A wind-solar-water microgrid reactive power coordinated control method and system

Abstract

The application discloses a kind of wind light water microgrid reactive power coordination control method and system, method includes: with the minimum comprehensive economic loss and the minimum grid equivalent load volatility as objective function, consider pumped storage unit hydraulic constraint, working condition conversion constraint and system power balance constraint, establish multi-objective optimization scheduling model;Enhanced epsilon constraint method is used to convert multi-objective problem into single objective problem, obtain Pareto frontier by mixed integer linear programming solution, and determine the generation / pumping power and reactive power output plan of pumped storage unit and the switching scheme of discrete reactive power equipment based on fuzzy membership function screening optimal compromise solution.The application cooperates optimization of pumped storage unit and wind light water resources, effectively reduces network loss and equipment action loss while suppressing new energy output fluctuation and improving voltage quality, reduces the action times of traditional reactive power regulating equipment, prolongs the service life of equipment, and realizes microgrid safe and economic operation.

Description

Technical Field

[0001] This invention belongs to the field of microgrid operation and control technology, and particularly relates to a method and system for reactive power coordinated control of wind, solar and hydropower microgrids. Background Technology

[0002] The output of new energy sources is characterized by significant randomness, volatility, and intermittency. The high proportion of new energy access changes the unidirectional flow characteristics of power flow in microgrids, which can easily lead to frequent, rapid, and severe voltage fluctuations, especially at the end of the feeder, where voltage over-limit problems are particularly prominent.

[0003] In traditional microgrids, capacitor banks (CBs) and on-load tap changers (OLTCs), as discrete operating devices, cannot be continuously adjusted to adapt to rapid changes in renewable energy output. When the output of distributed photovoltaic, small hydropower, and other power sources fluctuates, voltage exceeding limits triggers the automatic voltage control system to frequently adjust OLTC taps and switch capacitor banks. This not only increases the number of equipment operations, causing mechanical losses and electrical wear, and shortening equipment lifespan, but may also cause unbalanced reactive power distribution, further exacerbating voltage fluctuations and creating a vicious cycle.

[0004] Small-scale pumped-storage hydroelectric units (PSHs) are characterized by fast response, wide adjustment range, and bidirectional power regulation. They can operate in both power generation and pumping modes, providing active power support while also offering reactive power support through excitation system regulation. This makes them an ideal resource for addressing voltage fluctuations in microgrids. However, existing research largely focuses on the peak-shaving and valley-filling functions of PSHs, neglecting to explore their role in reactive power and voltage regulation. There is a lack of comprehensive methods for synergistically optimizing PSHs with wind, solar, and hydropower resources to simultaneously suppress voltage fluctuations and minimize system operating losses.

[0005] Therefore, there is an urgent need for a multi-objective coordinated control method for microgrids that can synergistically optimize pumped storage units and wind, solar and hydropower resources. This method can smooth out fluctuations in renewable energy output, improve voltage quality, reduce network losses and equipment operating costs, decrease the number of operations of traditional reactive power regulation equipment, and extend equipment lifespan. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of existing technologies and provide a multi-objective coordinated control method and system for wind, solar, and hydropower microgrids. The core concept of this invention is as follows: A multi-objective optimization framework is adopted, using the enhanced Epsilon constraint method to transform the two conflicting objectives of minimizing the equivalent load fluctuation of the power grid and minimizing the overall system operating loss into a series of single-objective problems. These problems are then solved using mixed-integer linear programming to obtain the Pareto front. Based on fuzzy membership functions, the optimal compromise solution is selected from the Pareto front, achieving coordinated optimization of the pumped-storage unit's power generation / pumping power curves, on-load tap changer tap positions, and capacitor bank switching schemes. By constructing a complete microgrid mathematical model, the power flow constraints of the distribution network, renewable energy output constraints, discrete reactive power compensation equipment constraints, and pumped-storage unit operating constraints are incorporated into the optimization framework. This ensures the feasibility and safety of the control strategy, thereby smoothing renewable energy output fluctuations, improving voltage quality, reducing network losses and equipment operating costs, decreasing the number of operations of traditional reactive power regulation equipment, and extending equipment lifespan.

[0007] In a first aspect, the present invention provides a reactive power coordinated control method for a wind-solar-hydro microgrid, comprising:

[0008] A mathematical model of a microgrid is constructed, which includes distributed photovoltaic power, small hydropower, on-load tap-changing transformers, capacitor banks and micro pumped storage units. The mathematical model includes power flow constraints of the distribution network, power output model of new energy sources, discrete reactive power compensation equipment model and pumped storage unit operation model.

[0009] With the objectives of minimizing the equivalent load fluctuation of the power grid and minimizing the overall system operating loss, and considering the power limitations, operating condition constraints, and system power balance constraints of pumped storage units, a multi-objective optimization scheduling model is constructed.

[0010] The enhanced Epsilon constraint method is used to transform the multi-objective optimization problem into a series of single-objective optimization problems. Each single-objective problem is solved by mixed-integer linear programming to obtain the Pareto front.

[0011] The optimal compromise solution is selected from the Pareto front based on the fuzzy membership function to determine the power generation / pumping power curve of the pumped storage unit, as well as the tap position of the on-load tap changer and the switching scheme of the capacitor bank.

[0012] Secondly, the present invention provides a reactive power coordinated control system for a wind-solar-hydro microgrid, comprising:

[0013] The first construction module is configured to construct a microgrid mathematical model containing distributed photovoltaic, small hydropower, on-load tap-changing transformers, capacitor banks and micro pumped storage units. The mathematical model includes distribution network power flow constraints, new energy output model, discrete reactive power compensation equipment model and pumped storage unit operation model.

[0014] The second construction module is configured to take the minimum power grid equivalent load fluctuation and the minimum system comprehensive operating loss as the objective functions, and consider the power limit, operating condition constraints and system power balance constraints of pumped storage units to build a multi-objective optimization scheduling model.

[0015] The solution module is configured to use the enhanced Epsilon constraint method to transform the multi-objective optimization problem into a series of single-objective optimization problems, and solve each single-objective problem by mixed-integer linear programming to obtain the Pareto front;

[0016] The module is configured to select the optimal compromise solution from the Pareto front based on the fuzzy membership function, and determine the power generation / pumping power curve of the pumped storage unit, as well as the tap position of the on-load tap changer and the switching scheme of the capacitor bank.

[0017] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the reactive power coordinated control method for wind-solar-hydro microgrids according to any embodiment of the present invention.

[0018] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the reactive power coordination control method for wind-solar-hydro microgrids according to any embodiment of the present invention.

[0019] This application presents a reactive power coordinated control method and system for wind-solar-hydro microgrids. By constructing a four-layer coordinated control architecture of "multi-objective optimization - enhanced Epsilon constraints - MILP solution - fuzzy decision-making," it achieves efficient coordinated optimization of discrete-continuous mixed variables in microgrids. First, by establishing a complete microgrid mathematical model, it incorporates DistFlow power flow constraints, renewable energy output characteristics, OLTC and CB discrete regulation constraints, and pumped-storage unit operation constraints into a unified framework, ensuring the physical feasibility of the optimization strategy. Second, with the objectives of minimizing equivalent load fluctuation and minimizing overall system operating losses, the enhanced Epsilon constraint method transforms the multi-objective problem into a series of single-objective sub-problems. Combined with mixed-integer linear programming, this method can fully obtain the Pareto front, avoiding the subjectivity and loss of non-convex fronts inherent in traditional weighted methods. Furthermore, based on fuzzy membership functions, it selects the optimal compromise solution from the Pareto front, balancing grid security and operational economy, and achieving a scientific trade-off among multiple objectives. Ultimately, the organic integration of the above architecture, while ensuring overall optimization quality, simultaneously improves the voltage stability, renewable energy absorption capacity, and pumped storage operation economy of the microgrid. It significantly reduces the number of OLTC and capacitor operations, reduces losses caused by frequent start-ups and shutdowns of pumped storage units, and extends equipment lifespan, providing an effective solution for the safe, stable, and economical operation of the microgrid. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of a reactive power coordinated control method for a wind-solar-hydro microgrid provided in an embodiment of the present invention;

[0022] Figure 2 This is a structural block diagram of a reactive power coordination control system for a wind-solar-hydro microgrid provided in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figure 1 The diagram shows a flowchart of a reactive power coordinated control method for a wind-solar-hydro microgrid according to this application.

[0026] like Figure 1 As shown, the reactive power coordinated control method for wind-solar-hydro microgrids specifically includes the following steps:

[0027] Step S101: Construct a microgrid mathematical model that includes distributed photovoltaic, small hydropower, on-load tap-changing transformer, capacitor bank and micro pumped storage unit. The mathematical model includes power flow constraints of distribution network, new energy output model, discrete reactive power compensation equipment model and pumped storage unit operation model.

[0028] In this step, the power flow constraints of the distribution network are in the second-order cone form of the three-phase DistFlow branch power flow model, expressed as:

[0029] ,

[0030] In the formula, For node i The square of the phase voltage amplitude, For the flow through branch ij Phase active power, For the flow through branch jk Phase active power, For the flow through branch ij Phase reactive power, For branch ij Phase line resistance, For node j Net active power injection For branch ij Phase line reactance, For node j Correction coefficient or correction amount for fluctuations in the output of new energy sources. For node j Phase reactive load, For node j Dynamic reactive power regulation correction coefficient for pumped storage units Let j be the set of downstream nodes starting from node j. For the flow through branch ij Phase current amplitude.

[0031] The new energy output model includes a robust optimization model for photovoltaic output, expressed as:

[0032] ,

[0033] In the formula, Let be the maximum actual active power output of the distributed photovoltaic system connected to node j at time t. For node j at time t Predicted photovoltaic output of the distributed photovoltaic system connected to the phase. The 0-1 variable controls the level of conservatism in the model; a value of 0 indicates the minimum photovoltaic fluctuation, and a value of 1 indicates the maximum photovoltaic fluctuation. Relaxation auxiliary variables in the robust optimization model for photovoltaic power output. As a bias auxiliary variable in the robust optimization model for photovoltaic power output, As a bias auxiliary variable in the robust optimization model for photovoltaic power output, , These are the lower and upper bounds for photovoltaic fluctuations.

[0034] The discrete reactive power compensation equipment model includes discrete turns ratio constraints for on-load tap-changing transformers and segmented switching constraints for capacitor banks, expressed as follows:

[0035] ,

[0036] In the formula, For integer variables, For the d-th capacitor bank The compensation amount for each gear. For the d-th capacitor bank The corresponding operating capacity, For the number of CB groups, The set of nodes that contain CB;

[0037] ,

[0038] In the formula, Let be the state variable of node j at time period t when the φ phase CB capacitor bank is in the c-th switching position. Let be the state variable of node j at time period t when the φ phase CB capacitor bank is in the qth switching position. Let be the state variable of node j when the φ-phase CB capacitor bank is in the q-th switching position during time period t−1. For CB (Center for Bottom Throw) shifting gear, Increase the size of the CB gear indicator. Reduce markings for CB gear. This represents the maximum number of adjustments allowed for CB.

[0039] For pumped-storage units, the main considerations are the start-up and shutdown of power generation and pumping operations, as well as the minimum duration constraints. Specifically, the start-up and shutdown constraints for power generation and the minimum duration constraints are as follows:

[0040] ,

[0041] In the formula, , These are the start-up and shutdown operation variables for unit power generation. For example, if unit i is started for power generation in time period t... =1, otherwise =0, if the power station shuts down unit i in time period t, then =1, otherwise =0, , These represent the shortest durations of unit power generation operation and shutdown, respectively. This is a time index variable used to represent the time period following the current time period t that is included in the shortest duration constraint. Let be the shutdown operation variable for unit i in time period t. Let be the shutdown operation variable for unit i in time period t;

[0042] ,

[0043] In the formula, Let be the startup operation variable for unit i in the (t+1)th time period. Let be the shutdown operation variable for unit i in the (t+1)th time period. Let i be the operating state variable of unit i in the (t+1)th time period. Let i be the operating state variable of unit i in time period t. To optimize the total number of time periods within the scheduling cycle, , These represent the maximum number of times the unit can operate for power generation and the maximum number of times it can be shut down, respectively.

[0044] ,

[0045] In the formula, This refers to the shortest duration of pumping operation and shutdown of the unit. Let η be the pumping start-up operation variable for unit i in the ηth time period. Let be the variable for the pumping stop operation of unit i in time period t. Let be the variable for the pumping stop operation of unit i in the ηth time period;

[0046] ,

[0047] In the formula, Let be the pumping start-up operation variable for unit i in the (t+1)th time period. Let be the variable representing the pumping stop operation of unit i in time period t+1. Let be the pumping operation state variable of unit i in the (t+1)th time period. Let be the pumping operation state variable of unit i in time period t. , These represent the maximum number of times the unit can be pumped out and shut down, respectively.

[0048] Step S102: Taking the minimum equivalent load fluctuation of the power grid and the minimum comprehensive operating loss of the system as the objective functions, and considering the power limitations, operating condition constraints and system power balance constraints of pumped storage units, a multi-objective optimization scheduling model is constructed.

[0049] In this step, the objective function for minimizing the equivalent load fluctuation of the power grid is:

[0050] ,

[0051] In the formula, To minimize the fluctuation of the equivalent load of the power grid, The peak-to-valley difference of the equivalent load. The standard deviation of the equivalent load, The equivalent load after optimization in time period t. To optimize the total number of time periods within the scheduling cycle, The equivalent load before optimization, The equivalent load before optimization is reduced by the total output of the pumped storage unit. Let t be the total electrical load of the regional system during time period t. For the predicted wind power output in time period t, For the predicted photovoltaic power output in time period t, Let be the total output of the pumped-storage units in time period t, where positive values ​​represent power generation and negative values ​​represent pumping, and is a decision variable. Let t be the power transmitted from the power grid to the external grid during time period t. The power received by the power grid from the external network during time period t;

[0052] The objective function for minimizing the overall system operating loss is:

[0053] ,

[0054] In the formula, To minimize the overall system operating losses, Let j be the set of terminal nodes of the branch with node j as the first terminal node. For network loss costs, Costs associated with the start-up and shutdown losses of pumped storage units. To incur the penalties for abandoning wind and solar power, The electricity price that the distribution network purchases from the upstream power grid. The total number of distribution network nodes. Let be the line resistance of branch ij. Let be the flow through branch ij at time t. The square of the phase current amplitude, The price of electricity generated by photovoltaic power generation sold to the grid. Let be the maximum actual active power output of the distributed photovoltaic system connected to node j at time t. For node j at time t The actual active power output of the distributed photovoltaic system connected to the phase is Cost of adding or removing a single capacitor The number of CB actions within one cycle. Cost of adding or removing a single capacitor, The number of actions performed by OLTC within one cycle.

[0055] The system power balance constraint is:

[0056] ,

[0057] In the formula, , These represent the predicted power output of wind power and the predicted power output of photovoltaic power, respectively, for time period t. For the predicted hydropower output in time period t, Let be the total output of the pumped-storage units in time period t, where positive values ​​represent power generation and negative values ​​represent pumping, and is a decision variable. Let t be the power received by the power grid from the external network. Let t be the power transmitted from the power grid to the external grid during time period t. Let t be the total electrical load of the regional system during time period t;

[0058] The operating constraints for pumped-storage units are as follows:

[0059] ,

[0060] ,

[0061] In the formula, It is a 0-1 variable, indicating whether unit i is generating electricity in the t-th time period (1 indicates generating electricity, 0 indicates not generating electricity). It is a 0-1 variable, indicating whether unit i is in the pumping state in the time period t (1 indicates pumping, 0 indicates not pumping). , These represent the minimum and maximum output of unit i during power generation, respectively. Let i be the power generation of unit i in time period t. This refers to the fixed power output of unit i during pumping. Let i be the pumping power of unit i in time period t. Let i be the net output of unit i in time period t;

[0062] When the water volume in the upper and lower reservoirs of a pumped storage power station is balanced, and without considering other inflows to the upper and lower reservoirs, the upper reservoir is:

[0063] ,

[0064] Lower warehouse: ,

[0065] In the formula, This represents the water storage volume at the beginning of time period t+1 in the upper reservoir. , These represent the water storage volumes at the beginning of time period t for the upper and lower reservoirs, respectively. Let be the flow rate through the unit in time period t. For unit conversion factors, This represents the water storage volume at the beginning of time period t+1 in the lower reservoir.

[0066] Pumped storage power station upper and lower reservoir water storage constraints:

[0067] Upper Warehouse: ,

[0068] Lower warehouse: ,

[0069] In the formula, For the dead storage capacity of Shangku, Reserved emergency backup storage capacity for accidents, The reservoir capacity is the equivalent of the normal water level of the upper reservoir. The reservoir capacity is the equivalent of the normal water level in the lower reservoir. Dead storage capacity for the lower warehouse;

[0070] Step S103: The multi-objective optimization problem is transformed into a series of single-objective optimization problems using the enhanced Epsilon constraint method. Each single-objective problem is solved by mixed-integer linear programming to obtain the Pareto front.

[0071] In this step, the minimum value of the overall operating loss of the calculation system is selected. The maximum value of the overall system operating loss , will the interval Divide the grid into N equal segments and generate N+1 grid points. Solve the single-objective problem for each grid point:

[0072] ,

[0073] In the formula, These are the weighting coefficients. For the introduced auxiliary variables, The maximum value of the overall system operating loss Minimum of overall system operating losses The difference is used to scale the slack variable. To characterize the auxiliary parameters of the Epsilon constraint, The index is the number of segments. This represents the total number of segments in the Epsilon constraint method. The objective function is the overall system operating loss. Let the objective function be the equivalent load fluctuation of the power grid. This serves as a reference value for the objective function of overall system operating losses.

[0074] Step S104: Based on the fuzzy membership function, the optimal compromise solution is selected from the Pareto front to determine the power generation / pumping power curve of the pumped storage unit, as well as the tap position of the on-load tap changer and the switching scheme of the capacitor bank.

[0075] In this step, for each Pareto solution, the membership degrees between the two objectives are calculated:

[0076] ,

[0077] ,

[0078] In the formula, Let m be the satisfaction level of the m-th objective function. Let m be the value of the objective function. and Let be the maximum and minimum values ​​of the m-th objective function, respectively, and S be the satisfaction level of the optimal solution. Let be the satisfaction weight of the m-th objective function.

[0079] In summary, the method of this application addresses the loss problem caused by frequent operation of discrete reactive power regulation equipment due to voltage exceedance in microgrids under high-proportion renewable energy access. It constructs a microgrid mathematical model including pumped storage units, distributed wind, solar, and small hydropower, on-load tap-changing transformers, and capacitor banks. With the objective functions of minimizing comprehensive economic losses and minimizing grid equivalent load fluctuations, and considering hydraulic constraints, operating condition transition constraints, and system power balance constraints of the pumped storage units, a multi-objective optimization scheduling model is established. The enhanced Epsilon constraint method is used to transform the multi-objective problem into a single-objective problem. The Pareto front is obtained through mixed-integer linear programming, and the optimal compromise solution is selected based on fuzzy membership functions to determine the power generation / pumping power and reactive power output plan of the pumped storage units, as well as the switching scheme for discrete reactive power equipment. Through the synergistic optimization of pumped storage units and wind, solar, and hydropower resources, while smoothing out renewable energy output fluctuations and improving voltage quality, the method effectively reduces network losses and equipment operation losses, decreases the number of operations of traditional reactive power regulation equipment, extends equipment lifespan, and achieves safe and economical operation of the microgrid.

[0080] Please see Figure 2 The diagram shows a structural block diagram of a reactive power coordinated control system for a wind-solar-hydro microgrid according to this application.

[0081] like Figure 2 As shown, the reactive power coordinated control system 200 for wind, solar and hydro microgrids includes a first construction module 210, a second construction module 220, a solution module 230 and a determination module 240.

[0082] The first construction module 210 is configured to construct a microgrid mathematical model including distributed photovoltaic, small hydropower, on-load tap-changing transformers, capacitor banks, and micro pumped storage units. The mathematical model includes distribution network power flow constraints, new energy output models, discrete reactive power compensation equipment models, and pumped storage unit operation models. The second construction module 220 is configured to construct a multi-objective optimization scheduling model with the objective functions of minimizing the equivalent load fluctuation of the power grid and minimizing the comprehensive operating loss of the system, considering the power limitations, operating condition constraints, and system power balance constraints of the pumped storage units. The solution module 230 is configured to transform the multi-objective optimization problem into a series of single-objective optimization problems using the enhanced Epsilon constraint method, solve each single-objective problem through mixed integer linear programming, and obtain the Pareto front. The determination module 240 is configured to select the optimal compromise solution from the Pareto front based on fuzzy membership functions, determine the power generation / pumping power curves of the pumped storage units, the tap positions of the on-load tap-changing transformers, and the switching schemes of the capacitor banks.

[0083] It should be understood that Figure 2 The modules and references described in the document Figure 1The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0084] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the reactive power coordination control method for wind-solar-hydro microgrids in any of the above method embodiments.

[0085] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0086] A mathematical model of a microgrid is constructed, which includes distributed photovoltaic power, small hydropower, on-load tap-changing transformers, capacitor banks and micro pumped storage units. The mathematical model includes power flow constraints of the distribution network, power output model of new energy sources, discrete reactive power compensation equipment model and pumped storage unit operation model.

[0087] With the objectives of minimizing the equivalent load fluctuation of the power grid and minimizing the overall system operating loss, and considering the power limitations, operating condition constraints, and system power balance constraints of pumped storage units, a multi-objective optimization scheduling model is constructed.

[0088] The enhanced Epsilon constraint method is used to transform the multi-objective optimization problem into a series of single-objective optimization problems. Each single-objective problem is solved by mixed-integer linear programming to obtain the Pareto front.

[0089] The optimal compromise solution is selected from the Pareto front based on the fuzzy membership function to determine the power generation / pumping power curve of the pumped storage unit, as well as the tap position of the on-load tap changer and the switching scheme of the capacitor bank.

[0090] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the wind-solar-hydro microgrid reactive power coordination control system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, which can be connected to the wind-solar-hydro microgrid reactive power coordination control system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0091] Figure 3This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the reactive power coordinated control method for the wind-solar-hydro microgrid described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the wind-solar-hydro microgrid reactive power coordinated control system. The output device 340 may include a display screen or other display device.

[0092] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0093] In one implementation, the aforementioned electronic device is applied in a reactive power coordination control system for a wind-solar-hydro microgrid, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0094] A mathematical model of a microgrid is constructed, which includes distributed photovoltaic power, small hydropower, on-load tap-changing transformers, capacitor banks and micro pumped storage units. The mathematical model includes power flow constraints of the distribution network, power output model of new energy sources, discrete reactive power compensation equipment model and pumped storage unit operation model.

[0095] With the objectives of minimizing the equivalent load fluctuation of the power grid and minimizing the overall system operating loss, and considering the power limitations, operating condition constraints, and system power balance constraints of pumped storage units, a multi-objective optimization scheduling model is constructed.

[0096] The enhanced Epsilon constraint method is used to transform the multi-objective optimization problem into a series of single-objective optimization problems. Each single-objective problem is solved by mixed-integer linear programming to obtain the Pareto front.

[0097] The optimal compromise solution is selected from the Pareto front based on the fuzzy membership function to determine the power generation / pumping power curve of the pumped storage unit, as well as the tap position of the on-load tap changer and the switching scheme of the capacitor bank.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coordinated reactive power control in a wind-solar-hydro microgrid, characterized in that, The method includes: A mathematical model of a microgrid is constructed, which includes distributed photovoltaic power, small hydropower, on-load tap-changing transformers, capacitor banks and micro pumped storage units. The mathematical model includes power flow constraints of the distribution network, power output model of new energy sources, discrete reactive power compensation equipment model and pumped storage unit operation model. With the objectives of minimizing the equivalent load fluctuation of the power grid and minimizing the overall system operating loss, and considering the power limitations, operating condition constraints, and system power balance constraints of pumped storage units, a multi-objective optimization scheduling model is constructed. The enhanced Epsilon constraint method is used to transform the multi-objective optimization problem into a series of single-objective optimization problems. Each single-objective problem is solved by mixed-integer linear programming to obtain the Pareto front. The optimal compromise solution is selected from the Pareto front based on the fuzzy membership function to determine the power generation / pumping power curve of the pumped storage unit, as well as the tap position of the on-load tap changer and the switching scheme of the capacitor bank.

2. The reactive power coordinated control method for a wind-solar-hydro microgrid according to claim 1, characterized in that, The power flow constraint of the distribution network is in the second-order cone form of the three-phase DistFlow branch power flow model, and its expression is: ， In the formula, For node i The square of the phase voltage amplitude, For the flow through branch ij Phase active power, For the flow through branch jk Phase active power, For the flow through branch ij Phase reactive power, For branch ij Phase line resistance, For node j Net active power injection For branch ij Phase line reactance, For node j Correction coefficient or correction amount for fluctuations in the output of new energy sources. For node j Phase reactive load, For node j Dynamic reactive power regulation correction coefficient for pumped storage units Let j be the set of downstream nodes starting from node j. For the flow through branch ij Phase current amplitude.

3. The reactive power coordinated control method for a wind-solar-hydro microgrid according to claim 1, characterized in that, The new energy output model includes a robust optimization model for photovoltaic output, expressed as follows: ， In the formula, Let be the maximum actual active power output of the distributed photovoltaic system connected to node j at time t. For node j at time t Predicted photovoltaic output of the distributed photovoltaic system connected to the phase. The 0-1 variable controls the level of conservatism in the model; a value of 0 indicates the minimum photovoltaic fluctuation, and a value of 1 indicates the maximum photovoltaic fluctuation. Relaxation auxiliary variables in the robust optimization model for photovoltaic power output. As a bias auxiliary variable in the robust optimization model for photovoltaic power output, As a bias auxiliary variable in the robust optimization model for photovoltaic power output, , These are the lower and upper bounds for photovoltaic fluctuations.

4. The reactive power coordinated control method for a wind-solar-hydro microgrid according to claim 1, characterized in that, The discrete reactive power compensation device model includes discrete turns ratio constraints for the on-load tap-changing transformer and segmented switching constraints for the capacitor bank, expressed as follows: ， In the formula, For integer variables, For the d-th capacitor bank The compensation amount for each gear. For the d-th capacitor bank The corresponding operating capacity, For the number of CB groups, The set of nodes that contain CB; ， In the formula, Let be the state variable of node j at time period t when the φ phase CB capacitor bank is in the c-th switching position. Let be the state variable of node j at time period t when the φ phase CB capacitor bank is in the qth switching position. Let be the state variable of node j when the φ-phase CB capacitor bank is in the q-th switching position during time period t−1. For CB (Center for Bottom Throw) shifting gear, Increase the size of the CB gear indicator. Reduce markings for CB gear. This represents the maximum number of adjustments allowed for CB.

5. The reactive power coordinated control method for a wind-solar-hydro microgrid according to claim 1, characterized in that, The objective function for minimizing the fluctuation of the equivalent load of the power grid is: ， In the formula, To minimize the fluctuation of the equivalent load of the power grid, The peak-to-valley difference of the equivalent load. The standard deviation of the equivalent load, The optimized equivalent load for time period t is... To optimize the total number of time periods within the scheduling cycle, The equivalent load before optimization, The equivalent load before optimization is reduced by the total output of the pumped storage unit. Let t be the total electrical load of the regional system during time period t. For the predicted wind power output in time period t, For the predicted photovoltaic power output in time period t, Let be the total output of the pumped-storage units in time period t, where positive values ​​represent power generation and negative values ​​represent pumping, and is a decision variable. Let t be the power transmitted from the power grid to the external grid during time period t. The power received by the power grid from the external network during time period t; The objective function for minimizing the overall system operating loss is: ， In the formula, To minimize the overall system operating losses, Let j be the set of terminal nodes of the branch with node j as the first terminal node. For network loss costs, Costs associated with the start-up and shutdown losses of pumped storage units. The cost of penalties for abandoning wind and solar power, The electricity price that the distribution network purchases from the upstream power grid. This represents the total number of nodes in the distribution network. Let be the line resistance of branch ij. Let be the flow through branch ij at time t. The square of the phase current amplitude, The price of electricity generated by photovoltaic power generation sold to the grid. Let be the maximum actual active power output of the distributed photovoltaic system connected to node j at time t. For node j at time t The actual active power output of the distributed photovoltaic system connected to the phase is Cost of adding or removing a single capacitor The number of CB actions within one cycle. Cost of adding or removing a single capacitor The number of actions performed by OLTC within one cycle.

6. The reactive power coordinated control method for a wind-solar-hydro microgrid according to claim 5, characterized in that, The method of transforming a multi-objective optimization problem into a series of single-objective optimization problems using the enhanced Epsilon constraint method includes: Select the minimum value of the overall operating loss of the calculation system. The maximum value of the overall system operating loss , will the interval Divide the grid into N equal segments and generate N+1 grid points. Solve the single-objective problem for each grid point: ， In the formula, These are the weighting coefficients. For the introduced auxiliary variables, The maximum value of the overall system operating loss Minimum of overall system operating losses The difference is used to scale the slack variable. To characterize the auxiliary parameters of the Epsilon constraint, The index is the number of segments. This represents the total number of segments in the Epsilon constraint method. The objective function is the overall system operating loss. Let the objective function be the equivalent load fluctuation of the power grid. This serves as a reference value for the objective function of overall system operating losses.

7. A reactive power coordinated control system for a wind-solar-hydro microgrid, characterized in that, The method includes: The first construction module is configured to construct a microgrid mathematical model containing distributed photovoltaic, small hydropower, on-load tap-changing transformers, capacitor banks and micro pumped storage units. The mathematical model includes distribution network power flow constraints, new energy output model, discrete reactive power compensation equipment model and pumped storage unit operation model. The second construction module is configured to take the minimum power grid equivalent load fluctuation and the minimum system comprehensive operating loss as the objective functions, and consider the power limit, operating condition constraints and system power balance constraints of pumped storage units to build a multi-objective optimization scheduling model. The solution module is configured to use the enhanced Epsilon constraint method to transform the multi-objective optimization problem into a series of single-objective optimization problems, and solve each single-objective problem by mixed-integer linear programming to obtain the Pareto front; The module is configured to select the optimal compromise solution from the Pareto front based on the fuzzy membership function, and determine the power generation / pumping power curve of the pumped storage unit, as well as the tap position of the on-load tap changer and the switching scheme of the capacitor bank.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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