Low-carbon optimal operation method and system for power distribution network under multi-stakeholder energy sharing

Abstract

The application discloses a multi-stakeholder energy sharing based power distribution network low-carbon optimal operation method and system, relates to the technical field of energy analysis, and comprises the following steps: receiving power distribution network optimal operation information and producer-consumer energy transaction information, generating a producer-consumer model according to operation parameters of the producer-consumer and constraint conditions of distributed resource aggregation characteristics; inputting the power distribution network optimal operation information and the energy transaction mode of the producer-consumer into the producer-consumer model to output a multi-stakeholder energy sharing clearing model considering carbon quotas; obtaining subject transaction data based on a solution strategy of an alternating direction multiplier method, inputting the subject transaction data into the multi-stakeholder energy sharing clearing model considering carbon quotas, solving by using the alternating direction multiplier algorithm, and outputting power distribution network low-carbon optimal operation results, wherein the subject transaction data comprises operation plans of each producer-consumer itself, an operation plan of the power distribution network and transaction decisions among multiple subjects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy analysis, in particular to a power distribution network low-carbon optimal operation method and system under multi-stakeholder energy sharing. BACKGROUND

[0002] In order to effectively reduce carbon emissions to cope with climate change, the country has put forward the "double carbon" goal, which brings new challenges to the low-carbon operation of the power distribution network. Distributed renewable energy, with its clean and flexible advantages, has gradually increased its penetration rate in the power system, and more and more distributed renewable energy participates in operation by acting as an internal power provider. Prosumers can participate in power market activities in the dual role of energy producers and consumers, and the multi-prosumer system they constitute can also provide mutual aid within the power distribution network, thereby reducing the dependence of prosumers on power supply. In order to promote the green and low-carbon transformation of energy structure and improve the consumption rate of renewable energy, carbon emissions should be considered in the dispatching and operation strategy of the power distribution network. Therefore, encouraging multi-stakeholders to participate in the market and designing a reasonable optimization dispatching strategy that considers carbon emissions and supply and demand is conducive to the internal coordination of the power distribution network system and the realization of low-carbon emission reduction. SUMMARY

[0003] In order to solve the problems mentioned in the background, the purpose of the present application is to provide a power distribution network low-carbon optimal operation method and system under multi-stakeholder energy sharing, which realizes the low-carbon optimal operation of the power distribution network through energy sharing between prosumers, and provides technical and mechanism reference for low-carbon power management of the main body connected to the power distribution network, and promotes the low-carbon optimal operation of the power distribution network of the connected main body.

[0004] In the first aspect, the purpose of the present application can be realized by the following technical scheme: a power distribution network low-carbon optimal operation method under multi-stakeholder energy sharing, the method comprising the following steps:

[0005] Receiving power distribution network optimal operation information and prosumer energy transaction information, generating a prosumer model according to the operation parameters of the prosumer and the distributed resource aggregation characteristic constraint condition, wherein the prosumer is a city building;

[0006] Inputting the power distribution network optimal operation information and the prosumer energy transaction mode into the prosumer model, and outputting a multi-stakeholder energy sharing clearing model considering carbon quota, wherein the multi-stakeholder energy sharing clearing model considering carbon quota takes the minimum cost of energy and carbon quota clearing of all prosumers as the objective function;

[0007] The solving strategy based on the alternating direction multiplier method obtains the main transaction data, inputs the main transaction data into the multi-stakeholder energy sharing dispatching model considering carbon quota, solves by using the alternating direction multiplier algorithm, and outputs to obtain the low-carbon optimal operation result of the power distribution network, wherein the main transaction data includes the operation plan of each producer and consumer, the operation plan of the power distribution network, and the transaction decision among the multiple subjects.

[0008] With reference to the first aspect, in some implementations of the first aspect, the method further includes: the power distribution network optimal operation information and the producer and consumer energy transaction information are used to construct a low-carbon optimal operation architecture of the power distribution network under multi-stakeholder energy sharing based on the relationship between the power distribution network optimal operation and the producer and consumer energy transaction.

[0009] With reference to the first aspect, in some implementations of the first aspect, the method further includes: the low-carbon optimal operation architecture of the power distribution network under multi-stakeholder energy sharing includes:

[0010] The urban building and the low-carbon optimal operation of the power distribution network are in the physical layer, and the producer and consumer energy transaction is in the information layer.

[0011] With reference to the first aspect, in some implementations of the first aspect, the method further includes: the distributed resource aggregation characteristic constraint condition includes:

[0012] The distributed photovoltaic operation characteristic constraint condition includes:

[0013]

[0014] The temperature control load operation characteristic constraint condition includes:

[0015]

[0016]

[0017] The electric energy storage operation characteristic constraint condition includes:

[0018]

[0019]

[0020]

[0021]

[0022] In the formula, I={1, 2,..., I}, i∈I represents a producer and consumer set; T={1, 2,..., T}, t∈I represents a time interval set; Δt represents a unit time; P i PV (t) and respectively represent the active power of the distributed photovoltaic of the producer-consumer i in the time interval t and its day-ahead predicted maximum value; P i AC (t), T i in (t), T i out respectively represent the running power of the temperature-controlled load of the producer-consumer i in the time interval t, the internal and external ambient temperature; R, C, η respectively represent the equivalent thermal resistance, equivalent heat capacity and performance coefficient of the temperature-controlled load; represent the temperature limit value corresponding to the comfort range of the producer-consumer i; respectively represent the charging and discharging power and the state of charge of the electric energy storage of the producer-consumer i in the time interval t; respectively represent the upper limit of the charging and discharging power, the state of charge limit value and the capacity of the electric energy storage of the producer-consumer i.

[0023] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the expression of the objective function of clearing the energy and carbon quota of all producer-consumers at the lowest cost is as follows:

[0024]

[0025] In the formula, represent the energy transaction cost of all producer-consumers in a transaction period; represent the carbon quota transaction cost of all producer-consumers in a transaction period; represent the cost of the producer-consumer i in the t time interval for purchasing electric energy from the DSO; represent the income of the producer-consumer i in the t time interval for selling electric energy to the DSO; represent the dissatisfaction degree of the producer-consumer i in the t time interval for deviating from the set temperature of the temperature-controlled load; represent the cost of the producer-consumer i in the t time interval for purchasing carbon quota from the carbon market.

[0026] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the constraint condition of the multi-stakeholder energy sharing clearing model considering carbon quota is the producer-consumer electric energy balance constraint based on energy sharing and the producer-consumer cost and utility constraint based on energy sharing.

[0027] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: the producer-consumer electric energy balance constraint based on energy sharing is:

[0028]

[0029]

[0030] In the formula, Pi netbuy (t) represents the electricity energy purchased by prosumer i from the grid in time interval t; P i netsell (t) represents the electricity energy sold by prosumer i to the grid in time interval t; P i LOAD (t) represents the rigid load forecast parameter of prosumer i in time interval t; represents the electricity energy transferred by prosumer i to prosumer j in P2P transaction in time interval t; represents the electricity energy received by prosumer j from prosumer i in P2P transaction;

[0031] The prosumer cost and utility constraint based on energy sharing:

[0032]

[0033]

[0034]

[0035]

[0036] wherein, represents the cost of electricity energy purchased by prosumer i from the grid in time interval t; represents the dissatisfaction degree of prosumer i to the temperature-controlled load deviating from the set temperature in time interval t; represents the loss cost of the electric energy storage of prosumer i in time interval t, represents the income of prosumer i from selling electricity energy to the grid in time interval t; β i TCL represents the sensitivity degree of the temperature-controlled load temperature of prosumer i; T i set represents the set most comfortable temperature of the temperature-controlled load of prosumer i; represents the loss coefficient of the electric energy storage; λ TOU represents the real-time electricity price of the grid; λ FIT represents the on-grid electricity price of the grid.

[0037] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: based on the carbon quota transaction cost of the prosumer sharing the carbon quota:

[0038]

[0039] wherein, is the cost of prosumer i purchasing carbon quota from the carbon market in time interval t; is the price of carbon quota sold by the carbon market, and Δt is a unit time interval.

[0040] In a second aspect, to achieve the above object, the present application discloses a power distribution network low-carbon optimal operation system under multi-stakeholder energy sharing, comprising:

[0041] a model generation module, configured to receive power distribution network optimal operation information and prosumer energy transaction information, generate a prosumer model according to operation parameters of the prosumer and distributed resource aggregation characteristic constraint conditions, wherein the prosumer is a city building;

[0042] a model training module, configured to input the power distribution network optimal operation information and the prosumer energy transaction mode into the prosumer model, and output a multi-stakeholder energy sharing clearing model considering carbon quota, wherein the multi-stakeholder energy sharing clearing model considering carbon quota takes the lowest energy and carbon quota clearing cost of all the prosumers as an objective function;

[0043] an optimal operation module, configured to obtain subject transaction data based on a solution strategy of an alternating direction multiplier method, input the subject transaction data into the multi-stakeholder energy sharing clearing model considering carbon quota, solve by using an alternating direction multiplier algorithm, and output power distribution network low-carbon optimal operation results, wherein the subject transaction data includes operation plans of each prosumer itself, an operation plan of the power distribution network, and transaction decisions among multiple subjects.

[0044] In combination with the second aspect, in some implementations of the second aspect, the system further comprises: the model generation module, which, based on a relationship between power distribution network optimal operation and prosumer energy transaction, constructs a power distribution network low-carbon optimal operation architecture under multi-stakeholder energy sharing.

[0045] The power distribution network low-carbon optimal operation architecture under multi-stakeholder energy sharing in the model generation module comprises:

[0046] the city building and the power distribution network low-carbon optimal operation are in a physical layer, and the prosumer energy transaction is in an information layer;

[0047] The distributed resource aggregation characteristic constraint conditions in the model generation module comprise:

[0048] a distributed photovoltaic operation characteristic constraint condition:

[0049]

[0050] a temperature control load operation characteristic constraint condition:

[0051]

[0052]

[0053] The electric energy storage operation characteristic constraint condition is:

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, I = {1, 2,..., I}, i ∈ I represents a set of producers and consumers; T = {1, 2,..., T}, t ∈ I represents a set of time intervals; Δt represents a unit time; P i PV (t) and respectively represent the active power of the distributed photovoltaic of the producer and consumer i at the time interval t and the day-ahead predicted maximum value thereof; P i AC (t), T i in (t), T i out (t) respectively represent the operation power of the temperature-controlled load of the producer and consumer i at the time interval t, the internal and external ambient temperature; R, C, η respectively represent the equivalent thermal resistance, the equivalent heat capacity and the performance coefficient of the temperature-controlled load; represent the temperature limit value corresponding to the comfort range of the producer and consumer i; respectively represent the charging and discharging power and the state of charge of the electric energy storage of the producer and consumer i at the time interval t; respectively represent the upper limit of the charging and discharging power, the state of charge limit value and the capacity of the electric energy storage of the producer and consumer i;

[0059] The expression of the objective function of the model training module in which the energy and carbon quota clearing cost of all producers and consumers is the lowest is as follows:

[0060]

[0061] In the formula, represents the energy transaction cost of all producers and consumers within one transaction period; represents the carbon quota transaction cost of all producers and consumers within one transaction period; represents the cost of the producer and consumer i for purchasing electric energy from the DSO at the time interval t; represents the income of the producer and consumer i for selling electric energy to the DSO at the time interval t; represents the dissatisfaction degree of the producer and consumer i for deviating from the set temperature of the temperature-controlled load at the time interval t; represents the cost of the producer and consumer i for purchasing carbon quota from the carbon market at the time interval t;

[0062] The constraint condition of the multi-stakeholder energy sharing clearing model considering carbon quota in the model training module is the energy sharing-based producer-consumer power balance constraint and the energy sharing-based producer-consumer cost and utility constraint;

[0063] The energy sharing-based producer-consumer power balance constraint condition in the model training module is:

[0064]

[0065]

[0066] In the formula, P i netbuy (t) represents the power purchased by the producer-consumer i from the power grid in the time interval t; P i netsell (t) represents the power sold by the producer-consumer i to the power grid in the time interval t; P i LOAD (t) represents the rigid load prediction parameter of the producer-consumer i in the time interval t; represents the power transferred by the producer-consumer i to the producer-consumer j in the P2P transaction in the time interval t; represents the power received by the producer-consumer j from the producer-consumer i in the P2P transaction;

[0067] The energy sharing-based producer-consumer cost and utility constraint is:

[0068]

[0069]

[0070]

[0071]

[0072] In the formula, represents the cost of the producer-consumer i for purchasing power from the power grid in the time interval t; represents the dissatisfaction degree of the producer-consumer i for the temperature-controlled load deviating from the set temperature in the time interval t; represents the loss cost of the electric energy storage of the producer-consumer i in the time interval t, represents the income of the producer-consumer i for selling power to the power grid in the time interval t; β i TCL represents the sensitivity degree of the temperature-controlled load of the producer-consumer i; T i set represents the most comfortable temperature of the temperature-controlled load set by the producer-consumer i; η i ES represents the loss coefficient of the electric energy storage; λ TOUrepresents the real-time electricity price of the power grid; λ FIT represents the on-grid electricity price of the power grid;

[0073] Optimizing the carbon quota transaction cost of the producer and consumer based on carbon quota sharing in the operation module:

[0074]

[0075] In the formula, is the cost of the carbon quota purchased by the producer and consumer i from the carbon market in the time interval t; is the price of the carbon quota sold by the carbon market, and Δt is the unit time interval.

[0076] Advantages of the present application:

[0077] The present application constructs an energy sharing mechanism for the electric energy transaction between the producer and consumer, and on this basis, establishes a transaction model for the energy clearing of all producers and consumers, encourages the producer and consumer to carry out low-carbon electric energy management, and promotes the low-carbon operation of the producer and consumer. In addition, in order to ensure the fair and safe operation of the market, while taking into account the protection of the privacy information of the producer and consumer users, the above-mentioned model is solved by using the alternating direction multiplier algorithm, which helps to reduce the energy cost of the producer and consumer through market means, and further promotes the low-carbon optimal operation of the distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor;

[0079] Figure 1 is a schematic diagram of the method flow of the present application;

[0080] Figure 2 is a schematic diagram of the overall framework of the low-carbon optimal operation of the distribution network of the present application;

[0081] Figure 3 is a schematic diagram of the system structure of the present application. DETAILED DESCRIPTION

[0082] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0083] Embodiment one:

[0084] Below, the related terms involved in the embodiments of the present application are introduced:

[0085] Distribution network: The distribution network refers to the power grid that accepts electric energy from the power transmission network or regional power plants, and distributes it to various users through distribution facilities or step-by-step distribution according to voltage. It is composed of overhead lines, cables, towers, distribution transformers, disconnectors, reactive power compensators, and some auxiliary facilities, and plays an important role in distributing electric energy in the power grid.

[0086] As shown in Figure 1 The method for low-carbon optimal operation of the distribution network under multi-stakeholder energy sharing includes the following steps:

[0087] Receive distribution network optimal operation information and prosumer energy transaction information, generate a prosumer model according to the operating parameters of the prosumer and the distributed resource aggregation characteristic constraint condition, wherein the prosumer is a city building;

[0088] The distribution network optimal operation information and the prosumer energy transaction information are based on the relationship between the distribution network optimal operation and the prosumer energy transaction, and a low-carbon optimal operation architecture of the distribution network under multi-stakeholder energy sharing is constructed;

[0089] The low-carbon optimal operation architecture of the distribution network under multi-stakeholder energy sharing includes:

[0090] The city building and the low-carbon optimal operation of the distribution network are in the physical layer, and the prosumer energy transaction is in the information layer.

[0091] In the physical layer, the city building is connected to the same distribution network, and energy interaction can be carried out between the city building and the distribution network, i.e., electric energy can be transmitted between the city building and the distribution network. Each city building has intelligent control equipment, through which the city building can exchange information in the information layer. In the information layer, the prosumers can trade electric energy and carbon quotas to achieve energy and carbon quota sharing. In addition, the prosumers and the distribution network operator can trade electric energy, and the prosumers and the carbon market can trade carbon quotas.

[0092] The behavior in the physical layer corresponds to the behavior in the information layer. In the information layer, the behavior of the prosumers trading electric energy with the distribution network operator corresponds to the behavior of electric energy transfer between the city building and the distribution network in the physical layer; the behavior of the prosumers trading electric energy with each other corresponds to the behavior of electric energy transfer from the city building to the distribution network, and then from the distribution network to other city buildings in the physical layer. Therefore, the electric energy transfer between the city building and the distribution network in the physical layer can be guided by the electric energy transaction of the prosumers in the information layer. The electric energy transaction of the prosumers in the information layer guides the energy interaction between the city building and the distribution network, thereby realizing the low-carbon optimal operation of the distribution network.

[0093] The distributed resource aggregation characteristic constraint conditions include:

[0094] The distributed photovoltaic operation characteristic constraint conditions include:

[0095]

[0096] The temperature-controlled load operation characteristic constraint conditions include:

[0097]

[0098]

[0099] The electric energy storage operation characteristic constraint conditions include:

[0100]

[0101]

[0102]

[0103]

[0104] In the formula, I={1, 2,..., I}, i∈I represents a set of producers and consumers; T={1, 2,..., T}, t∈I represents a set of time intervals; Δt represents a unit time; P i PV (t) and respectively represent the active power of the distributed photovoltaic of the producer and consumer i at the time interval t and the day-ahead predicted maximum value thereof; P i AC (t), T i in (t), T i out (t) respectively represent the operation power, internal and external ambient temperature of the temperature-controlled load of the producer and consumer i at the time interval t; R, C and η respectively represent the equivalent thermal resistance, equivalent heat capacity and performance coefficient of the temperature-controlled load; represent the temperature limit value corresponding to the comfort range of the producer and consumer i; respectively represent the charging and discharging power and the state of charge of the electric energy storage of the producer and consumer i at the time interval t; respectively represent the upper limit of the charging and discharging power, the state of charge limit value and the capacity of the electric energy storage of the producer and consumer i.

[0105] The power grid optimization operation information and the energy trading mode with the producer and consumer are input into the producer and consumer model, and a multi-stakeholder energy sharing clearing model considering carbon quota is output, wherein the multi-stakeholder energy sharing clearing model considering carbon quota takes the minimum energy and carbon quota clearing cost of all producers and consumers as an objective function;

[0106] The objective function with the lowest cost of all producers and consumers' energy and carbon quota is expressed as follows:

[0107]

[0108] In the formula, represents the energy transaction cost of all producers and consumers in a transaction period; represents the carbon quota transaction cost of all producers and consumers in a transaction period; represents the cost of producer i purchasing electricity from the DSO in the t time interval; represents the income of producer i selling electricity to the DSO in the t time interval; represents the dissatisfaction of producer i deviating from the set temperature of the temperature control load in the t time interval; represents the cost of producer i purchasing carbon quota from the carbon market in the t time interval.

[0109] The constraint conditions of the multi-stakeholder energy sharing clearing model considering carbon quota are the energy sharing-based producer and consumer electricity balance constraint and the energy sharing-based producer and consumer cost and utility constraint.

[0110] The energy sharing-based producer and consumer electricity balance constraint condition:

[0111]

[0112]

[0113] In the formula, P i netbuy represents the electricity purchased by producer i from the grid in the t time interval; P i netsell represents the electricity sold by producer i to the grid in the t time interval; P i LOAD represents the rigid load prediction parameter of producer i in the t time interval; represents the electricity transferred by producer i to producer j in the P2P transaction in the t time interval; represents the electricity received by producer j from producer i in the P2P transaction;

[0114] The energy sharing-based producer and consumer cost and utility constraint:

[0115]

[0116]

[0117]

[0118]

[0119] wherein, represents the cost of the producer-consumer i to purchase electricity from the power grid in the time interval t; represents the degree of dissatisfaction of the producer-consumer i to deviate from the set temperature of the temperature control load in the time interval t; represents the loss cost of the electric energy storage of the producer-consumer i in the time interval t, represents the income of the producer-consumer i to sell electricity to the power grid in the time interval t; represents the sensitivity of the temperature of the temperature control load of the producer-consumer i; represents the most comfortable temperature of the temperature control load set by the producer-consumer i; represents the loss coefficient of the electric energy storage; λ TOU represents the real-time electricity price of the power grid; λ FIT represents the on-grid electricity price of the power grid.

[0120] The solving strategy based on the alternating direction multiplier method obtains subject transaction data, inputs the subject transaction data into the multi-stakeholder energy sharing dispatching model considering carbon quota, solves by using the alternating direction multiplier algorithm, and outputs to obtain the low-carbon optimal operation result of the distribution network, wherein the subject transaction data includes the operation plan of each producer-consumer itself, the operation plan of the distribution network, and the transaction decision among the multiple subjects.

[0121] Specifically, the scheme of the present application is further described below through embodiments:

[0122] The carbon quota transaction cost of the producer-consumer based on carbon quota sharing:

[0123]

[0124] wherein, is the cost of the producer-consumer i to purchase carbon quota from the carbon market in the time interval t; is the price of the carbon quota sold by the carbon market, and Δt is a unit time interval.

[0125] Embodiment two: as shown in the second aspect, Figure 3 in order to achieve the above-mentioned purpose, the present application discloses a low-carbon optimal operation system of a distribution network under multi-stakeholder energy sharing, comprising:

[0126] A model generation module is configured to receive distribution network optimal operation information and producer-consumer energy transaction information, generate a producer-consumer model according to the operation parameters of the producer-consumer and the constraint conditions of the aggregation characteristics of distributed resources, wherein the producer-consumer is a city building.

[0127] The model training module is configured to input the power distribution network optimal operation information and the energy trading mode of the producers and consumers into the producer and consumer model, and output a multi-stakeholder energy sharing clearing model considering carbon quota, wherein the multi-stakeholder energy sharing clearing model considering carbon quota takes the minimum energy and carbon quota clearing cost of all producers and consumers as an objective function.

[0128] The optimal operation module is configured to obtain subject transaction data based on a solution strategy of an alternating direction multiplier method, input the subject transaction data into the multi-stakeholder energy sharing clearing model considering carbon quota, solve the model by using an alternating direction multiplier algorithm, and output a low-carbon optimal operation result of the power distribution network, wherein the subject transaction data includes operation plans of each producer and consumer, an operation plan of the power distribution network, and transaction decisions among the multiple subjects.

[0129] In some implementations of the second aspect, the system further includes that the model generation module includes power distribution network optimal operation information and producer and consumer energy trading information, and constructs a low-carbon optimal operation architecture of the power distribution network under multi-stakeholder energy sharing based on a relationship between the power distribution network optimal operation and the producer and consumer energy trading.

[0130] The low-carbon optimal operation architecture of the power distribution network under multi-stakeholder energy sharing in the model generation module includes:

[0131] The low-carbon optimal operation of the urban building and the power distribution network is in a physical layer, and the producer and consumer energy trading is in an information layer.

[0132] The distributed resource aggregation characteristic constraint condition in the model generation module includes:

[0133] The distributed photovoltaic operation characteristic constraint condition includes:

[0134]

[0135] The temperature control load operation characteristic constraint condition includes:

[0136]

[0137]

[0138] The electric energy storage operation characteristic constraint condition includes:

[0139]

[0140]

[0141]

[0142]

[0143] where I = {1, 2, …, I}, i ∈ I represents the set of prosumers; T = {1, 2, …, T}, t ∈ I represents the set of time intervals; Δt represents the unit time; P i PV (t) and represent the active power of the distributed photovoltaic of the prosumer i at the time interval t and its day-ahead predicted maximum value, respectively; P i AC (t), T i in (t), T i out (t) represent the running power, internal and external ambient temperature of the temperature-controlled load of the prosumer i at the time interval t; R, C, η represent the equivalent thermal resistance, equivalent heat capacity and performance coefficient of the temperature-controlled load, respectively; represent the temperature limit value corresponding to the comfort range of the prosumer i; represent the charging and discharging power and the state of charge of the electric energy storage of the prosumer i at the time interval t; represent the upper limit of the charging and discharging power, the state of charge limit value and the capacity of the electric energy storage of the prosumer i;

[0144] The expression of the objective function of the model training module to minimize the energy and carbon quota clearing cost of all prosumers is as follows:

[0145]

[0146] where represents the energy trading cost of all prosumers within a trading cycle; represents the carbon quota trading cost of all prosumers within a trading cycle; represents the cost of the prosumer i to purchase electricity from the DSO at the time interval t; represents the income of the prosumer i to sell electricity to the DSO at the time interval t; represents the dissatisfaction degree of the prosumer i to deviate from the set temperature of the temperature-controlled load at the time interval t; represents the cost of the prosumer i to purchase carbon quota from the carbon market at the time interval t;

[0147] The constraint conditions of the multi-stakeholder energy sharing clearing model considering carbon quota in the model training module are the prosumer electricity balance constraint based on energy sharing and the prosumer cost and utility constraint based on energy sharing;

[0148] The prosumer electricity balance constraint based on energy sharing in the model training module is:

[0149]

[0150]

[0151] where P i netbuy (t) denotes the electricity energy purchased by prosumer i from the grid at time interval t; P i netsell (t) denotes the electricity energy sold by prosumer i to the grid at time interval t; P i LOAD (t) denotes the rigid load forecast parameter of prosumer i at time interval t; denotes the electricity energy transferred by prosumer i to prosumer j at time interval t in P2P trading; denotes the electricity energy received by prosumer j from prosumer i in P2P trading;

[0152] The prosumer cost and utility constraints based on energy sharing:

[0153]

[0154]

[0155]

[0156]

[0157] where denotes the cost of electricity energy purchased by prosumer i from the grid at time interval t; denotes the dissatisfaction level of prosumer i for the temperature-controlled load deviating from the set temperature at time interval t; denotes the loss cost of the electric energy storage of prosumer i at time interval t, denotes the revenue of electricity energy sold by prosumer i to the grid at time interval t; denotes the sensitivity of the temperature-controlled load temperature of prosumer i; T i set denotes the most comfortable temperature of the temperature-controlled load set by prosumer i; denotes the loss coefficient of the electric energy storage; λ TOU denotes the real-time electricity price of the grid; λ FIT denotes the on-grid electricity price of the grid;

[0158] The carbon quota trading cost of the prosumer based on carbon quota sharing in the optimization operation module:

[0159]

[0160] where denotes the cost of carbon quota purchased by prosumer i from the carbon market at time interval t; The price of the carbon quota sold for the carbon market, Δt is a unit time interval.

[0161] Based on the same inventive concept, the present application further provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are configured to implement one or more instructions, and are specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0162] It needs to be further explained that, based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is executed by a processor to perform the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection with one or more conductive wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, device or component.

[0163] In the description of the specification, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner.

[0164] The basic principles, main features and advantages of the present disclosure are shown and described above. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements can be made to the present disclosure, and these changes and improvements all fall within the scope of the claimed present disclosure.

Claims

1. A method for low-carbon optimal operation of a power distribution network under multi-stakeholder energy sharing, characterized in that, The method comprises the following steps: Receiving power grid optimization operation information and prosumer energy transaction information, generating a prosumer model according to the operation parameters of the prosumer and the constraint conditions of the aggregation characteristics of the distributed resources, wherein the prosumer is a city building; Inputting the power grid optimization operation information and the prosumer energy transaction mode into the prosumer model to output a multi-stakeholder energy sharing clearing model considering carbon quota, wherein the multi-stakeholder energy sharing clearing model considering carbon quota takes the minimum energy and carbon quota clearing cost of all prosumers as an objective function; Based on the solving strategy of the alternating direction multiplier method, the subject transaction data is obtained, the subject transaction data is input into the multi-stakeholder energy sharing clearing model considering carbon quota, and the alternating direction multiplier algorithm is used for solving to output a low-carbon optimization operation result of the power grid, wherein the subject transaction data includes the operation plan of each prosumer itself, the operation plan of the power grid and the transaction decision among multiple subjects.

2. The method of claim 1, wherein, The power grid optimization operation information and the prosumer energy transaction information are based on the relationship between the power grid optimization operation and the prosumer energy transaction to construct a low-carbon optimization operation architecture of the power grid under multi-stakeholder energy sharing.

3. The method of claim 2, wherein, The low-carbon optimization operation architecture of the power grid under multi-stakeholder energy sharing comprises: The city building and the low-carbon optimization operation of the power grid are in the physical layer, and the prosumer energy and carbon quota transaction is in the information layer.

4. The method of claim 1, wherein, The constraint conditions of the aggregation characteristics of the distributed resources include: Distributed photovoltaic operation characteristic constraint condition: (1) Temperature control load operation characteristic constraint condition: (2) (3) Electric energy storage operation characteristic constraint condition: (4) (5) (6) (7) where I = {1, 2,..., I}, i e I represents the set of prosumers; T = {1, 2,..., T}, t e I represents the set of time intervals; represents the unit time; and respectively represent the active power of the distributed photovoltaic of the prosumer i at the time interval t and its day-ahead predicted maximum value; , , respectively represent the running power, internal and external ambient temperature of the temperature-controlled load of the prosumer i at the time interval t; R, C, η respectively represent the equivalent thermal resistance, equivalent heat capacity and performance coefficient of the temperature-controlled load; , represents the temperature limit value corresponding to the comfort range of the prosumer i; , , respectively represent the charging and discharging power and the state of charge of the electric energy storage of the prosumer i at the time interval t; , , , respectively represent the upper limit of the charging and discharging power, the state of charge limit value and the capacity of the electric energy storage of the prosumer i.

5. The method of claim 1, wherein, The expression of the objective function taking the minimum energy and carbon quota clearing cost of all prosumers as the objective function is as follows: (8) where, represents the total energy trading cost of all producers and consumers in a trading period; represents the total carbon quota trading cost of all producers and consumers in a trading period; represents the cost of producer-consumer i to buy electricity from DSO in time interval t; represents the revenue of producer-consumer i to sell electricity to DSO in time interval t; represents the dissatisfaction of producer-consumer i to the temperature deviation of temperature-controlled load in time interval t; represents the cost of producer-consumer i to buy carbon quota from carbon market in time interval t.

6. The method of claim 4, wherein, The constraint conditions of the multi-stakeholder energy sharing clearing model considering carbon quota are the prosumer electric energy balance constraint based on energy sharing and the prosumer cost and utility constraint based on energy sharing.

7. The method of claim 6, wherein, The prosumer electric energy balance constraint based on energy sharing is: (9) (10) wherein, represents the electrical energy purchased by prosumer i from the grid in time interval t; represents the electrical energy sold by prosumer i to the grid in time interval t; represents the rigid load forecast parameter of prosumer i in time interval t; represents the electrical energy transferred by prosumer i to prosumer j in time interval t in P2P trading; represents the electrical energy received by prosumer j from prosumer i in P2P trading; The prosumer cost and utility constraint based on energy sharing is: (11) (12) (13) (14) wherein, represents the cost of procsumer i purchasing electricity from the grid at time interval t; represents the dissatisfaction of procsumer i with the temperature deviation of the temperature- controlled load from the set temperature at time interval t; represents the cost of the loss of the electric energy storage of procsumer i at time interval t, represents the revenue of procsumer i selling electricity to the grid at time interval t; represents the sensitivity of the temperature-controlled load temperature of procsumer i; represents the set most comfortable temperature of the temperature-controlled load of procsumer i; represents the loss coefficient of the electric energy storage; represents the real-time electricity price of the grid; represents the on-grid electricity price of the grid.

8. The method of claim 1, wherein, The carbon quota transaction cost of the prosumer based on carbon quota sharing is: (15) wherein Cp is the cost to producer i of purchasing carbon allowances from the carbon market during time interval t; Pc is the price of carbon allowances sold by the carbon market, t is the unit time interval.

9. A low-carbon optimal operation system for a power distribution network under multi-stakeholder energy sharing, characterized in that, It comprises: A model generation module is configured to receive power grid optimization operation information and prosumer energy transaction information, generate a prosumer model according to the operation parameters of the prosumer and the constraint conditions of the aggregation characteristics of the distributed resources, wherein the prosumer is a city building; A model training module is configured to input the power grid optimization operation information and the prosumer energy transaction mode into the prosumer model to output a multi-stakeholder energy sharing clearing model considering carbon quota, wherein the multi-stakeholder energy sharing clearing model considering carbon quota takes the minimum energy and carbon quota clearing cost of all prosumers as an objective function; An optimization operation module is configured to obtain subject transaction data based on the solving strategy of the alternating direction multiplier method, input the subject transaction data into the multi-stakeholder energy sharing clearing model considering carbon quota, use the alternating direction multiplier algorithm for solving, and output a low-carbon optimization operation result of the power grid, wherein the subject transaction data includes the operation plan of each prosumer itself, the operation plan of the power grid and the transaction decision among multiple subjects.

10. The power distribution network low-carbon optimal operation system under multi-stakeholder energy sharing according to claim 9, characterized in that, The model generation module generates power distribution network optimization operation information and energy transaction information of producers and consumers, constructs a low-carbon optimization operation architecture of the power distribution network under the energy sharing of multiple interest subjects based on the relationship between the optimization operation of the power distribution network and the energy transaction of the producers and consumers; The low-carbon optimization operation architecture of the power distribution network under the energy sharing of multiple interest subjects in the model generation module comprises: The low-carbon optimization operation of urban buildings and the power distribution network is in a physical layer, and the energy transaction of the producers and consumers is in an information layer; The distributed resource aggregation characteristic constraint condition in the model generation module comprises: A distributed photovoltaic operation characteristic constraint condition: (1) A temperature control load operation characteristic constraint condition: (2) (3) An electric energy storage operation characteristic constraint condition: (4) (5) (6) (7) where I = {1, 2,..., I} and i e I represent the set of prosumers; T = {1, 2,..., T} and t e I represent the set of time intervals; denotes the unit time; and denote the active power of the distributed photovoltaic of the prosumer i at the time interval t and its day-ahead predicted maximum value, respectively; , , denote the running power, internal and external ambient temperature of the temperature-controlled load of the prosumer i at the time interval t, respectively; R, C, η denote the equivalent thermal resistance, equivalent thermal capacity and performance coefficient of the temperature-controlled load, respectively; , denote the temperature limit value corresponding to the comfort range of the prosumer i; , , denote the charging and discharging power and the state of charge of the electric energy storage of the prosumer i at the time interval t, respectively; , , , denote the upper limit of the charging and discharging power, the state of charge limit value and the capacity of the electric energy storage of the prosumer i, respectively. The expression of the objective function in the model training module is as follows: taking the minimum energy and carbon quota clearing cost of all producers and consumers as the objective function: (8) wherein, represents the total energy trading cost of all producers and consumers in a trading period; represents the total carbon quota trading cost of all producers and consumers in a trading period; represents the cost of producer-consumer i to purchase electricity from the DSO in the t time interval; represents the revenue of producer-consumer i to sell electricity to the DSO in the t time interval; represents the dissatisfaction of producer-consumer i to the temperature deviation of the temperature-controlled load from the set temperature in the t time interval; represents the cost of producer-consumer i to purchase carbon quota from the carbon market in the t time interval; The constraint condition of the energy sharing clearing model of multiple interest subjects considering carbon quotas in the model training module is a producer and consumer electric energy balance constraint based on energy sharing and a producer and consumer cost and utility constraint based on energy sharing; The producer and consumer electric energy balance constraint condition based on energy sharing in the model training module is: (9) (10) wherein, represents the electrical energy purchased by prosumer i from the grid in time interval t; represents the electrical energy sold by prosumer i to the grid in time interval t; represents the rigid load forecast parameter of prosumer i in time interval t; represents the electrical energy transferred by prosumer i to prosumer j in time interval t in P2P trading; represents the electrical energy received by prosumer j from prosumer i in P2P trading; The producer and consumer cost and utility constraint based on energy sharing is: (11) (12) (13) (14) wherein, represents the cost of producer-consumer i purchasing electricity from the grid in time interval t; represents the dissatisfaction of producer-consumer i with the temperature deviation of the temperature-controlled load from the set temperature in time interval t; represents the wear and tear cost of the electric energy storage of producer-consumer i in time interval t, represents the income of producer-consumer i from selling electricity to the grid in time interval t; represents the sensitivity of the temperature of the temperature-controlled load of producer-consumer i; represents the set most comfortable temperature of the temperature-controlled load of producer-consumer i; represents the wear and tear coefficient of the electric energy storage; represents the real-time electricity price of the grid; represents the on-grid electricity price of the grid; The carbon quota transaction cost of the producers and consumers based on carbon quota sharing in the optimization operation module is: (15) wherein Cp is the cost to producer i of purchasing carbon allowances from the carbon market during time interval t; Pc is the price of carbon allowances sold by the carbon market, t is the unit time interval.

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