CN115907981B - Low-carbon joint transaction optimization method for virtual power plant - Google Patents
Low-carbon joint transaction optimization method for virtual power plant Download PDFInfo
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Abstract
The application discloses a low-carbon combined transaction optimization method of a virtual power plant, which specifically comprises the following steps of firstly analyzing the developable capacity of a source side and a load side of the virtual power plant and considering the upper limit value of an adjustable resource; and secondly, designing a low-carbon virtual power plant operation trading framework, and respectively modeling each unit in the virtual power plant. And finally, considering economic targets and environmental protection targets, constructing a low-carbon combined transaction optimization strategy of the virtual power plant participating in the electric energy market, the carbon transaction market and the peak shaving auxiliary service market based on a carbon transaction mechanism. By the method provided by the application, the advice of participating in the market can be provided for the virtual power plant operators, the enthusiasm of the virtual power plant operators to participate in the market transaction is improved, and the policy advice is provided for the virtual power plant operators to participate in the market transaction through the policy analysis of the maximum benefit. Meanwhile, the model is set based on a low-carbon background, and reasonable suggestions can be provided according to actual conditions by combining the requirements of double carbon.
Description
Technical Field
The application relates to the technical field of power grid economy research, in particular to a low-carbon combined transaction optimization method for a virtual power plant.
Background
Under the 'double carbon' target, clean energy is greatly developed, but the distributed clean energy is distributed and dispersed, has strong randomness and volatility, and cannot be used as an independent main body to participate in the electric power market. The virtual power plant not only can improve the stability of the virtual power plant by aggregating various source side distributed energy sources and various load side adjustable loads, but also can participate in various markets by aggregation effect, and promote the consumption of new energy sources. However, the virtual power plant still has the condition of unknown strategy in the actual operation process, so that the income mode of the virtual power plant operator is limited, and a good virtual power plant operation environment is difficult to build, so that large-scale operation is formed. In order to promote the enthusiasm of virtual power plant operators, increase the new energy consumption level and form stable coordination and interaction capacity of the virtual power plant and a power grid. Therefore, firstly, starting from a source side and a load side, evaluating the scale of the polymerizable resources of the virtual power plant; then, designing an operation frame of the low-carbon virtual power plant, and modeling the low-carbon virtual power plant; secondly, constructing a low-carbon joint transaction optimization model of the virtual power plant participating in an electric energy market, a natural gas market and a carbon transaction market, and implementing a step carbon transaction mechanism in the carbon transaction market; and finally, carrying out model solving.
Disclosure of Invention
The application provides a low-carbon combined transaction optimization method for a virtual power plant, which aims to solve the technical problems that in the prior art, the profit mode of the virtual power plant is unknown in the operation process, so as to form the coordination and interaction capability of the operator of the virtual power plant and a power grid, solve according to a model, and explore a three-level market combined transaction optimization strategy of the virtual power plant, which meets the maximization of the profit and the maximization of new energy consumption.
In order to achieve the above object, the present application provides the following solutions:
a low-carbon joint transaction optimization method of a virtual power plant comprises the following steps:
s1, establishing a polymerizable resource model based on source side technology development quantity, load side technology development quantity and economic development quantity;
s2, designing a low-carbon virtual power plant operation transaction frame based on calculation and analysis of the polymerizable resource model, and modeling each unit in the transaction frame;
s3, based on the transaction framework and combined with a carbon transaction mechanism, a low-carbon combined transaction optimization strategy model of the virtual power plant participating in the electric energy market, the carbon transaction market and the peak shaving auxiliary service market is constructed, and the low-carbon combined transaction optimization strategy model is used for low-carbon combined transaction optimization.
Preferably, the method for establishing the polymerizable resource model in S1 includes: source side polymerizable resource analysis and load side polymerizable resource analysis.
Preferably, the source side polymerizable resource analysis includes: wind power resource technology developable amount analysis, wind power resource economic developable amount analysis, photovoltaic resource technology developable amount analysis and photovoltaic resource economic developable amount analysis.
Preferably, the load side polymerizable resource analysis includes:
based on the standardized processing of various load values and the whole-society load demand values, the formula for obtaining the matching degree of various load curves and the whole-society load curves is as follows:
in the method, in the process of the application,Y t and->y t Respectively representing the k-th adjustable load and the t-moment load value and the original load value after the standardization processing of the whole social load; d (X) k Y) represents the euclidean distance between the kth class load and the global social load; a is that i Representing an i-th level load adjustable coefficient; x is x i Indicating the total amount of electricity used in the ith level.
Preferably, the modeling method in S2 includes: source side modeling, load side modeling, and other unit modeling.
Preferably, the source side modeling includes: wind power generation modeling, photovoltaic power generation modeling and biomass power generation modeling.
Preferably, the load side modeling includes: according to the grading of the load side polymerizable resources, the formula for constructing the load side model is as follows:
in the method, in the process of the application,the load is adjustable at the moment t; />Respectively an adjustable industrial load and an adjustable resident load;the first-stage adjustable industrial load coefficient, the second-stage adjustable industrial load coefficient and the third-stage adjustable industrial load coefficient are respectively adopted; />The industrial load scale of the first stage, the second stage and the third stage is respectively;the first-stage adjustable resident load coefficient, the second-stage adjustable resident load coefficient and the third-stage adjustable resident load coefficient are respectively; />The first-stage resident load scale, the second-stage resident load scale and the third-stage resident load scale are respectively adopted.
Preferably, the other unit modeling includes: battery modeling, carbon capture device modeling, carbon storage device modeling, and electrical conversion device modeling.
Preferably, the S3 low-carbon joint transaction optimization policy model includes: total virtual power plant revenue, total virtual power plant cost, and net virtual power plant revenue;
the virtual power plant total revenue includes: electric energy market revenue, peak shaving market revenue, carbon market revenue and sales energy revenue;
the virtual power plant total cost includes: power generation costs, operational maintenance costs, demand response costs, bias penalty costs, and risk costs.
The beneficial effects of the application are as follows:
the application provides a low-carbon combined transaction optimization method of a virtual power plant, which specifically comprises the following steps of firstly analyzing the developable capacity of a source side and a load side of the virtual power plant and considering the upper limit value of an adjustable resource; and secondly, designing a low-carbon virtual power plant operation trading framework, and respectively modeling each unit in the virtual power plant. And finally, considering economic targets and environmental protection targets, constructing a low-carbon combined transaction optimization strategy of the virtual power plant participating in the electric energy market, the carbon transaction market and the peak shaving auxiliary service market based on a carbon transaction mechanism. By the method provided by the application, the advice of participating in the market can be provided for the virtual power plant operators, the enthusiasm of the virtual power plant operators to participate in the market transaction is improved, and the policy advice is provided for the virtual power plant operators to participate in the market transaction through the policy analysis of the maximum benefit. Meanwhile, the model is set based on a low-carbon background, and reasonable suggestions are provided according to actual conditions by combining the requirements of double carbon.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a three-level market transaction operation strategy analysis flow provided in an embodiment of the present application;
FIG. 2 is a schematic diagram of a low-carbon virtual power plant transaction operating framework according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a three-level market transaction operation flow provided in accordance with an embodiment of the present application;
FIG. 4 is a schematic diagram of various types of power generation curves in a virtual power plant according to an embodiment of the application;
FIG. 5 is a diagram of a second load curve according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an adjustable load in two or four scenarios according to an embodiment of the present application;
fig. 7 is a schematic diagram of objective function values in a second different scenario according to the embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Example 1
At present, the demand side resources have the characteristics of diversification, high flexibility and the like, and huge adjustable resources are reserved. The virtual power plant not only can improve the stability of the virtual power plant by aggregating various source side distributed energy sources and various load side adjustable loads, but also can participate in various markets by aggregation effect, and promote the consumption of new energy sources. However, the virtual power plant still has the condition of unknown strategy in the actual operation process, so that the income mode of the virtual power plant operator is limited, and a good virtual power plant operation environment is difficult to build, so that large-scale operation is formed. In order to promote the enthusiasm of virtual power plant operators, increase the new energy consumption level and form stable coordination and interaction capacity of the virtual power plant and a power grid. Based on this, the application aims to provide a low-carbon combined transaction optimization method for a virtual power plant, so as to solve the technical problems of unknown profit mode and the like of the virtual power plant in the operation process in the prior art, so as to form the coordination interaction capability of the operator profit capability of the virtual power plant and the power grid, and explore a three-level market combined transaction optimization strategy for the virtual power plant, which meets the maximization of profit and the maximization of new energy consumption, according to model solution, as shown in fig. 2 and 3.
FIG. 1 is a schematic flow chart of an embodiment of the present application; establishing a polymerizable resource model based on the source side technology development quantity, the load side technology development quantity and the economic development quantity;
source side polymerizable resource analysis and load side polymerizable resource analysis;
source side polymerizable resource analysis includes: wind power resource technology developable amount analysis, wind power resource economic developable amount analysis, photovoltaic resource technology developable amount analysis and photovoltaic resource economic developable amount analysis;
wind power resource technology developable amount analysis includes: the theoretical development quantity is calculated, and the areas of undevelvable and limited areas are removed on the basis, so that a technical development quantity measuring model is obtained:
in the method, in the process of the application,representing the development amount of wind power plant technology in the area; />Representing a wind farm technology mountable area within the area; />P i WT Representing the area between the equivalent lines of each wind power densityAnd wind power; n (N) WT 、F WT Representing total areas that are completely undevelcable and limiting their development, respectively; />Representing an i-th type undevelvable area; f (F) t WT And->Respectively represent development areas and unavailability of the t-th constraint type.
Wind power resource economic developable amount analysis includes: the economic development amount measurement model is specifically as follows:
in the method, in the process of the application,economic developable amount for wind power plants in the area; />Is T k Technology exploitable amount in the region; alpha k As a Boolean variable, if the electricity degree cost of wind power generation in the subarea is smaller than the local average online electricity price in the current year, assigning 1, otherwise, assigning 0; i 0 Initial investment, namely project integral unit cost; n is year; n is the full life cycle; r is R n The annual operation cost of the power station in the nth year; v (V) n Other tax such as value-added tax of the nth year is added; w (W) n Loan interest for the nth project; b (B) n Revenue for other channels of the nth year;r is the discount rate; r is R E Risk cost for external factors->For the nth year T k The electricity measuring cost of the area measurement; p (P) local-grid Average online electricity price for the local power grid of the nth year; p (P) local-thermal The average online electricity price of the local thermal power in the nth year; p (P) cross-province And sending the average internet power price locally for the nth year.
Photovoltaic resource technology developable quantity analysis includes: one of the fundamental conditions for the development of photovoltaic power plant sites is that the annual solar energy radiation (GHI) of the area must be greater than a specific value. The technical developable measurement model is specifically as follows:
in the method, in the process of the application,representing the technical development quantity of the photovoltaic power station in the area; />Representing the technical mountable area of the photovoltaic power station in the area; w (W) PV Representing the capacity of a photovoltaic power plant mountable per unit area within the area; n (N) PV 、F PV Representing total areas that are completely undevelcable and limiting their development, respectively; />Representing an i-th type undevelvable area; f (F) t PV And->Respectively represent development areas and unavailability of the t-th constraint type.
Photovoltaic resource economic developable analysis includes: referring to a wind power generation base economic development amount measurement model, a photovoltaic economic development amount formula is obtained as follows:
in the method, in the process of the application,economic development amount for the photovoltaic power station in the area; />Is T k Photovoltaic technology within an area can be exploited in volume.
The load side polymerizable resource analysis includes: by analyzing the load side polymerizable resources, the industrial load and the resident load are mainly classified according to industry;
carrying out standardized processing on various load values and all-society load demand values, calculating Euclidean distance, and further comparing the matching degree between various load curves and all-society load curves, wherein the calculation formula is as follows:
in the method, in the process of the application,Y t and->y t Respectively representing the k-th adjustable load and the t-moment load value and the original load value after the standardization processing of the whole social load; d (X) k Y) represents the euclidean distance between the kth class load and the global social load.
The total load side load adjustable quantity L can be obtained by adjusting the load quantity of each grade, and the formula is as follows:
wherein A is i Representing an i-th level load adjustable coefficient; xi represents the i-th level total amount of electricity.
Designing a low-carbon virtual power plant operation trading frame based on the calculation and analysis of the polymerizable resource model, and modeling each unit in the trading frame; comprising the following steps: source side modeling, load side modeling, and other unit modeling; source side modeling includes: wind power generation modeling, photovoltaic power generation modeling and biomass power generation modeling;
wind power modeling includes: the wind power generation is affected by regional wind speed, and a Weibull distribution fitting regional wind speed distribution rule is adopted. The wind power generation output is shown as the formula:
in the method, in the process of the application,the output of the wind power generation at the moment t; />The natural wind speed at the moment t; /> The rated wind speed, the running lowest wind speed and the running highest wind speed of the wind generating set are respectively; />Is the rated power of the wind turbine generator.
The photovoltaic power generation modeling includes: the photovoltaic power generation output is shown as the formula:
in the method, in the process of the application,generating power for the photovoltaic power at the moment t; m is m pv The mounting area of the photovoltaic cell panel is; />The illumination intensity at the time t; lambda (lambda) pv The efficiency of absorbing the illumination intensity for the photovoltaic cell panel; lambda (lambda) tran Is photoelectric conversion efficiency.
The biomass energy power generation modeling comprises the following steps: the biogas power generation is affected by power, pressure and biogas consumption, and a biomass energy power generation model is constructed as shown in the formula:
in the method, in the process of the application,the output of biomass energy power generation at the moment t; η (eta) 1 、η 2 、η 3 、η 4 Respectively are provided withThe constant term, the primary term coefficient of the pressure, the primary term coefficient of the biogas consumption and the secondary term coefficient of the pressure; f (F) sw 、Q xh The pressure and the methane consumption are respectively.
The load side modeling includes: the load side is mainly used for modeling the demand response of the load side, and the load side model is constructed on the basis of industrial load and resident load according to the grading of the load side polymerizable resources. The specific formula is as follows:
in the method, in the process of the application,the load is adjustable at the moment t; />Respectively an adjustable industrial load and an adjustable resident load;the first-stage adjustable industrial load coefficient, the second-stage adjustable industrial load coefficient and the third-stage adjustable industrial load coefficient are respectively adopted; />The industrial load scale of the first stage, the second stage and the third stage is respectively;the first-stage adjustable resident load coefficient, the second-stage adjustable resident load coefficient and the third-stage adjustable resident load coefficient are respectively; />The first-stage resident load scale, the second-stage resident load scale and the third-stage resident load scale are respectively adopted.
Other unit modeling mainly includes storage batteries, carbon capture devices, carbon storage devices, electrical conversion devices.
The battery modeling includes: the storage battery participates in the operation of the virtual power plant through charge and discharge, and the modeling formula is as follows:
in the method, in the process of the application,the electric quantity of the storage battery at the time t and the time t-1 respectively; /> The charge and discharge power of the storage battery; η (eta) ESS_cha 、η ESS_dis Is the charge and discharge efficiency of the storage battery.
The carbon capture plant modeling includes: modeling of the carbon capture apparatus is shown in the formula:
in the method, in the process of the application,the total energy consumption, the fixed energy consumption and the operation energy consumption of the CCS system at the moment t are respectively; />Is the Boolean variable of the carbon capture plant, +.>Capturing CO for carbon capture system start-up 2 Otherwise, not starting; />To capture CO of unit mass 2 Consumed byElectric energy; lambda (lambda) tbj CO for carbon capture system 2 The trapping rate; />Biomass energy unit CO 2 Emission intensity.
Carbon storage device modeling includes:
in the method, in the process of the application,the carbon storage quantity of the carbon storage equipment at the time t and t-1; />Is the loss coefficient of carbon storage; />CO for a carbon storage device at time t 2 A released amount.
Modeling an electrical switching apparatus includes: the specific formula of the P2G is shown as follows by utilizing the carbon storage amount in the carbon storage equipment and the synthetic natural gas generated by electrolysis of water:
in the method, in the process of the application,the natural gas power generated at the moment t; />The electric power consumed for the time instant P2G; lambda (lambda) P2G Conversion efficiency for P2G; h g Is the high-order heat value of natural gas.
Based on the transaction framework and in combination with a carbon transaction mechanism, a low-carbon combined transaction optimization strategy model of the virtual power plant participating in the electric energy market, the carbon transaction market and the peak shaving auxiliary service market is constructed, and the low-carbon combined transaction optimization strategy model is used for low-carbon combined transaction optimization. Total virtual power plant revenue, total virtual power plant cost, and net virtual power plant revenue;
first, from an economic perspective, virtual power plants are targeted to maximize net revenue; the net benefit of a virtual power plant is the difference between the benefit of the virtual power plant and the cost of the virtual power plant. The benefits of the virtual power plant are the sum of the combined benefits and the sales benefits of the three-level markets of participation of the virtual power plant in the electric energy market, the natural gas market and the carbon trade market.
The total benefits of the virtual power plant include: electric energy market revenue, peak shaving market revenue, carbon market revenue and sales energy revenue;
the electric energy market benefits include: the virtual power plant superimposes the scale of the polymerizable resource and the total load curve in the electric energy market to obtain an equivalent output curve, declares in the electric energy market, and settles according to the clearing price to obtain the income of the virtual power plant in the electric energy market, wherein the income is specifically shown in the formula:
in the method, in the process of the application,and (5) the total income, the settlement amount and the unit clearing price of the virtual power plant in the electric energy market at the moment t are obtained.
Peak shaving market revenue includes: the virtual power plant declares peak shaving amount according to peak shaving demand in the peak shaving market, and obtains the income of the virtual in the peak shaving market according to peak shaving price settlement, and the concrete formula is shown as follows:
in the method, in the process of the application,and the method is the income, the settlement amount and the clearing price of the virtual power plant in the peak shaver market at the moment t.
Carbon market benefits include: the carbon trade volume of the virtual power plant participating in the market is determined according to the initial carbon emission volume and the actual carbon emission volume of the virtual power plant in the carbon trade market. The initial carbon quota of the virtual power plant is determined by adopting the power generation intensity as shown in the formula:
in the method, in the process of the application,the initial carbon quota of the virtual power plant at the moment t; a, a i Is the initial carbon quota coefficient for unit i.
The profit of the virtual power plant in the carbon trade market is obtained according to the initial carbon quota as shown in the formula:
in the method, in the process of the application,the benefits of the virtual power plant in the carbon trade market; />The actual carbon emission amount at the time t of the virtual power plant; />Trade price for carbon dioxide per unit; l is the carbon trade emission interval length; u is the carbon emission price increase coefficient.
The sales energy benefits include: the selling energy benefit of the virtual power plant is obtained by meeting various load demands in the virtual power plant, and the selling energy benefit is specifically shown as the following formula:
in the method, in the process of the application,the energy selling benefit at the time t of the virtual power plant is obtained; />Selling electricity quantity for the virtual power plant to industrial users and residential users; />And selling electricity prices for units of electricity sold by the virtual power plant to the industrial users and the residential users.
The total yield of the virtual power plant is shown as:
the total cost of the virtual power plant includes the following components: power generation costs, operational maintenance costs, demand response costs, bias penalty costs, risk costs, and the like.
The power generation cost comprises: the power generation cost of the virtual power plant mainly refers to the cost of wind power generation, photovoltaic power generation and biomass energy power generation, and is specifically shown as the formula:
in the method, in the process of the application,and the constant term, the primary term and the secondary term of the unit i are used for generating cost coefficients.
The operation and maintenance cost comprises: the operation and maintenance cost of the virtual power plant is the cost for ensuring the normal operation of the unit, and is specifically shown as the following formula:
in the method, in the process of the application,generating power for wind power, photovoltaic and biomass energy at t moment,>for the total energy consumption of the carbon capture plant at time t, < >>Maximum power out, θ, for a P2G device i 、θ tbj 、θ P2G Respectively the unit maintenance costs.
The demand response costs include: the demand response cost of the virtual power plant refers to the cost spent by the virtual power plant for calling the resident adjustable load and the industrial adjustable load, and the cost is specifically shown as the following formula:
in the method, in the process of the application,the demand response cost generated at the moment t of the virtual power plant is calculated; />The price is responded to for the virtual power plant unit demand.
The bias penalty cost includes: the deviation punishment cost of the demand response participating in the three-level market refers to punishment of the virtual power plant when the deviation between the declared quantity and the actual quantity exceeds a certain range, and the punishment cost is shown in the formula:
in the method, in the process of the application,the total penalty cost for the virtual power plant; />Penalty costs in the electric energy and peak shaving market for the virtual power plant; />Shen Baoliang in the electric energy market and peak shaving market for the virtual power plant t moment; epsilon dnl 、ε tf Maximum deviation rate without penalty; />And punishing the cost for unit deviation of the virtual power plant in the electric energy market and the peak shaving market respectively.
The risk costs include: uncertainty exists in wind power generation and photovoltaic power generation in trade operation of virtual power plants, so that the risk cost of the virtual power plants is represented by CVaR and investment preference on N sample data rho 1 ,ρ 2 ,…,ρ N The CVaR estimate of (c) is shown in:
the risk cost of the virtual power plant is shown as:
where κ is the risk bias.
The total cost of the virtual power plant is shown as:
in the method, in the process of the application,for the power generation costs of the virtual power plant, < >>Maintenance costs for the operation of a virtual power plant, +.>Demand response cost for virtual power plant, +.>For the total penalty cost of the virtual power plant, +.>Is a risk cost of the virtual power plant.
Virtual power plant net benefitThe specific formula is as follows:
in the method, in the process of the application,representing the total income of the virtual power plant->Representing the total cost of the virtual power plant.
Secondly, from the viewpoint of environmental protection, the virtual power plant should improve the absorption rate of renewable energy sources such as wind and light, and the new energy source absorption is maximized. The method comprises the following steps:
the new energy consumption maximization refers to the maximization of the ratio of the actual wind-light consumption to the actual output in the virtual power plant, and is specifically shown as the following formula:
in the method, in the process of the application,the new energy consumption rate at the moment t; />Is the actual consumption of wind power generation and photovoltaic power generation.
Furthermore, constraints in the operation of the virtual power plant are considered, and the virtual power plant constraints include unit constraints and balance constraints. The unit constraint mainly comprises a source side unit constraint, a load side constraint and other unit constraints, and the unit constraint is as follows:
the source side unit constraints are as follows:
in the method, in the process of the application,and generating the maximum polymerizable scale for biomass energy of the virtual power plant.
The load side unit constraint is as follows:
in the method, in the process of the application,maximum adjustable load for industrial users and residential users.
The remaining element constraints include:
in the method, in the process of the application,the storage battery capacity is limited up and down; />Maximum power for charging and discharging the storage battery; />Maximum power for the carbon capture plant; />Maximum capacity for the carbon storage device; />Is the maximum power output of the P2G device.
The balancing constraint includes: the balance constraint is the power supply and demand balance and the carbon balance of the virtual power plant, and the specific formula is as follows:
finally, the model constructed by the method is a multi-objective optimization model, and the epsilon constraint method is adopted for solving. The method comprises the following steps:
(1) The maximized objective function is transformed prior to solving, as shown in the following formula:
in the method, in the process of the application,net benefit for virtual power plant: />The new energy consumption rate at the moment t; h is a 1 Net revenue for the converted virtual power plant; h is a 2 Is the new energy consumption rate of the conversion.
The transaction operation optimization model of the virtual power plant can be converted as follows by adopting an epsilon constraint method:
in minh 1 Represents the maximization of net benefit, h 2 Satisfies epsilon constraint.
The epsilon is shown as the formula:
in the method, in the process of the application,to be h 2 When optimizing for a single target, the obtained clean energy consumption rate; />In h 1 When optimizing for a single target, the obtained clean energy consumption rate; n (N) max Is the maximum of the number of cycles.
(2) Based on the above, the Pareto optimal solution set is s= {1,2, …, S }, and the optimal solution is obtained by adopting a fuzzy decision method, wherein membership functions are defined in the fuzzy decision method as follows:
in the method, in the process of the application,respectively the maximum value and the minimum value of the jth objective function; />The value in the ith Pareto set is the jth objective function.
(3) Further, the membership degree of each Pareto set is selected:
in the method, in the process of the application,represents h 1 Membership in the i-th Pareto set; />Represents h 2 Membership in the ith Pareto set.
(4) The optimal solution of the model is shown in the formula:
m max ＝max(m 1 ,m 2 ,…,m s )
wherein m is 1 、m 2 、……、m s The membership of the 1 st, 2 nd, … … th and s th Pareto sets are shown.
Example two
And taking a certain area with potential of wind power generation, photovoltaic power generation and biomass energy power generation as a research object, and performing calculation and analysis. Setting the first-stage adjustable industrial load coefficient, the second-stage adjustable industrial load coefficient and the third-stage adjustable industrial load coefficient to be 0.12, 0.10 and 0.08 respectively; the first-stage adjustable resident load coefficient, the second-stage adjustable resident load coefficient and the third-stage adjustable resident load coefficient are respectively 0.10, 0.08 and 0.06. The maximum deviation rate of the peak shaving market and the electric energy market which are not punished is 5 percent. The unit deviation cost in the peak shaving market and the electric energy market is 0.03 yuan/kWh. The parameters of each type of equipment are shown in table 1:
TABLE 1
The power generation cost and initial carbon quota coefficient of each unit are shown in table 2:
TABLE 2
The electricity selling in the virtual power plant adopts time-sharing electricity price, and the time-sharing electricity price is shown in a table 3:
TABLE 3 Table 3
The power generation curves of various types in the virtual power plant are shown in fig. 4, and the load curves of the areas are shown in fig. 5.
The result of the aggregate scale assessment of the virtual power plant;
source side polymerizable resource analysis: specific results of obtaining wind and light developable amounts (including the current developed amounts and the newly added developed amounts) of the region according to the source side wind and light renewable resource technology developable amounts and the economic developable amount measuring model are shown in table 4:
TABLE 4 Table 4
From the table, wind energy and solar energy resources in the area are rich, and after a few resources are removed and a poor land is formed, the economic development quantity of wind power generation and photovoltaic power generation is not greatly different from the technical development quantity, and the wind power and photovoltaic power generation electricity cost is continuously reduced in the future along with the improvement of technical equipment level and operation environment, and the development of the wind power and photovoltaic power generation electricity cost is not limited by the economic constraint.
Load side polymerizable resource analysis: first, the Euclidean distance between the industrial load 15 kinds of electric loads and the electric loads of the whole society is calculated, and the sequencing result is shown in table 5:
TABLE 5
The euclidean distance and ranking of the same resident load and the whole society electricity load are shown in table 6:
TABLE 6
Dividing the industrial load and the residential load into 3 adjustment levels according to the ranking result: ranks 1-5 are first, 6-10 are second, and 11-15 are third. The adjustable load amounts for each level of industrial load and residential load obtained according to the formula are shown in table 7:
TABLE 7
Synergistic effect of carbon capture device and P2G: to verify the effectiveness of the virtual power plant configuration herein, three scenarios are set as shown in Table 8, where X indicates that the device is not configured in the virtual power plant, and V indicates that the device is configured in the virtual power plant.
TABLE 8
The results of the operation of the virtual power plant in three scenarios are shown in table 9:
TABLE 9
As can be seen from table 9, although scenario 1, in which no carbon capture device, no carbon storage device, and no P2G device were arranged, did not generate P2G costs and no carbon storage costs, the profits in the carbon trade market were significantly reduced by 100.24% and 136.98% respectively compared to scenarios 2 and 3; this results in a net gain of the virtual power plant that is also lower than scenario 2, 3, which is only 1026.28 yuan. Meanwhile, due to the lack of the configuration of the carbon capture equipment, the carbon storage equipment and the P2G equipment, the electricity-carbon-electricity recycling cannot be performed, so that the clean energy consumption rate of the scene 1 is also the lowest. Although the carbon storage cost of scenario 2 is 0 compared with that of scenario 3, the P2G and the carbon capturing device cannot be matched in real time, so that the utilization rate of P2G is reduced, the running cost of P2G is increased, and as can be seen from table 9, the sum of the P2G cost and the carbon storage cost of scenario 3 is 307.30 yuan, which is smaller than the P2G cost of scenario 2. Scenario 3 the net benefit of the virtual power plant and clean energy rate are highest among the three scenarios. Therefore, the configuration of the carbon capture equipment, the carbon storage equipment and the P2G equipment in the virtual power plant structure can improve the net income of the virtual power plant and promote the consumption of new energy.
Optimizing effect of demand response: the following four scenarios are set to further analyze the effectiveness of demand response by mobilizing the load side resources by configuring the carbon capture device, the carbon storage device, and the P2G device in the virtual power plant.
Table 10
The four scenario adjustable loads are shown in fig. 6:
as can be seen from fig. 6, in four scenarios, when the adjustable load of the resident and the adjustable load of the industry are not considered in scenario 1, the adjustable load scale is 0, and when the adjustable load of the resident and the adjustable load of the industry are simultaneously considered in scenario 4, the adjustable load is the largest.
The operation of the virtual power plant in four scenarios is obtained based on the adjustable loads in the four scenarios, and the amount of settlement in the electric energy market and the peak shaving auxiliary service market is higher in scenario 4 than in the other three scenarios, because the adjustable load scale is increased and the scale in which the virtual power plant can participate in external transactions is also increased. Meanwhile, the wind power generation, the photovoltaic power generation and the biomass energy power generation output under the scenario 4 are far higher than those of other three scenarios, because the flexibility of the virtual power plant can be improved by utilizing the adjustable load, the clean energy can be promoted to be absorbed, and the load demands are reduced in a grading manner in peak period of industrial load and resident load based on adjustable potential. The description considers the adjustability of both residential and industrial loads to be effective for the operation of a virtual power plant.
Combined effect of three-stage market: in order to analyze the effectiveness of the three-level market joint operation, four scenarios are set as follows:
TABLE 11
As shown in fig. 7, as compared with scenario 2, scenario 1 is compared with scenario 2, since scenario 1 does not participate in the carbon trade market, the carbon deposit reduction polarity of the virtual power plant cannot be promoted, so that the clean energy consumption rate of the virtual power plant is lower than that of scenario 2; compared with scenario 2 and scenario 3 and scenario 4, the digestion rate of scenario 3 is far lower than that of scenario 4, and the income of scenario 2 is far lower than that of scenario 4, because the virtual power plant participates in the three-level market, the optimal allocation of the resources of the virtual power plant can be realized through the declaration amount allocated in each market, and the income of the virtual power plant is improved. Meanwhile, the combined operation can improve clean energy consumption, so that the system has cleanliness.
In order to further analyze the low carbon impact of the three-level market joint transaction optimization strategy, two indexes of the actual carbon emission of the system and the income of the system in the carbon transaction market are adopted to evaluate four situations in table 11, and the situations are specifically shown in table 12:
table 12
As can be seen from table 12, compared with scenario 1, scenario 2 and scenario 3, the actual carbon emissions of scenario 4 considering the three-level market combined operation are reduced by 59.35%, 48.13% and 55.84%, respectively, which indicates that the combined operation participating market enables the system to control carbon emissions, has a low carbon effect, and the benefit of the combined optimization operation in the carbon trade market is higher from the economical point of view.
The above embodiments are merely illustrative of the preferred embodiments of the present application, and the scope of the present application is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present application pertains are made without departing from the spirit of the present application, and all modifications and improvements fall within the scope of the present application as defined in the appended claims.
Claims (7)
1. The low-carbon combined transaction optimization method for the virtual power plant is characterized by comprising the following steps of:
s1, establishing a polymerizable resource model based on source side technology development quantity, load side technology development quantity and economic development quantity;
s2, designing a low-carbon virtual power plant operation transaction frame based on calculation and analysis of the polymerizable resource model, and modeling each unit in the transaction frame;
s3, constructing a low-carbon combined transaction optimization strategy model of the virtual power plant participating in the electric energy market, the carbon transaction market and the peak shaving auxiliary service market based on the transaction framework and combining a carbon transaction mechanism, wherein the low-carbon combined transaction optimization strategy model is used for low-carbon combined transaction optimization;
the method for establishing the polymerizable resource model in S1 comprises the following steps: source side polymerizable resource analysis and load side polymerizable resource analysis;
the load side polymerizable resource analysis includes: analyzing load measurement polymerizable resources, and classifying industrial loads and resident loads according to industries;
firstly, calculating and sequencing Euclidean distances between industrial loads and all-society electricity loads;
calculating and sequencing Euclidean distances between resident loads and all-society electricity loads;
dividing the industrial load and the resident load into 3 adjustment levels according to the sorting result, and calculating to obtain the adjustable load quantity of each level of the industrial load and the resident load;
based on the standardized processing of various load values and the whole-society load demand values, the formula for obtaining the matching degree of various load curves and the whole-society load curves is as follows:
in the method, in the process of the application,Y t and->y t Respectively representing the k-th adjustable load and the t-moment load value and the original load value after the standardization processing of the whole social load; d (X) k Y) represents the euclidean distance between the kth class load and the global social load; a is that i Representing an i-th level load adjustable coefficient; x is x i Indicating the total electricity consumption of the ith grade; l represents the total load-side load adjustable amount; x is x k max Represents the maximum value of the k-th class adjustable load adjustable capability; x is x k min A minimum value representing a k-th class adjustable load adjustability; y is max Representing a maximum value of the global social load demand; y is min Representing a minimum value of the global social load demand; parameter I represents the total number;
the load side unit constraint is as follows:
in the method, in the process of the application,maximum adjustable load for industrial users and residential users; l (L) t in 、L t re Respectively representing an adjustable industrial load and an adjustable residential load; l (L) t fx Representing an adjustable load at the time t;
the model is solved by adopting an epsilon constraint method, and the steps are as follows:
the maximized objective function is transformed before solving, and the method is specifically shown as the following formula:
in the method, in the process of the application,net benefit for virtual power plant: />The new energy consumption rate at the moment t; h is a 1 Net revenue for the converted virtual power plant; h is a 2 The new energy consumption rate is converted;
the transaction operation optimization model of the virtual power plant is converted as follows by adopting an epsilon constraint method:
in minh 1 Represents the maximization of net benefit, h 2 Satisfies epsilon constraint;
the epsilon is shown as the formula:
in the method, in the process of the application,to be h 2 When optimizing for a single target, the obtained clean energy consumption rate; />In h 1 When optimizing for a single target, the obtained clean energy consumption rate; n (N) max Is the maximum value of the cycle times;
obtaining a Pareto optimal solution set as S= {1,2, …, S }, and obtaining an optimal solution by adopting a fuzzy decision method, wherein a membership function is defined in the fuzzy decision method as follows:
in the method, in the process of the application,respectively the maximum value and the minimum value of the jth objective function; />Values in the ith Pareto set for the jth objective function;
selecting the membership degree of each Pareto set:
in the method, in the process of the application,represents h 1 Membership in the i-th Pareto set; />Represents h 2 Membership in the i-th Pareto set;
the model optimal solution is shown as the formula:
wherein m is 1 、m 2 、……、m s The membership of the 1 st, 2 nd, … … th and s th Pareto sets are shown.
2. The method for optimizing low-carbon joint transaction in a virtual power plant according to claim 1, wherein the source side polymerizable resource analysis comprises: wind power resource technology developable amount analysis, wind power resource economic developable amount analysis, photovoltaic resource technology developable amount analysis and photovoltaic resource economic developable amount analysis.
3. The method for optimizing low-carbon joint transaction of virtual power plant according to claim 1, wherein the modeling method in S2 comprises: source side modeling, load side modeling, and other unit modeling.
4. A virtual power plant low-carbon joint transaction optimization method according to claim 3, wherein the source side modeling includes: wind power generation modeling, photovoltaic power generation modeling and biomass power generation modeling.
5. A virtual power plant low-carbon joint transaction optimization method according to claim 3, wherein the load side modeling includes: according to the grading of the load side polymerizable resources, the formula for constructing the load side model is as follows:
in the method, in the process of the application,the load is adjustable at the moment t; />Respectively an adjustable industrial load and an adjustable resident load;the first-stage adjustable industrial load coefficient, the second-stage adjustable industrial load coefficient and the third-stage adjustable industrial load coefficient are respectively adopted; />The industrial load scale of the first stage, the second stage and the third stage is respectively;the first-stage adjustable resident load coefficient, the second-stage adjustable resident load coefficient and the third-stage adjustable resident load coefficient are respectively; />The first-stage resident load scale, the second-stage resident load scale and the third-stage resident load scale are respectively adopted.
6. A virtual power plant low-carbon joint transaction optimization method according to claim 3, wherein the other unit modeling includes: battery modeling, carbon capture device modeling, carbon storage device modeling, and electrical conversion device modeling.
7. The virtual power plant low-carbon joint transaction optimization method according to claim 1, wherein the S3 low-carbon joint transaction optimization strategy model comprises: total virtual power plant revenue, total virtual power plant cost, and net virtual power plant revenue;
the virtual power plant total revenue includes: electric energy market revenue, peak shaving market revenue, carbon market revenue and sales energy revenue;
the virtual power plant total cost includes: power generation costs, operational maintenance costs, demand response costs, bias penalty costs, and risk costs.
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