CN117526339A - Load resource benefit distribution method, device, computer equipment and storage medium - Google Patents
Load resource benefit distribution method, device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN117526339A CN117526339A CN202311460982.8A CN202311460982A CN117526339A CN 117526339 A CN117526339 A CN 117526339A CN 202311460982 A CN202311460982 A CN 202311460982A CN 117526339 A CN117526339 A CN 117526339A
- Authority
- CN
- China
- Prior art keywords
- load resource
- demand response
- alliance
- load
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000008901 benefit Effects 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000009826 distribution Methods 0.000 title claims abstract description 32
- 238000003860 storage Methods 0.000 title claims abstract description 13
- 230000004044 response Effects 0.000 claims abstract description 122
- 230000006870 function Effects 0.000 claims description 42
- 238000004590 computer program Methods 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000010276 construction Methods 0.000 claims description 6
- 239000002131 composite material Substances 0.000 claims description 3
- 206010011985 Decubitus ulcer Diseases 0.000 claims description 2
- 230000005611 electricity Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000009194 climbing Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 5
- 238000004146 energy storage Methods 0.000 description 5
- 238000010248 power generation Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000009434 installation Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 230000002650 habitual effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
Abstract
The application relates to a load resource benefit distribution method, a device, computer equipment and a storage medium, comprising the following steps: constructing a comprehensive cost model according to the multi-type cost of the demand response task; determining an objective function of the demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating and obtaining the maximum alliance income; and distributing the alliance benefits to each load resource based on the target Xia Puli value corresponding to each load resource. According to the method, the comprehensive cost of the demand response task is considered, the demand response comprehensive cost model is built, the objective function of the demand response task participating in the frequency modulation market is determined, and finally, the benefits of the load resource are distributed through the improved Charpy value, so that the reasonable benefits of the load resource are greatly improved.
Description
Technical Field
The present invention relates to the field of power resource revenue distribution technologies, and in particular, to a load resource revenue distribution method, a load resource revenue distribution device, a computer device, and a storage medium.
Background
In recent years, energy-saving and environment-friendly power production modes are increasingly popularized. Since renewable energy sources have larger instability, the increase in permeability of renewable energy sources puts no small pressure on the stable operation of the grid.
Demand response is an effective way to promote stable operation of the grid on the demand side, and it guides the user to change electricity usage habit through contract or electricity price change, and can reduce or transfer electricity load in a certain period. The demand response of the power load is used as a key technology for solving the stability problem, so that large-scale new energy consumption can be realized, and the high efficiency of the asset utilization and the clean generation energy of the power grid are promoted.
At present, the research on participation of a demand response technology in an electric power frequency modulation market is still blank, and a decision scheme for participation of a standard demand response resource in the frequency modulation market is lacking.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a load resource revenue distribution method, apparatus, computer device, and storage medium that can quickly determine a load resource principal revenue distribution scheme.
In a first aspect, the present application provides a load resource benefit allocation method, including:
constructing a comprehensive cost model according to the multi-type cost of the demand response task;
determining an objective function of the demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating and obtaining the maximum alliance income;
and distributing the alliance benefits to each load resource based on the target Xia Puli value corresponding to each load resource.
In one embodiment, the constructing the composite cost model according to the multi-type cost of the demand response task includes:
constructing a comprehensive cost sub-model corresponding to various types of demand response resources;
determining a target historical credit value and a target demand response matching degree based on a preset historical credit value model and a preset demand response matching degree model;
and constructing a comprehensive cost model according to each comprehensive cost sub-model, the target historical credit value and the target demand response matching degree.
In one embodiment, the determining that the demand response task is engaged in an electric frequency modulation market objective function includes:
determining the operation parameters of a target unit according to preset constraint conditions, wherein the target unit is a unit called by a demand response task;
and determining the objective function according to the operation parameters of the target unit and the compensation parameters of the electric power frequency modulation market.
In one embodiment, the allocating the alliance revenue to each load resource based on the target Xia Puli value corresponding to each load resource includes:
calculating a first profit fluctuation amount, a second profit fluctuation amount and a third profit fluctuation amount corresponding to each load resource;
calculating a target Xia Puli value corresponding to each load resource according to the first profit fluctuation amount, the second profit fluctuation amount, the third profit fluctuation amount and the weight corresponding to each profit fluctuation amount;
and distributing the alliance benefits to each load resource according to the target decubitus value.
In one embodiment, the target Xia Puli value is calculated as:
W Z +W T +W F ＝1
K＝{1,2,…,n}
wherein Q is i For Xia Puli value of the ith load resource, S is a load resource subset, S is the number of load resources in the load resource subset, K is a participant set of the cooperative game, E (S) is the alliance benefits when the ith load resource participates in the electric power frequency modulation market, E (S-i) is the alliance benefits when the ith load resource does not participate in the electric power frequency modulation market, n is the number of load resources in the alliance, Z i A first profit fluctuation amount for the ith load resource, W Z Is Z i Corresponding weight, T i A second profit fluctuation amount for the ith load resource, W T Is T i Corresponding weights, F i A third profit fluctuation amount for the ith load resource, W F Is F i And (5) corresponding weight.
In one embodiment, the calculation formula of the first profit fluctuation amount is:
Z i ＝G·(α i -1/n)
wherein G is the total alliance yield of the load resource alliance, alpha i The weight of the comprehensive strength of the ith load resource relative to the load resource alliance;
the calculation formula of the second profit fluctuation amount is as follows:
wherein I is i Investment amount for the ith load resource;
the calculation formula of the third profit fluctuation amount is as follows:
wherein R is i Considering the benefit under risk for the ith load resource, P i The success probability of the cooperation of the ith load resource and other load resources except the ith load resource in the load resource alliance is obtained.
In a second aspect, the present application further provides a load resource benefit allocation apparatus, including:
the model construction module is used for constructing a comprehensive cost model according to the multi-type cost of the demand response task;
the function calculation module is used for determining an objective function of the demand response task participating in the electric power frequency modulation market, wherein the objective function is used for calculating and obtaining the maximized alliance income;
and the income distribution module is used for distributing the alliance income to each load resource based on the target Xia Puli value corresponding to each load resource.
In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the load resource benefit allocation method according to the first aspect when the computer program is executed.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the load resource benefit allocation method of the first aspect.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the load resource benefit allocation method of the first aspect.
In summary, the present application provides a load resource benefit allocation method, apparatus, computer device and storage medium, including: constructing a comprehensive cost model according to the multi-type cost of the demand response task; determining an objective function of the demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating and obtaining the maximum alliance income; and distributing the alliance benefits to each load resource based on the target Xia Puli value corresponding to each load resource. According to the method, the comprehensive cost of the demand response task is considered, the demand response comprehensive cost model is built, the objective function of the demand response task participating in the frequency modulation market is determined, and finally, the benefits of the load resource are distributed through the improved Charpy value, so that the reasonable benefits of the load resource are greatly improved.
Drawings
FIG. 1 is an application environment diagram of a load resource benefit allocation method in one embodiment;
FIG. 2 is a flow chart of a load resource benefit allocation method according to one embodiment;
FIG. 3 is a flow chart illustrating steps for constructing an integrated cost model in one embodiment;
FIG. 4 is a flowchart illustrating steps for determining an objective function in one embodiment;
FIG. 5 is a flowchart illustrating steps for allocating revenue to load resources in one embodiment;
FIG. 6 is a block diagram of a load resource benefit distribution device in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The load resource benefit distribution method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and internet of things devices. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a load resource benefit allocation method is provided, and an application scenario in fig. 1 to which the method is applied is taken as an example to describe the description, and the method includes the following steps:
s201, constructing a comprehensive cost model according to the multi-type cost of the demand response task.
Specifically, demand Response (DR) refers to a short-term behavior in which when the reliability of a power system is affected or the price of a wholesale market of power increases, a power consumer changes the intrinsic habitual power consumption mode of the power consumer according to adjustment information sent by a power supplier, and reduces or shifts a power load for a certain period of time to respond to power supply, thereby ensuring the stability of a power grid and suppressing the increase of power price. The adjustment information may be direct compensation notification information for inducing a load reduction or an electric power price increase signal.
In the actual application process, the demand response task is that demand response resources in the power system respond to the electricity utilization adjustment task to be executed according to the demand.
In a specific embodiment, the demand response resource can adjust the demand load measurement, the distributed power supply, the energy storage and the like of the running state according to the demand of the demand response task. For example, for an economic demand response, the economic demand response may participate in the capacity market and the auxiliary service market, at which time the demand response resources participate in the day-ahead market in the form of virtual generators, and capacity obtained by bidding in the day-ahead market is allocated to users by the virtual farm as a dispatch plan. In emergency states such as insufficient system operation standby and load peaks, the demand response resource is used as a capacity resource, and load is reduced according to a dispatching center instruction.
It should be appreciated that the specific form of the demand response resource may be adaptively defined according to the requirements of the actual application scenario, which is not limited in this embodiment.
In a specific embodiment, the cost of the demand response resource at least includes a device modification cost, a device start-stop cost, a device new installation cost, a battery loss cost, a network loss cost, a market deviation assessment cost, and the like.
According to the embodiment, one or more demand response resources in the demand response task are aggregated, and the comprehensive cost model is built based on the cost of the aggregated demand response resources, so that bidding decision conditions of the demand response resources in the participation electric power frequency modulation market can be analyzed from the perspective of comprehensive cost, and a standard and comprehensive allocation scheme is provided.
S202, determining an objective function of the demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating and obtaining the maximized alliance income.
Specifically, before allocating federation benefits to each load resource in a load resource federation, the maximum federation benefit that the load resource federation may acquire needs to be calculated based on an objective function.
In the actual application process, the objective function consists of the comprehensive cost of the demand response resources corresponding to the demand response tasks, the power generation income, the basic compensation of the electric power frequency modulation market and the call compensation of the electric power frequency modulation market.
The electric power frequency modulation market is an electric power market of secondary frequency modulation auxiliary service.
In determining the objective function, constraint conditions are determined with the goal of maximizing the yield of the load resource alliance. In a specific embodiment, the constraint conditions at least include a unit climbing constraint, a State Of Charge (SOC) constraint, and a fm State constraint. And within the constraint condition, calculating to obtain the power generation benefits of the demand response resources and the call compensation of the power frequency modulation market.
According to the method and the device, the composition mode and the calculation purpose of the objective function are clarified, so that maximum profit distribution can be performed during load resource profit distribution, distribution efficiency is improved, distribution quality is improved, and a better profit distribution decision is obtained.
S203, the alliance benefits are distributed to the load resources based on the target Xia Puli values corresponding to the load resources.
Specifically, the load resource alliance in this embodiment is a cooperative alliance that performs electric energy interaction between multiple load aggregators, and the load resource represents the load aggregator in the load resource alliance.
The present embodiment calculates a target Xia Puli value (shape value) from the comprehensive actual profit fluctuation amount, the investment amount profit fluctuation amount, and the bearing risk profit fluctuation amount.
The comprehensive actual profit fluctuation quantity represents the comprehensive actual strength of the load resources, and the load resources with higher comprehensive actual strength can acquire higher profit allocation proportion in the profit allocation negotiation of the load resource alliance. The comprehensive strength of the load resource is evaluated by factors such as the scale and development potential of the load resource, and can be defined according to the requirements of the actual application scene, which is not limited in this embodiment.
The input amount profit fluctuation represents the input degree of the load resource, and the load resource with higher input degree can acquire higher profit allocation proportion in the profit allocation negotiation.
The amount of fluctuation of the bearing risk gain characterizes the risk bearing capacity of the load resource, and in the risk situation, the gain of the load resource changes along with the change of the risk bearing capacity.
And carrying out profit distribution according to the Charpy value, and completing fair distribution under the condition of considering specific requirements of all parties. According to the embodiment, the target Xia Puli value is calculated based on the comprehensive actual profit fluctuation amount, the investment amount profit fluctuation amount and the bearing risk profit fluctuation amount, and the contribution degree of each load resource in the load resource alliance to the profit in the electric power frequency modulation market can be comprehensively considered, so that a more fair allocation decision can be made.
In summary, the present embodiment provides a load resource profit allocation method, which can consider the comprehensive cost of demand response resources in a demand response task, obtain the total alliance profit of a load resource alliance according to the comprehensive cost, the power generation profit and the compensation calculation of the power frequency modulation market, and calculate Xia Puli values corresponding to each load resource in the load resource alliance based on multiple aspects of fluctuation amounts, thereby obtaining a fair profit allocation decision.
In one embodiment, as shown in fig. 3, S201 includes:
s301, constructing a comprehensive cost sub-model corresponding to each type of demand response resource.
Specifically, in the actual application process, the demand response task is executed by one or more demand response resources. The embodiment builds a comprehensive cost sub-model corresponding to various types of demand response resources. The integrated cost submodel in this embodiment includes at least submodels of equipment modification cost, equipment new installation cost, battery loss cost, grid loss cost, and market deviation assessment cost.
Specifically, the construction mode of the model of the equipment transformation cost is as follows:
C gz ＝k g *P*(1-α)
wherein k is g The unit capacity price for energy saving transformation is unit per kWh, P is the total power of the demand response equipment, and alpha is the subsidy rate.
The construction mode of the model of the equipment new installation cost is as follows:
C xz ＝k h *P*(1-α)
wherein k is h The price per unit capacity of the new high-efficiency equipment is shown as the unit per kWh, P is the total power of the demand response equipment, and alpha is the subsidy rate.
The construction mode of the model of the battery loss cost is as follows:
C sh ＝C bat *(S dis +S cha )
wherein C is bat The cost of breaking the unit cells of the energy storage unit C brc Is the unit investment cost of the battery, sigma DOD N is the allowable depth of discharge of the battery CL For the cycle life of the battery, S dis To discharge the battery during operation, S cha Charge the battery during operation.
The construction mode of the model of the network loss cost is as follows:
V ws ＝ΔQ loss *ρ df
wherein DeltaQ loss Is the total network loss of the power system, ρ df And the electric charge paid for the demand response resource is required. The model of the market deviation assessment cost is constructed in the following manner:
C kh ＝ρ df *max{P dec *R u -P real ,0}
wherein ρ is df Electric charge required to be paid for demand response resources, P dec Reporting response capacity for user, P real For the actual response capacity of the user, R u Is the response ratio threshold.
S302, determining a target historical credit value and a target demand response matching degree based on a preset historical credit value model and a preset demand response matching degree model.
Specifically, the preset historical credit value model corresponding to the historical credit value p is as follows:
wherein beta is a weight factor, is a constant, and P dec,n Reporting response capacity, P, for the nth participation of demand response resource principals in demand response real,n And for the actual response capacity corresponding to the nth participation requirement of the requirement response resource main body, N is the total number of participation of the requirement response resource main body in the requirement response.
Demand response matching degree r i The corresponding preset demand response matching degree model is as follows:
wherein r is i,k A k-th item of the demand response resource main body i represents a parameter level related to the demand response task; r is (r) 0,k Representing an optimal parameter level at which the demand response task is performed; lambda (lambda) k An influence factor of the kth parameter related to the demand response task on the demand response task is represented; lambda (lambda) 0 Representing a non-zero constant.
In a specific embodiment, the demand response matching degree is related to the response capacity, the response speed and other attributes of the adjustable resource, and the lower the value is, the more the demand response matching degree is matched with the demand of the demand response task. The parameter levels and the impact factors are matched according to a load resource library composed of acquired historical adjustable data of participation of the demand response resource main body in the demand response.
S303, constructing a comprehensive cost model according to each comprehensive cost sub-model, the target historical credit value and the target demand response matching degree.
In a specific embodiment, the expression of the integrated cost model is:
wherein C is ALL The comprehensive cost of all demand response resources based on historical credit values and demand response matching; i represents the total number of resource principals in the demand response resource, i= {1,2, …, I };the cost is improved for equipment; />The equipment start-stop cost is a fixed value; />The new installation cost of the equipment is realized; />Cost for battery loss; />Is the cost of network loss; />Checking cost for market deviation; />Other costs; p is p i A historical credit value representing a demand response resource principal i; r is (r) i And the resource attribute value of the demand response resource main body i is represented to be matched with the demand response task.
In one embodiment, as shown in fig. 4, S202 includes:
s401, determining operation parameters of a target unit according to preset constraint conditions, wherein the target unit is a unit called by a demand response task.
Specifically, the preset constraint conditions at least comprise a unit climbing constraint, a battery SOC constraint and a frequency modulation state constraint. In the actual application process, the preset constraint conditions can be configured according to the actual application scene.
It should be appreciated that, after the demand response task is determined, the electric power unit to be invoked may be further determined according to the specific task details of the demand response task.
In a specific embodiment, the expression of the unit climbing constraint is:
P - ≤P t -P t-1 ≤P +
wherein P is - For the upward climbing speed of the electric power unit, P + For the downward climbing speed of the electric machine set, P t And P t-1 The power of the electric power unit at the time t and the time t-1.
The expression of the battery SOC constraint is:
wherein S is t The battery SOC at the time t; s is S max Is the maximum value of the SOC of the battery; s is S min Is the minimum value of the battery SOC;the battery is followed by charge and discharge energy generated by the frequency modulation signal in a scheduling period; e (E) rated Is the rated capacity of energy storage; />Charging power for the battery; />Is the discharge power of the battery; η is the charge and discharge efficiency of the battery.
The expression of the frequency modulation state constraint is:
when the reserved capacity is declared to participate in the frequency modulation market, the charge or discharge behavior is required according to the frequency modulation signal, and the SOC value of the energy storage system is at a moderate level, so 0-1 variable is added
Wherein S in the formula lb 、S ub The upper limit value and the lower limit value of the SOC of the energy storage allowed to participate in the frequency modulation market are respectively,for the adjustable capacity of the electric power unit i +.>Maximum for electric power unit iCapacity of (I)>Is a 0-1 variable.
In particular embodiments, when the variables areWhen 1, it indicates that the charging or discharging operation is required according to the frequency modulation signal. When the variable->When 0, this indicates that no charge or discharge behavior is required based on the fm signal.
S402, determining an objective function according to the operation parameters of the target unit and the compensation parameters of the electric power frequency modulation market.
In a specific embodiment, the formula for the objective function is:
wherein I is a target unit participating in the electricity frequency modulation market quotation, and I is a set of target units; v i Quoting the generated energy of the target unit i, e i Generating power for the target unit i;basic compensation for the electricity frequency modulation auxiliary service market;calling and compensating for the electric power frequency modulation auxiliary service market; />Is the integrated cost of demand response resources.
Specifically, the basic compensation of the market is basic compensation cost calculated by all target units with qualified automatic gain control (Automatic Gain Control, AGC for short) functions in the frequency modulation market according to frequency modulation performance, frequency modulation capacity and operation rate, and the calculation formula of the basic compensation of the market is as follows:
wherein K is Base Compensating a standard for a basic service; k (k) i The comprehensive frequency modulation performance index of the target unit i is used for measuring comprehensive performance, and comprises three factors including an adjustment rate, response time and adjustment accuracy;the adjustable capacity of the unit i is obtained according to the pre-declaration of the target unit.
The market call compensation is call compensation cost obtained by calculating a target unit collected and called in the actual operation of an electric power frequency modulation market according to frequency modulation mileage, frequency modulation performance and mileage unit price, and the calculation formula of the market call compensation is as follows:
wherein m is i Scalar, k in the frequency modulation mileage for target unit i i Is the comprehensive frequency modulation performance index of the target unit i,and (5) price is cleared for the frequency modulation mileage of the target unit i.
The objective function in the embodiment can accurately calculate the maximum benefit of the load resource alliance when each demand response resource main body participates in the electric power frequency modulation market under the constraint condition.
In one embodiment, as shown in fig. 5, S203 includes:
s501, calculating a first profit fluctuation amount, a second profit fluctuation amount and a third profit fluctuation amount corresponding to each load resource.
In one embodiment, the first revenue fluctuation amount is a composite real revenue fluctuation amount, the second revenue fluctuation amount is a investment amount revenue fluctuation amount, and the third revenue fluctuation amount is a bearing risk revenue fluctuation amount.
In one embodiment, the calculation formula of the first profit fluctuation amount is:
Z i ＝G·(α i -1/n)
wherein G is the total alliance yield of the load resource alliance, alpha i The weight of the comprehensive strength of the ith load resource relative to the load resource alliance;
the calculation formula of the second profit fluctuation amount is:
wherein I is i Investment amount for the ith load resource;
the calculation formula of the third profit fluctuation amount is as follows:
wherein R is i Considering the benefit under risk for the ith load resource, P i For the success probability of the cooperation of the ith load resource and other load resources except the ith load resource in the load resource alliance, S is a load resource subset, S is the number of load resources in the load resource subset, K is a participant set of cooperative games, E (S) is the alliance income when the ith load resource participates in the electric power frequency modulation market, E (S-i) is the alliance income when the ith load resource does not participate in the electric power frequency modulation market, and n is the number of load resources in the alliance.
S502, calculating a target Xia Puli value corresponding to each load resource according to the first profit fluctuation amount, the second profit fluctuation amount, the third profit fluctuation amount and the weight corresponding to each profit fluctuation amount.
In one embodiment, the target Xia Puli value is calculated as:
W Z +W T +W F ＝1
K＝{1,2,…,n}
wherein Q is i For Xia Puli value of the ith load resource, S is a load resource subset, S is the number of load resources in the load resource subset, K is a participant set of the cooperative game, E (S) is the alliance benefits when the ith load resource participates in the electric power frequency modulation market, E (S-i) is the alliance benefits when the ith load resource does not participate in the electric power frequency modulation market, n is the number of load resources in the alliance, Z i A first profit fluctuation amount for the ith load resource, W Z Is Z i Corresponding weight, T i A second profit fluctuation amount for the ith load resource, W T Is T i Corresponding weights, F i A third profit fluctuation amount for the ith load resource, W F Is F i And (5) corresponding weight.
It is to be noted that W Z 、W T And W is F The specific numerical values of the weights are not limited and can be obtained through calculation by an entropy increasing method, and the specific numerical values can be configured according to actual application scenes.
And S503, distributing alliance benefits to each load resource according to the target decumbent value.
In an actual application scene, according to the target Xia Puli value of each load resource in the load resource alliance, the corresponding profit allocation proportion of each load resource is obtained, and according to the corresponding profit allocation proportion of the load resource, a corresponding profit allocation decision is obtained. And completing the profit allocation of the load resource main body according to the profit allocation decision.
In summary, the embodiment of the application provides a load resource benefit distribution method, which is used for establishing a comprehensive cost model by combining multiple types of demand response resources of a demand response task, determining an objective function based on comprehensive cost, power generation benefits and basic compensation and call compensation of an electric power frequency modulation market, and calculating to obtain the maximum alliance benefits of a load resource alliance. And the alliance benefits are distributed based on the improved Xia Puli value, so that the reasonable benefits of the load resources are greatly improved, and the fairness of the benefit distribution scheme is improved while the benefit distribution scheme can be rapidly determined.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a load resource benefit distribution device for realizing the above-mentioned load resource benefit distribution method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the load resource benefit distribution apparatus provided below may refer to the limitation of the load resource benefit distribution method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 6, there is provided a load resource profit sharing apparatus 600 comprising: a model building module 610, a function calculation module 620, and a revenue distribution module 630, wherein:
the model building module 610 is configured to build a comprehensive cost model according to the multi-type cost of the demand response task;
the function calculation module 620 is configured to determine an objective function of the demand response task to participate in the electric power frequency modulation market, where the objective function is used to calculate the maximized alliance revenue;
the revenue distribution module 630 is configured to distribute the alliance revenue to each load resource based on the target Xia Puli value corresponding to each load resource.
In one embodiment, the model building module 610 is specifically configured to build a comprehensive cost sub-model corresponding to each type of demand response resource; determining a target historical credit value and a target demand response matching degree based on a preset historical credit value model and a preset demand response matching degree model; and constructing a comprehensive cost model according to each comprehensive cost sub-model, the target historical credit value and the target demand response matching degree.
In one embodiment, the function calculation module 620 is specifically configured to determine an operation parameter of a target unit according to a preset constraint condition, where the target unit is a unit called by a demand response task; and determining an objective function according to the operation parameters of the target unit and the compensation parameters of the electric power frequency modulation market.
In one embodiment, the profit sharing module 630 is specifically configured to calculate a first profit fluctuation amount, a second profit fluctuation amount, and a third profit fluctuation amount corresponding to each load resource; calculating a target Xia Puli value corresponding to each load resource according to the first profit fluctuation amount, the second profit fluctuation amount, the third profit fluctuation amount and the weight corresponding to each profit fluctuation amount; and distributing alliance benefits to each load resource according to the target eplerian value.
In summary, the present embodiment provides a load resource benefit distribution device, which establishes a comprehensive cost model in combination with multiple types of demand response resources of a demand response task, determines an objective function based on comprehensive cost, power generation benefits and basic compensation and call compensation of a power frequency modulation market, and calculates to obtain the maximum alliance benefits of a load resource alliance. And the alliance benefits are distributed based on the improved Xia Puli value, so that the reasonable benefits of the load resources are greatly improved, and the fairness of the benefit distribution scheme is improved while the benefit distribution scheme can be rapidly determined.
The above-described respective modules in the load resource benefit distribution device may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a load resource benefit allocation method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
constructing a comprehensive cost model according to the multi-type cost of the demand response task;
determining an objective function of a demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating to obtain the maximum alliance income;
the coalition benefits are assigned to each load resource based on the target Xia Puli value corresponding to each load resource.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
constructing a comprehensive cost model according to the multi-type cost of the demand response task;
determining an objective function of a demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating to obtain the maximum alliance income;
the coalition benefits are assigned to each load resource based on the target Xia Puli value corresponding to each load resource.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
constructing a comprehensive cost model according to the multi-type cost of the demand response task;
determining an objective function of a demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating to obtain the maximum alliance income;
the coalition benefits are assigned to each load resource based on the target Xia Puli value corresponding to each load resource.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method for allocating load resource benefits, comprising:
constructing a comprehensive cost model according to the multi-type cost of the demand response task;
determining an objective function of the demand response task to participate in the electric power frequency modulation market, wherein the objective function is used for calculating and obtaining the maximum alliance income;
and distributing the alliance benefits to each load resource based on the target Xia Puli value corresponding to each load resource.
2. The method of claim 1, wherein constructing the composite cost model from the multi-type costs of demand response tasks comprises:
constructing a comprehensive cost sub-model corresponding to various types of demand response resources;
determining a target historical credit value and a target demand response matching degree based on a preset historical credit value model and a preset demand response matching degree model;
and constructing a comprehensive cost model according to each comprehensive cost sub-model, the target historical credit value and the target demand response matching degree.
3. The method of claim 1, wherein the determining that the demand response task is engaged in an electric frequency modulation market objective function comprises:
determining the operation parameters of a target unit according to preset constraint conditions, wherein the target unit is a unit called by a demand response task;
and determining the objective function according to the operation parameters of the target unit and the compensation parameters of the electric power frequency modulation market.
4. The method of claim 1, wherein the assigning the coalition benefit to each load resource based on the target Xia Puli value corresponding to each load resource comprises:
calculating a first profit fluctuation amount, a second profit fluctuation amount and a third profit fluctuation amount corresponding to each load resource;
calculating a target Xia Puli value corresponding to each load resource according to the first profit fluctuation amount, the second profit fluctuation amount, the third profit fluctuation amount and the weight corresponding to each profit fluctuation amount;
and distributing the alliance benefits to each load resource according to the target decubitus value.
5. The method of claim 4, wherein the target Xia Puli value is calculated as:
W Z +W T +W F ＝1
K＝{1，2，...，n}
wherein Q is i Xia Puli for the ith load resource, S is the load resource subset, S is the number of load resources in the load resource subset, K is the participant set for the collaborative game, E (S) is the thE (S-i) is the alliance revenue when the ith load resource participates in the electric power frequency modulation market, n is the number of load resources in the alliance, Z i A first profit fluctuation amount for the ith load resource, W Z Is Z i Corresponding weight, T i A second profit fluctuation amount for the ith load resource, W T Is T i Corresponding weights, F i A third profit fluctuation amount for the ith load resource, W F Is F i And (5) corresponding weight.
6. The method of claim 5, wherein the first profit fluctuation amount is calculated by the formula:
Z i ＝G·(α i -1/n)
wherein G is the total alliance yield of the load resource alliance, alpha i The weight of the comprehensive strength of the ith load resource relative to the load resource alliance;
the calculation formula of the second profit fluctuation amount is as follows:
wherein I is i Investment amount for the ith load resource;
the calculation formula of the third profit fluctuation amount is as follows:
wherein R is i Considering the benefit under risk for the ith load resource, P i The success probability of the cooperation of the ith load resource and other load resources except the ith load resource in the load resource alliance is obtained.
7. A load resource profit sharing apparatus, comprising:
the model construction module is used for constructing a comprehensive cost model according to the multi-type cost of the demand response task;
the function calculation module is used for determining an objective function of the demand response task participating in the electric power frequency modulation market, wherein the objective function is used for calculating and obtaining the maximized alliance income;
and the income distribution module is used for distributing the alliance income to each load resource based on the target Xia Puli value corresponding to each load resource.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the load resource profit allocation method of any one of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the load resource benefit allocation method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program when executed by a processor implements the steps of the load resource profit allocation method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311460982.8A CN117526339A (en) | 2023-11-03 | 2023-11-03 | Load resource benefit distribution method, device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311460982.8A CN117526339A (en) | 2023-11-03 | 2023-11-03 | Load resource benefit distribution method, device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117526339A true CN117526339A (en) | 2024-02-06 |
Family
ID=89750501
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311460982.8A Pending CN117526339A (en) | 2023-11-03 | 2023-11-03 | Load resource benefit distribution method, device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117526339A (en) |
-
2023
- 2023-11-03 CN CN202311460982.8A patent/CN117526339A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Two-stage community energy trading under end-edge-cloud orchestration | |
| CN108183473A (en) | A kind of cluster electric vehicle participates in the optimization Bidding system of assisted hatching | |
| CN111682536B (en) | Random-robust optimization operation method for virtual power plant participating in dual market before day | |
| Jember et al. | Game and contract theory-based energy transaction management for internet of electric vehicle | |
| Lu et al. | Data center aggregators’ optimal bidding and benefit allocation strategy considering the spatiotemporal transfer characteristics | |
| CN114997665A (en) | Virtual power plant optimal scheduling method and system considering controllable load response performance difference | |
| CN110941798A (en) | Energy storage shared data processing system and method, equipment and medium | |
| Zhang et al. | Dispatchable capacity optimization strategy for battery swapping and charging station aggregators to participate in grid operations | |
| Zhang et al. | Shared energy storage-assisted and tolerance-based alliance strategy for wind power generators based on cooperative game and resource dependence theories | |
| CN111987719B (en) | Method and device for bidding electric vehicle aggregator to participate in frequency modulation | |
| CN107025610A (en) | A kind of data center's Cost Optimization Approach | |
| CN110929978A (en) | Electric power market risk assessment method and system | |
| CN117526339A (en) | Load resource benefit distribution method, device, computer equipment and storage medium | |
| CN113162066B (en) | Game behavior analysis method considering participation of electrolytic aluminum industrial users in frequency modulation market | |
| CN115189409A (en) | Power system production simulation method and device, computer equipment and storage medium | |
| CN114792191A (en) | Virtual power plant internal income distribution method, device, storage medium and equipment | |
| CN114154670A (en) | Hybrid energy transaction scheduling method combined with block chain technology and application | |
| CN111445154A (en) | Power market resource self-scheduling optimization method, system and equipment | |
| CN115713211B (en) | Resource transfer method and device based on electric power energy and computer equipment | |
| Liu et al. | Optimal Allocation of Shared Energy Storage Based on Cooperative Game Theory | |
| CN113435651B (en) | Three-stage multi-subject optimized game method and system considering user comfort for virtual power plant | |
| CN114243681B (en) | Power utilization regulation and control method, device, equipment and storage medium of power system | |
| CN115906608A (en) | Game theory-based virtual power plant model and optimized operation method | |
| CN115660300A (en) | Genetic algorithm-based power resource processing method and device and computer equipment | |
| CN117710151A (en) | New energy station energy storage configuration method considering cost recovery in market environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |