A method and device for dynamic scheduling of an AC-DC hybrid power distribution network

Abstract

The application relates to an alternating current-direct current hybrid power distribution network dynamic scheduling method and device, which comprises the following steps: adjusting load demand according to real-time electricity price and constructing a multi-source uncertainty set; constructing first and second stage optimization scheduling models according to the adjusted load demand; constructing a total expected scheduling cost function according to the first and second stage optimization scheduling models; traversing all scene sets in the multi-source uncertainty set, adopting an improved column constraint generation algorithm based on total constraint conditions, and solving the first and second stage optimization scheduling models through strong duality transformation and iterative approximation of optimal solutions, so that the total expected scheduling cost function is minimized under the worst scene set, and a scheduling strategy which can keep the scheduling cost minimum under all scene sets is obtained. The application ensures that the system can still keep high efficient operation under the worst condition, improves the adaptability of the power grid to renewable energy and load fluctuation, and improves the economy and reliability of the whole system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system optimal scheduling, in particular to a dynamic scheduling method and device for AC-DC hybrid distribution network. BACKGROUND

[0002] With a large number of distributed power sources, energy storage devices and the like connected to the power grid, the distribution network has changed from the traditional one-way power supply mode to a two-way interactive smart network, and the power flow has also changed from one-way flow to two-way flow, making the operation mode of the AC-DC hybrid distribution network more diversified. On the other hand, the randomness and volatility of distributed photovoltaic output make the AC-DC hybrid distribution network face the risk of random disturbance. Therefore, the coordinated operation of distributed power sources, energy storage and loads is conducive to solving the problem of renewable energy consumption and plays an important supporting role in promoting the AC-DC hybrid distribution network to achieve the development goals of clean, low carbon, safe and efficient.

[0003] In the prior art, the two-stage optimal scheduling method for the AC-DC hybrid distribution network has the following steps: a model of the AC side and the DC side of the converter station is constructed, and a cost function is established considering the uncertainty factors; a random optimization, robust optimization or chance constraint programming method is used. Although these methods can theoretically handle certain uncertainties, they still have many limitations in practical applications.

[0004] The two-stage optimal scheduling method for the traditional AC-DC hybrid distribution network has the following disadvantages: it cannot adapt to the high volatility of renewable energy output and load demand; it relies too much on deterministic optimization models and lacks effective handling of uncertainties, resulting in insufficient robustness of the scheduling strategy in actual operation. In terms of price mechanism, the traditional optimal scheduling method mostly uses a fixed electricity price mode, which cannot dynamically adjust the electricity price according to the real-time supply and demand relationship and system operation state, and it is difficult to fully utilize the flexibility and response capability of the demand side. SUMMARY

[0005] The present application provides a dynamic scheduling method and device for AC-DC hybrid distribution network to solve at least one of the above technical problems.

[0006] The technical solution of the present application to solve the above technical problems is as follows: a dynamic scheduling method for AC-DC hybrid distribution network, comprising:

[0007] S1, for the AC-DC hybrid distribution network, dynamically adjusting the load demand of the converter station in the AC-DC hybrid distribution network according to the real-time electricity price to obtain the adjusted load demand; and constructing a multi-source uncertainty set including photovoltaic output uncertainty, wind power output uncertainty, load uncertainty and demand response uncertainty;

[0008] S2, according to the adjusted load demand, a first-stage optimal scheduling model considering load response benefits is constructed; and according to the adjusted load demand, a second-stage optimal scheduling model based on light and wind curtailment, load shedding and load response deviation penalty cost is constructed in consideration of the uncertainties in the multi-source uncertainty set;

[0009] S3, according to the first-stage optimal scheduling model and the second-stage optimal scheduling model, a total expected scheduling cost function is constructed;

[0010] S4, all scenario sets in the multi-source uncertainty set are traversed, and the first-stage optimal scheduling model and the second-stage optimal scheduling model are solved by using an improved column and constraint generation algorithm, and through strong duality transformation and iterative approximation of optimal solution, so that the total expected scheduling cost function is minimized under the worst scenario set, and a scheduling strategy capable of keeping the scheduling cost minimum under all scenario sets is obtained.

[0011] Based on the above-mentioned dynamic scheduling method for an AC / DC hybrid distribution network, the application further provides a dynamic scheduling device for an AC / DC hybrid distribution network.

[0012] A dynamic scheduling device for an AC / DC hybrid distribution network comprises a processor, a memory and a computer program stored in the memory, and the computer program is executed by the processor to implement the above-mentioned dynamic scheduling method for an AC / DC hybrid distribution network.

[0013] The application has the following beneficial effects: the dynamic scheduling method and device for an AC / DC hybrid distribution network can effectively cope with the uncertainties of photovoltaic and wind power output, load demand and demand response by introducing an uncertainty set based on multi-source uncertainty, ensure the efficient operation of the system under the worst condition, and improve the adaptability of the power grid to renewable energy and load fluctuation; meanwhile, the load demand is adjusted based on real-time electricity price in the first stage, which can improve the flexibility and predictability of the demand side, so that the uncertainty can be more effectively handled in the second stage, the wind and light curtailment and load reduction events caused by the uncertainty are reduced, and the economy and reliability of the whole system are improved; in addition, the two-stage optimal scheduling model is solved by using an improved column and constraint generation algorithm, and the optimal solution is approximated through strong duality transformation and iteration, so that the cost minimization is realized under the premise of ensuring the reliability of the system, the scheduling cost is effectively reduced, and the economy of the power grid operation is improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The flowchart of the dynamic scheduling method for an AC / DC hybrid distribution network is shown in the figure;

[0015] Figure 2 The structure diagram of the AC / DC hybrid distribution network is shown in the figure.

[0016] Figure 3 Fig. 1 is a schematic diagram of a converter station model;

[0017] Figure 4 Fig. 2 is a flowchart of solving a two-stage optimal scheduling model by using an improved column and constraint generation algorithm;

[0018] Figure 5 Fig. 3 is a structure block diagram of a dynamic scheduling device for an AC-DC hybrid distribution network. DETAILED DESCRIPTION

[0019] The principles and features of the present application are described below in conjunction with the accompanying drawings, and the examples are only used to explain the present application and not to limit the scope of the present application.

[0020] Embodiment one:

[0021] As shown in Fig. 1, a dynamic scheduling method for an AC-DC hybrid distribution network comprises: Figure 1 S1, for the AC-DC hybrid distribution network, a multi-source uncertainty set including photovoltaic output uncertainty, wind power output uncertainty, load uncertainty and demand response uncertainty is constructed; and according to real-time electricity price, the load demand of a converter station in the AC-DC hybrid distribution network is dynamically adjusted to obtain an adjusted load demand;

[0022] S2, according to the adjusted load demand, a first-stage optimal scheduling model considering load response revenue is constructed; and according to the adjusted load demand, a second-stage optimal scheduling model based on light and wind curtailment, load shedding and load response deviation penalty cost is constructed by taking into account the uncertainty in the multi-source uncertainty set;

[0023] S3, according to the first-stage optimal scheduling model and the second-stage optimal scheduling model, a total expected scheduling cost function is constructed;

[0024] S4, all scenario sets in the multi-source uncertainty set are traversed, and based on the total constraint condition of the AC-DC hybrid distribution network operation, an improved column and constraint generation algorithm is used, and the first-stage optimal scheduling model and the second-stage optimal scheduling model are solved by strong duality transformation and iterative approximation optimal solution method, so that the total expected scheduling cost function is minimized under the worst scenario set, and a scheduling strategy that can keep the scheduling cost minimum under all scenario sets is obtained.

[0025] The present application is specifically described below in conjunction with specific examples.

[0026]

[0027] Figure 2 ​A typical AC / DC hybrid distribution network structure diagram is shown in FIG. 1, which is integrated by distributed renewable energy, energy storage system and AC / DC load units; wherein, DL represents a DC load, AL represents an AC load; DP represents a DC photovoltaic station, AP represents an AC photovoltaic station, ESS represents an energy storage system, WT represents a wind turbine, and FL represents a flexible load.

[0028] The basic parameters of the AC / DC hybrid distribution network are obtained, and a converter station model is obtained as shown in FIG. 2; wherein, Figure 3 , and represent different nodes in the converter station, represents an AC side input node of the converter station, responsible for interaction with the AC power grid, and the power of the AC power grid is input to the converter station through the AC side input node of the converter station; represents an internal conversion node of the converter station, responsible for AC / DC conversion; represents a DC side output node of the converter station, responsible for interaction with the DC power grid; , and three nodes are connected to each other through power electronic devices inside the converter station, realizing AC / DC power conversion and transmission.

[0029] The converter station model has the following voltage relationship and power relationship:

[0030] ;

[0031] wherein, and represent the AC side voltage amplitude and the DC side voltage amplitude of the internal conversion node at time , represents the conversion coefficient of the converter station, represents the modulation ratio of the internal conversion node , and represent the AC side voltage reference value and the DC side voltage reference value of the converter station, and represent the AC side voltage per unit value and the DC side voltage per unit value of the internal conversion node at time , and represent the AC side active power and the DC side active power of the internal conversion node at time , represents the conversion efficiency of the converter station, ​Indicates the internal switching node of the converter station exist Reactive power on the AC side at any given time. This represents the AC-side power factor angle of the converter station.

[0032] In some embodiments of S1, dynamically adjusting the load demand of converter stations in the AC / DC hybrid distribution network according to real-time electricity prices specifically includes:

[0033] The real-time electricity price is determined based on market supply and demand and system operating status.

[0034] The adjusted load demand is obtained by dynamically adjusting the load demand of converter stations in the AC / DC hybrid distribution network by linking the real-time electricity price with the user price elasticity coefficient.

[0035] Furthermore, the formula for determining the real-time electricity price is as follows:

[0036] ;

[0037] The formula for dynamically adjusting the load demand of converter stations in a hybrid AC / DC distribution network is:

[0038] ;

[0039] in, express Real-time electricity price at any given moment express The benchmark electricity price at any given time. express Net load at any given time This represents the historical average net load. This represents the electricity price fluctuation ratio coefficient; Indicates the AC input node of the converter station exist Adjusted load demand at different times Indicates the AC input node of the converter station exist The basic load requirement at any given time Indicates the AC input node of the converter station exist Price elasticity coefficient at any given time.

[0040] Specifically, the formula for determining the real-time electricity price and the formula for dynamically adjusting the load demand of converter stations in the AC / DC hybrid distribution network are combined to form a load demand response model based on the real-time electricity price. The load demand response model is used to dynamically adjust the load demand through the linkage between the real-time electricity price and the user price elasticity coefficient, and uses the adjusted load demand as the input parameter of the subsequent two-stage optimization scheduling model to calculate the load response revenue and load deviation penalty cost, thereby guiding demand-side resources to participate in scheduling optimization through price signals.

[0041] In some embodiments of S1, to allow the set of multi-source uncertainties to be flexibly adjusted to adjust the conservatism of the optimized decision, the set of multi-source uncertainties is characterized by the following formula:

[0042] The uncertainty in photovoltaic output is expressed as:

[0043] ;

[0044] in, express The uncertainty of photovoltaic output at this moment express Photovoltaic forecast values ​​at this time and They represent At this time, the maximum positive and maximum negative deviations of photovoltaic output are allowed. and They represent The current photovoltaic power output is a binary variable that fluctuates upwards and downwards. This represents the adjustable uncertainty parameter of photovoltaics. This indicates the photovoltaic station index, i.e., the [number]th [unit]. One photovoltaic station;

[0045] The uncertainty in wind power output is expressed as:

[0046] ;

[0047] in, express The uncertainty of wind power output at any given moment express Wind power forecast at the current moment, and They represent The maximum positive and maximum negative deviations of wind power output at any given time. and They represent A binary variable representing the upward and downward fluctuations in wind power output at any given moment. This represents the adjustable uncertain parameters of wind power. This represents the wind turbine index, i.e., the [number]th [unit]. Typhoon generator sets;

[0048] The load uncertainty is expressed as:

[0049] ;

[0050] in, express Load uncertainty at any given time express Forecasted load power at time 10:00 and They represent The maximum positive and maximum negative deviations of the load allowable output at any given time. and They represent A binary variable representing the upward and downward fluctuations of load output at any given time. This represents the adjustable uncertainty parameter of the load. Indicates the load cell index, i.e., the first... One load unit;

[0051] The uncertainty in demand response is expressed as:

[0052] ;

[0053] in, express Demand response uncertainty at any given moment express Predicted elasticity coefficient at time t. and They represent At any given time, the maximum positive and maximum negative deviations of the elastic coefficient allowable for the output are... and These represent the two variables that represent the upward and downward fluctuations of the elasticity coefficient output, respectively. The adjustable uncertainty parameter representing the elastic coefficient. This represents the index of the user participating in the demand response, i.e., the i-th user participating in the demand response.

[0054] Specifically, demand response uncertainty is reflected through the uncertainty of the elasticity coefficient, which reflects the sensitivity of the load to changes in electricity prices. Its uncertainty can directly reflect the fluctuation range of user response behavior. In addition, the positive / negative deviation of the elasticity coefficient simulates the degree to which the actual user response deviates from the expected response.

[0055] In some embodiments of S2, the first-stage optimized scheduling model is specifically as follows:

[0056] ;

[0057] in, Indicates the decision variables in the first stage. This represents the scheduling cost in the first phase. , , , and They represent The current cost of dispatching traditional generator sets, energy storage systems, electricity purchase from the main grid, grid loss costs, and total revenue from load demand are calculated. , and These represent the secondary cost, linear cost, and fixed cost per unit of power, respectively. express The power generation of traditional generator sets at this time Represents the cost per unit of power. express The charging and discharging power of the energy storage system at this time. Indicates time interval, Indicates the unit network loss cost. and Let them represent the AC / DC branch set and the VSC equivalent branch set, respectively. Indicates the AC input node of the converter station internal conversion nodes of the converter station Between Current at any moment Indicates the AC input node of the converter station internal conversion nodes of the converter station The resistance between them express Real-time electricity price at any given moment Indicates the AC input node of the converter station exist Adjusted load demand at different times Indicates the AC input node of the converter station exist The basic load requirement at any given time , , and These represent the total number of scheduling time slots, the cost coefficient of traditional generator sets, the scheduling cost coefficient of energy storage systems, and the time period, respectively.

[0058] The second-stage optimized scheduling model is as follows:

[0059] ;

[0060] in, Indicates the decision variables for the second stage. This indicates the scheduling cost for the second phase. , , and They represent The costs of curtailing solar power, wind power, load shedding, and load response deviation at any given time are: , , and These represent the unit cost of curtailing solar power, the unit cost of curtailing wind power, the unit cost of load shedding, and the unit cost of load response deviation penalty, respectively. and They represent Current photovoltaic and wind power forecasts and They represent Actual photovoltaic power generation and actual wind power generation at the current time. and They represent AC load shedding and DC load shedding at any given time. Indicates the AC input node of the converter station exist Actual load demand at any given time Indicates the AC input node of the converter station exist Adjusted load demand at different times , , and These represent the total number of photovoltaic power generation nodes, the total number of wind power generation nodes, the total number of load nodes, and the number of users participating in demand response, respectively.

[0061] In some embodiments of S3, the total expected cost function is specifically:

[0062] ;

[0063] in, Minimize the first-stage decision variables ; This represents the first-stage scheduling cost in the first-stage optimized scheduling model. Represents maximizing the set of uncertainties Scene set in , Minimize the decision variables given in the first stage. and scene set The second stage decision variables , This represents the second-stage scheduling cost in the second-stage optimized scheduling model.

[0064] Specifically, the total expected cost function consists of two parts: the first-stage cost generated by the first-stage optimization scheduling model and the second-stage cost generated by the second-stage optimization scheduling model. The first-stage cost includes the scheduling cost of controllable power generation resources, the cost of purchasing electricity from the main grid, and the cost incurred due to line losses. The second-stage cost includes the cost of curtailment due to deviations between photovoltaic and wind power output and predicted values, and the cost of load shedding due to the inability to meet actual load demand. Based on the aforementioned multi-source uncertainty set, all possible combinations of the maximum positive / negative deviations in photovoltaic output, wind power output, load demand, and demand response elasticity coefficient are traversed to generate a scenario set. This scenario set serves as the input parameter for the two-stage optimization scheduling model, used to calculate the extreme values ​​of the second-stage penalty cost, thereby ensuring that the scheduling strategy meets robustness requirements under all possible scenario sets.

[0065] In some embodiments of S4, to enable the two-stage optimization scheduling model to find a scheduling strategy that remains effective even in the worst-case scenario, thereby minimizing total cost while maintaining system reliability, constraints need to be imposed on the operating conditions of the first and second stages. The two-stage optimization scheduling model needs to satisfy the constraints of AC / DC hybrid distribution network operation; the total constraints of AC / DC hybrid distribution network operation include power balance constraints, converter station constraints, interaction constraints with the main grid, energy storage battery constraints, price level constraints, and demand response constraints. Specifically:

[0066] The power balance constraint is expressed as:

[0067] ;

[0068] in, express At this moment The power generation of a traditional generator set ( express (Total power generation of all traditional generator sets at the current moment). express The electricity purchased from the main grid at the current time; express Photovoltaic output power at the current moment; express Current wind power output; express The power supplied by the energy storage system during discharge at any given moment; express The power consumed by the energy storage system during charging at any given time. express The total load demand at any given time; specifically, the power balance constraint is set based on the aforementioned converter station model.

[0069] The converter station constraints are expressed as follows:

[0070] ;

[0071] in, , and These represent the conversion nodes within the converter station. exist The active power on the AC side, the active power on the DC side, and the reactive power on the AC side at any given time. and These represent the conversion nodes within the converter station. exist The upper and lower limits of the AC-side active power at any given time. and These represent the conversion nodes within the converter station. exist The upper and lower limits of DC-side active power at any given time. and These represent the conversion nodes within the converter station. exist The upper and lower limits of reactive power on the AC side at any given time. This represents the set of conversion nodes within the converter station. The time period is indicated; specifically, the converter station constraints are set based on the aforementioned converter station model.

[0072] The interaction constraint with the main network is expressed as follows:

[0073] ;

[0074] in, and They represent Current converter station AC input node Active power and reactive power, and These represent the upper and lower limits of active power, respectively. and These represent the upper and lower limits of the reactive power of the converter station, respectively. This represents the set of AC input nodes for the converter station.

[0075] The energy storage battery constraint is expressed as follows:

[0076] ;

[0077] in, Indicates in The active power output of the energy storage system at all times. and They represent in The upper and lower limits of the active power output of the energy storage system at all times. express The operating status of the energy storage system at all times, and This indicates that the energy storage system has stopped operating. This indicates that the energy storage system is in grid-connected operation. This indicates the maximum number of hours a day that an energy storage system is allowed to connect to the power distribution network.

[0078] The price level constraint is expressed as follows:

[0079] ;

[0080] in, Indicates price level users in Electricity price at any time and These represent the minimum and maximum values ​​of the electricity price, respectively. Indicates the AC input node of the converter station exist Adjusted load demand at different times Indicates the AC input node of the converter station exist Original load demand before the adjustment time. express The original electricity price at that moment, Indicates price level Users below Price elasticity coefficient at any given time.

[0081] The demand response constraint is expressed as follows:

[0082] ;

[0083] in, Indicates the AC input node of the converter station exist At any time at the price level The following demand response adjustment amount, and These represent the minimum and maximum values ​​of the demand response, respectively.

[0084] In some embodiments of S4, such as Figure 4 As shown, S4 specifically includes:

[0085] S41, the problem of minimizing the total expected scheduling cost function is decomposed into a main problem and sub-problems; wherein, the main problem is to determine the feasible value range of the first-stage decision variables in the first-stage optimal scheduling model based on the current scenario set and the total constraints; the sub-problems are the combination of second-stage decision variables that maximize the second-stage scheduling cost in the second-stage optimal scheduling model based on the multi-source uncertainty set and the energy storage battery constraints, price level constraints, and demand response constraints in the total constraints;

[0086] S42, construct the objective function of the main problem based on the main problem, and reconstruct the objective function by transforming the constraints of the subproblems into dual variables through strong dual transformation, thereby obtaining the objective function of the subproblems;

[0087] S43, Set convergence error Initialize the number of iterations. Initialize the multi-source uncertainty set to obtain an initial scenario set, and use the initial scenario set as the current scenario set; initialize the total constraints to obtain an initial feasible solution; initialize the lower bound of the objective function of the main problem. and the Upper Realm ;

[0088] S44, taking the initial feasible solution as the starting point of the main problem, and based on the objective function of the main problem, according to the current scenario set and the overall constraints, perform the following steps on the main problem: The first-stage decision variables are obtained through multiple iterations. In the The optimal solution after the next iteration and the main problem objective value and update the lower bound. ;

[0089] S45, based on the objective function of the sub-problem, and according to the decision variables of the first stage... In the The optimal solution after the next iteration And the aforementioned multi-source uncertainty set, to perform the first step on the sub-problem. The solution is obtained in the nth iteration, and the set of multi-source uncertainties is obtained in the nth iteration. The worst-case scenario set after the next iteration Second-stage decision variables In the The optimal solution after the next iteration and the target value of the sub-problem and update the upper bound. ;

[0090] S46, Judgment Check if the condition is met; if so, stop the iteration and change the first-stage decision variables. In the The optimal solution after the next iteration and second-stage decision variables In the The optimal solution after the next iteration As the output of the scheduling strategy; if not, then the multi-source uncertainty set is set in the th... The worst-case scenario set after the next iteration and second-stage decision variables In the The optimal solution after the next iteration Add to the current scene set and update the iteration count. Then return to S44 and iterate until... Established.

[0091] Furthermore, the objective function of the main problem is expressed as:

[0092] , ;

[0093] in, Represents the decision variables in the first stage. The cost, Represents a cost coefficient vector. This represents the additional costs under the worst-case scenario in the second phase. Represents a cost coefficient vector. This represents the upper bound vector of the inequality constraint. Represents the value vector of equality constraints. This represents the upper bound vector of the inequality constraint. Represents the decision variables in the first stage. linear combination, , , , Both represent coefficient matrices.

[0094] By transforming the constraints of the subproblems into dual variables through strong duality transformation, the objective function is reconstructed. This transforms the insolvable main problem and subproblems into computable single-level problems, resulting in an equivalent single-level max problem. The dual variable corresponding to each constraint in the subproblems is defined as follows: , , , Therefore, the objective function expression for the subproblem can be obtained as follows:

[0095] ,

[0096] ;

[0097] in, , , and Both represent dual variables. Additionally, superscripts... This indicates the matrix transpose.

[0098] Example 2:

[0099] Based on the above-mentioned dynamic dispatching method for AC / DC hybrid distribution networks, the present invention also provides a dynamic dispatching device for AC / DC hybrid distribution networks.

[0100] A dynamic dispatching device for a hybrid AC / DC distribution network includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the dynamic dispatching method for the hybrid AC / DC distribution network as described above.

[0101] In other words, the AC / DC hybrid distribution network dynamic dispatching device of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store computer programs; the processor is used to execute the AC / DC hybrid distribution network dynamic dispatching method shown in any embodiment of the present invention by calling the computer programs.

[0102] In one optional embodiment, a dynamic dispatching device for a hybrid AC / DC distribution network is provided, such as Figure 5 As shown. Figure 5 The AC / DC hybrid distribution network dynamic dispatching device shown includes a processor and a memory. The processor and memory are connected, for example, via a bus. Optionally, the AC / DC hybrid distribution network dynamic dispatching device may further include a transceiver, which can be used for data interaction between the AC / DC hybrid distribution network dynamic dispatching device and other electronic devices, such as data transmission and / or data reception. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of this AC / DC hybrid distribution network dynamic dispatching device does not constitute a limitation on the embodiments of the present invention.

[0103] The processor can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a PLC (Programmable Controllers), a FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0104] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0105] The memory may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these.

[0106] The memory stores application code (computer program) that executes the present invention, and its execution is controlled by a processor. The processor executes the application code stored in the memory to implement the content shown in the foregoing method embodiments.

[0107] Among them, the AC / DC hybrid distribution network dynamic dispatching device can also be a terminal device. The terminal device can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart vehicle devices.

[0108] It should be noted that, Figure 5 The AC / DC hybrid distribution network dynamic dispatching device shown is merely an example and should not impose any limitations on the functionality and scope of application of the embodiments of the present invention.

[0109] This invention discloses a dynamic scheduling method and apparatus for AC / DC hybrid distribution networks. By introducing an uncertainty set based on multi-source uncertainty, it can effectively address the uncertainties in photovoltaic and wind power output, load demand, and demand response, ensuring efficient system operation even under the worst-case scenario and enhancing the grid's adaptability to renewable energy and load fluctuations. Simultaneously, the introduction of a load demand response model based on real-time electricity prices in the first stage improves the flexibility and predictability of the demand side, thereby more effectively handling uncertainty in the second stage, reducing wind and solar power curtailment and load reduction events caused by uncertainty, and improving the overall system's economy and reliability. Furthermore, this invention employs an improved column and constraint generation algorithm to solve the two-stage optimal scheduling model. Through strong dual transformation and iterative approximation of the optimal solution, it minimizes costs while ensuring system reliability, effectively reducing scheduling costs and improving the economic efficiency of grid operation.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic dispatching method for an AC / DC hybrid distribution network, characterized in that, include: S1, for AC / DC hybrid distribution networks, dynamically adjusts the load demand of converter stations in AC / DC hybrid distribution networks according to real-time electricity prices to obtain the adjusted load demand; Furthermore, a multi-source uncertainty set is constructed, including photovoltaic output uncertainty, wind power output uncertainty, load uncertainty, and demand response uncertainty; S2, based on the adjusted load demand, construct a first-stage optimized scheduling model that considers load response benefits; and, taking into account the uncertainties in the multi-source uncertainty set, construct a second-stage optimized scheduling model based on the adjusted load demand and the costs of curtailment of solar power, wind power, load shedding, and load response deviation penalties. S3. Construct the total expected scheduling cost function based on the first-stage optimized scheduling model and the second-stage optimized scheduling model; S4. Traverse all scenario sets in the multi-source uncertainty set. Based on the total constraints of AC / DC hybrid distribution network operation, adopt an improved column and constraint generation algorithm, and solve the first-stage optimization scheduling model and the second-stage optimization scheduling model by strong dual transformation and iterative approximation of the optimal solution, so as to minimize the total expected scheduling cost function under the worst scenario set, and obtain the scheduling strategy that can keep the scheduling cost minimum under all scenario sets. The total expected scheduling cost function is specifically as follows: ； in, Minimize the first-stage decision variables ; This represents the first-stage scheduling cost in the first-stage optimized scheduling model. Represents maximizing the set of uncertainties Scene set in , Minimize the decision variables given in the first stage. and scene set Second-stage decision variables , This represents the second-stage scheduling cost in the second-stage optimized scheduling model.

2. The dynamic dispatching method for AC / DC hybrid distribution networks according to claim 1, characterized in that, The load demand of converter stations in the AC / DC hybrid distribution network is dynamically adjusted based on real-time electricity prices, specifically including: The real-time electricity price is determined based on market supply and demand and system operating status. The adjusted load demand is obtained by dynamically adjusting the load demand of converter stations in the AC / DC hybrid distribution network by linking the real-time electricity price with the user price elasticity coefficient.

3. The dynamic dispatching method for AC / DC hybrid distribution networks according to claim 2, characterized in that, The formula for determining the real-time electricity price is: ； The formula for dynamically adjusting the load demand of converter stations in a hybrid AC / DC distribution network is: ； in, express Real-time electricity price at any given moment express The benchmark electricity price at any given time. express Net load at any given time This represents the historical average net load. This represents the electricity price fluctuation ratio coefficient; Indicates the AC input node of the converter station exist Adjusted load demand at different times Indicates the AC input node of the converter station exist The basic load requirement at any given time Indicates the AC input node of the converter station exist Price elasticity coefficient at any given time.

4. The dynamic dispatching method for AC / DC hybrid distribution networks according to claim 1, characterized in that, The uncertainty in photovoltaic output is expressed as: ； in, express The uncertainty of photovoltaic output at any given moment express Photovoltaic forecast values ​​at this time and They represent At this time, the maximum positive and maximum negative deviations of photovoltaic output are allowed. and They represent The current photovoltaic power output is a binary variable that fluctuates upwards and downwards. This represents the adjustable uncertainty parameter of photovoltaics. Indicates the index of photovoltaic stations; The uncertainty in wind power output is expressed as: ； in, express The uncertainty of wind power output at any given moment express Wind power forecast at the current moment, and They represent The maximum positive and maximum negative deviations of wind power output at any given time. and They represent A binary variable representing the upward and downward fluctuations in wind power output at any given moment. This represents the adjustable uncertain parameters of wind power. Indicates the wind turbine index; The load uncertainty is expressed as: ； in, express Load uncertainty at any given time express Forecasted load power at time 10:

00. and They represent The maximum positive and maximum negative deviations of the load allowable output at any given time. and They represent A binary variable representing the upward and downward fluctuations of load output at any given time. This represents the adjustable uncertainty parameter of the load. Indicates the load cell index; The uncertainty in demand response is expressed as: ； in, express Demand response uncertainty at any given moment express Predicted elasticity coefficient at time t. and They represent At any given time, the maximum positive and maximum negative deviations of the elastic coefficient allowable for the output are... and These represent the two variables that represent the upward and downward fluctuations of the elasticity coefficient output, respectively. The adjustable uncertainty parameter representing the elastic coefficient. This represents the index of users who participated in the demand response.

5. The dynamic dispatching method for AC / DC hybrid distribution networks according to claim 1, characterized in that, The first stage of the optimized scheduling model is as follows: ； in, Indicates the decision variables in the first stage. This represents the scheduling cost in the first phase. , , , and They represent The current cost of dispatching traditional generator sets, energy storage systems, electricity purchase from the main grid, grid loss costs, and total revenue from load demand are calculated. , and These represent the secondary cost, linear cost, and fixed cost per unit of power, respectively. express The power generation of traditional generator sets at this time Represents the cost per unit of power. express The charging and discharging power of the energy storage system at that time. Indicates time interval, Indicates the unit network loss cost. and Let them represent the AC / DC branch set and the VSC equivalent branch set, respectively. Indicates the AC input node of the converter station internal conversion nodes of the converter station Between Current at any moment Indicates the AC input node of the converter station internal conversion nodes of the converter station The resistance between them express Real-time electricity price at any given moment Indicates the AC input node of the converter station exist Adjusted load demand at different times Indicates the AC input node of the converter station exist The basic load requirement at any given time , , and These represent the total number of scheduling time slots, the cost coefficient of traditional generator sets, the scheduling cost coefficient of energy storage systems, and the time period, respectively. The second-stage optimized scheduling model is as follows: ； in, Indicates the decision variables for the second stage. This indicates the scheduling cost for the second phase. , , and They represent The costs of curtailing solar power, wind power, load shedding, and load response deviation at any given time are: , , and These represent the unit cost of curtailing solar power, the unit cost of curtailing wind power, the unit cost of load shedding, and the unit cost of load response deviation penalty, respectively. and They represent Current photovoltaic and wind power forecasts and They represent The actual photovoltaic power generation and actual wind power generation at the current moment. and They represent AC load shedding and DC load shedding at any given time. Indicates the AC input node of the converter station exist Actual load demand at any given time Indicates the AC input node of the converter station exist Adjusted load demand at different times , , and These represent the total number of photovoltaic power generation nodes, the total number of wind power generation nodes, the total number of load nodes, and the number of users participating in demand response, respectively.

6. The dynamic dispatching method for AC / DC hybrid distribution networks according to claim 1, characterized in that, The overall constraints include power balance constraints, converter station constraints, grid interaction constraints, energy storage battery constraints, price level constraints, and demand response constraints. The power balance constraint is expressed as: ； in, express At this moment The power output of a traditional generator set; express The electricity purchased from the main grid at the current time; express Photovoltaic output power at the current moment; express Current wind power output; express The power supplied by the energy storage system during discharge at any given moment; express The power consumed by the energy storage system during charging at any given time. express Total load demand at any given time; The converter station constraints are expressed as follows: ； in, , and These represent the conversion nodes within the converter station. exist The active power on the AC side, the active power on the DC side, and the reactive power on the AC side at any given time. and These represent the conversion nodes within the converter station. exist The upper and lower limits of the AC-side active power at any given time. and These represent the conversion nodes within the converter station. exist The upper and lower limits of DC-side active power at any given time. and These represent the conversion nodes within the converter station. exist The upper and lower limits of reactive power on the AC side at any given time. This represents the set of conversion nodes within the converter station. Indicates a time period; The constraint on interaction with the main network is expressed as follows: ； in, and They represent Current converter station AC input node Active power and reactive power, and These represent the upper and lower limits of active power, respectively. and These represent the upper and lower limits of the reactive power of the converter station, respectively. This represents the set of AC input nodes of the converter station; The energy storage battery constraint is expressed as follows: ； in, Indicates in The active power output of the energy storage system at all times. and They represent in The upper and lower limits of the active power output of the energy storage system at all times. express The operating status of the energy storage system at all times, and This indicates that the energy storage system has stopped operating. This indicates that the energy storage system is in grid-connected operation. This indicates the maximum number of hours a day that an energy storage system is allowed to connect to the power distribution network; The price level constraint is expressed as follows: ； in, Indicates price level users in Electricity price at any time and These represent the minimum and maximum values ​​of the electricity price, respectively. Indicates the AC input node of the converter station exist Adjusted load demand at different times Indicates the AC input node of the converter station exist Original load demand before the adjustment time. express The original electricity price at that moment, Indicates price level Users below Price elasticity coefficient at any given time; The demand response constraint is expressed as follows: ； in, Indicates the AC input node of the converter station exist At any time at the price level The following demand response adjustment amount, and These represent the minimum and maximum values ​​of the demand response, respectively.

7. The dynamic dispatching method for AC / DC hybrid distribution networks according to claim 1, characterized in that, The overall constraints include power balance constraints, converter station constraints, grid interaction constraints, energy storage battery constraints, price level constraints, and demand response constraints. S4 specifically includes: S41, the problem of minimizing the total expected scheduling cost function is decomposed into a main problem and sub-problems; wherein, the main problem is to determine the feasible value range of the first-stage decision variables in the first-stage optimal scheduling model based on the current scenario set and the total constraints; the sub-problems are the combination of second-stage decision variables that maximize the second-stage scheduling cost in the second-stage optimal scheduling model based on the multi-source uncertainty set and the energy storage battery constraints, price level constraints, and demand response constraints in the total constraints; S42, construct the objective function of the main problem based on the main problem, and reconstruct the objective function by transforming the constraints of the subproblems into dual variables through strong dual transformation, thereby obtaining the objective function of the subproblems; S43, Set convergence error Initialize the number of iterations. Initialize the multi-source uncertainty set to obtain an initial scenario set, and use the initial scenario set as the current scenario set; initialize the total constraints to obtain an initial feasible solution; initialize the lower bound of the objective function of the main problem. and the Upper Realm ; S44, taking the initial feasible solution as the starting point of the main problem, and based on the objective function of the main problem, according to the current scenario set and the overall constraints, perform the following steps on the main problem: The first-stage decision variables are obtained through multiple iterations. In the The optimal solution after the next iteration and the main problem objective value and update the lower bound. ; S45, based on the objective function of the sub-problem, and according to the decision variables of the first stage... In the The optimal solution after the next iteration And the aforementioned multi-source uncertainty set, to perform the first step on the sub-problem. The multi-source uncertainty set is obtained by solving the problem in the nth iteration. The worst-case scenario set after the next iteration Second-stage decision variables In the The optimal solution after the next iteration and the target value of the sub-problem and update the upper bound. ; S46, Judgment Check if the condition is met; if so, stop the iteration and change the first-stage decision variables. In the The optimal solution after the next iteration and second-stage decision variables In the The optimal solution after the next iteration As the output of the scheduling strategy; if not, then the multi-source uncertainty set is set in the 1st... The worst-case scenario set after the next iteration and second-stage decision variables In the The optimal solution after the next iteration Add to the current scene set and update the iteration count. Then return to S44 and iterate until... Established.

8. The dynamic dispatching method for AC / DC hybrid distribution networks according to claim 7, characterized in that, The objective function of the main problem is expressed as: ， ； in, Represents the decision variables in the first stage. The cost, Represents a cost coefficient vector. This represents the additional costs under the worst-case scenario in the second phase. Represents a cost coefficient vector. This represents the upper bound vector of the inequality constraint. Represents the value vector of equality constraints. This represents the upper bound vector of the inequality constraint. Represents the decision variables in the first stage. linear combination, , , , Each represents a coefficient matrix; The objective function of the subproblem is expressed as: ， ； in, , , and Both represent dual variables.

9. A dynamic dispatching device for an AC / DC hybrid distribution network, characterized in that, It includes a processor, a memory, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the dynamic dispatching method for AC / DC hybrid distribution networks as described in any one of claims 1 to 8.

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