CN112329988A - Demand side response calculation method based on park adjustability analysis - Google Patents

Demand side response calculation method based on park adjustability analysis Download PDF

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CN112329988A
CN112329988A CN202011114572.4A CN202011114572A CN112329988A CN 112329988 A CN112329988 A CN 112329988A CN 202011114572 A CN202011114572 A CN 202011114572A CN 112329988 A CN112329988 A CN 112329988A
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side response
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heat
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关杰
孙德挺
冯皓清
洪文骁
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Hangzhou Hongsheng Electric Power Design Consulting Co ltd
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Abstract

The application relates to a demand side response calculation method and system based on campus adjustability analysis, wherein the demand side response calculation method based on the campus adjustability analysis comprises the following steps: modeling and parameter configuration are carried out on a functional framework and equipment of the industrial park system, and the maximum adjustability of the system is calculated; reading the peak clipping requirement of a superior power grid from a database, and determining the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity; determining peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculating multi-proportion electricity demand side response and multi-proportion heat demand side response of a park system; and finally, calculating IDR compensation quotations responded by the multi-proportion electricity and the heat demand side, and reporting the multi-proportion electricity and heat demand side response plans and the IDR compensation quotations to a superior power grid to realize interactive feedback with the superior power grid. Through this application, for the regulation potentiality in degree of depth excavation industrial park, satisfy higher level's distribution network peak clipping and valley filling demand and provide important support.

Description

Demand side response calculation method based on park adjustability analysis
Technical Field
The application relates to the field of energy systems, in particular to a demand side response calculation method and system based on park adjustability analysis.
Background
With the rapid development of energy internet and comprehensive energy system, the decentralized energy market and energy network structure make the traditional power demand side response gradually develop towards the direction of integrated demand side response (IDR for short). IDR is a mechanism and means which depend on a multi-energy intelligent management system at a user side and change the comprehensive energy using behavior of the user through cooperative conversion among different energy sources, and is an important embodiment that energy flow, information flow and value flow in an energy internet are converged and fused at the user side, including transfer of various types of load demands and substitution among the load demands, and the implementation can fully dig the load adjusting potential at the demand side, improve the utilization rate of equipment in an energy hub and reduce the energy using cost.
In the related technology, the influence of IDR resources on the overall operation cost or the comprehensive energy utilization efficiency of the system is mainly researched, the operation control strategy of the multi-energy network is mostly modeled and analyzed with the aim of minimizing the system operation cost, and the influence factors in the IDR resource modeling process are rarely considered.
At present, aiming at the related technology, the research on the influence factors in the IDR resource modeling process is less, the optimization problems of environmental protection and total operation cost are considered on the basis of meeting the requirements of the response peak clipping and valley filling of the demand side, and an effective solution is not provided.
Disclosure of Invention
The embodiment of the application provides a demand side response calculation method and system based on park adjustability analysis, and at least solves the problems that in the related technology, research on influencing factors in an IDR resource modeling process is less, and multi-objective optimization on environmental protection and total operation cost is lacked on the basis of meeting demand side response peak clipping and valley filling.
In a first aspect, an embodiment of the present application provides a demand side response calculation method based on campus scalability analysis, where the method includes:
modeling and parameter configuration are carried out on a functional framework and equipment of the industrial park system, and the maximum adjustability of the industrial park system is calculated;
reading the peak clipping requirement of a superior power grid from a database, and determining the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity;
determining peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculating multi-proportion electricity demand side response and multi-proportion heat demand side response of a park system;
and calculating IDR compensation quotations of the multi-proportion electricity demand side response and the multi-proportion heat demand side response, and reporting the multi-proportion electricity demand side response plan, the multi-proportion heat demand side response plan and the IDR compensation quotations to the superior power grid.
In some of these embodiments, calculating the maximum scaleability, the multi-proportion electrical demand side response, and the multi-proportion thermal demand side response of the industrial park system itself comprises:
and utilizing the constructed industrial park system model to supplement corresponding constraint conditions to calculate the maximum adjustability, the multi-proportion electric demand side response and the multi-proportion heat demand side response.
In some of these embodiments, said calculating the IDR compensation quote for the multi-proportion electric demand side response and the multi-proportion thermal demand side response comprises:
determining the IDR compensation quote in combination with the electricity cost and the customer satisfaction of the industrial park.
In some of these embodiments, after the modeling of the industrial park system is complete, the method includes:
and performing initial optimization scheduling on the side-demand-free response to obtain an electricity utilization energy plan and a heat utilization energy plan.
In some of these embodiments, the modeling the functional architecture and equipment of the industrial park system includes:
and constructing an optimized scheduling constraint condition of the modeling equipment, wherein the optimized scheduling constraint condition comprises an electric power constraint, a thermal power constraint and a cold power constraint condition.
In some of these embodiments, the parameter configuration includes:
the parameter Z is configured by setting corresponding 0 and 1 to the modeling equipmentmod.iAnd an operating parameter Zrun.iWherein, if Zmod.iIf Z is equal to 1, the device i exists in the industrial park and plays a role, otherwise, the device i does not exist, and if Z is not equal to 1, the device i does not existrun.iA running constraint i is active, whereas the constraint is ignored.
In some of these embodiments, the functional architecture and equipment of the industrial park system includes:
the system comprises a battery energy storage system, an ice cold storage device, an electric refrigeration/heat central air conditioner, a lithium bromide refrigeration system, a gas turbine, a gas boiler, a photovoltaic unit, an absorption refrigerator, a household air conditioner, a waste heat boiler and various steam driving devices.
In a second aspect, an embodiment of the present application provides a demand side response computing system based on campus scalability analysis, the system including:
the server carries out modeling and parameter configuration on the functional framework and the equipment of the industrial park system, and calculates the maximum adjustability of the industrial park system;
the server reads the peak clipping requirement of a superior power grid from a database, and determines the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity;
the server determines peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculates multi-proportion electricity demand side response and multi-proportion heat demand side response of a park system;
and the server calculates IDR compensation quotations of the multi-proportion electricity demand side response and the multi-proportion heat demand side response, and reports the multi-proportion electricity demand side response plan, the multi-proportion heat demand side response plan and the IDR compensation quotations to the superior power grid.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the computer program to perform any one of the above methods for demand-side response calculation based on campus scalability analysis.
In a fourth aspect, an embodiment of the present application provides a storage medium, in which a computer program is stored, where the computer program is configured to execute, when running, any one of the above methods for demand-side response calculation based on campus scalability analysis.
Compared with the related technology, the demand side response calculation method based on park adjustability analysis provided by the embodiment of the application models and configures parameters of the functional framework and equipment of the industrial park system, and calculates the maximum adjustability of the industrial park system; reading the peak clipping requirement of a superior power grid from a database, and determining the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity; determining peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculating multi-proportion electricity demand side response and multi-proportion heat demand side response of a park system; the method comprises the steps of calculating IDR compensation quotations of multi-proportion electricity demand side responses and multi-proportion heat demand side responses, reporting multi-proportion electricity demand side response plans, multi-proportion heat demand side response plans and IDR compensation quotations to a superior power grid, realizing interactive feedback with the superior power grid, solving the problems of less research on influencing factors in an IDR resource modeling process, lacking of multi-objective optimization on environmental protection and total operation cost on the basis of meeting demand side response peak clipping and valley filling, providing important support for deeply excavating regulation potential of an industrial park and meeting peak clipping and valley filling requirements of the superior power distribution network.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of an application environment of a method for demand-side response computation based on campus scalability analysis according to an embodiment of the present application;
FIG. 2 is a flow chart of a demand side response calculation method based on campus scalability analysis according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a functional framework system of a multi-energy complementary industrial park according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating interactive feedback between an industrial park and a superior power grid according to an embodiment of the present application;
FIG. 5 is a block diagram of a demand side response computing system based on campus scalability analysis according to an embodiment of the present application;
FIG. 6 is a schematic flow chart illustrating an implementation of a demand-side response calculation method based on campus scalability analysis according to an embodiment of the present application;
fig. 7 is an internal structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The method for demand side response calculation based on campus scalability analysis provided by the present application can be applied to an application environment shown in fig. 1, where fig. 1 is an application environment schematic diagram of a method for demand side response calculation based on industrial campus scalability analysis according to an embodiment of the present application, as shown in fig. 1, where a system of the application environment includes a server 10 and modeling devices 11, where the server 10 models and configures parameters of functional frameworks and the devices 11 of an industrial campus system, and calculates the maximum scalability of the industrial campus system itself; reading the peak clipping requirement of a superior power grid from a database, and determining the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity; determining peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculating multi-proportion electricity demand side response and multi-proportion heat demand side response of the park system; and finally, calculating IDR compensation quotations of multi-proportion electricity demand side responses and multi-proportion heat demand side responses, and reporting the multi-proportion electricity demand side response plans, the multi-proportion heat demand side response plans and the IDR compensation quotations to a superior power grid to realize interactive feedback with the superior power grid, so that the problems of less research on influencing factors in an IDR resource modeling process, lack of multi-objective optimization on environmental protection and total operation cost on the basis of meeting demand side response peak clipping and valley filling are solved, and important support is provided for deeply excavating the regulation potential of an industrial park and meeting peak clipping and valley filling requirements of the superior power distribution network.
The present embodiment provides a demand side response calculation method based on campus scalability analysis, and fig. 2 is a flowchart of a demand side response calculation method based on campus scalability analysis according to an embodiment of the present application, and as shown in fig. 2, the flowchart includes the following steps:
step S201, modeling and parameter configuration are carried out on the functional framework and the equipment 11 of the industrial park system, and the maximum adjustability of the industrial park system is calculated, wherein the maximum adjustability is divided into electric maximum adjustability and thermal maximum adjustability. In the production process of a factory, coupling and conversion of four energy forms of cold, heat, electricity and gas are involved, the load is various in types and the energy supply of equipment is rich, fig. 3 is a schematic diagram of a functional framework system equipment of a multi-energy complementary industrial park according to the embodiment of the application, and as shown in fig. 3, the embodiment mainly realizes modeling and parameter configuration of the functional framework and the equipment of the multi-energy complementary industrial park system and calculates the maximum adjustability of the industrial park system;
step S202, reading the peak clipping requirement of the superior power grid from the database, determining the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity,the peak clipping requirement refers to reduction of the load requirement of a power grid during the peak period of power utilization. In the embodiment, the park receives the peak clipping time period and the peak clipping requirement issued by the power grid, and compares the peak clipping requirement delta Pref(t) and the calculated maximum adjustability of park electricity Δ Pmax(t) determining the actual electrical maximum adjustment if Δ Pmax(t)＞ΔPref(t) shows that the park can fully meet the peak clipping requirement of the power grid, and the maximum adjustment quantity delta P of the actual electricitym(t)＝ΔPref(t), since the heat demand side response is subsequently performed based on the maximum heat adjustment amount, the sum Δ P is calculatedm(t)＝ΔPref(t) corresponding Δ Qm(t) of (d). If Δ Pmax(t)＜ΔPref(t), the park can not completely meet the peak clipping requirement of the power grid, the actual maximum electricity adjustment quantity is the maximum electricity adjustable capacity of the park, and delta Pm(t)＝ΔPmax(t) the maximum heat adjustment amount is equal to Δ Pmax(t) corresponding Δ Qmax(t), i.e. Δ Qm(t)＝ΔQmax(t), the adjustment potential of the industrial park can be deeply excavated;
step S203, determining the peak load shifting demand, the total operation cost and the weight coefficient of the environment-friendly optimization target, and calculating the multi-proportion electricity demand side response and the multi-proportion heat demand side response of the park system, wherein the peak load shifting is a measure for adjusting the electricity load, and means that the power enterprises reduce the peak load of the power grid, improve the valley load, smooth the load curve, improve the load rate, reduce the power load demand, reduce the generator set investment and stabilize the power grid operation through necessary technical means. The embodiment meets the peak clipping and valley filling requirements F1In the case of (2), the total running cost C is also taken into considerationATCOptimization and environment-friendly optimization by a weight coefficient lambda1，λ2，λ3And realizing the determination of the multi-optimization objective function, as shown in formula 1:
Figure BDA0002728829930000061
the target function of the peak clipping and valley filling requirements is divided into two types of electric demand response and thermal demand response, and the two types of electric demand response and the thermal demand response are shown in a formula 2;
Figure BDA0002728829930000062
in the formula, epsilonjIs the proportion of the response;
total operating cost CATCThe optimized objective function is shown in equation 3:
minCATC＝COM+CES+CHS+Cbw+Cf+CSS (3)
in the formula, COMCost for operating and maintaining, CESTo purchase the cost of electricity, CHSFor purchase of heat cost, CbwDepreciation cost for energy storage, CfIs the cost of fuel, CSSThe start-stop cost.
The environmental protection optimization mainly considers the pollution emission of the gas turbine, and is quantified by the emission punishment of the park system, and the mathematical model of the environmental cost is shown as a formula 4:
Figure BDA0002728829930000063
in the formula: c'ErPunishment for the discharge of the r pollutant; fMTrAn r pollutant produced for the gas turbine; l is the number of gas turbine settings. Because the amount of pollutants generated by the gas turbine is not constant and difficult to measure, the pollutants generated by the gas turbine are considered to be balanced and not influenced by uncertain factors such as temperature, gas pressure, gas combustion value and the like, wherein the main pollutants comprise SO2、NOx、CO2CO, the corresponding pollution emission penalty is shown in table 1 below:
TABLE 1
Figure BDA0002728829930000064
In addition, after determining the multi-optimization objective function, the embodiment comprehensively considers the electric and thermal adjustability computing parkDetermining an optimized dispatching model by the multi-proportion electric demand side response and the multi-proportion heat demand side response of the system, wherein the obtained objective function formula 1 is combined to the response proportion epsilonjPlan value of epsilon1＝100％，ε2＝80％，ε3＝60％，ε4＝40％，ε5At 20%, 5 times of the multiple proportion electrical demand side response calculations are performed as shown in equation 5:
Figure BDA0002728829930000071
similarly, in conjunction with the objective function equation 1 obtained above, the response ratio εjPlan value of epsilon1＝100％，ε2＝80％，ε3＝60％，ε4＝40％，ε5At 20%, 5 multiple proportional heat demand side response calculations were performed as shown in equation 6:
Figure BDA0002728829930000072
according to the method, through determination of the proportional coefficients of the optimization targets, on the basis of meeting the requirement side response peak clipping and valley filling, other optimization targets can be considered as much as possible, the utilization rate of equipment is improved, and the energy consumption cost is reduced;
step S204, calculating IDR compensation quotations of multi-proportion electricity demand side responses and multi-proportion heat demand side responses, and reporting a multi-proportion electricity demand side response plan, a multi-proportion heat demand side response plan, and an IDR compensation quotation to an upper-level power grid, and fig. 4 is a schematic diagram of interaction feedback between an industrial park and the upper-level power grid according to an embodiment of the present application, as shown in fig. 4, in this embodiment, 5 groups of IDR compensation quotations of electricity demand side responses and heat demand side responses are calculated, and 5 groups of electricity demand side response plans, a heat demand side response plan, and corresponding IDR compensation quotations are reported to the upper-level power grid, so as to implement interaction feedback with the upper-level power grid, and provide important support for meeting peak clipping and valley filling requirements of the upper-level.
Through the steps S201 to S204, compared to the prior art, the influence of the IDR resource on the overall operation cost of the system or the comprehensive energy utilization efficiency is mainly studied, and the problem of influencing factors in the IDR resource modeling process is rarely considered, because the modeling analysis is mostly performed on the operation control strategy of the multi-energy network with the objective of minimum system operation cost. In the embodiment, the functional framework and the equipment 11 of the industrial park system are modeled and configured with parameters, and the maximum adjustability of the industrial park system is calculated; reading the peak clipping requirement of a superior power grid from a database, and determining the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity; determining peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculating multi-proportion electricity demand side response and multi-proportion heat demand side response of a park system; the method comprises the steps of calculating IDR compensation quotations of multi-proportion electricity demand side responses and multi-proportion heat demand side responses, reporting multi-proportion electricity demand side response plans, multi-proportion heat demand side response plans and IDR compensation quotations to a superior power grid, realizing interactive feedback with the superior power grid, solving the problems of less research on influencing factors in an IDR resource modeling process, lacking of multi-objective optimization on environmental protection and total operation cost on the basis of meeting demand side response peak clipping and valley filling, providing important support for deeply excavating regulation potential of an industrial park and meeting peak clipping and valley filling requirements of the superior power distribution network.
In some of these embodiments, the functional architecture and equipment 11 of the industrial park system includes: the system comprises a battery energy storage system, an ice cold storage device, an electric refrigeration/heat central air conditioner, a lithium bromide refrigeration system, a gas turbine, a gas boiler, a photovoltaic unit, an absorption refrigerator, a household air conditioner, a waste heat boiler and various steam driving devices, wherein in the embodiment, the industrial park exchanges power with a public power grid through a centralized power bus and purchases power from an external power grid so as to meet the electric load requirement of a factory. In addition, a Combined Heat and Power (CHP) system exists in the industrial park, electricity generated by the CHP is transmitted to a public power grid, and industrial users in the park can purchase hot steam from the CHP as a heat source of steam-driven equipment. The gas turbine, the waste heat boiler and the absorption refrigerator jointly form a combined cooling heating and power system, in the cooling/heating system, a household air conditioner can provide space cooling and heating load and serve as peak regulation equipment of the space cooling and heating load, the ice cold accumulation device stores cooling capacity when the cooling capacity meets the current requirement and the electricity price is low, and releases the cooling capacity when the cooling capacity is needed. Optionally, the comprehensive gradient utilization of heat energy is an essential link in industrial production, in a factory, different heat grade contra-aperture thermodynamic cycle systems are different from energy utilization technologies, the higher the heat energy grade is, the more the utilization modes are. Because the high-grade heat, the medium-grade heat and the low-grade heat in the plant are different in grade and quantity, the temperature of a plant heat distribution pipe network is generally fixed, and the steam flow in a pipeline is mainly controlled by a valve, the available energy of the plant heat distribution pipe network is expressed by the equivalent heat load available for heat energy, and the equivalent heat load and the steam consumption rate have the following relation, as shown in formula 7:
Figure BDA0002728829930000084
in the formula:
Figure BDA0002728829930000085
λgradand
Figure BDA0002728829930000086
respectively representing the consumption rate (ton/h), the calorific value (kJ/ton) and the equivalent thermal load (kW) of the grade steam, wherein 1kwh is 3600 kJ;
in addition, the modeling equipment 11 which needs to be controlled in the embodiment comprises a battery energy storage system, an ice storage air conditioning system, an electric refrigeration/heat central air conditioner, a lithium bromide refrigeration system, a gas turbine and a gas boiler;
the battery energy storage system controls the charge and discharge state and the charge and discharge Power of the battery at each time period through a Power Conversion System (PCS), as shown in formula 8;
Figure BDA0002728829930000081
in the formula,
Figure BDA0002728829930000082
is the self-loss factor of the battery energy storage, e.g. the
Figure BDA0002728829930000083
Is 0.0025; etaES_inAnd ηES_outRespectively representing the charging efficiency and the discharging efficiency of the battery energy storage; pES_in(t) and PES_out(t) representing the charging power and the discharging power of the battery stored energy respectively; sES(t) is the capacity of the battery to store energy; t is a unit time interval;
when the ice storage air conditioner is in an ice storage working condition, the ice storage power is controlled by controlling the inlet temperature and the volume of the glycol solution of the ice storage device; and during ice melting and cold supply, the ice melting and cold supply power is controlled by controlling the outlet temperature and the volume of the glycol solution of the ice storage device. The refrigerating machine controls the refrigeration/heat quantity by controlling the temperature and volume of the ethylene glycol inlet water and the ethylene glycol return water. The cold supply power, the cold accumulation power and the ice melting and cold supply power of the ice cold accumulation air conditioner are controlled by controlling the temperature and the volume of the inlet water and the return water of the ethylene glycol solution. The temperature and volume of the inlet water and the return water of the glycol solution and the cold supply power, the ice storage power and the ice melting and cold supply power of the ice storage air conditioner have the following relations as shown in the formulas 9-16:
Figure BDA0002728829930000091
in the formula, CglySpecific heat capacity, rho, of ethylene glycol solutionglyIs the density, Δ T, of the ethylene glycol solutiongly_iceThe temperature difference between the return water and the water supply of the glycol solution passing through the ice storage tank is Vgly_ice(t) the ice storage condition is the volume of the glycol solution passing through the ice storage tank, etaiceTo produce ice efficiently;
Figure BDA0002728829930000092
in the formula, CglyIs a secondSpecific heat capacity of alcoholic solutions, pglyIs the density, Δ T, of the ethylene glycol solutiongly_refThe temperature difference between the return water and the water supply of the glycol solution passing through the refrigerating machine in the refrigerating working condition is Vgly_ref(t) is the volume of glycol solution passing through the refrigerator under refrigeration conditions, etaiceTo the refrigeration efficiency;
Figure BDA0002728829930000098
in the formula, CglySpecific heat capacity, rho, of ethylene glycol solutionglyIs the density, Δ T, of the ethylene glycol solutiongly_meltThe temperature difference between the return water of the glycol solution and the water supply, V, passing through the ice storage tank under the ice melting working conditiongly_melt(t) the volume of the glycol solution passing through the ice storage tank under the ice-melting condition, etameltThe ice melting efficiency is improved;
Qref(t)＝Pref(t)EERref (12)
Figure BDA0002728829930000093
Pice(t)＝Pref(t)+Ptank(t) (14)
Qice(t)＝Qref(t)+Qtank(t) (15)
Figure BDA0002728829930000094
in the formula, Pref(t) and Qref(t) the refrigeration power consumption and the refrigeration power of the refrigerator are respectively; EERrefFor the refrigerating energy efficiency ratio of the refrigerating machine, e.g. the EERrefIs 3; qtank(t) and PtankAnd (t) the refrigeration power and the ice storage power consumption power of the ice storage tank are respectively, and the refrigeration and the ice storage operation can not be carried out at the same time. PiceAnd (t) is the power consumption of the ice storage air conditioning system. Stank(t) is the ice storage capacity of the ice storage tank,
Figure BDA0002728829930000095
is the self-loss factor of the ice storage tank, e.g. the
Figure BDA0002728829930000096
The value of (A) is 0.002,
Figure BDA0002728829930000097
energy efficiency ratio for ice making of ice storage tank, e.g. the
Figure BDA0002728829930000099
The value of (a) is 4,
Figure BDA00027288299300000910
for the efficiency of the refrigeration release of the ice storage tank, e.g. of
Figure BDA00027288299300000911
Is 0.95;
the electric refrigeration/heat central air conditioner controls the refrigeration/heat of the main machine by controlling the temperature and the volume of the inlet and the return water of the chilled water of the main machine, and the refrigeration/heat of the electric refrigeration/heat central air conditioner is in direct proportion to the input electric power of the electric refrigeration/heat central air conditioner because the temperature of the inlet and the return water of the chilled water is kept constant, as shown in formulas 17 and 18;
Qac(t)＝Pac(t)EERcold (17)
Figure BDA0002728829930000101
in the formula, Qac(t) and Pac(t) the refrigeration/heat power and the power consumption power of the electric refrigeration air conditioner are respectively; EERcoldFor the energy efficiency ratio of the electric refrigeration air conditioner, e.g. the EERcoldIs 4.3;
Figure BDA0002728829930000102
the maximum power consumption of the electric refrigeration air conditioner. The refrigerating capacity of the electric refrigerating central air conditioner and the refrigerating power Q of the central air conditioner are controlled by controlling the flow of the refrigerated waterac(t), then Qac(t) has the following relationship with the temperature and volume of the chilled water, as shown in equation 19:
Figure BDA0002728829930000103
in the formula, CwaterIs the specific heat capacity of the chilled water, ρwaterFor frozen water density, Δ TacFor temperature difference between water return and water supply, etaacFor cooling efficiency;
the gas turbine generates electricity by burning natural gas to generate high-grade heat, and the electricity generating power of the gas turbine is PGT(t) the electric power generation and heat generation powers are as follows, as shown in equations 20-23:
PGT(t)＝ηGT.eλgasFGT(t) (20)
HGT(t)＝ηGT.h(1-ηGT.e)λgasFGT(t) (21)
-DGT(t)≤PGT(t+1)-PGT(t)≤BGT(t) (22)
Figure BDA0002728829930000104
in the formula, FGT(t) is a gas consumption rate (m) of the gas internal combustion engine during a period t3/h)；PGT(t) the power (kW) generated by the gas combustion engine;
Figure BDA0002728829930000105
and
Figure BDA0002728829930000106
the minimum and maximum power generation of the gas internal combustion engine; etaGT.eAnd ηGT.hRespectively, the power generation efficiency of the gas internal combustion engine and the heat recovery rate of the waste heat boiler, e.g. etaGT.eAnd ηGT.hThe values are respectively 0.33 and 0.6; hGT(t) the thermal power (kW) recovered by the waste heat boiler in the period of t; b isGT(t) and DGT(t) representing an upper limit and a lower limit of the gas engine climbing slope, respectively; lambda [ alpha ]gasIs the heat value (kwh/m) of natural gas3) E.g. the lambdagasIs 9.9;
the gas boiler heats hot water by burning natural gas to generate high-temperature steam, the temperature and the pressure of the high-temperature steam are kept constant, and the control of the running state of the gas boiler is realized by controlling the steam flow, and the following relations exist, as shown in formulas 24 and 25:
HGB(t)＝FGB(t)λgasηGB (24)
Figure BDA0002728829930000107
in the formula, HGB(t)、ηGBPower (kW) and efficiency of heat production, respectively, of the gas boiler, e.g. etaGBIs 9.9; fGB(t) gas boiler gas consumption rate (m) for a period of t3/h)；λgasIs the heat value of natural gas;
the steam flow consumed by the lithium bromide absorption refrigerator is controlled by the pipeline valve, so that the cooling capacity of the absorption refrigerator can be controlled, the refrigerating capacity is in direct proportion to the heat energy input quantity, and the formula is shown as 26 and 27:
QBr(t)＝HBr.in(t)COPBr.c (26)
Figure BDA0002728829930000112
in the formula, QBr(t) is the refrigeration power of the lithium bromide absorption refrigerator; hBr.in(t) is the heat consumption of the lithium bromide absorption refrigerator;
Figure BDA0002728829930000111
the maximum heat consumption power of the lithium bromide absorption refrigerator; COPBr.cIs of lithium bromide absorption type. The heat value of the steam is lambda according to the temperature and the pressure of the steamBr(kJ/ton) in the lithium bromide absorption systemThe steam consumption rate (ton/h) of the chiller is shown in equation 28:
FBr(t)＝3600·HBr(t)/λBr (28)
in some of these embodiments, configuring the parameters of the model comprises: the parameter Z is configured by setting corresponding 0, 1 to the modeling apparatus 11mod.iAnd an operating parameter Zrun.iWherein, if Zmod.iIf Z is equal to 1, the device i exists in the industrial park and plays a role, otherwise, the device i does not exist, and if Z is not equal to 1, the device i does not existrun.iA running constraint i is active, whereas the constraint is ignored. Optionally, in a complex industrial park, different types of industrial and commercial subjects exist, the production requirements thereof are greatly different, and the device configurations and operation modes are also different, so that in this embodiment, corresponding 0 and 1 configuration parameters Z are set for each modeling device 11mod.iAnd an operating parameter Zrun.iThe invention is ensured to have universality for various industrial and commercial subjects.
In some of these embodiments, modeling the functional architecture and equipment of an industrial park system includes: and constructing the optimized scheduling constraint conditions of the modeling device 11, wherein the optimized scheduling constraint conditions comprise an electric power constraint, a thermal power constraint and a cold power constraint. Optionally, in this embodiment, the construction of the optimal scheduling constraint condition is performed on the modeling equipment in the multi-energy complementary comprehensive energy industrial park, and the optimal scheduling constraint condition mainly includes a power constraint, a thermal power constraint and a cold power constraint condition, where the electric power constraint includes an ac bus electric power constraint, an ac-dc converter efficiency constraint, a dc bus total load constraint, a battery energy storage constraint, an ice storage air conditioning system equipment constraint and a gateway power constraint;
the ac bus electrical power constraints are as shown in equation 29:
Pbuy(t)+PGT(t)＝PAC-load(t)+PAC-DC(t)+γice(t)Pice(t)+γac(t)Pac(t)+PBr(t) (29)
in the formula, considering that in the ice storage air conditioning system and the electric refrigeration air conditioning system,the cooling tower and the pump have corresponding energy consumption, so that the accuracy of the model is ensured by multiplying the energy consumption of the main engine by a certain proportionality coefficient, wherein gamma isice(t) and γac(t) is the proportionality coefficient of ice storage and electric refrigeration air conditioning systems, e.g. 1.05, PBr(t) is the power consumed by the cooling tower and the pump in the lithium bromide absorption refrigerator;
the efficiency constraint of the ac-dc converter is shown in equation 30:
Figure BDA0002728829930000121
in the formula etaA-DEfficiency of conversion of AC to DC, etaD-AConversion efficiency for converting DC to AC, PDC(t) is the total load of the direct current bus in the time period t;
the total load constraint condition of the direct current bus is shown as equation 31:
PDC(t)+PPV(t)＝PDC-load(t)+PES_in(t)+PES_out(t) (31)
in the formula, PPV(t) photovoltaic power generation power, PDC-load(t) is a DC load;
the battery energy storage constraint comprises a charge and discharge power constraint, a capacity constraint, a climbing rate constraint and a daily electric quantity accumulation constraint, and the charge and discharge power constraint conditions are shown in the formulas 32-34:
Figure BDA0002728829930000122
Figure BDA0002728829930000123
0≤γin+γout≤1 (34)
in the formula,
Figure BDA0002728829930000124
in order to maximize the efficiency of the discharge,
Figure BDA0002728829930000125
for maximum charging efficiency, gammainAnd gammaoutRespectively representing the energy storage device in a 0-1 state variable of charging and discharging energy in a time period t, wherein gamma in1 represents charging energy, gammaoutTaking 1 to show energy release;
capacity constraints, as shown in equations 35-38:
Figure BDA0002728829930000126
Figure BDA0002728829930000127
Figure BDA0002728829930000128
SES(0)＝SOCintR (38)
in the formula,
Figure BDA0002728829930000129
the maximum electric quantity of the energy storage battery is,
Figure BDA00027288299300001210
is the lowest electric quantity of the energy storage battery, SES(t) the battery energy storage state, SOC, for a time period tint、SOCminAnd SOCmaxRespectively an initial state of charge, a minimum state of charge and a maximum state of charge, wherein R is the battery capacity;
the climbing rate constraint condition is shown in formulas 39 and 40:
Figure BDA00027288299300001211
Figure BDA00027288299300001212
in the formula,
Figure BDA00027288299300001213
maximum charge and discharge power, gamma, respectively, for battery energy storageESIs a ramp rate constraint coefficient, e.g. the gammaESThe value is 1.05;
the daily charge accumulation constraint condition is shown in equation 41:
Figure BDA00027288299300001214
the ice storage air conditioning system equipment constraints comprise power consumption constraint, capacity constraint and daily accumulation constraint;
the power consumption constraint, as shown in equation 42:
Figure BDA0002728829930000131
capacity constraints, as shown in equations 43-46:
Figure BDA0002728829930000132
Figure BDA0002728829930000133
Figure BDA0002728829930000134
Figure BDA0002728829930000135
in the formula,
Figure BDA0002728829930000136
is the maximum ice storage amount of the ice storage tank,
Figure BDA0002728829930000137
is the lowest ice storage quantity S of the ice storage tanktank(t) is the ice storage state for time period t.
Figure BDA0002728829930000138
And
Figure BDA0002728829930000139
respectively in an initial ice storage state, a minimum ice storage state and a maximum ice storage state, and S is the capacity of the ice storage tank;
the daily accumulation constraint, as shown in equation 47:
Figure BDA00027288299300001310
the gateway power constraint, as shown in equation 48:
P(t)≤Pupp(t) (48)
wherein, Pupp(t) power constraint of the power grid gateway;
thermal power constraints, as shown in equations 49-51:
Figure BDA00027288299300001311
Figure BDA00027288299300001312
Hac(t)+HBr(t)≥Hspace(t) (51)
cold power constraints, as shown in equation 52:
QBr(t)+Qice(t)+EERcoldPac(t)＝Qsys(t) (52)
in the formula, Qsys(t) is the cooling load demand.
In some embodiments, after the modeling of the industrial park system is completed, the initial optimization scheduling is performed without considering the demand-side response scheduling and with the total operating cost as the minimum objective function, so as to obtain the power utilization plan and the heat utilization plan, where the objective function is shown in formula 53:
minCATC＝COM+CES+CHS+Cbw+Cf+CSS (53)
in the formula, COMCost for operating and maintaining, CESTo purchase the cost of electricity, CHSFor purchase of heat cost, CbwDepreciation cost for energy storage, CfIs the cost of fuel, CSSThe cost is the start-stop cost;
wherein, the operation and maintenance cost is shown in formula 54:
Figure BDA00027288299300001313
in the formula,
Figure BDA0002728829930000141
the operating maintenance cost per unit output power of the device s,
Figure BDA0002728829930000142
represents the output power of the s-th device at time T, where T is the unit period length;
the electricity purchase cost is shown in equation 55:
Figure BDA0002728829930000143
where 96 is the total number of time periods throughout the day, and optionally, the total number of time periods throughout the day may be other suitable values, Cbuy(t) time-of-use electricity price, P, for a time period tbuy(T) the power supply power obtained from the main network in a time period T, wherein T is the length of a unit time period;
in general, as the depth of discharge increases, the number of cycles of charging and discharging the battery decreases, but the total charge and discharge cycle amount remains substantially constant, so in this embodiment, assuming that the total charge and discharge amount of the battery in the entire life cycle is constant, the depreciation cost of the battery accumulated charge of 1kWh is obtained, as shown in equations 56 and 57:
Figure BDA0002728829930000144
Cbw＝∑tcbwPES_in(t)，PES_in(t)＞0 (57)
in the formula, Cbat.repFor replacement cost of batteries, QlifetimeOutputting the total electric quantity for the whole service life of the battery monomer, wherein T is the length of a unit time interval;
the fuel cost is shown in equation 58:
Figure BDA0002728829930000145
in the formula, Cgas(t) is hourly gas value, FGB(t) is the gas consumption of the gas boiler at t time period, wherein the natural gas price is 3.45 yuan/m3The price is reduced to 0.349 yuan/kWh per unit heat value, and T is the length of a unit time interval;
in addition to the power purchase from the grid, the heat purchase from the upper-level energy system may be performed, and the cost of the heat purchase is shown in formula 59:
Figure BDA0002728829930000146
in the formula, cHSThe steam price is 348 yuan/ton, the heat value is 2694800 kJ/ton, the price per heat value is 0.465 yuan/kWh, T is the unit time interval length, H isbuy(t) Heat purchase Power, steam consumption Rate Fbuy(t)＝Hbuy(t)/996, amount of steam purchase Pbuy(t)＝Fbuy(t)T；
The start-stop cost is shown in equation 60:
Figure BDA0002728829930000147
in the formula, cSS.iRepresents the on-off cost of the equipment i at a unit time, Ui(t) the starting and stopping state of the equipment i at the moment t, the stopping state is represented when the value is 0, and the starting state is represented when the value is 1;
in the embodiment, the demand side response scheduling is not considered, the total operation cost is the minimum, and the electricity utilization energy plan and the heat utilization energy plan are obtained through the initial optimization scheduling calculation, so that a reference value is provided for the subsequent maximum adjustable capacity calculation.
In some of these embodiments, calculating the maximum scaleability, the multi-proportion electrical demand side response, and the multi-proportion thermal demand side response of the industrial park system itself includes: and (3) utilizing the constructed industrial park system model to supplement corresponding constraint conditions to calculate the maximum adjustability, the multi-proportion electric demand side response and the multi-proportion heat demand side response. In this embodiment, a constructed industrial park system model is used, and non-peak clipping period gateway power constraint, peak clipping power upper limit constraint, gateway thermal power lower limit constraint and smooth energy curve constraint are supplemented to calculate the maximum adjustability, where the non-peak clipping period gateway power constraint condition is as shown in formula 61:
Figure BDA0002728829930000151
wherein, Pref(t) is a peak clipping target reference power applicable to each plant substation in the whole park, optionally, the peak clipping period is the peak clipping target reference power, the non-peak clipping period is a gateway power constraint curve, t is the non-peak clipping period, and P' (t) is a new gateway power curve;
the upper limit constraint of peak clipping power is shown in equation 62:
P0(t)-P′(t)≤ΔPref(t)，t∈[t0，t1] (62)
wherein, Δ Pref(t) is the power of the park needing peak clipping issued by the whole park in the peak clipping period, t is the peak clipping period, because of the delta Pref(t)＝P0(t)-Pref(t)Therefore, the above formula can be simplified to P' (t) ≧ Pref(t)；
The constraint condition of the lower limit of the thermal power of the gateway is shown as an equation 63:
Figure BDA0002728829930000152
wherein,
Figure BDA0002728829930000153
a lower power limit for such thermal loads;
the smoothing energy use curve constraint is shown in equation 64:
Figure BDA0002728829930000154
the maximum adjustability of the electricity of the industrial park calculates an objective function, as shown in equation 65:
Figure BDA0002728829930000155
wherein P' (t) is the electric energy curve after peak clipping, λ1＞＞λ2The maximum adjustable capacity delta P of the plant electricity can be obtained by solving the optimization problemmax(t)＝P0(t) -P '(t), and simultaneously obtaining a thermal power curve Q' (t) of the corresponding time period, so that the maximum adjustable capacity delta Q of the plant heat is obtainedmax(t)＝Q0(t)-Q′(t)。
In addition, in this embodiment, the constructed industrial park system model is used, and non-peak clipping period gateway power constraint, peak clipping power upper limit constraint, gateway thermal power lower limit constraint and smooth energy consumption curve constraint are supplemented, and multi-proportion electric demand side response calculation is performed according to formula 5, so as to obtain 5 groups of corresponding electric energy consumption curves and corresponding thermal energy consumption curves, where the non-peak clipping period gateway power constraint condition is as shown in formula 66:
Figure BDA0002728829930000156
wherein, Pref(t) is a peak clipping target reference power applicable to each plant substation in the whole park, optionally, the peak clipping period is the peak clipping target reference power, the non-peak clipping period is a gateway power constraint curve, t is the non-peak clipping period, and P' (t) is a new gateway power curve;
the upper limit constraint condition of peak clipping power is shown as formula 67:
P0(t)-P′(t)≤ΔPref(t)，t∈[t0，t1] (67)
wherein, Δ Pref(t) is the power of the park needing peak clipping issued by the whole park in the peak clipping period, t is the peak clipping period, because of the delta Pref(t)＝P0(t)-Pref(t), therefore, the above formula can be simplified to P' (t) ≧ Pref(t)；
The thermal power lower limit constraint of the gateway is shown in equation 68:
Figure BDA0002728829930000161
wherein,
Figure BDA0002728829930000162
a lower power limit for such thermal loads;
the smoothing energy use curve constraint is shown in equation 69:
Figure BDA0002728829930000163
in addition, in this embodiment, the constructed industrial park system model is utilized, and non-peak clipping period gate thermal power constraint, peak clipping power upper limit constraint, gate electric power constraint and smoothing energy use curve constraint are supplemented, and multi-proportion thermal demand side response calculation is performed according to formula 6, so as to obtain 5 groups of corresponding electric energy use curves and corresponding thermal energy use curves, where the non-peak clipping period gate thermal power constraint condition is as shown in formula 70:
Figure BDA0002728829930000164
the upper limit constraint of peak clipping power is shown in equation 71:
Q′(t)-Q0(t)≤ΔQref(t)，t∈[t0，t1] (71)
wherein, is Δ Qref(t) the park issued for the whole park at the peak clipping period requires the heat load power of the factory for multiple purposes, t belongs to the peak clipping period, because of the delta Qref(t)＝Q0(t)-Qref(t), therefore, the above formula can be simplified to Qref(t)≥Q′(t)；
The gateway electrical power constraint, as shown in equation 72:
P′(t)≤P′m(t) (72)
the smoothing energy curve constraint is shown in equation 73:
Figure BDA0002728829930000165
in some of these embodiments, calculating the IDR compensation quote for the multi-proportion electric demand side response and the multi-proportion thermal demand side response includes: an IDR compensation quote is determined in conjunction with the electricity costs and customer satisfaction for the industrial park. Optionally, in this embodiment, the IDR compensation quotation calculation is performed on the obtained 5 groups of electric demand side responses and 5 groups of thermal demand side responses, and the calculation formula of the IDR quotation B is determined comprehensively by combining the power consumption cost and the user satisfaction of the industrial park, as shown in formula 74:
B＝λs(2-S′user)(CATC-C′ATCj) (74)
in the formula, CATCAnd C'ATCjCost of energy use and epsilon respectively for optimization of side response not requiredjEnergy cost, lambda, at corresponding proportional electric/thermal demand side responsesA user satisfaction degree weight coefficient;
user satisfaction degree SuserFree energy satisfaction degree SloadAnd power supply satisfaction degree SpowerJoint decision, as shown in equation 75:
Figure BDA0002728829930000171
wherein S isuserThe satisfaction degree of the user is obtained; sloadThe use can be satisfied; spowerThe satisfaction degree of energy supply is satisfied; rload.shiftIs the load transfer amount, i.e. the demand response capacity; rload.allIs the total user load. RdirectDirectly supplying energy of a load to renewable energy sources; rBESSSupplying energy for supplying energy to a load after energy storage and storage for renewable energy sources; rallTotal production energy as renewable energy;
the satisfaction degree of the user can influence the enthusiasm of the user for participating in the response of the demand side, if the satisfaction degree of the user is not considered, the benefit of the user can be damaged by the optimization result, and the purpose of coordinating the user to participate in the comprehensive demand response is difficult to achieve.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The present embodiment further provides a demand side response calculation system based on campus adjustability analysis, where the system is used to implement the foregoing embodiments and preferred embodiments, and the description already made is omitted here for brevity. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for 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. 5 is a block diagram of a structure of demand-side response calculation based on campus scalability analysis according to an embodiment of the present application, and as shown in fig. 5, the system includes a campus system modeling module 51, a maximum scalability determining module 52, a multi-target multi-scale demand-side response calculating module 53, and an IDR compensation quotation calculating and reporting module 54:
the park system modeling module 51 is used for modeling and parameter configuration of functional frameworks and equipment of the industrial park system, and calculating the maximum adjustability of the industrial park system; the maximum adjustable quantity determining module 52 is configured to read a peak clipping requirement of the upper-level power grid from a database, and determine an actual maximum adjustable quantity by comparing the peak clipping requirement with the maximum adjustable capacity; the multi-target multi-proportion demand side response calculating module 53 is used for determining peak clipping and valley filling requirements, total operating cost and weight coefficients of an environment-friendly optimization target, and calculating multi-proportion electric demand side response and multi-proportion heat demand side response of the park system; and an IDR compensation quotation calculation and reporting module 54, configured to calculate IDR compensation quotations of multi-proportion electric demand side responses and multi-proportion heat demand side responses, and report a multi-proportion electric demand side response plan, a multi-proportion heat demand side response plan, and an IDR compensation quotation to a higher-level power grid, so as to implement interactive feedback with the higher-level power grid.
Through the system, the park system modeling module 51 mainly realizes the modeling and parameter configuration of the functional framework and equipment of the multi-energy complementary industrial park system and calculates the maximum adjustability of the industrial park system; the maximum adjustable quantity determining module 52 reads the peak clipping requirement of the superior power grid from the database, determines the actual maximum adjustable quantity by comparing the peak clipping requirement with the maximum adjustable capacity, and can deeply excavate the adjustment potential of the industrial park; the multi-target multi-proportion demand side response calculation module 53 determines the proportion coefficient of each optimization target, so that other optimization targets can be considered as much as possible on the basis of meeting the demand side response peak clipping and valley filling, the utilization rate of equipment is improved, and the energy consumption cost is reduced; the IDR compensation quotation calculation and reporting module 54 calculates 5 groups of IDR compensation quotations of the electric demand side response and the thermal demand side response, and reports 5 groups of electric demand side response plans, the thermal demand side response plans and corresponding IDR compensation quotations to the upper-level power grid, so as to realize interactive feedback with the upper-level power grid, and provide important support for meeting the peak clipping and valley filling requirements of the upper-level power distribution network. The whole system solves the problems that research on influencing factors in the IDR resource modeling process is less, multi-objective optimization on environmental protection and total operation cost is lacked on the basis of meeting the demand side response peak clipping and valley filling, and provides important support for deeply excavating the regulation potential of an industrial park and meeting the peak clipping and valley filling requirements of a superior power distribution network.
The present invention will be described in detail with reference to the following application scenarios.
The invention provides a demand side response calculation method based on campus adjustability analysis, and fig. 6 is a schematic implementation flow diagram of the demand side response calculation method based on campus adjustability analysis according to the embodiment of the application, and is shown in fig. 6.
The specific process steps of the technical scheme of demand side response calculation based on campus adjustability analysis in this embodiment include:
s1, modeling the typical functional architecture and equipment of the park system;
s2, modeling a park system scheduling constraint condition;
s3, calculating the initial dispatching of the park non-demand side response and calculating the maximum adjustability of the park system;
s4, determining the actual maximum adjustment amount and the proportion coefficient of each optimization target;
s5, calculating multi-proportion electric demand response and multi-proportion heat demand response of the park system;
s6, IDR compensation offer calculation.
The present embodiment also provides an electronic device comprising a memory having a computer program stored therein and a processor configured to execute 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.
In addition, in combination with the method for demand side response calculation based on campus scalability analysis in the foregoing embodiment, the present application embodiment may provide a storage medium to implement. The storage medium having stored thereon a computer program; the computer program when executed by a processor implements any of the above described embodiments of a method for demand side response computation based on campus scalability analysis.
In one embodiment, fig. 7 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 7, there is provided an electronic device, which may be a server, and an internal structure diagram of which may be as shown in fig. 7. The electronic device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the electronic device is used for storing data. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method for demand side response computation based on campus scalability analysis.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is a block diagram of only a portion of the architecture associated with the subject application, and does not constitute a limitation on the electronic devices to which the subject application may be applied, and that a particular electronic device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be understood by those skilled in the art that various features of the above-described embodiments can be combined in any combination, and for the sake of brevity, all possible combinations of features in the above-described embodiments are not described in detail, but rather, all combinations of features which are not inconsistent with each other should be construed as being within the scope of the present disclosure.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for demand side response computation based on campus tunability analysis, the method comprising:
modeling and parameter configuration are carried out on a functional framework and equipment of the industrial park system, and the maximum adjustability of the industrial park system is calculated;
reading the peak clipping requirement of a superior power grid from a database, and determining the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity;
determining peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculating multi-proportion electricity demand side response and multi-proportion heat demand side response of a park system;
and calculating IDR compensation quotations of the multi-proportion electricity demand side response and the multi-proportion heat demand side response, and reporting the multi-proportion electricity demand side response plan, the multi-proportion heat demand side response plan and the IDR compensation quotations to the superior power grid.
2. The method of claim 1, wherein calculating the maximum scaleability, the multi-proportion electrical demand side response, and the multi-proportion thermal demand side response of the industrial park system itself comprises:
and utilizing the constructed industrial park system model to supplement corresponding constraint conditions to calculate the maximum adjustability, the multi-proportion electric demand side response and the multi-proportion heat demand side response.
3. The method of claim 1, wherein said calculating the IDR compensation quote for the multi-proportion electrical demand side response and the multi-proportion thermal demand side response comprises:
determining the IDR compensation quote in combination with the electricity cost and the customer satisfaction of the industrial park.
4. The method of claim 1, wherein after the industrial park system modeling is complete, the method comprises:
and performing initial optimization scheduling on the side-demand-free response to obtain an electricity utilization energy plan and a heat utilization energy plan.
5. The method of claim 1, wherein modeling the functional architecture and equipment of the industrial park system comprises:
and constructing an optimized scheduling constraint condition of the modeling equipment, wherein the optimized scheduling constraint condition comprises an electric power constraint, a thermal power constraint and a cold power constraint condition.
6. The method of claim 1, wherein the parameter configuration comprises:
the parameter Z is configured by setting corresponding 0 and 1 to the modeling equipmentmod.iAnd an operating parameter Zrun.iWherein, if Zmod.iIf Z is equal to 1, the device i exists in the industrial park and plays a role, otherwise, the device i does not exist, and if Z is not equal to 1, the device i does not existrun.iA running constraint i is active, whereas the constraint is ignored.
7. The method of claim 1, wherein the functional architecture and equipment of the industrial park system comprises:
the system comprises a battery energy storage system, an ice cold storage device, an electric refrigeration/heat central air conditioner, a lithium bromide refrigeration system, a gas turbine, a gas boiler, a photovoltaic unit, an absorption refrigerator, a household air conditioner, a waste heat boiler and various steam driving devices.
8. A system for demand side response computing based on campus tunability analysis, the system comprising:
the server carries out modeling and parameter configuration on the functional framework and the equipment of the industrial park system, and calculates the maximum adjustability of the industrial park system;
the server reads the peak clipping requirement of a superior power grid from a database, and determines the actual maximum adjustment amount by comparing the peak clipping requirement with the maximum adjustable capacity;
the server determines peak clipping and valley filling requirements, total operation cost and weight coefficients of an environment-friendly optimization target, and calculates multi-proportion electricity demand side response and multi-proportion heat demand side response of a park system;
and the server calculates IDR compensation quotations of the multi-proportion electricity demand side response and the multi-proportion heat demand side response, and reports the multi-proportion electricity demand side response plan, the multi-proportion heat demand side response plan and the IDR compensation quotations to the superior power grid.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and the processor is configured to execute the computer program to perform the method of campus scalability analysis based demand-side response calculation of any of claims 1 to 7.
10. A storage medium having stored thereon a computer program, wherein the computer program is arranged to perform the method of demand side response calculation based on campus scalability analysis as claimed in any one of claims 1 to 7 when run.
CN202011114572.4A 2020-10-16 2020-10-16 Demand side response calculation method based on park adjustability analysis Pending CN112329988A (en)

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CN113344273A (en) * 2021-06-08 2021-09-03 中国农业大学 Building energy consumption based method and system for adjusting and optimizing peak-valley difference of regional distribution network
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CN115046246A (en) * 2022-08-15 2022-09-13 东方电子股份有限公司 Self-optimization-seeking control system for comprehensive energy
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CN116432818A (en) * 2023-03-02 2023-07-14 广州东方电科自动化有限公司 Power demand response optimization method for energy storage park
CN116451830A (en) * 2023-03-02 2023-07-18 广州东方电科自动化有限公司 Industrial park planning power curve tracking method
CN116432818B (en) * 2023-03-02 2024-02-23 广州东方电科自动化有限公司 Power demand response optimization method for energy storage park
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Application publication date: 20210205