Power distribution network collaborative optimization operation method, equipment and medium

Abstract

The invention relates to a power distribution network collaborative optimization operation method and device and a medium, and the method comprises the steps: a day-ahead global optimization stage: constructing a day-ahead source-network-load-storage collaborative optimization model, and taking the minimization of the day-ahead total operation cost containing the green certificate transaction cost as an optimization target, day-ahead scheduling is carried out on a distributed power supply, an interconnection switch, an interruptible load, a transferable load and a three-terminal intelligent soft switch containing energy storage in the active power distribution network; and an intra-day rolling optimization stage: constructing an intra-day rolling optimization model, and performing intra-day scheduling on a switchable capacitor and a three-terminal intelligent soft switch containing energy storage in the active power distribution network by taking the minimum intra-day total operation cost containing voltage offset cost as an optimization target. Compared with the prior art, the day-ahead-intra-day two-stage collaborative optimization method is provided based on the dynamic response characteristics of the equipment, the refinement level of a scheduling scheme is remarkably improved through layered progressive optimization, and the consumption rate of new energy is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimal scheduling, and in particular, to a method, device and medium for collaborative optimal operation of a distribution network. Background Art

[0002] The randomness and volatility of a high proportion of new energy will lead to problems such as reverse power flow, voltage over-limit, increased network loss, and curtailment of wind and light during grid connection, posing a huge challenge to the safe and stable operation of the distribution network. Therefore, it is urgent to coordinate and utilize the flexible entities in each link of "source-network-load-storage" in the active distribution network to improve the economy and reliability of the distribution network operation. At the same time, through a reasonable scheduling strategy, that is, multi-time scale scheduling, the prediction error of a high proportion of new energy is reduced to achieve the goal of new energy consumption.

[0003] Green Certificate Trading (GCT), as a type of green power market, is a typical policy-based market. Using green certificates as proof of electricity users' consumption of green power can flexibly meet the green electricity demands of a large number of electricity users.

[0004] After retrieval, Chinese Patent Application CN115564191A discloses a distribution network planning method considering green certificate trading and carbon trading. This method constructs a two-layer planning model for the new distribution network planning and simulation operation with new energy as the main body based on the green certificate trading and carbon trading mechanisms. On the basis of the three-stage green certificate trading model and the two-stage carbon trading model, the CO2 emission intensity per unit power supply of the distribution network is introduced, and the impacts of green certificate trading and carbon trading on the new distribution network planning are organically combined. As a result, the overall new energy installation ratio, the carbon emission intensity per unit power supply, and the total revenue of the distribution network are greatly improved.

[0005] However, the above solution does not consider the characteristics of different entities in each link of "source-network-load-storage" in the active distribution network, and the refinement level of the scheduling plan is not high, which needs to be further improved. Summary of the Invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a method, device and medium for collaborative optimal operation of a distribution network. Based on the dynamic response characteristics of equipment, this method proposes a day-ahead and intra-day two-stage collaborative optimization method, and the hierarchical progressive optimization significantly improves the refinement level of the scheduling plan and greatly improves the new energy consumption rate.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] According to a first aspect of the present invention, there is provided a method for collaborative optimal operation of a distribution network, including:

[0009] Day-ahead global optimization stage: Construct a day-ahead source-network-load-storage collaborative optimization model, with the goal of minimizing the day-ahead total operating cost including green certificate trading costs, and perform day-ahead scheduling on distributed power sources, tie switches, interruptible loads, shiftable loads, and three-terminal intelligent soft switches with energy storage in the active distribution network;

[0010] Intraday rolling optimization stage: Construct an intraday rolling optimization model, with the goal of minimizing the intraday total operating cost including voltage deviation costs, and perform intraday scheduling on switchable capacitors and three-terminal intelligent soft switches with energy storage in the active distribution network.

[0011] Preferably, the source-network-load-storage collaborative optimization model specifically includes:

[0012] Optimization goal: With the goal of minimizing the day-ahead total operating cost including green certificate trading costs, the day-ahead total operating cost F ahead includes the cost of purchasing electricity from the upstream power grid by the distribution network, the operation and maintenance cost of tie switches, the compensation cost of interruptible loads, the incentive cost of shiftable loads, the operation and maintenance cost of energy storage, the green certificate trading cost, and the cost of purchasing new energy. The mathematical expression is:

[0013] minF ahead = F SS + F CSW + F CL + F SL + F BS + F GC + F GE (1)

[0014]

[0015] In the formula: F SS represents the total cost of purchasing active and reactive power from the superior power grid; and respectively represent the prices of purchasing active and reactive power from the superior power grid at time t; and respectively represent the active and reactive power of the i-th substation at time t; F CSW represents the total operation and maintenance cost of the tie switch, represents the operation and maintenance cost of the tie switch on the ij section line; F CL represents the total cost paid to the interruptible load; represents the cost paid to the i-th interruptible load; F SL represents the total incentive cost of the shiftable load; represents the incentive cost of the i-th shiftable load; F BS represents the total operation and maintenance cost of the energy storage unit, denotes the operation and maintenance cost of the i-th energy storage unit, and denote the charging power and discharging power of the i-th energy storage unit at time t; and denote the charging efficiency and discharging efficiency of the i-th energy storage unit respectively; F GE denotes the cost of purchasing electricity from new energy sources by the distribution network; λ r is the on-grid electricity price of new energy; T is the time set; Φ, Φ LD and Φ SS are the total line set, load line set and substation line set respectively; Φ BS is the node set containing energy storage units; F GC is the green certificate trading cost.

[0016] Preferably, the green certificate trading cost F GC , specifically:

[0017]

[0018] In the formula: P GC is the green certificate trading price; G GC,o is the number of green certificates obtained by the distribution network; G GC,q is the green certificate quota number; P r,t is the on-grid power of the r-th new energy source at time t; R is the number of new energy sources; P q,t is the new energy quota demand of the distribution network; ω is the quota coefficient; P s is the predicted value of the total system power consumption; ξ is the influence coefficient of the historical green certificate quota completion; ε is the influence weight of the historical green certificate quota completion; δ is the historical green certificate quota completion; δ n is the average value of the green certificate quota completion; ψ is the influence parameter of the new energy output prediction; ζ is the influence weight of the new energy output prediction; τ is the accuracy of the new energy output prediction in the previous period; τ n is the standard value of the new energy output prediction accuracy.

[0019] Preferably, the intra-day rolling optimization model specifically includes:

[0020] Optimization objective: The optimization objective is to minimize the intra-day total operation cost including the voltage deviation cost. The intra-day total operation cost F intra includes the distribution network's upward power purchase cost, switchable capacitor operation and maintenance cost, energy storage BESS operation and maintenance cost, node voltage deviation cost, green certificate trading cost and new energy purchase cost. The mathematical expression is:

[0021] minF intra = F SS + F SCB + F BS + FV +F GC +F GE (4)

[0022]

[0023] Where: F SCB represents the total operation and maintenance cost of the switchable capacitor; represents the operation and maintenance cost of the i-th switchable capacitor; and represent the on-state and off-state of the i-th switchable capacitor at time t, respectively; F V represents the node voltage deviation cost; C V represents the voltage deviation cost coefficient; U v,i,t and U v,n,t are the actual voltage and reference voltage of node i at time t, respectively; Φ SCB is the set of nodes containing switchable capacitors.

[0024] Preferably, the constraint conditions of the source-network-load-storage collaborative optimization model and / or the intraday rolling optimization model include power flow constraints, switchable capacitor constraints, and power balance constraints.

[0025] Preferably, the switchable capacitor constraint is specifically:

[0026]

[0027] Where: represents the available number of switchable capacitors at node i; represents the number of switchable capacitor banks SCB put into operation in the i-th group at time t;

[0028] and / or, the power balance constraint is specifically:

[0029]

[0030] Preferably, the constraint conditions of the source-network-load-storage collaborative optimization model and / or the intraday rolling optimization model further include:

[0031] Distributed power source constraint:

[0032]

[0033] Where: and are the actual reactive power of the wind turbine and photovoltaic connected to the grid at node i at time t, respectively; and are the maximum reactive power generated by the wind turbine and photovoltaic at node i at time t, respectively, and They are the minimum power factor angles of the fan and the photovoltaic respectively; Φ PV and Φ WT They are the node sets containing the fan and the photovoltaic respectively;

[0034] Constraints of the three-terminal intelligent soft switch with energy storage:

[0035]

[0036] In the formula: P SOP,i,t and Q SOP,i,t They are the active power and reactive power output from the i-th port of the three-terminal intelligent soft switch at time t respectively; S SOP,i,t,max is the maximum capacity of the converter at the i-th port of the three-terminal intelligent soft switch; P loss,i,t is the active power loss at the i-th port of the three-terminal intelligent soft switch at time t; P ESS,i,t is the charge and discharge power of the energy storage at time t, m loss is the active power loss coefficient of each port of the three-terminal intelligent soft switch;

[0037] Interruptible load constraints:

[0038]

[0039] In the formula: and They represent the active and reactive powers of the i-th interruptible load at time t respectively, and They represent the reducible active and reactive powers at the i-th node at time t respectively; Φ CL is the node set containing the interruptible load;

[0040] And / or, transferable load constraints:

[0041]

[0042] In the formula: and They represent the incoming and outgoing states of the i-th transferable load at time t respectively; and They represent the predicted load and power factor angle of the i-th transferable load at time t respectively; and They represent the active and reactive powers of the i-th transferable load at time t respectively; and They represent the incoming and outgoing states of the active power of the i-th transferable load at time t respectively; and They represent the incoming and outgoing states of the reactive power of the i-th transferable load at time t respectively; Φ SLis a set of nodes containing transferable loads.

[0043] Preferably, in the intraday rolling optimization stage, the intraday plan is adjusted every 15 minutes, covering 4 hours each time, and only the plan for the first 15-minute period is optimized and adjusted.

[0044] According to the second aspect of the present invention, an electronic device is provided, including a memory and a processor. A computer program is stored on the memory, and when the processor executes the program, any of the above methods is implemented.

[0045] According to the third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, any of the above methods is implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) The present invention constructs an active distribution network "source-network-load-storage" collaborative day-ahead and intraday two-stage optimal operation strategy integrating the green certificate trading mechanism, and proposes a day-ahead and intraday two-stage collaborative optimization method based on the dynamic response characteristics of equipment. In the day-ahead stage, resources with slow response speed and those requiring advance contract signing are scheduled, while in the intraday stage, resources that can be quickly adjusted are dynamically optimized. The hierarchical progressive optimization significantly improves the refinement level of the scheduling scheme and greatly improves the consumption rate of new energy.

[0048] (2) By introducing the green certificate trading mechanism, the matching degree between green certificate trading and the actual operation requirements of the distribution network is effectively improved, providing a market-based incentive for the consumption of renewable energy.

[0049] (3) Connecting the energy storage to the DC side of the three-terminal intelligent soft switch improves the dynamic response ability of the system.

[0050] (4) In the intraday rolling optimization stage, each rolling adjustment is based on the latest system state information to optimize the first 15-minute period, which is equivalent to performing progressive optimization on a relatively short time scale, continuously approaching the optimal operation state, and can avoid the local optimum problem that may occur when optimizing a long time span at one time, making the distribution network scheduling operation closer to the global optimal solution. Description of the Drawings

[0051] Figure 1 is the day-ahead and intraday two-stage optimization strategy of the distribution network;

[0052] Figure 2 is the output curve of renewable energy and load;

[0053] Figure 3 is the improved IEEE33 node system;

[0054] Figure 4 is the comparison of wind and light consumption;

[0055] Figure 5 is the voltage comparison;

[0056] Figure 6 is the comparison of network losses. Specific implementation manner

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Embodiment 1

[0059] As Figure 1 shown, this embodiment provides a method for coordinated optimal operation of a distribution network, including:

[0060] Day-ahead global optimization stage: Construct a source-network-load-storage coordinated optimization model, with the optimization goal of minimizing the day-ahead total operating cost including green certificate trading costs, and perform day-ahead scheduling on distributed power sources, tie switches CSW, interruptible loads CL, shiftable loads SL, and three-terminal intelligent soft switches SOP with energy storage in the active distribution network;

[0061] Intra-day rolling optimization stage: Construct an intra-day rolling scheduling optimization model, with the optimization goal of minimizing the intra-day total operating cost including voltage deviation costs, and perform intra-day scheduling on switchable capacitors SCB and three-terminal intelligent soft switches SOP with energy storage in the active distribution network.

[0062] Among them, the optimization goal of the source-network-load-storage coordinated optimization model is specifically: with the optimization goal of minimizing the day-ahead total operating cost including green certificate trading costs, the day-ahead total operating cost F ahead includes the cost of purchasing electricity from the distribution network upwards, the operation and maintenance cost of tie switches, the compensation cost of interruptible loads, the incentive cost of shiftable loads, the operation and maintenance cost of energy storage, the green certificate trading cost, and the cost of purchasing new energy.

[0063] The optimization goal of the intra-day rolling optimization model is specifically: with the optimization goal of minimizing the intra-day total operating cost including voltage deviation costs, the intra-day total operating cost F intra includes the cost of purchasing electricity from the distribution network upwards, the operation and maintenance cost of switchable capacitors, the operation and maintenance cost of energy storage BESS, the node voltage deviation cost, the green certificate trading cost, and the cost of purchasing new energy.

[0064] The constraints of the source-grid-load-storage coordinated optimization model and / or the intraday rolling optimization model include power flow constraints, switchable capacitor constraints, and power balance constraints.

[0065] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or computer program instructions loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0066] Multiple components in the device are connected to the I / O interface, including: an input unit, such as a keyboard, a mouse, etc.; an output unit, such as various types of displays, speakers, etc.; a storage unit, such as a disk, an optical disc, etc.; and a communication unit, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0067] The processing unit executes the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the method described above can be executed. Alternatively, in other embodiments, the CPU can be configured to execute the method by any other suitable means (e.g., by means of firmware).

[0068] The functions described above herein can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), and so on.

[0069] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0070] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0071] Embodiment 2

[0072] This embodiment provides a method for collaborative optimal operation of a distribution network, including:

[0073] Day-ahead global optimization stage: Construct a source-network-load-storage collaborative optimization model, with the goal of minimizing the day-ahead total operating cost including green certificate trading costs, and perform day-ahead scheduling on distributed power sources, tie switches CSW, interruptible loads CL, shiftable loads SL, and three-terminal intelligent soft switches SOP with energy storage in the active distribution network;

[0074] Intra-day rolling optimization stage: Construct an intra-day rolling scheduling optimization model, with the goal of minimizing the intra-day total operating cost including voltage deviation costs, and perform intra-day scheduling on switchable capacitors SCB and three-terminal intelligent soft switches SOP with energy storage in the active distribution network.

[0075] Next, the method of this embodiment will be described in detail from several aspects such as model establishment, design principle, design method, and effectiveness verification.

[0076] (1) Green certificate trading model

[0077] Green certificates can not only play a role in optimizing the operation of the electricity market through trading, but also force electricity sellers and electricity users to sell and use a fixed amount of green electricity, thereby stimulating the vitality of green electricity consumption on the consumption side. The GCT cost is as follows:

[0078]

[0079] In the formula: F GC represents the green certificate trading cost of the distribution network, P GC is the green certificate trading price, yuan / certificate, G GC,o is the number of green certificates obtained by the distribution network, certificates, G GC,q is the number of green certificate quotas, certificates, P r,t is the grid-connected power of the r-th new energy source at time t, MW, R is the number of new energy sources, P q,t is the new energy quota demand of the distribution network, MW, ω is the quota coefficient; P s is the predicted value of the total system electricity consumption; ξ is the influence coefficient of the historical green certificate quota completion degree; ε is the influence weight of the historical green certificate quota completion degree; δ is the historical green certificate quota completion degree; δ n is the average value of the green certificate quota completion degree; ψ is the influence parameter of the new energy output prediction; ζ is the influence weight of the new energy output prediction; τ is the accuracy of the new energy output prediction in the previous period; τ n is the standard value of the new energy output prediction accuracy.

[0080] (2) "Source-network-load-storage" collaborative optimization model

[0081] By introducing distributed power sources, connection switches (controllable switches, CSW), shiftable loads (schedulable Loads, SL), curtailable loads (curtailable loads, CL) and three-terminal SOP with energy storage, the resources of the four links of "source-network-load-storage" are integrated. After a high proportion of renewable energy is connected to the grid, various related devices can be quickly regulated according to the dispatching results, reducing the operation cost of the distribution network while achieving the goals of reducing line losses and stabilizing the operating voltage.

[0082] Distributed power source:

[0083]

[0084] In the formula, are the actual reactive powers of the wind turbine (wind-turbine generation, WTG) and photovoltaic (photovoltaic generation, PVG) connected to the grid at node i at time t, respectively; are the maximum reactive powers generated by WTG and PVG at node i at time t, respectively, They are the minimum power factor angles of WTG and PVG respectively. Φ PV / Φ WT They are the node sets containing PVG and WTG respectively.

[0085] Three-terminal SOP with energy storage:

[0086]

[0087] In the formula, P SOP,i,t / Q SOP,i,t They are the active power and reactive power output from the i-th port of the three-terminal SOP at time t respectively; S SOP,i,t,max is the maximum capacity of the converter at the i-th port of the three-terminal SOP, P loss,i,t is the active power loss at the i-th port of the three-terminal SOP at time t; P ESS,i,t is the charging and discharging power of the ESS at time t, m loss is the active power loss coefficient of each port of the three-terminal SOP.

[0088] Interruptible load:

[0089]

[0090] In the formula, They represent the active and reactive powers of the i-th CL at time t respectively, They represent the reducible active and reactive powers of the i-th node at time t respectively; Φ CL is the node set containing CL.

[0091] Shiftable load:

[0092]

[0093] In the formula, They represent the transfer-in and transfer-out states of the i-th SL at time t respectively; They represent the predicted load and power factor angle of the i-th SL at time t respectively; They represent the active and reactive powers of the i-th SL at time t respectively; They represent the transfer-in and transfer-out states of the active power of the i-th SL at time t respectively; They represent the transfer-in and transfer-out states of the reactive power of the i-th SL at time t respectively. Φ SL is the node set containing SL.

[0094] (3) Day-ahead scheduling strategy

[0095] Such as Figure 1As shown in the figure, during the day-ahead global optimization phase, day-ahead scheduling is carried out for distributed power sources, CSWs, CLs, SLs, and three-terminal SOPs with energy storage in the active distribution network. The day-ahead plan is formulated once every 24 hours, covering 24 hours each time with a resolution of 1 hour.

[0096] Total day-ahead operating cost F ahead It consists of the cost of purchasing electricity from the distribution network, the operation and maintenance cost of CSWs, the compensation cost of CLs, the incentive cost of SLs, the operation and maintenance cost of BESSs, the GCT cost, and the cost of purchasing new energy:

[0097] minF ahead = F SS + F CSW + F CL + F SL + F BS + F GC + F GE (8)

[0098]

[0099] In the formula, F SS represents the total cost of purchasing active and reactive power from the superior power grid, represents the price of purchasing active and reactive power from the superior power grid at time t, represents the active and reactive power of the i-th substation at time t. F CSW represents the total operation and maintenance cost of CSWs, represents the operation and maintenance cost of CSWs on the ij section of the line. F CL represents the total cost paid to CLs, represents the cost paid to the i-th CL. F SL represents the total incentive cost of SLs, represents the incentive cost of the i-th SL. F BS represents the total operation and maintenance cost of BESSs, including the loss cost during its discharge period, represents the operation and maintenance cost of the i-th BESS, represents the charging and discharging power of the i-th BESS at time t, represents the charging and discharging efficiency of the i-th BESS. F GE represents the cost of purchasing electricity from new energy by the distribution network, λ r is the on-grid electricity price of new energy. T is the time set, Φ / Φ LD / Φ SS are the total line set, the load line set, and the substation line set respectively, and Φ BS is the node set containing BESSs.

[0100] (4) Intra-day scheduling strategy

[0101] As Figure 1 shown, during the intraday rolling optimization stage, the intraday scheduling is carried out for the switchable capacitor banks (SCB) and the three-terminal SOP with energy storage in the active distribution network. The intraday plan is adjusted every 15 minutes and each adjustment covers 4 hours. To avoid frequent adjustment of the scheduling plan, only the plan for the first 15-minute period is adjusted.

[0102] The total daily operating cost F intra consists of the cost of purchasing electricity from the distribution network, the operation and maintenance cost of SCB, the operation and maintenance cost of BESS, the node voltage deviation cost, the green certificate trading cost, and the cost of purchasing new energy:

[0103] minF intra = F SS + F SCB + F BS + F V + F GC + F GE (10)

[0104]

[0105] In the formula, F SCB represents the total operation and maintenance cost of SCB, represents the operation and maintenance cost of the i-th SCB, respectively represent the input and withdrawal states of the i-th SCB at time t. F V represents the node voltage deviation cost, C V represents the voltage deviation cost coefficient, U v,i,t / U v,n,t are the actual voltage and the reference voltage of node i at time t respectively. Φ SCB is the set of nodes containing SCB.

[0106] (5) Constraint conditions

[0107] 1) Power flow constraint

[0108] The power flow constraint based on network reconfiguration is as follows:

[0109]

[0110] In the formula, represents the binary variable of line ij. If node j is the parent node of node i, it is equal to 1; otherwise, it is equal to 0; represents the binary variable of line ij. If this line is connected, it is equal to 1; otherwise, it is equal to 0. For the power flow constraint model, by introducing intermediate variables U i,t 、 Perform replacement to linearize the non - linear model:

[0111]

[0112] In the formula, V i,t represents the voltage amplitude of the i - th line at time t in the system.

[0113] Furthermore, the relationship between distribution network reconfiguration and power flow variables can be described as:

[0114]

[0115] In the formula, represents the independent lines. When , these variables are set to zero; when , these variables are set to be greater than zero. The power flow constraint is expressed as:

[0116]

[0117] In the formula, g ij +jb ij =1 / (r ij +jx ij ).

[0118] 2) SCB constraint

[0119]

[0120] In the formula, represents the number of available capacitors of SCB at node i; represents the number of switched - on SCB groups at time t for the i - th group.

[0121] 3) Power balance constraint

[0122]

[0123] (6) Case study

[0124] 1) Case description

[0125] An improved IEEE 33 - node distribution system is adopted for analysis, as Figure 3 shown. The rated voltage of the distribution network is 12.66 kV, the rated capacity is 10 MW, and the upper and lower voltage limits are 0.95 and 1.05 (per - unit value). Taking the typical daily load curve and typical wind - solar output curves as examples, the output curves are shown in Figure 2。The electricity price adopts time-of-use electricity price, which is divided into electricity price data for peak, valley, and flat periods. Set detailed parameters such as the access locations and capacities of WTG, PVG, SCB, BESS, SL, CL, and the three-terminal SOP with energy storage. Set a control case: Case 1 is the day-ahead and intra-day two-stage optimal dispatching strategy for the "source-network-load-storage" coordination of the distribution network without considering green certificate trading, and Case 2 is the day-ahead and intra-day two-stage optimal dispatching strategy for the "source-network-load-storage" coordination of the distribution network considering green certificate trading.

[0126] 2) Analysis of optimization results

[0127] Figure 4 The following is a comparison chart of the wind and light consumption situation. The PV consumption rate of Case 2 is 83.93%, which is 0.04% higher than that of Case 1. The wind power consumption rate of Case 2 is 99.36%, which is 20.74% higher than that of Case 1. This shows that the introduction of the green certificate trading mechanism can significantly improve the consumption rate of new energy. Figure 5 The following shows the operating voltage situation. There is no over-limit situation in both cases. The voltage offset of Case 2 is 32.7527 kV, which is 3.32% lower than that of Case 1, and the system operating voltage is more stable. Figure 6 The following shows the network loss situation. The network loss of Case 2 is 0.629 MWh, which is 8.04% lower than that of Case 1, with less network loss and more economical system operation. The total operating cost of Case 2 is 333.1 yuan lower than that of Case 1, the upward power purchase cost is reduced by 621.3 yuan, and the cost of purchasing new energy is increased by 800.38 yuan.

[0128] Other settings in this embodiment are the same as those in Embodiment 1.

[0129] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A collaborative optimization operation method for a distribution network, characterized in that Including: Day-ahead global optimization stage: Construct a day-ahead source-network-load-storage collaborative optimization model, with the optimization goal of minimizing the day-ahead total operating cost including green certificate trading costs, and conduct day-ahead scheduling for distributed power sources, tie switches, interruptible loads, shiftable loads, and three-terminal intelligent soft switches with energy storage in the active distribution network; Intra-day rolling optimization stage: Construct an intra-day rolling optimization model, with the optimization goal of minimizing the intra-day total operating cost including voltage deviation costs, and conduct intra-day scheduling for switchable capacitors and three-terminal intelligent soft switches with energy storage in the active distribution network.

2. The collaborative optimization operation method of a distribution network according to claim 1, characterized in that The source-network-load-storage collaborative optimization model specifically includes: Optimization objective: The objective is to minimize the total day-ahead operating cost including the green certificate trading cost, and the total day-ahead operating cost F ahead includes the cost of purchasing electricity from the distribution network upwards, the operation and maintenance cost of the tie switch, the compensation cost of interruptible load, the incentive cost of transferable load, the operation and maintenance cost of energy storage, the green certificate trading cost, and the cost of purchasing new energy. The mathematical expression is: minF ahead = F SS + F CSW + F CL + F SL + F BS + F GC + F GE (1) Where: F SS represents the total cost of purchasing active and reactive power from the superior power grid; and respectively represent the prices of purchasing active and reactive power from the superior power grid at time t; and respectively represent the active and reactive power of the i-th substation at time t; F CSW represents the total operation and maintenance cost of the tie switches, represents the operation and maintenance cost of the tie switch on the ij line segment; F CL represents the total cost paid to the interruptible load; represents the cost paid to the i-th interruptible load; F SL represents the total incentive cost of the shiftable load; represents the incentive cost of the i-th shiftable load; F BS represents the total operation and maintenance cost of the energy storage unit, represents the operation and maintenance cost of the i-th energy storage unit, and represent the charging power and discharging power of the i-th energy storage unit at time t; and respectively represent the charging efficiency and discharging efficiency of the i-th energy storage unit; F GE represents the cost of purchasing electricity from new energy sources by the distribution network; λ r is the on-grid electricity price of new energy; T is the time set; Φ, Φ LD and Φ SS respectively are the total line set, load line set, and substation line set; Φ BS is the node set containing the energy storage unit; F GC is the green certificate trading cost.

3. The method for collaborative optimal operation of a distribution network according to claim 2, characterized in that The green certificate trading cost F GC , specifically: Where: P GC is the trading price of green certificates; G GC,o is the number of green certificates obtained by the distribution network; G GC,q is the green certificate quota; P r,t is the grid connection power of the r-th new energy source at time t; R is the number of new energy sources; P q,t is the new energy quota demand of the distribution network; ω is the quota coefficient; P s is the predicted value of the total system power consumption; ξ is the influence coefficient of the historical green certificate quota completion degree; ε is the influence weight of the historical green certificate quota completion degree; δ is the historical green certificate quota completion degree; δ n is the average value of the green certificate quota completion degree; ψ is the influence parameter of the new energy output prediction; ζ is the influence weight of the new energy output prediction; τ is the prediction accuracy of the new energy output in the previous cycle; τ n is the standard value of the prediction accuracy of the new energy output.

4. A method for collaborative optimization operation of a distribution network according to claim 1, characterized in that, The intra-day rolling optimization model specifically includes: Optimization objective: The optimization objective is to minimize the total intraday operating cost including the cost of voltage deviation, and the total intraday operating cost F intra includes the cost of purchasing electricity from the distribution network upwards, the operation and maintenance cost of switchable capacitors, the operation and maintenance cost of energy storage BESS, the node voltage deviation cost, the green certificate trading cost, and the cost of purchasing new energy. The mathematical expression is: minF intra = F SS + F SCB + F BS + F V + F GC + F GE (4) Where: F SCB represents the total operation and maintenance cost of the switchable capacitor; represents the operation and maintenance cost of the i-th switchable capacitor; and respectively represent the on-state and off-state of the i-th switchable capacitor at time t; F V represents the node voltage deviation cost; C V represents the voltage deviation cost coefficient; U v,i,t and U v,n,t are respectively the actual voltage and the reference voltage of node i at time t; Φ SCB is the set of nodes containing switchable capacitors.

5. A method for collaborative optimization operation of a distribution network according to claim 1, characterized in that, The constraint conditions of the source-network-load-storage collaborative optimization model and / or the intra-day rolling optimization model include power flow constraints, switchable capacitor constraints, and power balance constraints.

6. A coordinated optimization operation method for a distribution network according to claim 5, characterized in that The switchable capacitor constraint is specifically: Wherein: represents the available number of switchable capacitors at node i; represents the number of switched - in capacitors of the i - th group of switchable capacitors SCB at time t; And / or, the power balance constraint is specifically:

7. A coordinated optimization operation method for a distribution network according to claim 5, characterized in that The constraint conditions of the source-network-load-storage collaborative optimization model and / or the intra-day rolling optimization model further include: Distributed power source constraint: Wherein: and are respectively the actual reactive power of the wind turbine and the photovoltaic power generation connected to the grid at node i at time t; and are respectively the maximum reactive power generated by the wind turbine and the photovoltaic power generation at node i at time t, and are respectively the minimum power factor angles of the wind turbine and the photovoltaic power generation; Φ PV and Φ WT are respectively the node sets containing the wind turbine and the photovoltaic power generation; Three-terminal intelligent soft switch constraint with energy storage: Where: P SOP,i,t and Q SOP,i,t are respectively the active power and reactive power output from the i-th port of the three-terminal intelligent soft switch at time t; S SOP,i,t,max is the maximum capacity of the converter at the i-th port of the three-terminal intelligent soft switch; P loss,i,t is the active power loss at the i-th port of the three-terminal intelligent soft switch at time t; P ESS,i,t is the charge and discharge power of the energy storage at time t, m loss is the active power loss coefficient of each port of the three-terminal intelligent soft switch. Interruptible load constraint: Wherein: and respectively represent the active and reactive power of the i-th interruptible load at time t, and respectively represent the reducible active and reactive power of the i-th node at time t; Φ CL is the set of nodes containing interruptible loads; And / or, shiftable load constraint: Wherein: and respectively represent the in - transfer and out - transfer states of the i - th transferable load at time t; and respectively represent the predicted load and power factor angle of the i - th transferable load at time t; and respectively represent the active and reactive powers of the i - th transferable load at time t; and respectively represent the in - transfer and out - transfer states of the active power of the i - th transferable load at time t; and respectively represent the in - transfer and out - transfer states of the reactive power of the i - th transferable load at time t; Φ SL is the set of nodes containing transferable loads.

8. A coordinated optimization operation method for a distribution network according to claim 1, characterized in that In the intra-day rolling optimization stage, the intra-day plan is adjusted every 15 minutes, covering 4 hours each time, and only the plan for the first 15-minute period is optimized and adjusted.

9. An electronic device, comprising a memory and a processor, wherein a computer program is stored on the memory, characterized in that, When the processor executes the program, it implements the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 8.

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