CN114218865A - Multi-target collaborative optimization method and device for distributed heating system - Google Patents
Multi-target collaborative optimization method and device for distributed heating system Download PDFInfo
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Abstract
The invention belongs to the technical field of distributed heating systems, and provides a multi-target collaborative optimization method and device of a distributed heating system. The method comprises the steps of obtaining source storage load data, energy price information and emission factors of the distributed heating system; obtaining a multi-target collaborative optimization result of the distributed heat supply system under a set constraint condition based on the obtained related data of the distributed heat supply system and the multi-target collaborative optimization model; wherein the multi-objective collaborative optimization model is represented as a weighted sum of three objectives of economic cost, carbon emission and penalty cost representing operational complexity; the constraints include energy balance constraints, equipment model constraints, and operational complexity constraints.
Description
Technical Field
The invention belongs to the technical field of distributed heating systems, and particularly relates to a multi-target collaborative optimization method and device of a distributed heating system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Distributed heat supply does not need to build a large centralized heat source station and a long heat supply pipe network, and instead builds a small heat source station using renewable energy, and can implement personalized customized service according to the specific conditions of regions according to the energy requirements of local users. An accurate and reasonable optimization scheduling model is the key for ensuring that the distributed heat system gives full play to the economy and the environmental protection in the operation process, certain research is carried out on distributed heat supply at present, and a built distributed heat supply system is provided. However, most of the current research is developed through theoretical analysis and simulation in a laboratory environment, and field data analysis and optimized operation research on practical application systems are lacked.
Retrieval finds that problems exist in the aspects of scheduling and operation of the distributed heating system, and a proper optimal scheduling model cannot be established for part of the distributed heating system, or a traditional economic single-target optimization model is adopted, so that the rationality of practical application is lacked. Patent CN112696723A proposes a distributed clean heating system with electric energy replacement, in which a wind-light complementary power generation system cooperates with a storage battery to drive an electric boiler system to operate so as to realize clean heating, but this patent does not establish an optimized scheduling model for the system, and cannot ensure whether the system operates optimally. Patent CN112132332A proposes an optimized scheduling method for a clean heating energy system, which determines heating plan instructions of devices such as an electric boiler, an air source heat pump, a ground source heat pump unit, etc. in the clean heating energy system, but the method only uses the lowest operation cost as a single optimization target, does not consider system carbon emission, and is easy to cause frequent start and stop of the devices, and is not beneficial to long-term operation of the system.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a multi-target collaborative optimization method and a multi-target collaborative optimization device for a distributed heat supply system, wherein an operation complexity model and related constraints are introduced into the multi-target collaborative optimization model based on a multi-target collaborative optimization model which comprehensively considers economic cost, carbon emission and operation complexity, so that the start-stop frequency and the operation duration of equipment can be effectively controlled, and the distributed heat supply system can be ensured to stably and efficiently operate for a long time.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a multi-objective collaborative optimization method for a distributed heating system, which comprises the following steps:
acquiring source storage load data, energy price information and emission factors of the distributed heating system;
obtaining a multi-target collaborative optimization result of the distributed heat supply system under a set constraint condition based on the obtained related data of the distributed heat supply system and the multi-target collaborative optimization model;
wherein the multi-objective collaborative optimization model is represented as a weighted sum of three objectives of economic cost, carbon emission and penalty cost representing operational complexity; the constraints include energy balance constraints, equipment model constraints, and operational complexity constraints.
As an embodiment, the economic cost includes a cost of purchasing power from the grid and a cost of purchasing waste heat from the plant for a set period of time.
In one embodiment, the carbon emission represents a product of the grid interaction power and a carbon emission coefficient of the grid within a set period of time.
In one embodiment, the penalty cost representing the operation complexity is represented as a weighted cumulative sum of the unit working state and a corresponding penalty coefficient within a set time period.
As an embodiment, the operational complexity constraint is expressed as:
0≤Sstartup(t)+Sshutdown(t)≤1
Sstartup(t),Sshutdown(t)∈{0,1}
wherein: sstartup(t) and Sshutdown(t) each isRepresenting a power-on/off state when Sstartup(t) 1 or Sshutdown(t) 1, meaning that the unit operating state changes at this time; k is a radical ofpenalty,startupAnd kpenalty,shutdownRespectively representing the punishment coefficients of power-on and power-off;
and
respectively representing the time of power-on and power-off, k1And k2Respectively representing the number of cycles of single startup and shutdown; t is the optimization period.
As an embodiment, the plant model constraints include: the photovoltaic and photothermal integrated system comprises rated power generation power constraint and rated heating power constraint of the photovoltaic and photothermal integrated system, output power constraint of a ground source heat pump, maximum output constraint of a waste heat recovery device, input and output constraint of a heat storage device, and input and output constraint and capacity constraint of a capacity constraint and a power storage device.
A second aspect of the present invention provides a multi-objective collaborative optimization apparatus for a distributed heating system, including:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring source storage load data, energy price information and emission factors of the distributed heating system;
the collaborative optimization module is used for obtaining a multi-objective collaborative optimization result of the distributed heat supply system under a set constraint condition based on the acquired related data of the distributed heat supply system and the multi-objective collaborative optimization model;
wherein the multi-objective collaborative optimization model is represented as a weighted sum of three objectives of economic cost, carbon emission and penalty cost representing operational complexity; the constraints include energy balance constraints, equipment model constraints, and operational complexity constraints.
A third aspect of the invention provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for multi-objective collaborative optimization of a distributed heating system as described above.
A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the multi-objective collaborative optimization method for a distributed heating system as described above.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention provides a multi-objective collaborative optimization model comprehensively considering economic cost, carbon emission and operation complexity, and the operation complexity model is introduced into the collaborative optimization model, so that the start-stop frequency and the operation duration of equipment are effectively controlled, and the economic and low-carbon advantages of a distributed heat supply system are fully exerted on the premise that the distributed heat supply system can stably and efficiently operate for a long time.
(2) The method considers factors such as external environment change, production condition limitation and uncertainty interference existing in the engineering field, the load model is based on thermodynamic energy balance relation and parameter mixed modeling of an actual building, the equipment model is based on working principle of equipment and actual field data mixed modeling, the real working condition of the distributed heating system in the actual engineering application field is fully reflected, field research and energy efficiency analysis on the actual operation effect of the distributed heating system are realized, and the rationality of the actual engineering application of the multi-target collaborative optimization model is improved. Meanwhile, the modeling process is synchronously carried out, and the reasoning construction process and the required time of the model are shortened.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a multi-objective collaborative optimization method for a distributed heating system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a multi-objective cooperative optimization device of a distributed heating system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 1, the embodiment provides a multi-objective collaborative optimization method for a distributed heating system, which includes:
s101: and acquiring source storage load data, energy price information and emission factors of the distributed heating system.
In particular implementations, the source storage data, energy price information, and emission factors may be obtained directly from a database associated with the distributed heating system.
It is understood herein that those skilled in the art may also collect source storage data from the distributed heating system through the monitoring device, and may obtain energy price information and emission factors through the existing data storage server.
S102: and obtaining a multi-target cooperative optimization result of the distributed heat supply system under a set constraint condition based on the obtained related data of the distributed heat supply system and the multi-target cooperative optimization model.
Wherein the multi-objective collaborative optimization model is represented as a weighted sum of three objectives of economic cost, carbon emission and penalty cost representing operational complexity; the constraints include energy balance constraints, equipment model constraints, and operational complexity constraints.
The objective function is an important index for solving the optimal solution space of the multi-objective collaborative optimization model and is a standard for evaluating the advantages and disadvantages of the optimized scheduling scheme.
The embodiment incorporates the operation complexity into the optimization model in the form of penalty cost and related constraint, and the larger the penalty cost is, the more complicated the operation of optimizing the scheduling policy is. The economic cost, the carbon emission and the operation complexity are mutually contradictory multiple targets, the mutually contradictory multiple-target solving problem is converted into a single-target solving problem by a linear weighted combination method, and the target function of the multi-target collaborative optimization model is expressed as follows:
minobj＝α1·CDHS+α2·EDHS+α3·Cpenalty
wherein: obj is the optimization goal, CDHS、EDHSAnd CpenaltyRespectively, economic cost, carbon emission and penalty cost representing the complexity of the operation within a set period of time (e.g., one day), alpha1、α2And alpha3Respectively representing the weight coefficients of different objects.
The following takes the set time period as one day (24h) as an example:
the economic cost includes the cost of purchasing power from the grid and the cost of purchasing waste heat from the plant, and the cost model is represented as follows:
wherein: egrid(t) represents the amount of power interacting with the grid at time t, Qwh(t) represents the amount of waste heat purchased from the plant at time t, pgrid(t) and pwh(t) represents electricity prices and heat prices at different times, respectively. Egrid(t)>0 represents purchasing electricity from the grid; egrid(t)<0 represents selling electricity to the grid.
The prices for time-sharing electricity purchasing and electricity selling are different and are expressed as follows:
wherein: p is a radical ofgrid,p(t) and pgrid,s(t) represents the prices for electricity purchase and sale at different times, respectively.
Carbon emissions are primarily carbon emissions produced on the primary side of the grid from the purchase of electricity from the grid. The waste heat is a byproduct of industrial production, carbon emission is classified in the production process of main products, and corresponding carbon emission is generated even if the waste heat is not used. Therefore, the recovery and reuse of waste heat is zero carbon emissions. The carbon emission model is expressed as follows:
wherein: egrid(t) represents the amount of power interacting with the grid at time t, egridRepresenting the carbon emission coefficient of the grid. Egrid(t)>0 represents purchasing electricity from the grid; egrid(t)<0 represents selling electricity to the grid.
The penalty cost is expressed as follows:
wherein: sstartup(t) and Sshutdown(t) represents the on and off states, respectively, when Sstartup(t) 1 or Sshutdown(t) 1, which means that the operating state of the unit changes (on or off) at this time, kpenalty,startupAnd kpenalty,shutdownRespectively representing the punishment coefficients of power-on and power-off.
The constraint conditions are the limitations of the decision process for solving the optimal solution for the collaborative optimization model, and are often expressed in the form of inequality or equation. In general, equality constraints determine the relationship between different decision variables, and inequality constraints determine the complexity of the decision process.
In the present embodiment, the energy balance is a basic constraint condition for optimizing the scheduling model, and mainly includes the balance of thermal energy and electric energy. The heating energy balance constraint is expressed as follows:
Qpv/t(t)+Qghp(t)+Qhru(t)+Qtes(t)＝Qload(t)
wherein: qload(t) is the thermal load, Qpv/t(t)、Qghp(t) and Qhru(t) represents heat output by the photovoltaic and photothermal integrated system, the ground source heat pump and the waste heat recovery unit respectively, Qtes(t) represents the inputs (Q) of the heat storage devices, respectivelytes(t)<0) Or output (Q)tes(t)>0)。
The distributed heating system is not responsible for the electricity supply of the building of the demander. Thus, the power balance constraint is expressed as follows:
Epv/t(t)+Ees(t)+Egrid(t)＝Eghp(t)+Eo(t)
wherein: epv/t(t) is the amount of electricity generated by the integrated photovoltaic-thermal system, Ees(t) is the input (E) of the storage devicees(t)<0) Or output (E)es(t)>0)，Egrid(t) indicates the purchase of electricity from the power grid (E)grid(t)>0) Or selling electricity to the grid (E)grid(t)<0)，Eghp(t) and EoAnd (t) represents the power consumption of the ground source heat pump and the power circulating pump respectively.
In this embodiment, the device model constraints include: the photovoltaic and photothermal integrated system comprises rated power generation power constraint and rated heating power constraint of the photovoltaic and photothermal integrated system, output power constraint of a ground source heat pump, maximum output constraint of a waste heat recovery device, input and output constraint of a heat storage device, and input and output constraint and capacity constraint of a capacity constraint and a power storage device.
The load and the individual devices are first modeled below:
accurate thermal load values are an important reference for optimal scheduling. Building thermal loads are related to many factors, including building parameters, environmental conditions, and human factors. In order to accurately simulate and represent the heat load, the invention adopts the indoor temperature as the direct reflection of the heat load, and models the heat load from the thermodynamic angle, and the specific expression is as follows:
wherein: qload(T) is the thermal load, Tin(T) and Tout(t) represents indoor and outdoor temperatures, R is thermal resistance of a building, Cair、VairAnd ρairThe specific heat capacity, volume and density of the air in the building, respectively.
The components of the distributed heating system, i.e. the individual devices, are important controlled objects. The equipment model reflects the capacity and efficiency of the equipment from the mathematical perspective, is an important reference basis for determining energy scheduling at a single moment, and is important for solving an optimal scheme.
Static optimization based on a mathematical mechanism model of a controlled object is an ideal optimization method. However, although the distributed heating system is designed to continuously operate according to a certain normal working condition, because a large amount of external environment changes, production condition limitations, uncertain interferences and other factors exist on the site, it is often difficult for an actual distributed heating system to establish an accurate mathematical mechanism model for describing a controlled object on the site, and thus an originally formulated unit scheduling strategy is not necessarily optimal. Therefore, a control theory and a method based on an accurate mathematical mechanism model are difficult to be applied to an actual distributed heating system, and field research and energy efficiency analysis on the actual operation effect of the distributed heating system are important aspects for realizing the optimized operation of the system.
The method based on mechanism and on-site real data mixed modeling is adopted, the real working condition of the distributed heating system in actual engineering application is fully reflected, and the rationality and the usability of the multi-objective collaborative optimization scheduling model are further improved.
(1) Photovoltaic and photo-thermal integrated system model
The photovoltaic and photothermal integrated system combines the photothermal collector and the photovoltaic power generation plate together, so that more sun can be utilized in unit area. The photovoltaic power generation board absorbs solar radiation, produces required electric energy in the distributed heating system, and the required heat energy of heat supply is converted into with the solar energy that does not utilize to the light and heat collector, improves the solar energy utilization ratio. Because the factors influencing the output of the photovoltaic and photothermal integrated system are many, mainly comprise solar radiation, radiation area and temperature, the simplified model is as follows:
Epv/t＝ηpv·Spv·Isolar·[1-kpv·(Tpv-Tpv,s)]
Qpv/t＝ηt·St·Isolar
wherein: epv/tAnd Qpv/tRespectively the generating power and the heating power of the photovoltaic and photothermal integrated system; is IsolarIs the solar irradiance, ηpvAnd ηtEfficiency of photovoltaic panel power generation and heat generation of heat collector, S, respectivelypvAnd StThe areas of the photovoltaic panel and the collector, T, respectively, receiving solar radiationpvAnd Tpv,sActual and standard temperature, k, respectively, of the surface of the photovoltaic panelpvIs the temperature difference coefficient.
(2) Ground source heat pump model
The ground source heat pump is a heat lifting device, realizes conversion from low-grade heat energy to high-grade heat energy by consuming a small amount of high-grade energy, and has remarkable economic benefit. The ground source heat pump technology belongs to the renewable energy utilization technology, and when the unit normally operates, no waste gas, waste water and waste residue are discharged to the outside, so that the environmental benefit is remarkable. The ground source heat pump model is as follows:
wherein: eghp(t)、Qghp(t)、COPghpAnd rghpRespectively the input, output, coefficient of performance and load factor of the ground source heat pump. The input and output data of the ground source heat pump are obtained by the actual measurement of the engineering application site, c0、c1、…、cnThe fitting model coefficient is obtained by performing least square method and QR decomposition on data measured by experiments, and n is the highest order number of the fitting model.
(3) Waste heat recovery device model
The waste heat recovery device is an energy recovery and reuse device, and can recover waste liquid, waste gas and other waste heat resources discharged by partial factories and realize secondary utilization through heat exchange, heat-power conversion and other technologies. This helps to reduce energy waste, reduce primary energy consumption, and improve energy utilization. The model of the waste heat recovery device is as follows:
Qhru(t)＝Iwh(t)·ηhru
Iwh(t)＝Qwh(t)
wherein: i iswh(t)、Qhru(t) and ηhruRepresenting input, output and efficiency, Q, respectively, of the waste heat recovery devicewh(t) represents the amount of waste heat purchased from the plant at time t. The waste heat recovery efficiency is obtained by actual measurement of an engineering application field.
(4) Heat storage device model
The heat storage device is capable of achieving load transfer, i.e., storing heat during peak periods of heat production and releasing heat during peak periods of heat use. The heat storage link is added in the heat supply system, so that the defects of intermittence and fluctuation of solar energy can be effectively overcome, the pressure of peak heat load on the heat supply system is reduced, the phenomena of unbalance and mismatching of heat energy supply and demand in time are further solved, and the flexibility of energy supply and transmission of the heat supply system is improved. The dynamic equilibrium equation for the thermal storage device is as follows:
Qtes,s(t+1)＝Qtes,s(t)·ηtes-Qtes(t)
wherein: qtes,s(t +1) and Qtes,s(t) the amount of heat stored in the heat storage device at times t +1 and t, ηtesThe heat storage efficiency is obtained by actual measurement on engineering application sites.
(5) Electricity storage device model
The electricity storage device is connected with all electric equipment in the heat supply system, a photovoltaic part of the photovoltaic and photothermal integrated system and an electric network. The dynamic balance equation of the power storage device is as follows:
Ees,s(t+1)＝Ees,s(t)·ηes-Ees(t)
wherein: ees,s(t +1) and Ees,s(t) the respective amounts of electricity stored in the electricity storage device at times t +1 and t, ηesThe electricity storage efficiency is obtained by actual measurement on the engineering application site.
The electric energy and the heat energy generated by the photovoltaic and photothermal integrated system depend on the solar radiation intensity and the total irradiation area, the efficiency of the photovoltaic power generation panel and the photothermal collector, and the installed capacity of the photovoltaic power generation panel and the photothermal collector. For the integrated photovoltaic and photothermal system, the constraint conditions can be expressed as follows:
0≤Epv/t(t)≤CAPpv/t,e
0≤Qpv/t(t)≤CAPpv/t,h
wherein: CAP (common Place Capacity)pv/t,eAnd CAPpv/t,hRespectively rated power generation power and rated heating power.
The ground source heat pump under low load is difficult to exert the best heating effect, and in order to avoid the low load state, the output of the ground source heat pump should be controlled within a certain range, so that the ground source heat pump can be ensured to have good performance. The constraints that ensure efficient operation of a ground source heat pump are expressed as follows:
rmin·CAPghp≤Qghp(t)≤rmax·CAPghp
wherein: r isminAnd rmaxRespectively representing the lowest and highest load rates, CAP, of the ground source heat pumpghpIs the rated output power of the ground source heat pump.
The relationship between the output and the input of the waste heat recovery device is approximately linear. Therefore, the maximum output of the waste heat recovery device should be less than the rated capacity, and the constraint conditions are expressed as follows:
0≤Qhru(t)≤CAPhru
wherein: CAP (common Place Capacity)hruIs rated output power of the waste heat recovery device
Besides the energy dynamic balance constraint, the heat storage device also comprises an input and output constraint and a capacity constraint, which are specifically expressed as follows:
Qtes,i≤Qtes(t)≤Qtes,o
0≤Qtes,s(t)≤CAPtes
wherein: qtes,i、Qtes,oAnd CAPtesThe rated output power, the rated input power, and the rated capacity of the heat storage device, respectively. In the optimization model, the input is defined as negative number (Q) in consideration of the calculation requirementtes(t)<0) The output is a positive number (Q)tes(t)>0)。
In addition, to avoid the heat storage device being in a full heat state (Q)tes,s(t)＝CAPtes) Lower excess heat storage, extra input (Q)tes(t)<0) The constraints are as follows:
Qtes,s(t)-CAPtes≤Qtes(t)<0
similarly, to avoid the heat storage device being in a zero heat state (Q)tes,s(t)＝CAPtes) Lower excess exotherm, additional output (Q)tes(t)>0) The constraints are as follows:
0<Qtes(t)≤Qtes,s(t)
thus, the input-output constraints of the thermal storage device are expressed as follows:
max{Qtes,s(t)-CAPtes,Qtes,i(t)}≤Qtes(t)≤min{Qtes,s(t),Qtes,o(t)}
similar to the thermal storage device, the input-output constraints and capacity constraints of the electrical storage device are as follows:
max{Ees,s(t)-CAPes,Ees,i(t)}≤Ees(t)≤min{Ees,s(t),Ees,o(t)}
0≤Ees,s(t)≤CAPes
wherein: ees,i(t)、Ees,o(t) and CAPesThe rated output power, the rated input power and the rated capacity of the electrical storage device, respectively.
In this embodiment, the operation complexity constraint is expressed as:
0≤Sstartup(t)+Sshutdown(t)≤1
Sstartup(t),Sshutdown(t)∈{0,1}
wherein: sstartup(t) and Sshutdown(t) represents the on and off states, respectively, when Sstartup(t) 1 or Sshutdown(t) 1, meaning that the unit operating state changes at this time; k is a radical ofpenalty,startupAnd kpenalty,shutdownRespectively representing the punishment coefficients of power-on and power-off;
and
respectively representing the time of power-on and power-off, k1And k2Respectively representing the number of cycles of single startup and shutdown; t is the optimization period.
In the modeling process of the multi-objective collaborative optimization model, the establishment of the objective function, the load model and the equipment model is carried out synchronously, and the reasoning construction process and the required time of the model are shortened. The constraint conditions are determined jointly according to the established objective function, the load model and the equipment model, and the complexity of the decision process is reduced.
Example two
As shown in fig. 2, the present embodiment provides a multi-objective collaborative optimization device for a distributed heating system, which includes:
(1) the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring source storage load data, energy price information and emission factors of the distributed heating system;
(2) and the collaborative optimization module is used for obtaining a multi-objective collaborative optimization result of the distributed heat supply system under a set constraint condition based on the acquired related data of the distributed heat supply system and the multi-objective collaborative optimization model.
Wherein the multi-objective collaborative optimization model is represented as a weighted sum of three objectives of economic cost, carbon emission and penalty cost representing operational complexity; the constraints include energy balance constraints, equipment model constraints, and operational complexity constraints.
In a specific implementation, the economic cost includes the cost of purchasing power from the grid and the cost of purchasing waste heat from the plant for a set period of time. The carbon emission represents the product of the power grid interaction electric quantity and the carbon emission coefficient of the power grid in a set time period. And the penalty cost representing the operation complexity is represented as the weighted accumulation sum of the working state of the unit and the corresponding penalty coefficient in a set time period.
Wherein the operational complexity constraint is expressed as:
0≤Sstartup(t)+Sshutdown(t)≤1
Sstartup(t),Sshutdown(t)∈{0,1}
wherein: sstartup(t) and Sshutdown(t) represents the on and off states, respectively, when Sstartup(t) 1 or Sshutdown(t) 1, meaning that the unit operating state changes at this time; k is a radical ofpenalty,startupAnd kpenalty,shutdownRespectively representing the punishment coefficients of power-on and power-off;
and
respectively representing the time of power-on and power-off, k1And k2Respectively representing the number of cycles of single startup and shutdown; t is the optimization period.
The plant model constraints include: the photovoltaic and photothermal integrated system comprises rated power generation power constraint and rated heating power constraint of the photovoltaic and photothermal integrated system, output power constraint of a ground source heat pump, maximum output constraint of a waste heat recovery device, input and output constraint of a heat storage device, and input and output constraint and capacity constraint of a capacity constraint and a power storage device.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described herein again.
EXAMPLE III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for multi-objective collaborative optimization of a distributed heating system as described above.
Example four
The embodiment provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the multi-objective collaborative optimization method for a distributed heating system as described above.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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 spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A multi-objective collaborative optimization method of a distributed heating system is characterized by comprising the following steps:
acquiring source storage load data, energy price information and emission factors of the distributed heating system;
obtaining a multi-target collaborative optimization result of the distributed heat supply system under a set constraint condition based on the obtained related data of the distributed heat supply system and the multi-target collaborative optimization model;
wherein the multi-objective collaborative optimization model is represented as a weighted sum of three objectives of economic cost, carbon emission and penalty cost representing operational complexity; the constraints include energy balance constraints, equipment model constraints, and operational complexity constraints.
2. The multi-objective collaborative optimization method of a distributed heating system according to claim 1, wherein the economic cost includes a cost of purchasing electricity from a power grid and a cost of purchasing surplus heat from a plant for a set period of time.
3. The multi-objective collaborative optimization method for the distributed heating system according to claim 1, wherein the carbon emission represents a product of grid interaction power and a carbon emission coefficient of a grid within a set period of time.
4. The multi-objective collaborative optimization method of the distributed heating system according to claim 1, wherein the penalty cost representing the operation complexity is represented as a weighted cumulative sum of the unit operating state and a corresponding penalty coefficient within a set time period.
5. The multi-objective collaborative optimization method for a distributed heating system according to claim 1, wherein the operational complexity constraint is expressed as:
0≤Sstartup(t)+Sshutdown(t)≤1
Sstartup(t),Sshutdown(t)∈{0,1}
wherein: sstartup(t) and Sshutdown(t) represents the on and off states, respectively, when Sstartup(t) 1 or Sshutdown(t) 1, meaning that the unit operating state changes at this time; k is a radical ofpenalty,startupAnd kpenalty,shutdownRespectively representing the punishment coefficients of power-on and power-off;
and
respectively representing the time of power-on and power-off, k1And k2Respectively representing the number of cycles of single startup and shutdown; t is the optimization period.
6. The multi-objective collaborative optimization method of a distributed heating system according to claim 1, wherein the equipment model constraints include: the photovoltaic and photothermal integrated system comprises rated power generation power constraint and rated heating power constraint of the photovoltaic and photothermal integrated system, output power constraint of a ground source heat pump, maximum output constraint of a waste heat recovery device, input and output constraint of a heat storage device, and input and output constraint and capacity constraint of a capacity constraint and a power storage device.
7. The utility model provides a distributed heating system's multiobjective collaborative optimization device which characterized in that includes:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring source storage load data, energy price information and emission factors of the distributed heating system;
the collaborative optimization module is used for obtaining a multi-objective collaborative optimization result of the distributed heat supply system under a set constraint condition based on the acquired related data of the distributed heat supply system and the multi-objective collaborative optimization model;
wherein the multi-objective collaborative optimization model is represented as a weighted sum of three objectives of economic cost, carbon emission and penalty cost representing operational complexity; the constraints include energy balance constraints, equipment model constraints, and operational complexity constraints.
8. The multi-objective collaborative optimization device of a distributed heating system according to claim 7, wherein the economic cost includes a cost of purchasing electricity from a power grid and a cost of purchasing surplus heat from a plant for a set period of time;
or the carbon emission represents the product of the power grid interaction electric quantity and the carbon emission coefficient of the power grid in a set time period;
or the penalty cost representing the operation complexity is represented as the weighted accumulation sum of the unit working state and the corresponding penalty coefficient in a set time period.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method for multi-objective collaborative optimization of a distributed heating system according to any one of claims 1-6.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the method for multi-objective co-optimization of a distributed heating system according to any of claims 1-6.
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