CN117350419A - Park comprehensive energy system optimization operation method considering flexible load and carbon flow - Google Patents
Park comprehensive energy system optimization operation method considering flexible load and carbon flow Download PDFInfo
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Abstract
The invention discloses a park comprehensive energy system optimizing operation method considering flexible load and carbon flow, which comprises the following steps: constructing a park comprehensive energy system; introducing step carbon transaction and determining corresponding gratuitous carbon emission quota and actual carbon emission; constructing a carbon transaction mechanism model according to the gratuitous carbon emission quota and the actual carbon emission amount; constructing a demand response model according to the time-sharing electricity price; describing source load uncertainty by adopting a radix uncertainty set; and constructing an objective function, constraint conditions and an optimization scheduling model, and completing energy system optimization through the optimization scheduling model. According to the invention, electric, thermal, cold and gas loads are integrated into flexible demand response, so that peak-valley difference is effectively reduced, energy supply pressure of equipment is reduced, and energy efficiency level is optimized; the step-type carbon transaction model is introduced, so that the carbon emission level is effectively restrained, the output level of energy supply equipment is optimized, and the energy consumption loss is reduced; the uncertainty of the source load is fully considered, and the safe and stable operation of the renewable energy system is effectively improved.
Description
Technical Field
The invention relates to the technical field of park comprehensive energy, in particular to a park comprehensive energy system optimizing operation method considering flexible load and carbon flow.
Background
The park integrated energy system (Park Integrated Energy System, PIES) is increasingly the dominant form of the new integrated system. PIES not only can exert complementary advantages of renewable energy sources and sharing advantages of energy source coupling, but also can further promote energy utilization by integrating system resources such as electric power, natural gas, heat, cold energy and the like. The optimal scheduling of PIES in the future has become a main method for solving the contradiction between energy development and environment, and the optimal scheduling of PIES with low-carbon economy as background is the next hot spot. The initial research of the comprehensive energy system mainly covers the internal structure and the energy types of the system, and the research is mainly developed from the three aspects of the system, the objective function, the constraint condition and the intelligent algorithm. The current research systems include an electric-gas interconnection system, an electric-thermal interconnection system, an electric-heat-cold interconnection system, an electric-heat-gas interconnection system and the like. On the basis, part of research work is brought into the digestion of photovoltaic and wind power, and a multi-element energy storage device is introduced to reduce the phenomenon of wind and light abandoning, so that the utilization efficiency of renewable resources is increased. The objective function of the existing PIES is mainly economical and has environmental benefits, but the influence of a carbon emission trading mechanism, the effect of a user demand side on the optimized operation in the comprehensive energy system, uncertainty of a multi-element load and the research of the system on the analysis related to the environmental pollution degree and the comprehensive demand response are not considered, so that the PIES is low in efficiency and poor in stability, and the carbon emission is not effectively restrained.
Disclosure of Invention
Aiming at the defects in the prior art, the optimization operation method of the park comprehensive energy system considering the flexible load and the carbon flow solves the problems of low optimization efficiency and poor stability of the park comprehensive energy system in the prior art.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
there is provided a method of optimizing operation of a campus integrated energy system accounting for flexible loads and carbon flows, comprising the steps of:
s1, constructing a park comprehensive energy system containing an electric-heat-cold-gas-carbon flow system;
s2, introducing step carbon transaction and determining gratuitous carbon emission quota and actual carbon emission of the park comprehensive energy system; constructing a carbon transaction mechanism model of the park comprehensive energy system according to the gratuitous carbon emission quota and the actual carbon emission;
s3, constructing a demand response model according to the time-sharing electricity price;
s4, describing source load uncertainty of the park comprehensive energy system by adopting a base uncertainty set;
s5, constructing an objective function and constraint conditions according to the carbon transaction mechanism model, the demand response model and the source load uncertainty;
s6, constructing an optimized scheduling model according to the objective function and the constraint condition; and (3) according to the requirements of the park comprehensive energy system, an optimal output plan is formulated through an optimal scheduling model, and the energy system optimization is completed.
The beneficial effects of the invention are as follows:
1. according to the optimal operation method, electric, thermal, cold and air loads are integrated into flexible demand response to participate in optimal scheduling analysis, so that peak-valley difference can be effectively reduced, energy supply pressure of equipment is reduced, and energy efficiency level of a system is further optimized;
2. the optimizing operation method introduces the ladder-shaped carbon transaction model with punishment, so that the carbon emission level can be well restrained, the output level of energy supply equipment can be optimized, and the energy consumption loss is reduced;
3. according to the optimal operation method, the uncertainty of the output and the load of the renewable energy sources is fully considered through the constructed source-load uncertainty set model, and uncertainty parameters are adjusted, so that the safe and stable operation of the renewable energy source system can be effectively improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a campus integrated energy system architecture;
FIG. 3 is a graph of burnup for a piecewise linearized gas turbine;
FIG. 4 is a schematic diagram of a ladder carbon transaction cost;
FIG. 5 is a typical user load graph;
FIG. 6 is a graph of photovoltaic, wind power prediction;
FIG. 7 is a graph of various energy prices;
FIG. 8 is a graph of power balance scheduling results;
FIG. 9 is a graph of heat balance schedule results;
FIG. 10 is a graph of cold balance scheduling results;
fig. 11 is a diagram of the result of gas balance scheduling.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a method for optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows includes the steps of:
s1, constructing a park comprehensive energy system containing an electric-heat-cold-gas-carbon flow system;
s2, introducing step carbon transaction and determining gratuitous carbon emission quota and actual carbon emission of the park comprehensive energy system; constructing a carbon transaction mechanism model of the park comprehensive energy system according to the gratuitous carbon emission quota and the actual carbon emission;
s3, constructing a demand response model according to the time-sharing electricity price;
s4, describing source load uncertainty of the park comprehensive energy system by adopting a base uncertainty set;
s5, constructing an objective function and constraint conditions according to the carbon transaction mechanism model, the demand response model and the source load uncertainty;
s6, constructing an optimized scheduling model according to the objective function and the constraint condition; and (3) according to the requirements of the park comprehensive energy system, an optimal output plan is formulated through an optimal scheduling model, and the energy system optimization is completed.
As shown in fig. 2, the park comprehensive energy system of step S1 includes an energy supply unit model, an energy coupling unit model, an energy storage unit model, and an external distribution network model; the energy supply unit model comprises a photovoltaic unit and a wind generating unit; the energy coupling unit model comprises a gas turbine, a gas boiler, a waste heat boiler, an absorption refrigerator, an electric heating boiler, carbon capturing equipment and electric gas converting equipment; the energy storage unit comprises electricity storage, heat, cold and gas equipment.
The photovoltaic unit comprises photovoltaic cells, and a predicted power model of the photovoltaic cells is as follows:
T c ＝T a +30G c /1000
wherein T is c Representing the actual sun illumination intensity, T a Represents the ambient temperature, G c Representing the actual temperature of the photovoltaic array;
the power prediction model of the wind turbine generator set is as follows:
wherein P is WT (t) represents the output power of the fan at the moment t, P r Represents the rated power of the fan, v represents the actual wind speed, v r Represents the rated wind speed of the fan, v ci Represents the cut-in wind speed of the fan, v co The cut-out wind speed of the fan is represented;
as shown in fig. 3, the power relationship of the gas turbine is:
wherein,indicating the gas consumption of the gas turbine at time t +.>U, which represents the lower limit of natural gas consumed by the gas turbine GT (t) represents a start/stop flag bit of the gas turbine, f i A gas turbine power curve, d, representing section i i+1 、d i Gas turbine power curve parameters for the (i+1) th and (i) th sections, respectively,/->Representing the output electric power of the gas turbine at time t, and Σ (·) representing a summation function;
the power relation of the gas boiler is as follows:
wherein,represents the output heat power of the gas boiler at the time t, eta GB Represents the heat generating efficiency lambda of the gas boiler gas Indicating natural gas low calorific value, < >>Represents the natural gas quantity input by the gas boiler at the time t, < + >>An upper limit value indicating the output heat power of the gas boiler;
the power relation of the waste heat boiler is as follows:
wherein,represents the output heat power, eta of the waste heat boiler at the moment t GT Representing the efficiency coefficient, eta of the gas turbine loss Represents the coefficient of thermal energy self-dissipation rate,/->An upper limit value of the output heat power of the waste heat boiler at the time t is represented;
the power relation of the absorption refrigerator is as follows:
wherein,represents the output refrigerating power, eta of the absorption refrigerator at the time t AR Representing the efficiency coefficient of an absorption chiller, +.>Indicating the heat power absorbed by the absorption chiller at time t,/>An upper limit value of the output refrigeration power of the absorption refrigerator at the time t;
the power relation of the electric refrigerator is as follows:
wherein,indicating the output refrigerating power of the electric refrigerator at the time t, eta ER Indicating the efficiency coefficient of the electric refrigerator,indicating the input electric power of the electric refrigerator at time t, < >>An upper limit value of the output refrigerating power of the electric refrigerator at the time t;
the power relation of the electric heating boiler is as follows:
wherein,represents the output heating power, eta of the electric heating boiler at the moment t EB Indicating the efficiency coefficient of the electric heating boiler,indicating the input heating power of the electric heating boiler at the moment t, < >>An upper limit value of output heating power of the electric heating boiler at the time t is shown;
the power relationship of the carbon capture device is:
wherein P is CCS (t) represents the power consumption of the carbon capture apparatus at time t,represents the energy consumption in the carbon capture process at the time t, P CCS,e (t) represents the intrinsic energy loss when the device is operated at time t, < >>Representing the unit CO 2 Trapping the power consumption coefficient, ">Representing the unit CO 2 Efficiency coefficient of trapping->Indicating CO capture emissions 2 The total amount of the components is calculated,respectively representing carbon dioxide trappable coefficients, P, of a coal-fired generator set and a gas turbine under unit power generation e (t) represents the amount of power generation at time t, N e 、N GT The number of the coal-fired units and the number of the gas turbines are respectively represented;
the power relation of the electric gas conversion equipment is as follows:
wherein,represents the natural gas quantity synthesized by the electric gas conversion equipment at the moment t, eta P2G Representing the efficiency coefficient of the electrical switching apparatus, +.>Represents the power consumption of the electric switching device at time t, < >>Indicating the electric power to air conversion device at the time tPreparing an upper limit value of the synthesized natural gas quantity;
the expressions of electricity storage, heat, cold and gas equipment are:
λ∈{ES,HS,CS,GS}
wherein E is λ (t)、E λ (t-1) represents the capacity stored at time t and time t-1, σ, respectively λ Represents the energy storage consumption rate, deltat represents the time variation,respectively represents the charging and discharging power of the energy storage device at the time t,the energy charging and discharging efficiency coefficients of the stored energy at the time t are respectively represented, and ES, HS, CS and GS respectively represent electricity storage, heat storage, cold storage and gas storage equipment;
the power expression of the external distribution network model is:
wherein,respectively representing the electricity purchasing state and the electricity selling state of the external distribution network model at the time t, and P b,grid (t)、P s,grid (t) respectively representing the electricity purchasing efficiency and the electricity selling efficiency of the external distribution network model at the t moment,/->The upper limit value of the electricity purchasing efficiency of the external distribution network model at the time t is represented,/>and the upper limit value of the electricity selling efficiency of the external distribution network model at the time t is shown.
The formula of the gratuitous carbon emission quota of step S2 is:
E IES,c ＝E grid,c +E GB,c +E gas,c +E CCHP,c
wherein E is IES,c Gratuitous carbon emission quota representing comprehensive energy system of park, E grid,c 、E GB,c 、E gas,c And E is CCHP,c Respectively represents the carbon emission allowance of the coal-fired power plant, the carbon emission allowance of the gas-fired boiler, the carbon emission allowance of the natural gas and the carbon emission allowance of heat produced by the combined cooling, heating and power equipment,and->Respectively representing the carbon emission allowance coefficient of the coal-fired power plant, the carbon emission allowance coefficient of the natural gas, the carbon emission allowance coefficient of the gas boiler and the combined cooling, heating and power unit, and N e 、N GB And N CCHP Respectively represent coal-fired unit, gas boiler and coldNumber of combined heat and power units, P gas (t) represents the output power of natural gas at time t, < ->Representing the electrothermal conversion coefficient, < >>The output electric power of the gas turbine at the time t is indicated,represents the output heat power of the waste heat boiler at the moment t, < + >>The output refrigeration power of the absorption refrigerator at the time t is shown;
the formula of the actual carbon emission is:
wherein E is IES,s Representing the actual carbon emissions of the integrated energy system for the campus,represents the carbon emission quantity of the coal-fired unit at the time t +.>Represents the carbon emission of the gas boiler at the time t, < + >>Represents the carbon emission quantity of the combined cooling heating power unit at the t moment,/->Represents municipal thermal carbon emission at time t +.>Represents the carbon emission of natural gas at the time t, +.>Indicating the carbon emission consumption of the electric converting device at time t,/->Representing CO of coal-fired power plant 2 Emission coefficient, < >>CO representing municipal thermal power 2 Emission coefficient, < >>CO representing natural gas 2 Emission coefficient, < >>Representing CO of an electrical conversion apparatus 2 Consumption coefficient, N represents the total number of corresponding devices, < ->Representing actual CO of gas boiler 2 Emission coefficient, < >>Representing the actual CO of the combined heat and power plant 2 Emission coefficient, P gas Representing the supply power of natural gas purchased from the network, P hot Representing the municipal heating power consumption, P P2G Indicating the amount of gas supplied by the electrotransport device.
As shown in fig. 4, the formula of the stepwise carbon trade cost of the carbon trade mechanism model in step S2 is:
wherein f ct Represents the cost of stepped carbon trade, lambda c Represents the carbon emissions trading price, β represents the price increase factor, Δe represents the carbon emissions trading price interval length.
The demand response model of the step S3 carries out optimization adjustment on the transferable load; transferable loads include electrical, gas, thermal and cold loads, corresponding to the formula:
wherein,representing transferable electric power load, < >>Respectively representing the electric load which can be turned in and out of the demand side, sigma (-) represents the summation function, T represents the time period,> respectively representing the maximum turnable in and maximum turnable out electric loads, < >>Representing transferable gas load, +.>Indicating the gas load which can be turned in and out at the demand side,/->Respectively represents the maximum turnable and turnable gas load, H t Representing transferable thermal load, Q t Indicating transferable cold load,/->Respectively, a fixed, transferable thermal load in the t period,/->Respectively, a fixed, transferable cooling load in the t period, < >>Respectively representing the upper limit value of the transferable heat load and cold load in the t time period, +.>The total amount of transferable heat load and cold load in the T period is represented by Δt, respectively.
The formula of the source load uncertainty in step S4 is:
wherein D represents the uncertainty of the source load,respectively represent the prediction interval d w Lower limit, upper limit, d of (t) w (t) represents a prediction interval,>an offset representing the predicted upper limit value, T representing the scheduling period, φ w Represents the maximum deviation coefficient of the prediction upper limit value Γ w Representing uncertainty adjustment parameters, L w (t) is a variable of 0-1, representing the prediction interval d w (t) whether a boundary value is reached; the uncertainty in the source charge includes an uncertainty value for the electrical load, an uncertainty value for the gas load, an uncertainty value for the thermal load, and an uncertainty value for the cold load.
The constraint conditions include:
electric power balance constraint:
wherein,indicating the electrical load demand, i.e. the uncertainty value of the electrical load, P e (t) represents the amount of power generation at time t, P WP (t) represents the power generated by the wind turbine at time t, P PV (t) represents the actual output power of the photovoltaic unit at the moment t,indicating the output power of the gas turbine at time t, < >>Representing the generated energy of the electricity storage equipment at the moment t, P CCS (t) represents the power consumption of the carbon capture device at time t, < >>Represents the power consumption of the electric switching device at time t, < >>Indicating the input heating power of the electric heating boiler at the moment t, < >>Indicating the input electric power of the electric refrigerator at time t, < >>The power consumption of the power storage equipment at the moment t is represented;
air power balance constraint:
wherein,represents the air load demand, i.e. an indeterminate value of air load, +.>Represents the natural gas quantity synthesized by the electric gas conversion equipment at the t moment, < + >>The air quantity provided by the air storage device at the moment t is represented by +.>The air quantity released by the air storage equipment at the moment t is represented by +.>Represents the amount of air consumed by the GT device at time t, +.>The natural gas quantity input by the gas boiler at the time t is shown;
thermal power balance constraint:
wherein,indicating the heat load demand, i.e. an indeterminate value of the heat load,/->Represents the output heat power of the waste heat boiler at the moment t, < + >>Indicating the output heating power of the electric heating boiler at the moment t, < >>Represents the output heat power of the gas boiler at the time t, P hot (t) represents the heat supplied by municipal heating power at time t, < + >>Represents the heat released by the heat storage device at time t +.>The heat consumed by the heat storage equipment at the time t is represented;
cold power balance constraint:
wherein,indicating the amount of cold load demand, i.e. an indeterminate value of cold load,/->Indicating the output refrigerating power of the electric refrigerator at time t, < >>Indicating the output refrigerating power of the absorption refrigerator at time t, < >>Indicating the cold quantity released by the cold storage device at the moment t, < >>The air quantity consumed by the cold storage equipment at the moment t is represented;
energy storage constraint conditions:
E λ (0)＝E λ (T)
wherein,represents the discharging power of the energy storage device at time t, < >>Indicating the rate of release, +.>Represents the charging power of the energy storage device at time t, < >>Represents the charge rate, C λ The rated capacity is indicated as such,representing the minimum and maximum charge states of the energy storage device, E λ (t) represents the energy of the energy storage device at time t, E λ (0)、E λ (T) respectively representing the stored energy of the energy storage device at the initial moment and the final moment;
and (3) the climbing constraint condition of the controllable unit:
-r n,d Δt≤P n (t)-P n (t-1)≤r n,u Δt
wherein r is n,d 、r n,u Respectively representing the rate limit of load shedding and loading of the controllable output unit, delta t represents the time variation and P represents n (t) represents the operating power of the device n at time t, P n (t-1) represents the operating power of the device n at time t-1.
The objective function is:
F min ＝f grid +f gas +f hot +f cost +f waste +u 1 f c +u 2 f ct
f cost ＝C n P n (t)
f waste ＝λ PV,waste P PV,waste +λ WP,waste P WP,waste
wherein F is min Representing an objective function, f grid Representing the interaction cost of the park comprehensive energy system and the power grid, f hot Representing the cost of purchasing heat, f gas Representing the cost of purchasing gas, f cost Representing equipment operation and maintenance cost, f waste Indicating the punishment cost of renewable energy resource abandonment, u 1 、u 2 Represents a variable of 0-1, f c Representing the cost of conventional carbon transactions, f ct Represents the stepwise carbon trade cost, Σ (·) represents the summation function, λ p Representing the electricity purchase price, P s→g (t) represents the electricity purchase amount at time t, lambda up Represents the price of electricity selling, P d→g (t) represents the power of surfing the internet at the t moment,λ g represents the price of unit natural gas, P gas (t) represents the amount of air purchased at time t, lambda h Representing the price per unit of heat, P hot (T) represents the amount of heat purchased at time T, T represents the time period, C n Apparatus for representing a comprehensive energy system of the park, P n (t) represents the operating power of the device n at time t, lambda PV,waste Indicating the unit price of the photovoltaic unit to be abandoned, P PV,waste Represents the abandoned power lambda of the photovoltaic unit WP,waste Indicating the abandon unit price of the wind turbine generator, P WP,waste And (5) representing the waste power of the wind turbine generator.
In one embodiment of the invention, the demand response model may divide the load into two categories, rigid and flexible. The rigid load mainly refers to a fixed load in which the load is substantially not changed by interference from external factors. Typically, the rigidity requirement does not change with price or the like. While the flexible load is primarily a variable load that is subject to change by external or self factors. The distribution network system can guide the user at the demand side to cut down, transfer or adjust the service condition of the flexible load by adjusting the electricity price, signing a contract and the like. Depending on the manner in which the user responds, the flexible load can be divided into three categories: load may be cut, transferred and replaced. The load is at a high power rate in peak periods and at a low power rate in valley periods, affected by the time-of-use power rate. The user can adjust the electricity consumption time to shift from price peak time to price valley time.
The demand response model of the method optimizes and adjusts the transferable load, wherein the transferable electric load is 15 percent of the total load, the transferable gas load is 10 percent of the total load, and the transferable heat and cold loads are 10 percent of the total load; the experiment takes 24 hours as a period, and the scheduling time is 1 hour; natural gas low heat value lambda gas 9.78 kW.h/m was taken 3 The method comprises the steps of carrying out a first treatment on the surface of the Partial parameters inside the PIES are set according to Table 1; referring to typical electricity, heat, cold and gas load prediction curves, wind and light output curves, various energy price curves and carbon emission transaction parameters of a certain area, as shown in fig. 5, 6, 7 and table 2; five scenes are constructed for verification, and the five scenes are respectively:
scene one: and the uncertainty 'worst scene' parameter change is considered, and the PIES multi-energy complementary optimization model is not considered, so that the carbon emission trading mechanism and the comprehensive demand response are not considered.
Scene II: and a deterministic PIES multi-energy complementary optimization model is considered, and a carbon emission trading mechanism and a comprehensive demand response are not considered.
Scene III: based on the second scene, the PIES multi-energy complementary optimization model is considered to only consider the comprehensive demand response.
Scene four: based on a third scene, the PIES multi-energy complementary optimization model is considered to consider the traditional carbon emission trading mechanism and the comprehensive demand response.
Scene five: based on the fourth scene, the PIES multi-energy complementary optimization model is considered to consider the ladder-type carbon transaction mechanism and the comprehensive demand response.
TABLE 1 model parameters
TABLE 2 carbon emissions trading parameters
Table 3 is the running cost combination in different scenarios.
TABLE 3 running cost combinations under different scenarios
Compared to scene one selected uncertaintyScene two selection +.>The energy supply pressure at different moments is reduced, the energy supply pressure is flexibly adjusted according to renewable energy sources and load changes, and safe and stable operation of regional energy sources is ensured; as can be seen from table 3, the total cost of operation for scenario two was reduced by about 16.1% and the carbon emissions by about 15.1%. Wherein Γ is pv Uncertainty adjustment parameter, Γ, representing the photovoltaic output wp Uncertainty adjustment parameter indicative of fan output, +.>Uncertainty adjustment parameter indicative of electrical load, +.>Uncertainty adjustment parameter indicative of thermal load, +.>Uncertainty adjustment parameter representing the cooling load, +.>An uncertainty adjustment parameter indicative of the gas load.
Compared with the second scene, the third scene introduces the flexible demand response of the load side, and the user can change the energy utilization strategy in time according to the time-sharing energy price. As shown in table 3, the user transfers the load of the peak period of the price to the valley period or the flat period of the price under the influence of the time-sharing electricity price, so that the energy supply pressure of the peak period is further reduced, the safe operation of the power system can be ensured, the total cost of the third operation of the scene is reduced by about 3.7% compared with the total cost of the second operation of the scene, and the carbon emission is increased by about 2.7%.
Compared with the third scene, the fourth scene introduces a traditional carbon transaction mechanism model. Because the unit power of the combined heat and cold unit and the gas boiler has less carbon emission, the carbon emission trading volume of the selling part obtains a certain amount of benefit subsidy, and the unit output is encouraged so as to obtain more effects. As the carbon capture device is added in the fourth scene, the electric load is increased compared with the third scene, so that the system carbon emission index is increased. As can be seen from table 3, the total cost of operation for scenario four was reduced by about 8.5% and the carbon emissions increased by about 14.2%.
Compared with the fourth scenario, the fifth scenario introduces the ladder-type carbon transaction model, divides the carbon emission interval and the price, and further controls the carbon emission of the system, and as shown in table 3, the total cost of operation of the fifth scenario is reduced by about 4.5% and the carbon emission is reduced by about 4%.
And analyzing the optimal scheduling result scene five as scheduling supply and demand balance, wherein the optimized electric, thermal, cold and gas scheduling results are shown in fig. 8, 9, 10 and 11.
As can be seen from fig. 8, during the valley period 00:00-07:00, the electricity price is the lowest, the coupling device using electric energy as the input energy source and the energy storage device as the priority enabling device, the output device of this period is mainly provided by the gas turbine, the photovoltaic unit and the wind turbine, and the electricity storage device stores electricity, and the shortage is provided by the external distribution network. During the periods 07:00-10:00, 16:00-18:00 and 22:00-24:00, the electric load output equipment is mainly provided by a gas turbine and a wind turbine generator, and the insufficient part is provided by an external distribution network. During peak periods 10:00-15:00 and 19:00-21:00, the electric load is in peak period, electric equipment is added into the electric equipment except for a gas turbine, a photovoltaic unit and a wind turbine unit to be charged, and then the rest part is charged through an external distribution network.
As can be seen from fig. 9, during the valley period 00:00-07:00, the electricity price is the lowest, the electric heating boiler is used as the preferential heating device to exert force, the deficiency is supplemented by the waste heat boiler, the gas boiler and the municipal heat, and the heat storage device stores heat. During the period of time 07:00-10:00, 16:00-18:00 and 22:00-24:00, the electric heating boiler outputs force preferentially as compared with other equipment because the electric heating boiler has better output benefit, and the deficiency is supplemented by the waste heat boiler, the gas boiler and municipal heat. During peak periods 10:00-15:00, 19:00-21:00, electricity prices are at peak moments, the electric heating boiler equipment stops outputting, and the period heat load is provided by the waste heat boiler, the gas boiler, municipal heating power and heat storage equipment.
As can be seen from fig. 10, during the valley period 00:00-07:00, the electricity price is the lowest, the electric refrigerator device in this period preferentially outputs force, and the insufficient part is supplemented by the absorption refrigerator device and the cold storage device stores cold. During the periods 07:00-10:00, 16:00-18:00, 22:00-24:00, the output is still exerted by the electric refrigerator and the absorption refrigerator device. During peak periods 10:00-15:00 and 19:00-21:00, electricity prices are at an elevated price, and the power of the electric refrigerator and the absorption refrigerator equipment is output at the moment, and the electric refrigerator and the absorption refrigerator equipment are supplemented by the cold storage equipment.
As can be seen from fig. 11, the electricity price is the lowest during the valley period 00:00-07:00, the electricity-to-gas device in this period preferentially outputs power, the deficiency is supplemented by the natural gas net, and the gas storage device stores gas. During the periods 07:00-10:00, 16:00-18:00, and 22:00-24:00, the power is still exerted by the electric power to gas and natural gas network. During peak periods of 10:00-15:00 and 19:00-21:00, electricity prices are high, and the period of electricity removal, gas conversion and natural gas network output are carried out, and the gas storage equipment is used for supplementing.
In summary, the invention integrates electric, thermal, cold and air loads into flexible demand response to participate in optimization scheduling analysis, so that peak-valley difference can be effectively reduced, energy supply pressure of equipment is reduced, and energy efficiency level of a system is further optimized; the introduction of the ladder-shaped carbon transaction model with rewards and punishments can not only better inhibit the carbon emission level, but also optimize the output level of energy supply equipment and reduce the energy consumption loss; the uncertainty of the output and the load of the renewable energy sources is fully considered through the constructed source-load uncertainty set model, and uncertainty parameters are adjusted, so that the safe and stable operation of the renewable energy source system can be effectively improved.
Claims (9)
1. A park comprehensive energy system optimizing operation method considering flexible load and carbon flow is characterized in that: the method comprises the following steps:
s1, constructing a park comprehensive energy system containing an electric-heat-cold-gas-carbon flow system;
s2, introducing step carbon transaction and determining gratuitous carbon emission quota and actual carbon emission of the park comprehensive energy system; constructing a carbon transaction mechanism model of the park comprehensive energy system according to the gratuitous carbon emission quota and the actual carbon emission;
s3, constructing a demand response model according to the time-sharing electricity price;
s4, describing source load uncertainty of the park comprehensive energy system by adopting a base uncertainty set;
s5, constructing an objective function and constraint conditions according to the carbon transaction mechanism model, the demand response model and the source load uncertainty;
s6, constructing an optimized scheduling model according to the objective function and the constraint condition; and (3) according to the requirements of the park comprehensive energy system, an optimal output plan is formulated through an optimal scheduling model, and the energy system optimization is completed.
2. The method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows of claim 1, wherein: the park comprehensive energy system in the step S1 comprises an energy supply unit model, an energy coupling unit model, an energy storage unit model and an external distribution network model; the energy supply unit model comprises a photovoltaic unit and a wind generating unit; the energy coupling unit model comprises a gas turbine, a gas boiler, a waste heat boiler, an absorption refrigerator, an electric heating boiler, carbon capturing equipment and electric gas converting equipment; the energy storage unit includes electricity storage, heat, cold and gas devices.
3. The method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows of claim 2, wherein: the photovoltaic unit comprises a photovoltaic cell, and a predicted power model of the photovoltaic cell is as follows:
T c ＝T a +30G c /1000
wherein T is c Representing the actual sun illumination intensity, T a Represents the ambient temperature, G c Representing the actual temperature of the photovoltaic array;
the power prediction model of the wind turbine generator is as follows:
wherein P is WT (t) represents the output power of the fan at the moment t, P r Represents the rated power of the fan, v represents the actual wind speed, v r Represents the rated wind speed of the fan, v ci Represents the cut-in wind speed of the fan, v co The cut-out wind speed of the fan is represented;
the power relation of the gas turbine is as follows:
wherein,indicating the gas consumption of the gas turbine at time t +.>U, which represents the lower limit of natural gas consumed by the gas turbine GT (t) represents a start/stop flag bit of the gas turbine, f i A gas turbine power curve, d, representing section i i+1 、d i Gas turbine power curve parameters for the (i+1) th and (i) th sections, respectively,/->Representing the output electric power of the gas turbine at time t, and Σ (·) representing a summation function;
the power relation of the gas boiler is as follows:
wherein,represents the output heat power of the gas boiler at the time t, eta GB Indicating the heat production efficiency of the gas boiler,λ gas indicating natural gas low calorific value, < >>Represents the natural gas quantity input by the gas boiler at the time t, < + >>An upper limit value indicating the output heat power of the gas boiler;
the power relation of the waste heat boiler is as follows:
wherein,represents the output heat power, eta of the waste heat boiler at the moment t GT Representing the efficiency coefficient, eta of the gas turbine loss Represents the coefficient of thermal energy self-dissipation rate,/->An upper limit value of the output heat power of the waste heat boiler at the time t is represented;
the power relation of the absorption refrigerator is as follows:
wherein,represents the output refrigerating power, eta of the absorption refrigerator at the time t AR Representing the efficiency coefficient of an absorption chiller, +.>Representing the heat absorbed by an absorption refrigerator at time tMeasuring power, < >>An upper limit value of the output refrigeration power of the absorption refrigerator at the time t;
the power relation of the electric refrigerator is as follows:
wherein,indicating the output refrigerating power of the electric refrigerator at the time t, eta ER Representing the efficiency coefficient of an electric refrigerator, +.>Indicating the input electric power of the electric refrigerator at time t, < >>An upper limit value of the output refrigerating power of the electric refrigerator at the time t;
the power relation of the electric heating boiler is as follows:
wherein,represents the output heating power, eta of the electric heating boiler at the moment t EB Representing the efficiency coefficient of an electric boiler, < >>Indicating the input heating power of the electric heating boiler at the moment t, < >>An upper limit value of output heating power of the electric heating boiler at the time t is shown;
the power relation of the carbon capture device is as follows:
P CCS (t)＝P CCS,CO2 (t)+P CCS,e (t)
wherein P is CCS (t) represents the power consumption of the carbon capture apparatus at time t,represents the energy consumption in the carbon capture process at the time t, P CCS,e (t) represents the intrinsic energy loss when the device is operated at time t, < >>Representing the unit CO 2 Trapping the power consumption coefficient, ">Representing the unit CO 2 Efficiency coefficient of trapping->Indicating CO capture emissions 2 The total amount of the components is calculated,respectively representing carbon dioxide trappable coefficients, P, of a coal-fired generator set and a gas turbine under unit power generation e (t) represents the amount of power generation at time t, N e 、N GT Respectively represent coal-fired unitsAnd the number of gas turbines;
the power relation of the electric gas conversion equipment is as follows:
wherein,represents the natural gas quantity synthesized by the electric gas conversion equipment at the moment t, eta P2G Representing the efficiency coefficient of the electrical switching apparatus,represents the power consumption of the electric switching device at time t, < >>The upper limit value of the natural gas quantity synthesized by the electric gas conversion equipment at the time t is represented;
the expressions of the electricity storage, heat, cold and gas equipment are as follows:
λ∈{ES,HS,CS,GS}
wherein E is λ (t)、E λ (t-1) represents the capacity stored at time t and time t-1, σ, respectively λ Represents the energy storage consumption rate, deltat represents the time variation,respectively representing the charge and discharge power of the energy storage device at the time t,/>The energy charging and discharging efficiency coefficients of the stored energy at the time t are respectively represented, and ES, HS, CS and GS respectively represent electricity storage, heat storage, cold storage and gas storage equipment;
the power expression of the external distribution network model is as follows:
wherein,respectively representing the electricity purchasing state and the electricity selling state of the external distribution network model at the time t, and P b,grid (t)、P s,grid (t) respectively representing the electricity purchasing efficiency and the electricity selling efficiency of the external distribution network model at the t moment,/->The upper limit value of the electricity purchasing efficiency of the external distribution network model at the t moment is represented by +.>And the upper limit value of the electricity selling efficiency of the external distribution network model at the time t is shown.
4. A method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows according to claim 3, characterized by: the formula of the gratuitous carbon emission quota of the step S2 is as follows:
E IES,c ＝E grid,c +E GB,c +E gas,c +E CCHP,c
wherein E is IES,c Gratuitous carbon emission quota representing comprehensive energy system of park, E grid,c 、E GB,c 、E gas,c And E is CCHP,c Respectively represents the carbon emission allowance of the coal-fired power plant, the carbon emission allowance of the gas-fired boiler, the carbon emission allowance of the natural gas and the carbon emission allowance of heat produced by the combined cooling, heating and power equipment,and->Respectively representing the carbon emission allowance coefficient of the coal-fired power plant, the carbon emission allowance coefficient of the natural gas, the carbon emission allowance coefficient of the gas boiler and the combined cooling, heating and power unit, and N e 、N GB And N CCHP The number P of the coal-fired unit, the gas-fired boiler and the combined unit of cooling, heating and power are respectively represented gas (t) represents the output power of natural gas at time t, < ->Representing the electrothermal conversion coefficient, < >>Indicating the output power of the gas turbine at time t, < >>Represents the output heat power of the waste heat boiler at the moment t, < + >>The output refrigeration power of the absorption refrigerator at the time t is shown;
the formula of the actual carbon emission is as follows:
wherein E is IES,s Representing the actual carbon emissions of the integrated energy system for the campus,represents the carbon emission quantity of the coal-fired unit at the time t +.>Represents the carbon emission of the gas boiler at the time t, < + >>The carbon emission quantity of the combined cooling heating power unit at the moment t is expressed,represents municipal thermal carbon emission at time t +.>Represents the carbon emission of natural gas at the time t, +.>Indicating the carbon emission consumption of the electric converting device at time t,/->Representing CO of coal-fired power plant 2 Emission coefficient, < >>CO representing municipal thermal power 2 Emission coefficient, < >>CO representing natural gas 2 Emission coefficient, < >>Representing CO of an electrical conversion apparatus 2 Consumption coefficient, N represents the total number of corresponding devices, < ->Representing actual CO of gas boiler 2 Emission coefficient, < >>Representing the actual CO of the combined heat and power plant 2 Emission coefficient, P gas Representing the supply power of natural gas purchased from the network, P hot Representing the municipal heating power consumption, P P2G Indicating the amount of gas supplied by the electrotransport device.
5. The method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows of claim 4, wherein: the formula of the stepwise carbon transaction cost of the carbon transaction mechanism model in the step S2 is as follows:
wherein fct represents the stepwise carbon trade cost, lambda c Represents the carbon emissions trading price, β represents the price increase factor, Δe represents the carbon emissions trading price interval length.
6. The method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows of claim 1, wherein: the demand response model of the step S3 carries out optimization adjustment on the transferable load; the transferable loads include an electrical load, a gas load, a thermal load, and a cold load, and the corresponding formulas are:
wherein,representing transferable electric power load, < >>Respectively representing the electric load which can be turned in and out of the demand side, sigma (-) represents the summation function, T represents the time period,> respectively representing the maximum turnable in and maximum turnable out electric loads, < >>Representing transferable gas load, +.>Indicating the gas load which can be turned in and out at the demand side,/->Respectively represents the maximum turnable and turnable gas load, H t Representing transferable thermal load, Q t Indicating transferable cold load,/->Respectively a fixed and transferable thermal load over a period of t,respectively, a fixed, transferable cooling load in the t period, < >>Respectively representing the upper limit value of the transferable heat load and cold load in the t time period, +.>The total amount of transferable heat load and cold load in the T period is represented by Δt, respectively.
7. The method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows of claim 6, wherein: the formula of the source load uncertainty in the step S4 is as follows:
wherein D represents the uncertainty of the source load,respectively represent the prediction interval d w Lower limit, upper limit, d of (t) w (t) represents a prediction interval,>an offset representing the predicted upper limit value, T representing the scheduling period, φ w Represents the maximum deviation coefficient of the prediction upper limit value Γ w Representing uncertainty adjustment parameters, L w (t) is a variable of 0-1, representing the prediction interval d w (t) whether a boundary value is reached; the uncertainty in the source charge includes an uncertainty value for the electrical load, an uncertainty value for the gas load, an uncertainty value for the thermal load, and an uncertainty value for the cold load.
8. The method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows of claim 7, wherein: the constraint conditions include:
electric power balance constraint:
wherein,indicating the electrical load demand, i.e. the uncertainty value of the electrical load, P e (t) represents the amount of power generation at time t, P WP (t) represents the power generated by the wind turbine at time t, P PV (t) represents the actual output power of the photovoltaic unit at time t, < >>Indicating the output power of the gas turbine at time t, < >>Representing the generated energy of the electricity storage equipment at the moment t, P CCS (t) represents the power consumption of the carbon capture device at time t, < >>Represents the power consumption of the electric switching device at time t, < >>Indicating the input heating power of the electric heating boiler at the moment t, < >>Indicating the input electric power of the electric refrigerator at time t, < >>The power consumption of the power storage equipment at the moment t is represented; the method comprises the steps of carrying out a first treatment on the surface of the
Air power balance constraint:
wherein,represents the air load demand, i.e. an indeterminate value of air load, +.>Represents the natural gas quantity synthesized by the electric gas conversion equipment at the t moment, < + >>The air quantity provided by the air storage device at the moment t is represented by +.>The air quantity released by the air storage equipment at the moment t is represented by +.>Represents the amount of air consumed by the GT device at time t, +.>The natural gas quantity input by the gas boiler at the time t is shown;
thermal power balance constraint:
wherein,indicating the heat load demand, i.e. an indeterminate value of the heat load,/->Represents the output heat power of the waste heat boiler at the moment t, < + >>Indicating the output heating power of the electric heating boiler at the moment t, < >>Represents the output heat power of the gas boiler at the time t, P hot (t) represents the heat supplied by municipal heating power at time t, < + >>Represents the heat released by the heat storage device at time t +.>The heat consumed by the heat storage equipment at the time t is represented;
cold power balance constraint:
wherein,indicating the amount of cold load demand, i.e. an indeterminate value of cold load,/->Indicating the output refrigerating power of the electric refrigerator at time t, < >>Indicating the output refrigerating power of the absorption refrigerator at time t, < >>Indicating the cold quantity released by the cold storage device at the moment t, < >>The air quantity consumed by the cold storage equipment at the moment t is represented;
energy storage constraint conditions:
E λ (0)＝E λ (T)
wherein,represents the discharging power of the energy storage device at time t, < >>Indicating the rate of release, +.>Represents the charging power of the energy storage device at time t, < >>Represents the charge rate, C λ Indicating rated capacity +.>Representing the minimum and maximum charge states of the energy storage device, E λ (t) represents the energy of the energy storage device at time t, E λ (0)、E λ (T) respectively representing the stored energy of the energy storage device at the initial moment and the final moment;
and (3) the climbing constraint condition of the controllable unit:
-r n,d Δt≤P n (t)-P n (t-1)≤r n,u Δt
wherein r is n,d 、r n,u Respectively representing the rate limit of load shedding and loading of the controllable output unit, delta t represents the time variation and P represents n (t) represents the operating power of the device n at time t, P n (t-1) represents the operating power of the device n at time t-1.
9. The method of optimizing the operation of a campus integrated energy system that accounts for flexible loads and carbon flows of claim 8, wherein: the objective function is:
F min ＝f grid +f gas +f hot +f cost +f waste +u 1 f c +u 2 f ct
f cost ＝C n P n (t)
f waste ＝λ PV,waste P PV,waste +λ WP,waste P WP,waste
wherein F is min Representing an objective function, f grid Representing the interaction cost of the park comprehensive energy system and the power grid, f hot Representing the cost of purchasing heat, f gas Representing the cost of purchasing gas, f cost Representing equipment operation and maintenance cost, f waste Indicating the punishment cost of renewable energy resource abandonment, u 1 、u 2 Represents a variable of 0-1, f c Representing the cost of conventional carbon transactions, f ct Represents the stepwise carbon trade cost, Σ (·) represents the summation function, λ p Representing the electricity purchase price, P s→g (t) represents the electricity purchase amount at time t, lambda up Represents the price of electricity selling, P d→g (t) represents the power of surfing the Internet at t time, lambda g Represents the price of unit natural gas, P gas (t) represents the amount of air purchased at time t, lambda h Representing the price per unit of heat, P hot (T) represents the amount of heat purchased at time T, T represents the time period, C n Apparatus for representing a comprehensive energy system of the park, P n (t) represents the operating power of the device n at time t, lambda PV,waste Indicating the unit price of the photovoltaic unit to be abandoned, P PV,waste Represents the abandoned power lambda of the photovoltaic unit WP,waste Indicating the abandon unit price of the wind turbine generator, P WP,waste And (5) representing the waste power of the wind turbine generator.
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| CN117933500A (en) * | 2024-03-25 | 2024-04-26 | 青岛鼎信通讯科技有限公司 | Park multi-element energy optimization scheduling method based on energy router |
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| CN117808171A (en) * | 2024-02-29 | 2024-04-02 | 山东大学 | Low-carbon optimal scheduling method, system, storage medium and equipment for comprehensive energy system |
| CN117933500A (en) * | 2024-03-25 | 2024-04-26 | 青岛鼎信通讯科技有限公司 | Park multi-element energy optimization scheduling method based on energy router |
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