Virtual power plant and power distribution network master-slave game optimization method and device

Abstract

The invention relates to the technical field of power distribution network and virtual power plant joint optimization, and particularly provides a virtual power plant and power distribution network master-slave game optimization method and device, and the method comprises the steps: solving an upper layer model corresponding to a power distribution network, and obtaining an upper layer power distribution network optimization variable; substituting the upper-layer power distribution network optimization variable into a lower-layer model corresponding to the power distribution network and solving to obtain a lower-layer virtual power plant optimization variable; determining the distributed power of various electric vehicles under the charging pile in the intra-day stage; performing scheduling optimization on the power distribution network and the virtual power plant by using the upper-layer power distribution network optimization variable, the lower-layer virtual power plant optimization variable and the power distributed by various electric vehicles under the charging pile in the intraday stage; according to the technical scheme provided by the invention, active-reactive coupled distributed resources in the system are fully utilized through the constructed upper-layer model and the constructed lower-layer model, and active-reactive joint optimization scheduling is carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of joint optimization of distribution networks and virtual power plants, and in particular to a master-slave game optimization method and device for virtual power plants and distribution networks. Background Art

[0002] The distribution network is a crucial component of the power system. In the context of building a new power system dominated by renewable energy, distribution networks face the challenge of integrating massive amounts of distributed renewable energy and flexible loads. The random nature of these distributed energy sources exacerbates fluctuations in distribution network flows and increasingly highlights voltage overshoots, impacting power system stability. Therefore, ensuring the stability of distribution network load fluctuations and voltage excursions is crucial.

[0003] With the continuous improvement of various flexible load resource aggregation technologies, it has brought assistance to the optimization of distribution networks. Among them, virtual power plants play an important role in improving the operating efficiency of distribution networks and enhancing the management capabilities of distributed power generation and flexible load resources. They can effectively alleviate the problem of insufficient flexibility adjustment capabilities of power systems. However, the energy and load resources of power systems are complex, and the optimization objectives are diverse. It is difficult to comprehensively consider various factors and indicators, which is the difficulty in optimizing distribution networks and virtual power plants.

[0004] Currently, joint optimization of distribution networks and virtual power plants (VPPs) mostly employs a two-tier collaborative optimization model. However, existing hierarchical optimization mechanisms for distribution networks and VPPs containing a large number of distributed resources remain incomplete. During hierarchical optimization and scheduling of each component, the regulation capacity of distributed resources is not fully utilized. These optimizations often only consider active or reactive power, with few considering both active and reactive scheduling simultaneously, limiting the regulation capacity of the entire system. Furthermore, during two-tier optimization of distribution networks and VPPs, the satisfaction and compensation received by flexible loads participating in ancillary services are rarely fully considered. This is especially true when flexible loads, such as electric vehicles, are included in the power system. Improper handling can easily lead to a decrease in their willingness to participate in scheduling and excessive resource loss for certain flexible loads. Furthermore, the interaction and information exchange between the distribution network and VPP are difficult to fully analyze and implement. Summary of the Invention

[0005] In order to overcome the above-mentioned defects, the present invention proposes a master-slave game optimization method and device for a virtual power plant and a distribution network.

[0006] In a first aspect, a master-slave game optimization method for a virtual power plant and a distribution network is provided, the master-slave game optimization method for a virtual power plant and a distribution network comprising:

[0007] Solve the upper-level model corresponding to the distribution network and obtain the upper-level distribution network optimization variables;

[0008] Substituting the upper-layer distribution network optimization variables into the lower-layer model corresponding to the distribution network and solving them to obtain the lower-layer virtual power plant optimization variables;

[0009] Determine the power allocated to each type of electric vehicle under the charging pile during the intraday phase based on the active power on the charging pile side during the day-ahead phase and the reactive power on the charging pile side during the intraday phase in the lower-layer virtual power plant optimization variables;

[0010] A distribution network scheduling optimization scheme is constructed using the upper-layer distribution network optimization variables, and a virtual power plant scheduling optimization scheme is constructed using the lower-layer virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles during the daily stage. The distribution network scheduling optimization scheme and the virtual power plant scheduling optimization scheme are respectively used to optimize the scheduling of the distribution network and the virtual power plant;

[0011] Among them, the upper-level distribution network optimization variables include: peak-shaving compensation coefficient and voltage regulation compensation coefficient, and the lower-level virtual power plant optimization variables include: gas turbine output in the day-ahead stage, power purchase from the power grid in the day-ahead stage, active power and reactive power on the charging pile side in the day-ahead stage, gas turbine output in the intraday stage, and active power and reactive power on the charging pile side in the intraday stage.

[0012] Preferably, the upper-level model corresponding to the distribution network includes: an upper-level objective function and its corresponding upper-level constraint conditions.

[0013] Furthermore, the upper layer objective function is as follows:

[0014]

[0015] In the above formula, ω1, ω2 and ω3 are the target weights of peak load regulation and voltage regulation requirements during the actual operation of the distribution network, respectively. pvd With F fd is the peak-to-valley difference and fluctuation variance of the overall net load of the distribution network, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, N is the set consisting of all nodes in the distribution network, V i 、 are the voltage at node i, voltage upper limit, voltage lower limit and voltage rated value respectively.

[0016] Furthermore, the upper-level constraints are as follows:

[0017]

[0018]

[0019] V i min ≤V i ≤V i max

[0020]

[0021] In the above formula, C pk with C rv are the peak load compensation coefficient and voltage regulation compensation coefficient provided by the upper distribution network of the game for the virtual power plant, α and β are the compensation standards for the peak-to-valley difference and fluctuation degree of the net load of the distribution network, respectively. i is the weight coefficient of node i, k is the adjustment coefficient, and are the ideal values ​​of the peak-to-valley difference and fluctuation variance of the net load of the distribution network, and are the maximum and minimum peak-to-valley differences of the net load of the distribution network, and are the maximum and minimum values ​​of the net load fluctuation variance of the distribution network, is the ideal voltage of node i, is the set of branch head nodes with j as the terminal node, ψ(j) is the set of branch terminal nodes with j as the terminal node, r ij and x ij is the resistance and reactance of the branch between node i and node j, P ij,t and Q ij,t The active and reactive power flowing from node i to node j at time t, V i,t is the voltage of node i at time t, V j,t is the voltage of node j at time t, P j,t and Q j,t are the injected active and reactive power of node j at time t, P jk,t and Q jk,t are the active and reactive power flowing from node j to node k at time t, and are the active power outputs of the photovoltaic power plant and wind farm at node j at time t, and are the active and reactive power outputs of the gas turbine at node j at time t, and are the active and reactive power of the electric vehicle charging station at node j at time t, and are the active and reactive power of the load at node j at time t, P ij and Q ij are the active power and reactive power flowing through the branch between node i and node j, I ij is the current amplitude flowing through the branch between node i and node j, and are the maximum and minimum current amplitudes flowing through the branch between node i and node j, respectively.

[0022] Preferably, the lower layer model corresponding to the distribution network includes: a first-stage objective function and its corresponding first-stage constraint conditions and a second-stage objective function and its corresponding second-stage constraint conditions.

[0023] Furthermore, the objective function of the first stage is as follows:

[0024]

[0025] In the above formula, C0 is the objective function value of the first stage, N T With N G are the dispatch period and the number of gas turbines aggregated in the virtual power plant, and are the electricity price and electricity quantity purchased by the virtual power plant from the distribution network during period t, and are the charging electricity price and rechargeable power provided by the charging piles in the virtual power plant to each electric vehicle user, ρ g and is the fuel cost function and the active power output of the g-th gas turbine during the day-ahead period t, and are the upward reserve cost function of the g-th gas turbine and the upward reserve capacity provided during period t, and are the downward reserve cost function of the g-th gas turbine and the downward reserve capacity provided during period t, ΔF pvd and ΔF var are the changes in the peak-to-valley difference and fluctuation variance of the net load of the distribution network compared with the previous dispatching cycle, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, C pk It is used to calculate the peak load compensation coefficient provided by the upper distribution network to the virtual power plant.

[0026] Furthermore, the constraints of the first stage are as follows:

[0027]

[0028] In the above formula, and are the active power and reactive power of the cth charging pile in the virtual power plant during the t period, is the maximum power capacity of the cth charging pile, and is the charging and discharging active power of the v-th type of electric vehicle user at the c-th charging pile in the day-ahead period, and is the charging and discharging efficiency of the vth type of electric vehicle user at the cth charging pile, N c,vis the total number of electric vehicle user types under the c-th charging pile, λ c,v,t is a Boolean variable, when λ c,v,t = 1, the electric vehicle user of type v at the cth charging pile in period t is in the charging state. c,v,t = 0, the electric vehicle user of type v at the cth charging pile in period t is in the discharging state. represents the maximum charging and discharging power of the vth type of electric vehicle user at the cth charging pile, is the reactive power output of the g-th gas turbine during the day-ahead period t, represents the rated capacity of the g-th gas turbine, and are the maximum values ​​of upward and downward active reserves that can be provided by unit g when it transitions from the day-ahead phase to the intraday phase, and are the minimum values ​​of upward and downward active reserve, respectively, and are the upward and downward reactive power reserves that the gas turbine unit g can provide during period t, and are the active power output of photovoltaic power and wind power in period t during the day-ahead period, and are the available predicted values ​​of active power of photovoltaic and wind power in period t during the day-ahead period.

[0029] Furthermore, the objective function of the second stage is as follows:

[0030]

[0031] In the above formula, C S is the objective function value of the second stage, C rv is the voltage regulation compensation coefficient provided by the upper distribution network to the virtual power plant, C pv with C wt are the penalty cost coefficients for curtailing solar power and wind power, N pv With N w are photovoltaic power plant collection and wind power plant collection respectively. and are the absorption capacity of photovoltaic power field pv and wind power field w at time t in the day, and are the available predicted values ​​of active power of photovoltaic power plant pv and wind power plant w at time t in the day, and are the cost functions for calling the upward and downward reactive reserve of gas turbine unit g during the intraday phase, and are the upward and downward reactive reserve amounts called by unit g at time t in the day phase, is the reduction value of the integrated node voltage deviation of the distribution network system compared with the previous scheduling period, and U is the uncertainty set as an additional constraint condition.

[0032] Furthermore, the uncertain set is as follows:

[0033]

[0034]

[0035] In the above formula, and are the available power of photovoltaic and wind power in the second stage t period, and is a 0-1 integer variable, used to indicate whether photovoltaic power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of photovoltaic at time t, u t,pv with u pv is the uncertainty limit and total uncertainty limit of photovoltaic at time t, and is a 0-1 integer variable, used to indicate whether the wind farm power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of wind power at time t, u t,w with u w are the uncertainty limit and total uncertainty limit of wind power at time t, is the reactive power load of node j in the virtual power plant during period t after considering the uncertainty, is the reactive power load of node j in period t during the day ahead, represents the maximum fluctuation deviation allowed for reactive load power at node j during period t, and is a 0-1 integer variable, which indicates that the reactive load of node j at time t fluctuates upward and downward when it is 0 or 1, respectively. ql is the uncertainty limit of reactive load.

[0036] Furthermore, the constraints of the second stage are as follows:

[0037]

[0038]

[0039] In the above formula, It is the reactive power output of unit g at time t after reactive power reserve is called upon during the day.

[0040] Furthermore, the power allocated to each type of electric vehicle under the charging pile during the day is as follows:

[0041]

[0042] In the above formula, is the power allocated to the vth type of electric vehicle at charging pile c at time t, is the power adjustment amount allocated to the vth type of electric vehicle at the charging pile c at time t, is the chargeable and dischargeable power of the electric vehicle user v at time t. The power adjustment amount allocated to the v-th type of electric vehicle under the charging pile c at time t is as follows:

[0043]

[0044] In the above formula, is the weight of the vth type of electric vehicle under charging pile c, N c,v is the total number of electric vehicles of type v under charging pile c, is the change in active power of the electric vehicle during the intra-day period. The change in active power of the electric vehicle during the intra-day period is as follows:

[0045]

[0046] In the above formula, is the reactive power output of charging pile c during period t within the day.

[0047] Furthermore, the weight of the vth type of electric vehicle under the charging pile c is as follows:

[0048]

[0049] In the above formula, is the weight of the jth indicator of the vth type of electric vehicle under charging pile c, m is the total number of indicators, Y j is the evaluation coefficient of the j-th indicator, s is the proportional coefficient, is the current battery health status of the v-th type of electric vehicle, is the battery health status hazard value, and the weight of the jth indicator of the vth type of electric vehicle under the charging pile c is as follows:

[0050]

[0051] In the above formula, is the information entropy of the v-th type of electric vehicle under charging pile c on the j-th indicator, The information entropy of the v-th type of electric vehicle at the j-th indicator under charging pile c is as follows:

[0052]

[0053] In the above formula, is the proportion of the vth type of electric vehicles in the jth indicator under charging pile c, is the standardized data of various indices of the vth type of electric vehicle under charging pile c, n is the number of electric vehicle types, and the standardized data of various indices of the vth type of electric vehicle under charging pile c is as follows:

[0054]

[0055] In the above formula, is the jth indicator of the vth type of electric vehicle under charging pile c, is the jth indicator of all electric vehicles under charging pile c.

[0056] In a second aspect, a master-slave game optimization device for a virtual power plant and a distribution network is provided, wherein the master-slave game optimization device for a virtual power plant and a distribution network comprises:

[0057] The first analysis module is used to solve the upper-level model corresponding to the distribution network and obtain the upper-level distribution network optimization variables;

[0058] The second analysis module is used to substitute the upper-layer distribution network optimization variables into the lower-layer model corresponding to the distribution network and solve them to obtain the lower-layer virtual power plant optimization variables;

[0059] The third analysis module is used to determine the power allocated to each type of electric vehicle under the charging pile in the intraday stage based on the active power on the charging pile side in the day-ahead stage and the reactive power on the charging pile side in the intraday stage in the lower-layer virtual power plant optimization variables;

[0060] An optimization module is configured to construct a distribution network scheduling optimization plan using the upper-layer distribution network optimization variables, construct a virtual power plant scheduling optimization plan using the lower-layer virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles during the daily stage, and respectively optimize the distribution network and virtual power plant scheduling using the distribution network scheduling optimization plan and the virtual power plant scheduling optimization plan;

[0061] Among them, the upper-level distribution network optimization variables include: peak-shaving compensation coefficient and voltage regulation compensation coefficient, and the lower-level virtual power plant optimization variables include: gas turbine output in the day-ahead stage, power purchase from the power grid in the day-ahead stage, active power and reactive power on the charging pile side in the day-ahead stage, gas turbine output in the intraday stage, and active power and reactive power on the charging pile side in the intraday stage.

[0062] Preferably, the upper-level model corresponding to the distribution network includes: an upper-level objective function and its corresponding upper-level constraint conditions.

[0063] Furthermore, the upper layer objective function is as follows:

[0064]

[0065] In the above formula, ω1, ω2 and ω3 are the target weights of peak load regulation and voltage regulation requirements during the actual operation of the distribution network, respectively. pvd With F fd is the peak-to-valley difference and fluctuation variance of the overall net load of the distribution network, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, N is the set consisting of all nodes in the distribution network, V i 、 are the voltage at node i, voltage upper limit, voltage lower limit and voltage rated value respectively.

[0066] Furthermore, the upper-level constraints are as follows:

[0067]

[0068] V i min ≤V i ≤V i max

[0069]

[0070] In the above formula, C pk with C rv are the peak load compensation coefficient and voltage regulation compensation coefficient provided by the upper distribution network of the game for the virtual power plant, α and β are the compensation standards for the peak-to-valley difference and fluctuation degree of the net load of the distribution network, respectively. i is the weight coefficient of node i, k is the adjustment coefficient, and are the ideal values ​​of the peak-to-valley difference and fluctuation variance of the net load of the distribution network, and are the maximum and minimum peak-to-valley differences of the net load of the distribution network, and are the maximum and minimum values ​​of the net load fluctuation variance of the distribution network, V i ideal is the ideal voltage of node i, is the set of branch head nodes with j as the terminal node, ψ(j) is the set of branch terminal nodes with j as the terminal node, r ij and x ij is the resistance and reactance of the branch between node i and node j, P ij,t and Q ij,t The active and reactive power flowing from node i to node j at time t, V i,t is the voltage of node i at time t, V j,t is the voltage of node j at time t, P j,t and Q j,tare the injected active and reactive power of node j at time t, P jk,t and Q jk,t are the active and reactive power flowing from node j to node k at time t, and are the active power outputs of the photovoltaic power plant and wind farm at node j at time t, and are the active and reactive power outputs of the gas turbine at node j at time t, and are the active and reactive power of the electric vehicle charging station at node j at time t, and are the active and reactive power of the load at node j at time t, P ij and Q ij are the active power and reactive power flowing through the branch between node i and node j, I ij is the current amplitude flowing through the branch between node i and node j, and are the maximum and minimum current amplitudes flowing through the branch between node i and node j, respectively.

[0071] Preferably, the lower layer model corresponding to the distribution network includes: a first-stage objective function and its corresponding first-stage constraint conditions and a second-stage objective function and its corresponding second-stage constraint conditions.

[0072] Furthermore, the objective function of the first stage is as follows:

[0073]

[0074] In the above formula, C0 is the objective function value of the first stage, N T With N G are the dispatch period and the number of gas turbines aggregated in the virtual power plant, and are the electricity price and electricity quantity purchased by the virtual power plant from the distribution network during period t, and are the charging electricity price and rechargeable power provided by the charging piles in the virtual power plant to each electric vehicle user, ρ g and is the fuel cost function and the active power output of the g-th gas turbine during the day-ahead period t, and are the upward reserve cost function of the g-th gas turbine and the upward reserve capacity provided during period t, and are the downward reserve cost function of the g-th gas turbine and the downward reserve capacity provided during period t, ΔF pvd and ΔF varare the changes in the peak-to-valley difference and fluctuation variance of the net load of the distribution network compared with the previous dispatching cycle, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, C pk It is used to calculate the peak load compensation coefficient provided by the upper distribution network to the virtual power plant.

[0075] Furthermore, the constraints of the first stage are as follows:

[0076]

[0077] In the above formula, and are the active power and reactive power of the cth charging pile in the virtual power plant during the t period, is the maximum power capacity of the cth charging pile, and is the charging and discharging active power of the v-th type of electric vehicle user at the c-th charging pile in the day-ahead period, and is the charging and discharging efficiency of the vth type of electric vehicle user at the cth charging pile, N c,v is the total number of electric vehicle user types under the c-th charging pile, λ c,v,t is a Boolean variable, when λ c,v,t = 1, the electric vehicle user of type v at the cth charging pile in period t is in the charging state. c,v,t = 0, the electric vehicle user of type v at the cth charging pile in period t is in the discharging state. represents the maximum charging and discharging power of the vth type of electric vehicle user at the cth charging pile, is the reactive power output of the g-th gas turbine during the day-ahead period t, represents the rated capacity of the g-th gas turbine, and are the maximum values ​​of upward and downward active reserves that can be provided by unit g when it transitions from the day-ahead phase to the intraday phase, and are the minimum values ​​of upward and downward active reserve, respectively, and are the upward and downward reactive power reserves that the gas turbine unit g can provide during period t, and are the active power output of photovoltaic power and wind power in period t during the day-ahead period, and are the available predicted values ​​of active power of photovoltaic and wind power in period t during the day-ahead period.

[0078] Furthermore, the objective function of the second stage is as follows:

[0079]

[0080] In the above formula, C S is the objective function value of the second stage, C rv is the voltage regulation compensation coefficient provided by the upper distribution network to the virtual power plant, C pv with C wt are the penalty cost coefficients for curtailing solar power and wind power, N pv With N w are photovoltaic power plant collection and wind power plant collection respectively. and are the absorption capacity of photovoltaic power field pv and wind power field w at time t in the day, and are the available predicted values ​​of active power of photovoltaic power plant pv and wind power plant w at time t in the day, and are the cost functions for calling the upward and downward reactive reserve of gas turbine unit g during the intraday phase, and are the upward and downward reactive reserve amounts called by unit g at time t in the day phase, is the reduction value of the integrated node voltage deviation of the distribution network system compared with the previous scheduling period, and U is the uncertainty set as an additional constraint condition.

[0081] Furthermore, the uncertain set is as follows:

[0082]

[0083]

[0084] In the above formula, and are the available power of photovoltaic and wind power in the second stage t period, and is a 0-1 integer variable, used to indicate whether photovoltaic power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of photovoltaic at time t, u t,pv with u pv is the uncertainty limit and total uncertainty limit of photovoltaic at time t, and is a 0-1 integer variable, used to indicate whether the wind farm power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of wind power at time t, u t,w with u w are the uncertainty limit and total uncertainty limit of wind power at time t, is the reactive power load of node j in the virtual power plant during period t after considering the uncertainty, is the reactive power load of node j in period t during the day ahead, represents the maximum fluctuation deviation allowed for reactive load power at node j during period t, and is a 0-1 integer variable, which indicates that the reactive load of node j at time t fluctuates upward and downward when it is 0 or 1, respectively. ql is the uncertainty limit of reactive load.

[0085] Furthermore, the constraints of the second stage are as follows:

[0086]

[0087] In the above formula, It is the reactive power output of unit g at time t after reactive power reserve is called upon during the day.

[0088] Furthermore, the power allocated to each type of electric vehicle under the charging pile during the day is as follows:

[0089]

[0090] In the above formula, is the power allocated to the vth type of electric vehicle at charging pile c at time t, is the power adjustment amount allocated to the vth type of electric vehicle at the charging pile c at time t, is the chargeable and dischargeable power of the electric vehicle user v at time t. The power adjustment amount allocated to the v-th type of electric vehicle under the charging pile c at time t is as follows:

[0091]

[0092] In the above formula, is the weight of the vth type of electric vehicle under charging pile c, N c,v is the total number of electric vehicles of type v under charging pile c, is the change in active power of the electric vehicle during the intra-day period. The change in active power of the electric vehicle during the intra-day period is as follows:

[0093]

[0094] In the above formula, is the reactive power output of charging pile c during period t within the day.

[0095] Furthermore, the weight of the vth type of electric vehicle under the charging pile c is as follows:

[0096]

[0097] In the above formula, is the weight of the jth indicator of the vth type of electric vehicle under charging pile c, m is the total number of indicators, Y j is the evaluation coefficient of the j-th indicator, s is the proportional coefficient, is the current battery health status of the v-th type of electric vehicle, is the battery health status hazard value, and the weight of the jth indicator of the vth type of electric vehicle under the charging pile c is as follows:

[0098]

[0099] In the above formula, is the information entropy of the v-th type of electric vehicle under charging pile c on the j-th indicator, The information entropy of the v-th type of electric vehicle at the j-th indicator under charging pile c is as follows:

[0100]

[0101] In the above formula, is the proportion of the vth type of electric vehicles in the jth indicator under charging pile c, is the standardized data of various indices of the vth type of electric vehicle under charging pile c, n is the number of electric vehicle types, and the standardized data of various indices of the vth type of electric vehicle under charging pile c is as follows:

[0102]

[0103] In the above formula, is the jth indicator of the vth type of electric vehicle under charging pile c, is the jth indicator of all electric vehicles under charging pile c.

[0104] In a third aspect, a computer device is provided, comprising: one or more processors;

[0105] The processor is configured to store one or more programs;

[0106] When the one or more programs are executed by the one or more processors, the master-slave game optimization method of the virtual power plant and the distribution network is implemented.

[0107] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the master-slave game optimization method of the virtual power plant and the distribution network is implemented.

[0108] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0109] The present invention provides a master-slave game optimization method and device for a virtual power plant and a distribution network, comprising: solving an upper-layer model corresponding to the distribution network to obtain upper-layer distribution network optimization variables; substituting the upper-layer distribution network optimization variables into a lower-layer model corresponding to the distribution network and solving the variables to obtain lower-layer virtual power plant optimization variables; determining the power allocated to various types of electric vehicles under the charging piles in the intraday stage based on the active power on the charging pile side in the day-ahead stage and the reactive power on the charging pile side in the intraday stage in the lower-layer virtual power plant optimization variables; constructing a distribution network scheduling optimization scheme using the upper-layer distribution network optimization variables, constructing a virtual power plant scheduling optimization scheme using the lower-layer virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles in the intraday stage, and respectively utilizing the distribution network scheduling optimization scheme and the virtual power plant scheduling optimization scheme to perform scheduling optimization on the distribution network and the virtual power plant; the technical solution provided by the present invention, through the constructed upper-layer model and lower-layer model, makes full use of the distributed resources of active-reactive coupling in the system to perform active-reactive joint optimization scheduling, specifically:

[0110] On the virtual power plant side of the game's lower layer, a two-stage robust optimization approach was proposed to handle uncertainty. Compared with deterministic optimization, the maximum voltage deviation under the distribution network's worst operating conditions decreased by 81.36%, enhancing the robustness and risk resistance of the virtual power plant and distribution network joint operation system.

[0111] In the second phase of the day, when the electric vehicle charging piles adjust their own residual reactive capacity to participate in the voltage regulation of the distribution network, a power allocation strategy based on a fair weight algorithm is further proposed, which increases the satisfaction of EV users connected to the electric vehicle charging piles with the participation of auxiliary services by 14.70%. In addition, at this time, after 1,000 charge and discharge cycles, EV users can significantly improve their battery health. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 This is a flow chart of the main steps of the master-slave game optimization method for a virtual power plant and a distribution network according to an embodiment of the present invention;

[0113] Figure 2 is an improved IEEE33 node system diagram of an embodiment of the present invention;

[0114] Figure 3 is a node voltage distribution curve diagram before optimization according to an embodiment of the present invention;

[0115] Figure 4 is a graph showing the node voltage distribution after optimization according to an embodiment of the present invention;

[0116] Figure 5 is a distribution network net load curve diagram according to an embodiment of the present invention;

[0117] Figure 6 This is a graph showing predicted / actual wind and solar output curves according to an embodiment of the present invention;

[0118] Figure 7 is a diagram showing the results of optimal scheduling of a virtual power plant under uncertainty according to an embodiment of the present invention;

[0119] Figure 8 This is a diagram of system reactive power standby usage according to an embodiment of the present invention;

[0120] Figure 9 This is a voltage distribution curve diagram of nodes connected to the optimized charging pile at each moment in the embodiment of the present invention;

[0121] Figure 10 This is a diagram showing changes in the health status of a power plant according to an embodiment of the present invention. DETAILED DESCRIPTION

[0122] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0123] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0124] As disclosed in the background, the distribution network is a crucial component of the power system. In the context of building a new power system dominated by renewable energy, the distribution network faces the challenge of integrating a massive amount of distributed renewable energy and flexible loads. The random nature of these distributed energy sources exacerbates fluctuations in the distribution network's current flow and increasingly highlights voltage overshoots, impacting the stability of the power system. Therefore, ensuring the stability of load fluctuations and voltage excursions in the distribution network is crucial.

[0125] With the continuous improvement of various flexible load resource aggregation technologies, it has brought assistance to the optimization of distribution networks. Among them, virtual power plants play an important role in improving the operating efficiency of distribution networks and enhancing the management capabilities of distributed power generation and flexible load resources. They can effectively alleviate the problem of insufficient flexibility adjustment capabilities of power systems. However, the energy and load resources of power systems are complex, and the optimization objectives are diverse. It is difficult to comprehensively consider various factors and indicators, which is the difficulty in optimizing distribution networks and virtual power plants.

[0126] Currently, joint optimization of distribution networks and virtual power plants (VPPs) mostly employs a two-tier collaborative optimization model. However, existing hierarchical optimization mechanisms for distribution networks and VPPs containing a large number of distributed resources remain incomplete. During hierarchical optimization and scheduling of each component, the regulation capacity of distributed resources is not fully utilized. These optimizations often only consider active or reactive power, with few considering both active and reactive scheduling simultaneously, limiting the regulation capacity of the entire system. Furthermore, during two-tier optimization of distribution networks and VPPs, the satisfaction and compensation received by flexible loads participating in ancillary services are rarely fully considered. This is especially true when flexible loads, such as electric vehicles, are included in the power system. Improper handling can easily lead to a decrease in their willingness to participate in scheduling and excessive resource loss for certain flexible loads. Furthermore, the interaction and information exchange between the distribution network and VPP are difficult to fully analyze and implement.

[0127] In order to improve the above problems, the present invention provides a master-slave game optimization method and device for a virtual power plant and a distribution network, including: solving an upper-level model corresponding to the distribution network to obtain upper-level distribution network optimization variables; substituting the upper-level distribution network optimization variables into the lower-level model corresponding to the distribution network and solving them to obtain lower-level virtual power plant optimization variables; determining the power allocated to various types of electric vehicles under the charging piles in the intraday stage based on the active power on the charging pile side in the day-ahead stage and the reactive power on the charging pile side in the intraday stage in the lower-level virtual power plant optimization variables; constructing a distribution network scheduling optimization scheme using the upper-level distribution network optimization variables, constructing a virtual power plant scheduling optimization scheme using the lower-level virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles in the intraday stage, and respectively utilizing the distribution network scheduling optimization scheme and the virtual power plant scheduling optimization scheme to perform scheduling optimization on the distribution network and the virtual power plant; the technical solution provided by the present invention, through the constructed upper-level model and lower-level model, makes full use of the distributed resources of active-reactive coupling in the system to perform active-reactive joint optimization scheduling, specifically:

[0128] On the virtual power plant side of the game's lower layer, a two-stage robust optimization approach was proposed to handle uncertainty. Compared with deterministic optimization, the maximum voltage deviation under the distribution network's worst operating conditions decreased by 81.36%, enhancing the robustness and risk resistance of the virtual power plant and distribution network joint operation system.

[0129] In the second phase of the day, when the electric vehicle charging piles adjust their own residual reactive capacity to participate in the voltage regulation of the distribution network, a power allocation strategy based on a fair weight algorithm is further proposed, which increases the satisfaction of EV users connected to the electric vehicle charging piles with the participation of auxiliary services by 14.70%. In addition, at this time, after 1,000 charge and discharge cycles, EV users can significantly improve their battery health.

[0130] The above scheme is described in detail below.

[0131] Example 1

[0132] See attached Figure 1 , Figure 1 This is a flow chart of the main steps of the master-slave game optimization method for virtual power plants and distribution networks according to an embodiment of the present invention. Figure 1 As shown, the master-slave game optimization method of the virtual power plant and the distribution network in the embodiment of the present invention mainly includes the following steps:

[0133] Step S101: solving the upper-layer model corresponding to the distribution network to obtain the upper-layer distribution network optimization variables;

[0134] Step S102: Substituting the upper-layer distribution network optimization variables into the lower-layer model corresponding to the distribution network and solving the variables to obtain the lower-layer virtual power plant optimization variables;

[0135] Step S103: determining the power allocated to each type of electric vehicle at the charging pile during the intraday phase based on the active power at the charging pile side during the day-ahead phase and the reactive power at the charging pile side during the intraday phase in the lower-layer virtual power plant optimization variables;

[0136] Step S104: constructing a distribution network scheduling optimization plan using the upper-layer distribution network optimization variables, constructing a virtual power plant scheduling optimization plan using the lower-layer virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles during the day, and respectively optimizing the distribution network and the virtual power plant using the distribution network scheduling optimization plan and the virtual power plant scheduling optimization plan;

[0137] Among them, the upper-level distribution network optimization variables include: peak-shaving compensation coefficient and voltage regulation compensation coefficient, and the lower-level virtual power plant optimization variables include: gas turbine output in the day-ahead stage, power purchase from the power grid in the day-ahead stage, active power and reactive power on the charging pile side in the day-ahead stage, gas turbine output in the intraday stage, and active power and reactive power on the charging pile side in the intraday stage.

[0138] In this embodiment, the upper-level model corresponding to the distribution network includes: an upper-level objective function and its corresponding upper-level constraint conditions.

[0139] In one embodiment, the upper layer objective function is as follows:

[0140]

[0141] In the above formula, ω1, ω2 and ω3 are the target weights of peak load regulation and voltage regulation requirements during the actual operation of the distribution network, respectively. pvd With F fd is the peak-to-valley difference and fluctuation variance of the overall net load of the distribution network, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, N is the set consisting of all nodes in the distribution network, V i 、 are the voltage at node i, voltage upper limit, voltage lower limit and voltage rated value respectively.

[0142] In one embodiment, the upper layer constraints are as follows:

[0143]

[0144] V i min ≤V i ≤V i max

[0145]

[0146]

[0147] In the above formula, C pk with C rv are the peak load compensation coefficient and voltage regulation compensation coefficient provided by the upper distribution network of the game for the virtual power plant, α and β are the compensation standards for the peak-to-valley difference and fluctuation degree of the net load of the distribution network, respectively. i is the weight coefficient of node i, k is the adjustment coefficient, and are the ideal values ​​of the peak-to-valley difference and fluctuation variance of the net load of the distribution network, and are the maximum and minimum peak-to-valley differences of the net load of the distribution network, and are the maximum and minimum values ​​of the net load fluctuation variance of the distribution network, V i ideal is the ideal voltage of node i, is the set of branch head nodes with j as the terminal node, ψ(j) is the set of branch terminal nodes with j as the terminal node, r ij and x ij is the resistance and reactance of the branch between node i and node j, P ij,t and Q ij,t The active and reactive power flowing from node i to node j at time t, V i,t is the voltage of node i at time t, V j,t is the voltage of node j at time t, P j,t and Q j,t are the injected active and reactive power of node j at time t, P jk,t and Q jk,t are the active and reactive power flowing from node j to node k at time t, and are the active power outputs of the photovoltaic power plant and wind farm at node j at time t, and are the active and reactive power outputs of the gas turbine at node j at time t, and are the active and reactive power of the electric vehicle charging station at node j at time t, and are the active and reactive power of the load at node j at time t, P ij and Q ij are the active power and reactive power flowing through the branch between node i and node j, I ij is the current amplitude flowing through the branch between node i and node j, and are the maximum and minimum current amplitudes flowing through the branch between node i and node j, respectively.

[0148] In this embodiment, the lower layer model corresponding to the distribution network includes: a first-stage objective function and its corresponding first-stage constraint conditions and a second-stage objective function and its corresponding second-stage constraint conditions.

[0149] In one embodiment, the first stage objective function is as follows:

[0150]

[0151] In the above formula, C0 is the objective function value of the first stage, N T With N G are the dispatch period and the number of gas turbines aggregated in the virtual power plant, and are the electricity price and electricity quantity purchased by the virtual power plant from the distribution network during period t, and are the charging electricity price and rechargeable power provided by the charging piles in the virtual power plant to each electric vehicle user, ρ g and is the fuel cost function and the active power output of the g-th gas turbine during the day-ahead period t, and are the upward reserve cost function of the g-th gas turbine and the upward reserve capacity provided during period t, and are the downward reserve cost function of the g-th gas turbine and the downward reserve capacity provided during period t, ΔF pvd and ΔF var are the changes in the peak-to-valley difference and fluctuation variance of the net load of the distribution network compared with the previous dispatching cycle, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, C pk It is used to calculate the peak load compensation coefficient provided by the upper distribution network to the virtual power plant.

[0152] In one embodiment, the first stage constraints are as follows:

[0153]

[0154] In the above formula, and are the active power and reactive power of the cth charging pile in the virtual power plant during the t period, is the maximum power capacity of the cth charging pile, and is the charging and discharging active power of the v-th type of electric vehicle user at the c-th charging pile in the day-ahead period, and is the charging and discharging efficiency of the vth type of electric vehicle user at the cth charging pile, N c,v is the total number of electric vehicle user types under the c-th charging pile, λ c,v,t is a Boolean variable, when λ c,v,t = 1, the electric vehicle user of type v at the cth charging pile in period t is in the charging state. c,v,t = 0, the electric vehicle user of type v at the cth charging pile in period t is in the discharging state. represents the maximum charging and discharging power of the vth type of electric vehicle user at the cth charging pile, is the reactive power output of the g-th gas turbine during the day-ahead period t, represents the rated capacity of the g-th gas turbine, and are the maximum values ​​of upward and downward active reserves that can be provided by unit g when it transitions from the day-ahead phase to the intraday phase, and are the minimum values ​​of upward and downward active reserve, respectively, and are the upward and downward reactive power reserves that the gas turbine unit g can provide during period t, and are the active power output of photovoltaic power and wind power in period t during the day-ahead period, and are the available predicted values ​​of active power of photovoltaic and wind power in period t during the day-ahead period.

[0155] In one embodiment, the second stage objective function is as follows:

[0156]

[0157] In the above formula, C S is the objective function value of the second stage, C rv is the voltage regulation compensation coefficient provided by the upper distribution network to the virtual power plant, Cpv with C wt are the penalty cost coefficients for curtailing solar power and wind power, N pv With N w are photovoltaic power plant collection and wind power plant collection respectively. and are the absorption capacity of photovoltaic power field pv and wind power field w at time t in the day, and are the available predicted values ​​of active power of photovoltaic power plant pv and wind power plant w at time t in the day, and are the cost functions for calling the upward and downward reactive reserve of gas turbine unit g during the intraday phase, and are the upward and downward reactive reserve amounts called by unit g at time t in the day phase, is the reduction value of the integrated node voltage deviation of the distribution network system compared with the previous scheduling period, and U is the uncertainty set as an additional constraint condition.

[0158] In one embodiment, the uncertainty set is as follows:

[0159]

[0160]

[0161] In the above formula, and are the available power of photovoltaic and wind power in the second stage t period, and is a 0-1 integer variable, used to indicate whether photovoltaic power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of photovoltaic at time t, u t,pv with u pv is the uncertainty limit and total uncertainty limit of photovoltaic at time t, and is a 0-1 integer variable, used to indicate whether the wind farm power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of wind power at time t, u t,w with u w are the uncertainty limit and total uncertainty limit of wind power at time t, is the reactive power load of node j in the virtual power plant during period t after considering the uncertainty, is the reactive power load of node j in period t during the day ahead, represents the maximum fluctuation deviation allowed for reactive load power at node j during period t, and is a 0-1 integer variable, which indicates that the reactive load of node j at time t fluctuates upward and downward when it is 0 or 1, respectively. ql is the uncertainty limit of reactive load.

[0162] In one embodiment, the second stage constraints are as follows:

[0163]

[0164] In the above formula, It is the reactive power output of unit g at time t after reactive power reserve is called upon during the day.

[0165] In one embodiment, the power allocated to each type of electric vehicle at the charging pile during the day is as follows:

[0166]

[0167] In the above formula, is the power allocated to the vth type of electric vehicle at charging pile c at time t, is the power adjustment amount allocated to the vth type of electric vehicle at the charging pile c at time t, is the chargeable and dischargeable power of the electric vehicle user v at time t. The power adjustment amount allocated to the v-th type of electric vehicle under the charging pile c at time t is as follows:

[0168]

[0169] In the above formula, is the weight of the vth type of electric vehicle under charging pile c, N c,v is the total number of electric vehicles of type v under charging pile c, is the change in active power of the electric vehicle during the intra-day period. The change in active power of the electric vehicle during the intra-day period is as follows:

[0170]

[0171] In the above formula, is the reactive power output of charging pile c during period t within the day.

[0172] In one embodiment, the weight of the vth type of electric vehicle at the charging pile c is as follows:

[0173]

[0174] In the above formula, is the weight of the jth indicator of the vth type of electric vehicle under charging pile c, m is the total number of indicators, Y j is the evaluation coefficient of the j-th indicator, s is the proportional coefficient, is the current battery health status of the v-th type of electric vehicle, is the battery health status hazard value, and the weight of the jth indicator of the vth type of electric vehicle under the charging pile c is as follows:

[0175]

[0176] In the above formula, is the information entropy of the v-th type of electric vehicle under charging pile c on the j-th indicator, The information entropy of the v-th type of electric vehicle at the j-th indicator under charging pile c is as follows:

[0177]

[0178] In the above formula, is the proportion of the vth type of electric vehicles in the jth indicator under charging pile c, is the standardized data of various indices of the vth type of electric vehicle under charging pile c, n is the number of electric vehicle types, and the standardized data of various indices of the vth type of electric vehicle under charging pile c is as follows:

[0179]

[0180] In the above formula, is the jth indicator of the vth type of electric vehicle under charging pile c, is the jth indicator of all electric vehicles under charging pile c.

[0181] Through the above steps and the basic information of each type of electric vehicle, when the battery health status of the electric vehicle is 100%, the power allocation weights shown in Table 1 are obtained.

[0182] Table 1

[0183]

[0184] In a specific embodiment, the present invention uses an ant colony optimization algorithm to initialize the upper-level model; and uses a Benders decomposition algorithm to solve the lower-level model. First, the original problem is decomposed into a main problem and sub-problems in the form of max-min. Then, the strong duality theory is used to transform the two-level optimization sub-problems into a single-level optimization problem. The Benders cutting plane method is used to add the information of the sub-problems to the main problem in the form of cutting planes. Finally, the main and sub-problems are continuously iterated to obtain the optimal solution of the original problem.

[0185] This embodiment is applied to an improved IEEE33 node model. The specific grid topology and virtual power plant source and load resource distribution are as follows: Figure 2As shown. This system is equipped with three gas turbine units GT1-3 with active and reactive backup capabilities. Wind farms WT1 and WT2, both with an installed capacity of 15MW, are connected to nodes 12 and 23 respectively. Photovoltaic generators PV1 and PV2 are connected to nodes 24 and 31, with a capacity of 10MW each. Nodes 8, 14, 19, 25, and 32 are electric vehicle charging stations EVS1-5. The total load of each node in the distribution system is set to 434.60MW + 222.83Mvar. The system reference voltage is set to U B =12.66kV, the reference power is S B =10MVA, the voltage limit of each node in the system is [0.94,1.06]pu, where node 1 is the connection point between the distribution network and the main grid, and is defined as the balancing node of the entire network. Its voltage amplitude per unit value is set to 1.05. The node voltage distribution curves of the IEEE33-node distribution network system before and after the distribution network optimization are shown in the figure. Figure 3 and Figure 4 shown.

[0186] The present invention proposes multiple scenarios to verify the necessity of considering the uncertain factors of wind, solar and reactive loads.

[0187] Scenario 1: The uncertainty of wind and solar power output and the uncertainty of reactive load power are not considered.

[0188] Scenario 2: Considering only the uncertainty of wind and solar output, the robustness index u pv 、u t,pv 、u w 、u t,w They are set to 1, 6, 1, and 10 respectively, without considering the uncertainty of reactive load power.

[0189] Scenario 3: Considering the uncertainty of wind and solar output and the uncertainty of reactive load power, the robustness index u pv 、u t,pv 、u w 、u t,w 、u ql 、u t,ql Set to 1, 6, 1, 10, 8, and 10 respectively.

[0190] As shown in Table 2:

[0191] Table 2

[0192]

[0193] As can be seen, in Scenario 1, which does not account for uncertainties, the maximum voltage deviation under the worst-case operating condition reached 0.059, exceeding the normal allowable deviation range. Comparing Scenario 2 with Scenario 1, it is found that accounting for the temporal and spatial uncertainties of photovoltaic and wind power output within the virtual power plant increases the total operating cost of the virtual power plant by 3.49%. However, the maximum voltage deviation under the worst-case operating condition in Scenario 2 is reduced to 0.046, which is within the normal voltage deviation range, but the maximum voltage deviation is still large. Considering the uncertainty of the reactive load within the virtual power plant, Scenario 3 in Table 2 shows that although the total operating cost and cumulative voltage deviation are larger in Scenario 3, the maximum voltage deviation under the worst-case operating condition is reduced to 0.011, demonstrating the necessity of considering uncertainties within the virtual power plant.

[0194] exist Figure 3 and Figure 4 In the figure, the red and blue translucent planes represent the upper bound (1.06) and lower bound (0.94) of the voltage at each node in the distribution network system, respectively. This shows that after the master-slave game model of the distribution network and the virtual power plant is optimized, the node voltage limit problem of the distribution network system has been significantly improved.

[0195] The peak-shaving performance of the master-slave game optimization model of the virtual power plant and distribution network proposed in this invention is verified.

[0196] The net load curve of the distribution network is as follows: Figure 5 As shown: Scenario 1 is that the virtual power plant does not conduct master-slave game and two-layer optimization with the distribution network, and Scenario 2 is the model proposed in this invention.

[0197] Depend on Figure 5 It can be seen that after taking into account the master-slave game model of the virtual power plant and the distribution network system with consideration of the voltage regulation problem, compared with scenario 1, the system peak-to-valley difference and fluctuation variance are reduced by 27.46% and 29.64% respectively, optimizing the net load peak regulation of the distribution network.

[0198] Voltage regulation performance and controllable resource output optimization analysis

[0199] In this invention, the maximum allowable fluctuation deviation of reactive load power and wind and solar output in the virtual power plant is set according to the maximum deviation of historical prediction, which is 20%, 15%, and 10% respectively. Under this regulation, the actual / predicted wind and solar output curve of the optimized virtual power plant is as follows: Figure 6 shown.

[0200] Depend on Figure 6 It can be seen that photovoltaic and wind power fluctuate at the same time at 12 o'clock. This moment is used as a typical moment to study the game model with the distribution network, and is optimized by combining the two-stage robust master-slave game algorithm model. The voltage distribution curves of the distribution network nodes before and after optimization and the optimized output of the controllable resources of each node in the virtual power plant are shown in the figure. Figure 7shown.

[0201] like Figure 7 As shown in the figure: at this moment, the voltage over-limit problem of each node has been greatly improved after optimization, and the voltage over-limit situation no longer occurs at each node.

[0202] Analysis on how electric vehicle charging piles improve system robustness and flexibility.

[0203] The system considers charging pile voltage regulation compensation and electric vehicle power allocation based on a fair weight algorithm. The total cost of the virtual power plant in the two stages and the satisfaction of electric vehicles participating in auxiliary services are 37,625.8 yuan and 72.3% respectively. The system reactive power reserve usage is shown in the following figure:

[0204] like Figure 8 As shown: It can be seen that after the implementation of the voltage regulation compensation of the electric vehicle charging pile, the operating time of the reactive power standby unit in the system is 42 hours, and the total reactive power standby usage of the system is relatively low. Figure 9 As shown in Figure 3, after considering the electric vehicle voltage regulation compensation, the voltage distribution at the charging pile access node is greatly improved.

[0205] (8) Response analysis based on fair weight algorithm.

[0206] When the battery health status of an electric vehicle is low, its allocation weight will be lower to avoid excessive consumption of the battery health status of a single type of electric vehicle. Figure 10 The figure shows the updated graph of the battery health status of EV3 and EV4 after 1000 charge and discharge cycles.

[0207] Depend on Figure 10 As can be seen, when EV3 and EV4 users participated in the virtual power plant voltage regulation assistance service under the power allocation strategy without the fair weight algorithm, their battery health status declined to a relatively low level after 1000 charge and discharge cycles. The EV3's battery health status even fell below the critical value. This critical value standard refers to GB / T 31484-2015, which stipulates that after 1000 charge and discharge cycles, the battery capacity of an EV should be no less than 80% of its initial state. However, after optimizing the power allocation strategy based on the fair weight algorithm, the battery health of both EV3 and EV4 significantly improved, by 5.5% and 6.7%, respectively. Furthermore, after the optimization, the EV3's battery health status reached a safe level after 1000 cycles.

[0208] Example 2

[0209] Based on the same inventive concept, the present invention also provides a master-slave game optimization device for a virtual power plant and a distribution network, the master-slave game optimization device for a virtual power plant and a distribution network comprising:

[0210] The first analysis module is used to solve the upper-level model corresponding to the distribution network and obtain the upper-level distribution network optimization variables;

[0211] The second analysis module is used to substitute the upper-layer distribution network optimization variables into the lower-layer model corresponding to the distribution network and solve them to obtain the lower-layer virtual power plant optimization variables;

[0212] The third analysis module is used to determine the power allocated to each type of electric vehicle under the charging pile in the intraday stage based on the active power on the charging pile side in the day-ahead stage and the reactive power on the charging pile side in the intraday stage in the lower-layer virtual power plant optimization variables;

[0213] An optimization module is configured to construct a distribution network scheduling optimization plan using the upper-layer distribution network optimization variables, construct a virtual power plant scheduling optimization plan using the lower-layer virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles during the daily stage, and respectively optimize the distribution network and virtual power plant scheduling using the distribution network scheduling optimization plan and the virtual power plant scheduling optimization plan;

[0214] Among them, the upper-level distribution network optimization variables include: peak-shaving compensation coefficient and voltage regulation compensation coefficient, and the lower-level virtual power plant optimization variables include: gas turbine output in the day-ahead stage, power purchase from the power grid in the day-ahead stage, active power and reactive power on the charging pile side in the day-ahead stage, gas turbine output in the intraday stage, and active power and reactive power on the charging pile side in the intraday stage.

[0215] Preferably, the upper-level model corresponding to the distribution network includes: an upper-level objective function and its corresponding upper-level constraint conditions.

[0216] Furthermore, the upper layer objective function is as follows:

[0217]

[0218] In the above formula, ω1, ω2 and ω3 are the target weights of peak load regulation and voltage regulation requirements during the actual operation of the distribution network, respectively. pvd With F fd is the peak-to-valley difference and fluctuation variance of the overall net load of the distribution network, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, N is the set consisting of all nodes in the distribution network, V i 、 are the voltage at node i, voltage upper limit, voltage lower limit and voltage rated value respectively.

[0219] Furthermore, the upper-level constraints are as follows:

[0220]

[0221]

[0222] Vi min ≤V i ≤V i max

[0223]

[0224] In the above formula, C pk with C rv are the peak load compensation coefficient and voltage regulation compensation coefficient provided by the upper distribution network of the game for the virtual power plant, α and β are the compensation standards for the peak-to-valley difference and fluctuation degree of the net load of the distribution network, respectively. i is the weight coefficient of node i, k is the adjustment coefficient, and are the ideal values ​​of the peak-to-valley difference and fluctuation variance of the net load of the distribution network, and are the maximum and minimum peak-to-valley differences of the net load of the distribution network, and are the maximum and minimum values ​​of the net load fluctuation variance of the distribution network, V i ideal is the ideal voltage of node i, is the set of branch head nodes with j as the terminal node, ψ(j) is the set of branch terminal nodes with j as the terminal node, r ij and x ij is the resistance and reactance of the branch between node i and node j, P ij,t and Q ij,t The active and reactive power flowing from node i to node j at time t, V i,t is the voltage of node i at time t, V j,t is the voltage of node j at time t, P j,t and Q j,t are the injected active and reactive power of node j at time t, P jk,t and Q jk,t are the active and reactive power flowing from node j to node k at time t, and are the active power outputs of the photovoltaic power plant and wind farm at node j at time t, and are the active and reactive power outputs of the gas turbine at node j at time t, and are the active and reactive power of the electric vehicle charging station at node j at time t, and are the active and reactive power of the load at node j at time t, P ij and Q ij are the active power and reactive power flowing through the branch between node i and node j, Iij is the current amplitude flowing through the branch between node i and node j, and are the maximum and minimum current amplitudes flowing through the branch between node i and node j, respectively.

[0225] Preferably, the lower layer model corresponding to the distribution network includes: a first-stage objective function and its corresponding first-stage constraint conditions and a second-stage objective function and its corresponding second-stage constraint conditions.

[0226] Furthermore, the objective function of the first stage is as follows:

[0227]

[0228] In the above formula, C0 is the objective function value of the first stage, N T With N G are the dispatch period and the number of gas turbines aggregated in the virtual power plant, and are the electricity price and electricity quantity purchased by the virtual power plant from the distribution network during period t, and are the charging electricity price and rechargeable power provided by the charging piles in the virtual power plant to each electric vehicle user, ρ g and is the fuel cost function and the active power output of the g-th gas turbine during the day-ahead period t, and are the upward reserve cost function of the g-th gas turbine and the upward reserve capacity provided during period t, and are the downward reserve cost function of the g-th gas turbine and the downward reserve capacity provided during period t, ΔF pvd and ΔF var are the changes in the peak-to-valley difference and fluctuation variance of the net load of the distribution network compared with the previous dispatching cycle, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, C pk It is used to calculate the peak load compensation coefficient provided by the upper distribution network to the virtual power plant.

[0229] Furthermore, the constraints of the first stage are as follows:

[0230]

[0231]

[0232] In the above formula, and are the active power and reactive power of the cth charging pile in the virtual power plant during the t period, is the maximum power capacity of the cth charging pile, and is the charging and discharging active power of the v-th type of electric vehicle user at the c-th charging pile in the day-ahead period, and is the charging and discharging efficiency of the vth type of electric vehicle user at the cth charging pile, N c,v is the total number of electric vehicle user types under the c-th charging pile, λ c,v,t is a Boolean variable, when λ c,v,t = 1, the electric vehicle user of type v at the cth charging pile in period t is in the charging state. c,v,t = 0, the electric vehicle user of type v at the cth charging pile in period t is in the discharging state. represents the maximum charging and discharging power of the vth type of electric vehicle user at the cth charging pile, is the reactive power output of the g-th gas turbine during the day-ahead period t, represents the rated capacity of the g-th gas turbine, and are the maximum values ​​of upward and downward active reserves that can be provided by unit g when it transitions from the day-ahead phase to the intraday phase, and are the minimum values ​​of upward and downward active reserve, respectively, and are the upward and downward reactive power reserves that the gas turbine unit g can provide during period t, and are the active power output of photovoltaic power and wind power in period t during the day-ahead period, and are the available predicted values ​​of active power of photovoltaic and wind power in period t during the day-ahead period.

[0233] Furthermore, the objective function of the second stage is as follows:

[0234]

[0235] In the above formula, C S is the objective function value of the second stage, C rv is the voltage regulation compensation coefficient provided by the upper distribution network to the virtual power plant, C pv with C wt are the penalty cost coefficients for curtailing solar power and wind power, N pv With N w are photovoltaic power plant collection and wind power plant collection respectively. and are the absorption capacity of photovoltaic power field pv and wind power field w at time t in the day, and are the available predicted values ​​of active power of photovoltaic power plant pv and wind power plant w at time t in the day, and are the cost functions for calling the upward and downward reactive reserve of gas turbine unit g during the intraday phase, and are the upward and downward reactive reserve amounts called by unit g at time t in the day phase, is the reduction value of the integrated node voltage deviation of the distribution network system compared with the previous scheduling period, and U is the uncertainty set as an additional constraint condition.

[0236] Furthermore, the uncertain set is as follows:

[0237]

[0238] In the above formula, and are the available power of photovoltaic and wind power in the second stage t period, and is a 0-1 integer variable, used to indicate whether photovoltaic power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of photovoltaic at time t, u t,pv with u pv is the uncertainty limit and total uncertainty limit of photovoltaic at time t, and is a 0-1 integer variable, used to indicate whether the wind farm power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of wind power at time t, u t,w with u w are the uncertainty limit and total uncertainty limit of wind power at time t, is the reactive power load of node j in the virtual power plant during period t after considering the uncertainty, is the reactive power load of node j in period t during the day ahead, represents the maximum fluctuation deviation allowed for reactive load power at node j during period t, and is a 0-1 integer variable, which indicates that the reactive load of node j at time t fluctuates upward and downward when it is 0 or 1, respectively. ql is the uncertainty limit of reactive load.

[0239] Furthermore, the constraints of the second stage are as follows:

[0240]

[0241] In the above formula, It is the reactive power output of unit g at time t after reactive power reserve is called upon during the day.

[0242] Furthermore, the power allocated to each type of electric vehicle under the charging pile during the day is as follows:

[0243]

[0244] In the above formula, is the power allocated to the vth type of electric vehicle at charging pile c at time t, is the power adjustment amount allocated to the vth type of electric vehicle at the charging pile c at time t, is the chargeable and dischargeable power of the electric vehicle user v at time t. The power adjustment amount allocated to the v-th type of electric vehicle under the charging pile c at time t is as follows:

[0245]

[0246] In the above formula, is the weight of the vth type of electric vehicle under charging pile c, N c,v is the total number of electric vehicles of type v under charging pile c, is the change in active power of the electric vehicle during the intra-day period. The change in active power of the electric vehicle during the intra-day period is as follows:

[0247]

[0248] In the above formula, is the reactive power output of charging pile c during period t within the day.

[0249] Furthermore, the weight of the vth type of electric vehicle under the charging pile c is as follows:

[0250]

[0251] In the above formula, is the weight of the jth indicator of the vth type of electric vehicle under charging pile c, m is the total number of indicators, Y j is the evaluation coefficient of the j-th indicator, s is the proportional coefficient, is the current battery health status of the v-th type of electric vehicle, is the battery health status hazard value, and the weight of the jth indicator of the vth type of electric vehicle under the charging pile c is as follows:

[0252]

[0253] In the above formula, is the information entropy of the v-th type of electric vehicle under charging pile c on the j-th indicator, The information entropy of the v-th type of electric vehicle at the j-th indicator under charging pile c is as follows:

[0254]

[0255] In the above formula, is the proportion of the vth type of electric vehicles in the jth indicator under charging pile c, is the standardized data of various indices of the vth type of electric vehicle under charging pile c, n is the number of electric vehicle types, and the standardized data of various indices of the vth type of electric vehicle under charging pile c is as follows:

[0256]

[0257] In the above formula, is the jth indicator of the vth type of electric vehicle under charging pile c, is the jth indicator of all electric vehicles under charging pile c.

[0258] Example 3

[0259] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may 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. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the master-slave game optimization method of a virtual power plant and a distribution network in the above embodiment.

[0260] Example 4

[0261] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a master-slave game optimization method for a virtual power plant and a distribution network in the above embodiment.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A master-slave game optimization method for a virtual power plant and a distribution network, characterized in that: The method comprises: Solve the upper-level model corresponding to the distribution network and obtain the upper-level distribution network optimization variables; Substituting the upper-layer distribution network optimization variables into the lower-layer model corresponding to the distribution network and solving them to obtain the lower-layer virtual power plant optimization variables; Determine the power allocated to each type of electric vehicle under the charging pile during the intraday phase based on the active power on the charging pile side during the day-ahead phase and the reactive power on the charging pile side during the intraday phase in the lower-layer virtual power plant optimization variables; A distribution network scheduling optimization scheme is constructed using the upper-layer distribution network optimization variables, and a virtual power plant scheduling optimization scheme is constructed using the lower-layer virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles during the daily stage. The distribution network scheduling optimization scheme and the virtual power plant scheduling optimization scheme are respectively used to optimize the scheduling of the distribution network and the virtual power plant; Among them, the upper-level distribution network optimization variables include: peak-shaving compensation coefficient and voltage regulation compensation coefficient, and the lower-level virtual power plant optimization variables include: gas turbine output in the day-ahead stage, power purchase from the power grid in the day-ahead stage, active power and reactive power on the charging pile side in the day-ahead stage, gas turbine output in the intraday stage, and active power and reactive power on the charging pile side in the intraday stage.

2. The method according to claim 1, wherein The upper-level model corresponding to the distribution network includes: an upper-level objective function and its corresponding upper-level constraint conditions.

3. The method according to claim 2, wherein The upper objective function is as follows: In the above formula, ω1, ω2 and ω3 are the target weights of peak load regulation and voltage regulation requirements during the actual operation of the distribution network, respectively. pvd With F fd is the peak-to-valley difference and fluctuation variance of the overall net load of the distribution network, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, N is the set consisting of all nodes in the distribution network, V i 、 are the voltage at node i, voltage upper limit, voltage lower limit and voltage rated value respectively.

4. The method according to claim 3, wherein The upper-level constraints are as follows: In i min ≤V i ≤V i max In the above formula, C pk with C rv are the peak load compensation coefficient and voltage regulation compensation coefficient provided by the upper distribution network of the game for the virtual power plant, α and β are the compensation standards for the peak-to-valley difference and fluctuation degree of the net load of the distribution network, respectively. i is the weight coefficient of node i, k is the adjustment coefficient, and are the ideal values ​​of the peak-to-valley difference and fluctuation variance of the net load of the distribution network, and are the maximum and minimum peak-to-valley differences of the net load of the distribution network, and are the maximum and minimum values ​​of the net load fluctuation variance of the distribution network, V i ideal is the ideal voltage of node i, is the set of branch head nodes with j as the terminal node, ψ(j) is the set of branch terminal nodes with j as the terminal node, r ij and x ij is the resistance and reactance of the branch between node i and node j, P ij,t and Q ij,t The active and reactive power flowing from node i to node j at time t, V i,t is the voltage of node i at time t, V j,t is the voltage of node j at time t, P j,t and Q j,t are the injected active and reactive power of node j at time t, P jk,t and Q jk,t are the active and reactive power flowing from node j to node k at time t, and are the active power outputs of the photovoltaic power plant and wind farm at node j at time t, and are the active and reactive power outputs of the gas turbine at node j at time t, and are the active and reactive power of the electric vehicle charging station at node j at time t, and are the active and reactive power of the load at node j at time t, P ij and Q ij are the active power and reactive power flowing through the branch between node i and node j, I ij is the current amplitude flowing through the branch between node i and node j, and are the maximum and minimum current amplitudes flowing through the branch between node i and node j, respectively.

5. The method according to claim 1, wherein The lower layer model corresponding to the distribution network includes: a first-stage objective function and its corresponding first-stage constraint conditions and a second-stage objective function and its corresponding second-stage constraint conditions.

6. The method according to claim 5, wherein The objective function of the first stage is as follows: In the above formula, C0 is the objective function value of the first stage, N T With N G are the dispatch period and the number of gas turbines aggregated in the virtual power plant, and are the electricity price and electricity quantity purchased by the virtual power plant from the distribution network during period t, and are the charging electricity price and rechargeable power provided by the charging piles in the virtual power plant to each electric vehicle user, ρ g and is the fuel cost function and the active power output of the g-th gas turbine during the day-ahead period t, and are the upward reserve cost function of the g-th gas turbine and the upward reserve capacity provided during period t, and are the downward reserve cost function of the g-th gas turbine and the downward reserve capacity provided during period t, ΔF pvd and ΔF var are the changes in the peak-to-valley difference and fluctuation variance of the net load of the distribution network compared with the previous dispatching cycle, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, C pk It is used to calculate the peak load compensation coefficient provided by the upper distribution network to the virtual power plant.

7. The method according to claim 6, wherein The constraints of the first stage are as follows: In the above formula, and are the active power and reactive power of the cth charging pile in the virtual power plant during the t period, is the maximum power capacity of the cth charging pile, and is the charging and discharging active power of the v-th type of electric vehicle user at the c-th charging pile in the day-ahead period, and is the charging and discharging efficiency of the vth type of electric vehicle user at the cth charging pile, N c,v is the total number of electric vehicle user types under the c-th charging pile, λ c,v,t is a Boolean variable, when λ c,v,t = 1, the electric vehicle user of type v at the cth charging pile in period t is in the charging state. c,v,t = 0, the electric vehicle user of type v at the cth charging pile in period t is in the discharging state. represents the maximum charging and discharging power of the vth type of electric vehicle user at the cth charging pile, is the reactive power output of the g-th gas turbine during the day-ahead period t, represents the rated capacity of the g-th gas turbine, and are the maximum values ​​of upward and downward active reserves that can be provided by unit g when it transitions from the day-ahead phase to the intraday phase, and are the minimum values ​​of upward and downward active reserve, respectively, and are the upward and downward reactive power reserves that the gas turbine unit g can provide during period t, and are the active power output of photovoltaic power and wind power in period t during the day-ahead period, and are the available predicted values ​​of active power of photovoltaic and wind power in period t during the day-ahead period.

8. The method according to claim 7, wherein The objective function of the second stage is as follows: In the above formula, C S is the objective function value of the second stage, C rv is the voltage regulation compensation coefficient provided by the upper distribution network to the virtual power plant, C pv with C wt are the penalty cost coefficients for curtailing solar power and wind power, N pv With N w are photovoltaic power plant collection and wind power plant collection respectively. and are the absorption capacity of photovoltaic power field pv and wind power field w at time t in the day, and are the available predicted values ​​of active power of photovoltaic power plant pv and wind power plant w at time t in the day, and are the cost functions for calling the upward and downward reactive reserve of gas turbine unit g during the intraday phase, and are the upward and downward reactive reserve amounts called by unit g at time t in the day phase, is the reduction value of the integrated node voltage deviation of the distribution network system compared with the previous scheduling period, and U is the uncertainty set as an additional constraint condition.

9. The method according to claim 8, wherein The uncertain set is as follows: In the above formula, and are the available power of photovoltaic and wind power in the second stage t period, and is a 0-1 integer variable, used to indicate whether photovoltaic power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of photovoltaic at time t, u t,pv with u pv is the uncertainty limit and total uncertainty limit of photovoltaic at time t, and is a 0-1 integer variable, used to indicate whether the wind farm power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of wind power at time t, u t,w with u w are the uncertainty limit and total uncertainty limit of wind power at time t, is the reactive power load of node j in the virtual power plant during period t after considering the uncertainty, is the reactive power load of node j in period t during the day ahead, represents the maximum fluctuation deviation allowed for reactive load power at node j during period t, and is a 0-1 integer variable, which indicates that the reactive load of node j at time t fluctuates upward and downward when it is 0 or 1, respectively. ql is the uncertainty limit of reactive load.

10. The method according to claim 9, wherein The constraints of the second stage are as follows: In the above formula, It is the reactive power output of unit g at time t after reactive power reserve is called upon during the day.

11. The method according to claim 9, wherein The power allocated to each type of electric vehicle under the charging pile during the day is as follows: In the above formula, is the power allocated to the vth type of electric vehicle at charging pile c at time t, is the power adjustment amount allocated to the vth type of electric vehicle at the charging pile c at time t, is the chargeable and dischargeable power of the electric vehicle user v at time t. The power adjustment amount allocated to the v-th type of electric vehicle under the charging pile c at time t is as follows: In the above formula, is the weight of the vth type of electric vehicle under charging pile c, N c,v is the total number of electric vehicles of type v under charging pile c, is the change in active power of the electric vehicle during the intra-day period. The change in active power of the electric vehicle during the intra-day period is as follows: In the above formula, is the reactive power output of charging pile c during period t within the day.

12. The method according to claim 11, wherein The weight of the vth type of electric vehicle under the charging pile c is as follows: In the above formula, is the weight of the jth indicator of the vth type of electric vehicle under charging pile c, m is the total number of indicators, Y j is the evaluation coefficient of the j-th indicator, s is the proportional coefficient, is the current battery health status of the v-th type of electric vehicle, is the battery health status hazard value, and the weight of the jth indicator of the vth type of electric vehicle under the charging pile c is as follows: In the above formula, is the information entropy of the v-th type of electric vehicle under charging pile c on the j-th indicator, The information entropy of the v-th type of electric vehicle at the j-th indicator under charging pile c is as follows: In the above formula, is the proportion of the vth type of electric vehicles in the jth indicator under charging pile c, is the standardized data of various indices of the vth type of electric vehicle under charging pile c, n is the number of electric vehicle types, and the standardized data of various indices of the vth type of electric vehicle under charging pile c is as follows: In the above formula, is the jth indicator of the vth type of electric vehicle under charging pile c, is the jth indicator of all electric vehicles under charging pile c.

13. A master-slave game optimization device for a virtual power plant and a distribution network, characterized in that: The device comprises: The first analysis module is used to solve the upper-level model corresponding to the distribution network and obtain the upper-level distribution network optimization variables; The second analysis module is used to substitute the upper-layer distribution network optimization variables into the lower-layer model corresponding to the distribution network and solve them to obtain the lower-layer virtual power plant optimization variables; The third analysis module is used to determine the power allocated to each type of electric vehicle under the charging pile in the intraday stage based on the active power on the charging pile side in the day-ahead stage and the reactive power on the charging pile side in the intraday stage in the lower-layer virtual power plant optimization variables; An optimization module is configured to construct a distribution network scheduling optimization plan using the upper-layer distribution network optimization variables, construct a virtual power plant scheduling optimization plan using the lower-layer virtual power plant optimization variables and the power allocated to various types of electric vehicles under the charging piles during the daily stage, and respectively optimize the distribution network and virtual power plant scheduling using the distribution network scheduling optimization plan and the virtual power plant scheduling optimization plan; Among them, the upper-level distribution network optimization variables include: peak-shaving compensation coefficient and voltage regulation compensation coefficient, and the lower-level virtual power plant optimization variables include: gas turbine output in the day-ahead stage, power purchase from the power grid in the day-ahead stage, active power and reactive power on the charging pile side in the day-ahead stage, gas turbine output in the intraday stage, and active power and reactive power on the charging pile side in the intraday stage.

14. The device according to claim 13, wherein The upper-level model corresponding to the distribution network includes: an upper-level objective function and its corresponding upper-level constraint conditions.

15. The device according to claim 14, wherein The upper objective function is as follows: In the above formula, ω1, ω2 and ω3 are the target weights of peak load regulation and voltage regulation requirements during the actual operation of the distribution network, respectively. pvd With F fd is the peak-to-valley difference and fluctuation variance of the overall net load of the distribution network, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, N is the set consisting of all nodes in the distribution network, V i 、 are the voltage at node i, voltage upper limit, voltage lower limit and voltage rated value respectively.

16. The device according to claim 15, characterized in that The upper-level constraints are as follows: In i min ≤V i ≤V i max In the above formula, C pk with C rv are the peak load compensation coefficient and voltage regulation compensation coefficient provided by the upper distribution network of the game for the virtual power plant, α and β are the compensation standards for the peak-to-valley difference and fluctuation degree of the net load of the distribution network, respectively. i is the weight coefficient of node i, k is the adjustment coefficient, and are the ideal values ​​of the peak-to-valley difference and fluctuation variance of the net load of the distribution network, and are the maximum and minimum peak-to-valley differences of the net load of the distribution network, and are the maximum and minimum values ​​of the net load fluctuation variance of the distribution network, V i ideal is the ideal voltage of node i, is the set of branch head nodes with j as the terminal node, ψ(j) is the set of branch terminal nodes with j as the terminal node, r ij and x ij is the resistance and reactance of the branch between node i and node j, P ij,t and Q ij,t The active and reactive power flowing from node i to node j at time t, V i,t is the voltage of node i at time t, V j,t is the voltage of node j at time t, P j,t and Q j,t are the injected active and reactive power of node j at time t, P jk,t and Q jk,t are the active and reactive power flowing from node j to node k at time t, and are the active power outputs of the photovoltaic power plant and wind farm at node j at time t, and are the active and reactive power outputs of the gas turbine at node j at time t, and are the active and reactive power of the electric vehicle charging station at node j at time t, and are the active and reactive power of the load at node j at time t, P ij and Q ij are the active power and reactive power flowing through the branch between node i and node j, I ij is the current amplitude flowing through the branch between node i and node j, and are the maximum and minimum current amplitudes flowing through the branch between node i and node j, respectively.

17. The device according to claim 13, wherein The lower layer model corresponding to the distribution network includes: a first-stage objective function and its corresponding first-stage constraint conditions and a second-stage objective function and its corresponding second-stage constraint conditions.

18. The device according to claim 17, wherein The objective function of the first stage is as follows: In the above formula, C0 is the objective function value of the first stage, N T With N G are the dispatch period and the number of gas turbines aggregated in the virtual power plant, and are the electricity price and electricity quantity purchased by the virtual power plant from the distribution network during period t, and are the charging electricity price and rechargeable power provided by the charging piles in the virtual power plant to each electric vehicle user, ρ g and is the fuel cost function and the active power output of the g-th gas turbine during the day-ahead period t, and are the upward reserve cost function of the g-th gas turbine and the upward reserve capacity provided during period t, and are the downward reserve cost function of the g-th gas turbine and the downward reserve capacity provided during period t, ΔF pvd and ΔF var are the changes in the peak-to-valley difference and fluctuation variance of the net load of the distribution network compared with the previous dispatching cycle, a and b are the weight coefficients of peak-to-valley difference optimization and fluctuation variance optimization in peak load regulation demand, C pk It is used to calculate the peak load compensation coefficient provided by the upper distribution network to the virtual power plant.

19. The device according to claim 18, wherein The constraints of the first stage are as follows: In the above formula, and are the active power and reactive power of the cth charging pile in the virtual power plant during the t period, is the maximum power capacity of the cth charging pile, and is the charging and discharging active power of the v-th type of electric vehicle user at the c-th charging pile in the day-ahead period, and is the charging and discharging efficiency of the vth type of electric vehicle user at the cth charging pile, N c,v is the total number of electric vehicle user types under the c-th charging pile, λ c,v,t is a Boolean variable, when λ c,v,t = 1, the electric vehicle user of type v at the cth charging pile in period t is in the charging state. c,v,t = 0, the electric vehicle user of type v at the cth charging pile in period t is in the discharging state. represents the maximum charging and discharging power of the vth type of electric vehicle user at the cth charging pile, is the reactive power output of the g-th gas turbine during the day-ahead period t, represents the rated capacity of the g-th gas turbine, and are the maximum values ​​of upward and downward active reserves that can be provided by unit g when it transitions from the day-ahead phase to the intraday phase, and are the minimum values ​​of upward and downward active reserve, respectively, and are the upward and downward reactive power reserves that the gas turbine unit g can provide during period t, and are the active power output of photovoltaic power and wind power in period t during the day-ahead period, and are the available predicted values ​​of active power of photovoltaic and wind power in period t during the day-ahead period.

20. The device according to claim 19, wherein The objective function of the second stage is as follows: In the above formula, C S is the objective function value of the second stage, C rv is the voltage regulation compensation coefficient provided by the upper distribution network to the virtual power plant, C pv with C wt are the penalty cost coefficients for curtailing solar power and wind power, N pv With N w are photovoltaic power plant collection and wind power plant collection respectively. and are the absorption capacity of photovoltaic power field pv and wind power field w at time t in the day, and are the available predicted values ​​of active power of photovoltaic power plant pv and wind power plant w at time t in the day, and are the cost functions for calling the upward and downward reactive reserve of gas turbine unit g during the intraday phase, and are the upward and downward reactive reserve amounts called by unit g at time t in the day phase, is the reduction value of the integrated node voltage deviation of the distribution network system compared with the previous scheduling period, and U is the uncertainty set as an additional constraint condition.

21. The device according to claim 20, characterized in that The uncertain set is as follows: In the above formula, and are the available power of photovoltaic and wind power in the second stage t period, and is a 0-1 integer variable, used to indicate whether photovoltaic power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of photovoltaic at time t, u t,pv with u pv is the uncertainty limit and total uncertainty limit of photovoltaic at time t, and is a 0-1 integer variable, used to indicate whether the wind farm power generation fluctuates at time t. and are the upward and downward fluctuation values ​​of wind power at time t, u t,w with u w are the uncertainty limit and total uncertainty limit of wind power at time t, is the reactive power load of node j in the virtual power plant during period t after considering the uncertainty, is the reactive power load of node j in period t during the day ahead, represents the maximum fluctuation deviation allowed for reactive load power at node j during period t, and is a 0-1 integer variable, which indicates that the reactive load of node j at time t fluctuates upward and downward when it is 0 or 1, respectively. ql is the uncertainty limit of reactive load.

22. The device according to claim 21, wherein The constraints of the second stage are as follows: In the above formula, It is the reactive power output of unit g at time t after reactive power reserve is called upon during the day.

23. The device according to claim 21, wherein The power allocated to each type of electric vehicle under the charging pile during the day is as follows: In the above formula, is the power allocated to the vth type of electric vehicle at charging pile c at time t, is the power adjustment amount allocated to the vth type of electric vehicle at the charging pile c at time t, is the chargeable and dischargeable power of the electric vehicle user v at time t. The power adjustment amount allocated to the v-th type of electric vehicle under the charging pile c at time t is as follows: In the above formula, is the weight of the vth type of electric vehicle under charging pile c, N c,v is the total number of electric vehicles of type v under charging pile c, is the change in active power of the electric vehicle during the intra-day period. The change in active power of the electric vehicle during the intra-day period is as follows: In the above formula, is the reactive power output of charging pile c during period t within the day.

24. The device according to claim 23, wherein The weight of the vth type of electric vehicle under the charging pile c is as follows: In the above formula, is the weight of the jth indicator of the vth type of electric vehicle under charging pile c, m is the total number of indicators, Y j is the evaluation coefficient of the j-th indicator, s is the proportional coefficient, is the current battery health status of the v-th type of electric vehicle, is the battery health status hazard value, and the weight of the jth indicator of the vth type of electric vehicle under the charging pile c is as follows: In the above formula, is the information entropy of the v-th type of electric vehicle under charging pile c on the j-th indicator, The information entropy of the v-th type of electric vehicle at the j-th indicator under charging pile c is as follows: In the above formula, is the proportion of the vth type of electric vehicles in the jth indicator under charging pile c, is the standardized data of various indices of the vth type of electric vehicle under charging pile c, n is the number of electric vehicle types, and the standardized data of various indices of the vth type of electric vehicle under charging pile c is as follows: In the above formula, is the jth indicator of the vth type of electric vehicle under charging pile c, is the jth indicator of all electric vehicles under charging pile c.

25. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the master-slave game optimization method of the virtual power plant and distribution network as described in any one of claims 1 to 12 is implemented.

26. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, it implements the master-slave game optimization method of the virtual power plant and distribution network as described in any one of claims 1 to 12.

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