An autonomous microgrid wind-storage combined frequency regulation method and system

Abstract

The present invention provides an autonomous microgrid wind-storage combined frequency regulation method and system. The solution includes establishing a state-space model for a wind turbine and an energy storage device to participate in the frequency regulation of the microgrid system; establishing an optimization objective function for the wind-storage to participate in frequency regulation based on the minimum frequency deviation and the lowest wind curtailment rate; setting an adaptive weight coefficient in the optimization objective function; setting the wind turbine constraint conditions and the energy storage device constraint conditions at the k-th sampling moment; and carrying out autonomous microgrid wind-storage combined frequency regulation. The solution establishes a state-space model in combination with the operating characteristics of the wind turbine and the energy storage device, takes the minimum frequency deviation and the lowest wind curtailment rate as optimization indicators, and designs a weight coefficient that can be adaptively adjusted according to frequency changes and constraint conditions that are dynamically adjusted according to wind speed and state of charge, so as to convert wind energy into electrical energy more fully to participate in frequency regulation, solve the problems of unstable frequency and high wind curtailment rate in the autonomous microgrid containing wind-storage, and reduce energy waste.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid coordinated control, and more specifically, to an autonomous microgrid wind-storage combined frequency regulation method and system. Background Art

[0002] The access of high-penetration renewable energy has posed great challenges to the planning and operation of power systems. As a small power system that can effectively integrate various distributed generations (DGs), microgrids have received great attention in recent years. However, when operating autonomously, microgrids lose the support of the large power grid, and due to the randomness of new energy generation such as wind power and photovoltaic power, the frequency control of autonomous microgrids is particularly important for the stable operation of the system; at the same time, when renewable energy participates in frequency regulation, how to reduce energy waste is also of great significance for promoting the realization of China's dual-carbon goal.

[0003] Before the technology of the present invention, wind turbines and energy storage have become important components in microgrids. The strategies for the two to participate in microgrid frequency regulation mainly focus on the combined response of wind storage to frequency changes. However, existing methods such as droop control and virtual synchronous generator control mainly focus on improving the frequency control performance, ignoring the influence of the time-varying characteristics of wind speed on the active power reserve of wind turbines and the influence of the state of charge (SoC) of energy storage devices on the charge and discharge power. There is no dynamic constraint on the active power increment when the two participate in frequency regulation. On the one hand, their frequency regulation advantages cannot be fully utilized, and on the other hand, the operation safety of wind turbines and energy storage devices cannot be guaranteed, and it may be difficult to obtain ideal frequency regulation performance; in addition, no dynamic power distribution technology considering wind-storage combined frequency regulation with energy efficiency as the goal has been proposed, so it is impossible to maximize the utilization of wind energy and give full play to the advantage of energy storage absorbing active power surplus to reduce the wind curtailment rate and improve the energy efficiency of the system. Summary of the Invention

[0004] In view of the above problems, the present invention proposes an autonomous microgrid wind-storage combined frequency regulation method and system, which establishes a state-space model by combining the operating characteristics of wind turbines and energy storage devices, and takes the minimum frequency deviation and the lowest wind curtailment rate as optimization indicators, and designs weight coefficients that can be adaptively adjusted according to frequency changes and constraint conditions that are dynamically adjusted according to wind speed and state of charge, so as to more fully convert wind energy into electrical energy to participate in frequency regulation, solve the problems of frequency instability and high wind curtailment rate in autonomous microgrids containing wind storage, and reduce energy waste.

[0005] According to the first aspect of the embodiments of the present invention, an autonomous microgrid wind-storage combined frequency regulation method is provided.

[0006] In one or more embodiments, preferably, the autonomous microgrid wind-storage combined frequency regulation method includes a weight self-adjusting model predictive control strategy with flexible constraint boundaries, which is used to enable the wind turbine and energy storage device to participate in the flexible power distribution during the frequency regulation process of the autonomous microgrid. Specifically, it includes:

[0007] Establish a state-space model for the wind turbine and energy storage device to participate in the frequency regulation of the microgrid system;

[0008] Establish an optimization objective function for the wind energy storage to participate in frequency regulation based on the minimum frequency deviation and the lowest curtailment rate;

[0009] Set the adaptive weight coefficient in the optimization objective function;

[0010] Set the constraint conditions of the wind turbine and the energy storage device at the k-th sampling moment;

[0011] Carry out the autonomous microgrid wind-storage combined frequency regulation.

[0012] In one or more embodiments, preferably, establishing the state-space model for the wind turbine and energy storage device to participate in the frequency regulation of the microgrid system specifically includes:

[0013] According to the proportional relationship between the initial frequency change rate and the initial power deviation after the disturbance of the AC microgrid, establish the state-space model of the microgrid system;

[0014] According to the state-space model, coordinate the wind turbine and energy storage to generate active power opposite to the frequency fluctuation, so that the wind turbine and energy storage device participate in the frequency regulation of the microgrid system;

[0015] Among them, the state-space model of the microgrid system is:

[0016]

[0017] Among them, k is the k-th sampling moment of the discrete system, x k is the state variable at the k-th sampling moment, x k+1 is the state variable at the (k + 1)-th sampling moment, u k is the control input variable at the k-th sampling moment, y k is the output variable of the AC microgrid at the k-th sampling moment, A c is the system matrix, B c is the input matrix, C c is the output matrix, D c is the direct transmission matrix, x k 、u k and y k The expressions of are:

[0018]

[0019] A c 、B c 、C c 、D c The expressions of are as follows:

[0020]

[0021] where f k 、P 1,k and P 2,k are, in sequence, the microgrid frequency, the active power output of the wind turbine, and the active power output of the energy storage at the k-th moment, ΔP 1,k and ΔP 2,k are, in sequence, the increment of the active power borne by the wind turbine and the increment of the active power borne by the energy storage system at the k-th moment, T s is the sampling time, f0 is the rated frequency, and H is the inertia coefficient.

[0022] In one or more embodiments, preferably, establishing an optimization objective function for the participation of wind energy storage in frequency regulation based on minimizing frequency deviation and minimizing the curtailment rate of wind power specifically includes:

[0023] Setting the control time domain and the prediction time domain;

[0024] Setting a first penalty coefficient corresponding to the control time domain;

[0025] Setting a second penalty coefficient corresponding to the prediction time domain, where the second penalty coefficient is less than the first penalty coefficient;

[0026] Setting the optimization objective function;

[0027] where the optimization objective function is:

[0028]

[0029] where J is the objective function, P is the control time domain, N is the prediction time domain, f k+i|k is the predicted value of the frequency at the (k + i)-th moment by the control algorithm at the k-th moment, △P1(k + i|k) is the predicted value of the increment of the active power borne by the wind turbine at the (k + i)-th moment by the control algorithm at the k-th moment, △P2(k + i|k) is the predicted value of the increment of the active power borne by the energy storage device at the (k + i)-th moment by the control algorithm at the k-th moment, α and β are, in sequence, the first penalty coefficient and the second penalty coefficient, and γ and δ are, in sequence, the first weight coefficient and the second weight coefficient.

[0030] In one or more embodiments, preferably, setting the adaptive weight coefficient in the optimization objective function specifically includes:

[0031] Setting the weight coefficient in the objective function as a piecewise function of frequency, where the piecewise function is:

[0032]

[0033] Among them, γ is the first weight coefficient, δ is the second weight coefficient, a1, a2, a3, a4, a5, a6, b1, and b2 are all value coefficients of the first weight coefficient, and a1 = a2 = a3 = a4 = a5 = a6, b1 = b2 are all value coefficients of γ, c1, c2, c3, c4, c5, c6 are all value coefficients of the second weight coefficient, and c1 = c2 = c3, c4 = c5 = c6, f1 and f2 are respectively preset critical values of frequency deviation.

[0034] In one or more embodiments, preferably, setting the wind turbine constraint conditions and energy storage device constraint conditions at the k-th sampling moment specifically includes:

[0035] The wind turbine constraint conditions are:

[0036]

[0037] Among them, P MPPT (v k ) is the maximum active power output of the wind turbine at the k-th sampling moment, T s is the sampling time, △P 1,k+i|k is the predicted value of the active power output increment borne by the wind turbine from the k-th moment to the k + i-th moment, P 1,k+i|k is the predicted value of the active power output borne by the wind turbine from the k-th moment to the k + i-th moment, T s is the sampling time, r1 is the upper ramp rate of the wind turbine power increment within each sampling interval, △P 1,min is the lower limit of the wind turbine power increment within each sampling interval;

[0038] The energy storage device constraint conditions are:

[0039]

[0040] Among them, P ch ES (SoC k ) and P disch ES (SoC k ) are respectively the safety thresholds of the energy storage charging power and discharging power calculated according to the state of charge SoC k at the k-th moment, r2 is the upper and lower ramp rates of the energy storage device charge and discharge power increment within each sampling interval, T s is the sampling time, △P 2,k+i|k is the predicted value of the active power output increment borne by the energy storage device from the k-th moment to the k + i-th moment, P 2,k+i|kis the predicted value of the active power output borne by the energy storage device at the (k + i)-th moment at the k-th moment.

[0041] In one or more embodiments, preferably, the implementation of autonomous microgrid wind-storage combined frequency regulation specifically includes:

[0042] Obtain the microgrid frequency measurement data at the k-th sampling moment, obtain the preset frequency regulation demand, and determine whether the microgrid frequency measurement data meets the frequency regulation demand. If the frequency regulation demand is not met, enable the weight self-adjusting model predictive controller with flexible constraint boundaries;

[0043] If it is detected that the weight self-adjusting model predictive controller with flexible constraint boundaries is enabled, obtain the microgrid system state variables at the k-th sampling moment, adjust the weight coefficients according to the frequency measurement data at the k-th sampling moment, determine the optimization objective function, and according to the wind speed v k and the SoC of the energy storage device k Update the wind turbine constraint conditions and the energy storage device constraint conditions;

[0044] Use the weight self-adjusting model predictive controller with flexible constraint boundaries to calculate and obtain a solution sequence of the control input according to the state space model, and use the solution sequence as the command value of the active power output increment of the wind turbine and the energy storage device;

[0045] Obtain the optimal output command of the wind turbine and the energy storage device, where the optimal output command includes the wind turbine releasing active power reserve or reducing output, and the energy storage device adjusting the charge and discharge power;

[0046] Judge whether the microgrid operation state is normal according to the preset frequency range until the microgrid frequency returns to the normal value or the normal operation range.

[0047] In one or more embodiments, preferably, the k-th moment is based on the state of charge (SoC) of the energy storage k The safety thresholds of the charging power and discharging power of the energy storage calculated specifically include:

[0048] During the charging and discharging process of the energy storage device, control the instantaneous power not to exceed the preset maximum charging and discharging power.

[0049] In one or more embodiments, preferably, the weight self-adjusting model predictive controller with flexible constraint boundaries specifically includes:

[0050] A state space model module for inputting the state space model according to the characteristic quantities of the microgrid system, where the characteristic quantities include the inertia coefficient and the rated frequency;

[0051] An objective function module for writing the objective function and adjusting the weight coefficients according to the received microgrid system state variables;

[0052] A constraint condition generator module, configured to online correct constraint conditions according to the wind speed and the energy storage state of charge at each time section, and generate new constraint conditions;

[0053] A control input generation module, configured to generate a control input sequence according to the acquired state quantities at each sampling moment by using a state space and an objective function;

[0054] An optimization and solution module, configured to solve the optimal output values of the wind turbine and the energy storage device, and send the optimal output values of the wind turbine and the energy storage device to a wind turbine controller and an energy storage device controller respectively.

[0055] According to a second aspect of an embodiment of the present invention, a self-governing microgrid wind-storage combined frequency modulation system is provided.

[0056] In one or more embodiments, preferably, the self-governing microgrid wind-storage combined frequency modulation system includes:

[0057] A state space subsystem, configured to establish a state space model for the wind turbine and the energy storage device to participate in the frequency modulation of the microgrid system;

[0058] An objective optimization subsystem, configured to establish an optimization objective function for the wind storage to participate in frequency modulation based on the minimum frequency deviation and the lowest wind curtailment rate;

[0059] An active power distribution subsystem, configured to set an adaptive weight coefficient in the optimization objective function;

[0060] A real-time correction subsystem, configured to set the wind turbine constraint conditions and the energy storage device constraint conditions at the k-th sampling moment;

[0061] A power output subsystem, configured to perform self-governing microgrid wind-storage combined frequency modulation.

[0062] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described in any one of the first aspects of the embodiments of the present invention is implemented.

[0063] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided, including a memory and a processor, where the memory is configured to store one or more computer program instructions, and wherein the one or more computer program instructions are executed by the processor to implement the steps described in any one of the first aspects of the embodiments of the present invention.

[0064] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0065] 1) In the embodiments of the present invention, aiming at the problems of frequency instability and high wind curtailment rate in the autonomous microgrid with wind power and energy storage, a weight self - adjustment model predictive control strategy with flexible change of constraint boundary is designed, and based on this, a method for coordinated frequency regulation of wind power and energy storage in an autonomous microgrid is proposed. This method establishes a state - space model by combining the operating characteristics of wind turbines and energy storage devices, and takes the minimum frequency deviation and the lowest wind curtailment rate as optimization indexes, taking into account both frequency control and energy efficiency optimization functions.

[0066] 2) In the embodiments of the present invention, a weight coefficient that can be adaptively adjusted according to frequency change is designed in the objective function, realizing the flexible distribution of active power output when wind turbines and energy storage devices participate in the frequency regulation of the autonomous microgrid, improving the utilization rate of wind energy resources while ensuring the frequency regulation performance.

[0067] 3) In the embodiments of the present invention, a flexible constraint boundary that can be corrected online based on the wind speed of the wind turbine and the state of charge of the energy storage is designed, improving the safe operation level during the coordinated frequency regulation of wind power and energy storage in the autonomous microgrid.

[0068] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification, claims, and drawings.

[0069] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the following drawings are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0071] Figure 1 It is a flowchart of a method for coordinated frequency regulation of wind power and energy storage in an autonomous microgrid according to an embodiment of the present invention.

[0072] Figure 2 This is an applicable scenario of a method for coordinated frequency regulation of wind power and energy storage in an autonomous microgrid provided by an embodiment of the present invention.

[0073] Figure 3 It is a system diagram of the coordinated frequency regulation of wind power and energy storage in an autonomous microgrid in an embodiment of the invention.

[0074] Figure 4 It is a flowchart of establishing a state - space model for wind turbines and energy storage devices to participate in the frequency regulation of the microgrid system in a method for coordinated frequency regulation of wind power and energy storage in an autonomous microgrid according to an embodiment of the present invention.

[0075] Figure 5 It is a graph of the weight coefficient - frequency relationship according to an embodiment of the present invention.

[0076] Figure 6 It is a flowchart of the autonomous microgrid wind - storage combined frequency regulation execution according to an embodiment of the present invention.

[0077] Figure 7 It is a schematic diagram of the structure of a weight self - adjusting model predictive controller with flexible changes in the constraint boundary according to an embodiment of the present invention.

[0078] Figure 8 It is a structural diagram of an autonomous microgrid wind - storage combined frequency regulation system according to an embodiment of the present invention.

[0079] Figure 9 It is a structural diagram of an electronic device in an embodiment of the present invention. Detailed implementation manners

[0080] In some processes described in the specification, claims and above - mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0081] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present invention.

[0082] The access of high - penetration renewable energy makes the planning and operation of the power system face huge challenges. As a small - scale power system that can effectively integrate various distributed generations (DGs), the microgrid has received great attention in recent years. However, when the microgrid operates autonomously, due to the loss of the support of the large power grid and the randomness of new - energy power generation such as wind power and photovoltaic power, the frequency control of the autonomous microgrid is particularly important for the stable operation of the system; at the same time, when renewable energy participates in frequency regulation, how to reduce energy waste is also of great significance for promoting the realization of China's dual - carbon goal.

[0083] Before the technology of the present invention, the fan and energy storage have become important components in the microgrid. When they participate in the frequency regulation of the microgrid, the strategies mainly focus on the combined response of the wind energy storage to the frequency change. However, the existing methods such as droop control and virtual synchronous generator control mainly focus on improving the frequency control performance, ignoring the influence of the time-varying characteristics of the wind speed on the active reserve power of the fan and the influence of the state of charge (SoC) of the energy storage device on the charge and discharge power. There is no dynamic constraint on the increment of the active power output when they participate in the frequency regulation based on this. On the one hand, their frequency regulation advantages cannot be fully exerted. On the other hand, the operation safety of the fan and the energy storage device cannot be guaranteed, and it may be difficult to obtain ideal frequency regulation performance. In addition, there is no proposed dynamic power distribution technology considering the combined wind energy storage frequency regulation with the goal of energy efficiency. Therefore, it is impossible to maximize the utilization of wind energy and give full play to the advantage of the energy storage absorbing the active power surplus to reduce the wind curtailment rate and improve the system energy efficiency.

[0084] In an embodiment of the present invention, a method and system for combined wind energy storage frequency regulation in an autonomous microgrid are provided. This solution establishes a state space model by combining the operating characteristics of the fan and the energy storage device, and takes the minimum frequency deviation and the lowest wind curtailment rate as the optimization indexes. It designs a weight coefficient that can be adaptively adjusted according to the frequency change and constraint conditions that are dynamically adjusted according to the wind speed and the state of charge, so that the wind energy can be more fully converted into electric energy to participate in the frequency regulation, solve the problems of unstable frequency and high wind curtailment rate in the autonomous microgrid containing wind energy storage, and reduce energy waste.

[0085] Specifically, for example, when the wind speed increases, by dynamically adjusting the constraint conditions, the wind energy can be more fully converted into electric energy to participate in the frequency regulation, reducing energy waste. For example, when the wind speed decreases, the power that the fan can participate in the frequency regulation must be less than when the wind speed is larger. If the constraint conditions remain unchanged at this time and the fan forcibly provides active power output by reducing the rotor speed, it may cause a secondary frequency drop and also affect the safety of the fan itself. When the SoC is about to exceed the limit, if the constraint conditions for its active power output are not changed and the energy storage continues to charge and discharge, once the SoC exceeds the limit, it will cause great damage to the life of the energy storage device. The core technology of dynamically adjusting the constraint conditions according to the time-varying nature can solve this problem. On this basis, this solution adds a dynamic power distribution technology considering the combined wind energy storage frequency regulation with the goal of energy efficiency. Therefore, it can achieve the maximum utilization of wind energy and give full play to the advantage of the energy storage absorbing the active power surplus to reduce the wind curtailment rate, improve the system energy efficiency, and reduce the waste of new energy.

[0086] According to the first aspect of the embodiment of the present invention, a method for combined wind energy storage frequency regulation in an autonomous microgrid is provided.

[0087] Figure 1 It is a flowchart of a method for combined wind energy storage frequency regulation in an autonomous microgrid according to an embodiment of the present invention.

[0088] As Figure 1 shown, in one or more embodiments, preferably, the autonomous microgrid wind and energy storage combined frequency regulation method includes a weight self - adjustment model predictive control strategy with flexible constraint boundaries, which is used to enable the wind turbine and energy storage device to participate in the flexible power distribution during the frequency regulation process of the autonomous microgrid. Specifically, it includes:

[0089] S101. Establish a state - space model for the wind turbine and energy storage device to participate in the frequency regulation of the microgrid system;

[0090] S102. Establish an optimization objective function for the wind and energy storage to participate in frequency regulation based on minimizing frequency deviation and the lowest wind curtailment rate;

[0091] S103. Set the adaptive weight coefficient in the optimization objective function;

[0092] S104. Set the constraint conditions of the wind turbine and energy storage device at the k - th sampling moment;

[0093] S105. Carry out the autonomous microgrid wind and energy storage combined frequency regulation.

[0094] In the embodiment of the present invention, a weight self - adjustment model predictive control (Flexible boundary&weight adjustive model predictive control, FBWA - MPC) strategy with flexible constraint boundaries is designed. Figure 2 This provides an applicable scenario for the autonomous microgrid wind and energy storage combined frequency regulation method in the embodiment of the present invention. Figure 3 This is the system diagram of the autonomous microgrid wind and energy storage combined frequency regulation in the embodiment of the invention. And based on this, the flexible power distribution of the wind turbine and energy storage device during the participation in the frequency regulation of the autonomous microgrid is realized. It has two important features: 1) The weight coefficient of the optimization objective can be adaptively adjusted according to the frequency, realizing the reasonable distribution of the active power output of the wind turbine and energy storage device in different situations, optimizing the system energy efficiency while ensuring the frequency regulation effect and reducing the wind curtailment rate; 2) The constraint conditions can be corrected online according to the wind speed of the wind turbine and the state of charge of the energy storage, improving the safe operation level of the two during the participation in the frequency regulation of the autonomous microgrid.

[0095] Figure 4 This is the flow chart of establishing the state - space model for the wind turbine and energy storage device to participate in the frequency regulation of the microgrid system in an autonomous microgrid wind and energy storage combined frequency regulation method according to an embodiment of the present invention.

[0096] As Figure 4 shown, in one or more embodiments, preferably, the establishment of the state - space model for the wind turbine and energy storage device to participate in the frequency regulation of the microgrid system specifically includes:

[0097] S401. Establish the state - space model of the micro - grid system according to the proportional relationship between the initial frequency change rate and the initial power deviation after the AC micro - grid is disturbed;

[0098] S402. Generate active power opposite to the frequency fluctuation by coordinating the wind turbines and energy storage according to the state - space model, so that the wind turbines and energy storage devices participate in the frequency regulation of the micro - grid system;

[0099] Among them, the state - space model of the micro - grid system is:

[0100]

[0101] Among them, \(k\) is the \(k\) - th sampling moment of the discrete system, \(x\) k is the state variable at the \(k\) - th sampling moment, \(x\) k+1 is the state variable at the \((k + 1)\) - th sampling moment, \(u\) k is the control input variable at the \(k\) - th sampling moment, \(y\) k is the output variable of the AC micro - grid at the \(k\) - th sampling moment, \(A\) c is the system matrix, \(B\) c is the input matrix, \(C\) c is the output matrix, \(D\) c is the direct transmission matrix, and the expressions of \(x\) k , \(u\) k and \(y\) k are:

[0102]

[0103] The expressions of \(A\) c , \(B\) c , \(C\) c , \(D\) c are:

[0104]

[0105] Among them, \(f\) k , \(P\) 1,k and \(P\) 2,k are the micro - grid frequency, the active power output of the wind turbine, and the active power output of the energy storage at the \(k\) - th moment in sequence, \(\Delta P\) 1,k and \(\Delta P\) 2,k are the increment of the active power borne by the wind turbine and the increment of the active power borne by the energy storage system at the \(k\) - th moment in sequence, \(T\) s is the sampling time, \(f_0\) is the rated frequency, and \(H\) is the inertia coefficient.

[0106] In the embodiment of the present invention, according to the fact that the initial frequency change rate after the AC microgrid is disturbed is proportional to the initial power deviation, a power dynamic model is established; the frequency fluctuation caused by active power imbalance is eliminated by coordinating the output of the fan and the energy storage system. Based on the above criteria, the expression of the state space model of the microgrid system is established.

[0107] In one or more embodiments, preferably, the establishment of the optimization objective function for the wind energy storage to participate in frequency regulation based on the minimum frequency deviation and the lowest wind curtailment rate specifically includes:

[0108] Set the control time domain and the prediction time domain;

[0109] Set the first penalty coefficient corresponding to the control time domain;

[0110] Set the second penalty coefficient corresponding to the prediction time domain, where the second penalty coefficient is less than the first penalty coefficient;

[0111] Set the optimization objective function;

[0112] Among them, the optimization objective function is:

[0113]

[0114] Among them, J is the objective function, P is the control time domain, N is the prediction time domain, f k+i|k is the predicted value of the frequency at the k + i moment by the control algorithm at the k moment, △P1(k + i|k) is the predicted value of the active power output increment borne by the fan at the k + i moment by the control algorithm at the k moment, △P2(k + i|k) is the predicted value of the active power output increment borne by the energy storage device at the k + i moment by the control algorithm at the k moment, α and β are the first penalty coefficient and the second penalty coefficient in sequence, and γ and δ are the first weight coefficient and the second weight coefficient in sequence.

[0115] In the embodiment of the present invention, the objective function needs to meet the following requirements: 1) Find the optimal control input so that when the microgrid has power imbalance, the frequency can be restored to the rated value as soon as possible; 2) When the frequency drops due to power shortage in the microgrid, the active power reserve of the fan is preferentially used to provide power output. If the demand cannot be met through prediction calculation, the energy storage discharges to provide active power output to achieve power balance in the microgrid and improve the utilization rate of wind energy; 3) When the frequency rises due to power surplus in the microgrid, the energy storage is preferentially charged to store the excess electric energy. If the demand cannot be met through prediction calculation, the fan reduces its output to achieve power balance in the microgrid and reduce the wind curtailment rate. Therefore, the objective function in the above form is set.

[0116] Specifically, the design method of the weight coefficient is introduced in the following embodiments.

[0117] In one or more embodiments, preferably, setting the adaptive weight coefficient in the optimization objective function specifically includes:

[0118] Setting the weight coefficient in the objective function as a piecewise function with respect to frequency, where the piecewise function is:

[0119]

[0120] where γ is the first weight coefficient, δ is the second weight coefficient, a1, a2, a3, a4, a5, a6, b1, b2 are all value coefficients of the first weight coefficient, and a1 = a2 = a3 = a4 = a5 = a6, b1 = b2 are all value coefficients of γ, c1, c2, c3, c4, c5, c6 are all value coefficients of the second weight coefficient, and c1 = c2 = c3, c4 = c5 = c6, f1, f2 are respectively preset critical frequency deviation values.

[0121] Figure 5 is the weight coefficient - frequency relationship diagram of the embodiment of the present invention. As Figure 5 shown, in one or more embodiments, preferably, the first weight coefficient takes values a1 = a2 = a3 = a4 = a5 = a6 = 500, b1 = b2 = 500 in the following manner; the second weight coefficient takes values c1 = c2 = c3 = 2, c4 = c5 = c6 = 0.5 in the following manner; f1 = 0.02 and f2 = 0.3 represent the critical frequency deviation values. Based on this, the values can be adaptively adjusted according to the specific parameters of the system, and the corresponding curves of frequency and weight system can be generated.

[0122] In the embodiment of the present invention, the weight coefficient in the objective function is designed as a function of frequency: when f k < f0, take 0 < γ < δ, when f k > f0, take 0 < δ < γ; meanwhile, in order to prevent the frequency overshoot from being too large in the later stage of frequency recovery, resulting in energy waste, γ and δ increase as the frequency deviation Δf (i.e., f k - f0) decreases, so that the target values of the corresponding terms of the active power increment of the fan and the energy storage device decrease.

[0123] In one or more embodiments, preferably, setting the fan constraint condition and the energy storage device constraint condition at the k - th sampling moment specifically includes:

[0124] The fan constraint condition is:

[0125]

[0126] where P MPPT (v k) is the maximum active output of the fan at the kth sampling moment, T s is the sampling time, △P 1,k+i|k is the predicted value of the incremental active output of the wind turbine at time k to time k+i, P 1,k+i|k is the predicted value of the active power output of the wind turbine at time k+i, s is the sampling time, r1 is the upper limit climbing rate of the wind turbine power increment within each sampling interval, △P 1,min is the lower limit of the fan power increment within each sampling interval;

[0127] The energy storage device constraints are:

[0128]

[0129] Among them, P ch ES (SoC k ) and P disch ES (SoC k ) are respectively based on the energy storage charge state SoC at the kth moment k The calculated energy storage charging power and discharging power safety thresholds, r2 is the upper and lower limit climbing rate of the energy storage device charging and discharging power increment within each sampling interval, T s is the sampling time, △P 2,k+i|k is the predicted value of the active output increment of the energy storage device at time k+i at time k, P 2,k+i|k It is the predicted value of the active output of the energy storage device at the kth moment to the k+i moment.

[0130] In the embodiment of the present invention, the constraints of the wind turbine and the energy storage device are considered respectively. In terms of the active power constraint of the wind turbine: at time k, its total output active power P1 cannot be greater than the maximum active output P MPPT (v k ), the active power increment in each sampling interval must meet the wind turbine climbing rate r1 limit, and when it is necessary to reduce the active power output, the active power reduction amount cannot exceed the specified value ΔP 1,min (ΔP 1,min <0), therefore, the corresponding constraint formula that the wind turbine needs to meet at time k. In terms of energy storage equipment, in order to protect the safe operation of energy storage elements, the actual energy storage system has strict requirements on its charging and discharging power, and its power ramp rate is set to a finite value. Therefore, in the design of the constraint conditions, the upper and lower limits of the ramp rate r2 of the charging and discharging power increment of the energy storage device within each sampling interval are limited. When the rate of change of the power response demand exceeds a certain value, the energy storage does not respond, otherwise it will cause physical damage to the battery body, posing a huge safety hazard.

[0131] Figure 6This is the execution flow chart of autonomous microgrid wind-storage combined frequency regulation according to the embodiments of the present invention.

[0132] As Figure 6 shown, in one or more embodiments, preferably, the implementation of autonomous microgrid wind-storage combined frequency regulation specifically includes:

[0133] Obtain the microgrid frequency measurement data at the k-th sampling moment, obtain the preset frequency regulation demand, and determine whether the microgrid frequency measurement data meets the frequency regulation demand. If the frequency regulation demand is not met, enable the FBWA-MPC controller;

[0134] If it is detected that the FBWA-MPC controller is enabled, obtain the microgrid system state variables at the k-th sampling moment, adjust the weight coefficient according to the frequency measurement data at the k-th sampling moment, determine the optimization objective function, and according to the wind speed v k and the SoC of the energy storage device k update the wind turbine constraint conditions and the energy storage device constraint conditions;

[0135] Use the FBWA-MPC controller to calculate and obtain the solution sequence of the control input according to the state space model, and use the solution sequence as the command value of the active power output increment of the wind turbine and the energy storage device;

[0136] Obtain the optimal output command of the wind turbine and the energy storage device, where the optimal output command includes the wind turbine releasing active power reserve or reducing output, and the energy storage device adjusting the charge and discharge power;

[0137] Judge whether the microgrid operation state is normal according to the preset frequency range until the microgrid frequency returns to the normal value or the normal operation range.

[0138] In the embodiments of the present invention, specifically, the process of the wind turbine performing autonomous microgrid wind-storage combined frequency regulation is provided.

[0139] In one or more embodiments, preferably, the safety thresholds of the energy storage charging power and discharging power calculated according to the energy storage state of charge SoC at the k-th moment specifically include: k During the charge and discharge process of the energy storage device, control the instantaneous power not to exceed the preset maximum charge and discharge power.

[0140] During the charge and discharge process of the energy storage device, control the instantaneous power not to exceed the preset maximum charge and discharge power.

[0141] Figure 7 This is the structural schematic diagram of the FBWA-MPC controller according to the embodiments of the present invention.

[0142] As Figure 7 shown, in one or more embodiments, preferably, the FBWA-MPC controller specifically includes:

[0143] A state space model module for inputting the state space model according to the characteristic quantities of the microgrid system, where the characteristic quantities include the inertia coefficient and the rated frequency;

[0144] A target function module for writing the target function and adjusting the weight coefficient according to the received state quantity of the microgrid system;

[0145] A constraint condition generator module for online correcting the constraint conditions according to the wind speed and the energy storage state of charge at each time section and generating new constraint conditions;

[0146] A control input generation module for generating a control input sequence using the state space and the target function according to the state quantity obtained by acquisition at each sampling moment;

[0147] An optimization solution module for solving the optimal output values of the wind turbine and the energy storage device and sending the optimal output values of the wind turbine and the energy storage device to the wind turbine controller and the energy storage device controller respectively.

[0148] According to the second aspect of the embodiments of the present invention, an autonomous microgrid wind-storage combined frequency regulation system is provided.

[0149] Figure 8 It is a structural diagram of an autonomous microgrid wind-storage combined frequency regulation system according to an embodiment of the present invention.

[0150] As Figure 8 shown, in one or more embodiments, preferably, the autonomous microgrid wind-storage combined frequency regulation system includes:

[0151] A state space subsystem 801 for establishing a state space model for the wind turbine and the energy storage device to participate in the frequency regulation of the microgrid system;

[0152] A target optimization subsystem 802 for establishing an optimization target function for the wind storage to participate in frequency regulation based on the minimum frequency deviation and the lowest wind curtailment rate;

[0153] An active power distribution subsystem 803 for setting the adaptive weight coefficient in the optimization target function;

[0154] A real-time correction subsystem 804 for setting the wind turbine constraint conditions and the energy storage device constraint conditions at the k-th sampling moment;

[0155] A power output subsystem 805 for carrying out autonomous microgrid wind-storage combined frequency regulation.

[0156] According to the third aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions, when executed by a processor, implement the method according to any one of the first aspects of the embodiments of the present invention.

[0157] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Figure 9 It is a structural diagram of an electronic device in an embodiment of the present invention. Figure 9 The electronic device shown is a general autonomous combined frequency regulation device, which includes a general computer hardware structure, and at least includes a processor 901 and a memory 902. The processor 901 and the memory 902 are connected through a bus 903. The memory 902 is adapted to store instructions or programs executable by the processor 901. The processor 901 can be an independent microprocessor or a set of one or more microprocessors. Thus, by executing the instructions stored in the memory 902, the processor 901 executes the method flow of the embodiment of the present invention as described above to implement data processing and control of other devices. The bus 903 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to a display controller 904, a display device, and an input / output (I / O) device 905. The input / output (I / O) device 905 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a body sense input device, a printer, and other devices well-known in the art. Typically, the input / output device 905 is connected to the system through an input / output (I / O) controller 906.

[0158] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0159] 1) In the embodiment of the present invention, aiming at the problems of frequency instability and high wind curtailment rate in an autonomous microgrid with wind energy storage, a weight self-adjusting model predictive control strategy with flexible change of constraint boundaries is designed, and an autonomous microgrid wind energy storage combined frequency regulation method is proposed based on this. This method establishes a state space model by combining the operating characteristics of wind turbines and energy storage devices, and takes the minimum frequency deviation and the lowest wind curtailment rate as optimization indicators, taking into account both frequency control and energy efficiency optimization functions.

[0160] 2) In the embodiment of the present invention, a weight coefficient that can be adaptively adjusted according to frequency changes is designed in the objective function, realizing flexible distribution of the active power output of wind turbines and energy storage devices when participating in the frequency regulation of an autonomous microgrid, improving the utilization rate of wind energy resources while ensuring the frequency regulation performance.

[0161] 3) In the embodiment of the present invention, a flexible constraint boundary that can be corrected online based on the wind speed of the wind turbine and the state of charge of the energy storage is designed, improving the safe operation level during the combined frequency regulation of wind energy storage in an autonomous microgrid.

[0162] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory and optical memory, etc.) that contain computer-usable program code.

[0163] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0164] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0166] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. An autonomous microgrid wind and energy storage combined frequency regulation method, characterized in that The method includes a weight self - adjustment model predictive control strategy for constraining the flexible change of the boundary, which is used to enable the wind turbine and energy storage device to participate in the flexible power distribution during the frequency regulation process of the autonomous microgrid. Specifically, it includes: Establish a state - space model for the wind turbine and energy storage device to participate in the frequency regulation of the microgrid system; Establish an optimization objective function for the wind energy storage to participate in frequency regulation based on the minimum frequency deviation and the lowest curtailment rate. Specifically, it includes: Set the control time domain and the prediction time domain; Set the first penalty coefficient corresponding to the control time domain; Set the second penalty coefficient corresponding to the prediction time domain, where the second penalty coefficient is less than the first penalty coefficient; Set the optimization objective function; Among them, the optimization objective function is: where J is the objective function, P is the control time domain, N is the prediction time domain, and f k+i|k is the predicted value of the frequency at time k+i by the control algorithm, △P1(k+i|k) is the predicted value of the active power output increment borne by the wind turbine at time k+i by the control algorithm at time k, △P2(k+i|k) is the predicted value of the active power output increment borne by the energy storage device at time k+i by the control algorithm at time k, α and β are the first penalty coefficient and the second penalty coefficient in sequence, and γ and δ are the first weight coefficient and the second weight coefficient in sequence; Set the adaptive weight coefficient in the optimization objective function; Set the constraint conditions of the wind turbine and the energy storage device at the k - th sampling moment; Carry out the wind - energy storage combined frequency regulation of the autonomous microgrid.

2. The autonomous microgrid wind-storage combined frequency regulation method according to claim 1, characterized in that The establishment of the state - space model for the wind turbine and energy storage device to participate in the frequency regulation of the microgrid system specifically includes: Establish the state - space model of the microgrid system according to the proportional relationship between the initial frequency change rate and the initial power deviation after the AC microgrid is disturbed; According to the state - space model, generate active power opposite to the frequency fluctuation by coordinating the wind turbine and the energy storage, and participate in the frequency regulation of the microgrid system through the wind turbine and the energy storage device; Among them, the state - space model of the microgrid system is: where k is the k-th sampling instant of the discrete system, x k is the state variable at the k-th sampling instant, x k+1 is the state variable at the (k + 1)-th sampling instant, u k is the control input variable at the k-th sampling instant, y k is the AC microgrid output variable at the k-th sampling instant, A c is the system matrix, B c is the input matrix, C c is the output matrix, D c is the direct transmission matrix, and the expressions of x k , u k and y k are as follows: A c , B c , C c , D c The expressions for are: Among them, f k , P 1,k and P 2,k are respectively the microgrid frequency, the active power output of the wind turbine, and the active power output of the energy storage at the k-th moment. ΔP 1,k and ΔP 2,k are respectively the increment of the active power borne by the wind turbine and the increment of the active power borne by the energy storage system at the k-th moment. T s is the sampling time, f0 is the rated frequency, and H is the inertia coefficient.

3. The autonomous microgrid wind and storage combined frequency regulation method according to claim 1, characterized in that Setting the adaptive weight coefficient in the optimization objective function specifically includes: Set the weight coefficient in the objective function as a piece - wise function of frequency, where the piece - wise function is: Among them, γ is the first weight coefficient, δ is the second weight coefficient, a1, a2, a3, a4, a5, a6, b1, b2 are all the value coefficients of the first weight coefficient, and a1 = a2 = a3 = a4 = a5 = a6, b1 = b2 are all the value coefficients of γ, c1, c2, c3, c4, c5, c6 are all the value coefficients of the second weight coefficient, and c1 = c2 = c3, c4 = c5 = c6, f1, f2 are respectively the preset critical values of frequency deviation, and △f represents the frequency deviation.

4. The autonomous microgrid wind-storage combined frequency regulation method according to claim 1, wherein The setting of the constraint conditions of the wind turbine and the energy storage device at the k - th sampling moment specifically includes: The constraint conditions of the wind turbine are: Among them, P MPPT (v k ) is the maximum active power output of the wind turbine at the k-th sampling moment, T s is the sampling time, △P 1,k+i|k is the predicted value of the active power output increment borne by the wind turbine from the k-th moment to the k + i-th moment, P 1,k+i|k is the predicted value of the active power output borne by the wind turbine from the k-th moment to the k + i-th moment, T s is the sampling time, r1 is the upper ramp rate of the wind turbine power increment within each sampling interval, △P 1,min is the lower limit of the wind turbine power increment within each sampling interval; The constraint conditions of the energy storage device are: Among them, P ch ES (SoC k ) and P disch ES (SoC k ) are the safety thresholds of the charging power and discharging power of the energy storage calculated according to the state of charge SoC k at the k-th moment respectively. r2 is the upper and lower limit ramp rate of the charge and discharge power increment of the energy storage device within each sampling interval. T s is the sampling time, and △P 2,k+i|k is the predicted value of the active power output increment borne by the energy storage device at the (k + i)-th moment at the k-th moment. P 2,k+i|k is the predicted value of the active power output borne by the energy storage device at the (k + i)-th moment at the k-th moment.

5. The autonomous microgrid wind-storage combined frequency regulation method according to claim 1, characterized in that The implementation of the wind - energy storage combined frequency regulation of the autonomous microgrid specifically includes: Obtain the microgrid frequency measurement data at the k - th sampling moment, obtain the preset frequency regulation demand, and judge whether the microgrid frequency measurement data meets the frequency regulation demand. If it does not meet the frequency regulation demand, then enable the weight self - adjustment model predictive controller with flexible change of the constraint boundary; If it is detected that the weight self-adjusting model predictive controller for the flexible change of the constraint boundary is enabled, obtain the state variables of the microgrid system at the k-th sampling moment, adjust the weight coefficient according to the frequency measurement data at the k-th sampling moment, determine the optimization objective function, and according to the wind speed v k , the SoC of the energy storage device k Update the fan constraint conditions and the energy storage device constraint conditions; Use the weight self - adjustment model predictive controller with flexible change of the constraint boundary to calculate the solution sequence of the control input according to the state - space model, and use the solution sequence as the command value of the active power output increment of the wind turbine and the energy storage device; Obtain the optimal output command of the wind turbine and the energy storage device, where the optimal output command includes the wind turbine releasing active power reserve or reducing output, and the energy storage device adjusting the charge - discharge power; Judge whether the operation state of the microgrid is normal according to the preset frequency range until the microgrid frequency returns to the normal value or the normal operation range.

6. The autonomous microgrid wind and energy storage combined frequency regulation method according to claim 4, characterized in that, The safety thresholds of the energy storage charging power and discharging power calculated based on the state of charge (SoC) of the energy storage at the k-th moment specifically include: k ​ During the charging and discharging process of the energy storage device, control the instantaneous power not to exceed the preset maximum charging and discharging power.

7. The autonomous microgrid wind-storage combined frequency regulation method according to claim 5, characterized in that The weight self-adjusting model predictive controller with flexible change of the constraint boundary specifically includes: A state space model module for inputting the state space model according to the characteristic quantities of the microgrid system, where the characteristic quantities include the inertia coefficient and the rated frequency; A target function module for writing the target function and adjusting the weight coefficient according to the received state quantity of the microgrid system; A constraint condition generator module for online correcting the constraint conditions according to the wind speed and the energy storage state of charge at each time section and generating new constraint conditions; A control input generation module for generating a control input sequence using the state space and the target function according to the state quantities acquired at each sampling moment; An optimization solution module for solving the optimal output values of the wind turbine and the energy storage device and sending the optimal output values of the wind turbine and the energy storage device to the wind turbine controller and the energy storage device controller respectively.

8. An autonomous microgrid wind and energy storage combined frequency regulation system, which is used to execute the autonomous microgrid wind and energy storage combined frequency regulation method described in any one of claims 1-7, and is characterized in that, The system includes: A state space subsystem for establishing a state space model of the wind turbine and the energy storage device participating in the frequency modulation of the microgrid system; A target optimization subsystem for establishing an optimization target function for the wind energy storage to participate in frequency modulation based on the minimum frequency deviation and the lowest curtailment rate; An active power distribution subsystem for setting the adaptive weight coefficient in the optimization target function; A real-time correction subsystem for setting the wind turbine constraint conditions and the energy storage device constraint conditions at the kth sampling moment; A power output subsystem for carrying out the combined frequency modulation of the wind energy storage in the autonomous microgrid.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The computer program instructions, when executed by a processor, implement the method according to any one of claims 1-7.

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