Flexible power distribution network electricity transaction method and device based on intelligent soft switching pricing

Abstract

The present application relates to the flexible power distribution network electricity transaction method based on intelligent soft switch pricing, comprising: obtaining the parameters of each regional power distribution network; constructing an intelligent soft switch pricing model; each regional power distribution network takes the minimum purchase cost and voltage offset cost as the target, considers the power flow constraint and safe operation constraint of the regional power distribution network, and constructs the electricity transaction model of each regional power distribution network; constructing the double-layer master-slave game model of the flexible interconnected power distribution network electricity transaction, taking the intelligent soft switch as the upper leader and each regional power distribution network as the lower follower; using the KKT optimality condition to equivalently convert the lower regional power distribution network electricity transaction model into the constraint condition of the upper intelligent soft switch pricing model, and constructing the single-layer game equilibrium constraint pricing model of the flexible interconnected power distribution network electricity transaction; linearizing the single-layer game equilibrium constraint pricing model; and performing transaction settlement according to the obtained electricity transaction price and electricity transaction power; the method effectively promotes the investment cost recovery of the intelligent soft switch.

Description

Technical Field

[0001] This invention relates to a method for power trading in a multi-regional flexible interconnected distribution network, and particularly to a method and apparatus for power trading in a flexible interconnected distribution network where active and reactive power trading is priced under the guidance of intelligent soft switching. Background Technology

[0002] The large-scale integration of new elements such as distributed power sources and electric vehicles has placed higher demands on the operation of distribution networks. To meet the diversified and customized electricity demands of users, traditional distribution networks, supported by flexible distribution equipment such as multi-terminal soft open points (SOPs) at multiple voltage levels, will gradually evolve from a traditional radial structure to a highly flexible and controllable interconnected structure. Distribution network areas interconnected through multi-terminal soft open points will have multiple energy exchange paths and support precise and controllable power transmission. Intelligent energy storage soft open points can achieve feeder load balancing through flexible active power transmission between areas and optimize the voltage distribution of regional distribution networks through reactive power regulation, improving the operational efficiency of each area and laying the physical foundation for power trading in multi-regional flexible interconnected distribution networks.

[0003] In response to the demand for power trading in multi-regional flexible interconnected distribution networks, how to reduce the operating costs of distribution networks in each region while promoting the recovery of investment in flexible interconnected power electronic devices such as smart soft switches has become an issue that needs to be addressed.

[0004] In power trading involving multiple stakeholders, reasonable trading rules are needed to accommodate the diverse interests of each participant. In multi-regional flexible interconnected distribution networks, multiple regional distribution networks are interconnected by intelligent soft-switching devices, which control transmission power. The intelligent soft-switching operator holds a dominant position in the trading process, improving its operational efficiency through pricing, while each regional distribution network formulates its optimal trading plan based on the pricing. A master-slave game theory architecture can be effectively applied to power trading in multi-regional flexible interconnected distribution networks.

[0005] In master-slave game-based power trading, efficient and stable solution of game equilibrium is crucial. Currently, iterative solutions are widely used, but they suffer from slow solution speed and poor convergence. By using KKT optimality conditions to replace the follower power trading model, the master-slave game problem can be transformed into a single-layer game equilibrium constraint optimization problem, which can effectively solve the power trading problem of flexible interconnected distribution networks based on smart soft-switching pricing.

[0006] Currently, domestic and international research on multi-regional flexible interconnected distribution networks mainly focuses on the formulation of operation optimization strategies for multi-terminal smart energy storage soft switches. Further research is needed on how to achieve proactive pricing for smart soft switches in electricity trading. Therefore, to meet the electricity trading needs of multi-regional flexible interconnected distribution networks, a smart soft switch trading pricing method is required that promotes reduced operating costs in each region and increases the operating profits of smart energy storage soft switches. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide a pricing method for smart soft switches based on master-slave game theory, addressing the issue of proactive profit enhancement for smart soft switches in power trading of flexible interconnected distribution networks.

[0008] The flexible distribution network power trading method based on smart soft-switching pricing includes the following steps:

[0009] 1) Obtain the distribution network parameters for each region based on the selected flexible interconnected distribution network;

[0010] 2) Based on the smart soft switch parameters provided in step 1), and taking into account the operational constraints of the smart soft switch, construct a smart soft switch pricing model with the goal of maximizing the revenue of smart soft switches participating in regional power trading.

[0011] 3) Based on the distribution network parameters and smart soft switch parameters provided in step 1), each regional distribution network k aims to minimize the power purchase cost and voltage deviation cost, and considers the power flow constraints and safe operation constraints of the regional distribution network to construct the power trading model of each regional distribution network.

[0012] 4) Based on the smart soft-switching pricing model in step 2) and the regional distribution network power trading model in step 3), construct a two-layer master-slave game model for flexible interconnected distribution network power trading with smart soft-switching as the upper-level leader and regional distribution networks as the lower-level followers. Use KKT optimality conditions to convert the lower-level regional distribution network power trading model into the constraint conditions of the upper-level smart soft-switching pricing model, and construct a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading.

[0013] 5) Linearize the single-layer game equilibrium constraint pricing model constructed in step 4), and solve it using the mathematical solver CPLEX to obtain the power trading price and power trading capacity of the smart soft switch and the distribution network in each region during the operating day.

[0014] 6) The distribution networks and smart soft switches in each region shall conduct transaction settlements according to the electricity trading price and electricity trading power obtained in step 5).

[0015] Furthermore, step 2), which aims to maximize the revenue from participating in regional electricity trading with intelligent soft switches, can be expressed as:

[0016]

[0017] In the formula, f SOP,t The transaction revenue of the smart soft switch in time period t is the sum of the transaction amounts with the distribution networks of each district; These represent the active and reactive power trading prices for the smart soft switch in time period t for region k. For the active and reactive power of the time period t region k plan and smart soft switch trading; Ω R It represents a set of flexible interconnected regional distribution networks; Δt is the duration of the transaction period.

[0018] Furthermore, step 3), where the distribution network k in each region aims to minimize both electricity purchase cost and voltage offset cost, can be expressed as:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] In the formula, The electricity price is determined by the higher-level power grid. These represent the active power of the distribution network k in region t that purchases and sells electrical energy to the upper-level power grid during time period t. Let ω be the set of nodes in region k; U This is the cost reduction factor for load shedding when voltage exceeds the limit; V represents the active power load at node i during time period t; i,t The square of the voltage at node i during time period t; This is an indicator of the voltage offset of node i in time period t; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V σ represents the squares of the upper and lower limits of the node voltage, respectively. k,t , The dual variable for the voltage offset cost constraint is used to solve the dual problem of the original problem and the KKT conditions.

[0025] Furthermore, the characteristic of step 4) is that the constraint conditions for using the KKT optimality condition to convert the lower-level regional distribution network power trading model into the upper-level smart soft-switching pricing model are as follows: In the KKT optimality condition, all partial derivatives of the Lagrange function are zero, which can be expressed as:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] In the formula, Let k be the set of nodes in the SOP access area. Let k be the set of nodes in region k. Let k be the set of source nodes for region k. Let k be the set of branches in region k; Let k be the set of branches connected to the source node of region k. Let k be the set of distributed power supply access nodes in region k. These represent the active and reactive power trading prices for the smart soft switch in time period t for region k. The electricity price of the upper-level power grid; ω U This is the cost reduction factor for load shedding when voltage exceeds the limit; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; R ij X ij These are the resistance and reactance of branch ij, respectively; σ k,t , For the voltage offset cost constraint, it is the dual variable; ζ ij,t τ and υ are the dual variables of the power flow constraint; The dual variable for the safety operation constraint.

[0039] The complementary relaxation condition in the KKT optimality condition can be expressed as:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, For the set of distributed power access nodes in region k; V represents the set of k nodes in the regional distribution network. i,t The square of the voltage at node i during time period t; This is an indicator of the voltage offset of node i in region k during time period t; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; The active power output of the distributed power source at node i during time period t; These are the upper and lower limits of the active power of the distributed power source connected to node i, respectively. For the voltage offset cost constraint, it is the dual variable; The dual variable for safety constraints; "⊥" indicates that the product of the expressions on both sides is zero.

[0046] Furthermore, the linearization process described in step 5) of the single-layer game equilibrium-constrained pricing model constructed in step 4), including the complementary relaxation condition in the linearized KKT optimality condition, can be expressed as:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061] In the formula, M is a sufficiently large positive number; For the set of distributed power access nodes in region k; V represents the set of k nodes in the regional distribution network. i,t The square of the voltage at node i during time period t; This is an indicator of the voltage offset of node i in region k during time period t; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; The active power output of the distributed power source at node i during time period t; These are the upper and lower limits of the active power of the distributed power source connected to node i, respectively. For the voltage offset cost constraint, it is the dual variable; The dual variable for safety operation constraints; The binary variable introduced for complementary relaxation condition linearization takes the value 0 or 1.

[0062] The objective function of the linearized game equilibrium-constrained pricing model can be expressed as:

[0063]

[0064] In the formula, These represent the active and reactive power trading prices for the smart soft switch in time period t for region k.

[0065] For the active and reactive power of the time period t region k plan and smart soft switch trading; Ω R This refers to a flexible interconnected regional distribution network set; Δt is the duration of the transaction period. V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; These represent the active and reactive loads at node i during time period t; These represent the upper and lower limits of the active power of the distributed power source connected to node i; V s The square of the source node voltage; As a whole variable, f represents the active power of region k purchasing electrical energy from the upper-level power grid; U,k,t Let k be the voltage offset cost of the distribution network in region k with objective function for time period t. The electricity price is determined by the higher-level power grid. For the voltage offset cost constraint, it is the dual variable; τ is the dual variable of the power flow constraint; The dual variable for the safety operation constraint.

[0066] Further, in step 1), obtain the distribution network parameters for each region, including network topology connections, load and distributed generation access locations and capacities, daily operating curve prediction results for load and distributed generation, system safe operating voltage range, ideal voltage range, branch current limits, unit cost of load loss under extreme voltage deviations, and the electricity trading price between the distribution network and the upper-level grid for each time period during the operating day. Input the intelligent soft switch parameters, including the intelligent soft switch connection location and the capacity of each converter; set the duration Δt of each trading session during the operating day.

[0067] Flexible distribution network power trading devices based on smart soft-switching pricing include:

[0068] The distribution network parameter acquisition module is used to acquire the distribution network parameters of each region based on the selected flexible interconnected distribution network.

[0069] The intelligent soft switch pricing model construction module is used to construct an intelligent soft switch pricing model based on the provided intelligent soft switch parameters, with the goal of maximizing the revenue of intelligent soft switches participating in regional power trading, and taking into account the operational constraints of intelligent soft switches.

[0070] The power distribution network power trading model construction module is used to construct power trading models for each regional power distribution network based on the provided regional power distribution network parameters and smart soft switch parameters, with the goal of minimizing the power purchase cost and voltage deviation cost, and taking into account the power flow constraints and safe operation constraints of the regional power distribution network.

[0071] The module for constructing a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading is used to construct a two-layer master-slave game model for flexible interconnected distribution network power trading, with intelligent soft switching as the upper-layer leader and regional distribution networks as the lower-layer followers, based on the intelligent soft switching pricing model and the power trading models of each regional distribution network. The KKT optimality condition is used to convert the lower-layer regional distribution network power trading model into the constraint condition of the upper-layer intelligent soft switching pricing model, thus constructing a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading.

[0072] The single-layer game equilibrium constraint pricing model solution module is used to linearize the constructed single-layer game equilibrium constraint pricing model. It is solved using the mathematical solver CPLEX to obtain the power trading price and power trading capacity of smart soft switches and distribution networks in each region during the operating day.

[0073] The transaction settlement module is used for transaction settlement between regional distribution networks and smart soft switches based on the obtained electricity transaction price and electricity transaction power.

[0074] A computing device, comprising:

[0075] One or more processing units;

[0076] A storage unit is used to store one or more programs.

[0077] When the one or more programs are executed by the one or more processing units, the one or more processing units execute the flexible distribution network power trading method based on smart soft-switching pricing.

[0078] A computer-readable storage medium having processor-executable non-volatile program code, wherein the computer program, when executed by a processor, implements the steps of the flexible distribution network power trading method based on smart soft-switching pricing.

[0079] The technical solution adopted in this invention is:

[0080] This invention presents a flexible interconnected distribution network power trading method based on smart soft-switching pricing. It aims to proactively increase the profits of smart soft switches in flexible interconnected distribution network power trading. Using the active and reactive power transmission of smart soft switches as the trading objects, it fully considers the voltage control needs of each region. It constructs power trading models for each region's distribution network with the goal of minimizing the power purchase cost and voltage deviation cost. Furthermore, it constructs a smart soft-switching pricing model with the goal of maximizing the returns of smart soft switches participating in power trading in each region. Based on a master-slave game architecture, it models the power trading between smart soft switches and regional distribution networks. Further, through KKT optimality conditions, it transforms the two-layer master-slave game model of flexible interconnected distribution network power trading into a single-layer game equilibrium constraint pricing model, enabling rapid solution to the smart soft-switching trading pricing problem and effectively promoting the recovery of investment costs for smart soft switches. Attached Figure Description

[0081] Figure 1 This is a schematic diagram of a flexible interconnected distribution network with four-terminal intelligent soft switches.

[0082] Figure 2 These are the operating curves for photovoltaic systems, wind turbines, and load.

[0083] Figure 3 It is the electricity price curve of the upper-level power grid;

[0084] Figure 4 It is the active power pricing of each area by the intelligent soft switch at each time period;

[0085] Figure 5 It is the pricing of reactive power auxiliary services for each area by intelligent soft switches at different times;

[0086] Figure 6 It is the active power exchange between the distribution network and smart soft switches in each region;

[0087] Figure 7 It is the reactive power trading power of the distribution network and smart soft switches in each region;

[0088] Figure 8 It represents the transaction revenue of the power distribution network in each region at different times.

[0089] Figure 9 It refers to the revenue generated from the operation of smart soft switches in electricity trading at different times;

[0090] Figure 10 This is a diagram showing the node voltage distribution of the power distribution network in each region at 17:00;

[0091] Figure 11 This is a map showing the voltage distribution range of the power distribution network in different time periods and regions. Detailed Implementation

[0092] The following detailed description of the power trading method for flexible interconnected distribution networks based on intelligent soft-switching pricing proposed in this invention, with reference to the embodiments and accompanying drawings, will be provided in detail.

[0093] The present invention provides a flexible distribution network power trading method based on intelligent soft-switching pricing, such as... Figure 1 As shown, it includes the following steps:

[0094] 1) Based on the selected flexible interconnected distribution network, input the distribution network parameters for each region, including network topology connections, load and distributed generation access locations and capacities, daily operating curve prediction results for load and distributed generation, system safe operating voltage range, ideal voltage range, branch current limits, unit cost of load loss under extreme voltage deviations, and electricity trading prices between the distribution network and the upper-level grid for each time period during the operating day. Input the intelligent soft switch parameters, including the intelligent soft switch connection location and the capacity of each converter; set the duration Δt of each trading session during the operating day;

[0095] In this embodiment, the constructed four-terminal intelligent soft-switching flexible interconnected distribution network is as follows: Figure 2 As shown, the distribution networks in the four regions are all upgraded based on the IEEE 33-node example. The voltage level, topology, branch parameters, and node load parameters of each region's distribution network are consistent with the standard IEEE 33-node example. The voltage level is 12.66 kV, and the total active power demand and total reactive power demand are 3.1750 MW and 2.3000 Mvar, respectively. Detailed parameters are shown in Tables 1 and 2. The node numbers and branch numbers in the four regions are obtained by sequentially adding the node numbers from the IEEE 33-node example. Figure 2 Consistent.

[0096] The four regional distribution networks differ only in the location of the smart soft switch and the configuration of distributed generation. The distributed generation configuration is shown in Table 3, and the wind turbine, photovoltaic, and load curves are as follows: Figure 3 As shown, the power factor of all distributed power sources is set to 1.0.

[0097] The converters at each port of the four-terminal intelligent soft switch are connected to nodes 8, 59, 78, and 104 in each zone, with a capacity of 3 MW and a loss factor of 0.01, to achieve flexible interconnection between zones. Power loss in the DC link of the intelligent soft switch is not considered. The per-unit value for the safe operating voltage range of the distribution network in each zone is set to 0.90–1.10, and the per-unit value for the ideal voltage range is 0.97–1.03.

[0098] During the operating day, each trading session (Δt) is set to 1 hour, totaling 24 trading sessions. The electricity price curves of the upper-level power grid for each session are as follows: Figure 4 As shown.

[0099] 2) Based on the smart soft switch parameters provided in step 1), and taking into account the operational constraints of the smart soft switch, construct a smart soft switch pricing model with the goal of maximizing the revenue of smart soft switches participating in regional power trading.

[0100] (1) The objective of maximizing the revenue from participating in regional power trading with intelligent soft switches can be expressed as:

[0101]

[0102] In the formula, f SOP，t The transaction revenue of the smart soft switch in time period t is the sum of the transaction amounts with the distribution networks of each district; These represent the active and reactive power trading prices for the smart soft switch in time period t for region k. For the active and reactive power of the time period t region k plan and smart soft switch trading; Ω R It represents a set of flexible interconnected regional distribution networks; Δt is the duration of the transaction period.

[0103] (2) The operating constraints of the intelligent soft switch can be expressed as:

[0104]

[0105]

[0106]

[0107] In the formula, Let A be the power loss of the intelligent energy storage soft switch at the converter port of region k during time period t; SOP S represents the loss factor of the intelligent soft-switching converter. SOP Capacity of intelligent soft-switching converter.

[0108] 3) Based on the distribution network parameters and smart soft switch parameters provided in step 1), each regional distribution network k aims to minimize the power purchase cost and voltage deviation cost, and considers the power flow constraints and safe operation constraints of the regional distribution network to construct the power trading model of each regional distribution network.

[0109] (1) The distribution network k in each region aims to minimize the cost of electricity purchase and the cost of voltage deviation, which can be expressed as:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] In the formula, The electricity price is determined by the higher-level power grid. These represent the active power of the distribution network k in region t that purchases and sells electrical energy to the upper-level power grid during time period t. Let ω be the set of nodes in region K; U This is the cost reduction factor for load shedding when voltage exceeds the limit; V represents the active power load at node i during time period t; i,t The square of the voltage at node i during time period t; This is an indicator of the voltage offset of node i in time period t; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V σ represents the squares of the upper and lower limits of the node voltage, respectively. k,t , The dual variable for the voltage offset cost constraint is used to solve the dual problem of the original problem and the KKT conditions.

[0116] (2) The power flow constraint can be expressed as:

[0117]

[0118]

[0119]

[0120]

[0121] V i,t -V j,t =2(R) ij P ij,t +X ij Q ij,t )∶ζ ij,t (12)

[0122]

[0123]

[0124] In the formula, Let k be the set of nodes in region k. Let k be the set of source nodes for region k. Let k be the set of branches in region k; Let k be the set of branches connected to the source node of region k. Let P be the set of nodes in SOP access area k; ji,t Q ji,t These represent the active and reactive power flowing through branch ji during time period t; P i,t Q i,t These represent the net injected active and reactive power at node i during time period t, respectively. The active power output of the distributed power source at node i during time period t; These represent the active and reactive loads at node i during time period t; R ij X ij These are the resistance and reactance of branch ij, respectively; V s The square of the source node voltage; ζ ij,t τ and v are the dual variables of the power flow constraint.

[0125] (3) The aforementioned safe operation constraints can be expressed as:

[0126]

[0127]

[0128] In the formula, These are the upper and lower limits of the active power of the distributed power source connected to node i, respectively. The dual variable for the safety operation constraint.

[0129] 4) Based on the smart soft-switching pricing model in step 2) and the regional distribution network power trading model in step 3), construct a two-layer master-slave game model for flexible interconnected distribution network power trading with smart soft-switching as the upper-level leader and regional distribution networks as the lower-level followers. Use KKT optimality conditions to convert the lower-level regional distribution network power trading model into the constraint conditions of the upper-level smart soft-switching pricing model, and construct a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading.

[0130] (1) The aforementioned two-layer master-slave game model for flexible interconnected distribution network power trading, with intelligent soft switches as the upper-level leader and regional distribution networks as the lower-level followers, can be described as follows:

[0131] The smart soft switch operator formulates active and reactive power trading prices for each region based on the response of each region; the regional distribution network solves the regional distribution network power trading model based on the smart soft switch trading prices and formulates the optimal trading strategy under the current trading prices; the smart soft switch adjusts the active and reactive power trading prices according to the trading strategies given by each region to improve the operating efficiency of the smart soft switch and ensure that the trading results meet the operating constraints of the smart soft switch until the operating revenue of the smart soft switch no longer increases, and finally obtains the optimal pricing strategy of the smart soft switch.

[0132] (2) The KKT optimality condition is used to convert the lower-level regional distribution network power trading model into the constraint condition of the upper-level smart soft-switching pricing model. In the KKT optimality condition, all partial derivatives of the Lagrangian function are zero, which can be expressed as:

[0133]

[0134]

[0135]

[0136] 1-σ k,t =0 (20)

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] In the formula, Let k be the set of nodes in the SOP access area. Let k be the set of nodes in region k. Let k be the set of source nodes for region k. Let k be the set of branches in region k; Let k be the set of branches connected to the source node of region k. Let k be the set of distributed power supply access nodes in region k. These represent the active and reactive power trading prices for the smart soft switch in time period t for region k. The electricity price of the upper-level power grid; ω U This is the cost reduction factor for load shedding when voltage exceeds the limit; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; R ij X ij These are the resistance and reactance of branch ij, respectively; σ k,t, For the voltage offset cost constraint, it is the dual variable; ζ ij,t τ and υ are the dual variables of the power flow constraint; The dual variable for the safety operation constraint.

[0146] (3) Among the KKT optimality conditions, the complementary relaxation condition can be expressed as:

[0147]

[0148]

[0149]

[0150]

[0151]

[0152] In the formula, For the set of distributed power access nodes in region k; V represents the set of k nodes in the regional distribution network. i,t The square of the voltage at node i during time period t; This is an indicator of the voltage offset of node i in region k during time period t; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; The active power output of the distributed power source at node i during time period t; These are the upper and lower limits of the active power of the distributed power source connected to node i, respectively. For the voltage offset cost constraint, it is the dual variable; The dual variable for safety constraints; "⊥" indicates that the product of the expressions on both sides is zero.

[0153] (4) The single-layer game equilibrium constraint pricing model for power trading in flexible interconnected distribution networks can be expressed as:

[0154]

[0155] st(2)~(4), (17)~(30) (32)

[0156] 5) Linearize the single-layer game equilibrium constraint pricing model constructed in step 4), and solve it using the mathematical solver CPLEX to obtain the power trading price and power trading capacity of the smart soft switch and the distribution network in each region during the operating day.

[0157] (1) The single-layer game equilibrium constraint pricing model constructed in the linearization process step 4), including the complementary relaxation condition in the linearized KKT optimality condition, can be expressed as:

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] In the formula, M is a sufficiently large positive number; For the set of distributed power access nodes in region k; V represents the set of k nodes in the regional distribution network. i,t The square of the voltage at node i during time period t; This is an indicator of the voltage offset of node i in region k during time period t; V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; The active power output of the distributed power source at node i during time period t; These are the upper and lower limits of the active power of the distributed power source connected to node i, respectively. For the voltage offset cost constraint, it is the dual variable; The dual variable for safety operation constraints; The binary variable introduced for complementary relaxation condition linearization takes the value 0 or 1.

[0168] (2) The single-layer game equilibrium-constrained pricing model constructed in the linearization process step 4) includes the objective function of the linear game equilibrium-constrained pricing model, which can be expressed as:

[0169]

[0170]

[0171] In the formula, These represent the active and reactive power trading prices for the smart soft switch in time period t for region k. For the active and reactive power of the time period t region k plan and smart soft switch trading; Ω R This refers to a flexible interconnected regional distribution network set; Δt is the duration of the transaction period. V thr These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; V These are the squares of the upper and lower limits of the node voltage, respectively; These represent the active and reactive loads at node i during time period t; These represent the upper and lower limits of the active power of the distributed power source connected to node i; V s The square of the source node voltage; As a whole variable, f represents the active power of region k purchasing electrical energy from the upper-level power grid; U,k,t Let k be the voltage offset cost of the distribution network in region k with objective function for time period t. The electricity price is determined by the higher-level power grid. For the voltage offset cost constraint, it is the dual variable; τ is the dual variable of the power flow constraint; The dual variable for the safety operation constraint.

[0172] (3) The single-layer game equilibrium-constrained pricing model, after linearization, can be represented as a mixed-integer second-order cone programming model:

[0173]

[0174] st(2)~(4), (17)~(27), (33)~(39) (42)

[0175] 6) The distribution networks and smart soft switches in each region shall conduct transaction settlements according to the electricity trading price and electricity trading power obtained in step 5).

[0176] For this embodiment, the voltage offset cost factor ω U Set it to 0.443.

[0177] To verify the feasibility and effectiveness of the flexible interconnected distribution network power trading method based on intelligent soft-switching pricing in this invention, the following two scenarios are used for verification and analysis in this embodiment:

[0178] Option 1: Each regional distribution network does not engage in power trading with smart soft switches, but only with the upstream power grid, to obtain the initial operating level and cost of the distribution network.

[0179] Option 2: Adopt the proposed flexible interconnected distribution network power trading method based on smart soft switching pricing, and conduct active power trading and reactive power auxiliary service trading between the distribution networks in each region and the smart soft switch.

[0180] Table 4 shows the daily operating costs for each district in both Scheme 1 and Scheme 2. The active power pricing results for the intelligent soft switch in Scheme 1 at different times of the day are shown below. Figure 5 As shown, the reactive power pricing results are as follows: Figure 6 As shown in the figure. The active power trading between the distribution network and the smart soft switch in each area of ​​Scheme 2 is as follows: Figure 7 As shown in Figure 8, the reactive power trading power is as follows. Compared to Scheme 1, the trading revenue of each regional distribution network in Scheme 2 at each time period is as follows: Figure 9 As shown in the diagram. In Scheme 2, the transaction revenue of the smart soft switch at different times is as follows: Figure 10 As shown. In Scheme 1 and Scheme 2, the voltage distribution of the distribution network in each area at 17:00 is as follows. Figure 11 As shown in Scheme 1 and Scheme 2, the voltage distribution range of the distribution network in each time period and area is as follows: Figure 11 As shown.

[0181] The computer hardware environment for performing the optimization calculations was an Intel(R) Core(TM) i5-5200U CPU with a clock speed of 2.20GHz and 4GB of memory; the software environment was a Windows 10 operating system.

[0182] Compared to Option 1, which does not participate in market transactions, Option 2, based on a flexible interconnected distribution network power trading method using smart soft-switching pricing, effectively reduces operating costs in each region and improves voltage distribution. In Option 2, smart soft-switching achieves maximum profitability through an optimal pricing scheme, thus promoting investment cost recovery.

[0183] A comparison of the two schemes shows that the flexible interconnected distribution network power trading method based on intelligent soft switching pricing proposed in this invention can effectively improve the economic efficiency of system operation, enhance the operating efficiency of intelligent soft switching, and improve the voltage distribution of the system.

[0184] Table 1 Load Connection Locations and Power in an IEEE 33-Node Distribution Network Example

[0185]

[0186] Table 2 Line parameters for IEEE 33-node distribution network example

[0187]

[0188] Table 3. Distributed Power Supply Connection Locations and Capacities

[0189] area Access Node Photovoltaic capacity (MVA) Fan capacity (megavolt-amperes) 1 17 1.0 - 1 30 1.0 - 1 33 - 0.8 2 42 - 1.0 2 46 1.0 - 2 65 - 1.0 3 79 0.8 - 3 84 - 0.7 3 97 1.1 - 4 114 1.2 - 4 132 1 -

[0190] Table 4 Daily operating costs of power distribution networks in various regions

[0191]

[0192]

[0193] Example 2

[0194] Flexible distribution network power trading devices based on smart soft-switching pricing include:

[0195] The distribution network parameter acquisition module is used to acquire the distribution network parameters of each region based on the selected flexible interconnected distribution network.

[0196] The intelligent soft switch pricing model construction module is used to construct an intelligent soft switch pricing model based on the provided intelligent soft switch parameters, with the goal of maximizing the revenue of intelligent soft switches participating in regional power trading, and taking into account the operational constraints of intelligent soft switches.

[0197] The power distribution network power trading model construction module is used to construct power trading models for each regional power distribution network based on the provided regional power distribution network parameters and smart soft switch parameters, with the goal of minimizing the power purchase cost and voltage deviation cost, and taking into account the power flow constraints and safe operation constraints of the regional power distribution network.

[0198] The module for constructing a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading is used to construct a two-layer master-slave game model for flexible interconnected distribution network power trading, with intelligent soft switching as the upper-layer leader and regional distribution networks as the lower-layer followers, based on the intelligent soft switching pricing model and the power trading models of each regional distribution network. The KKT optimality condition is used to convert the lower-layer regional distribution network power trading model into the constraint condition of the upper-layer intelligent soft switching pricing model, thus constructing a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading.

[0199] The single-layer game equilibrium constraint pricing model solution module is used to linearize the constructed single-layer game equilibrium constraint pricing model. It is solved using the mathematical solver CPLEX to obtain the power trading price and power trading capacity of smart soft switches and distribution networks in each region during the operating day.

[0200] The transaction settlement module is used for transaction settlement between regional distribution networks and smart soft switches based on the obtained electricity transaction price and electricity transaction power.

[0201] In addition, this embodiment also provides a computing device, including:

[0202] One or more processing units;

[0203] A storage unit is used to store one or more programs.

[0204] When the one or more programs are executed by the one or more processing units, the one or more processing units execute the above-described flexible distribution network power trading method based on smart soft-switching pricing. It should be noted that the computing device may include, but is not limited to, processing units and storage units. Those skilled in the art will understand that the inclusion of processing units and storage units in the computing device does not constitute a limitation on the computing device. It may include more components, or combine certain components, or different components. For example, the computing device may also include input / output devices, network access devices, buses, etc.

[0205] A computer-readable storage medium having processor-executable non-volatile program code is also provided. When executed by a processor, the computer program implements the steps of the above-described flexible distribution network power trading method based on smart soft-switching pricing. It should be noted that the readable storage medium can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. The program contained on the readable medium can be transmitted using any suitable medium, including, but not limited to, wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. For example, the program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as C or similar programming languages. The program code can be executed entirely on a user's computing device, partially on a user's device, as a standalone software package, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to the user's computing device via any type of network—including a local area network (LAN) or a wide area network (WAN), or they can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

Claims

1. A flexible distribution network power trading method based on intelligent soft-switching pricing, characterized in that, Includes the following steps: 1) Obtain the distribution network parameters for each region based on the selected flexible interconnected distribution network; 2) Based on the smart soft switch parameters provided in step 1), and taking into account the operational constraints of the smart soft switch, construct a smart soft switch pricing model with the goal of maximizing the revenue of smart soft switches participating in regional power trading. 3) Based on the distribution network parameters and smart soft switch parameters provided in step 1), the distribution network of each region... With the goal of minimizing electricity purchase cost and voltage offset cost, and considering the power flow constraints and safe operation constraints of the regional distribution network, an energy trading model for each regional distribution network is constructed. 4) Based on the smart soft-switching pricing model in step 2) and the regional distribution network power trading model in step 3), construct a two-layer master-slave game model for flexible interconnected distribution network power trading with smart soft-switching as the upper-level leader and regional distribution networks as the lower-level followers. Use KKT optimality conditions to convert the lower-level regional distribution network power trading model into the constraint conditions of the upper-level smart soft-switching pricing model, and construct a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading. 5) Linearize the single-layer game equilibrium constraint pricing model constructed in step 4), and solve it using the mathematical solver CPLEX to obtain the power trading price and power trading capacity of the smart soft switch and the distribution network in each region during the operating day. 6) The distribution networks and smart soft switches in each region will settle transactions according to the electricity trading price and electricity trading power obtained in step 5); Step 2), which aims to maximize the returns from participating in regional electricity trading with smart soft switches, can be expressed as: In the formula, For intelligent soft switches during time periods The transaction revenue is the sum of the transaction amounts with the distribution networks of each district; , Time periods Intelligent soft switch for area Pricing of active and reactive power transactions; , For time period area The active and reactive power to be traded with smart soft switches; A collection of flexible interconnected regional distribution networks; This refers to the duration of the trading session.

2. The flexible distribution network power trading method based on intelligent soft-switching pricing according to claim 1, characterized in that, Step 3) describes the distribution networks in each area. The objective of minimizing both electricity purchase cost and voltage offset cost can be expressed as: In the formula, The electricity price is determined by the higher-level power grid. , Time periods Regional distribution network The active power used to purchase or sell electrical energy from the superior power grid; For the region The set of nodes; This is the cost reduction factor for load shedding when voltage exceeds the limit; For time period node Active load; For time period node The square of the voltage; For time period Middle node The voltage deviation index; , These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; , These are the squares of the upper and lower limits of the node voltage, respectively; , , , The dual variable for the voltage offset cost constraint is used to solve the dual problem of the original problem and the KKT conditions.

3. The flexible distribution network power trading method based on intelligent soft-switching pricing according to claim 1, characterized in that... Step 4) describes using KKT optimality conditions to convert the lower-level regional distribution network power trading model into the upper-level smart soft-switching pricing model's constraints. In the KKT optimality conditions, all partial derivatives of the Lagrange function are zero, which can be expressed as: In the formula, SOP access area The set of nodes; For the region The set of nodes; For the region The set of source nodes; For the region The set of branch paths; For the region The set of branches connected to the source node; For the region A set of distributed power supply access nodes; , Time periods Intelligent soft switch for area Pricing of active and reactive power transactions; The electricity price is determined by the higher-level power grid. This is the cost reduction factor for load shedding when voltage exceeds the limit; , These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; , These are the squares of the upper and lower limits of the node voltage, respectively; , Branch roads Resistance and reactance; , , , For the voltage offset cost constraint, it is the dual variable; , , , , , , For the dual variable of the power flow constraint; , , , The dual variable for safety operation constraints; The complementary relaxation condition in the KKT optimality condition can be expressed as: In the formula, For the region Set of distributed power supply access nodes; For regional power distribution network Node set; For time period node The square of the voltage; For time period area Middle node The voltage deviation index; , These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; , These are the squares of the upper and lower limits of the node voltage, respectively; For time period node Distributed power sources have active power output; , They are nodes The upper and lower limits of the active power of the connected distributed power sources; , , For the voltage offset cost constraint, it is the dual variable; , , , The dual variable for safety constraints; "⊥" indicates that the product of the expressions on both sides is zero.

4. The flexible distribution network power trading method based on intelligent soft-switching pricing according to claim 1, characterized in that, Step 5) describes the linearization process used in step 4) to construct a single-layer game equilibrium-constrained pricing model, including the complementary relaxation condition in the linearized KKT optimality condition, which can be expressed as: In the formula, It is a sufficiently large positive number; For the region Set of distributed power supply access nodes; For regional power distribution network Node set; For time period node The square of the voltage; For time period area Middle node The voltage deviation index; , These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; , These are the squares of the upper and lower limits of the node voltage, respectively; For time period node Distributed power sources have active power output; , They are nodes The upper and lower limits of the active power of the connected distributed power sources; , , For the voltage offset cost constraint, it is the dual variable; , , , The dual variable for safety operation constraints; , , , , , , The binary variable introduced for linearization of the complementary relaxation condition takes the value 0 or 1; The objective function of the linearized game equilibrium-constrained pricing model can be expressed as: ; In the formula, , Time periods Intelligent soft switch for area Pricing of active and reactive power transactions; , For time period area The active and reactive power to be traded with smart soft switches; A collection of flexible interconnected regional distribution networks; The duration of the trading session; , These are the squares of the upper and lower limits of the ideal voltage range of the node, respectively; , These are the squares of the upper and lower limits of the node voltage, respectively; , Time periods node Active and reactive loads; , They are nodes The upper and lower limits of the active power of the connected distributed power sources; The square of the source node voltage; As a whole variable, it represents the region. The active power that purchases electrical energy from the upper-level power grid; For time period Regional distribution network Voltage offset cost of the objective function; The electricity price is determined by the higher-level power grid. , For the voltage offset cost constraint, it is the dual variable; , , For the dual variable of the power flow constraint; , , , The dual variable for the safety operation constraint.

5. The flexible distribution network power trading method based on intelligent soft-switching pricing according to claim 1, characterized in that, Step 1) Obtain the distribution network parameters for each region, including network topology connections, load and distributed generation connection locations and capacities, daily operating curve prediction results for load and distributed generation, system safe operating voltage range, ideal voltage range, branch current limits, unit cost of load loss under extreme voltage deviations, and time periods during the operating day. Electricity trading prices between distribution networks and upstream power grids ; Input the intelligent soft switch parameters, including the connection location of the intelligent soft switch and the capacity of each converter; set the duration of each trading session during the operating day. .

6. A flexible distribution network power trading device based on intelligent soft-switching pricing, used to implement the method described in any one of claims 1-5, characterized in that, include: The distribution network parameter acquisition module is used to acquire the distribution network parameters of each region based on the selected flexible interconnected distribution network. The intelligent soft switch pricing model construction module is used to construct an intelligent soft switch pricing model based on the provided intelligent soft switch parameters, with the goal of maximizing the revenue of intelligent soft switches participating in regional power trading, and taking into account the operational constraints of intelligent soft switches. The power distribution network trading model construction module is used to construct power distribution network models based on the provided regional power distribution network parameters and smart soft-switching parameters. With the goal of minimizing electricity purchase cost and voltage offset cost, and considering the power flow constraints and safe operation constraints of the regional distribution network, an energy trading model for each regional distribution network is constructed. The module for constructing a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading is used to construct a two-layer master-slave game model for flexible interconnected distribution network power trading, with intelligent soft switching as the upper-layer leader and regional distribution networks as the lower-layer followers, based on the intelligent soft switching pricing model and the power trading models of each regional distribution network. The KKT optimality condition is used to convert the lower-layer regional distribution network power trading model into the constraint condition of the upper-layer intelligent soft switching pricing model, thus constructing a single-layer game equilibrium constraint pricing model for flexible interconnected distribution network power trading. The single-layer game equilibrium constraint pricing model solution module is used to linearize the constructed single-layer game equilibrium constraint pricing model. It is solved using the mathematical solver CPLEX to obtain the power trading price and power trading capacity of smart soft switches and distribution networks in each region during the operating day. The transaction settlement module is used for transaction settlement between regional distribution networks and smart soft switches based on the obtained electricity transaction price and electricity transaction power.

7. A computing device, characterized in that: include: One or more processing units; A storage unit is used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having processor-executable non-volatile program code, characterized in that, When a computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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