CN110929938B - Energy system optimization method and device, storage medium and electronic device - Google Patents
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Abstract
The invention provides an energy system optimization method and device, a storage medium and an electronic device, wherein the method comprises the following steps: determining energy units constituting an energy system; establishing a data model of the energy system, wherein the data model comprises constraint conditions for setting the energy units; constructing an objective function of the energy system optimization based on the data model; and utilizing the objective function to perform application adjustment of energy data on the energy system. The invention solves the technical problem that energy modeling can only be carried out on single equipment in an energy system in the prior art.
Description
Technical Field
The invention relates to the field of energy information, in particular to an energy system optimization method and device, a storage medium and an electronic device.
Background
In the prior art, in recent years, China makes great efforts in promoting the technology of energy conservation and emission reduction and the efficient utilization of clean energy. The prior single coupling mode is supplemented by diversified technical means, and the development and utilization of multivariate interactive comprehensive energy becomes an important measure for reducing fossil energy consumption and controlling emission in China.
Coupling complementation and cascade utilization of various energy forms are effective ways for reducing impact of distributed energy fluctuation on a power grid, promoting development and application of renewable energy, relieving shortage of fossil energy and reducing environmental pollution. The universal energy station has become one of important research directions of the comprehensive energy system by virtue of the advantages of high energy efficiency, good environmental benefit and the like.
The existing integrated energy modeling is basically that each device is independently modeled, and then respective corresponding constraint conditions are generated. The thinking of modeling is purely at the level of mathematical modeling, and when new equipment is added, a great deal of effort is also invested to build a model of the new equipment.
In view of the above problems in the prior art, no effective solution has been found.
Disclosure of Invention
The embodiment of the invention provides an energy system optimization method and device, a storage medium and an electronic device.
According to an embodiment of the present invention, there is provided an optimization method of an energy system, including:
determining energy units forming an energy system;
establishing a data model of the energy system, wherein the data model comprises constraint conditions for setting the energy units;
constructing an optimized objective function of the energy system based on the data model;
and utilizing the objective function to perform application adjustment of energy data on the energy system.
Optionally, a start-stop constraint condition of the energy unit is set, where the start-stop constraint condition includes: an on-off state, an on-state, an off-state, and an initial on-off state;
setting input and output constraint conditions of the energy units, wherein the input and output constraint conditions comprise input and output relations of one-dimensional univariates;
setting a constraint condition of input and output energy storage of the energy unit, wherein the constraint condition of the input and output energy storage comprises a differential equation;
setting a load constraint for the energy unit, wherein the load constraint comprises a slack variable;
setting network constraint conditions of the energy units, wherein the pipeline channel flow attributes of the energy units comprise a convergent structure and a divergent structure, and the network constraint conditions comprise that the energy received by the energy units is the integration of energy input;
and setting a pipeline constraint condition of the energy unit, wherein the pipeline constraint condition comprises a pipe loss rate.
Optionally, setting the network constraint condition of the energy unit comprises:
determining a channel flow attribute of the energy unit;
when the pipeline slot flow attribute of the energy unit is a convergent structure, setting the network constraint condition of the energy unit as:A in ＝A 1 +A 2 +…+A n (ii) a When the pipeline slot flow attribute of the energy unit is a divergent structure, setting the network constraint condition of the energy unit as: b is out ＝B 1 +B 2 +…+B n ；
Wherein A is in For the purpose of energy impoundment, A i (i is 1, …, n) is the energy amount of the energy input pipeline i, B out To distribute energy, B i (i-1, 2, …, n) is the energy amount of the energy output pipeline i.
Optionally, determining the energy units constituting the energy system comprises:
the energy system is determined as a total energy unit, and the subsystems constituting the energy system are determined as sub-energy units of the total energy unit.
Optionally, constructing the energy system optimized objective function based on the data model comprises:
the following objective function min F was constructed:
wherein the content of the first and second substances,
representing a first energy price input during a time period t,
representing the second energy price entered during time period t,
representing a first energy price output during a time period t,
representing the second energy price output during time period t,
is shown inThe first amount of energy output for the time period t,
representing the second amount of energy output during a period t, D representing the type of energy device, N d Represents the number of said energy source devices,
a penalty price representing that the first energy amount is less than a first threshold,
a penalty price representing that the first energy amount is greater than a second threshold,
a penalty price representing that the second energy amount is less than a third threshold,
a penalty price, P, representing that said second energy amount is greater than a fourth threshold pv pv_eE_out[t]Represents a penalty, P, of not using the third energy source at time t wg wg_eE_out[t]Indicating a penalty for not using the fourth energy source at time t.
According to another embodiment of the present invention, there is provided an optimization apparatus of an energy system, including:
the determining module is used for determining energy units forming the energy system;
the establishing module is used for establishing a data model of the energy system, wherein the data model comprises a constraint condition for setting the energy unit;
the construction module is used for constructing an objective function of the energy system optimization based on the data model;
and the adjusting module is used for carrying out application adjustment on the energy data of the energy system by utilizing the objective function.
Optionally, the setting module comprises at least one of:
the first setting unit is used for setting the start-stop constraint condition of the energy unit, wherein the start-stop constraint condition comprises: an on-off state, an on-state, an off-state, and an initial on-off state;
the second setting unit is used for setting input and output constraint conditions of the energy unit, wherein the input and output constraint conditions comprise a one-dimensional univariate input and output relationship;
the third setting unit is used for setting a constraint condition of input and output energy storage of the energy unit, wherein the constraint condition of the input and output energy storage comprises a differential equation;
a fourth setting unit for setting a load constraint of the energy unit, wherein the load constraint comprises a slack variable;
the fifth setting unit is used for setting a network constraint condition of the energy unit, wherein the pipeline slot flow attribute of the energy unit comprises a convergent structure and a divergent structure, and the network constraint condition comprises that the energy received by the energy unit is the integration of energy input;
and the sixth setting unit is used for setting a pipeline constraint condition of the energy unit, wherein the pipeline constraint condition comprises a pipe loss rate.
Optionally, the fifth setting unit includes:
a determining subunit, configured to determine a channel flow property of the energy unit;
the setting subunit is used for setting the network constraint conditions of the energy unit as follows when the pipeline slot flow attribute of the energy unit is a convergent structure: a. the in ＝A 1 +A 2 +…+A n (ii) a When the pipeline slot flow attribute of the energy unit is a divergent structure, setting the network constraint condition of the energy unit as: b is out ＝B 1 +B 2 +…+B n ；
Wherein A is in For the purpose of energy impoundment, A i (i is 1, …, n) is the energy amount of the energy input pipeline i, B out To distribute the energy, B i (i-1, 2, …, n) is the energy amount of the energy output pipeline i.
Optionally, the determining module includes:
and the determining unit is used for determining the energy system as a total energy unit and determining the subsystems forming the energy system as the sub energy units of the total energy unit.
Optionally, the building module comprises:
a construction unit configured to construct the following objective function min F:
wherein the content of the first and second substances,
representing a first energy price entered during a time period t,
representing the second energy price entered during time period t,
representing a first energy price output during a time period t,
representing the second energy price output during time period t,
representing a first amount of energy output during a time period t,
representing the second amount of energy output during a period t, D representing the type of energy device, N d Represents the number of said energy source devices,
a penalty price representing that the first energy amount is less than a first threshold,
a penalty price representing that the first energy amount is greater than a second threshold,
a penalty price representing that the second energy amount is less than a third threshold,
a penalty price, P, representing that said second energy amount is greater than a fourth threshold pv pv_eE_out[t]Represents a penalty, P, of not using the third energy source at time t wg wg_eE_out[t]Indicating a penalty for not using the fourth energy source at time t.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the steps of any of the method embodiments described above.
According to the invention, the energy units forming the energy system are determined, then the data model of the energy system is established, the constraint conditions of the energy units are set, the target function for optimizing the energy system is established based on the data model, finally, the application adjustment of the energy data is carried out on the energy system by utilizing the target function, and the equipment and the network in the energy system are abstracted into the energy units, so that the modeling process is simplified, the technical problem that the energy modeling can only be carried out on single equipment in the energy system in the prior art is solved, and the scheduling optimization of large-scale comprehensive energy can be applied.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of an energy system optimizing computer according to an embodiment of the present invention;
fig. 2 is a flowchart of an optimization method of an energy system according to an embodiment of the present invention;
fig. 3 is a schematic view of an energy unit of an embodiment of the invention;
FIG. 4 is a schematic diagram of an integrated energy station according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an SOS system of an embodiment of the present invention;
FIG. 6 is a schematic illustration of a convergent configuration and a divergent configuration of an embodiment of the invention;
fig. 7 is a block diagram illustrating an apparatus for optimizing an energy system according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
The method provided by the first embodiment of the present application may be executed in a server, a network terminal, a computer, or a similar computing device. Taking an example of the method running on a computer, fig. 1 is a block diagram of a hardware structure of an optimization computer of an energy system according to an embodiment of the present invention. As shown in fig. 1, computer 10 may include one or more (only one shown in fig. 1) processors 102 (processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data, and optionally may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the configuration shown in FIG. 1 is merely illustrative and is not intended to limit the configuration of the computer described above. For example, computer 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the method for optimizing an energy system in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the method described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communications provider of the computer 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, an optimization method of an energy system is provided, and fig. 2 is a flowchart of the optimization method of an energy system according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, determining energy units forming an energy system, wherein the energy units comprise: energy devices, energy networks;
the Energy Unit (EU) of this embodiment is a set of devices and/or networks with energy conversion in an energy system, and is a basic unit for constructing an energy model function, and all the devices, the universal energy, the regional energy network, and even the cross-regional energy network can be represented by energy units.
Step S204, establishing a data model of the energy system, wherein the data model comprises constraint conditions for setting the energy units;
the constraint condition of this embodiment is a physical relationship, such as a time relationship, an energy conservation relationship, etc., in which the energy unit conforms to a natural rule.
Step S206, constructing an optimized objective function of the energy system based on the data model;
and S208, utilizing the objective function to perform application adjustment of energy data on the energy system.
Through the steps, the energy units forming the energy system are determined, then the data model of the energy system is established, the constraint conditions of the energy units are set, the target function for optimizing the energy system is established based on the data model, finally, the application adjustment of the energy data is carried out on the energy system by utilizing the target function, the equipment and the network in the energy system are abstracted into the energy units, the modeling process is simplified, the technical problem that in the prior art, the energy modeling can only be carried out on single equipment in the energy system is solved, and the scheduling optimization of large-scale comprehensive energy can be applied.
Optionally, the main body of the execution of the above steps may be a data processing device, a server, a terminal, and the like, and may specifically be a processor, an algorithm module, and the like, but is not limited thereto.
The application scenario of this embodiment can be applied to energy prediction, flow prediction, energy regulation and control, and in artificial intelligence's scenarios such as energy configuration, the energy system can be a comprehensive general energy system, include: electric energy, heat energy, photovoltaic energy and other energy sources.
The problem to be solved by the integrated energy modeling of the present embodiment is how to handle the flow and conversion of energy, and the present embodiment refers to each energy conversion unit as an energy unit (energy unit). Fig. 3 is a schematic diagram of an energy unit according to an embodiment of the present invention, and an energy unit object includes input and output of the object, a relationship between the input and output, and parameters of characteristics of the object such as a state variable, start-up and shut-down. The energy unit is a universal model interpretation, and the embodiment of the model is used for performing modeled interpretation on any thing in the physical world, namely small equipment, universal energy station, large area network and even cross-area network. The object that the energy unit thus defined can describe can cover all physical equipment, facilities and systems, and is the basis for building comprehensive energy.
The present embodiment refers to a specific energy unit as an object, and the input and output of the object include input and output of energy and input and output of information. The input and output of information mainly comprise instructions of internal factors such as state variables and state parameters of the control object, and the information directly determines the characteristics of the object and the interaction mode with the outside. While the input and output of energy are extrinsic to the object, they can be of a wide variety. This embodiment may represent the input and output of the energy source by X and Y, respectively, and represent the dimensions of the input and output of the energy source by N and M distributions. Theoretically, any object has input and output, but according to the research category, the energy unit is divided into three categories, which are: input-output type, input type, output type. The energy unit does not necessarily have both input and output, and the energy consumption side (user side for short) is a typical input type energy unit. The input and output of the energy unit may also be the same kind of energy, such as a battery or the like. However, different input and output combinations define different types of energy units, which is also the main basis for distinguishing objects from other energy units.
For equipment such as a boiler, a Combined Cooling and Power (CCHP) system, an energy storage device, a generator, a waste heat recovery device, and the like, the present embodiment summarizes and summarizes some dozens of features mainly including input, output, energy storage, input and output relationships, a difference relationship between the input and output energy storage, CO2 discharge, load, a climbing rate, and the like.
For a decomposable energy unit, in other words, the energy unit is formed by connecting a plurality of small energy units through a certain topological structure, and the embodiment is called as an energy source station. The energy station can be a universal energy station, and can also be a regional energy network or even a cross-regional energy network. Therefore, two important features for the new addition of energy stations are: subsystems and topologies. In addition, the energy station is regarded as an independent whole in the embodiment, and the interaction is unified to the outside. The purpose of this is that after the amount of interaction with the outside world is determined, an independent operating system is formed inside each energy station or subsystem, and the subsystems inside the energy station and the interaction information outside the subsystems are all inside the energy station and are therefore uniquely determined by the energy station.
Fig. 4 is a schematic diagram of an integrated power plant in accordance with an embodiment of the present invention, wherein the solid lines represent natural gas, the dashed lines represent electricity, and the dashed dotted lines represent steam. The integrated energy station has two sub-energy stations MES _1 and MES _2, MES _1 providing power and steam to MES _2, but as can be seen from the internal topology of MES _2, it uses only the power provided by MES _1 and not its steam. MES _1 contains a Source Source, two User users, 2 CCHP, and 3 boiler GSB. Source provides gas to the CCHP and boiler GSB, which supplies the electricity or steam generated to the User. Notably, the second CCHP may deliver the excess electricity to MES _2 via MES _1, and the first GSB may deliver the excess steam electricity to MES _2 via MES _ 1. MES _2 has contained 2 CCHPs, 1 photovoltaic station, 1 energy memory, a Source and a User. Wherein, the photovoltaic can store electricity through the energy storage device by the surplus energy production besides transmitting electricity to the user. Although the first CCHP, the photovoltaic station and the energy storage are connected to the external interface of MES _2, the energy is not transmitted since this interface is not used.
In this embodiment, determining the energy units that make up the energy system includes: the energy system is determined as a total energy unit, and the subsystems constituting the energy system are determined as sub-energy units of the total energy unit. For example, the energy system is determined as a primary energy unit, each subsystem constituting the energy system is determined as a secondary energy unit, and each next-stage subsystem constituting the subsystem is determined as a tertiary energy unit up to the last-stage energy unit.
The energy system of the present embodiment is a system of systems (SoS), and based on the above concept, it is considered that the whole integrated energy system can be described as one energy unit (energy station, and the following embodiments are collectively referred to as system), and the system is composed of many subsystems, and the subsystems are composed of subsystems of the next stage, and so on.
Fig. 5 is a schematic structural diagram of an SOS system according to an embodiment of the present invention, such as the integrated energy system in fig. 5 comprising different subsystems sys1, sys5 and sys6, wherein the subsystems are connected to each other. The subsystem can be composed of a plurality of complete energy system connections, and can also be composed of single or a plurality of energy device connections. Wherein sys1 is a subsystem of an integrated energy system, which comprises a plurality of subsystems sys2, sys3 and sys 4. In secondary subsystems, such as sys2 and sys3, they are connected by basic energy devices.
In this embodiment, the constraint conditions for setting the energy unit may be, but are not limited to: setting a start-stop constraint condition of the energy unit, wherein the start-stop constraint condition comprises: an on-off state, an on-state, an off-state, and an initial on-off state; setting input and output constraint conditions of the energy unit, wherein the input and output constraint conditions comprise a one-dimensional univariate input and output relationship; setting a constraint condition of input and output energy storage of the energy unit, wherein the constraint condition of the input and output energy storage comprises a differential equation; setting a load constraint for the energy unit, wherein the load constraint comprises a slack variable; setting a network constraint condition of the energy unit, wherein the pipeline channel flow attribute of the energy unit comprises a convergent structure and a divergent structure, and the network constraint condition comprises that the energy received by the energy unit is the integration of energy input; and setting a pipeline constraint condition of the energy unit, wherein the pipeline constraint condition comprises a pipe loss rate. Which are explained and illustrated in detail below, respectively:
start-stop restraint:
the energy unit has a problem of starting and stopping, and the four variables 'eu _ flag', 'eu _ stupflag', 'eu _ sDnflag' and 'eu _ initState' of the embodiment are 0-1 to describe the on-off state, the starting state, the closing state and the initial on-off state of the energy unit 'eu'. Assuming that within't num' periods, then the limit condition for starting and stopping the energy unit is,
eu_flag[0]-eu_initState-eu_sUpflag[0]+eu_sDnflag[0]＝0
eu_flag[i+1]-eu_flag[i]-eu_sUpflag[i+1]+eu_sDnflag[i+1]＝0
eu_sDnflag[i]+eu_sUpflag[i]<＝1,(i＝0,1,…,t_num-2)
eu_sDnflag[t_num-1]+eu_sUpflag[t_num-1]<＝1
input and output constraint conditions:
the input-output constraints of the energy unit are complex, especially for this relationship at the energy station level. The high-dimensional input and output relationship can be converted into a one-dimensional univariate relationship in a dimension reduction mode, and then the constraint is linearized by applying an SOS method. It is also possible to achieve linearization by triangulating regions of high-dimensional functions and similar SOS.
Constraint conditions of input and output energy storage:
unlike the direct functional relationship of input and output, the constraint of energy cells like stored energy is a relationship of differential equations. For example, assuming that the energy storage efficiency of the stored energy is λ, it means that the rate of change of the stored energy is λ, i.e.
Wherein S represents the amount of stored electricity at time t. Assuming that the charge/discharge efficiencies of the electrical storage device are 'stg _ charge rate' and 'stg _ discharge rate', respectively, the present embodiment discretizes them,
S_init+stg_chargeRate*stg_eE_in[0]-stg_eE_out[0]/stg_dischargeRate＝S[0]
S[i]+stg_chargeRate*stg_eE_in[i+1]-stg_eE_out[i+1]/stg_dischargeRate＝S[i+1](i＝0,1,…,t_num-2)
wherein 'stg _ eE _ in' represents the amount of charge of the stored energy and 'stg _ eE _ out' is the amount of discharge of the stored energy.
Load-related constraints:
for an energy unit, if the load is not empty, it represents a user. For the user load, the embodiment may also consider the slack variable, that is, the embodiment does not necessarily satisfy the user's requirement completely. However, for unsatisfied quantities, a penalty value needs to be set, which is reflected in the optimization goal. The constraints for matching the supply and demand of the users with the slack variable are as follows:
elec_loads[i]+u1_slack1_elec[i]-u1_slack2_elec[i]-u1_eE_in[i]+u1_eE_out[i]＝0
wherein u1_ slack1_ elec represents a penalty amount for insufficient supply, u1_ slack2_ elec represents a penalty amount for excessive supply, u1_ eE _ in represents the amount of electricity purchased by the user, u1_ eE _ out represents the amount of electricity sold by the user, and elec _ loads represents the electrical load. And i denotes the ith time instant.
Network constraint conditions:
the comprehensive energy system is an energy network formed by connecting various energy units through various parallel structures, serial structures and the like. In order to make the network structure unit simpler and have universal applicability, the present embodiment uses the convergent structure and the divergent structure to jointly depict the whole integrated energy network. The advantage of this definition is that the original energy supply and demand interaction relationship between the energy output device and the energy input device is converted into the relationship between the energy output device and the pipeline and between the pipeline and the energy input device. The network thus defined combines the characteristics of the pipeline slot flow analysis, so that the program of the embodiment can also be applied to the slot flow analysis, and the slot flow analysis is the basis of the cross-region modeling of the embodiment. More importantly, the network can be suitable for any topological structure, and the network structure is not required to be analyzed intentionally whether the network structure is a ring or a tree, so that the network is convenient to use.
Fig. 6 is a schematic diagram of a convergent and divergent architecture of an embodiment of the invention, showing how the convergent and divergent architectures generate constraints, and showing how the physical world architecture is broken down into convergent and divergent architectures. For a convergent configuration (as shown in the upper right diagram of fig. 6), an energy or energy source flows into an energy unit, and the model constraint generated by the convergent configuration is the amount of energy or energy received by the energy unit A in Is the quantity A on all sides i (i is 1, …, n), i.e.
A in ＝A 1 +A 2 +…+A n
For a divergent configuration (as shown in the lower right diagram of FIG. 6), the energy cell will have a quantity B out Is distributed to n different energy units, provided that the energy unit is distributed to B in connection with a pipeline of n different energy units i (i is 1,2, …, n), then the present embodiment has
For a divergent configuration (as shown in the lower right of FIG. 6), the energy cell will have a magnitude B out Is distributed to n different energy units, provided that the energy unit is distributed to B in connection with a pipeline of n different energy units i (i is 1,2, …, n), then the present embodiment has
B out ＝B 1 +B 2 +…+B n
The following example illustrates how the network of the entire integrated energy source can be characterized by convergent and divergent configurations. In addition to the separate consideration of equipment (equipment modeling) and piping (piping modeling), one relationship for the overall integrated energy source is the relationship between equipment and piping. If the present embodiment has a CHP facility, the pipes associated with the CHP include a pipe for inputting water and natural gas and a pipe for outputting electricity and steam. Assuming that the source of water is only one, the source of natural gas is two, the user of steam is only one household, but the user of electricity is two (as shown in the left diagram of fig. 6), the present embodiment can split the structure into four according to the types of the inlet and outlet energy sources: water to CHP relationship (configuration 1), natural gas to CHP relationship (configuration 2), CHP to electricity relationship (configuration 3) and CHP to steam relationship (configuration).
And (3) pipe restraint:
this embodiment may view the pipe as a device with the same input as the input. There is a relationship between the input and output of the pipeline, taking the natural gas pipeline of the chp being delivered from the source as an example,
s_gE_out_in_chp_gE_in＝f(s_gE_out_out_chp_gE_in)
this relationship can be implemented by data-driven and SOS means to couple the input and output, or can be simplified by wear-rate. Assuming that the pipe loss rate per unit length is eta and the total length of the pipeline is L, then,
s_gE_out_out_chp_gE_in＝s_gE_out_in_chp_gE_in*η*L
in this embodiment, the constructing the target energy function according to the constraint condition based on the preset optimal condition may be, but is not limited to: constructing a target energy function according to the constraint condition by taking the maximum economic benefit as an optimal condition; constructing a target energy function according to the constraint condition by taking the lowest pollution emission as an optimal condition; and constructing a target energy function according to the constraint condition by taking the minimum energy loss as an optimal condition.
Taking the maximum economic benefit as an optimal condition as an example, an energy efficiency model with the aim of maximizing the economic benefit is established to meet the requirements of different users on electricity and heat under the conditions of supply and demand balance and operation constraint. Wherein, total profit subtracts total cost for energy sale profit, and system total cost includes the running cost, the start-up cost and the shut-down cost of power supply, heat supply and air feed, then constructs the following objective function min F:
wherein the content of the first and second substances,
representing a first energy price entered during a time period t,
representing a second energy price input during time period t,
representing a first energy price output during a time period t,
representing the second energy price output during time period t,
representing a first amount of energy output during a time period t,
representing the second amount of energy output during a time period t, D representing the type of energy device, N d Represents the number of said energy source devices,
a penalty price representing that the first energy amount is less than a first threshold,
a penalty price representing that the first amount of energy is greater than a second threshold,
a penalty price representing that said second amount of energy is less than a third threshold,
a penalty price, P, representing that said second amount of energy is greater than a fourth threshold pv pv_eE_out[t]Represents a penalty amount, P, of not using the third energy source at time t wg wg_eE_out[t]Indicating a penalty for not using the fourth energy source at time t.
The universal energy station is an infrastructure in a universal energy network, integrates comprehensive energy conversion equipment, such as a universal energy machine, a low-temperature catalytic combustion technology, a boiler, an energy efficiency booster and the like, realizes multi-energy coordination optimization and self-balancing, is a miniature comprehensive energy system close to a user side, and aims to optimally configure comprehensive energy according to user energy load and operation safety constraint so as to improve the use efficiency of energy, achieve economy, environmental protection, high efficiency and the like.
The energy optimization of the universal energy station aims at the maximum total profit of the universal energy station, the total profit is the energy selling profit minus the total cost, and the total system cost comprises the running cost, the starting cost and the shutdown cost of power supply, heat supply and gas supply; the constraint conditions of the balance of electricity, heat and natural gas power of each energy source, the characteristics of energy supply units and the like are met, and the constraint conditions can be classified into system operation constraint and operation constraint of each energy supply unit in the system.
Although the integration mode of cold, heat, electricity and gas energy coupling is beneficial to exerting the multi-energy complementation and the synergistic benefit of the system, the difficulty is brought to the comprehensive energy optimization solution, and the solution difficulty brought by the multi-energy coupling is mainly focused on the following three aspects: the input energy and the output energy between the energy units are coupled and mutually influenced; the total operation cost of the system comprises operation cost, starting cost and stopping cost, the energy equipment has starting and stopping variables (0-1 integer variables) and equipment operation state variables, and coupling relations exist among the variables; after distributed power supplies such as wind power and photovoltaic power are connected into the universal energy station, great uncertainty is brought to a system, and great interference and adverse effects can be caused to the existing energy supply network when the distributed power supplies are merged into the existing energy supply network, so that the model is configured with energy storage equipment to improve the reliability and the economy of the universal energy station.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art will appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are required by the invention.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
In this embodiment, an optimization apparatus for an energy system is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware or a combination of software and hardware is also possible and contemplated.
Fig. 7 is a block diagram illustrating an apparatus for optimizing an energy system according to an embodiment of the present invention, as shown in fig. 7, the apparatus including:
a determination module 70 for determining energy units constituting an energy system, wherein the energy units comprise: energy devices, energy networks;
a building module 72, configured to build a data model of the energy system, where the data model includes constraints for setting the energy units;
a construction module 74 for constructing an objective function of the energy system optimization based on the data model;
and an adjusting module 76, configured to perform application adjustment of the energy data on the energy system by using the objective function.
Optionally, the setting module includes at least one of: the first setting unit is used for setting the start-stop constraint condition of the energy unit, wherein the start-stop constraint condition comprises: an on-off state, an on-state, an off-state, and an initial on-off state; the second setting unit is used for setting input and output constraint conditions of the energy unit, wherein the input and output constraint conditions comprise a one-dimensional univariate input and output relationship; the third setting unit is used for setting a constraint condition of input and output energy storage of the energy unit, wherein the constraint condition of the input and output energy storage comprises a differential equation; a fourth setting unit for setting a load constraint of the energy unit, wherein the load constraint comprises a slack variable; the fifth setting unit is used for setting a network constraint condition of the energy unit, wherein the pipeline slot flow attribute of the energy unit comprises a convergent structure and a divergent structure, and the network constraint condition comprises that the energy received by the energy unit is the integration of energy input; and the sixth setting unit is used for setting a pipeline constraint condition of the energy unit, wherein the pipeline constraint condition comprises a pipe loss rate.
Optionally, the fifth setting unit includes: a determining subunit, configured to determine a channel flow property of the energy unit; the setting subunit is used for setting the network constraint condition of the energy unit as follows when the pipeline slot flow attribute of the energy unit is a convergent structure: a. the in ＝A 1 +A 2 +…+A n (ii) a When the pipeline slot flow attribute of the energy unit is a divergent structure, setting the network constraint condition of the energy unit as follows: b is out ＝B 1 +B 2 +…+B n (ii) a Wherein A is in For the purpose of energy impoundment, A i (i is 1, …, n) is the energy amount of the energy input pipeline i, B out To distribute energy, B i And (i is 1,2, …, n) is the energy quantity of the energy output pipeline i.
Optionally, the determining module includes: and the determining unit is used for determining the energy system as an overall energy unit and determining the subsystems forming the energy system as sub energy units of the overall energy unit.
Optionally, the building module comprises: a construction unit for constructing the following objective function min F:
wherein the content of the first and second substances,
representing a first energy price entered during a time period t,
representing the second energy price entered during time period t,
representing a first energy price output during a time period t,
representing the second energy price output during time period t,
representing a first amount of energy output during a time period t,
representing the second amount of energy output during a period t, D representing the type of energy device, N d Represents the number of said energy source devices,
a penalty price representing that the first energy amount is less than a first threshold,
a penalty price representing that the first amount of energy is greater than a second threshold,
a penalty price representing that the second energy amount is less than a third threshold,
a penalty price, P, representing that said second energy amount is greater than a fourth threshold pv pv_eE_out[t]Represents a penalty, P, of not using the third energy source at time t wg wg_eE_out[t]Indicating a penalty for not using the fourth energy source at time t.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 3
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, determining energy source units forming the energy source system, wherein the energy source units comprise: energy devices, energy networks;
s2, establishing a data model of the energy system, wherein the data model comprises constraint conditions for setting the energy unit;
s3, constructing an objective function of the energy system optimization based on the data model;
and S4, utilizing the objective function to perform application adjustment of energy data on the energy system.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, determining energy source units forming the energy source system, wherein the energy source units comprise: energy devices, energy networks;
s2, establishing a data model of the energy system, wherein the data model comprises constraint conditions for setting the energy unit;
s3, constructing an objective function of the energy system optimization based on the data model;
and S4, utilizing the objective function to perform application adjustment of energy data on the energy system.
Optionally, for a specific example in this embodiment, reference may be made to the examples described in the above embodiment and optional implementation, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for optimizing an energy system, comprising:
determining energy units constituting an energy system;
establishing a data model of the energy system, wherein the data model comprises constraint conditions for setting the energy units;
constructing an objective function of the energy system based on the data model;
performing application adjustment of energy data on the energy system by using the objective function;
constructing an objective function of the energy system based on the data model comprises:
the following objective function min F was constructed:
wherein the content of the first and second substances,
representing a first energy price entered during a time period t,
representing the second energy price entered during time period t,
representing a first energy price output during a time period t,
representing the second energy price output during time period t,
representing a first amount of energy output during a time period t,
representing the second amount of energy output during a period t, D representing the type of energy device, N d Represents the number of said energy source devices and,
representing a penalty price for said first energy amount being less than a first threshold for a period t,
representing a penalty price for said first amount of energy being greater than a second threshold for a time period t,
a penalty price representing that said second energy amount is less than a third threshold for a period t,
a penalty price, P, representing that said second energy quantity is greater than a fourth threshold during a time period t pv pv_eE_out[t]Represents a penalty, P, of not using the photovoltaic energy source during a time period t wg wg_eE_out[t]And the penalty amount of unused wind power energy in the time t is represented.
2. The method according to claim 1, wherein setting the constraints of the energy unit comprises at least one of:
setting a start-stop constraint condition of the energy unit, wherein the start-stop constraint condition comprises: an on-off state, an on-state, an off-state, and an initial on-off state;
setting input and output constraint conditions of the energy units, wherein the input and output constraint conditions comprise input and output relations of one-dimensional univariates;
setting a constraint condition of input and output energy storage of the energy unit, wherein the constraint condition of the input and output energy storage comprises a differential equation;
setting a load constraint for the energy unit, wherein the load constraint comprises a slack variable;
setting network constraint conditions of the energy units;
and setting a pipeline constraint condition of the energy unit, wherein the pipeline constraint condition comprises a pipe loss rate.
3. The method of claim 2, wherein setting network constraints for the energy unit comprises:
determining a channel flow attribute of the energy unit; the pipeline channel flow attribute of the energy unit comprises a convergent structure and a divergent structure;
when the pipeline slot flow attribute of the energy unit is a convergent structure, setting the network constraint condition of the energy unit as: a. the in ＝A 1 +A 2 +…+A n (ii) a When the pipeline slot flow attribute of the energy unit is a divergent structure, setting the network constraint condition of the energy unit as: b is out ＝B 1 +B 2 +…+B n ；
Wherein, A in For the purpose of energy impoundment, A i I is 1, …, n, which is the energy amount of the energy input pipeline i, B out To distribute energy, B j ，j＝1,2,…And n is the energy quantity of the energy output pipeline j.
4. The method of claim 1, wherein determining the energy units comprising the energy system comprises:
the energy system is determined as a total energy unit, and the subsystems constituting the energy system are determined as sub-energy units of the total energy unit.
5. An apparatus for optimizing an energy system, comprising:
the determining module is used for determining energy units forming the energy system;
the establishing module is used for establishing a data model of the energy system, wherein the data model comprises constraint conditions for setting the energy units;
a construction module for constructing an objective function of the energy system based on the data model;
the adjusting module is used for performing application adjustment on the energy data on the energy system by using the objective function;
the building module is specifically configured to: the following objective function min F was constructed:
wherein the content of the first and second substances,
representing a first energy price entered during a time period t,
representing the second energy price entered during time period t,
representing a first energy price output during a time period t,
representing the second energy price output during time period t,
representing a first amount of energy output during a time period t,
representing the second amount of energy output during a period t, D representing the type of energy device, N d Represents the number of said energy source devices and,
representing a penalty price for said first energy amount being less than a first threshold for a period t,
representing a penalty price for said first amount of energy being greater than a second threshold for a period t,
a penalty price representing that said second energy amount is less than a third threshold for a period t,
a penalty price, P, representing that said second energy quantity is greater than a fourth threshold during a time period t pv pv_eE_out[t]Represents a penalty, P, of not using the photovoltaic energy source during a time period t wg wg_eE_out[t]And the penalty of not using the wind power energy source in the time period t is represented.
6. The apparatus of claim 5, wherein the establishing means comprises at least one of:
the first setting unit is used for setting the start-stop constraint condition of the energy unit, wherein the start-stop constraint condition comprises: an on-off state, an on-state, an off-state, and an initial on-off state;
the second setting unit is used for setting input and output constraint conditions of the energy unit, wherein the input and output constraint conditions comprise input and output relations of one-dimensional univariates;
the third setting unit is used for setting a constraint condition of input and output energy storage of the energy unit, wherein the constraint condition of the input and output energy storage comprises a differential equation;
a fourth setting unit for setting a load constraint of the energy unit, wherein the load constraint comprises a slack variable;
a fifth setting unit, configured to set a network constraint condition of the energy unit;
and the sixth setting unit is used for setting a pipeline constraint condition of the energy unit, wherein the pipeline constraint condition comprises a pipe loss rate.
7. The apparatus according to claim 6, wherein the fifth setting unit includes:
a determining subunit, configured to determine a channel flow property of the energy unit; the pipeline channel flow attribute of the energy unit comprises a convergent structure and a divergent structure;
the setting subunit is used for setting the network constraint condition of the energy unit as follows when the pipeline slot flow attribute of the energy unit is a convergent structure: a. the in ＝A 1 +A 2 +…+A n (ii) a When the pipeline slot flow attribute of the energy unit is a divergent structure, setting the network constraint condition of the energy unit as follows: b is out ＝B 1 +B 2 +…+B n ；
Wherein A is in For the energy input, A i I is 1, …, n, which is the energy quantity of the energy input pipeline i, B out To distribute energy, B j And j is 1,2, …, n, which is the energy quantity of the energy output pipeline j.
8. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 4 when executed.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 4.
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