Low-carbon economic dispatch method based on virtual carbon emission intensity of power plant

Abstract

The application relates to a source-load-storage low-carbon economic dispatching method based on virtual carbon emission intensity of a power plant, which comprises the following specific steps: calculating the composite electricity price of a power generation subject based on the declared electricity price, power and virtual carbon emission intensity of the power generation subject, carrying out power spot clearing based on the composite electricity price of the power generation subject, and calculating the node electricity price; applying a carbon flow algorithm to calculate the node virtual carbon emission intensity and obtain the virtual carbon emission cost per kilowatt of the node based on the network topology, the first clearing result and the virtual carbon emission intensity declared by the power generation subject; since the node electricity price and the virtual carbon emission cost per kilowatt of the node exist price fluctuation within a preset dispatching period, the two parameters are used to guide users to adjust the power consumption behavior, and within the preset dispatching period, the virtual carbon emission cost is considered to carry out the second clearing with the total cost of the power system being minimum as an objective function, so that the dispatching result is obtained, and the dispatching result comprises unit combination, unit output, energy storage device combination and the charge-discharge curve of the energy storage power station.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system regulation, and more particularly to a source-load-storage low-carbon economic dispatching method based on virtual carbon emission intensity of power plants. BACKGROUND

[0002] In a power system, carbon emissions are directly generated on the power generation side, and power use is on the user side. Power dispatching on the power generation side is carried out according to the power generation needs on the user side. In the construction of a new power system mainly based on new energy, energy storage and user-side flexible load are important technical means for solving the volatility, randomness and intermittency of new energy. In combination with source-load-storage, low-carbon dispatching research is carried out to optimize resource allocation in a wider range, which is an important means to reduce carbon emissions of the power system.

[0003] The current carbon emission rights trading system mainly focuses on the power generation industry and some large demand-side users, while the majority of small and medium-sized power users have not been included in its coverage. How to associate the vast number of demand-side power users with carbon trading and carbon emission reduction is of great significance for further power emission reduction.

[0004] On the market level, in order to respond to real-time changes in power supply and demand, power spot trading is usually carried out every 15 minutes or every hour, while the frequency of carbon trading is usually lower. How to couple the two markets in a certain time dimension is the key to carrying out low-carbon power dispatching.

[0005] On the power generation side, real-time measurement of carbon emissions usually has the problem of low accuracy. Using inaccurate carbon emission data to transmit carbon emission intensity signals to the user side cannot effectively guarantee carbon reduction effect.

[0006] On the user side, the construction of the power market relies on time-of-use electricity prices to guide users to adjust their electricity consumption behavior, which can reduce carbon emissions to a certain extent. However, since the change trend of electricity price and carbon emission is not consistent, there is currently a lack of clear and direct signals to guide users to carry out low-carbon electricity consumption behavior adjustment.

[0007] The prior art such as the Chinese patent with the patent number "CN115511349A" discloses a user-side steam-heat-electricity coupling low-carbon scheduling method related to carbon emission quota. The method comprises the following steps: S1: collecting required data; S2: establishing a user-side steam-heat-electricity coupling conversion model; S3: constructing a user-side carbon emission cost model based on carbon emission quota; S4: constructing a user-side steam-heat-electricity coupling low-carbon scheduling model considering carbon emission quota; when the carbon emission is less than the carbon emission quota, scheduling is mainly based on electricity economy; when the carbon emission is higher than the carbon emission quota, scheduling is performed considering both carbon emission and economy; S5: based on a genetic algorithm, the user-side steam-heat-electricity coupling low-carbon scheduling model considering carbon emission quota is solved, and the optimal total electricity consumption, basic electricity consumption and gas consumption of the user in different time periods are output.

[0008] The prior art such as the Chinese patent with the patent number "CN108229865A" discloses a low-carbon economic scheduling method of an integrated energy system based on carbon trading. The method comprises the following steps: determining the free carbon emission amount of IES according to the purchased electricity and the electricity generated by the gas turbine; determining the actual carbon emission amount of IES; determining the step-type carbon trading cost of IES according to the free carbon emission amount of IES and the actual carbon emission amount of IES; constructing an IES low-carbon economic scheduling model based on the step-type carbon trading cost, and obtaining the required scheduling scheme according to the model. The present application establishes a carbon trading mode suitable for IES, introduces low-carbon cleanliness into IES scheduling, gives a low-carbon economic scheduling method suitable for IES, optimizes the carbon trading cost calculation model, and makes it have a more strict control on the carbon emission amount of IES.

[0009] The above prior art has the problem that only the carbon emission generated by the traditional power generation method is considered for optimization, and the introduction of clean energy to jointly constitute the power system is not considered. Or only the carbon emission amount on the market side and the power generation side is considered, and the user side is not considered as an optimization object. In view of the deficiencies of the prior art and the urgent improvement needs, the present application proposes a source-load-storage low-carbon economic scheduling method based on virtual carbon emission intensity of power plants. The method proposes virtual carbon emission intensity and adds virtual cost to the electricity price cost, which can bring a larger electricity price bidding range to the power generation subject, better guide the user side to adjust the electricity consumption behavior, and reduce carbon emission. SUMMARY

[0010] To solve the above technical problems, the present application proposes a source-load-storage low-carbon economic scheduling method based on virtual carbon emission intensity of power plants.

[0011] The technical scheme of the present application is as follows:

[0012] In one aspect, the present application proposes a source-load-storage low-carbon economic scheduling method based on virtual carbon emission intensity of power plants, characterized in that it comprises the following specific steps:

[0013] The composite electricity price of the power generation subject is calculated based on the declared electricity price, power, and virtual carbon emission intensity of the power generation subject, and electricity spot clearing is carried out based on the composite electricity price of the power generation subject to calculate the node electricity price;

[0014] Based on the network topology, the first clearing result, and the virtual carbon emission intensity declared by the power generation subject, the node virtual carbon emission intensity is calculated by applying the carbon flow algorithm to obtain the virtual carbon emission cost per kilowatt-hour of the node;

[0015] Since the node electricity price and the node virtual carbon emission price per kilowatt-hour fluctuate within a preset dispatching period, these two parameters are used to guide users to adjust their electricity consumption behavior. Within the preset dispatching period, the virtual carbon emission cost is considered, and the total cost of the power system is minimized as the objective function to carry out the second clearing to obtain the dispatching result, which includes unit commitment, unit output, energy storage device combination, and energy storage power station charging and discharging curve.

[0016] As a preferred embodiment, the specific formula of the composite electricity price is as follows:

[0017] P compond =P single +P carbon ×ρ carbon

[0018] Wherein P compond represents the composite electricity price, P single represents the electricity price, P carbon represents the carbon price, and ρ carbon represents the virtual carbon emission intensity.

[0019] As a preferred embodiment, the virtual carbon emission cost per kilowatt-hour of the node is shown in the following formula:

[0020] C j =P carbon ×ρ j

[0021] Wherein C j represents the virtual carbon emission cost per kilowatt-hour of the node, P carbon represents the carbon price, and ρ j represents the virtual carbon emission intensity of the jth node, which is calculated according to the carbon flow theory.

[0022] As a preferred embodiment, the total cost of the power system includes: energy storage power station charging and discharging cost, flexible load adjustment cost, thermal power fuel cost, virtual carbon emission cost, wind power operation and maintenance cost, and photovoltaic operation and maintenance cost. The total cost of the power system within the dispatching period is represented by the following formula:

[0023] C u，t =C bes，t+C wind，t +C solar，t +C fuel，t +C a，t +C carbon，t

[0024] wherein t is a dispatch cycle time, C bes，t is a charge-discharge loss cost of the t period, C wind，t is a wind turbine operation and maintenance cost of the t period, C solar，t is a photovoltaic turbine maintenance cost of the t period, C fuel，t is a thermal power turbine operation cost of the t period, C a，t is an adjustment cost of the t period due to user adjustment of power consumption strategy, C carbon，t is a virtual carbon emission cost of the t period;

[0025] The calculation formula with the optimization objective of minimum total cost is:

[0026]

[0027] As a preferred embodiment, the flexible load adjustment cost is specifically:

[0028] C a，t = a t (ΔQ l，k ) 2 +b t ΔQ l，t

[0029] wherein C a，t represents an adjustment cost of the t period due to user adjustment of power consumption strategy, a t and b t are coefficients of the adjustment cost function, and ΔQ l，t represents an adjustment power size of the user in the t period, which satisfies the following constraints:

[0030]

[0031] wherein, is a maximum adjustment amount of the load.

[0032] On the other hand, the present application also provides a source-load-storage low-carbon economic dispatching system based on virtual carbon emission intensity of power plants, comprising:

[0033] a price calculation module, based on the declared price, power and virtual carbon emission intensity of the power generation subject, carrying out power spot clearing based on the composite price of the power generation subject, and calculating the node price;

[0034] The carbon emission cost calculation module calculates the virtual carbon emission intensity of the node by applying a carbon flow algorithm based on the network topology, the first clearing result and the virtual carbon emission intensity declared by the power generation subject, and obtains the virtual carbon emission cost per kilowatt-hour of the node;

[0035] The dispatch optimization module guides users to adjust the power consumption behavior based on the node price and the virtual carbon emission cost per kilowatt-hour of the node, and obtains the dispatch result by taking the minimum total cost of the power system as the objective function in the dispatch period of a day;

[0036] The result output module outputs the unit combination, unit output, energy storage device combination and energy storage device charge-discharge curve of the dispatch result.

[0037] As a preferred embodiment, the carbon emission cost calculation module calculates the virtual carbon emission cost per kilowatt-hour of the node as shown in the following formula:

[0038] C j = P carbon × p j

[0039] Wherein C j represents the virtual carbon emission cost per kilowatt-hour of the node, P carbon represents the carbon price, and p j represents the virtual carbon emission intensity of the jth node.

[0040] As a preferred embodiment, the dispatch optimization module takes the total cost of the power system in the dispatch period as shown in the following formula:

[0041] C u，t = C bes，t + C wind，t + C solar，t + C fuel，t + C a，t + C carbon，t

[0042] Wherein t is the dispatch period time, C bes，t is the charge-discharge loss cost in the t period, C wind，t is the wind turbine operation and maintenance cost in the t period, C solar，t is the photovoltaic unit maintenance cost in the t period, C fuel，t is the thermal power unit operation cost in the t period, C a，t is the adjustment cost in the t period due to the adjustment of the user's power consumption strategy, and C carbon，t is the virtual carbon emission cost in the t period.

[0043] The calculation formula with the minimum total cost as the optimization objective is:

[0044]

[0045] In still another aspect, the present application also provides an electronic device having stored thereon a computer program which, when executed by a processor, implements the method for source-load-storage low-carbon economic dispatch based on virtual carbon emission intensity of power plants according to any embodiment of the present application.

[0046] In still another aspect, the present application also provides a computer readable medium for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the method for source-load-storage low-carbon economic dispatch based on virtual carbon emission intensity of power plants according to any embodiment of the present application.

[0047] The present application has the following beneficial effects:

[0048] 1. From the perspective of the power system, the method for source-load-storage low-carbon economic dispatch based on virtual carbon emission intensity proposed by the present application relaxes the close coupling between carbon emission responsibility and power flow on the basis of ensuring actual measurement of all-day carbon emission, proposes "virtual carbon emission intensity", makes the carbon emission intensity of the power generation subject a controllable variable, realizes free transfer of carbon emission responsibility, supports low-carbon guidance of user electricity consumption behavior, and reduces carbon emission of the power system.

[0049] 2. From the perspective of the power generation subject, the "virtual carbon emission intensity" proposed by the present application relaxes the close coupling between carbon emission responsibility and power flow on the basis of ensuring actual measurement of all-day carbon emission of the power generation subject, distinguishes between the electricity price and the carbon price contained therein, transfers the carbon emission responsibility to the user side, and expands the price adjustment space of the power generation subject, thereby providing optimization space for the price declaration strategy of the power generation subject.

[0050] 3. From the perspective of the user, the method proposed by the present application can receive the node electricity price containing time sequence information, the carbon emission cost per kilowatt-hour of the node, and the carbon emission intensity of the node at the user node, so as to guide the user to adjust the electricity consumption behavior in the light of the explicit carbon cost and carbon emission intensity, and reduce the overall carbon emission of the system. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and other related drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0052] Figure 1 Low-carbon dispatch logic diagram

[0053] Figure 2 Electricity-carbon coupling subject and cost composition of the power system

[0054] Figure 3 User-side electricity price deconstruction. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0056] It should be understood that the step numbers used herein are only for the convenience of description, and are not limited to the execution sequence of the steps.

[0057] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0058] The terms "comprise" and "include" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0059] The term "and / or" means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0060] Embodiment one:

[0061] In order to make the purposes, technical solutions and advantages of the present application clearer, the following will combine specific embodiments of the present application, and refer to the accompanying drawings Figure 1 The technical solutions of the present application are clearly and completely described.

[0062] To solve the problems in the prior art, the present application provides a source-load-storage low-carbon economic dispatching method based on virtual carbon emission intensity of power plants, which comprises the following steps:

[0063] 1) The present application proposes an electricity-carbon coupling mechanism, which takes the power system composed of power generation subjects such as thermal power plants, wind power plants and photovoltaic power stations, power users and energy storage power stations as the object, and carries out low-carbon economic dispatching.

[0064] The market subjects under the electricity-carbon coupling mechanism are shown in Figure 2 .

[0065] 2) The power generation body declares the electricity price, power and virtual carbon emission intensity, and the declared electricity price is processed by the electricity-carbon coupling mechanism to obtain the composite electricity price as the declared electricity price of the power generation body.

[0066] P compond = P single + P carbon x p carbon

[0067] where P compond represents the composite electricity price, P single represents the electricity price, P carbon represents the carbon price, and p carbon represents the virtual carbon emission intensity.

[0068] 3) The composite electricity price of the power generation body is used to carry out power spot clearing, and the node electricity price is calculated; based on the network topology and the virtual carbon emission intensity declared by the power generation body, the carbon flow algorithm is applied to calculate the node virtual carbon emission intensity and obtain the node virtual carbon emission cost per kilowatt-hour. The carbon emission flow theory is briefly introduced:

[0069] The branch carbon flow density can be expressed as the ratio of the cumulative carbon emission flowing through the branch to the power flowing through the branch in a period of time, i.e.:

[0070]

[0071] where L represents the set of branches, is the average branch carbon flow density of the ith branch, F i represents the cumulative carbon emission through branch i in a certain period of time, and Q i represents the power through branch i in a certain period of time. The branch carbon flow density can describe the relationship between the power flow and the carbon emission flow on the branch in the power network. Similarly, there is a physical quantity for describing the relationship between the carbon emission flow and the power flow on the node, i.e. the node carbon emission intensity:

[0072]

[0073] where N represents the set of nodes, p j represents the carbon emission intensity of the jth node, F j represents the carbon emission flowing into node j per unit time, and Q j represents the power consumed by node j per unit time.

[0074] The virtual carbon emission cost per kilowatt-hour of the node is:

[0075] C j = P carbon x p j

[0076] where C jVirtual degree carbon emission cost of the representative node, P carbon Representative carbon price, p j Indicates the virtual carbon emission intensity of the jth node.

[0077] 4) Since the node electricity price and the node degree carbon emission price exist price fluctuations in the preset scheduling period, as shown in Figure 3 , the two parameters are used to guide the user to adjust the power consumption behavior, in the preset scheduling period, considering the virtual carbon emission cost, taking the minimum total cost of the power system as the objective function, carrying out the second clearing, obtaining the scheduling result, the scheduling result including unit combination, unit output, energy storage device combination and energy storage power station charging and discharging curve.

[0078] 5) The present application takes the minimum total cost of the power system as the optimization target, and solves the optimal power generation and consumption strategy of the power system in a period of time. The total cost of the power system includes the charging and discharging cost of the energy storage power station, the flexible load regulation cost, the fuel cost of the thermal power station, the virtual carbon emission cost, the wind power operation and maintenance cost, and the photovoltaic operation and maintenance cost, as shown in Figure 2 .

[0079] 6) The state of charge of the energy storage device can be represented as:

[0080]

[0081] Among them, SOC t represents the state of charge of the energy storage in the period, and respectively represent the charging capacity and discharging capacity of the energy storage in the period, and c and d are the charging coefficient and discharging coefficient of the energy storage.

[0082] 7) The charging and discharging loss cost of the energy storage in the t period can be represented as: bes，t

[0083] C bes，t = tau bes | SOC t -SOC t-1 |

[0084] Where tau bes is a coefficient related to the charging and discharging loss cost of the energy storage.

[0085] 8) The randomness of wind speed determines the uncertainty of the output power of the wind turbine generator set, which can be described by Weibull distribution, and the wind speed calculation model is as follows:

[0086]

[0087] ​In the formula, v is the wind speed, c is the scale parameter of the Weibull distribution, and k is the state parameter. Using the above formula to calculate the wind speed probability density, the relationship between the output power of the wind turbine and the real-time wind speed is as follows:

[0088]

[0089] In the formula, C represents the power generation of the wind turbine at time t; p Wind energy utilization coefficient; air density; A ω The vertical projected area of ​​the wind speed across the area swept by the turbine blades; The rated power of the unit; v in For the cut-in wind speed; v rated Rated wind speed; v out To cut off the wind speed.

[0090] 9) User's wind turbine operation and maintenance cost C during time period t wind，t It can be represented as:

[0091]

[0092] In the formula, C Wind，t Maintenance costs for the wind turbines in the industrial park; K Wind M is the operation and maintenance coefficient of the r-th wind turbine unit; t The scheduling cycle is 24 hours; N Wind This refers to the number of wind turbine units.

[0093] 10) The randomness of solar irradiance intensity determines the uncertainty of photovoltaic power generation units, which can be described by the Beta distribution. The solar radiation intensity calculation model is as follows:

[0094]

[0095] In the formula, γ and γ max α and β are the solar irradiance and maximum solar irradiance at time t, respectively; α and β are the relevant parameters of the Beta distribution.

[0096] Output model of photovoltaic power generation:

[0097]

[0098] In the formula, χ Solar For conversion efficiency; ρ Solar θ represents the total area of ​​the photovoltaic modules; t Let be the solar radiation intensity at time t.

[0099] 11) User's photovoltaic unit operation and maintenance cost C during time period t solar，t It can be represented as:

[0100]

[0101] In the formula, C solar，t is the maintenance cost of the park photovoltaic unit; K Solar is the operation maintenance coefficient of the xth photovoltaic unit; M t is the scheduling period, 24 hours; N Solar is the number of photovoltaic units.

[0102] 12) The calculation model of the thermal power unit is as follows:

[0103] P H，b，t = P ruel，b，t η b，t

[0104]

[0105] In the formula, P H，b，t is the thermal power of the thermal power unit; P fuel，b，t is the fuel consumption power; η b，t is the thermal efficiency corresponding to the load rate of the thermal power unit at t; r b，t is the load rate of the thermal power unit at t; P H，b，max is the rated power of the thermal power unit; a1, a2, a3 are -0.6849, 1.525, and 0.0951.

[0106] 13) The operation cost C fuel，t of the thermal power unit at t can be expressed as:

[0107]

[0108] In the formula, C fuel，t is the operation cost of the thermal power unit; C fuel is the unit fuel cost coefficient; q is the fuel heat value; s gb (t) is the start-stop state of the thermal power unit at t; C gb，st is the single start-up cost of the thermal power unit.

[0109] 14) For a large number of flexible adjustable loads such as air conditioners, flexible user adjustment of power consumption strategies will inevitably cause some other problems such as comfort loss. The loss caused by these problems can be described by a cost function, and with the increase of the load adjustment amount, the accompanying regulation cost gradually rises. In view of this, the present application describes the adjustment cost of the user-side flexible load in the form of a quadratic function:

[0110] C a，t = a t (ΔQ l，t ) 2 +b t ΔQl，t

[0111] wherein C a，t represents the adjustment cost generated by the user adjusting the power consumption strategy at period t, a t and b t are coefficients of the adjustment cost function, AQ l，t represents the adjustment power size of the user at period t, which needs to satisfy the following constraints:

[0112]

[0113] wherein, is the maximum adjustment amount of the load.

[0114] 15) for the virtual carbon emission cost:

[0115]

[0116] wherein C conv，gb is the conversion coefficient of unit fuel to carbon dioxide; P carbon is the carbon price.

[0117] 16) finally, the total cost C u，t of the power system at period t is obtained:

[0118] C u，t = C bes，t + C wind，t + C solar，t + C fuel，t + C a，t + C carbon，t

[0119] The objective function to be optimized by the system within 0 to T period is:

[0120]

[0121] 17) taking the minimum total cost of the power system as the objective function, carrying out the second clearing, obtaining the dispatching result, the dispatching result including unit combination, unit output, energy storage device combination and energy storage power station charge-discharge curve.

[0122] 18) the constraints mainly considered by the function mainly include the self operation constraints of various devices such as units and energy storages, power balance constraints and the like, which are as follows:

[0123] a) system power balance constraint

[0124]

[0125] wherein, is the power generation power of the thermal power unit at t moment; is the wind power at t moment; Let T be the photovoltaic power generation at time t; t The power supplied to the tie line at time t; Let t be the power of the electrical load. The energy storage charging power at time t; Let t represent the power consumption of the common part of each subsystem at time t.

[0126] b) System standby constraints

[0127] System standby capacity constraints

[0128]

[0129] Where, α i，t α represents the start-up and shutdown status of thermal power unit i during time period t. i，t =0 indicates that the unit is shut down, α i，t =1 indicates that the unit is started; N is the total number of thermal power units; β represents the maximum output (typically rated capacity) of unit i during time period t; j，t This indicates the activation status of energy storage j during time period t, β j，t =0 indicates that the energy storage is not used, β j，t =1 indicates that the energy storage has been activated; D represents the energy storage discharge power; M represents the total number of energy storage devices; t RtU represents the system load for time period t, which has been reduced by the net power supplied to the tie line; RtU represents the system positive standby capacity requirement for time period t.

[0130] System negative reserve capacity constraints

[0131] The system negative reserve capacity constraint can be described as:

[0132]

[0133] in, Let i be the minimum output of thermal power unit i during time period t; The charging power of the energy storage is represented by k, the number of interconnecting lines, and T. k t Let be the input power of tie line k at time t; The system's negative backup capacity requirement for time period t.

[0134] c) Output constraints of thermal power units

[0135]

[0136] In the formula, These are the maximum and minimum output values ​​of the i-th thermal power unit, respectively.

[0137] d) Gradient constraints of thermal power units

[0138]

[0139] where, is the output of the i-th thermal power unit at time t, t-1; is the maximum ramp-up rate and the maximum ramp-down rate of the i-th thermal power unit.

[0140] e) Wind power and photovoltaic power output constraints

[0141]

[0142] where, is the maximum value of the output of the wind power and photovoltaic unit.

[0143] f) Energy storage constraints

[0144] Charging power constraints

[0145]

[0146] Discharging power constraints

[0147]

[0148] Charging and discharging state limits

[0149]

[0150] Energy storage power constraints

[0151]

[0152] Energy storage power at the end of the period constraints

[0153] E s，t＝0 = E s，t＝T

[0154] where,

[0155] is the charging and discharging power of the energy storage at time t;

[0156] is the upper and lower limits of the charging and discharging power of the energy storage;

[0157] is the charging and discharging state of the energy storage at time t;

[0158] E s，t , E s，t＝0 , E s，t＝T are the state of charge of the energy storage at time t, the beginning and the end of the period, respectively;

[0159] is the charging and discharging efficiency of the energy storage;

[0160] At is the time interval, in hours.

[0161] Embodiment two:

[0162] The embodiment provides a source-load-storage low-carbon economic dispatching system based on virtual carbon emission intensity of a power plant, and comprises the following modules.

[0163] A power price calculation module calculates a node power price based on a declared power price, power, and virtual carbon emission intensity of a power generation subject, and carries out power spot clearing based on a composite power price of the power generation subject.

[0164] A carbon emission cost calculation module calculates a node virtual carbon emission intensity and obtains a virtual carbon emission cost per kilowatt of the node based on a network topology, a first clearing result, and the virtual carbon emission intensity declared by the power generation subject, and applies a carbon flow algorithm.

[0165] A dispatching optimization module guides a user to adjust power consumption behavior based on the node power price and the virtual carbon emission cost per kilowatt of the node, and obtains a dispatching result by taking the minimum total cost of the power system as an objective function in a dispatching period of one day.

[0166] A result output module outputs a unit combination, unit output, energy storage device combination, and energy storage device charging and discharging curve of the dispatching result.

[0167] Embodiment three:

[0168] The embodiment provides an electronic device, which has a computer program stored thereon, and the computer program is executed by a processor to realize the source-load-storage low-carbon economic dispatching method based on virtual carbon emission intensity of a power plant according to any one of the embodiments of the present application.

[0169] Embodiment four:

[0170] The embodiment provides a computer readable medium for storing one or more programs, and the one or more programs are executed by one or more processors to make the one or more processors realize the source-load-storage low-carbon economic dispatching method based on virtual carbon emission intensity of a power plant according to any one of the embodiments of the present application.

[0171] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0172] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be realized in electronic hardware, computer software, and a combination of electronic hardware and computer software. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0173] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0174] In several embodiments provided in the present application, any function realized in the form of a software function unit and sold or used as an independent product can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory; hereinafter referred to as: ROM), a random access memory (Random Access Memory; hereinafter referred to as: RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0175] The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation based on the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A source-load-storage low-carbon economic dispatch method based on the virtual carbon emission intensity of power plants, characterized in that, The specific steps include the following: The composite electricity price for power generation entities is calculated based on their declared electricity price, power output, and virtual carbon emission intensity. This composite price is then used to clear the electricity spot market and calculate the nodal price. The specific formula for the composite electricity price is as follows: P compond =P single +P carbon ×ρ carbon Where P compond P represents the compound electricity price. single Represents electricity price, P carbon Represents carbon price, ρ carbon Represents virtual carbon intensity; Based on the network topology, the results of the first clearing process, and the virtual carbon emission intensity declared by the power generation entities, the carbon flow algorithm is applied to calculate the virtual carbon emission intensity of each node and obtain the virtual carbon emission cost per kilowatt-hour of the node; the virtual carbon emission cost per kilowatt-hour of the node is shown in the following formula: C j =P carbon ×ρ j Where C j The virtual carbon cost per kilowatt-hour of a node, P carbon Represents carbon price, ρ j This represents the virtual carbon emission intensity of the j-th node, calculated based on carbon flow theory; Because nodal electricity prices and virtual carbon emission prices per kilowatt-hour fluctuate within a preset dispatch period, these two parameters are used to guide users to adjust their electricity consumption behavior. Within the preset dispatch period, considering virtual carbon emission costs, a second clearing process is conducted with the objective function of minimizing the total cost of the power system. The dispatch results include unit combination, unit output, energy storage equipment combination, and energy storage power station charge / discharge curves. The total cost of the power system includes: energy storage power station charge / discharge costs, flexible load adjustment costs, thermal power fuel costs, virtual carbon emission costs, wind power operation and maintenance costs, and photovoltaic operation and maintenance costs. The total cost of the power system within the dispatch period is expressed by the following formula: C u,t =C bes,t +C wind,t +C solar,t +C fuel,t +C a,t +C carbon,t In the formula, t is the scheduling cycle time, and C bes,t C represents the cost of charge / discharge losses during time period t. wind,t C represents the operation and maintenance cost of the wind turbine during time period t. solar,t C represents the maintenance cost of the photovoltaic unit during time period t. fuel,t Let C be the operating cost of the thermal power unit during time period t. a,t C represents the adjustment cost incurred by users adjusting their electricity consumption strategies during time period t. carbon,t The virtual carbon emission cost for time period t; The calculation formula with the goal of minimizing total cost is as follows: The specific cost of flexible load adjustment is as follows: C a,t =a c (ΔQ l,t ) 2 +b t ΔQ l,t Among them, C a,t a represents the adjustment cost incurred by users in adjusting their electricity consumption strategies during time period t. t and b t To adjust the coefficients of the cost function, ΔQ l,t Let represent the amount of electricity adjusted by the user during time period t, which satisfies the following constraints: in, It is the maximum adjustment amount of the load.

2. A source-load-storage low-carbon economic dispatch system based on the virtual carbon emission intensity of power plants, characterized in that, include: The electricity price calculation module calculates the nodal price based on the electricity price, power, and virtual carbon emission intensity declared by the power generation entity, and conducts electricity spot clearing using the composite electricity price of the power generation entity. The carbon emission cost calculation module, based on network topology, the first clearing results, and the virtual carbon emission intensity declared by power generation entities, applies a carbon flow algorithm to calculate the node carbon emission intensity and obtain the node's virtual carbon emission cost per kilowatt-hour. The carbon emission cost calculation module calculates the node's virtual carbon emission cost per kilowatt-hour using the following formula: C j =P carbon ×ρ j Where C j The virtual carbon cost per kilowatt-hour of a node, P carbon Represents carbon price, ρ j This represents the virtual carbon emission intensity of the j-th node; The dispatch optimization module guides users to adjust their electricity consumption behavior based on nodal electricity prices and the virtual carbon emission cost per kilowatt-hour. Within a one-day dispatch cycle, considering the virtual carbon emission cost, the module uses the minimum total cost of the power system as the objective function to obtain the dispatch result. The total cost of the power system within the dispatch cycle is expressed by the following formula: C u,t =C bes,t +C wind,t +C solar,t +C fuel,t +C a,t +C carbon,t In the formula, t is the scheduling cycle time, and C bes,t C represents the cost of charge / discharge losses during time period t. wind,t C represents the operation and maintenance cost of the wind turbine during time period t. solar,t C represents the maintenance cost of the photovoltaic unit during time period t. fuel,t Let C be the operating cost of the thermal power unit during time period t. a,t C represents the adjustment cost incurred by users adjusting their electricity consumption strategies during time period t. carbon,t The virtual carbon emission cost for time period t; The calculation formula with the goal of minimizing total cost is as follows: The results output module outputs the scheduling results, including the unit combination, unit output, energy storage equipment combination, and energy storage equipment charge and discharge curves.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the source-load-storage low-carbon economic dispatch method based on the virtual carbon emission intensity of power plants as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the source-load-storage low-carbon economic dispatch method based on the virtual carbon emission intensity of power plants as described in claim 1.

Images