Power trading method and device for distributed power supply in distribution network based on master-slave game

Abstract

The present invention relates to a method and device for trading distributed power supply electricity in a distribution network based on a master-slave game. The method takes the active power of the distributed power supply as the trading object, and constructs a trading model for each non-renewable energy generation distributed power supply with the goal of minimizing the power generation cost of each non-renewable energy generation distributed power supply and maximizing the trading profit. The method also considers the supporting role of reactive power scheduling of the distributed power supply on the active power transaction, and takes the goal of maximizing the profit from electricity trading between the distribution network and each renewable and non-renewable energy generation distributed power supply to construct a distribution network trading pricing model. The method also models the electricity trading between the distribution network and the non-renewable energy generation distributed power supply based on the master-slave game architecture, and further transforms the two-layer master-slave game model of the distribution network distributed power supply electricity trading into a single-layer game equilibrium constraint pricing model through the KKT optimality condition, thereby realizing a rapid solution to the distribution network trading pricing problem and promoting the safe and economical operation of the distribution network.

Description

Technical Field

[0001] The present invention belongs to the field of power trading, and in particular relates to a method and device for trading distributed power sources in a distribution network based on a master-slave game. Background Art

[0002] Distributed power sources can be categorized as renewable energy-based distributed power sources (DGs) and non-renewable energy-based distributed power sources (DGs). Renewable energy-based DGs, typically represented by wind and photovoltaic power, offer low generation costs and are clean and pollution-free. However, their active power output is limited by environmental factors, requiring only power reduction for active power scheduling. Due to their zero-carbon and low-cost advantages, renewable energy-based DGs are gradually increasing their share of the DG landscape. Non-renewable energy-based DGs, represented by micro-gas turbines, also utilize converters for grid connection. Their marginal cost of generation increases with the output of random units, enabling planned output scheduling and supporting distribution network scheduling strategies. The high penetration of heterogeneous DGs and electric vehicle loads has led to operational issues such as voltage over-limit and line overloads in distribution networks, placing higher demands on their operation. Effectively scheduling the output of distributed power sources can ensure the safe and economic operation of distribution networks while promoting the integration of distributed generation and the economic scheduling of electric vehicles. How to effectively coordinate the scheduling strategies of these two types of DGs to promote the safe and economic operation of distribution networks remains an unresolved issue.

[0003] With the advancement of power market reform, distributed generation (DGs) are becoming the main players in power transactions with distribution networks, generating profits and promoting DG investment planning. Through node-based transaction pricing, distribution networks can guide DG trading strategies and coordinate the output plans of DGs across the entire network, thereby supporting the safe and economic operation of the distribution network. The master-slave game architecture can be effectively applied to the analysis and solution of power transaction pricing issues in distribution networks.

[0004] Currently, research on trading between distribution networks and distributed generation (DGs) both domestically and internationally focuses on active power trading. Further research is needed to consider the impact of DG reactive power scheduling on congestion relief and the economics of active power trading. Therefore, addressing the demand for DG power trading in distribution networks requires a trading pricing method that promotes the safe and economical operation of distribution networks. Summary of the Invention

[0005] In power trading based on a master-slave game, efficient and stable solution of the game equilibrium is crucial. Currently, iterative solutions are widely used, but these methods suffer from slow solution speed and poor convergence. Using the KKT optimality condition in place of the follower in the power trading model transforms the master-slave game problem into a single-layer game equilibrium constrained optimization problem, effectively solving the distributed generation power trading problem in distribution networks based on the master-slave game.

[0006] The present invention solves the technical problem by adopting the following technical solutions:

[0007] The method for trading electric energy of distributed power generation in a distribution network based on master-slave game includes the following steps:

[0008] According to the selected distribution network, obtain the parameters of non-renewable energy generation distributed power supply, distribution network parameters, and intelligent soft switch parameters;

[0009] Based on the obtained distribution network parameters and intelligent soft switch parameters, with the goal of maximizing the benefits of power trading between the distribution network and various renewable and non-renewable energy generation distributed power sources, a distribution network trading pricing model is constructed, taking into account distribution network flow constraints, system safety operation constraints, renewable and non-renewable energy generation distributed power generation scheduling constraints, and intelligent soft switch operation constraints.

[0010] Based on the obtained parameters of non-renewable energy generation distributed power sources, the non-renewable energy generation distributed power sources at each node aim to minimize power generation costs and maximize the revenue from selling electricity to the distribution network. This model is constructed by considering the unit output constraints and the piecewise linearization constraints of power generation costs.

[0011] Based on the constructed distribution network transaction pricing model and non-renewable energy generation distributed power supply transaction model, a two-layer master-slave game model of distribution network distributed power supply electric energy transaction is constructed, with the distribution network as the upper-layer leader and the non-renewable energy generation distributed power supplies as the lower-layer followers. The KKT optimality condition is used to equate the lower-layer non-renewable energy generation distributed power supply transaction model with the constraint condition of the upper-layer distribution network transaction pricing model, and a single-layer game equilibrium constraint pricing model of distribution network distributed power supply electric energy transaction is constructed.

[0012] Solve the constructed single-layer game equilibrium constraint pricing model to obtain the electricity transaction prices between the distribution network and each non-renewable energy generation distributed power source at each time period during the operation day, as well as the active and reactive outputs of each renewable and non-renewable energy generation distributed power source;

[0013] Without considering the distribution network's regulation of the reactive output of each renewable and non-renewable energy generation distributed power source, the reference power trading results are obtained based on the single-layer game equilibrium constraint pricing model; considering the distribution network's regulation of the reactive output of the distributed power source, the distribution network freely adjusts the reactive output of the distributed power source under the scheduling constraints of the renewable and non-renewable energy generation distributed power source, and the power trading results considering reactive auxiliary services are obtained based on the single-layer game equilibrium constraint pricing model;

[0014] Based on the obtained electric energy trading results taking reactive ancillary services into consideration, the distribution network and non-renewable energy distributed power sources conduct active energy trading settlements based on the electric energy trading price and trading power, and the distribution network conducts active energy trading settlements with each renewable energy distributed power source based on the on-grid electricity price of renewable energy power generation and the active power consumed; based on the obtained reference electric energy trading results, a portion of the increased operating income of the distribution network under the electric energy trading results taking reactive ancillary services into consideration will be distributed to each distributed power source according to the proportion of the distributed power source's reactive output, and the rest will be retained as the distribution network's own income.

[0015] Furthermore, the goal of maximizing the profit from the power transaction between the distribution network and the renewable and non-renewable energy generation distributed power sources can be expressed as:

[0016]

[0017] Where, f DN,t is the objective function of the distribution network in period t; is the electricity price of the upper power grid during period t; P DN,t is the active power purchased by the distribution network from the upper power grid during period t; is the active power of distributed power generation from renewable energy at node i during period t; RDG The on-grid tariff for active power of distributed power generation for renewable energy generation; is the active power output of the distributed generation of non-renewable energy at node i during period t; i,t The transaction pricing of the distributed power generation of non-renewable energy at node i in the distribution network during period t; Ω RDG is the node set of renewable energy distributed power generation connected to the distribution network, Ω NDG is the set of nodes where distributed power sources of non-renewable energy are connected to the distribution network; Δt is the duration of the trading period.

[0018] Furthermore, the scheduling constraints of distributed power generation taking into account renewable and non-renewable energy generation can be expressed as:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] Where, is the active power of distributed power generation from renewable energy sources at node i during period t; is the active power output of the distributed generation of non-renewable energy at node i during period t; are the reactive power output of renewable and non-renewable energy generation distributed power sources at node i during period t; are the total active and reactive power output by the two types of distributed power sources at node i during period t; are the capacities of distributed power sources for renewable and non-renewable energy generation, respectively; is the active power output of node i under the distributed power generation trading strategy of non-renewable energy generation in period t.

[0025] Furthermore, the KKT optimality condition is used to make the lower-layer non-renewable energy generation distributed power supply trading model equivalent to the constraint conditions of the upper-layer distribution network trading pricing model, where the lower-layer non-renewable energy generation distributed power supply trading model can be expressed as:

[0026]

[0027]

[0028]

[0029] Where, is the net transaction cost of distributed power generation from non-renewable energy sources; Δt is the duration of the transaction period; is the power generation cost of the distributed power generation of non-renewable energy at node i during period t; i,t The transaction pricing of the distributed generation of non-renewable energy at node i in the distribution network during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; P i NDG,min 、P i NDG,max are the lower and upper limits of the active power of distributed power generation from non-renewable energy sources; Ω T It is the set of trading sessions on a trading day; is the dual variable of the unit output constraint; Ω K,i is the piecewise linearization set of the distributed generation cost of non-renewable energy generation at node i; a k,i 、v k,i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; μ i,k,t is the dual variable of the piecewise linearization constraint for power generation cost;

[0030] The KKT optimality condition of the lower model is that the partial derivatives of the Lagrangian function are zero, which can be expressed as:

[0031]

[0032]

[0033] Where Δt is the duration of the trading session; Ω K,i is the piecewise linearization set of the distributed generation cost of non-renewable energy generation at node i; π i,t The transaction pricing of the distributed power generation of non-renewable energy at node i in the distribution network during period t; a k,i is the slope of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint for power generation cost;

[0034] The complementary slack condition in the KKT optimality condition can be expressed as:

[0035]

[0036]

[0037] Where, Ω K，i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; is the generation cost of the distributed generation of non-renewable energy at node i during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; a k,i 、b k,i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; P i NDG,min 、

[0038] P i NDG,max They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint of power generation cost; “⊥” indicates that the product of the expressions on both sides is zero.

[0039] Furthermore, the single-layer game equilibrium constraint pricing model constructed by the linearization process, including the complementary relaxation conditions in the linearized KKT optimality conditions, can be expressed as:

[0040]

[0041]

[0042]

[0043] Where M is a sufficiently large positive number; Ω K,i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; is the generation cost of the distributed generation of non-renewable energy at node i during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; a k,i 、b k,i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i, respectively; They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint for power generation cost; Binary variables introduced for linearization of complementary slack conditions.

[0044] Furthermore, the objective function of the linearized game equilibrium constraint pricing model can be expressed as:

[0045]

[0046] Where, f DN,t is the objective function of the distribution network in period t; Δt is the duration of the trading period; is the electricity price of the upper power grid during period t; P DN,t is the active power purchased by the distribution network from the upper power grid during period t; is the active power of distributed power generation from renewable energy at node i during period t; RDG Ω is the active power on-grid price of distributed power generation from renewable energy sources; RDG is the node set of renewable energy distributed power generation connected to the distribution network, Ω NDG The node set of distributed power generation from non-renewable energy sources connected to the distribution network; Ω K,i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; b is the power generation cost of the distributed power generation of non-renewable energy at node i during period t; k,i is the intercept of the kth segment of the piecewise linear cost function of distributed generation cost of non-renewable energy generation at node i; P i NDG,min 、P i NDG,max They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint on power generation cost.

[0047] Furthermore, the non-renewable energy generation distributed power supply parameters include the non-renewable energy generation distributed power supply generation cost function, capacity, and active output range; the distribution network parameters include the network topology connection relationship, the electric vehicle load and the distributed power supply access location and capacity, the electric vehicle load and the renewable energy generation distributed power supply daily operation curve prediction results, the system safe operation voltage range, the branch current limit, and the power transaction price π between the distribution network and the upper power grid at each time period t during the operation day. t g , Renewable energy power generation on-grid electricity price π RDG ; The intelligent soft switch parameters include the intelligent soft switch access position and the converter capacity at both ends.

[0048] A power trading device for distributed power generation in a distribution network based on a master-slave game, comprising:

[0049] The parameter acquisition module is used to obtain the parameters of the distributed power supply of non-renewable energy generation, the distribution network parameters, and the intelligent soft switch parameters according to the selected distribution network;

[0050] The distribution network transaction pricing model construction module is used to construct the distribution network transaction pricing model based on the obtained distribution network parameters and intelligent soft switch parameters, with the goal of maximizing the profit from the power transaction between the distribution network and various renewable and non-renewable energy generation distributed power sources, taking into account the distribution network power flow constraints, system safety operation constraints, renewable and non-renewable energy generation distributed power generation scheduling constraints, and intelligent soft switch operation constraints;

[0051] A non-renewable energy generation distributed power supply trading model construction module is used to construct a non-renewable energy generation distributed power supply trading model based on the obtained non-renewable energy generation distributed power supply parameters, with the non-renewable energy generation distributed power supply at each node taking the lowest power generation cost and the highest profit from selling electricity to the distribution network as the goal, taking into account the unit output constraint and the piecewise linearization constraint of power generation cost;

[0052] A module for constructing a single-layer game equilibrium constraint pricing model for distributed power generation in distribution networks is used to construct a two-layer master-slave game model for distributed power generation in distribution networks, with the distribution network as the upper-layer leader and the non-renewable energy generation distributed power generation as the lower-layer followers, based on the constructed distribution network transaction pricing model and the non-renewable energy generation distributed power generation transaction model. The KKT optimality condition is used to equate the lower-layer non-renewable energy generation distributed power generation transaction model with the constraint conditions of the upper-layer distribution network transaction pricing model, thereby constructing a single-layer game equilibrium constraint pricing model for distributed power generation in distribution networks.

[0053] The single-layer game equilibrium constraint pricing model solving module is used to solve the constructed single-layer game equilibrium constraint pricing model to obtain the electric energy transaction price between the distribution network and each non-renewable energy generation distributed power source at each time period during the operation day, as well as the active and reactive output of each renewable and non-renewable energy generation distributed power source;

[0054] The module for obtaining the results of electric energy transactions taking into account reactive power ancillary services is used to obtain reference electric energy transaction results based on a single-layer game equilibrium constraint pricing model, without considering the distribution network's regulation of the reactive power output of each renewable and non-renewable energy generation distributed power source. The module also considers the distribution network's regulation of the reactive power output of the distributed power source, allowing the distribution network to freely adjust the reactive output of the distributed power source under the scheduling constraints of the renewable and non-renewable energy generation distributed power source, and obtains the electric energy transaction results taking into account reactive power ancillary services based on a single-layer game equilibrium constraint pricing model.

[0055] The final electric energy transaction module between the distribution network and the distributed power sources is used to conduct active energy transaction settlement between the distribution network and the distributed power sources of non-renewable energy generation according to the electric energy transaction price and transaction power based on the obtained electric energy transaction results taking into account reactive auxiliary services. The distribution network conducts active energy transaction settlement with each distributed power source of renewable energy generation according to the on-grid electricity price of renewable energy generation and the active power consumed. Based on the obtained reference electric energy transaction results, a part of the increased operating income of the distribution network under the electric energy transaction results taking into account reactive auxiliary services will be distributed to each distributed power source according to the proportion of the reactive output of the distributed power source, and the rest will be retained as the income of the distribution network itself.

[0056] A computing device, comprising:

[0057] one or more processing units;

[0058] A storage unit for storing one or more programs,

[0059] Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the above-mentioned method for trading electric energy of distributed power sources in the distribution network based on master-slave game.

[0060] A computer-readable storage medium having a non-volatile program code executable by a processor, characterized in that when the computer program is executed by the processor, the steps of the above-mentioned method for trading electric energy of distributed power sources in a distribution network based on master-slave game are implemented.

[0061] The advantages and positive effects of the present invention are:

[0062] The master-slave game-based distributed power supply electric energy trading method for distribution network of the present invention is based on realizing the safe and economical operation of distribution network in the distributed power supply electric energy trading of distribution network, takes the active power of distributed power source as the trading object, and constructs the trading model of each non-renewable energy generation distributed power source with the goal of minimizing the power generation cost of each non-renewable energy generation distributed power source and maximizing the trading profit, considers the supporting role of reactive power scheduling of distributed power source on active power trading, and constructs the distribution network trading pricing model with the goal of maximizing the electric energy trading profit between the distribution network and each renewable and non-renewable energy generation distributed power source, and models the electric energy trading between the distribution network and the non-renewable energy generation distributed power source based on the master-slave game architecture, and further transforms the two-layer master-slave game model of the distribution network distributed power source electric energy trading into a single-layer game equilibrium constraint pricing model through the KKT optimality condition, thereby realizing the rapid solution of the distribution network trading pricing problem and promoting the safe and economical operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, unless otherwise specified, these drawings are intended only to conceptually illustrate the structures described herein and are not necessarily drawn to scale.

[0064] Figure 1 It is an improved IEEE 33-node distribution network calculation example structure in an embodiment of the present invention;

[0065] Figure 2 is the electricity price curve of the upper-level power grid in the embodiment of the present invention;

[0066] Figure 3 The output curve of the distributed power supply generated by renewable energy and the load curve including electric vehicles in the embodiment of the present invention;

[0067] FIG4( a ) shows the transaction pricing of the distribution network in Scheme 2 according to an embodiment of the present invention;

[0068] FIG4( b ) shows the transaction pricing of the distribution network in Scheme 3 according to an embodiment of the present invention;

[0069] FIG5( a ) shows the active transaction power of distributed power sources generated by non-renewable energy in solution 2 according to an embodiment of the present invention;

[0070] FIG5( b ) shows the active transaction power of distributed power sources generated by non-renewable energy in solution 3 of the embodiment of the present invention;

[0071] FIG5( c ) shows the active transaction power of distributed power sources generated by renewable energy in schemes 2 and 3 according to the embodiment of the present invention;

[0072] FIG6( a ) shows the operating benefits of distributed power generation using non-renewable energy sources in Scheme 2 according to an embodiment of the present invention;

[0073] FIG6( b ) shows the operating benefits of distributed power generation using non-renewable energy sources in Scheme 3 according to an embodiment of the present invention;

[0074] FIG6( c ) shows the operating benefits of distributed power generation using renewable energy in Solution 2 according to an embodiment of the present invention;

[0075] FIG6( d ) shows the operating benefits of distributed power generation using renewable energy in Solution 3 according to an embodiment of the present invention;

[0076] FIG7( a ) is a reactive output dispatch result of distributed power sources for non-renewable energy generation in Scheme 3 according to an embodiment of the present invention;

[0077] FIG7( b ) is a reactive output dispatch result of distributed power generation from renewable energy sources according to Scheme 3 of the embodiment of the present invention;

[0078] Figure 8 is the amount of reduction in distribution network cost in Schemes 2 and 3 of the embodiments of the present invention;

[0079] FIG9( a ) shows the active power injected by the intelligent soft switch at 12 nodes in Solution 3 of the embodiment of the present invention;

[0080] FIG9( b ) shows the reactive power injected by the intelligent soft switch at the nodes on both sides of the scheme 3 according to an embodiment of the present invention;

[0081] in, Figure 3 PV stands for photovoltaics, Wind stands for wind turbines, and Load stands for electric vehicle load. DETAILED DESCRIPTION

[0082] First, it should be noted that the specific structure, characteristics and advantages of the present invention will be described in detail below by way of example, but all descriptions are for illustration only and should not be understood as limiting the present invention. In addition, any single technical feature described or implied in the embodiments mentioned herein may still be combined or deleted between these technical features (or their equivalents) to obtain more other embodiments of the present invention that may not be directly mentioned herein. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other.

[0083] The present invention relates to a method for trading electric energy of distributed power sources in a distribution network based on a master-slave game, comprising the following steps:

[0084] 1) According to the selected distribution network, obtain the parameters of non-renewable energy generation distributed power supply, including the generation cost function, capacity, and active output range of non-renewable energy generation distributed power supply; obtain the distribution network parameters, including the network topology connection relationship, the electric vehicle load and the access location and capacity of the distributed power supply, the daily operation curve prediction results of the electric vehicle load and renewable energy generation distributed power supply, the system safe operation voltage range, the branch current limit, and the power transaction price between the distribution network and the upper power grid at each time period t during the operation day Renewable energy power generation on-grid price π RDG ; Obtain the parameters of the intelligent soft switch, including the intelligent soft switch connection location and the converter capacity at both ends; Set the duration Δt of each trading period during the operation day;

[0085] For this embodiment, the improved IEEE 33-node distribution network diagram is as follows: Figure 1 As shown in the figure, the system is modified based on the IEEE33 node distribution system example, and some renewable and non-renewable energy distributed power generation is configured, and a two-port intelligent soft switch is configured. The topology, branch impedance parameters, and node load parameters of the improved IEEE33 node example are consistent with the IEEE33 node standard example. The voltage level is 12.66 kV, the total active power demand and total reactive power demand of the load are 3.175 MW and 2.3 Mvar respectively, the system voltage base value is set to 12.66 kV, and the power base value is set to 1 MVA. Detailed parameters are shown in Tables 1 and 2.

[0086] The time-of-use electricity price of the upper power grid is as follows Figure 2 The configuration location and parameters of distributed power generation for renewable energy generation are shown in Table 3; the daily operation curves of wind turbines, photovoltaics and electric vehicle loads are shown in Figure 3 As shown, the on-grid electricity price for renewable energy distributed power generation is set at 0.4153 RMB / kWh. In this embodiment, the non-renewable energy distributed power generation type is a micro gas turbine, and its cost function is set as a quadratic function and divided into 15 segments for piecewise linearization. Its configuration location and parameters are shown in Table 4. A day is divided into 24 trading periods, each lasting 1 hour.

[0087] Table 1 Load access location and power of IEEE 33-node distribution network example

[0088]

[0089]

[0090] Table 2 IEEE 33-node distribution network example line parameters

[0091]

[0092] Table 3. Connection location and capacity of distributed power generation for renewable energy generation

[0093] Access Node Photovoltaic capacity (kVA) Fan capacity (kVA) 12 150 - 21 - 150 33 200 -

[0094] Table 4. Connection location and parameters of distributed power generation for non-renewable energy generation

[0095]

[0096] The port capacity of the two-port intelligent soft switch is 1MVA, and the loss coefficient is set to 0.01, ignoring the power loss of the intelligent soft switch in the DC link; the per-unit value of the safe operating voltage range of the distribution network is set to 0.9 to 1.1;

[0097] 2) Based on the distribution network parameters and intelligent soft switch parameters provided in step 1), with the goal of maximizing the revenue from power trading between the distribution network and each renewable and non-renewable energy generation distributed power source, taking into account distribution network flow constraints, system safety operation constraints, renewable and non-renewable energy generation distributed power source scheduling constraints, and intelligent soft switch operation constraints, a distribution network trading pricing model is constructed;

[0098] (1) The goal of maximizing the benefits of electricity trading between the distribution network and various renewable and non-renewable energy generation distributed power sources can be expressed as:

[0099]

[0100] Where, f DN,t is the objective function of the distribution network in period t; is the electricity price of the upper power grid during period t; P DN,t is the active power purchased by the distribution network from the upper power grid during period t; is the active power of distributed power generation from renewable energy at node i during period t; RDG The on-grid tariff for active power of distributed power generation for renewable energy generation; is the active power output of the distributed generation of non-renewable energy at node i during period t; i,t The transaction pricing of the distributed power generation of non-renewable energy at node i in the distribution network during period t; Ω RDG is the node set of renewable energy distributed power generation connected to the distribution network, Ω NDG is the set of nodes where distributed power sources of non-renewable energy are connected to the distribution network; Δt is the duration of the trading period.

[0101] (2) The distribution network flow constraint can be expressed as:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] Where, Ω b is the distribution network branch set; P ji,t , Q ji,t are the active and reactive power flowing through branch ji during period t respectively; is the square of the current amplitude of branch ij during period t; R ij 、X ij are the resistance and reactance of branch ij respectively; P i,t , Q i,t are the net injected active and reactive power of node i in period t, respectively; is the total active and reactive power injected by the distributed generation at node i during period t; are the active and reactive powers injected by the intelligent soft switch at node i during period t; is the active and reactive power of the load at node i during period t; is the square of the voltage amplitude at node i during period t.

[0109] (3) The system safety operation constraints can be expressed as:

[0110]

[0111]

[0112] Where, U are the upper and lower limits of the node i voltage, I ij are the upper and lower limits of the current amplitude of branch ij.

[0113] (4) The scheduling constraints of renewable and non-renewable energy generation distributed power sources can be expressed as:

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] Where, is the active power of distributed power generation from renewable energy sources at node i during period t; is the active power output of the distributed generation of non-renewable energy at node i during period t; are the reactive power output of renewable and non-renewable energy generation distributed power sources at node i during period t; are the total active and reactive power output by the two types of distributed power sources at node i during period t; are the capacities of distributed power sources for renewable and non-renewable energy generation, respectively; is the active power output of node i under the distributed power generation trading strategy of non-renewable energy generation in period t.

[0120] (5) The intelligent soft switch operation constraints can be expressed as:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] Where, is the converter loss coefficient of the intelligent soft switch at node i; is the active power loss of the intelligent soft switch at node i during period t; is the converter capacity of the intelligent soft switch at node i.

[0127] 3) Based on the non-renewable energy generation distributed power supply parameters provided in step 1), the non-renewable energy generation distributed power supply at each node i aims to minimize the power generation cost and maximize the revenue from selling electricity to the distribution network, taking into account the unit output constraint and the piecewise linearization constraint of power generation cost, and constructing a non-renewable energy generation distributed power supply trading model;

[0128] (1) The distributed power sources of non-renewable energy generation aim to minimize power generation costs and maximize revenue from selling electricity to the distribution network, which can be expressed as:

[0129]

[0130] Where, is the net transaction cost of distributed power generation from non-renewable energy sources; Δt is the duration of the transaction period; is the power generation cost of the distributed power generation of non-renewable energy at node i during period t; i,t The transaction pricing of the distributed generation of non-renewable energy at node i in the distribution network during period t; is the active power of the distributed generation of non-renewable energy at node i during period t.

[0131] (2) The unit output constraint can be expressed as:

[0132]

[0133] Where, P i NDG,min 、P i NDG,max are the lower and upper limits of the active power of distributed power generation from non-renewable energy sources; Ω T It is the set of trading sessions on a trading day; is the dual variable of the unit output constraint.

[0134] (3) The piecewise linearization constraint of power generation cost can be expressed as:

[0135]

[0136] Where, Ω K,i is the piecewise linearization set of the distributed generation cost of non-renewable energy generation at node i; a k,i 、b k,i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; μ i,k,t is the dual variable of the piecewise linearization constraint on power generation cost.

[0137] 4) Based on the distribution network transaction pricing model in step 2) and the non-renewable energy generation distributed power supply transaction model in step 3), a two-layer master-slave game model for distribution network distributed power supply electricity trading is constructed, with the distribution network as the upper-layer leader and the non-renewable energy generation distributed power supplies as the lower-layer followers. The KKT optimality condition is used to equate the lower-layer non-renewable energy generation distributed power supply transaction model with the constraint condition of the upper-layer distribution network transaction pricing model, and a single-layer game equilibrium constraint pricing model for distribution network distributed power supply electricity trading is constructed;

[0138] (1) The above-mentioned construction of a two-layer master-slave game model for power energy trading in a distribution network with the distribution network as the upper-layer leader and the non-renewable energy generation distributed power sources as the lower-layer followers can be described as follows:

[0139] The distribution network operator formulates the active power transaction price with each non-renewable energy distributed power source based on the response of each non-renewable energy distributed power source; each non-renewable energy distributed power source solves the non-renewable energy distributed power source transaction model based on the transaction pricing of the distribution network and formulates the optimal transaction strategy under the current transaction price; the distribution network adjusts the pricing of active power transactions based on the transaction strategies given by each non-renewable energy distributed power source, improves the operating efficiency of the distribution network, and makes the transaction results meet the constraints until the operating income of the distribution network no longer increases, and finally obtains the optimal pricing strategy for the distribution network.

[0140] (2) The KKT optimality condition is used to equate the lower-level non-renewable energy generation distributed power supply trading model with the constraint conditions of the upper-level distribution network trading pricing model. In the KKT optimality condition, the partial derivatives of the Lagrangian function are zero, which can be expressed as:

[0141]

[0142]

[0143] Where Δt is the duration of the trading session; Ω K,i is the piecewise linearization set of the distributed generation cost of non-renewable energy generation at node i; π i,t The transaction pricing of the distributed power generation of non-renewable energy at node i in the distribution network during period t; a k,i is the slope of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint on power generation cost.

[0144] (3) In the KKT optimality condition, the complementary relaxation condition can be expressed as:

[0145]

[0146]

[0147] Where, Ω K,i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; is the generation cost of the distributed generation of non-renewable energy at node i during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; a k,i 、b k,i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; P iNDG,min 、

[0148] P i NDG,max They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint of power generation cost; “⊥” indicates that the product of the expressions on both sides is zero.

[0149] (4) The single-layer game equilibrium constraint pricing model for electric energy trading in the flexible interconnected distribution network can be expressed as:

[0150]

[0151] st(2)~(19), (21)~(26) (45)

[0152] 5) Linearizing the single-layer game equilibrium constrained pricing model constructed in step 4) and solving it using the mathematical solver CPLEX to obtain the electric energy transaction prices between the distribution network and each non-renewable energy generation distributed power source at each time period during the operation day, as well as the active and reactive outputs of each renewable and non-renewable energy generation distributed power source;

[0153] The single-layer game equilibrium constraint pricing model constructed in the linearization processing step 4) described in (1), including the complementary relaxation conditions in the linearized KKT optimality conditions, can be expressed as:

[0154]

[0155]

[0156]

[0157] Where M is a sufficiently large positive number; Ω K,i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; is the generation cost of the distributed generation of non-renewable energy at node i during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; a k,i 、b k,i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; P i NDG ,min 、P i NDG,max They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint for power generation cost; A binary variable introduced for the linearization of complementary slack conditions, taking the value 0 or 1.

[0158] (2) The single-layer game equilibrium constraint pricing model constructed in the linearization processing step 4), including the objective function of the linearized game equilibrium constraint pricing model, can be expressed as:

[0159]

[0160] Where, f DN,t is the objective function of the distribution network in period t; Δt is the duration of the trading period; is the electricity price of the upper power grid during period t; P DN,t is the active power purchased by the distribution network from the upper power grid during period t; is the active power of distributed power generation from renewable energy at node i during period t; RDG Ω is the active power on-grid price of distributed power generation from renewable energy sources; RDG is the node set of renewable energy distributed power generation connected to the distribution network, Ω NDG The node set of distributed power generation from non-renewable energy sources connected to the distribution network; Ω K,i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; b is the power generation cost of the distributed power generation of non-renewable energy at node i during period t; k,i is the intercept of the kth segment of the piecewise linear cost function of distributed generation cost of non-renewable energy generation at node i; P i NDG,min 、P i NDG,max They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i,k,t is the dual variable of the piecewise linearization constraint on power generation cost.

[0161] (3) The mixed integer second-order cone programming model obtained by linearizing the single-level game equilibrium constraint pricing model can be expressed as:

[0162]

[0163] st(2)~(19), (21)~(24), (29)~(31) (51)

[0164] 6) Ignoring the distribution network's regulation of the reactive output of each renewable and non-renewable energy distributed power source, the distributed power source's reactive output is set to 0, and the power factor is 1, and the reference electric energy trading result is calculated according to step 5); considering the distribution network's regulation of the reactive output of the distributed power source, the distribution network freely adjusts the distributed power source's reactive output under the scheduling constraints of the renewable and non-renewable energy distributed power sources, and the electric energy trading result considering reactive auxiliary services is calculated according to step 5);

[0165] 7) Based on the power transaction results considering reactive auxiliary services obtained in step 6), the distribution network and the non-renewable energy generation distributed power source conduct active power transaction settlement according to the power transaction price and transaction power, and the distribution network settles the active power transaction according to the renewable energy generation grid electricity price π RDG And the active power consumed is traded and settled with each distributed power source for active power generation from renewable energy sources; based on the reference power transaction result in step 6), the distribution network further distributes a portion of the increased distribution network operating income under the power transaction result taking into account reactive auxiliary services to each distributed power source according to the proportion of the distributed power source's reactive output, and the rest is retained as the distribution network's own income.

[0166] In order to verify the feasibility and effectiveness of the distributed power generation power trading method based on master-slave game in the distribution network of the present invention, the following three scenarios are adopted for verification and analysis in this embodiment:

[0167] Option 1: Distributed power sources for renewable energy generation and non-renewable energy generation maintain zero on-grid power, and the distribution network only trades with the upper-level power grid as a reference benchmark for the operating benefits of all parties.

[0168] Scheme 2: The distribution network and the distributed power generation of renewable energy generation and non-renewable energy generation adopt the proposed energy trading method to trade only active energy. The power factor of the distributed power generation is set to 1. It is assumed that the current capacity of line 6-26 and its subsequent lines is 1.25 times the current base value.

[0169] Scheme 3: The distribution network and the distributed power sources of renewable energy generation and non-renewable energy generation adopt the proposed electric energy trading method to conduct active power trading and reactive power scheduling. The distribution network allocates 50% of the reactive power scheduling profit of the distributed power sources to the distributed power sources according to the reactive output ratio. It is assumed that the current capacity of line 6-26 and its subsequent lines is 1.25 times the current base value.

[0170] The transaction pricing of the distribution network of schemes 2 and 3 for non-renewable energy generation distributed power sources is shown in Figures 4(a) and 4(b); the active power transaction results of the distribution network of schemes 2 and 3 and the two types of distributed power sources are shown in Figure 5(a) 、 5(b), 5(c); the operating income of non-renewable energy generation distributed power supply and renewable energy generation distributed power supply of schemes 2 and 3 are as follows Figure 6(a) 、 6(b) , 6(c), 6(d); the reactive power dispatch results of distributed generation in scheme 3 are shown as follows Figure 7(a) 、 7(b) Compared with Scheme 1, the cost of distribution network in each period of Scheme 2 and 3 is reduced as follows: Figure 8 In Scheme 3, the active and reactive power scheduling results of the intelligent soft switch are shown in Figures 9 (a) and 9 (b).

[0171] The computer hardware environment for performing optimization calculations is Intel(R) Core(TM) i5-5200U CPU with a main frequency of 2.20GHz and a memory of 4GB; the software environment is Windows 10 operating system.

[0172] Compared to Option 1, which trades only with the upper-level power grid, Option 2 reduces operating costs by enabling active power trading through a distributed generation power trading method based on a master-slave game. Option 3 supports active power trading through reactive power dispatching of distributed generation power, further alleviating congestion and reducing operating costs.

[0173] From the comparison of the two schemes, it can be seen that the distributed power generation power energy trading method based on master-slave game in the distribution network proposed by the present invention can effectively improve the economic efficiency of the distribution network operation, ensure the profit of distributed power generation trading, and alleviate system congestion.

[0174] Example 2

[0175] This embodiment provides a power trading device for distributed power generation in a distribution network based on a master-slave game, comprising:

[0176] The parameter acquisition module is used to obtain the parameters of the distributed power supply of non-renewable energy generation, the distribution network parameters, and the intelligent soft switch parameters according to the selected distribution network;

[0177] The distribution network transaction pricing model construction module is used to construct the distribution network transaction pricing model based on the obtained distribution network parameters and intelligent soft switch parameters, with the goal of maximizing the profit from the power transaction between the distribution network and various renewable and non-renewable energy generation distributed power sources, taking into account the distribution network power flow constraints, system safety operation constraints, renewable and non-renewable energy generation distributed power generation scheduling constraints, and intelligent soft switch operation constraints;

[0178] A non-renewable energy generation distributed power supply trading model construction module is used to construct a non-renewable energy generation distributed power supply trading model based on the obtained non-renewable energy generation distributed power supply parameters, with the non-renewable energy generation distributed power supply at each node taking the lowest power generation cost and the highest profit from selling electricity to the distribution network as the goal, taking into account the unit output constraint and the piecewise linearization constraint of power generation cost;

[0179] A module for constructing a single-layer game equilibrium constraint pricing model for distributed power generation in distribution networks is used to construct a two-layer master-slave game model for distributed power generation in distribution networks, with the distribution network as the upper-layer leader and the non-renewable energy generation distributed power generation as the lower-layer followers, based on the constructed distribution network transaction pricing model and the non-renewable energy generation distributed power generation transaction model. The KKT optimality condition is used to equate the lower-layer non-renewable energy generation distributed power generation transaction model with the constraint conditions of the upper-layer distribution network transaction pricing model, thereby constructing a single-layer game equilibrium constraint pricing model for distributed power generation in distribution networks.

[0180] The single-layer game equilibrium constraint pricing model solving module is used to solve the constructed single-layer game equilibrium constraint pricing model to obtain the electric energy transaction price between the distribution network and each non-renewable energy generation distributed power source at each time period during the operation day, as well as the active and reactive output of each renewable and non-renewable energy generation distributed power source;

[0181] The module for obtaining the results of electric energy transactions taking into account reactive power ancillary services is used to obtain reference electric energy transaction results based on a single-layer game equilibrium constraint pricing model, without considering the distribution network's regulation of the reactive power output of each renewable and non-renewable energy generation distributed power source. The module also considers the distribution network's regulation of the reactive power output of the distributed power source, allowing the distribution network to freely adjust the reactive output of the distributed power source under the scheduling constraints of the renewable and non-renewable energy generation distributed power source, and obtains the electric energy transaction results taking into account reactive power ancillary services based on a single-layer game equilibrium constraint pricing model.

[0182] The final electric energy transaction module between the distribution network and the distributed power generation is used to settle active energy transactions between the distribution network and the non-renewable energy generation distributed power generation according to the electric energy transaction price and transaction power based on the obtained electric energy transaction results taking into account reactive auxiliary services. The distribution network settles active energy transactions with each renewable energy generation distributed power generation according to the on-grid electricity price of renewable energy generation and the active power consumed. Based on the obtained reference electric energy transaction results, a portion of the increased operating income of the distribution network under the electric energy transaction results taking into account reactive auxiliary services is allocated to each distributed power generation according to the proportion of the reactive output of the distributed power generation, and the rest is retained as the income of the distribution network itself.

[0183] In addition, this embodiment further provides a computing device, including:

[0184] one or more processing units;

[0185] A storage unit for storing one or more programs,

[0186] In which, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the above-mentioned master-slave game-based distributed power supply electricity trading method in the distribution network; it should be noted that the computing device may include but is not limited to a processing unit and a storage unit; those skilled in the art can understand that the computing device includes a processing unit and a storage unit and does not constitute a limitation on the computing device, and may include more components, or a combination of certain components, or different components. For example, the computing device may also include input and output devices, network access devices, buses, etc.

[0187] Also provided is a computer-readable storage medium having a processor-executable non-volatile program code, wherein when the computer program is executed by the processor, the steps of the above-mentioned method for trading distributed power generation in a distribution network based on master-slave game are implemented. It should be noted that the readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. The program contained on the readable medium can be transmitted using any appropriate medium, including, but not limited to, wireless, wired, optical cable, RF, etc., or any suitable combination thereof. For example, the program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., as well as conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a standalone software package, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

Claims

1. A method for trading electric energy of distributed power generation in a distribution network based on master-slave game, characterized in that: The steps include: According to the selected distribution network, obtain the parameters of non-renewable energy generation distributed power supply, distribution network parameters, and intelligent soft switch parameters; Based on the obtained distribution network parameters and intelligent soft switch parameters, with the goal of maximizing the benefits of power trading between the distribution network and various renewable and non-renewable energy generation distributed power sources, a distribution network trading pricing model is constructed, taking into account distribution network flow constraints, system safety operation constraints, renewable and non-renewable energy generation distributed power generation scheduling constraints, and intelligent soft switch operation constraints. Based on the obtained parameters of non-renewable energy generation distributed power sources, the non-renewable energy generation distributed power sources at each node aim to minimize power generation costs and maximize the revenue from selling electricity to the distribution network. This model is constructed by considering the unit output constraints and the piecewise linearization constraints of power generation costs. Based on the constructed distribution network transaction pricing model and non-renewable energy generation distributed power supply transaction model, a two-layer master-slave game model of distribution network distributed power supply electric energy transaction is constructed, with the distribution network as the upper-layer leader and the non-renewable energy generation distributed power supplies as the lower-layer followers. The KKT optimality condition is used to equate the lower-layer non-renewable energy generation distributed power supply transaction model with the constraint condition of the upper-layer distribution network transaction pricing model, and a single-layer game equilibrium constraint pricing model of distribution network distributed power supply electric energy transaction is constructed. Solve the constructed single-layer game equilibrium constraint pricing model to obtain the electricity transaction prices between the distribution network and each non-renewable energy generation distributed power source at each time period during the operation day, as well as the active and reactive outputs of each renewable and non-renewable energy generation distributed power source; Without considering the distribution network's regulation of the reactive output of each renewable and non-renewable energy generation distributed power source, the reference power trading results are obtained based on the single-layer game equilibrium constraint pricing model; considering the distribution network's regulation of the reactive output of the distributed power source, the distribution network freely adjusts the reactive output of the distributed power source under the scheduling constraints of the renewable and non-renewable energy generation distributed power source, and the power trading results considering reactive auxiliary services are obtained based on the single-layer game equilibrium constraint pricing model; Based on the obtained electric energy transaction results taking into account reactive ancillary services, the distribution network and the non-renewable energy generation distributed power sources conduct active energy transaction settlements based on the electric energy transaction price and transaction power, and the distribution network conducts active energy transaction settlements with each renewable energy generation distributed power source based on the renewable energy generation grid-connected electricity price and the active power consumed; Based on the reference electricity trading results obtained, part of the increase in distribution network operation revenue under the electricity trading results considering reactive auxiliary services will be allocated to each distributed power source according to the proportion of distributed power source reactive output, and the rest will be retained as the distribution network's own revenue.

2. The method for trading electric energy of distributed power supply in distribution network based on master-slave game according to claim 1, characterized in that: The goal of maximizing the benefits of electricity trading between the distribution network and various renewable and non-renewable energy generation distributed power sources can be expressed as: Where, f DN，t is the objective function of the distribution network in period t; is the electricity price of the upper power grid during period t; P DN，t is the active power purchased by the distribution network from the upper power grid during period t; is the active power of distributed power generation from renewable energy at node i during period t; RDG The on-grid tariff for active power of distributed power generation for renewable energy generation; is the active power output of the distributed generation of non-renewable energy at node i during period t; i，t The transaction pricing of the distributed power generation of non-renewable energy at node i in the distribution network during period t; Ω RDG is the node set of renewable energy distributed power generation connected to the distribution network, Ω NDG is the set of nodes where distributed power sources of non-renewable energy are connected to the distribution network; Δt is the duration of the trading period.

3. The method for trading electric energy of distributed power supply in distribution network based on master-slave game according to claim 1, characterized in that: The scheduling constraints of distributed power generation taking into account renewable and non-renewable energy generation can be expressed as: Where, are the reactive power output of renewable and non-renewable energy generation distributed power sources at node i during period t; are the total active and reactive power output by the two types of distributed power sources at node i during period t; are the capacities of distributed power sources for renewable and non-renewable energy generation, respectively; is the active power output of node i under the distributed power generation trading strategy of non-renewable energy generation in period t.

4. The method for trading electric energy of distributed power supply in distribution network based on master-slave game according to claim 1, characterized in that: The KKT optimality condition is used to make the lower-layer non-renewable energy generation distributed power supply trading model equivalent to the constraint conditions of the upper-layer distribution network trading pricing model, where the lower-layer non-renewable energy generation distributed power supply trading model can be expressed as: Where, is the net transaction cost of distributed power generation from non-renewable energy sources; Δt is the duration of the transaction period; is the power generation cost of the distributed power generation of non-renewable energy at node i during period t; i，t The transaction pricing of the distributed generation of non-renewable energy at node i in the distribution network during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; Ω K，i is the piecewise linearization set of the distributed generation cost of non-renewable energy generation at node i; Ω T is the set of trading hours on a trading day; a k，i 、b k，i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i, respectively; is the dual variable of the unit output constraint; μ i，k，t is the dual variable of the piecewise linearization constraint for power generation cost; The KKT optimality condition of the lower model is that the partial derivatives of the Lagrangian function are zero, which can be expressed as: Where Δt is the duration of the trading session; Ω K，i is the piecewise linearization set of the distributed generation cost of non-renewable energy generation at node i; π i，t The transaction pricing of the distributed power generation of non-renewable energy at node i in the distribution network during period t; a k，i is the slope of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; is the dual variable of the unit output constraint; μ i，k，t is the dual variable of the piecewise linearization constraint for power generation cost; The complementary slack condition in the KKT optimality condition can be expressed as: Where, Ω K，i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; is the generation cost of the distributed generation of non-renewable energy at node i during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; a k，i 、b k，i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i, respectively; They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i，k，t is the dual variable of the piecewise linearization constraint of power generation cost; "⊥" indicates that the product of the expressions on both sides is zero.

5. The method for trading electric energy of distributed power supply in distribution network based on master-slave game according to claim 1, characterized in that: The single-layer game equilibrium constraint pricing model constructed by the linearization process, including the complementary relaxation conditions in the linearized KKT optimality conditions, can be expressed as: Where M is a sufficiently large positive number; Ω K，i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; is the generation cost of the distributed generation of non-renewable energy at node i during period t; is the active power of the distributed generation of non-renewable energy at node i during period t; a k，i 、b k，i are the slope and intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i, respectively; They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i，k，t is the dual variable of the piecewise linearization constraint for power generation cost; A binary variable introduced for the linearization of complementary slack conditions, taking the value 0 or 1.

6. The method for trading electric energy of distributed power sources in a distribution network based on master-slave game according to claim 5, characterized in that: The objective function of the linearized game equilibrium constraint pricing model can be expressed as: Where, f DN，t is the objective function of the distribution network in period t; Δt is the duration of the trading period; is the electricity price of the upper power grid during period t; P DN，t is the active power purchased by the distribution network from the upper power grid during period t; is the active power of distributed power generation from renewable energy at node i during period t; RDG Ω is the active power on-grid price of distributed power generation from renewable energy sources; RDG is the node set of renewable energy distributed power generation connected to the distribution network, Ω NDG The node set of distributed power generation from non-renewable energy sources connected to the distribution network; Ω K，i is the piecewise linearization set of the generation cost of distributed generation of non-renewable energy at node i; b is the power generation cost of the distributed power generation of non-renewable energy at node i during period t; k，i is the intercept of the kth segment of the piecewise linear cost function of the distributed generation cost of non-renewable energy generation at node i; They are the lower and upper limits of active power of distributed power generation from non-renewable energy sources; is the dual variable of the unit output constraint; μ i，k，t is the dual variable of the piecewise linearization constraint on power generation cost.

7. The method for trading electric energy of distributed power supply in distribution network based on master-slave game according to claim 1, characterized in that: The parameters of the non-renewable energy generation distributed power supply include the generation cost function, capacity, and active output range of the non-renewable energy generation distributed power supply; the distribution network parameters include the network topology connection relationship, the electric vehicle load and the access location and capacity of the distributed power supply, the daily operation curve prediction results of the electric vehicle load and the renewable energy generation distributed power supply, the system safe operation voltage range, the branch current limit, and the power transaction price π between the distribution network and the upper power grid at each time period t during the operation day. t g , Renewable energy power generation on-grid electricity price π RDG ; The intelligent soft switch parameters include the intelligent soft switch access position and the converter capacity at both ends.

8. A power trading device for distributed power generation in a distribution network based on master-slave game, characterized in that: include: The parameter acquisition module is used to obtain the parameters of the distributed power supply of non-renewable energy generation, the distribution network parameters, and the intelligent soft switch parameters according to the selected distribution network; The distribution network transaction pricing model construction module is used to construct the distribution network transaction pricing model based on the obtained distribution network parameters and intelligent soft switch parameters, with the goal of maximizing the profit from the power transaction between the distribution network and various renewable and non-renewable energy generation distributed power sources, taking into account the distribution network power flow constraints, system safety operation constraints, renewable and non-renewable energy generation distributed power generation scheduling constraints, and intelligent soft switch operation constraints; A non-renewable energy generation distributed power supply trading model construction module is used to construct a non-renewable energy generation distributed power supply trading model based on the obtained non-renewable energy generation distributed power supply parameters, with the non-renewable energy generation distributed power supply at each node taking the lowest power generation cost and the highest profit from selling electricity to the distribution network as the goal, taking into account the unit output constraint and the piecewise linearization constraint of power generation cost; A module for constructing a single-layer game equilibrium constraint pricing model for distributed power generation in distribution networks is used to construct a two-layer master-slave game model for distributed power generation in distribution networks, with the distribution network as the upper-layer leader and the non-renewable energy generation distributed power generation as the lower-layer followers, based on the constructed distribution network transaction pricing model and the non-renewable energy generation distributed power generation transaction model. The KKT optimality condition is used to equate the lower-layer non-renewable energy generation distributed power generation transaction model with the constraint conditions of the upper-layer distribution network transaction pricing model, thereby constructing a single-layer game equilibrium constraint pricing model for distributed power generation in distribution networks. The single-layer game equilibrium constraint pricing model solving module is used to solve the constructed single-layer game equilibrium constraint pricing model to obtain the electric energy transaction price between the distribution network and each non-renewable energy generation distributed power source at each time period during the operation day, as well as the active and reactive output of each renewable and non-renewable energy generation distributed power source; The module for obtaining the results of electric energy transactions taking into account reactive power ancillary services is used to obtain reference electric energy transaction results based on a single-layer game equilibrium constraint pricing model, without considering the distribution network's regulation of the reactive power output of each renewable and non-renewable energy generation distributed power source. The module also considers the distribution network's regulation of the reactive power output of the distributed power source, allowing the distribution network to freely adjust the reactive output of the distributed power source under the scheduling constraints of the renewable and non-renewable energy generation distributed power source, and obtains the electric energy transaction results taking into account reactive power ancillary services based on a single-layer game equilibrium constraint pricing model. The final electric energy transaction module between the distribution network and the distributed power generation is used to conduct active energy transaction settlement between the distribution network and the non-renewable energy generation distributed power generation according to the electric energy transaction price and transaction power based on the obtained electric energy transaction results taking into account reactive auxiliary services. The distribution network conducts active energy transaction settlement with each renewable energy generation distributed power generation according to the renewable energy generation grid price and the active power consumed; Based on the reference electricity trading results obtained, part of the increase in distribution network operation revenue under the electricity trading results considering reactive auxiliary services will be allocated to each distributed power source according to the proportion of distributed power source reactive output, and the rest will be retained as the distribution network's own revenue.

9. A computing device, characterized in that: include: one or more processing units; A storage unit for storing one or more programs, When the one or more programs are executed by the one or more processing units, the one or more processing units execute the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium having a non-volatile program code executable by a processor, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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