objective optimization scheduling model facing the regional energy supply micro-grid and solving the model to obtain an optimization result;
the second analysis module is used for obtaining an optimized scheduling scheme facing the regional energy supply microgrid based on the optimization result;
wherein the optimization result comprises at least one of the following: energy storage output, electric heating power, electric refrigerating power, heat pump cold power output and heat pump heat power output.
11. A computer device, comprising: one or more processors;
the processor to store one or more programs;
the one or more programs, when executed by the one or more processors, implement the method for optimized scheduling of area-oriented power microgrid of any of claims 1 to 9.
12. A computer-readable storage medium, on which a computer program is stored, which, when executed, implements the method for optimized scheduling of area-oriented power micro grids according to any of claims 1 to 9.
CN202210792427.4A 2022-07-05 2022-07-05 Optimized scheduling method and device for regional energy supply microgrid Pending CN115081724A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117689184A (en) * 2024-02-02 2024-03-12 山东科技大学 Power system planning method and system considering cooperation of load side and low carbon-economy

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117689184A (en) * 2024-02-02 2024-03-12 山东科技大学 Power system planning method and system considering cooperation of load side and low carbon-economy
CN117689184B (en) * 2024-02-02 2024-04-19 山东科技大学 Power system planning method and system considering cooperation of load side and low carbon-economy

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