CN115660351A - Shapley value-based revenue distribution method and apparatus - Google Patents
Shapley value-based revenue distribution method and apparatus Download PDFInfo
- Publication number
- CN115660351A CN115660351A CN202211332933.1A CN202211332933A CN115660351A CN 115660351 A CN115660351 A CN 115660351A CN 202211332933 A CN202211332933 A CN 202211332933A CN 115660351 A CN115660351 A CN 115660351A
- Authority
- CN
- China
- Prior art keywords
- determining
- load
- model
- electric heating
- heating system
- 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
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a device for allocating revenue based on a shape value, wherein the method comprises the following steps: generating a profit model of multi-subject interactive influence in the regional heat accumulating type electric heating system, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator; determining constraint conditions corresponding to multiple subjects in the regional heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the income model; determining income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method; and determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall income model. By the method and the device, the problem that influence of multi-subject interaction on revenue distribution after the addition of the aggregator is not considered in the related technology is solved.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a revenue distribution method and device based on a shapey value.
Background
With the random access of new energy power generation such as wind power and photovoltaic to power grids and urban centers in a large scale, peak load increases year by year, and the demand of system balance resources is increased sharply. The conventional energy is replaced on a large scale on the power generation side, so that the regulation capability is limited, and the power balance requirement of the system is difficult to meet only by means of the regulation means on the power generation side. With the development of the source-network-load-storage technology, more and more subjects participate in system balance, and the method has very important practical significance for maintaining the multi-subject of system balance, equitable revenue distribution and maximization of collective benefits. The aggregator, as a party to maintain system balance, has flexible control over regional loads that allows each participant to benefit the market. At present, the problem of income distribution of a multi-main-body area heat accumulating type electric heating system is lack of consideration of interaction influence among multiple main bodies after a aggregator is added. Therefore, the problem that the influence of multi-subject interaction on revenue distribution after an aggregator joins is lacked to be considered in the prior art.
Disclosure of Invention
The invention provides a method and a device for allocating revenue based on a shape value, which are used for at least solving the problem that the influence of multi-subject interaction on revenue allocation after an aggregator is added is not considered in the related technology.
According to a first aspect of the embodiments of the present invention, there is provided a revenue allocation method based on a shapey value, the method including: generating a profit model of multi-subject interactive influence in a regional heat accumulating type electric heating system, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator; determining constraint conditions corresponding to multiple subjects in the regional heat accumulating type electric heating system according to the safe operation requirement, the risk conditions and the income model; determining income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method; and determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall income model.
Optionally, the generating a revenue model of interaction effect of multiple entities in the district regenerative electric heating system, where the multiple entities include a power grid company, a heat consumer, a wind power plant, and a load aggregator includes: determining a profit model of the wind power plant according to the electric quantity consumed by the load aggregator during the wind power consumption period; determining a profit model of the load aggregator according to a peak clipping subsidy reward provided by the grid company to the load aggregator, a peak clipping subsidy given by the load aggregator to the thermal consumer, a thermal mass heating profit managed by the load aggregator, and a thermal mass thermal storage cost; determining a profit model of the hot user according to subsidies given to the hot user by the load aggregator, the heat consumption cost of the hot user for reducing load saving and the heat consumption cost of the hot user for saving in the wind power consumption period; and determining a revenue model of the power grid company according to the load reduction proportion of the heat consumption peak period.
Optionally, the determining constraint conditions corresponding to multiple subjects in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the profit model includes: determining constraint conditions corresponding to the load aggregators according to heat supply requirements under normal conditions and power interruption conditions; determining a constraint condition corresponding to the power grid company according to the load reduction proportion and a revenue model of the power grid company; and determining the corresponding constraint conditions of the wind power plant according to the wind power price and the income model of the wind power plant.
Optionally, the determining, according to a shapeley value method, the income weight and the regional overall income model corresponding to multiple subjects in the regional heat accumulating type electric heating system includes: generating a federation set from the hot user, the wind power plant, and the load aggregator; determining profits corresponding to the hot users, the wind power plants and the load aggregators according to a union feature function and a shapey value calculation formula, wherein the union feature function represents union profits corresponding to any member combination in the union set; determining income weights corresponding to the thermal users, the wind power plants and the load aggregators in the regional overall income model according to the income corresponding to the thermal users, the wind power plants and the load aggregators; and generating a regional overall profit model according to the product of the profit models corresponding to the thermal users, the wind power plants and the load aggregators and the corresponding profit weights.
Optionally, the determining, by machine learning, the profit allocation strategy of the multiple subjects in the zone heat accumulating type electric heating system according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall profit model includes: determining an objective function according to the regional overall profit model; and determining a multi-subject profit allocation strategy in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multi-subjects in the zone heat accumulating type electric heating system and the objective function.
According to a second aspect of the embodiments of the present invention, there is further provided a revenue distribution apparatus based on a shapey value, the apparatus including: the system comprises a generation module, a control module and a control module, wherein the generation module is used for generating a profit model of multi-subject interaction influence in a regional heat accumulating type electric heating system, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator; the first determining module is used for determining constraint conditions corresponding to multiple main bodies in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the benefit model; the second determining module is used for determining income weight and a regional overall income model corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method; and the third determining module is used for determining the income distribution strategy of the multiple subjects in the regional heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the regional heat accumulating type electric heating system and the regional overall income model.
Optionally, the generating module includes: the first determining unit is used for determining a revenue model of the wind power plant according to the electric quantity consumed by the load aggregator in the wind power consumption period; a second determination unit, configured to determine a profit model of the load aggregator according to a peak clipping subsidy reward provided by the grid company to the load aggregator, a peak clipping subsidy given by the load aggregator to the hot consumer, a heat accumulator heating profit managed by the load aggregator, and the heat accumulator heat accumulation cost; a third determining unit, configured to determine a revenue model of the hot user according to a subsidy given to the hot user by the load aggregator, a heat consumption cost saved by the hot user for reducing load, and a heat consumption cost saved by the hot user in a wind power consumption period; and the fourth determination unit is used for determining the income model of the power grid company according to the load reduction proportion of the heat utilization peak period.
Optionally, the first determining module includes: a fifth determining unit, configured to determine a constraint condition corresponding to the load aggregator according to a heat supply demand under a normal condition and an electric power interruption condition; a sixth determining unit, configured to determine, according to the load shedding proportion and a revenue model of the power grid company, a constraint condition corresponding to the power grid company; and the seventh determining unit is used for determining the corresponding constraint conditions of the wind power plant according to the wind power price and the income model of the wind power plant.
Optionally, the second determining module includes: a first generating unit, configured to generate a federation set according to the hot user, the wind power plant, and the load aggregator; an eighth determining unit, configured to determine, according to a union feature function and a shapey value calculation formula, profits corresponding to the hot user, the wind power plant, and the load aggregator, where the union feature function represents a union benefit corresponding to any member combination in the union set; a ninth determining unit, configured to determine, according to the revenue corresponding to the thermal user, the wind power plant, and the load aggregator, revenue weights corresponding to the thermal user, the wind power plant, and the load aggregator in the area overall revenue model; and the second generation unit is used for generating a regional overall profit model according to the product of the profit models corresponding to the thermal users, the wind power plants and the load aggregators and the corresponding profit weights.
Optionally, the third determining module includes: a tenth determining unit, configured to determine an objective function according to the regional overall profit model; and the eleventh determining unit is used for determining the income distribution strategy of the multiple subjects in the regional heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the regional heat accumulating type electric heating system and the objective function.
According to a third aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus; wherein the memory is used for storing the computer program; a processor for performing the method steps in any of the above embodiments by running the computer program stored on the memory.
According to a fourth aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the method steps in any of the above embodiments when the computer program is run.
In the embodiment of the invention, a profit model of multi-subject interactive influence of a power grid company, a heat consumer, a wind power plant and a load aggregator in the regional heat accumulating type electric heating system is generated; determining constraint conditions corresponding to multiple main bodies in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the benefit model; determining income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method; and determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall income model. When the profit model corresponding to the multiple subjects is generated, the influence of interaction of the multiple subjects after the load aggregation business is added is considered, then the profit weight corresponding to the multiple subjects and the regional overall profit model are determined according to the shapey value method, and the profit fair distribution strategy for maximizing the profit of the multiple subjects is determined by machine learning according to the regional overall profit model and the constraint conditions corresponding to the multiple subjects. Therefore, the purpose of income distribution of multi-main-body interaction after the aggregator is added can be realized, and the problem that influence of the multi-main-body interaction after the aggregator is added on the income distribution is not considered in the related technology is solved.
In the embodiment of the invention, the income model of each main body is constructed by analyzing the multi-main-body interaction mode in the zone heat accumulating type electric heating system, so that the purpose of considering the interaction among the multi-main bodies when calculating the contribution value of each main body to the overall income of the zone through the income model of each main body is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic diagram of a hardware environment of an alternative sharley-value-based revenue allocation method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an alternative revenue allocation method based on shape values according to an embodiment of the present invention;
fig. 3 is a schematic view of an alternative zone heat accumulating type electric heating system according to an embodiment of the present invention;
fig. 4 is a block diagram illustrating an alternative revenue distribution apparatus based on shapey values, according to an embodiment of the present invention;
fig. 5 is a block diagram of an alternative electronic device according to an embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, in the description of the present invention, the terms "first", "second", and the like are used for distinguishing similar objects, and are not necessarily used for describing a particular order or sequence. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "mounted," "connected," and "coupled" are to be construed broadly and may include, for example, fixed connections, removable connections, or integral connections; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
According to an aspect of the embodiment of the invention, a revenue allocation method based on a shape value is provided. Optionally, in this embodiment, the revenue allocation method based on the shape value may be applied to a hardware environment as shown in fig. 1. As shown in fig. 1, the terminal 102 may include a memory 104, a processor 106, and a display 108 (optional components). The terminal 102 may be communicatively coupled to a server 112 via a network 110, the server 112 may be configured to provide services (e.g., application services, etc.) for the terminal or for clients installed on the terminal, and a database 114 may be provided on the server 112 or separate from the server 112 for providing data storage services for the server 112. Additionally, a processing engine 116 may be run in the server 112, and the processing engine 116 may be used to perform the steps performed by the server 112.
Alternatively, the terminal 102 may be, but is not limited to, a terminal capable of calculating data, such as a mobile terminal (e.g., a mobile phone, a tablet Computer), a notebook Computer, a PC (Personal Computer) Computer, and the like, and the network may include, but is not limited to, a wireless network or a wired network. Wherein, this wireless network includes: bluetooth, WIFI (Wireless Fidelity), and other networks that enable Wireless communication. Such wired networks may include, but are not limited to: wide area networks, metropolitan area networks, and local area networks. The server 112 may include, but is not limited to, any hardware device capable of performing calculations.
In addition, in this embodiment, the benefit allocation method based on the shapey value may also be applied to, but not limited to, an independent processing device with a relatively high processing capability without performing data interaction. For example, the processing device may be, but is not limited to, a terminal device with a relatively high processing capability, that is, each operation in the above revenue allocation method based on the shapey value may be integrated into a separate processing device. The above is merely an example, and this is not limited in this embodiment.
Optionally, in this embodiment, the profit sharing method based on the shapeley value may be executed by the server 112, the terminal 102, or both the server 112 and the terminal 102. The terminal 102 may execute the revenue allocation method based on the shapeley value according to the embodiment of the present invention, or may execute the revenue allocation method based on the shapeley value by a client installed thereon.
Taking the utilization of the Shapley-value-based profit sharing method in a central processing unit as an example, fig. 2 is a schematic flow chart of an optional Shapley-value-based profit sharing method according to an embodiment of the present invention, and as shown in fig. 2, the flow of the method may include the following steps:
step S201, a profit model of multi-subject interaction influence in the zone heat accumulating type electric heating system is generated, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator. Optionally, as shown in fig. 3, the district heat accumulating type electric heating system includes a power grid company, a heat consumer, a wind power plant, and a load aggregator, wherein the load aggregator is configured to aggregate thermal loads of distributed users, flexible control of the district loads by the load aggregator enables each participant in the system to benefit in the market, the heat consumer is a user using concentrated heat energy in the district heat accumulating type electric heating system, specifically, the load aggregator helps the power grid company to reduce loads during peak hours, so as to reduce capacity expansion cost and operation and maintenance cost of the power grid company, the power grid company divides different times to make different prices (time-of-use electricity prices), and the load aggregator makes peak reduction and subsidy prices; the hot users participate in response to actively reduce the load, and the load aggregators give subsidy prices to the users; the wind power generation plant sells wind power to a load aggregator with preferential wind power price in a period of vigorous wind power to obtain profit and improve wind power consumption, and the load aggregator helps wind power consumption of the wind power generation plant and peak clipping of a power grid company by managing heat storage/release amount of a heat storage type electric boiler. And generating a profit model corresponding to each main body by considering the final profit after interaction among the main bodies after the load aggregation trader is added.
And S202, determining constraint conditions corresponding to multiple subjects in the regional heat accumulating type electric heating system according to the safe operation requirement, the risk conditions and the income model. Optionally, in order to ensure that each main body in the zone heat accumulating type electric heating system can safely operate while obtaining benefits for the main body, constraint conditions corresponding to multiple main bodies in the system need to be determined, so that each main body operates under corresponding risk constraint conditions, and it is ensured that no risk occurs.
Step S203, income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system are determined according to the shapeley value method. Optionally, the profits are distributed fairly according to contributions of all subjects in the regional heat accumulating type electric heating system, specifically, a multi-subject cooperation scheme with the largest profit is calculated according to a shapey value method, and then the profits and the profit weights corresponding to all subjects in the scheme are determined, wherein the profit weights represent the importance degree of the profits of all subjects in the regional overall profits. And determining the regional overall profit model according to the profit weights corresponding to the subjects calculated by the shapeley value method and the profit models corresponding to the subjects generated in the step S201.
And S204, determining a multi-subject profit allocation strategy in the regional heat accumulating type electric heating system through machine learning according to constraint conditions corresponding to the multi-subjects in the regional heat accumulating type electric heating system and a regional overall profit model. Optionally, the optimization target and the constraint condition of machine learning are determined according to the regional overall profit model determined in step S203 and the constraint condition corresponding to the multiple subjects determined in step S202, and the benefit maximization of the multiple subjects and the reasonable distribution of the profits of the parties in the regional heat accumulating type electric heating system are realized through strategy optimization.
In the embodiment of the invention, a profit model of multi-main interaction influence of a power grid company, a heat consumer, a wind power plant and a load aggregator in a regional heat accumulating type electric heating system is generated; determining constraint conditions corresponding to multiple main bodies in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the benefit model; determining income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method; and determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall income model. When the income model corresponding to the multiple subjects is generated, the influence of interaction of the multiple subjects after the load aggregation business is added is considered, then the income weight corresponding to the multiple subjects and the regional overall income model are determined according to the shapey value method, and the income fair distribution strategy for maximizing the benefits of the multiple subjects is determined by machine learning according to the regional overall income model and the constraint conditions corresponding to the multiple subjects. Therefore, the purpose of income distribution of multi-main-body interaction after the aggregator is added can be realized, and the problem that influence of the multi-main-body interaction after the aggregator is added on the income distribution is not considered in the related technology is solved.
As an alternative embodiment, a profit model of interaction effect of multiple entities in a district regenerative electric heating system is generated, wherein the multiple entities include a power grid company, a heat consumer, a wind power plant, and a load aggregator: determining a profit model of the wind power plant according to the electric quantity consumed by the load aggregator during the wind power consumption period; determining a profit model of the load aggregator according to the peak clipping subsidy reward provided by the power grid company to the load aggregator, the peak clipping subsidy given by the load aggregator to the hot users, the heat supply profit of the heat accumulator managed by the load aggregator and the heat accumulation cost of the heat accumulator; determining a profit model of the hot user according to subsidies given to the hot user by the load aggregators, the heat consumption cost of the hot user for reducing load saving and the heat consumption cost of the hot user for saving in the wind power consumption period; and determining a revenue model of the power grid company according to the load reduction proportion of the heat utilization peak period.
Optionally, a model of the profit for the wind power plant is determined from the amount of electricity consumed by the load aggregator during the wind power consumption period, in particular, the profit for the wind power plant during the wind power consumption period is derived from the amount of electricity consumed by the load aggregator during the contract, including the amount of heat heated to the thermal user by operating the electric boiler (the thermal mass managed by the load aggregator) and the amount of heat stored to the thermal mass. It will be appreciated that, with the addition of load aggregators and thermal storage devices, the heat stored in the thermal storage mass will bring new wind curtailment consumptions, and the new gains from wind power plants will come from this portion of the new wind curtailment consumptions. Suppose that the wind power consumption at the time i is
The wind power plant yields are
New revenue generation for wind power plants
The corresponding formula is as follows:
in the formula (I), the compound is shown in the specification,
number of users responding,
User load (kW/user), at is the time step,
the heat storage capacity (kW) at the time of the load polymerization quotient i,
the price of wind power (yuan/kWh) is preferential.
And determining a profit model of the load aggregator according to the profit and the expenditure of the load aggregator, wherein the profit of the load aggregator is derived from the peak clipping subsidy reward given by the power grid, the heat supply profit of the managed heat accumulator, and the expenditure part is the peak clipping subsidy given to the user and the heat accumulation cost of the heat accumulator. So the load aggregator revenue at time i can be expressed as:
in the formula (I), the compound is shown in the specification,
a total load reduction (kW) for the subordinate region of the load aggregator, the portion containing the total load actively reduced by the user
And the heat released by the heat accumulator of the heat accumulating electric boiler
Subsidizing the peak load reduction price (yuan/kWh), P, for the grid to the load aggregator i Peak-to-valley time-of-use electricity prices (yuan/kWh) established for the grid,
subsidy price (dollar/kWh) to the customer for the load aggregator.
For hot users, the users only need to pay the electricity fee according to the heat of the users under the normal condition. After the user joins the load aggregator, the user can choose to participate in demand response, and actively reduce self thermal load to obtain subsidies. The profit for the user is partly from subsidies given by the aggregator and the heat cost saved by the reduction of the load itself. To further increase the user revenue, the aggregator will provide heat to the user at a lower peak-to-valley electricity rate than the grid established during the wind-power consumption period, so the user's revenue portion also includes the heat cost saved during this period. Thus, the user's revenue at time i is as follows:
in the formula (I), the compound is shown in the specification,
is a single household benefit (meta/household),
for the user load (kW/household),
actively cutting down the load (kW/household) for the user. In the time period other than the wind power consumption time period,
determining a profit model of the power grid company according to the load reduction ratio during the peak time of heat utilization, specifically, adding the electric heating load to the peak time of the power utilization, so that the power grid company is further increasedInvestment is required to increase the capacity of the power grid to ensure safe operation. After the load aggregation business is added, the power grid company pays a part of excitation subsidies to the load aggregation business, and the load aggregation business stores and releases heat through the heat accumulator and excites users to participate in demand response to achieve peak load reduction. The benefits of the grid company are mainly reflected in the amount of load reduction, and therefore the amount of load reduction through peak hours
The size of (2) measures the network profit. In order to more intuitively show the load reduction amount, the load reduction ratio θ is used for measuring:
in the embodiment, the purpose of determining the income model corresponding to the multiple agents in the regional heat accumulating type electric heating system is achieved by calculating the income of each party after the multiple agents interact.
As an optional embodiment, determining constraint conditions corresponding to multiple subjects in the zone regenerative electric heating system according to the safe operation requirement, the risk condition and the profit model includes: determining constraint conditions corresponding to load aggregators according to heat supply requirements under normal conditions and power interruption conditions; determining constraint conditions corresponding to the power grid company according to the load reduction proportion and a profit model of the power grid company; and determining the corresponding constraint conditions of the wind power plant according to the wind power price and the yield model of the wind power plant.
Alternatively, in actual operation, the aggregator earns income for itself and simultaneously takes on the task of heating subordinate users, so that in addition to ensuring heating under normal conditions of the power system, the heat demand of users in a period of time under the condition of power interruption needs to be met. The risk constraint of the load aggregator can therefore be expressed as a capacity constraint of the thermal mass:
S min ≤S i ≤S max
in the formula, S i Is the state of the heat accumulator i at time capacity, S max To storeMaximum thermal body volume (kWh), S min To ensure safe capacity (kWh) for thermal comfort of the user in the event of a power outage, the thermal mass thermal storage/release power constraint can be expressed by the following equation:
in the formula, HP max For the district regenerative electric heating system maximum heating power (kW), a, b are index coefficients for ensuring that regenerative/exothermic operations are not performed simultaneously.
The starting state of the heat accumulator capacity is constrained as follows:
S start ＝ end
in the formula, S start For the initial heat storage of the heat storage cycle, S end The heat storage amount is finished for the heat storage body period. The constraint of the electric heat conversion of the heat accumulating type electric boiler is expressed as follows:
L i ＝·Δt·HP i
in the formula, L i Supplying heat to the electric boiler at time i, HP i The power of the electric boiler at the moment i and the electric heat conversion efficiency eta.
The main purpose of the participation of the grid companies in regenerative electric heating systems is to reduce peak loads and thereby reduce the operational risks that may arise from excessive peak-to-valley differences. Therefore, the risk constraint condition of the power grid is used for ensuring that the load reduction proportion is not zero, and in combination with the revenue model of the power grid, the constraint condition is as follows:
θ≥ε
where ε represents an acceptable minimum reduction and θ is the revenue model, i.e., the load reduction rate, of the grid company.
For the constraint condition of the wind power plant, the load aggregator can use the wind power to store heat only when the wind power price is lower than the peak-valley time-of-use electricity price established by the power grid. Although the wind power plant has pricing right and the income increases with the increase of the price, the wind power plant cannot obtain the income if the preferential price of the wind power is higher than the peak-valley time-of-use price established by the power grid. The risk constraint of a wind power plant is therefore related to wind power pricing, and in combination with the revenue model of the wind power plant, its constraints can be expressed by the following formula:
in the embodiment, the constraint conditions corresponding to the multiple main bodies in the regional heat accumulating type electric heating system are determined according to the safe operation requirement, the risk conditions and the profit model, so that the purpose of ensuring that the main bodies can not run at risk while obtaining benefits for the main bodies is achieved.
As an optional embodiment, the determining of the profit weight and the regional overall profit model corresponding to the multiple subjects in the regional heat accumulating type electric heating system according to the shapeley value method includes: generating a union set according to the hot users, the wind power plants and the load aggregators; determining profits corresponding to the hot users, the wind power plants and the load aggregators according to the alliance feature functions and the shapey value calculation formula, wherein the alliance feature functions represent alliance profits corresponding to any member combination in the alliance set; and determining income weights corresponding to the hot users, the wind power plants and the load aggregators in the whole area income model according to the income corresponding to the hot users, the wind power plants and the load aggregators, and generating the whole area income model according to the product of the income models corresponding to the hot users, the wind power plants and the load aggregators and the corresponding income weights.
Optionally, a regional overall revenue model is constructed according to the revenue weights and corresponding revenue models corresponding to the wind power plant, the load aggregator and the hot user, as follows:
where α, β, γ are yield weights and represent how important each principal's yield is in the overall yield of the region. It should be noted that the above formula does not include the income obtained by the load reduction of the power grid company during the peak period of heat utilization, and because the income is not an economic parameter, the area overall income model is not added, and the power grid peak reduction ratio can be determined according to the overall income calculation results of the wind power plant, the load aggregator and the heat consumer.
Calculating weight coefficients in the regional overall profit model by using a shape value method, specifically, defining a set I = {1,2 \8230;, n } of n participants by using the shape value method, and defining a real function V (S) to satisfy the condition for any subset S ∈ I:
(1)
representing an empty set;
(2) When in use
S 1 ∈I，S 2 E is equal to I, V (S) 1 ∩S 2 )≥V(S 1 )+(S 2 )。
[ I, V ] is called an n-person cooperative game, V (S) is called a characteristic function of the n-person cooperative game, wherein S is any subset of I (alliance), and the characteristic function V (S) describes the income of the alliance. The feature function formula gives the corresponding subset (league) benefit for each possible subset (league) of the n-person cooperative game, i.e. a set function. For any n-person cooperative game [ I, V ], sharey value is unique and the subset (league) benefit is calculated using the formula:
in the formula (I), the compound is shown in the specification,
and the | S | is the number of elements in the set S, and the | S | is { } is the set after the element i is removed from the set S.
A, B and C respectively represent three main bodies of a wind power plant, a load aggregator and a hot user, different main bodies share 7 alliance combination modes, and the table 1 is a characteristic function expression of each alliance under different combination modes.
TABLE 1 expression of characteristic function of each alliance in different combinations (Wanyuan)
| S | A | B | C | AB | AC | BC | ABC |
| V(S) | 271.957 | 196.388 | 104.194 | 453.174 | 395.087 | 301.371 | 601.059 |
In summary, the maximum profit can be obtained by the cooperation of the wind power plant, the load aggregator and the hot user, and the allocation schemes in the overall profit of the region are expressed by the following formulas:
in the formula
Representing the overall benefit of the area,
corresponding to the wind power plant revenue, the load aggregator revenue and the hot user revenue, respectively. The wind power plant (a) revenue allocation calculation table of table 2, the load aggregator (B) revenue allocation calculation table of table 3, and the hot user (C) revenue allocation calculation table of table 4 are available according to the subset (alliance) revenue calculation formula.
TABLE 2 wind power plant (A) revenue allocation calculation sheet
TABLE 3 calculation of revenue distribution for load aggregators (B)
TABLE 4 Hot user (C) profit allocation calculation Table
From tables 2, 3 and 4, a profit weight calculation formula for each party's profit to the area's overall profit may be determined:
the wind farm (a), the load aggregator (B) and the hot user (C) may thus be calculated to have respective revenue weights of 0.469, 0.329, 0.203, so that the regional overall revenue model may be expressed as:
in the embodiment of the invention, the income weight corresponding to multiple subjects in the regional heat accumulating type electric heating system and the regional overall income model are determined according to the shapeley value method.
As an optional embodiment, the method for determining the profit allocation strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall profit model includes: determining an objective function according to the regional overall profit model; and determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the objective function. Optionally, the regional overall profit model is used as an objective function, the existing reinforcement learning algorithm can be specifically used for machine learning, the objective function is iteratively calculated through the reinforcement learning algorithm so that the strategy converges to the optimal value, and in the iterative calculation, the constraint conditions corresponding to the multiple determined subjects are used as limiting conditions, so that the reasonable distribution strategy with the maximum benefit can be finally obtained.
Specifically, taking the following application scenario as an example, a heat accumulating type electric heating system with 1000 household loads in a certain area has a heating season of 11 months and 1 day per year to 3 months and 31 days per year, the maximum heating power of an electric boiler controlled by a load aggregator is 10000kW, the total capacity of a heat accumulator is 20000kWh, the maximum heat accumulation and release power is 10000kW, the compensation range of compensated demand response in the area is 0-0.4 yuan/kWh, and the compensation given to a user by the load aggregator for obtaining a benefit is not higher than the price. In order to determine the reasonable allocation strategy with the maximum multi-subject profit, a reinforcement learning environment can be formed according to a regional overall profit model and constraint conditions, the reinforcement learning emphasizes how to act based on the environment to obtain the maximum expected benefit, the algorithm obtains the state from the environment to determine how to act, and the environment gives the algorithm positive action according to the logic of the environmentWhen the excitation, the state and the action in the direction of or in the opposite direction have a mapping relation (the relation between input and output), the task of reinforced learning is to find an optimal strategy so as to maximize the benefit. Specifically, the area overall profit model is:
as an objective function for seeking an optimal strategy in reinforcement learning, the strategy is converged to the optimal strategy by iterative computation of the objective function, and the existing reinforcement learning algorithm can be used for obtaining the optimal strategy through a regional overall profit model:
and each main body acquires constraint conditions corresponding to the earnings, inputs related parameters such as load quantity of users on a demand side, load reduction quantity and the like, and calculates an optimal pricing strategy given to the subsidies of the users by the load aggregators in the subsidy range enabling the overall earnings of the region to be maximized in the scene. Table 5 shows the income and expense for each party in the entire heating season determined by reinforcement learning, and the results show that the profit obtained by the entire area is the maximum and the profit distribution for each party is the most reasonable when the load aggregator subsidizes the price for the user at 0.25 yuan/kWh in the heating season and keeps the price unchanged.
TABLE 5 income and expenditure situation of each party in full heating season
| Categories | Gain(s) |
| Load aggregator revenue | 191.676 ten thousand yuan |
| Load reduction ratio of power grid | 6.862％ |
| Wind power plant revenue | 271.957 ten thousand yuan |
| Wind power heating user cost saving (Single household) | 929.517 yuan |
| Responding to user subsidy income (Single household) | 314.297 element |
| Responding to user's heating expenditure (Single family) | 9262.001 yuan |
| Not responding to user heating expenditure (Single household) | 9994.527 yuan |
In the embodiment of the invention, the profit distribution strategy which determines that the profit obtained by the whole area is the maximum and the profit distribution of each main body is the most reasonable is realized by taking the whole area profit model as the target function of machine learning according to the constraint conditions corresponding to the multiple main bodies in the area heat accumulating type electric heating system.
According to another aspect of the embodiment of the present invention, a swap-value-based profit allocation apparatus for implementing the swap-value-based profit allocation method is further provided. Fig. 4 is a block diagram of an alternative revenue distribution apparatus based on a shapey value according to an embodiment of the present invention, as shown in fig. 4, the apparatus may include: the generation module 401 is configured to generate a profit model of multi-subject interaction influence in a district heat accumulating type electric heating system, where the multi-subject includes a power grid company, a heat consumer, a wind power plant, and a load aggregator; the first determining module 402 is configured to determine constraint conditions corresponding to multiple subjects in the district heat accumulating type electric heating system according to a safe operation requirement, a risk condition and a profit model; the second determining module 403 is configured to determine, according to a shape value method, revenue weights and a regional overall revenue model corresponding to multiple subjects in the regional heat accumulating type electric heating system; the third determining module 404 is configured to determine a profit allocation strategy of multiple subjects in the zone heat accumulating type electric heating system through machine learning according to constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and a zone overall profit model.
It should be noted that the generating module 401 in this embodiment may be configured to execute the step S201, the first determining module 402 in this embodiment may be configured to execute the step S202, the second determining module 403 in this embodiment may be configured to execute the step S203, and the third determining module 404 in this embodiment may be configured to execute the step S204.
Through the modules, when the income model corresponding to the multiple subjects is generated, the influence of interaction of the multiple subjects after the load aggregation businessmen is added is considered, then the income weight corresponding to the multiple subjects and the regional overall income model are determined according to the shapey value method, and the income fair distribution strategy for maximizing the benefits of the multiple subjects is determined by machine learning according to the regional overall income model and the constraint conditions corresponding to the multiple subjects. Therefore, the purpose of income distribution of multi-main-body interaction after the addition of the aggregator is considered can be achieved, and the problem that the influence of the multi-main-body interaction after the addition of the aggregator on the income distribution is lacked to be considered in the related technology is solved.
As an alternative embodiment, the generating module comprises: the first determining unit is used for determining a profit model of the wind power plant according to the electric quantity consumed by the load aggregator during the wind power consumption period; the second determination unit is used for determining a profit model of the load aggregator according to the peak clipping subsidy reward provided by the power grid company to the load aggregator, the peak clipping subsidy given by the load aggregator to the heat user, the heat accumulator heat supply profit managed by the load aggregator and the heat accumulator heat storage cost; the third determining unit is used for determining a profit model of the hot user according to subsidies given to the hot user by the load aggregator, the heat consumption cost of the hot user for reducing load saving and the heat consumption cost of the hot user for saving in the wind power consumption period; and the fourth determination unit is used for determining a revenue model of the power grid company according to the load reduction proportion of the heat utilization peak period.
As an alternative embodiment, the first determining module comprises: the fifth determining unit is used for determining constraint conditions corresponding to the load aggregators according to the heat supply requirements under the normal condition and the power interruption condition; a sixth determining unit, configured to determine a constraint condition corresponding to the power grid company according to the load shedding proportion and the revenue model of the power grid company; and the seventh determining unit is used for determining the corresponding constraint conditions of the wind power plant according to the wind power price and the income model of the wind power plant.
As an alternative embodiment, the second determining module comprises: a first generation unit, configured to generate a federation set according to a hot user, a wind power plant, and a load aggregator; the eighth determining unit is used for determining profits corresponding to the hot users, the wind power plant and the load aggregator according to the union characteristic function and the shapey value calculation formula, wherein the union characteristic function represents union profits corresponding to any member combination in the union set; a ninth determining unit, configured to determine revenue weights corresponding to the thermal users, the wind power plants, and the load aggregators in the overall revenue model of the region according to the revenue corresponding to the thermal users, the wind power plants, and the load aggregators; and the second generation unit is used for generating the regional overall profit model according to the product of the profit models corresponding to the thermal users, the wind power plants and the load aggregators and the corresponding profit weights.
As an alternative embodiment, the third determining module comprises: a tenth determining unit, configured to determine an objective function according to the regional overall profit model; and the eleventh determining unit is used for determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the objective function.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as part of the apparatus may run in a hardware environment as shown in fig. 1, may be implemented by software, and may also be implemented by hardware, where the hardware environment includes a network environment.
According to another aspect of the embodiment of the present invention, there is further provided an electronic device, which may be a server, a terminal, or a combination thereof, for implementing the above method for allocating revenue based on a shape value.
Fig. 5 is a block diagram of an alternative electronic device according to an embodiment of the present invention, as shown in fig. 5, including a processor 501, a communication interface 502, a memory 503 and a communication bus 504, where the processor 501, the communication interface 502 and the memory 503 complete communication with each other through the communication bus 504, and the memory 503 is used for storing a computer program; the processor 501, when executing the computer program stored in the memory 503, implements the following steps:
generating a profit model of multi-subject interactive influence in the regional heat accumulating type electric heating system, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator; determining constraint conditions corresponding to multiple main bodies in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the benefit model; determining income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method; and determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall income model.
Alternatively, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 5, but that does not indicate only one bus or one type of bus. The communication interface is used for communication between the electronic equipment and other equipment. The memory may include RAM, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the aforementioned processor.
As an example, as shown in fig. 5, the memory 503 may include, but is not limited to, a generation module 401, a first determination module 402, a second determination module 403, and a third determination module 404 in the revenue allocation apparatus based on the shape value. In addition, other module units in the aforementioned revenue distribution apparatus based on the shape value may also be included, but are not limited to this, and are not described in detail in this example.
The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), NP (Network Processor), and the like; but also DSPs (Digital Signal Processing), ASICs (Application Specific Integrated circuits), FPGAs (Field-Programmable Gate arrays) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In addition, the electronic device further includes: and the display is used for displaying the revenue distribution result based on the shapeley value. Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
It can be understood by those skilled in the art that the structure shown in fig. 5 is only an illustration, and the device implementing the above revenue allocation method based on the shape value may be a terminal device, and the terminal device may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 5 does not limit the structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
According to still another aspect of an embodiment of the present invention, there is also provided a storage medium. Alternatively, in this embodiment, the storage medium may be configured to execute a program code of a revenue allocation method based on a shapey value.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment. Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
generating a profit model of multi-subject interactive influence in the regional heat accumulating type electric heating system, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator; determining constraint conditions corresponding to multiple main bodies in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the benefit model; determining income weights and a regional overall income model corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method; and determining the income distribution strategy of the multiple subjects in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone heat accumulating type electric heating system and the zone overall income model.
Optionally, the specific example in this embodiment may refer to the example described in the above embodiment, which is not described again in this embodiment. Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments. The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing one or more computer devices (which may be personal computers, servers, or network devices) to execute all or part of the steps of the method for allocating revenue based on the shapey value according to the embodiments of the present invention.
In the above embodiments of the present invention, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described in detail in a certain embodiment.
In the embodiments provided in the present invention, it should be understood that the disclosed client can be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, and may also be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in this embodiment. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and amendments can be made without departing from the principle of the present invention, and these modifications and amendments should also be considered as the protection scope of the present invention.
Claims (10)
1. A method for allocating revenue based on a shapey value, the method comprising:
generating a profit model of multi-subject interactive influence in a regional heat accumulating type electric heating system, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator;
determining constraint conditions corresponding to multiple main bodies in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the income model;
determining income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method;
and determining the income distribution strategy of the multiple subjects in the regional heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the regional heat accumulating type electric heating system and the regional overall income model.
2. The method of claim 1, wherein the generating of the revenue model of the interaction effect of the multiple entities in the district regenerative electric heating system comprises:
determining a profit model of the wind power plant according to the electric quantity consumed by the load aggregator during the wind power consumption period;
determining a profit model of the load aggregator according to a peak clipping subsidy reward provided by the grid company to the load aggregator, a peak clipping subsidy given by the load aggregator to the thermal consumer, a thermal mass heating profit managed by the load aggregator, and a thermal mass thermal storage cost;
determining a profit model of the hot user according to subsidies given to the hot user by the load aggregator, the heat consumption cost of the hot user for reducing load saving and the heat consumption cost of the hot user for saving in the wind power consumption period;
and determining a revenue model of the power grid company according to the load reduction proportion of the heat consumption peak period.
3. The shapey-value-based profit sharing method according to claim 2, wherein the determining of constraint conditions corresponding to a plurality of subjects in the district regenerative electric heating system according to a safe operation requirement, a risk condition and the profit model comprises:
determining constraint conditions corresponding to the load aggregators according to heat supply requirements under normal conditions and power interruption conditions;
determining a constraint condition corresponding to the power grid company according to the load reduction proportion and a revenue model of the power grid company;
and determining the corresponding constraint conditions of the wind power plant according to the wind power price and the income model of the wind power plant.
4. The method of claim 1, wherein the determining of the multi-subject corresponding profit weight and the regional overall profit model according to the shapey value method comprises:
generating a federation set from the hot user, the wind power plant, and the load aggregator;
determining profits corresponding to the hot users, the wind power plants and the load aggregators according to a union feature function and a shapey value calculation formula, wherein the union feature function represents union profits corresponding to any member combination in the union set;
determining income weights corresponding to the thermal users, the wind power plants and the load aggregators in the regional overall income model according to the income corresponding to the thermal users, the wind power plants and the load aggregators;
and generating a regional overall profit model according to the product of the profit models corresponding to the thermal users, the wind power plants and the load aggregators and the corresponding profit weights.
5. The method of any one of claims 1-4, wherein the determining the profit sharing strategy for the multiple subjects in the zone regenerative electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the zone regenerative electric heating system and the zone overall profit model comprises:
determining an objective function according to the regional overall profit model;
and determining a multi-subject profit allocation strategy in the zone heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multi-subjects in the zone heat accumulating type electric heating system and the objective function.
6. A device for allocating revenue based on shape value, the device comprising:
the generating module is used for generating a profit model of multi-subject interaction influence in the regional heat accumulating type electric heating system, wherein the multi-subject comprises a power grid company, a heat consumer, a wind power plant and a load aggregator;
the first determining module is used for determining constraint conditions corresponding to multiple main bodies in the zone heat accumulating type electric heating system according to the safe operation requirement, the risk condition and the benefit model;
the second determining module is used for determining income weights and regional overall income models corresponding to multiple subjects in the regional heat accumulating type electric heating system according to a shapeley value method;
and the third determining module is used for determining the income distribution strategy of the multiple subjects in the regional heat accumulating type electric heating system through machine learning according to the constraint conditions corresponding to the multiple subjects in the regional heat accumulating type electric heating system and the regional overall income model.
7. The apparatus for revenue sharing based on shape value of claim 6, wherein the generating module comprises:
the first determining unit is used for determining a revenue model of the wind power plant according to the electric quantity consumed by the load aggregator in the wind power consumption period;
a second determination unit, configured to determine a profit model of the load aggregator according to a peak clipping subsidy reward provided by the grid company to the load aggregator, a peak clipping subsidy given by the load aggregator to the hot consumer, a heat accumulator heating profit managed by the load aggregator, and the heat accumulator heat accumulation cost;
a third determining unit, configured to determine a profit model of the hot user according to a subsidy given to the hot user by the load aggregator, a heat consumption cost of the hot user for reducing load saving, and a heat consumption cost of the hot user saved in a wind power consumption period;
and the fourth determination unit is used for determining the income model of the power grid company according to the load reduction proportion of the heat utilization peak period.
8. The apparatus of claim 6, wherein the first determining module comprises:
a fifth determining unit, configured to determine a constraint condition corresponding to the load aggregator according to a heat supply demand under a normal condition and an electric power interruption condition;
a sixth determining unit, configured to determine, according to the load shedding proportion and a revenue model of the power grid company, a constraint condition corresponding to the power grid company;
and the seventh determining unit is used for determining the corresponding constraint conditions of the wind power plant according to the wind power price and the income model of the wind power plant.
9. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein said processor, said communication interface and said memory communicate with each other via said communication bus,
the memory for storing a computer program;
the processor for performing the method steps of any one of claims 1 to 5 by running the computer program stored on the memory.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211332933.1A CN115660351A (en) | 2022-10-28 | 2022-10-28 | Shapley value-based revenue distribution method and apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211332933.1A CN115660351A (en) | 2022-10-28 | 2022-10-28 | Shapley value-based revenue distribution method and apparatus |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115660351A true CN115660351A (en) | 2023-01-31 |
Family
ID=84993784
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211332933.1A Pending CN115660351A (en) | 2022-10-28 | 2022-10-28 | Shapley value-based revenue distribution method and apparatus |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115660351A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117628663A (en) * | 2024-01-16 | 2024-03-01 | 广州海颐软件有限公司 | Air conditioner load cluster participation demand response income distribution method based on interactive adjustment |
-
2022
- 2022-10-28 CN CN202211332933.1A patent/CN115660351A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117628663A (en) * | 2024-01-16 | 2024-03-01 | 广州海颐软件有限公司 | Air conditioner load cluster participation demand response income distribution method based on interactive adjustment |
| CN117628663B (en) * | 2024-01-16 | 2024-06-04 | 广州海颐软件有限公司 | Air conditioner load cluster participation demand response income distribution method based on interactive adjustment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wei et al. | An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining | |
| Zhao et al. | Virtual energy storage sharing and capacity allocation | |
| Li et al. | Optimal operation strategy for integrated natural gas generating unit and power-to-gas conversion facilities | |
| Schill et al. | Prosumage of solar electricity: pros, cons, and the system perspective | |
| Luo et al. | Distributed peer-to-peer energy trading based on game theory in a community microgrid considering ownership complexity of distributed energy resources | |
| Huang et al. | Optimal demand response with energy storage management | |
| Chen et al. | A multi-lateral trading model for coupled gas-heat-power energy networks | |
| Xuanyue et al. | Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach | |
| Chen et al. | Electricity demand response schemes in China: Pilot study and future outlook | |
| Li et al. | Incentivizing distributed energy trading among prosumers: A general Nash bargaining approach | |
| Jiang et al. | Extended multi‐energy demand response scheme for industrial integrated energy system | |
| CN109800927B (en) | Distributed optimization method for power distribution network in bilateral power market environment | |
| Li et al. | A game-based combinatorial double auction model for cloud resource allocation | |
| JP5497603B2 (en) | Method for introducing power generation equipment and promoting power saving | |
| CN115660351A (en) | Shapley value-based revenue distribution method and apparatus | |
| Miletić et al. | The effects of household automation and dynamic electricity pricing on consumers and suppliers | |
| Hussain et al. | Optimization of social welfare in P2P community microgrid with efficient decentralized energy management and communication-efficient power trading | |
| CN111402015B (en) | Virtual power plant double-layer bidding method and system based on purchase and sale risks | |
| CN116306050B (en) | Energy storage configuration determining method and device and electronic equipment | |
| CN117391718A (en) | Green electricity-CCER mutual recognition transaction system based on dynamic emission reduction factors | |
| CN112633771B (en) | Power resource numerical value calculation method, device, computer equipment and storage medium | |
| Zou et al. | Auction-based distributed efficient economic operations of microgrid systems | |
| CN115906608A (en) | Game theory-based virtual power plant model and optimized operation method | |
| Zarenia et al. | A new two-stage game-based approach for energy storage pricing in radial distribution system considering uncertainty | |
| Gao et al. | Electricity-carbon coupling retail package optimization considering low-carbon benefits of distributed renewable energy |
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 |