CN115081867A - Energy storage planning method, device and equipment for comprehensive energy system - Google Patents

Energy storage planning method, device and equipment for comprehensive energy system Download PDF

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CN115081867A
CN115081867A CN202210696068.2A CN202210696068A CN115081867A CN 115081867 A CN115081867 A CN 115081867A CN 202210696068 A CN202210696068 A CN 202210696068A CN 115081867 A CN115081867 A CN 115081867A
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戴攀
胡哲晟
王蕾
杨黎
朱超
张曼颖
林玲
刘曌煜
黄晶晶
邹波
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Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention provides a method, a device and equipment for planning energy storage of an integrated energy system, wherein historical thermoelectric load data are processed by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load; constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid; performing deterministic equivalence class conversion on the opportunity model based on a value probability distribution function to obtain a deterministic constraint model; acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment; and establishing a constraint condition and an objective function of the comprehensive energy system optimization scheduling problem based on a deterministic constraint model, a comprehensive energy system network transmission model and a model of the multi-energy storage equipment, and calculating to obtain a day-ahead optimal scheduling plan. Compared with the traditional energy storage planning, the method can accurately depict the fluctuation and the randomness of the load, and provides a more accurate system model for the energy storage planning.

Description

Energy storage planning method, device and equipment for comprehensive energy system
Technical Field
The invention relates to the technical field of power system planning, in particular to a comprehensive energy system energy storage planning method, device and equipment considering load uncertainty.
Background
In order to realize the aim of carbon neutralization, the analysis and prediction of the power load are key means for promoting the consumption of new energy such as wind power and photovoltaic and maintaining the operation stability of a power grid, and the method is beneficial to constructing a clean, efficient, safe and sustainable modern energy system. The power load analysis and prediction are carried out, the system risk prediction capability is facilitated to be improved, the load uncertainty quantification is realized, the risk brought by the load randomness and the uncertainty is remarkably reduced, and the key reliable data support is provided for power system cooperative regulation, energy storage configuration and control, production simulation and the like.
In recent years, the development of energy storage technology promotes the establishment of energy storage equipment, the introduction of the energy storage equipment can well solve the problem of a regional comprehensive energy system, the planning problem of an energy storage device is gradually reflected along with the improvement of modeling precision, but the traditional energy storage planning problem focuses more on deterministic load, and the influence of current load uncertainty is not considered.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, and a device for planning energy storage of an integrated energy system, so as to implement more reasonable planning of the integrated energy system.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
an integrated energy system energy storage planning method comprises the following steps:
acquiring historical thermoelectric load data of the comprehensive energy system;
processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid;
performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
and establishing a constraint condition and an objective function of the optimization scheduling problem of the comprehensive energy system based on the deterministic constraint model, the network transmission model of the comprehensive energy system and the model of the multi-energy storage equipment.
An integrated energy system energy storage planning apparatus, comprising:
the data acquisition unit is used for acquiring historical thermoelectric load data of the comprehensive energy system;
the distribution probability function calculation unit is used for processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
the constraint model building unit is used for building an opportunity constraint model for representing the load fluctuation and the randomness of the power grid; performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
the model acquisition unit is used for acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
and the planning problem establishing unit is used for obtaining an objective function for constructing the comprehensive energy system based on the deterministic constraint model, the comprehensive energy system network transmission model and the model of the multi-energy storage equipment, and constructing a scheduling optimization problem model of the comprehensive energy system in the future by combining the established constraint conditions. An integrated energy system energy storage planning apparatus, comprising:
a memory and a processor; the memory stores a program adapted for execution by the processor, the program for:
acquiring historical thermoelectric load data of the comprehensive energy system;
processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid;
performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
and calculating to obtain a target function of the comprehensive energy system based on the deterministic constraint model, the comprehensive energy system network transmission model and the model of the multi-energy storage equipment.
Based on the technical scheme, in the scheme provided by the embodiment of the invention, before the comprehensive energy system is constructed, historical thermoelectric load data of the comprehensive energy system is acquired; processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load; constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid; performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model; acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment; and establishing a constraint condition and an objective function of the optimization scheduling problem of the comprehensive energy system based on the deterministic constraint model, the network transmission model of the comprehensive energy system and the model of the multi-energy storage equipment. According to the scheme, uncertainty is introduced into the model by utilizing the opportunity constraint thought, the opportunity constraint problem is solved through deterministic processing, a comprehensive energy system model and a multi-energy storage model are established, and an economic-oriented multi-energy storage planning strategy is provided.
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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 described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of an energy storage planning method for an integrated energy system disclosed in an embodiment of the present application;
fig. 2 is a schematic structural diagram of an integrated energy system energy storage planning apparatus disclosed in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an integrated energy system energy storage planning device disclosed in an embodiment of the present application.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention aims to provide a comprehensive energy system multi-energy storage planning method capable of considering load uncertainty, which provides a more accurate model and guidance for multi-energy storage planning, and in order to achieve the aim, the load randomness and the volatility under the current condition are considered to be higher, a traditional energy storage planning scheme possibly cannot meet the operation conditions, and additional cost is generated to cause economic challenge.
Specifically, referring to fig. 1, the method for planning energy storage of an integrated energy system disclosed in an embodiment of the present application may include:
step S101: acquiring historical thermoelectric load data of the comprehensive energy system;
the historical thermoelectric load data is historical operating data of the built integrated energy system, and the data can be extracted by a data processing system in the existing integrated energy system.
Step S102: processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
in the step, considering the uncertainty of the electric heating load, adopting a direct quantile regression prediction algorithm, setting a series of conditional quantiles with different proportions to obtain electric heating load predicted values under different conditional quantiles, wherein specifically, the cumulative distribution function CDF generated by the electric power load can be approximated by a group of quantiles generated by nonparametric probability prediction and expressed as
Figure BDA0003702584070000051
In the formula
Figure BDA0003702584070000052
As a function of the probability distribution at time t + k, α 1 、α 2 …α r Is a number in [0,1]The quantile between can also be understood as a probability value, i.e. t + k has an alpha 1 、α 2 …α r The probability power load prediction value is
Figure BDA0003702584070000053
Namely, it is
Figure BDA0003702584070000054
Is quantile alpha i And processing the corresponding power load predicted value.
Step S103: constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid;
in this step, the opportunity constraint model is a model constructed based on energy that can be provided by the power grid node and energy consumed by the node, and specifically, the model may be:
Figure BDA0003702584070000055
in the formula (I), the compound is shown in the specification,
Figure BDA0003702584070000056
power output, R, of thermal power generating unit at node i i,t For the spinning reserve that the ith node of the distribution network can provide,
Figure BDA0003702584070000057
the representation CHP represents the active power of the cogeneration unit,
Figure BDA0003702584070000058
represents the charging power of the stored energy of the battery,
Figure BDA0003702584070000059
the discharge power of the stored energy of the battery is represented,
Figure BDA00037025840700000510
the active load of a node i of the power distribution network; the confidence level α is the probability value for which a constraint holds, said α ∈ (α) 1 、α 2 …α r ) That is, the confidence level α is a predetermined quantile. The opportunity constraint model mainly introduces uncertainty through a node of the power system, and for the power balance and standby requirements of the node, the probability that the energy transmitted outwards by the node and the standby energy are greater than the power consumption of the node needs to be greater than a certain confidence level alpha, namely more than a certain probability, so that the requirement of safe operation of the power system is met. By utilizing such an opportunistic constraint expression,by combining the predicted cumulative probability density function, the uncertainty of the load can be introduced into the model, and the established model is more accurate.
Step S104: performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
in this step, the opportunistic constraint model is converted into a deterministic constraint:
Figure BDA0003702584070000061
wherein, the
Figure BDA0003702584070000062
Is alpha i When it is equal to the confidence level
Figure BDA0003702584070000063
A value of (d);
Figure BDA0003702584070000064
the lower quantile point representing the predicted load, i.e. the integral value for the load value probability from minus infinity to this point, is α. The transformation is one-time equivalent transformation based on the probability theory, so that the limitation that the original problem is difficult to solve can be well solved.
Step S105: acquiring a network transmission model of the comprehensive energy system and a model of the multi-energy storage equipment;
in the step, a comprehensive energy system network transmission model is constructed based on physical characteristics of various energy flows and by combining operation constraints in an actual system;
specifically, the operation constraint of the thermal power generating unit is as follows:
Figure BDA0003702584070000065
Figure BDA0003702584070000066
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003702584070000067
the active power output by the thermal power generating unit i at the moment t,
Figure BDA0003702584070000068
the reactive power output by the thermal power generating unit i at the moment t is obtained;
Figure BDA0003702584070000069
and
Figure BDA00037025840700000610
respectively representing the upper limit and the lower limit of the i active power output of the thermal power generating unit;
Figure BDA00037025840700000611
and
Figure BDA00037025840700000612
respectively representing the upper limit and the lower limit of the reactive power output of the thermal power generating unit i;
Figure BDA00037025840700000613
and
Figure BDA00037025840700000614
respectively representing the upward/downward climbing capacity of the thermal power generating unit; the operating state of the thermal power generating unit is limited, and the constraints are all based on actual operating characteristics.
The gas turbine operating constraints are:
Figure BDA00037025840700000615
wherein: eta GB Representing the operating efficiency of the gas boiler; p t gas,GB Representing the gas consumption power of the gas boiler at the time t; h t GB The thermal power output by the gas boiler at the time t is represented;
Figure BDA00037025840700000616
and
Figure BDA00037025840700000617
the upper limit and the lower limit of the output force of the gas boiler are set; also similar to thermal power plants, the operation of gas turbines is subject to certain constraints.
And (3) operation constraint of the cogeneration unit:
Figure BDA0003702584070000071
Figure BDA0003702584070000072
wherein: p t e,CHP ，P t gas,CHP And H t CHP Respectively representing the output electric power, the gas consumption power and the thermal power of the CHP unit at the time t; eta CHP And R CHP The operation efficiency and the heat-electricity ratio of the CHP unit are represented; r is l CHP And r u CHP The upward/downward climbing capacity of the CHP unit;
Figure BDA0003702584070000073
and
Figure BDA0003702584070000074
the upper and lower limits of the output active power of the CHP unit are set;
the power system network model constructed based on the operation constraint of the thermal power generating unit, the operation constraint of the gas turbine and the operation constraint of the cogeneration unit is as follows:
Figure BDA0003702584070000075
U j ＝U i -(R ij P ij +X ij Q ij )/U 0
Figure BDA0003702584070000076
wherein: { O j And { I } j The branch set for power injection and the branch set for power outflow at the node j are respectively; p jk ，P ij ，Q jk And Q ij Respectively the incoming/outgoing active power and reactive power; load at node j is represented by S j ＝P j +Q j Represents; u shape i And U j The voltage amplitudes at nodes i and j, respectively; u shape 0 Is a reference voltage; the parameter of the line ij is a resistance R ij And reactance X ij ；P d,t ，Q d,t And H d,t Respectively the system electrical load and the thermal load level at the time t;
the thermodynamic system network model is constructed based on the operation constraint of the thermal power generating unit, the operation constraint of the gas turbine and the operation constraint of the cogeneration unit:
Figure BDA0003702584070000077
Figure BDA0003702584070000078
Figure BDA0003702584070000079
Figure BDA0003702584070000081
Figure BDA0003702584070000082
Figure BDA0003702584070000083
wherein the content of the first and second substances,
Figure BDA0003702584070000084
and
Figure BDA0003702584070000085
respectively representing the heat power absorbed by the heat exchange station positioned at the node n from a heat source or a primary pipe network at the moment t and the heat power transmitted to the primary pipe network or a secondary pipe network through the heat exchange station;
Figure BDA0003702584070000086
and
Figure BDA0003702584070000087
respectively representing the flow of the working medium flowing into and out of the node n at the moment t;
Figure BDA0003702584070000088
and
Figure BDA0003702584070000089
respectively representing the water supply temperature of the heat exchange station positioned at the node n at the time t and the temperature of hot water flowing through the main heat exchange station; in the same way, the method for preparing the composite material,
Figure BDA00037025840700000810
and
Figure BDA00037025840700000811
representing the temperatures at the water supply pipe outlet and the water return pipe inlet at the moment of node n; c represents the specific heat capacity of water in the pipeline; m is l Represents the mass flow of water inside the pipe l; ρ is the density of water; d l And L l The diameter and length of the pipe l, respectively; m l Represents the hot water mass inside the pipe l;
Figure BDA00037025840700000812
is the temperature at time t at the outlet of the conduit l;
Figure BDA00037025840700000813
and
Figure BDA00037025840700000814
are respectively (t-tau) l ) And (t-tau) l +1) the temperature of the working medium injected into the pipeline l at the moment; k l And T ground Respectively the heat conductivity coefficient of the pipeline in unit length and the ground temperature around the pipeline;
Figure BDA00037025840700000815
and
Figure BDA00037025840700000816
the temperatures of working media injected into and flowed out of the node n at the time t respectively;
Figure BDA00037025840700000817
and
Figure BDA00037025840700000818
respectively representing the flow of working media injected into and flowed out of the node n at the time t;
Figure BDA00037025840700000819
and
Figure BDA00037025840700000820
a set of all pipes, respectively ingress and egress node n;
Figure BDA00037025840700000821
and
Figure BDA00037025840700000822
the upper and lower temperature limits of each node in the water supply pipe network;
Figure BDA00037025840700000823
the lower limit of the temperature of each node of the return water pipe network is provided. The hydraulic pipe network model established here considers the characteristic of thermal inertia, that is, in a heat supply pipe network, the change of the heat of a source end cannot be rapidly transmitted to a load end, but a certain time delay exists.
The model of the multi-energy storage device comprises: a model constructed by battery energy storage and a model constructed by heat storage tank energy storage;
the model constructed by the battery energy storage is as follows:
Figure BDA0003702584070000091
in the formula:
Figure BDA0003702584070000092
respectively representing the maximum charge and discharge power of the battery energy storage system i; n is a radical of BESS The number of stored energy of the battery is represented; gamma ray self ,γ ch ,γ dis Respectively representing the self-discharge coefficient, the charging efficiency and the discharging efficiency of the battery; SOC represents the state of charge of the electrical energy storage device; c i N Representing the rated capacity of the battery energy storage system i; Δ t represents the optimization step; SOC i min ，SOC i max Respectively representing the minimum and maximum states of charge of the battery energy storage device,
Figure BDA0003702584070000093
representing a continuity constraint of the energy storage device.
The model for constructing the energy storage of the heat storage tank is as follows:
Figure BDA0003702584070000094
in the formula: e represents the residual energy in the heat storage device;
Figure BDA0003702584070000095
respectively representing the maximum heat accumulation and heat release power of heat energy storage;
Figure BDA0003702584070000096
and
Figure BDA0003702584070000097
respectively representing the heat storage/release power of the heat storage tank k at the time t;
Figure BDA0003702584070000098
respectively representing the minimum and maximum energy of the rest of the heat storage tank k; e k,t The heat storage state of the heat storage tank k at the time t is shown; xi ch And xi dis Respectively, heat accumulation/release efficiencies of the heat accumulation tanks, E k,0 ＝E k,T Representing a continuity constraint satisfied by a scheduled total period regenerator; n is a radical of TSS Indicates the number of all the heat storage tanks.
Step S106: and establishing a constraint condition and an objective function of the optimization scheduling problem of the comprehensive energy system based on the deterministic constraint model, the comprehensive energy system network transmission model and the model of the multi-energy storage equipment, and calculating to obtain the day-ahead optimal scheduling plan.
In the step, an objective function is constructed on the basis of the established deterministic constraint model, the comprehensive energy system network transmission model and the model of the multi-energy storage equipment,
the specific objective function may include:
Figure BDA0003702584070000101
Figure BDA0003702584070000102
Figure BDA0003702584070000103
Figure BDA0003702584070000104
Figure BDA0003702584070000105
Figure BDA0003702584070000106
Figure BDA0003702584070000107
in the formula:
Figure BDA0003702584070000108
represents the operating cost of the stored energy of the battery,
Figure BDA0003702584070000109
represents the operating cost of the heat storage energy,
Figure BDA00037025840700001010
represents the operating cost, σ, of a conventional generator set CHP Representing the operating cost coefficient, Γ, of a cogeneration unit CHP Representing all CHP unit sets, Γ GB Representing all GB sets, σ GB Representing operating cost of gas turbine coefficient thermal generator set
Figure BDA00037025840700001011
Expressed by a quadratic function, a, b and c are respectively a quadratic term coefficient, a linear term coefficient and a constant term of the cost function; sigma CHP ，σ GB Respectively are the operation and maintenance cost coefficients of a cogeneration unit and a gas boiler; c inv And
Figure BDA00037025840700001012
representing energy storage construction and operating costs; c. C BESS ，c TSS Respectively representing the cost coefficients of unit capacity of the battery energy storage and the heat storage device; epsilon BESS ，ε TSS The daily cost distribution coefficient of the battery energy storage and heat storage device is calculated;
Figure BDA00037025840700001013
and
Figure BDA00037025840700001014
respectively representing the installation capacities of the battery energy storage system and the heat storage device; r and n are respectively the depreciation rate and the service life of the equipment; m is BESS And m TSS Respectively representing the operation and maintenance cost coefficients of each energy storage system; n is a radical of BESS And N TSS Respectively representing the installation quantity of the battery energy storage system and the thermal storage device;
Figure BDA0003702584070000111
representing the active power of the diesel generator set I at the moment t;
Figure BDA0003702584070000112
representing the active power output by the CHP unit I at the moment t;
Figure BDA0003702584070000113
the thermal power output by the gas boiler at the moment t;
Figure BDA0003702584070000114
charging/discharging power of the battery energy storage system i at the moment t;
Figure BDA0003702584070000115
and
Figure BDA0003702584070000116
the charging/discharging power of the heat storage device k at the moment t; p t EX,e And P t EX,gas Respectively the outsourcing electric power and the outsourcing gas power of the comprehensive energy system at the moment t; lambda [ alpha ] t e And λ gas Respectively representing the electricity purchase price and the gas purchase price at the time t.
After the objective function is obtained, the objective function can be analyzed by taking economic optimization as a target, so that objective parameters can be obtained, wherein the objective parameters are values of each function and related functions in the objective function, and when the comprehensive energy system is built, the comprehensive energy system can be built according to the objective parameters.
The embodiment of the invention discloses an energy storage planning device for an integrated energy system, and the specific working contents of each unit in the device please refer to the contents of the method embodiment.
The energy storage planning apparatus for an integrated energy system according to an embodiment of the present invention is described below, and the energy storage planning apparatus for an integrated energy system described below and the energy storage planning method for an integrated energy system described above may be referred to correspondingly.
Referring to fig. 2, the present application discloses an integrated energy system energy storage planning apparatus, which may include:
the data acquisition unit A is used for acquiring historical thermoelectric load data of the comprehensive energy system;
the distribution probability function calculation unit B is used for processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
the constraint model building unit C is used for building an opportunity constraint model for representing the load fluctuation and the randomness of the power grid; performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
the model acquisition unit D is used for acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
and the planning problem establishing unit is used for establishing an objective function of the comprehensive energy system based on the deterministic constraint model, the comprehensive energy system network transmission model and the model of the multi-energy storage equipment, and establishing a day-ahead scheduling optimization problem model of the comprehensive energy system by combining the established constraint conditions.
The specific contents of the above units are described in the embodiment of the method, and are not described in detail.
An integrated energy system energy storage planning apparatus, comprising:
a memory and a processor; the memory stores a program adapted for execution by the processor, the program for:
acquiring historical thermoelectric load data of the comprehensive energy system;
processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid;
performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
and establishing a constraint condition and an objective function of the optimization scheduling problem of the comprehensive energy system based on the deterministic constraint model, the network transmission model of the comprehensive energy system and the model of the multi-energy storage equipment.
Fig. 3 is a hardware structure diagram of a server according to an embodiment of the present invention, which is shown in fig. 3 and may include: at least one processor 100, at least one communication interface 200, at least one memory 300, and at least one communication bus 400;
in the embodiment of the present invention, the number of the processor 100, the communication interface 200, the memory 300, and the communication bus 400 is at least one, and the processor 100, the communication interface 200, and the memory 300 complete the communication with each other through the communication bus 400; it is clear that the communication connections shown by the processor 100, the communication interface 200, the memory 300 and the communication bus 400 shown in fig. 3 are merely optional;
optionally, the communication interface 200 may be an interface of a communication module, such as an interface of a GSM module;
the processor 100 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present invention.
Memory 300 may comprise high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
Wherein, the processor 100 is specifically configured to:
acquiring historical thermoelectric load data of the comprehensive energy system;
processing the historical thermoelectric load data by adopting a DOR decomposition method to obtain a value probability distribution function corresponding to each load;
constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid;
performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
based on the deterministic constraint model, the integrated energy system network transmission model and the model of the multi-energy storage device.
For convenience of description, the above system is described with the functions divided into various modules, which are described separately. Of course, the functionality of the various modules may be implemented in the same software and/or hardware in the practice of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. An energy storage planning method for an integrated energy system is characterized by comprising the following steps:
acquiring historical thermoelectric load data of the comprehensive energy system;
processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid;
performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
acquiring a network transmission model of the comprehensive energy system and a model of the multi-energy storage equipment;
and establishing a constraint condition and an objective function of the optimization scheduling problem of the comprehensive energy system based on the deterministic constraint model, the network transmission model of the comprehensive energy system and the model of the multi-energy storage equipment.
2. The energy storage planning method for the integrated energy system according to claim 1, wherein the value-taking probability distribution function includes:
Figure FDA0003702584060000011
wherein the content of the first and second substances,
Figure FDA0003702584060000012
is a value probability distribution function at the time of t + k, alpha 1 、α 2 …α r Is a number in [0,1]A quantile of between, said
Figure FDA0003702584060000013
Is quantile alpha i And processing the corresponding power load predicted value.
3. The integrated energy system energy storage planning method of claim 1, wherein the opportunity constraint model is:
Figure FDA0003702584060000014
Figure FDA0003702584060000015
is the power output, R, of the thermal power generating unit at the node i i，t For the spinning reserve that the ith node of the distribution network can provide,
Figure FDA0003702584060000016
the representation CHP represents the active power of the cogeneration unit,
Figure FDA0003702584060000017
represents the charging power of the stored energy of the battery,
Figure FDA0003702584060000018
the discharge power of the stored energy of the battery is represented,
Figure FDA0003702584060000019
the active load of a node i of the power distribution network; the confidence level α is the probability value that the constraint holds, said α ∈ (α) 1 、α 2 …α r )。
4. The integrated energy system energy storage planning method of claim 1, wherein the deterministic constraint model is:
Figure FDA0003702584060000021
the above-mentioned
Figure FDA0003702584060000022
Is alpha i When it is equal to the confidence level
Figure FDA0003702584060000023
The value of (c).
5. The integrated energy system energy storage planning method of claim 1, wherein the integrated energy system network transmission model comprises:
and (3) operation constraint of the thermal power generating unit:
Figure FDA0003702584060000024
Figure FDA0003702584060000025
wherein the content of the first and second substances,
Figure FDA0003702584060000026
the active power output by the thermal power generating unit i at the moment t,
Figure FDA0003702584060000027
the reactive power output by the thermal power generating unit i at the moment t is obtained;
Figure FDA0003702584060000028
and
Figure FDA0003702584060000029
respectively representing the upper limit and the lower limit of the i active power output of the thermal power generating unit;
Figure FDA00037025840600000210
and
Figure FDA00037025840600000211
respectively representing the upper limit and the lower limit of the reactive power output of the thermal power generating unit i; r is l CGU And r u CGU Respectively representing the upward/downward climbing capacity of the thermal power generating unit, wherein the delta t represents the time difference between the t +1 moment and the t moment;
gas turbine operating constraints:
Figure FDA00037025840600000212
Figure FDA00037025840600000213
wherein: eta GB Representing the operating efficiency of the gas boiler; p is t gas，GB Representing the gas consumption power of the gas boiler at the time t;
Figure FDA00037025840600000214
the thermal power output by the gas boiler at the time t is represented;
Figure FDA00037025840600000215
and
Figure FDA00037025840600000216
is the upper and lower limits of the output of the gas boiler, r l GB And r u GB Respectively representing the upward/downward climbing capacity of the gas turbine;
and (3) operation constraint of the cogeneration unit: p t e，CHP ＝η CHP P t gas，CHP ，
Figure FDA00037025840600000217
Figure FDA00037025840600000218
Wherein: p t e，CHP ，P t gas，CHP And
Figure FDA00037025840600000219
respectively representing the output electric power, the gas consumption power and the thermal power of the CHP unit at the time t; eta CHP And R CHP The operation efficiency and the heat-electricity ratio of the CHP unit are represented; r is l CHP And r u CHP The upward/downward climbing capacity of the CHP unit;
Figure FDA00037025840600000220
and
Figure FDA00037025840600000221
is the upper and lower limits of the output active power r of the CHP unit l CHP And r u CHP The climbing capacity of the cogeneration unit upwards/downwards is respectively;
electric power system network model:
Figure FDA0003702584060000031
U j ＝U i -(R ij P ij +X ij Q ij )/U 0 ，
Figure FDA0003702584060000032
wherein: { O j And { I } j The branch set for power injection and the branch set for power outflow at the node j are respectively; p is jk ，P ij ，Q jk And Q ij Respectively the incoming/outgoing active power and reactive power; load at node j is represented by S j ＝P j +Q j Represents; u shape i And U j The voltage amplitudes at nodes i and j, respectively; u shape 0 Is a reference voltage; the parameter of the line ij is the resistance R ij And reactance X ij ；P d，t ，Q d，t Respectively the system electrical load level at the time t;
thermodynamic system network model:
Figure FDA0003702584060000033
c represents the specific heat capacity of the water quality at the heat exchange station,
Figure FDA0003702584060000034
Figure FDA0003702584060000035
Figure FDA0003702584060000036
Figure FDA0003702584060000037
wherein C represents the specific heat capacity of water quality at the heat exchange station,
Figure FDA0003702584060000038
and
Figure FDA0003702584060000039
respectively representing the heat power absorbed by the heat exchange station positioned at the node n from a heat source or a primary pipe network at the moment t and the heat power transmitted to the primary pipe network or a secondary pipe network through the heat exchange station;
Figure FDA00037025840600000310
and
Figure FDA00037025840600000311
respectively representing the flow of the working medium flowing into and out of the node n at the moment t;
Figure FDA00037025840600000312
and
Figure FDA00037025840600000313
respectively representing the water supply temperature of the heat exchange station positioned at the node n at the time t and the temperature of hot water flowing through the main heat exchange station;
Figure FDA00037025840600000314
and
Figure FDA00037025840600000315
representing the temperatures at the water supply pipe outlet and the water return pipe inlet at the moment of node n; m is l Representing the flow of the working medium in the pipeline l; ρ is the density of water; d l And L l The diameter and length of the pipe l, respectively; m l Represents the hot water mass inside the pipe l;
Figure FDA00037025840600000316
is the temperature at time t at the outlet of the conduit l;
Figure FDA0003702584060000041
and
Figure FDA0003702584060000042
are respectively (t-tau) l ) And (t- τ) l +1) the temperature of the working medium injected into the pipeline l at the moment; k l And T ground Respectively the heat conductivity coefficient of the pipeline in unit length and the ground temperature around the pipeline;
Figure FDA0003702584060000043
and
Figure FDA0003702584060000044
the temperatures of working media injected into and flowed out of the node n at the time t respectively;
Figure FDA0003702584060000045
and
Figure FDA0003702584060000046
respectively representing the flow of working media injected into and flowed out of the node n at the time t;
Figure FDA0003702584060000047
and
Figure FDA0003702584060000048
a set of all pipes, respectively ingress and egress node n;
Figure FDA0003702584060000049
and
Figure FDA00037025840600000410
the upper and lower temperature limits of each node in the water supply pipe network;
Figure FDA00037025840600000411
the lower limit of the temperature of each node of the return water pipe network is provided.
6. The integrated energy system energy storage planning method of claim 1, wherein the model of the multi-energy storage device comprises:
model of battery energy storage construction:
Figure FDA00037025840600000412
in the formula:
Figure FDA00037025840600000413
respectively represent the maximum charge and discharge power of the battery energy storage system i,
Figure FDA00037025840600000414
representing the charging power of the battery energy storage system i at time t,
Figure FDA00037025840600000415
representing the discharge power of the battery energy storage system i at time t,
Figure FDA00037025840600000416
i indicates knowledge as belonging to N BESS ，N BESS Indicates the number of stored energy of all batteries, T w The representation represents the total optimization time; gamma ray self ，γ ch ，γ dis Respectively representing the self-discharge coefficient, the charging efficiency and the discharging efficiency of the battery; SOC represents the state of charge of the electrical energy storage device;
Figure FDA00037025840600000417
representing the rated capacity of the battery energy storage system i; Δ t represents an optimization step;
the model of heat storage pipe energy storage construction:
Figure FDA0003702584060000051
in the formula: e represents the residual energy in the heat storage device;
Figure FDA0003702584060000052
respectively representing the maximum heat accumulation and heat release power of heat energy storage;
Figure FDA0003702584060000053
and
Figure FDA0003702584060000054
respectively representing the heat storage/release power of the heat storage tank k at the time t;
Figure FDA0003702584060000055
respectively representing the minimum and maximum energy of the rest of the heat storage tank k; e k，t The heat storage state of the heat storage tank k at the time t is shown; xi ch And xi dis Respectively, heat accumulation/release efficiencies of the heat accumulation tanks, E k，0 ＝E k，T Representing a continuity constraint satisfied by a scheduled total period regenerator; n is a radical of hydrogen TSS Indicates the number of all the heat storage tanks.
7. The integrated energy system energy storage planning method of claim 1, wherein the objective function comprises:
Figure FDA0003702584060000056
Figure FDA0003702584060000057
Figure FDA0003702584060000058
Figure FDA0003702584060000059
Figure FDA00037025840600000510
Figure FDA00037025840600000511
Figure FDA00037025840600000512
in the formula: the operation cost of the thermal generator set is expressed by a quadratic function, and a, b and c are a quadratic term coefficient, a linear term coefficient and a constant term of the quadratic function for the operation cost of the thermal generator set respectively; sigma CHP ，σ GB The operation and maintenance cost coefficients of a cogeneration unit and a gas boiler are respectively set; c inv And
Figure FDA0003702584060000061
representing energy storage construction and operating costs; c. C BESS ，c TSS Respectively representing the cost coefficients of unit capacity of the battery energy storage and the heat storage device; epsilon BESS ，ε TSS The cost daily distribution factor;
Figure FDA0003702584060000062
and
Figure FDA0003702584060000063
respectively representing the installation capacities of the battery energy storage system and the heat storage device; r and n are respectively the depreciation rate and the service life of the equipment; m is BESS And m TSS Respectively representing the operation and maintenance cost coefficients of each energy storage system; n is a radical of BESS And N TSS Respectively representing the installation quantity of the battery energy storage system and the thermal storage device;
Figure FDA0003702584060000064
representing the active power of the diesel generator set I at the moment t;
Figure FDA0003702584060000065
representing the active power output by the CHP unit I at the moment t;
Figure FDA0003702584060000066
the thermal power output by the gas boiler at the moment t;
Figure FDA0003702584060000067
charging/discharging power of the battery energy storage system i at the moment t;
Figure FDA0003702584060000068
and
Figure FDA0003702584060000069
the charging/discharging power of the heat storage device k at the moment t; p is t EX，e And P t EX，gas Respectively the outsourcing electric power and the outsourcing gas power of the comprehensive energy system at the moment t; lambda [ alpha ] t e And λ gas Respectively representing the electricity purchase price and the gas purchase price at the time t.
8. The integrated energy system energy storage planning method of claim 1, further comprising:
and solving the objective function based on a preset principle to obtain an optimal scheduling strategy.
9. An integrated energy system energy storage planning apparatus, comprising:
the data acquisition unit is used for acquiring historical thermoelectric load data of the comprehensive energy system;
the distribution probability function calculation unit is used for processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
the constraint model building unit is used for building an opportunity constraint model for representing the load fluctuation and the randomness of the power grid; performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
the model acquisition unit is used for acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
and the planning problem establishing unit is used for establishing an objective function of the comprehensive energy system based on the deterministic constraint model, the comprehensive energy system network transmission model and the model of the multi-energy storage equipment, and establishing a day-ahead scheduling optimization problem model of the comprehensive energy system by combining the established constraint conditions.
10. An integrated energy system energy storage planning apparatus, comprising:
a memory and a processor; the memory stores a program adapted for execution by the processor, the program for:
acquiring historical thermoelectric load data of the comprehensive energy system;
processing the historical thermoelectric load data by adopting a DQR decomposition method to obtain a value probability distribution function corresponding to each load;
constructing an opportunity constraint model for representing the load fluctuation and the randomness of the power grid;
performing deterministic equivalence class conversion on the opportunity model based on the value probability distribution function to obtain a deterministic constraint model;
acquiring a comprehensive energy system network transmission model and a model of the multi-energy storage equipment;
and establishing a constraint condition and an objective function of the optimization scheduling problem of the comprehensive energy system based on the deterministic constraint model, the network transmission model of the comprehensive energy system and the model of the multi-energy storage equipment.
CN202210696068.2A 2022-06-20 2022-06-20 Energy storage planning method, device and equipment for comprehensive energy system Pending CN115081867A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116820057A (en) * 2023-08-30 2023-09-29 四川远方云天食品科技有限公司 Hotpot condiment production monitoring method and system based on Internet of things

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116820057A (en) * 2023-08-30 2023-09-29 四川远方云天食品科技有限公司 Hotpot condiment production monitoring method and system based on Internet of things
CN116820057B (en) * 2023-08-30 2023-12-01 四川远方云天食品科技有限公司 Hotpot condiment production monitoring method and system based on Internet of things

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