Interactive operation method and device of source, grid, load and storage in distributed photovoltaic system

Abstract

The present invention provides a method and device for interactive operation of source, grid, load and storage in a distributed photovoltaic system, the method comprising: dividing a natural day into multiple time periods; judging whether photovoltaic feedback will occur in the current time period, or judging whether photovoltaic feedback will occur in the next time period when there is no photovoltaic feedback in the current time period; if photovoltaic feedback occurs in the current time period, or if there is no photovoltaic feedback in the current time period but photovoltaic feedback occurs in the next time period, performing source, grid, load and storage interactive demand response to obtain the load power consumption strategy for the current time period; if there is no photovoltaic feedback in both the current time period and the next time period, performing distribution network reactive power voltage optimization control to obtain the load power consumption strategy for the current time period; repeating the above steps with the next time period as the current time period to obtain the load power consumption strategy for each time period in turn to determine the load power consumption plan for the entire day. This application significantly improves the distributed photovoltaic absorption rate, takes into account the interests of both the distribution network side and the user side, and improves the user experience.

Description

Technical Field

[0001] The present invention relates to the field of distributed photovoltaic technology, and in particular to a method and device for interactive operation of source, grid, load and storage in a distributed photovoltaic system. Background Art

[0002] Currently, the construction of a new power system based on renewable energy is gradually advancing. The penetration rate of distributed photovoltaics in distribution networks continues to grow, and the number of flexible loads such as electric vehicles is also increasing. The large-scale integration of distributed photovoltaics into the distribution network brings serious problems such as power flow backflow and voltage over-limit, which endangers the safety and stability of the power system. The traditional "source follows load" distribution network operation mode cannot meet the operational needs of the new distribution system as the power market gradually opens up and renewable energy and flexible loads increase. Leveraging the adjustable potential of the source, grid, load, and storage components, utilizing the efficient interaction of the source, grid, load, and storage to promote the local consumption of distributed photovoltaics, and ensuring the safe and stable operation of distribution networks with a high proportion of distributed photovoltaics has been a new research trend in recent years. However, there is still much room for further technical research.

[0003] Currently, the construction of a new power system based on renewable energy is still in the experimental and exploratory stage, and research on promoting the local consumption of distributed photovoltaic power through source-grid-load-storage interaction has just begun. Literature on source-grid-load-storage interaction focuses solely on direct control methods for the adjustable potential of each component, with the distribution network as the control center, and explores key technical issues that need to be addressed. However, with the gradual opening of the electricity market, all parties are pursuing their own interests. Source-grid-load-storage interaction strategies that focus on the distribution network as the control center and maximize distribution network profits are not compatible with the increasingly open distribution system.

[0004] Therefore, it is necessary to provide a source-grid-load-storage interactive operation method that can fully guarantee the operating benefits of each part of the source, grid, load and storage, maintain the individual rational pursuit of each part, promote the local consumption of distributed photovoltaics, and maintain the safety and stability of the distribution network under the condition of the gradual opening of the power market on the distribution side. Summary of the Invention

[0005] In view of this, the present invention provides a method and device for interactive operation of source, grid, load and storage in a distributed photovoltaic system to solve at least one of the above-mentioned problems.

[0006] In order to achieve the above object, the present invention adopts the following scheme:

[0007] According to the first aspect of the present invention, an embodiment of the present invention provides a method for interactive operation of source, grid, load and storage in a distributed photovoltaic system, the method comprising: S1, dividing a natural day into multiple time periods; S2, judging whether photovoltaic feedback will occur in the normal electricity demand in the current time period, or judging whether photovoltaic feedback will occur in the next time period when there is no photovoltaic feedback in the normal electricity demand in the current time period; S3, if photovoltaic feedback occurs in the current time period, or if there is no photovoltaic feedback in the current time period but photovoltaic feedback occurs in the next time period, performing source, grid, load and storage interactive demand response to obtain the load power consumption strategy for the current time period; S4, if there is no photovoltaic feedback in both the current time period and the next time period, performing distribution network reactive voltage optimization control, and then obtaining the load power consumption strategy for the current time period; S5, repeating steps S2 to S4 with the next time period as the current time period to obtain the load power consumption strategy for each time period in turn to determine the load power consumption plan for the whole day.

[0008] Preferably, in the above steps of this embodiment, when there is no photovoltaic feedback in the current period due to normal electricity demand, determining whether photovoltaic feedback will occur in the next period includes: when there is no photovoltaic feedback in the current period due to normal electricity demand, based on the normal electricity consumption of all loads, predicting the power required to maintain the original setting state of all loads in the next period, and determining whether photovoltaic feedback will occur in the next period.

[0009] Preferably, the above steps of this embodiment for performing interactive demand response between source, grid, load and storage to obtain the load power consumption strategy for the current period include: evaluating the dynamic limit carrying capacity of the distribution network in the current period to obtain an evaluation result, determining the boundary conditions of the game model between the power sales partner and the load aggregator based on the evaluation result, conducting an electricity trading game according to the game model to determine the electricity demand response plan, taking the electricity demand response plan as the target, obtaining the active and reactive power control plan based on the active and reactive power coordinated control model to obtain the load power consumption strategy for the current period.

[0010] Preferably, the evaluation results obtained by evaluating the dynamic limit carrying capacity of the distribution network in the current period described in the above steps of this embodiment include: evaluating the adjustable potential of various flexible loads in the distribution network on the premise of meeting the basic needs of users to obtain the adjustment range of the adjustable potential; determining the adjustment range of energy storage in the distribution network; based on the adjustment range of the adjustable potential and the adjustment range of the energy storage, and taking the actual structure, flow and operation safety of the distribution network as constraints, evaluating and obtaining the maximum power value and minimum power value that each node of the distribution network can carry.

[0011] Preferably, the boundary conditions of the game model between the power sales partner and the load aggregator determined based on the evaluation results in the above steps of this embodiment include: if photovoltaic return occurs in the current period: the boundary conditions P of the demand response game between the power sales partner and the load aggregator are determined based on the power demand when maintaining the original set state of all loads, the dynamic limit carrying capacity of the distribution network and the maximum output of distributed photovoltaics. N,t and in P N,t is the lower limit of the power boundary in the demand response game determined by the power required to maintain the original set state of all loads, is the power boundary upper limit of the demand response game determined by the maximum power generation of distributed photovoltaics and the dynamic limit carrying capacity of the distribution network, P pv,max is the maximum output of distributed photovoltaics, is the maximum power value in the dynamic limit carrying capacity of the distribution network; if there is no photovoltaic feedback in the current period but there is photovoltaic feedback in the next period: the boundary condition P for the demand response game between the power sales partner and the load aggregator is determined based on the power demand when maintaining the original set state of all loads and the dynamic limit carrying capacity of the distribution network. N,t and in P N,t is the power boundary upper limit in the demand response game determined by the power required to maintain the original set state of all loads, is the lower limit of the power boundary in the demand response game determined by the dynamic limit carrying capacity of the distribution network, It is the minimum power value in the dynamic limit carrying capacity of the distribution network.

[0012] Preferably, the game model in the above steps of this embodiment includes a power sales partner game model and a load aggregator game model, and the electricity trading game based on the game model to determine the electricity demand response plan includes: taking the boundary conditions as constraints, conducting electricity trading games based on the power sales partner game model and the load aggregator game model to obtain an electricity demand response plan that achieves profit balance, and the electricity demand response plan includes electric energy trading volume and electricity price.

[0013] Preferably, the performing of distribution network reactive power and voltage optimization control in the above steps of this embodiment includes: performing distribution network reactive power and voltage optimization control on the distribution network's own adjustable devices using a distribution network reactive power and voltage optimization model.

[0014] According to the second aspect of the present invention, an embodiment of the present invention further provides an interactive operation device of source, grid, load and storage in a distributed photovoltaic system, the device comprising: a time period division unit, for dividing a natural day into multiple time periods; a first judgment unit, for judging whether photovoltaic feedback will occur in the normal power demand in the current time period; a second judgment unit, for judging whether photovoltaic feedback will occur in the next time period when the first judgment unit judges that there is no photovoltaic feedback for the normal power demand in the current time period; a demand response unit, for performing source, grid, load and storage interactive demand response to obtain the load power consumption strategy for the current time period when photovoltaic feedback occurs in the current time period, or when there is no photovoltaic feedback in the current time period but photovoltaic feedback occurs in the next time period; a reactive power optimization unit, for performing distribution network reactive voltage optimization control to obtain the load power consumption strategy for the current time period when there is no photovoltaic feedback in both the current time period and the next time period; a cyclic execution control unit, for controlling the judgment unit, demand response unit and reactive power optimization unit to sequentially obtain the load power consumption strategy for each time period to determine the load power consumption plan for the whole day.

[0015] Preferably, the second judgment unit in the above-mentioned device of this embodiment is specifically used for: when the first judgment unit determines that there is no photovoltaic feedback in the normal electricity demand of the current period, based on the normal electricity consumption of all loads, predicting the power required to maintain the original setting state of all loads in the next period, and judging whether photovoltaic feedback will occur in the next period.

[0016] Preferably, the demand response unit in the above-mentioned device of this embodiment includes: an extreme load assessment module, which is used to evaluate the dynamic extreme load capacity of the distribution network in the current time period to obtain an assessment result; a boundary condition determination module, which is used to determine the boundary conditions of the game model between the power sales partner and the load aggregator based on the assessment result; an electricity demand game module, which is used to conduct an electricity trading game according to the game model to determine the electricity demand response plan; and an electricity consumption strategy determination module, which is used to obtain the active and reactive power control plan based on the active and reactive power coordinated control model with the electricity demand response plan as the target, and then obtain the load electricity consumption strategy for the current time period.

[0017] Preferably, the limit load assessment module in the above-mentioned device of this embodiment includes: a flexible load assessment submodule, which is used to evaluate the adjustable potential of various flexible loads in the distribution network to obtain the adjustment range of the adjustable potential on the premise of meeting the basic needs of users; an energy storage assessment module, which is used to determine the adjustment range of energy storage in the distribution network; and a limit load assessment submodule, which is used to evaluate the maximum power value and minimum power value that each node of the distribution network can bear based on the adjustment range of the adjustable potential and the adjustment range of energy storage, with the actual structure, flow and operation safety of the distribution network as constraints.

[0018] Preferably, the boundary condition determination module in the above-mentioned device of this embodiment is specifically used to: if photovoltaic return occurs in the current period: determine the boundary condition P for the demand response game between the power sales partner and the load aggregator based on the power demand when maintaining the original set state of all loads, the dynamic limit carrying capacity of the distribution network and the maximum output of distributed photovoltaics N,t and in P N,t is the lower limit of the power boundary in the demand response game determined by the power required to maintain the original set state of all loads, is the power boundary upper limit of the demand response game determined by the maximum power generation of distributed photovoltaics and the dynamic limit carrying capacity of the distribution network, P pv,max is the maximum output of distributed photovoltaics, is the maximum power value in the dynamic limit carrying capacity of the distribution network; if there is no photovoltaic feedback in the current period but there is photovoltaic feedback in the next period: the boundary condition P for the demand response game between the power sales partner and the load aggregator is determined based on the power demand when maintaining the original set state of all loads and the dynamic limit carrying capacity of the distribution network. N,t and in Among them, P N,t is the power boundary upper limit in the demand response game determined by the power required to maintain the original set state of all loads, is the lower limit of the power boundary in the demand response game determined by the dynamic limit carrying capacity of the distribution network, It is the minimum power value in the dynamic limit carrying capacity of the distribution network.

[0019] Preferably, the game model in the above-mentioned device of this embodiment includes a power sales partner game model and a load aggregator game model, and the electricity demand game module is specifically used to: with the boundary conditions as constraints, conduct electricity trading games according to the power sales partner game model and the load aggregator game model, and obtain an electricity demand response plan that achieves balanced benefits, and the electricity demand response plan includes electricity trading volume and electricity price.

[0020] Preferably, the reactive power optimization unit in the above-mentioned device of this embodiment is specifically used for: utilizing the reactive power voltage optimization model of the distribution network to perform reactive power voltage optimization control on the adjustable equipment of the distribution network itself.

[0021] According to the third aspect of the present invention, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0022] According to a fourth aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0023] According to a fifth aspect of the present invention, an embodiment of the present invention further provides a computer program product, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0024] The interactive operation method and device of source, grid, load and storage proposed in the present invention realize the interactive optimized operation of source, grid, load and storage in distributed photovoltaic distribution system, which will promote the organic unity of distributed photovoltaic on-site power consumption demand response and active and reactive coordinated control of distribution system, significantly improve the distributed photovoltaic consumption rate, take into account the interests of distribution network side and user side, and enhance user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0026] Figure 1 This is a flow chart of a method for interactive operation of source, grid, load and storage in a distributed photovoltaic system provided by an embodiment of the present application;

[0027] Figure 2 Another embodiment of the present application provides a flow chart of a method for interactive operation of source, grid, load and storage in a distributed photovoltaic system;

[0028] Figure 3 This is a flow chart of evaluating the dynamic limit carrying capacity of a distribution network provided by an embodiment of the present application;

[0029] Figure 4 This embodiment of the present application provides a diagram of the operation structure of a power system in which power sales partners and load aggregators participate;

[0030] Figure 5 This is a schematic diagram of the structure of a distributed photovoltaic distribution network simulation model provided in an embodiment of the present application;

[0031] Figure 6 This is a schematic diagram of a typical scenario of distributed photovoltaic power generation tidal return before power demand response provided by an embodiment of the present application;

[0032] Figure 7 This is a schematic diagram of an electricity trading solution after an electricity demand response according to an embodiment of the present application;

[0033] Figure 8 This is a structural diagram of a source-grid-load-storage interactive operation device in a distributed photovoltaic system provided by an embodiment of the present application;

[0034] Figure 9 This is a schematic diagram of the structure of the demand response unit provided in an embodiment of the present application;

[0035] Figure 10 Schematic diagram of the structure of the ultimate load-bearing assessment module provided in an embodiment of the present application;

[0036] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0038] like Figure 1 FIG2 is a flow chart of a method for interactive operation of a source, grid, load and storage system in a distributed photovoltaic system provided by an embodiment of the present application. The method includes the following steps:

[0039] S1. Divide a natural day into multiple time periods.

[0040] In this embodiment, a natural day is first divided into multiple time periods. Preferably, the multiple time periods are equal in length, for example, a natural day is divided into 96 time periods, each 15 minutes long. Of course, it is also feasible to divide a natural day into unequal time periods, and this application does not limit this.

[0041] S2. Determine whether photovoltaic feedback will occur during normal electricity demand in the current period, or determine whether photovoltaic feedback will occur in the next period if photovoltaic feedback does not exist during normal electricity demand in the current period.

[0042] In this step, whether photovoltaic foldback will occur during the current period of normal electricity demand is determined. That is, whether the distributed photovoltaic output is greater than the normal electricity demand during the current period is determined. If so, photovoltaic foldback will occur; if less than or equal to the normal electricity demand, photovoltaic foldback will not occur. Similarly, if photovoltaic foldback does not exist during the current period, this step further determines whether photovoltaic foldback will occur during the next period of time. That is, based on the normal electricity demand of the next period, it is determined whether the distributed photovoltaic output will be greater than the normal electricity demand of the next period of time.

[0043] S3. If photovoltaic feedback occurs in the current period, or if there is no photovoltaic feedback in the current period but photovoltaic feedback occurs in the next period, a source-grid-load-storage interactive demand response is performed to obtain the load power consumption strategy for the current period.

[0044] In this embodiment, to obtain the load power utilization strategy for the current time period, it is necessary to determine whether photovoltaic feedback occurs in the current time period. If photovoltaic feedback does not occur in the current time period, it is necessary to further determine whether photovoltaic feedback will occur in the next time period. In this step, if photovoltaic feedback occurs in the current time period or in the next time period, a source-grid-load-storage interactive demand response is performed with the goal of promoting local consumption of distributed photovoltaic power and reducing power flow feedback to obtain the load power utilization strategy for the current time period.

[0045] S4. If there is no photovoltaic feedback in the current period and the next period, the distribution network reactive power voltage optimization control is performed to obtain the load power consumption strategy for the current period.

[0046] In this embodiment, if there is no photovoltaic feedback in the current period or the next period, there is no need to perform source-grid-load-storage interactive demand response operation, and only reactive voltage optimization control is performed on the distribution network side to obtain the load power consumption strategy for the current period.

[0047] S5. Repeat steps S2 to S4 with the next time period as the current time period to sequentially obtain the load power consumption strategy for each time period to determine the load power consumption plan for the entire day.

[0048] After the load power usage strategy for the current period is completed, the next period is taken as the current period, and steps S2-S4 are repeated to obtain the load power usage strategy for the next period, and so on to obtain the load power usage plan for the whole day. In this embodiment, it is preferred to start executing the above steps from the first period of the divided period. For example, when the period is divided into 96 periods, the execution starts from the period of 0:00-0:15. Of course, due to the continuity of time, this embodiment can also start from any period in the middle, as long as the load power usage strategy for all periods can be obtained, and then the load power usage plan for the whole day can be obtained. This application is not limited to this.

[0049] The interactive operation method of source, grid, load and storage proposed in the present invention realizes the interactive optimization operation of source, grid, load and storage in distributed photovoltaic distribution system, which will promote the organic unity of distributed photovoltaic on-site power consumption demand response and distribution system active and reactive coordinated control, significantly improve the distributed photovoltaic consumption rate, take into account the interests of the distribution network side and the user side, and enhance the user experience.

[0050] like Figure 2 FIG2 is a flow chart of a method for interactive operation of a source, grid, load and storage system in a distributed photovoltaic system according to another embodiment of the present application. The method includes the following steps:

[0051] Step S201: Divide a natural day into multiple time periods.

[0052] Step S202: Determine whether photovoltaic feedback will occur during normal electricity demand in the current period. If photovoltaic feedback occurs, proceed to step S205; if not, proceed to step S203.

[0053] Step S203: Based on the normal power consumption of all loads, the power required to maintain the original set state of all loads in the next period is predicted.

[0054] Step S204: Determine whether photovoltaic feedback will occur in the next period. If so, proceed to step S205; if not, proceed to step S208.

[0055] Step S205: Evaluate the dynamic limit carrying capacity of the distribution network in the current period to obtain an evaluation result.

[0056] This step mainly considers the potential for upward and downward regulation of various flexible loads and energy storage at each node in a distribution network with a high proportion of distributed photovoltaics at different times. It evaluates the maximum and minimum power values ​​that can be carried by each node in the distribution network, based on the actual structure, power flow, and operational safety of the distribution network.

[0057] Preferably, Figure 3 As shown, this step may include the following sub-steps:

[0058] Step S2051: Evaluate the adjustable potential of various flexible loads within the distribution network, while ensuring that basic user needs are met, to determine the adjustable range. Flexible loads are flexible loads that can actively participate in grid operation control and interact with the grid through energy exchange. These loads include air conditioners, water heaters, electric vehicles, and cold storage.

[0059] Step S2052: Determine the regulation range of energy storage in the distribution network.

[0060] Step S2053: Based on the adjustment range of the adjustable potential and the adjustment range of the energy storage, and with the actual structure, power flow, and operational safety of the distribution network as constraints, the maximum power value and the minimum power value that each node in the distribution network can bear are evaluated and obtained.

[0061] Preferably, the model for evaluating the upper limit of the dynamic ultimate carrying capacity of the distribution network can be the following model:

[0062]

[0063] In the above formula: P u,t is the upper limit power of the distribution network feeder carrying capacity at time t, P r,tis the total power of various flexible loads and energy storage in the distribution network substation r at time t, and R is the number of distribution network substations.

[0064]

[0065] In the above formula: are the upper and lower limits of the power adjustable potential of all flexible loads and energy storage in the distribution network area r at time t under the condition of meeting the power demand constraints.

[0066]

[0067] In the above formula: is the distributed photovoltaic power generation power in the distribution network area r at time t, P r,max 、P r,min The upper and lower limits of the allowable transmission power of the transformer in the distribution network area r.

[0068]

[0069] In the above formula: P i , Q i The active and reactive power injected into node i; U i 、U j is the voltage between node i and its connected node j; G ij 、B ij is the conductance and susceptance between node i and node j; θ ij is the voltage phase angle difference between node i and node j; N i is the number of branches connected to node i.

[0070] U min ≤U i ≤U max ;

[0071] In the above formula: U max and U min are the upper and lower limits of node voltage in the distribution network, respectively.

[0072] The method for solving the lower limit of the dynamic ultimate carrying capacity of the distribution network is the same as the upper limit solution method. Both use OpenDSS&Matlab "1:1" to build a distribution system simulation model containing various controllable resources, and use genetic algorithm to solve it.

[0073] Step S206: Determine the boundary conditions of the game model between the power sales partner and the load aggregator based on the evaluation result, and conduct an electricity trading game according to the game model to determine an electricity demand response plan.

[0074] Preferably, this step can be constrained by the boundary conditions, and an electricity trading game can be conducted according to the electricity sales partner game model and the load aggregator game model to obtain an electricity demand response plan that achieves balanced benefits. The electricity demand response plan includes electricity trading volume and electricity price.

[0075] In this embodiment, with the goal of promoting local consumption of distributed photovoltaic power, a power sales partner game model consisting of distributed photovoltaic power stations and distribution network operators, and a load aggregator game model that acts as an agent for power users to participate in power market demand response are established based on dynamic cooperative game. Figure 4 As shown, the electricity sales partners are now composed of distributed PV power plants and distribution network operators. Distributed PV power plants no longer sell electricity to distribution network operators at a fixed price, but can instead sell electricity directly to loads through cooperation with the distribution network. Load aggregators, on the other hand, have a certain degree of control over flexible loads such as electric vehicles, water heaters, and air conditioners. They leverage the adjustable potential of aggregated electricity consumption to negotiate with electricity sales partners on behalf of power users, reducing their electricity costs and generating demand response benefits.

[0076] Preferably, the boundary conditions of the game model between the power sales partner and the load aggregator determined by the evaluation results in this step may include:

[0077] If photovoltaic return occurs in the current period: the boundary conditions P for the demand response game between the power sales partner and the load aggregator are determined based on the power demand when maintaining the original set state of all loads, the dynamic limit carrying capacity of the distribution network and the maximum output of distributed photovoltaics. N,t and in P N,t is the lower limit of the power boundary in the demand response game determined by the power required to maintain the original set state of all loads, is the power boundary upper limit of the demand response game determined by the maximum power generation of distributed photovoltaics and the dynamic limit carrying capacity of the distribution network, P pv,max is the maximum output of distributed photovoltaics, It is the maximum power value in the dynamic limit carrying capacity of the distribution network.

[0078] If there is no photovoltaic feedback in the current period but there is photovoltaic feedback in the next period: the boundary condition P of the demand response game between the power sales partner and the load aggregator is determined based on the power demand when maintaining the original setting state of all loads and the dynamic limit carrying capacity of the distribution network. N,t and in P N,t is the power boundary upper limit in the demand response game determined by the power required to maintain the original set state of all loads, is the lower limit of the power boundary in the demand response game determined by the dynamic limit carrying capacity of the distribution network, It is the minimum power value in the dynamic limit carrying capacity of the distribution network.

[0079] Preferably, this step of conducting an electricity trading game according to the game model to determine an electricity demand response plan may include: taking the boundary conditions as constraints, conducting an electricity trading game according to the power sales partner game model and the load aggregator game model, and obtaining an electricity demand response plan that achieves a balanced profit, wherein the electricity demand response plan includes electricity trading volume and electricity price.

[0080] Preferably, in this embodiment, the power sales partner game model can be the following model:

[0081] maxU Psp,t =E in,t -C om,t -C de,t ;

[0082] E in,t =ρ sell,t P co2l,t Δt;

[0083] C om,t =α om (P co2l,t Δt) 2 +β om P co2l,t Δt+γ om ;

[0084] C de,t =ε(P co2l,t Δt-P pv,t Δt) 2 ;

[0085] In the above formula: U Psp,t is the individual rational pursuit of the electricity sales partner at time t, E in,t is the electricity sales revenue of the electricity sales partner, ρ sell,t P is the electricity sales price of the electricity sales partner, co2,t The power sold by the electricity sales partner, C om,t is the operation and maintenance cost of the electricity sales partner, α om , β om , γ om are the secondary, primary and constant coefficients of the operation and maintenance costs of the power sales partners respectively; C de,t The penalty for the deviation between the power sales of the power sales partner and the output of the distributed photovoltaic power station is P pv,t is the distributed photovoltaic output, ε is the penalty coefficient, and Δt is the duration of each period, for example, 15 minutes.

[0086] The load aggregator game model can be as follows:

[0087] maxU LA,t =a la (P la,t Δt) 2 +b la P la,t Δt+c la ;

[0088]

[0089]

[0090]

[0091] In the above formula: U LA,t is the individual rational pursuit of the load aggregator at time t, P la,t Purchase electricity for load aggregators, is the maximum power consumption of the load aggregator, is the minimum power consumption of the load aggregator, and is the electricity price boundary, φ is a constant, and is the demand response compensation parameter jointly determined by both parties in the game.

[0092] Step S207: Using the power demand response scheme as a target, an active and reactive power control scheme is obtained based on the active and reactive power coordinated control model, thereby obtaining a load power utilization strategy for the current period. Specifically, the electric energy trading volume in the power demand response scheme can be used as an upper-level guide, and then an active and reactive power control scheme is obtained based on the active and reactive power coordinated control model, thereby obtaining a load power utilization strategy for the current period.

[0093] Preferably, the active and reactive power coordinated control model may be the following model:

[0094]

[0095]

[0096]

[0097] In the above formula: P n,t is the power consumption of distribution network station area n, is the active power loss of equipment l in the distribution network, is the power of energy storage system i, is the unit operating cost of the energy storage system. The first goal is to minimize the deviation between the total power consumption in the distribution system and the power trading volume obtained in the power demand response game scheme; For goal two: Minimize active power loss in the distribution system; Goal 3: Minimize the operating cost of the energy storage system.

[0098]

[0099]

[0100] In the above formula: Q pv,t Reactive power output of distributed photovoltaic power station, is the inverter capacity of the distributed photovoltaic power station, is the minimum and maximum reactive power of the distributed photovoltaic power station;

[0101]

[0102]

[0103]

[0104]

[0105] In the above formula: P ess,t is the energy storage system power, is the maximum charging power of the energy storage system, is the maximum discharge power of the energy storage system, SOC ess,t is the state of charge of the energy storage system, Q is the minimum and maximum state of charge allowed by the energy storage system. ess,t is the reactive power output by the energy storage system, is the inverter capacity of the energy storage system, is the minimum and maximum reactive power of the energy storage system;

[0106]

[0107] In the above formula: P i , Q i The active and reactive power injected into node i; U i 、U j is the voltage between node i and its connected node j; G ij 、B ij is the conductance and susceptance between node i and node j; θ ij is the voltage phase angle difference between node i and node j; nline is the number of branches connected to node i;

[0108] 0.93U N ≤U i ≤1.07U N ;

[0109] Ui is the node voltage of the distribution network, U N is the rated voltage of the distribution network node.

[0110] Step S208: using the distribution network reactive power and voltage optimization model to perform distribution network reactive power and voltage optimization control on the adjustable devices of the distribution network itself to obtain the load power consumption strategy for the current period.

[0111] Preferably, the following distribution network reactive power voltage optimization model can be used to perform distribution network reactive power voltage optimization control:

[0112]

[0113] In the above formula: The first goal is to minimize the active power loss in the power distribution system; The second goal is to minimize the deviation between the voltage of each node in the distribution system and the rated voltage. i is the per-unit value of the node voltage, is the active power loss of equipment l in the distribution network.

[0114] Step S209: taking the next time period as the current time period, repeatedly executing steps S202 to S208 to sequentially obtain the load power usage strategy of each time period to determine the load power usage plan for the entire day.

[0115] The interactive operation method of source, grid, load and storage proposed in this invention realizes the interactive optimization operation of source, grid, load and storage in distributed photovoltaic distribution system, which will promote the organic unification of distributed photovoltaic on-site power consumption demand response and the coordinated control of active and reactive power of distribution system, significantly improve the distributed photovoltaic consumption rate, and take into account the interests of both distribution network and user side, thus improving the user experience. In addition, the concept of dynamic limit carrying capacity of distribution network proposed in this invention gives the demand response resource spatiotemporal characteristics, and provides support for source, grid, load and storage interactive demand response from the two aspects of flexible load adjustable potential and distribution network carrying capacity, which is consistent with actual engineering.

[0116] The present invention will be further described below in conjunction with a specific embodiment:

[0117] To verify the effectiveness of the above method, Figure 5As shown in the figure, a simulation model of a high-proportion distributed photovoltaic distribution network was built in OpenDSS & Matlab. The model includes 4 10kV distribution lines and 95 substations, of which 58 substations have distributed photovoltaics. The photovoltaic installed capacity reaches 8624kW, the photovoltaic penetration rate reaches 140%, and the power factor of the photovoltaic system is adjustable in the range of -0.9 to 0.9; the distribution network includes 9 energy storage systems, and the installed power of the energy storage system is set to 15% of the photovoltaic installed power. The power factor of the energy storage system is adjustable in the range of -0.9 to 0.9; 4 5*15kvar capacitor reactive compensator groups and 4 300kvar continuous reactive compensators (SVCs). 6500 air conditioners, each with a maximum cooling power of 0.85kW and an average indoor temperature adjustable range of 20℃ to 28℃; 1077 water heaters, each with a maximum heating power of 2.1kW and an adjustable temperature range of T N Set the temperature for the water heater; 10 electric vehicle charging stations, the original charging power of the electric vehicles is constant at 6kW, and the charging power of the electric vehicles can be adjusted after participating in demand response, and the total charging amount needs to be consistent with the original charging amount; a large cold storage with an adjustable range set at -18℃ to -12℃.

[0118] For a typical day with distributed photovoltaic output backflow, simulations were conducted to verify the effectiveness of the method described in the present invention. Figure 6 FIG. 1 is a schematic diagram of a typical scenario of distributed photovoltaic power generation current return before power demand response provided by this embodiment, as shown in FIG. Figure 7 The figure shows a schematic diagram of the power transaction scheme after the power demand response provided by this embodiment. Under the original operation of the distribution network, the distribution network returns power to the 110kV voltage level in 11 periods, with the maximum return power reaching 1766kW and the total return power reaching 2654kWh. Figure 6 It can be seen that due to the photovoltaic feedback phenomenon at 44, the power demand response is activated at 43, and the load aggregator reduces the total power consumption, which increases the normal power demand at the next moment to a certain extent. After the power demand response at 43, photovoltaic feedback still occurs at 44, and the power demand response is activated again at 44 to adjust the flexible load state and eliminate the photovoltaic feedback phenomenon. Figure 6 It can be seen that the method of the present invention eliminates all photovoltaic feedback situations, ensures the local consumption of renewable energy, and load aggregators can obtain demand response benefits, and power users can also reduce electricity costs.

[0119] like Figure 8The figure shows a structural schematic diagram of an interactive operation device of source, grid, load and storage in a distributed photovoltaic system provided by an embodiment of the present application, the device including: a time period division unit 810, a first judgment unit 820, a second judgment unit 830, a demand response unit 840, a reactive power optimization unit 850 and a loop control unit 860, wherein the first judgment unit 820 is respectively connected to the time period division unit 810, the second judgment unit 830 and the demand response unit 840, the second judgment unit 830 is also respectively connected to the demand response unit 840 and the reactive power optimization unit 850, and the loop control unit 860 is also respectively connected to the demand response unit 840 and the reactive power optimization unit 850.

[0120] The time period division unit 810 is used to divide a natural day into multiple time periods.

[0121] The first judgment unit 820 is used to judge whether photovoltaic feedback will occur during normal electricity demand in the current period.

[0122] The second judgment unit 830 is configured to judge whether photovoltaic feedback will occur in the next time period when the first judgment unit determines that there is no photovoltaic feedback in the current time period due to normal electricity demand.

[0123] The demand response unit 840 is used to perform source-grid-load-storage interactive demand response to obtain the load power consumption strategy for the current period when photovoltaic feedback occurs in the current period, or when there is no photovoltaic feedback in the current period but photovoltaic feedback occurs in the next period.

[0124] The reactive power optimization unit 850 is used to perform reactive power and voltage optimization control of the distribution network when there is no photovoltaic feedback in the current period and the next period, thereby obtaining the load power consumption strategy for the current period.

[0125] The cyclic execution control unit 860 is used to control the judgment unit, the demand response unit and the reactive power optimization unit to sequentially obtain the load power consumption strategy of each time period to determine the load power consumption plan for the whole day.

[0126] Preferably, the second judgment unit 830 can be specifically used for: when the first judgment unit 820 determines that there is no photovoltaic feedback in the normal electricity demand of the current period, based on the normal electricity consumption of all loads, predicting the power required to maintain the original setting state of all loads in the next period, and judging whether photovoltaic feedback will occur in the next period.

[0127] Preferably, Figure 9 As shown, the demand response unit 840 may include: a limit load assessment module 841, a boundary condition determination module 842, an electricity demand game module 843 and an electricity use strategy determination module 844, which are sequentially connected.

[0128] The ultimate load evaluation module 841 is used to evaluate the dynamic ultimate load capacity of the distribution network in the current period to obtain an evaluation result.

[0129] The boundary condition determination module 842 is used to determine the boundary conditions of the game model between the power sales partner and the load aggregator based on the evaluation results.

[0130] The power demand game module 843 is used to perform power transaction game according to the game model to determine a power demand response plan.

[0131] The power consumption strategy determination module 844 is used to obtain the active power and reactive power control scheme based on the active power and reactive power coordinated control model with the power demand response scheme as the target, and then obtain the load power consumption strategy for the current period.

[0132] Preferably, Figure 10 As shown, the extreme load assessment module 841 may include: a flexible load assessment submodule 8411, an energy storage assessment module 8412 and an extreme load assessment submodule 8413, wherein the extreme load assessment submodule 8413 is connected to the flexible load assessment submodule 8411 and the energy storage assessment module 8412 respectively.

[0133] The flexible load evaluation submodule 8411 is used to evaluate the adjustable potential of various flexible loads in the distribution network on the premise of meeting the basic needs of users to obtain the adjustment range of the adjustable potential.

[0134] The energy storage evaluation module 8412 is used to determine the regulation range of energy storage in the distribution network;

[0135] The limit load assessment submodule 8413 is used to assess the maximum and minimum power values ​​that each node in the distribution network can carry based on the adjustment range of the adjustable potential and the adjustment range of the energy storage, with the actual structure, power flow, and operational safety of the distribution network as constraints.

[0136] The preferred boundary condition determination module 842 can be specifically used to: if photovoltaic return occurs in the current period: determine the boundary condition P for the demand response game between the power sales partner and the load aggregator based on the power demand when maintaining the original set state of all loads, the dynamic limit carrying capacity of the distribution network and the maximum output of distributed photovoltaics N,t and in P N,t is the lower limit of the power boundary in the demand response game determined by the power required to maintain the original set state of all loads, is the power boundary upper limit of the demand response game determined by the maximum power generation of distributed photovoltaics and the dynamic limit carrying capacity of the distribution network, P pv,max is the maximum output of distributed photovoltaics, is the maximum power value in the dynamic limit carrying capacity of the distribution network. If there is no photovoltaic feedback in the current period but there is photovoltaic feedback in the next period: the boundary condition P for the demand response game between the power sales partner and the load aggregator is determined based on the power demand when maintaining the original set state of all loads and the dynamic limit carrying capacity of the distribution network. N,t and in Among them, P N,t is the power boundary upper limit in the demand response game determined by the power required to maintain the original set state of all loads, is the lower limit of the power boundary in the demand response game determined by the dynamic limit carrying capacity of the distribution network, It is the minimum power value in the dynamic limit carrying capacity of the distribution network.

[0137] Preferably, the above-mentioned game model includes a power sales partner game model and a load aggregator game model, and the electricity demand game module 843 is specifically used to: with the boundary conditions as constraints, conduct electricity trading games according to the power sales partner game model and the load aggregator game model, and obtain an electricity demand response plan that achieves balanced benefits, and the electricity demand response plan includes electricity trading volume and electricity price.

[0138] Preferably, the reactive power optimization unit 850 can be specifically used to: utilize the reactive power and voltage optimization model of the distribution network to perform reactive power and voltage optimization control on the adjustable devices of the distribution network itself.

[0139] The detailed description of the above units can be found in the response description of the aforementioned method embodiment, which will not be repeated here.

[0140] From the above, it can be seen that the interactive operation device of source, grid, load and storage proposed in the present invention realizes the interactive optimization operation of source, grid, load and storage in distributed photovoltaic distribution system, which will promote the organic unity of distributed photovoltaic on-site power consumption demand response and active and reactive power coordinated control of distribution system, significantly improve the distributed photovoltaic consumption rate, and take into account the interests of both distribution network side and user side, thus improving user experience. In addition, the concept of dynamic limit carrying capacity of distribution network proposed in the present invention gives the demand response resource spatiotemporal characteristics, and provides support for source, grid, load and storage interactive demand response from the two aspects of flexible load adjustable potential and distribution network carrying capacity, which is consistent with actual engineering.

[0141] Figure 11 is a schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 11The electronic device shown is a general-purpose data processing device comprising a general-purpose computer hardware structure, including at least a processor 801 and a memory 802. Processor 801 and memory 802 are connected via a bus 803. Memory 802 is adapted to store one or more instructions or programs executable by processor 801. These one or more instructions or programs are executed by processor 801 to implement the steps of the aforementioned source-grid-load-storage interactive operation method.

[0142] The above-mentioned processor 801 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 801 executes the commands stored in the memory 802, thereby executing the method flow of the embodiment of the present invention as described above to realize the processing of data and the control of other devices. The bus 803 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 804 and the display device and the input / output (I / O) device 805. The input / output (I / O) device 805 can be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer and other devices known in the art. Typically, the input / output (I / O) device 805 is connected to the system through the input / output (I / O) controller 806.

[0143] The memory 802 may store software components such as an operating system, a communication module, an interaction module, and an application program. Each of the modules and applications described above corresponds to a set of executable program instructions that implement one or more functions and methods described in the embodiments of the invention.

[0144] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned source-grid-load-storage interactive operation method.

[0145] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned source-grid-load-storage interactive operation method.

[0146] In summary, the interactive operation device of source, grid, load and storage proposed in the present invention realizes the interactive optimization operation of source, grid, load and storage in distributed photovoltaic distribution system, promotes the organic unity of distributed photovoltaic on-site power consumption demand response and active and reactive power coordinated control of distribution system, significantly improves the distributed photovoltaic consumption rate, takes into account the interests of both distribution network side and user side, and enhances user experience. In addition, the concept of dynamic limit carrying capacity of distribution network proposed in the present invention gives the demand response resource spatiotemporal characteristics, and provides support for source, grid, load and storage interactive demand response from the two aspects of flexible load adjustable potential and distribution network carrying capacity, which is consistent with actual engineering.

[0147] Preferred embodiments of the present invention have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are apparent from this detailed description, and thus the appended claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. Furthermore, since numerous modifications and changes will readily occur to those skilled in the art, the embodiments of the present invention are not intended to be limited to the precise construction and operation illustrated and described, but are intended to cover all suitable modifications and equivalents that fall within the scope thereof.

[0148] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0152] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for interactive operation of source, grid, load and storage in a distributed photovoltaic system, characterized in that: The method comprises: S1. Divide a natural day into multiple time periods; S2. Determine whether photovoltaic feedback will occur during normal electricity demand in the current period, or determine whether photovoltaic feedback will occur in the next period if photovoltaic feedback does not exist during normal electricity demand in the current period; S3. If photovoltaic feedback occurs in the current period, or if there is no photovoltaic feedback in the current period but photovoltaic feedback occurs in the next period, a source-grid-load-storage interactive demand response is performed to obtain the load power consumption strategy for the current period; S4. If there is no PV feedback in the current period and the next period, the distribution network reactive power voltage optimization control is performed to obtain the load power consumption strategy for the current period; S5. Repeat steps S2 to S4 with the next time period as the current time period to obtain the load power consumption strategy for each time period in turn to determine the load power consumption plan for the whole day; The method of performing source-grid-load-storage interactive demand response to obtain a load power consumption strategy for the current period includes: An evaluation result is obtained by evaluating the dynamic limit carrying capacity of the distribution network in the current period, and boundary conditions of a game model between power sales partners and load aggregators are determined based on the evaluation result. An electricity trading game is conducted according to the game model to determine an electricity demand response plan. With the electricity demand response plan as the target, an active and reactive power control plan is obtained based on an active and reactive power coordinated control model, thereby obtaining a load power utilization strategy for the current period. The evaluation results obtained by evaluating the dynamic limit carrying capacity of the distribution network in the current period include: Under the premise of meeting the basic needs of users, the adjustable potential of various flexible loads in the distribution network is evaluated to obtain the adjustable range of the adjustable potential; Determine the regulation range of energy storage within the distribution network; According to the adjustment range of the adjustable potential and the adjustment range of the energy storage, and with the actual structure, power flow and operational safety of the distribution network as constraints, the maximum power value and the minimum power value that each node of the distribution network can carry are evaluated and obtained.

2. The interactive operation method of source, grid, load and storage in a distributed photovoltaic system according to claim 1, characterized in that: When there is no photovoltaic feedback in the current period due to normal electricity demand, the following procedures are used to determine whether photovoltaic feedback will occur in the next period: When there is no photovoltaic feedback in the current period due to normal electricity demand, the power required to maintain the original set state of all loads in the next period is predicted based on the normal electricity consumption of all loads, and it is determined whether photovoltaic feedback will occur in the next period.

3. The interactive operation method of source, grid, load and storage in a distributed photovoltaic system according to claim 1, characterized in that: The boundary conditions of the game model between the power sales partner and the load aggregator determined based on the evaluation results include: If photovoltaic return occurs in the current period: the boundary conditions P for the demand response game between the power sales partner and the load aggregator are determined based on the power demand when maintaining the original set state of all loads, the dynamic limit carrying capacity of the distribution network and the maximum output of distributed photovoltaics. N,t and in P N,t is the lower limit of the power boundary in the demand response game determined by the power required to maintain the original set state of all loads, is the power boundary upper limit of the demand response game determined by the maximum power generation of distributed photovoltaics and the dynamic limit carrying capacity of the distribution network, P pv,max is the maximum output of distributed photovoltaics, is the maximum power value in the dynamic limit carrying capacity of the distribution network; If there is no photovoltaic feedback in the current period but there is photovoltaic feedback in the next period: the boundary conditions p for the demand response game between the power sales partner and the load aggregator are determined based on the power demand when maintaining the original setting state of all loads and the dynamic limit carrying capacity of the distribution network. N,t and in P N,t is the power boundary upper limit in the demand response game determined by the power required to maintain the original set state of all loads, is the lower limit of the power boundary in the demand response game determined by the dynamic limit carrying capacity of the distribution network, It is the minimum power value in the dynamic limit carrying capacity of the distribution network.

4. The interactive operation method of source, grid, load and storage in a distributed photovoltaic system according to claim 3, characterized in that: The game model includes a power sales partner game model and a load aggregator game model. The power transaction game based on the game model to determine the power demand response plan includes: With the boundary conditions as constraints, an electricity trading game is conducted according to the electricity sales partner game model and the load aggregator game model to obtain an electricity demand response plan that achieves balanced benefits. The electricity demand response plan includes electricity trading volume and electricity price.

5. The interactive operation method of source, grid, load and storage in a distributed photovoltaic system according to claim 1, characterized in that: The performing distribution network reactive power voltage optimization control includes: utilizing a distribution network reactive power voltage optimization model to perform distribution network reactive power voltage optimization control on adjustable equipment of the distribution network itself.

6. A source-grid-load-storage interactive operation device in a distributed photovoltaic system, characterized in that: The device comprises: The time period division unit is used to divide a natural day into multiple time periods; The first judgment unit is used to judge whether photovoltaic feedback will occur during the normal power demand in the current period; a second judgment unit, configured to judge whether photovoltaic feedback will occur in the next period when the first judgment unit determines that there is no photovoltaic feedback in the normal electricity demand in the current period; The demand response unit is used to perform source-grid-load-storage interactive demand response to obtain the load power consumption strategy for the current period when photovoltaic feedback occurs in the current period, or when there is no photovoltaic feedback in the current period but photovoltaic feedback occurs in the next period; The reactive power optimization unit is used to optimize the reactive power and voltage of the distribution network when there is no photovoltaic feedback in the current period and the next period, thereby obtaining the load power consumption strategy for the current period; A cyclic execution control unit, configured to control the judgment unit, the demand response unit, and the reactive power optimization unit to sequentially obtain the load power consumption strategy for each time period to determine the load power consumption plan for the entire day; The demand response unit includes: The ultimate load evaluation module is used to evaluate the dynamic ultimate load capacity of the distribution network in the current period and obtain the evaluation result; A boundary condition determination module, configured to determine the boundary conditions of the game model between the power sales partner and the load aggregator based on the evaluation results; An electricity demand game module, configured to conduct an electricity trading game according to the game model to determine an electricity demand response plan; A power consumption strategy determination module is used to obtain an active power and reactive power control scheme based on an active power and reactive power coordinated control model with the power demand response scheme as the target, and then obtain a load power consumption strategy for the current period; The ultimate load assessment module includes: The flexible load evaluation submodule is used to evaluate the adjustable potential of various flexible loads in the distribution network and obtain the adjustment range of the adjustable potential while meeting the basic needs of users; Energy storage assessment module, used to determine the regulation range of energy storage in the distribution network; The limit load assessment submodule is used to assess the maximum and minimum power values ​​that can be carried by each node in the distribution network based on the adjustment range of the adjustable potential and the adjustment range of the energy storage, with the actual structure, flow and operation safety of the distribution network as constraints.

7. The interactive operation device of source, grid, load and storage in a distributed photovoltaic system according to claim 6, characterized in that: The second judgment unit is specifically used to: when the first judgment unit determines that there is no photovoltaic feedback in the normal power demand in the current period, based on the normal power consumption of all loads, predict the power required to maintain the original set state of all loads in the next period, and determine whether photovoltaic feedback will occur in the next period.

8. The interactive operation device for source, grid, load and storage in a distributed photovoltaic system according to claim 6, characterized in that: The boundary condition determination module is specifically used for: If photovoltaic return occurs in the current period: the boundary conditions P for the demand response game between the power sales partner and the load aggregator are determined based on the power demand when maintaining the original set state of all loads, the dynamic limit carrying capacity of the distribution network and the maximum output of distributed photovoltaics. N,t and in p N,t is the lower limit of the power boundary in the demand response game determined by the power required to maintain the original set state of all loads, is the power boundary upper limit of the demand response game determined by the maximum power generation of distributed photovoltaics and the dynamic limit carrying capacity of the distribution network, p pv,max is the maximum output of distributed photovoltaics, is the maximum power value in the dynamic limit carrying capacity of the distribution network; If there is no photovoltaic feedback in the current period but there is photovoltaic feedback in the next period: the boundary condition P of the demand response game between the power sales partner and the load aggregator is determined based on the power demand when maintaining the original setting state of all loads and the dynamic limit carrying capacity of the distribution network. N,t and in Among them, P N,t is the power boundary upper limit in the demand response game determined by the power required to maintain the original set state of all loads, is the lower limit of the power boundary in the demand response game determined by the dynamic limit carrying capacity of the distribution network, It is the minimum power value in the dynamic limit carrying capacity of the distribution network.

9. The interactive operation device of source, grid, load and storage in a distributed photovoltaic system according to claim 8, characterized in that: The game model includes a power sales partner game model and a load aggregator game model. The power demand game module is specifically used to: With the boundary conditions as constraints, an electricity trading game is conducted according to the electricity sales partner game model and the load aggregator game model to obtain an electricity demand response plan that achieves balanced benefits. The electricity demand response plan includes electricity trading volume and electricity price.

10. The interactive operation device of source, grid, load and storage in a distributed photovoltaic system according to claim 6, characterized in that: The reactive power optimization unit is specifically used to: utilize the reactive power voltage optimization model of the distribution network to perform reactive power voltage optimization control on the adjustable equipment of the distribution network itself.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

13. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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