AGC (Automatic Gain Control) and AVC (Automatic Voltage Control) dynamic cooperative control method, device, equipment and medium

Abstract

The invention relates to an AGC and AVC dynamic cooperative control method and device, equipment and a medium. Comprising the following steps: based on active output characteristics and reactive output characteristics of multi-type energy storage, establishing active and reactive output models of the multi-type energy storage; establishing a hybrid energy storage system model based on the active and reactive output model, and determining an AGC and AVC cooperative control strategy based on the hybrid energy storage system model and a preset unit; based on the minute-level prediction result of the load demand and the real-time network data, establishing a periodic control model of the power system by using a periodic-real-time double-layer control structure; and establishing an emergency control model by using a preset cross iteration strategy based on a preset key control index, and performing dynamic cooperative control on AGC and AVC based on the periodic control model, the emergency control model and an AGC and AVC cooperative control strategy. Therefore, mutual influence between AGC and AVC is avoided, and dynamic optimization control of the time scale is realized at the same time.

Description

Technical Field

[0001] The present application relates to the technical field of power system control, and in particular to a method, device, equipment and medium for dynamic coordinated control of AGC (Automatic Generation Control) and AVC (Automatic Voltage Control). Background Art

[0002] In the current research field, most of the studies on active frequency regulation and reactive voltage control are based on the assumption of active and reactive decoupling. This means that in these studies, active and reactive power are regarded as two independent variables and studied separately. However, with the changes in the operating state of the power grid, a strong coupling relationship may appear between active and reactive power. In this case, it is difficult for a single control mode to solve the interaction and influence between the two. Therefore, it is urgent to establish a coordinated optimization calculation system to better solve the problems of frequent regulation of multiple energy sources and the safety and stability of the system.

[0003] In the related technologies, time scale stratification and event-driven cross-iteration strategy optimization control methods have been proposed. Time scale stratification belongs to periodic control, which divides the control process into different time scales to meet the control requirements in different time periods. The event-driven cross-iteration strategy belongs to emergency section control, which can respond quickly and perform effective control when an emergency occurs in the power grid.

[0004] However, this method does not fully consider the complexity of the dynamic control process, and in terms of regulating resources, current research mainly focuses on thermal power units, but lacks consideration of new resources. The dynamic characteristics and control requirements of new resources are very different from those of traditional thermal power units, and they urgently need to be improved. Summary of the invention

[0005] The present application provides a method, device, equipment and medium for dynamic collaborative control of AGC and AVC to solve the problems that the existing technology does not fully consider the complexity of the dynamic control process, and in terms of regulating resources, current research mainly focuses on thermal power units, but lacks consideration of new resources. The mutual influence between AGC and AVC is avoided, and dynamic optimization control on a time scale is realized, thereby improving the stability and efficiency of the entire power system.

[0006] To achieve the above object, a first aspect of the present application provides a method for dynamic coordinated control of AGC and AVC, comprising the following steps:

[0007] Based on the active and reactive output characteristics of multiple types of energy storage, active and reactive output models of multiple types of energy storage are established;

[0008] Based on the active and reactive output models of the multi-type energy storage, a hybrid energy storage system model is established, and based on the hybrid energy storage system model and the preset units, an AGC and AVC collaborative control strategy is determined;

[0009] Based on the minute-level forecast results of load demand and real-time network data, a periodic control model of the power system is established using the preset periodic-real-time double-layer control structure;

[0010] Based on preset key control indicators and using a preset cross-iteration strategy, an emergency control model is established, and based on the periodic control model, the emergency control model and the AGC and AVC collaborative control strategy, the AGC and the AVC are dynamically collaboratively controlled.

[0011] According to an embodiment of the present application, the active and reactive output models of multiple types of energy storage are established based on the active output characteristics and reactive output characteristics of multiple types of energy storage, including:

[0012] Obtaining a first output characteristic and a first energy storage state of charge of battery energy storage participating in power system control, a second output characteristic and a second energy storage state of charge of flywheel energy storage participating in power system control, and a third output characteristic and a third energy storage state of charge of gravity energy storage participating in power system control among the multiple types of energy storage;

[0013] Obtaining a first physical constraint model based on the first output characteristic and the first energy storage state of charge, obtaining a second physical constraint model based on the second output characteristic and the second energy storage state of charge, and obtaining a third physical constraint model based on the third output characteristic and the third energy storage state of charge;

[0014] According to the first physical constraint model, the second physical constraint model and the third physical constraint model, an active and reactive output model of the multi-type energy storage is established.

[0015] According to an embodiment of the present application, determining the AGC and AVC collaborative control strategy based on the hybrid energy storage system model and the preset unit includes:

[0016] Obtain unit control instructions;

[0017] Determine the droop coefficient corresponding to each energy storage unit in the hybrid control unit by using a preset droop coefficient adjustment method;

[0018] Based on the droop coefficient corresponding to each energy storage unit, the unit control instruction is distributed to each energy storage unit in the hybrid control unit to obtain the control instruction of each energy storage unit, wherein the hybrid control unit is obtained by the hybrid energy storage system model and the preset unit;

[0019] The AGC and AVC collaborative control strategy is determined according to the control instructions of each energy storage unit.

[0020] According to an embodiment of the present application, the minute-level prediction results based on load demand and real-time network data are used to establish a periodic control model of the power system using a preset periodic-real-time double-layer control structure, including:

[0021] Determining an economic optimal objective function and a safety objective function based on the minute-level forecast result of the load demand and the real-time network data;

[0022] Based on the economic optimal objective function and the safety objective function, a periodic control model of the power system is obtained by utilizing a preset periodic-real-time double-layer control structure.

[0023] According to one embodiment of the present application, the preset periodic-real-time two-layer control structure includes a periodic control layer and a real-time control layer, wherein the periodic control layer coordinates and controls the power system according to a preset period based on a preset coordinated control optimization index, and the real-time control layer uses a preset driving mechanism to perform cross-iterative control of the AGC or AVC in the power system.

[0024] According to an embodiment of the present application, the emergency control model is established based on preset key control indicators and using a preset cross-iteration strategy, including:

[0025] Based on the preset key control indicators, an AGC event module and an AVC event module are established, and a first control event and a first event triggering condition corresponding to the AGC event module, and a second control event and a second event triggering condition corresponding to the AVC event module are determined;

[0026] Construct AGC optimization control model and AVC optimization control model;

[0027] Based on the first control event, the first event triggering condition, the second control event, the second event triggering condition, the AGC optimization control model and the AVC optimization control model, the emergency control model is obtained by using the preset cross-iteration strategy.

[0028] According to the AGC and AVC dynamic collaborative control method proposed in the embodiment of the present application, based on the active and reactive output characteristics of multiple types of energy storage, an active and reactive output model of multiple types of energy storage can be established; based on the active and reactive output model, a hybrid energy storage system model is established, and based on the hybrid energy storage system model and the preset unit, the AGC and AVC collaborative control strategy is determined; based on the minute-level prediction results of load demand and real-time network data, a periodic control model of the power system is established using a periodic-real-time double-layer control structure; based on preset key control indicators, an emergency control model is established using a preset cross-iteration strategy, and based on the periodic control model, the emergency control model and the AGC and AVC collaborative control strategy, AGC and AVC are dynamically collaboratively controlled. In this way, the mutual influence between AGC and AVC is avoided, and the dynamic optimization control of the time scale is realized, thereby improving the stability and efficiency of the entire power system.

[0029] To achieve the above-mentioned purpose, the second embodiment of the present application proposes an AGC and AVC dynamic cooperative control device, including:

[0030] A first building module is used to build active and reactive output models of multiple types of energy storage based on active output characteristics and reactive output characteristics of multiple types of energy storage;

[0031] A determination module, used to establish a hybrid energy storage system model based on the active and reactive output models of the multi-type energy storage, and determine the AGC and AVC collaborative control strategy based on the hybrid energy storage system model and the preset units;

[0032] The second establishment module is used to establish a periodic control model of the power system based on the minute-level forecast results of load demand and real-time network data using a preset periodic-real-time double-layer control structure;

[0033] The control module is used to establish an emergency control model based on preset key control indicators and using a preset cross-iteration strategy, and to dynamically coordinate control of the AGC and the AVC based on the periodic control model, the emergency control model and the AGC and AVC coordinated control strategy.

[0034] According to an embodiment of the present application, the first establishing module is specifically used to:

[0035] Obtaining a first output characteristic and a first energy storage state of charge of battery energy storage participating in power system control, a second output characteristic and a second energy storage state of charge of flywheel energy storage participating in power system control, and a third output characteristic and a third energy storage state of charge of gravity energy storage participating in power system control among the multiple types of energy storage;

[0036] Obtaining a first physical constraint model based on the first output characteristic and the first energy storage state of charge, obtaining a second physical constraint model based on the second output characteristic and the second energy storage state of charge, and obtaining a third physical constraint model based on the third output characteristic and the third energy storage state of charge;

[0037] According to the first physical constraint model, the second physical constraint model and the third physical constraint model, an active and reactive output model of the multi-type energy storage is established.

[0038] According to an embodiment of the present application, the determining module is specifically used to:

[0039] Obtain unit control instructions;

[0040] Determine the droop coefficient corresponding to each energy storage unit in the hybrid control unit by using a preset droop coefficient adjustment method;

[0041] Based on the droop coefficient corresponding to each energy storage unit, the unit control instruction is distributed to each energy storage unit in the hybrid control unit to obtain the control instruction of each energy storage unit, wherein the hybrid control unit is obtained by the hybrid energy storage system model and the preset unit;

[0042] The AGC and AVC collaborative control strategy is determined according to the control instructions of each energy storage unit.

[0043] According to an embodiment of the present application, the second establishing module is specifically used to:

[0044] Determining an economic optimal objective function and a safety objective function based on the minute-level forecast result of the load demand and the real-time network data;

[0045] Based on the economic optimal objective function and the safety objective function, a periodic control model of the power system is obtained by utilizing a preset periodic-real-time double-layer control structure.

[0046] According to one embodiment of the present application, the preset periodic-real-time two-layer control structure includes a periodic control layer and a real-time control layer, wherein the periodic control layer coordinates and controls the power system according to a preset period based on a preset coordinated control optimization index, and the real-time control layer uses a preset driving mechanism to perform cross-iterative control of the AGC or AVC in the power system.

[0047] According to one embodiment of the present application, the control module is specifically used to:

[0048] Based on the preset key control indicators, an AGC event module and an AVC event module are established, and a first control event and a first event triggering condition corresponding to the AGC event module, and a second control event and a second event triggering condition corresponding to the AVC event module are determined;

[0049] Construct AGC optimization control model and AVC optimization control model;

[0050] Based on the first control event, the first event triggering condition, the second control event, the second event triggering condition, the AGC optimization control model and the AVC optimization control model, the emergency control model is obtained by using the preset cross-iteration strategy.

[0051] According to the AGC and AVC dynamic collaborative control device proposed in the embodiment of the present application, based on the active and reactive output characteristics of multiple types of energy storage, an active and reactive output model of multiple types of energy storage can be established; based on the active and reactive output model, a hybrid energy storage system model is established, and based on the hybrid energy storage system model and the preset unit, the AGC and AVC collaborative control strategy is determined; based on the minute-level prediction results of load demand and real-time network data, a periodic control model of the power system is established using a periodic-real-time double-layer control structure; based on preset key control indicators, an emergency control model is established using a preset cross-iteration strategy, and based on the periodic control model, the emergency control model and the AGC and AVC collaborative control strategy, AGC and AVC are dynamically collaboratively controlled. In this way, the mutual influence between AGC and AVC is avoided, and the dynamic optimization control of the time scale is realized, thereby improving the stability and efficiency of the entire power system.

[0052] To achieve the above-mentioned objectives, the third aspect embodiment of the present application proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the AGC and AVC dynamic collaborative control method as described in the above-mentioned embodiment.

[0053] To achieve the above objectives, the fourth aspect of the present application proposes a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement the AGC and AVC dynamic collaborative control method as described in the above embodiments.

[0054] To achieve the above objectives, the fifth aspect of the present application proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the AGC and AVC dynamic collaborative control method as described in the above embodiments.

[0055] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0057] Figure 1 A flowchart of a method for dynamic collaborative control of AGC and AVC provided according to an embodiment of the present application;

[0058] Figure 2 A flowchart of an emergency control layer cross iteration method according to an embodiment of the present application;

[0059] Figure 3 It is a schematic diagram of an overall hierarchical framework according to an embodiment of the present application;

[0060] Figure 4 A schematic diagram of a joint adjustment strategy for multiple types of energy storage according to an embodiment of the present application;

[0061] Figure 5 A schematic diagram of an output allocation strategy based on droop coefficients for multiple types of energy storage according to an embodiment of the present application;

[0062] Figure 6 It is a block diagram of an AGC and AVC dynamic cooperative control device provided according to an embodiment of the present application;

[0063] Figure 7 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0064] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0065] The following describes the AGC and AVC dynamic collaborative control method, device, equipment and medium proposed according to the embodiments of the present application with reference to the accompanying drawings.

[0066] Figure 1 It is a flowchart of a method for dynamic collaborative control of AGC and AVC according to an embodiment of the present application.

[0067] Before introducing the AGC and AVC dynamic collaborative control method proposed in the embodiment of the present application, the related technology is first introduced.

[0068] In the related technologies, (1) a coordinated control method of AGC and AVC in a power grid, based on the different control cycles of AGC and AVC, constructs an AGC and AVC coordinated control mode that is interconnected at the minute level and the second level at the time scale level, and proposes a corresponding control strategy. At the minute level, this method integrates the control objectives and constraints of AGC on the basis of the AVC reactive power optimization model, and constructs an optimal power flow model for joint optimization of active and reactive power to achieve coordinated control; at the second level, the overall guiding ideology of the coordinated correction strategy is to take AGC as the leading factor and AVC as an auxiliary adjustment means to achieve unidirectional coordination of AVC with AGC. (2) A study on the coordinated optimization control strategy of AGC and AVC based on the alternating iteration method defines two types of "events" for the control performance of the AGC system and the control performance of the AVC system. On this basis, an event-driven mechanism is adopted to propose a coordinated optimization control strategy of AGC and AVC based on the alternating iteration method. According to the characteristics of the actual power grid, an AGC control model with dual economy as the optimal goal is constructed, while considering the control variable constraints and network security constraints. In addition, an AVC control model with economy and safety as the optimal goals is also established. By using the idea and method of alternating iteration, the control reference values ​​of AGC and AVC can be accurately corrected, so as to eliminate the above two types of "events" in time, ensuring that the power grid can achieve efficient operation in terms of safety, quality and economy. (3) Research on the coordinated control of the network source of the Tibet power grid after the commissioning of the Tibet-China interconnection project, an evaluation index system for the operation of the power grid is established, and the control objectives of the network source coordination are proposed from the perspectives of economy, safety and quality; secondly, based on the event-driven mechanism, a hierarchical network source coordination optimization control system of the Tibet power grid is established, realizing the multi-objective optimization of the Tibet power grid; and the cross-iteration algorithm is used in the middle layer of the system to solve the problem of mutual influence between the AGC and AVC systems, improve the control effect of the automatic control system, and effectively alleviate the problems of large DC small system and long chain transmission in the Tibet power grid.

[0069] However, in the above technical solutions, the time scale hierarchical strategy and the event-driven cross-iteration strategy optimization control method are mainly proposed. The former belongs to the category of periodic control, while the latter belongs to emergency section control, and the dynamic control process is not taken into consideration.

[0070] In addition, at the current stage, emerging energy storage resources, including batteries, flywheels, and gravity energy storage, can respond quickly to instructions and keep up with the fluctuations of load and renewable energy output when participating in regulation services, thanks to their excellent climbing ability, fast response speed, and high-precision regulation performance. Therefore, these emerging energy storage resources have received extensive attention in the frequency regulation auxiliary service market and voltage control field. However, in the above-mentioned coordinated control research on AGC and AVC, only thermal power units are focused on as regulation resources, and new resources are not included in the research scope.

[0071] It is based on the above problems that the embodiment of the present application proposes a method for dynamic coordinated control of AGC and AVC. Compared with the traditional AGC system, this method innovatively proposes a collaborative optimization model, which incorporates batteries, flywheels and gravity energy storage into the frequency and voltage coordinated integrated control of the new power system. When constructing the collaborative control framework, a control model of the periodic control layer is established based on the principles of economy and safety. In response to the control requirements of the emergency control layer, an event-driven improved cross-iteration method is adopted to achieve dynamic optimization control. This method effectively avoids the mutual influence between AGC and AVC, and successfully realizes dynamic optimization control on a time scale.

[0072] Next, the AGC and AVC dynamic collaborative control method proposed in the embodiment of the present application will be introduced in detail.

[0073] For example, Figure 1 As shown, the AGC and AVC dynamic cooperative control method includes the following steps:

[0074] In step S101, based on the active output characteristics and reactive output characteristics of the multiple types of energy storage, active and reactive output models of the multiple types of energy storage are established.

[0075] It can be understood that multi-type energy storage refers to multi-type energy storage devices, including battery energy storage, flywheel energy storage and gravity energy storage. Energy storage has the characteristics of strong climbing ability, fast response speed and high regulation accuracy. When participating in regulation services, it can quickly respond to regulation instructions and follow the load and renewable energy output. Active output refers to the ability of energy storage equipment to actually provide available electrical energy, that is, electrical energy that can do work. Reactive output refers to a kind of electrical energy provided by energy storage equipment in an AC circuit that does not directly do work. It is related to maintaining the stability of the power system and improving efficiency. The active and reactive output models of multi-type energy storage and the hybrid energy storage system model are both mathematical models.

[0076] That is to say, through in-depth analysis of the active and reactive output characteristics of multiple types of energy storage, a comprehensive active and reactive output model of multiple types of energy storage can be established based on the analysis results. This model will cover the characteristics of active and reactive output to facilitate effective active and reactive control in the power system.

[0077] As a possible implementation method, in some embodiments, based on the active output characteristics and reactive output characteristics of multiple types of energy storage, an active and reactive output model of multiple types of energy storage is established, including: obtaining a first output characteristic and a first energy storage state of charge of battery energy storage participating in power system control, a second output characteristic and a second energy storage state of charge of flywheel energy storage participating in power system control, and a third output characteristic and a third energy storage state of charge of gravity energy storage participating in power system control; obtaining a first physical constraint model based on the first output characteristic and the first energy storage state of charge, and obtaining a second physical constraint model based on the second output characteristic and the second energy storage state of charge, and obtaining a third physical constraint model based on the third output characteristic and the third energy storage state of charge; establishing active and reactive output models of multiple types of energy storage according to the first physical constraint model, the second physical constraint model, and the third physical constraint model.

[0078] Specifically, first, the output characteristics (i.e., the first output characteristics) and the energy storage charge state (i.e., the first energy storage charge state) of battery energy storage participating in power system control are analyzed, and the corresponding physical constraint model (i.e., the first physical constraint model) is constructed. The active output of battery energy storage is affected by the state of charge (SOC, an indicator used to measure the amount of energy storage). Under normal circumstances, the active output of battery energy storage is positively correlated with SOC, and its active output size P B Maximum charging power P B,max and the maximum discharge power P B,min (negative value) restriction, therefore, the battery energy storage active power model mainly considers the upper and lower limit constraints of active power output and SOC constraints.

[0079] Among them, the upper and lower limits of active output are:

[0080] P B,min ≤P B ≤P B,max ; (1)

[0081] The SOC calculation formula is:

[0082]

[0083] Among them, SOC 0 is the initial charge state, E B is the rated capacity of the energy storage system, η B is the charging and discharging efficiency of the energy storage system, ΔP B is the change in energy storage output, and Δt is the unit time period.

[0084] The SOC constraint is:

[0085] SOC min ≤SOC≤SOC max(3)

[0086] Battery energy storage uses the inverter as the grid-connected interface, which can realize the two-way flow of energy between the grid and the power grid. Although the reactive and active outputs of the inverter can be decoupled and are not restricted by the fixed power factor, they are still limited by the capacity of the inverter. Therefore, its reactive output range Q B Constrained by inverter capacity and active power.

[0087] Among them, the reactive power output constraint is:

[0088] Q B,min ≤Q B ≤Q B,max ; (4)

[0089]

[0090] Among them, S PCS is the capacity of the energy storage converter, P BN is the rated active power of energy storage.

[0091] Secondly, the basic principles of the output characteristics (i.e., the second output characteristics) and the energy storage charge state (i.e., the second energy storage charge state) of flywheel energy storage participating in the control of the power system are analyzed, and the corresponding physical constraint model (i.e., the second physical constraint model) is constructed. The flywheel energy storage system (FESS) also has the characteristics of bidirectional output, instantaneous response, and precise tracking of power generation instructions. Therefore, the physical constraint model includes the electromechanical dynamic process of flywheel energy storage, the energy storage residual energy state (SOE) constraint link, and the energy storage PCS (Power Conversion System, converter) link.

[0092] Electromechanical dynamic process: The flywheel energy storage system can control its output power by driving the flywheel to accelerate or decelerate, completing the task of storing and releasing electrical energy. The process can be expressed as:

[0093] P FESS (t) = T FESS (t)×ω FESS (t); (7)

[0094]

[0095] Among them, P FESS (t) is the flywheel energy storage output power at time t, T FESS (t) is the electromagnetic torque of the flywheel at time t, J FESS is the flywheel moment of inertia, ω FESS (t) is the angular velocity of the flywheel at time t.

[0096] SOE constraint link: Considering the capacity limitation of the flywheel energy storage system, when the SOE of the flywheel energy storage system exceeds the limit, the flywheel energy storage system does not provide power output. The calculation formula of the SOE of the flywheel energy storage system at time t is:

[0097]

[0098] SOE min ≤SOE≤SOE max ; (10)

[0099] Among them, SOE max and SOE min They are the upper and lower limits of SOE of the flywheel energy storage system; SOE 0 t 0 Moment flywheel energy storage system SOE; E FESS is the rated capacity of the flywheel energy storage system.

[0100] The PCS link mainly affects the reactive output capacity of the flywheel energy storage. It has a similar structure and constraints to the battery energy storage, and can establish the same reactive output constraints as the battery energy storage.

[0101] Finally, the basic principles of the output characteristics (i.e., the third output characteristics) and the energy storage charge state (i.e., the third energy storage charge state) of gravity energy storage participating in the control of the power system are analyzed, and the corresponding physical constraint model (i.e., the third physical constraint model) is constructed. Gravity energy storage can store energy by utilizing the height difference and store electrical energy in the form of gravitational potential energy. When electricity is scarce, the heavy objects are released to drive the motor to operate in a power generation mode, generating electricity to feed back to the power grid, completing the energy conversion from gravitational potential energy to electrical energy. Compared with pumped storage and battery energy storage, it has certain advantages in adapting to different geographical environments and temperature conditions, and has good application prospects. Gravity energy storage power generation is directly related to the mass of the heavy objects and the rate of descent. Its remaining power is similar to that of flywheel energy storage. The constructed constraint model is as follows:

[0102] The energy release power satisfies the formula:

[0103] P GESS =F GESS ×v GESS ; (11)

[0104] For vertical gravity energy storage systems:

[0105] F GESS =mg-F μ ; (12)

[0106] For slope gravity energy storage system:

[0107] F GESS= mg·sinθ-F μ ; (13)

[0108] Among them, P GESS is the energy release power of the gravity energy storage system; F GESS The traction force of the generator when the gravity energy storage system releases the weight; v GESS is the falling speed of the weight, θ is the inclination angle of the slope, m is the mass of the weight, g is the acceleration due to gravity, F μ is the system resistance.

[0109] The SOE calculation formula of the gravity energy storage system at time t is:

[0110]

[0111] Among them, SOE 0 t 0 Momentary Gravity Energy Storage System SOE; E GESS is the rated capacity of the gravity energy storage system.

[0112] The reactive output capability of gravity energy storage is similar to that of battery energy storage, and depends on the capacity of the converter and the constraints of the active power. The same reactive power constraints as those of battery energy storage can be established.

[0113] Therefore, by integrating the first physical constraint model corresponding to battery energy storage, the second physical constraint model corresponding to flywheel energy storage and the third physical constraint model corresponding to gravity energy storage, the active and reactive output models of multiple types of energy storage can be obtained.

[0114] In step S102, a hybrid energy storage system model is established based on active and reactive output models of multiple types of energy storage, and an AGC and AVC collaborative control strategy is determined based on the hybrid energy storage system model and a preset unit.

[0115] It is understandable that since the overall regulation capacity of energy storage is quite different from that of traditional thermal power units, and battery energy storage, flywheel energy storage and gravity energy storage have similar active and reactive output characteristics, the embodiment of the present application proposes to aggregate different types of energy storage into a hybrid energy storage system (i.e., a hybrid energy storage system model), and jointly participate in the coordinated control process of AGC and AVC with the thermal power units (i.e., the preset units). The subsequent coordinated control process of AGC and AVC is only analyzed from the aggregation of the hybrid energy storage system model.

[0116] Specifically, according to the power output models of battery energy storage, flywheel energy storage and gravity energy storage (i.e., the active and reactive output models of multiple types of energy storage), a hybrid energy storage system model is established by aggregation, wherein the aggregation parameters include parameters such as active adjustment capacity, reactive adjustment capacity and system residual storage capacity. The specific calculation formula is as follows:

[0117] Active output constraints are:

[0118] P H,min =P B,min +P FESS,min +P GESS,min ; (15)

[0119] P H,max =P B,max +P FESS,max +P GESS,max ; (16)

[0120] P H,min ≤P H ≤P H,max ; (17)

[0121] The reactive power output constraint is:

[0122] Q H,min =Q B,min +Q FESS,min +Q GESS,min ; (18)

[0123] Q H,max =Q B,max +Q FESS,max +Q GESS,max ; (19)

[0124] Q H,min ≤Q H ≤Q H,max ; (20)

[0125] The remaining storage capacity is the same as above:

[0126] SOE min ≤SOE≤SOE max . (twenty one)

[0127] As a possible implementation method, in some embodiments, based on the hybrid energy storage system model and the preset unit, the AGC and AVC collaborative control strategy is determined, including: obtaining unit control instructions; using the preset droop coefficient adjustment method to determine the droop coefficient corresponding to each energy storage unit in the hybrid control unit; based on the droop coefficient corresponding to each energy storage unit, the unit control instructions are distributed to each energy storage unit in the hybrid control unit to obtain the control instructions of each energy storage unit, wherein the hybrid control unit is obtained by the hybrid energy storage system model and the preset unit; according to the control instructions of each energy storage unit, the AGC and AVC collaborative control strategy is determined.

[0128] Among them, the preset droop coefficient adjustment method is a technology used to maintain frequency and voltage stability in the power system. When the load of the power system changes, the power output of the generator set is adjusted to keep the system frequency and voltage within the specified range.

[0129] Specifically, the hybrid energy storage system model and the preset unit are regarded as a whole, that is, the hybrid control unit, and the unit control command is issued to the hybrid control unit. The significant feature of the discharge process of multi-type energy storage is that the output of the energy storage changes with the change of the frequency deviation. Its output characteristics are associated with the active power distribution of each unit, and the control command assigned to the unit is inversely proportional to the droop coefficient of the unit, that is, the load power borne by the unit with a small droop coefficient is relatively large, while the load power borne by the unit with a large droop coefficient is relatively small. After receiving the unit control command, the hybrid control unit can use the preset droop coefficient adjustment method to distribute the command between the energy storage units, that is, distribute the unit control command to each energy storage unit in the hybrid control unit. In this way, the outstanding problems of small frequency deviation but large change rate in the early stage of load disturbance and large frequency deviation but small change rate in the later stage can be solved.

[0130] Among them, the droop coefficient k of the energy storage unit is:

[0131]

[0132] Where Δf is the frequency change, ΔP G Energy storage power change.

[0133] Since it involves the distribution among various types of energy storage units, the droop coefficient can be expressed in the form of per-unit value:

[0134]

[0135] In the formula, f N is the system frequency rating, ΔP GN is the total power change, that is, the active power command received by the hybrid energy storage system model.

[0136] In step S103, based on the minute-level prediction results of load demand and real-time network data, a periodic control model of the power system is established using a preset periodic-real-time double-layer control structure.

[0137] It is understandable that in order to achieve the logic of AGC and AVC coordinated distributed control process to maintain a unified whole, the embodiment of the present application proposes a two-layer coordinated control strategy of AGC and AVC (i.e., a preset periodic-real-time two-layer control structure) to achieve time-segmented and multi-level coordinated control of AGC and AVC, ensuring the unity of the overall coordinated control objectives and the integrity of the overall operating mechanism in the hierarchical distributed control process. Combined with the predicted data of load demand and real-time network data, through the preset periodic-real-time two-layer control structure, a periodic control model of the power system can be established to achieve effective management of the power system.

[0138] Optionally, in some embodiments, the preset periodic-real-time two-layer control structure includes a periodic control layer and a real-time control layer, wherein the periodic control layer coordinates and controls the power system according to a preset period based on a preset coordinated control optimization index, and the real-time control layer uses a preset driving mechanism to perform cross-iterative control of the AGC or AVC in the power system.

[0139] Specifically, the embodiment of the present application proposes a two-layer coordinated control strategy of AGC and AVC (i.e., a preset periodic-real-time two-layer control structure) based on a hybrid control method of time base and event base, and divides the coordinated control of AGC and AVC into two layers according to the time base, namely, an AGC and AVC periodic control layer for periodic centralized control of the system at the upper layer and an AGC and AVC alternating drive layer for real-time control of each area based on the event base at the lower layer. Among them, the AGC and AVC periodic control layer can take the power generation cost, energy storage depreciation cost and reactive power margin of the optimal operating point as the optimization objectives of the coordinated control model, and consider the results of the advanced load demand forecast (i.e., the minute-level forecast results of the load demand) and the steady-state response of the voltage to the active power (i.e., the real-time network data), and optimize the overall coordination of the system with a period of 5 minutes to solve the AGC and AVC control setting values ​​of the system at the optimal operating point; the AGC and AVC alternating drive layer can consider maintaining the stability of the grid voltage and frequency in emergency situations. In order to avoid mutual interference between the simultaneous adjustment of AGC and AGC, the AGC or AVC is cross-iterated and adjusted by the event-driven mechanism to eliminate the voltage and tie line deviations.

[0140] As a possible implementation method, in some embodiments, based on the minute-level prediction results of load demand and real-time network data, a preset periodic-real-time two-layer control structure is used to establish a periodic control model of the power system, including: determining the economic optimal objective function and the safety objective function based on the minute-level prediction results of load demand and real-time network data; based on the economic optimal objective function and the safety objective function, using the preset periodic-real-time two-layer control structure, obtaining the periodic control model of the power system.

[0141] Specifically, in order to reduce the energy loss during the operation of the power system, the power system's power generation cost, network loss cost and the comprehensive operation cost index of the hybrid energy storage system can be used to correct the power grid energy consumption. The objective function of the comprehensive economic optimization of the power grid is:

[0142] f(P Gi ,Q Gi ) = f 1 (P Gi )+γ 2 f 2 (V i ,θ i )+γ 3 f 3 ;(twenty four)

[0143]

[0144] Among them, P Gi is the active power output of unit i, Q Gi is the reactive power output of unit i, f 1 is the system power generation cost, γ 2 is the weight of network loss in economic indicators, f 2 is the system network loss cost, V i is the voltage amplitude of node i, θ i is the phase angle value of node i, f 3 is the depreciation cost of energy storage, γ 3 is the weight coefficient, S G is the set of thermal power units, a 2i 、a 1i 、a 0i is the power generation cost coefficient of conventional units, N is the number of nodes, G ij is the real part of the node admittance matrix in row i and column j, θ ij is the phase difference between nodes i and j at both ends of the line, N H is the energy storage system set, α H is the unit charging and discharging cost of energy storage, is the charging power of the energy storage system during period t, is the discharge power of the energy storage system during period t, M H is the life cycle cost of the energy storage system, E HC is the discharge amount in the whole life cycle, E HP is the charge amount in the whole life cycle, β HC is the discharge efficiency, β HP For charging efficiency.

[0145] Optimizing the control quantity according to the safety index can effectively improve the reactive power margin of the generator in each control zone, balance the reactive power output of each unit, and take into account the safe operation of the energy storage system, reduce the number of charge and discharge times, and improve the stability of the system. Safety indicators include reactive power balance and energy storage power balance. The safety objective function is:

[0146] ψ(P Gi ,Q Gi )=ω M M k +ω K K Bi ; (29)

[0147] Among them, M k is the regional reactive power balance, ω M is its weight coefficient; K Bi is the energy storage balance, ω K is its weight coefficient.

[0148] The calculation formula of regional reactive power balance is:

[0149]

[0150] Among them, Q lev,i is the reactive power output level of a single unit, Q lev,k is the average reactive power output level of the regional units, Q i0 is the current reactive power output of unit i, Q i,min is the minimum reactive power output of unit i, Q i,max is the maximum reactive power output of unit i.

[0151] Regarding the balance of energy storage electricity, in order to ensure the cyclic operation of the energy storage system, the remaining stored energy after a full day of operation should return to the initial value as much as possible. In order to make the scheduling more flexible, the embodiment of the present application can relax the remaining stored energy to a certain range.

[0152]

[0153] Among them, K Bi is the remaining stored energy, E cha,i is the maximum chargeable amount of energy storage, E dis,i is the maximum discharge capacity of energy storage, E B,i is the power balance threshold.

[0154] Using the preset periodic-real-time double-layer control structure, the AGC and AVC control of the entire system are periodically optimized with a period of 5 minutes to obtain the control commands of each active and reactive controller. In order to achieve the coordinated optimization of AGC and AVC control, based on the optimization mathematical model of AVC, the control variables of AGC are introduced, and the corresponding operation, control constraints and optimization objectives of coordinated control are obtained. A new nonlinear mathematical programming model for coordinated optimization of AGC and AVC (i.e., the periodic control model of the power system) can be obtained:

[0155]

[0156] Among them, ω qua It is the weight coefficient of the safety target under the economic indicator.

[0157] The constraints may include the power flow balance equations of active power and reactive power of the power grid, upper and lower limits of active power output of the generator sets involved in the regulation, upper and lower limits of reactive power output, voltage amplitude constraints of each node, tie line power constraints and active power constraints of the transmission line, as shown in the following formulas:

[0158]

[0159]

[0160]

[0161] P ij ≤P ijmax ; (40)

[0162] P T,max ≤P T ≤P T,max (41)

[0163] In step S104, based on preset key control indicators and using a preset cross-iteration strategy, an emergency control model is established, and based on the periodic control model, the emergency control model and the AGC and AVC collaborative control strategy, dynamic collaborative control of the AGC and AVC is performed.

[0164] In other words, by combining the emergency control model and the periodic control model, as well as the AGC and AVC collaborative control strategy, dynamic collaborative control of AGC and AVC can be achieved. Through this collaborative control strategy, it can be ensured that AGC and AVC can cooperate with each other and work together under different operating conditions, thereby achieving comprehensive, accurate and efficient control of the power grid. Through this comprehensive control method, various complex power grid operation conditions can be effectively responded to to ensure the stability and safety of the power grid.

[0165] As a possible implementation method, in some embodiments, based on preset key control indicators, an emergency control model is established using a preset cross-iteration strategy, including: based on the preset key control indicators, an AGC event module and an AVC event module are established, and the first control event and the first event trigger condition corresponding to the AGC event module are determined, as well as the second control event and the second event trigger condition corresponding to the AVC event module; an AGC optimization control model and an AVC optimization control model are constructed; based on the first control event, the first event trigger condition, the second control event, the second event trigger condition, the AGC optimization control model and the AVC optimization control model, an emergency control model is obtained using a preset cross-iteration strategy.

[0166] Among them, for AGC control, the preset key control indicator is the regional control deviation; for AVC control, the preset key control indicator is the node voltage deviation.

[0167] Specifically, in order to distinguish the triggering conditions for starting AGC and AVC control, an AGC control event (i.e., the first control event) with the tie line exchange power as the first event triggering condition and an AVC control event (i.e., the second control event) with the key node voltage exceeding the limit as the second event triggering condition can be defined respectively.

[0168] The control conditions are defined as follows:

[0169]

[0170] Among them, ε 1 is the tie line power deviation threshold, ΔP T is the tie line power deviation value, E AGC is the result of AGC event judgment. AGC When it is 1, it indicates that the event is triggered.

[0171]

[0172] Among them, ε 2 Node voltage deviation threshold, ΔV P is the voltage deviation value of the central node, E AVC is the result of AVC event judgment. AVC When it is 1, it indicates that the event is triggered.

[0173] When the upper layer event is triggered, the real-time control system will ΔP T and ΔV P It is sent to the control layer for coordinated optimization of the AGC and AVC adjustment modules to reduce the deviation to within the threshold range.

[0174] Furthermore, an independent AGC optimization control model and an AVC optimization control model considering the participation of the hybrid energy storage system in the new power system are constructed respectively.

[0175] (1) The AGC optimization control model has an objective function that includes the regulation cost of traditional units and energy storage systems at the economic level, while also considering the impact of unit output on system network losses; at the quality level, it considers the core indicator of the control target, that is, the minimum regional control deviation. The overall optimization control objective function is as follows:

[0176]

[0177] Where ΔP Gi is the distribution adjustment output of unit i, f(ΔP Gi ) is the output cost function of thermal power unit i, f H is the comprehensive operating cost of the energy storage system, w ploss is the network loss weight coefficient, C i is the network loss sensitivity, c ACE To adjust the ACE randomness cost coefficient, ACE is the regional control deviation.

[0178] Active output constraints are:

[0179] P Gimin ≤P 0 +ΔP Gi ≤P Gimax ; (45)

[0180] Construct active output constraints for energy storage system participation control:

[0181] P H,min ≤P H0 +ΔP Bi ≤P Himax ; (46)

[0182] The power balance equation is established based on the power system balance condition:

[0183]

[0184] Where ΔP tie is the power change of the interconnection line. In the node network, under the section condition where the system load remains unchanged, the total value of the additional output of the units is approximately the change value of the interconnection line.

[0185] Based on the power system safety operation index, the transmission power constraints of the tie line established according to the line load rate are as follows:

[0186]

[0187] Among them, K overis the interconnection line power load rate, C i-K is the sensitivity of unit i to the over-limit line load rate, K sec The upper limit of line load rate.

[0188] (2) AVC optimization control model, which establishes an optimization model with the minimum voltage deviation at the grid connection point and the maximum reactive power margin. Its optimization objective function mainly consists of two parts. The first part is to minimize the deviation between the bus voltage and the set value; the second part is to minimize the reactive output ratio of the reactive output unit. The smaller the ratio, the larger the reactive power margin of the reactive output unit.

[0189] The objective function expression is:

[0190] min{R|a(V i -V i ref )+C g ΔQ Gi | 2 +ωM}; (49)

[0191] Among them, V i is the central node voltage, ΔQ Gi is the reactive power regulation of unit i, C g is the sensitivity matrix, R is the weight coefficient, a is the gain coefficient, ω is the safety factor, and M is the reactive balance degree.

[0192] Construct the boundary conditions of the regulating unit, improve the AVC optimization control model, and establish the reactive output constraints of the traditional thermal power units and energy storage systems participating in the control as follows:

[0193] Reactive power output upper and lower limits of thermal power units:

[0194] Q Gi,min ≤Q Gi,0 +ΔQ Gi ≤Q Gi,max ; (50)

[0195] Output constraints of energy storage units:

[0196] Q Hmin ≤Q H0 +ΔQ Hi ≤Q Himax ; (51)

[0197] Based on the quality and safety indicators of safe and stable operation of the power system, the voltage deviation constraints of the central node and the voltage constraints of the generator terminal are established as follows:

[0198] |ΔV p -C Q ΔQ Gi |<ε; (52)

[0199] V min ≤V i0 +C i ΔV i ≤V max ; (53)

[0200] Where, ΔV p is the voltage deviation of the central node, and its control dead zone is ε, C i is the sensitivity of the reactive output of the unit to the terminal voltage of its generator i, C Q is the sensitivity of the reactive output of the unit to the terminal voltage of generator i, V i0 and ΔV i are the initial value and control value of the machine-end node voltage respectively.

[0201] Considering the improved cross-iteration method of energy storage system participation in control, the embodiment of the present application performs cross-iteration control optimization control method based on event. The improved cross-iteration control method (i.e., the preset cross-iteration strategy) has the following process: Figure 2 As shown:

[0202] (1) Accept event module instructions, as well as parameters of each unit and multiple types of energy storage systems.

[0203] (2) Carry out power flow calculation and independently control AGC and AVC based on the system tie line deviation number and node voltage deviation as control indicators.

[0204] (3) Based on the calculation results of the independent AGC or AVC control module, perform the power flow calculation again and determine whether there is any mutual influence (determination method: determine whether the node voltage exceeds the limit after AGC control, and determine whether the tie line deviation exceeds the limit after AVC control). If so, continue to perform cross-solving until the influence is eliminated.

[0205] (4) After the control module solves the problem, it sends instructions to specific control units and energy storage systems. The hybrid energy storage is then distributed to battery energy storage units, flywheel energy storage units, and gravity energy storage units based on the droop coefficient.

[0206] This application combines the original time-layered and time-driven AGC and AVC coordinated control methods, and proposes a two-layer dynamic coordinated control method that combines periodic and real-time control. At the same time, it considers incorporating new energy storage as a regulating resource into the coordinated control research of AGC and AVC, and based on the characteristics of various types of energy storage regulating resources, establishes an aggregated hybrid energy storage system and traditional units to jointly participate in AGC and AVC collaborative optimization control.

[0207] To facilitate those skilled in the art to further understand the AGC and AVC dynamic collaborative control method proposed in the embodiment of the present application, it is further elaborated below with examples.

[0208] Step 1: According to the preset periodic-real-time dual-layer control structure provided in the embodiment of the present application, determine the time scale applicable to the coordinated control of AGC and AVC, and establish a dynamic AGC and AVC coordinated control system.

[0209] The overall framework provided by the embodiment of the present application includes two levels, such as Figure 3 As shown in the figure, they are the periodic control layer and the emergency control layer. The periodic control layer takes economy as the control target, establishes the optimal power flow model of active and reactive power, realizes periodic AGC and AVC optimization control, and outputs active and reactive power control instructions. In the emergency control layer, when a disturbance occurs, the event module controls and triggers the cross-iterative control of AGC and AVC, and outputs active and reactive power adjustment instructions.

[0210] According to the preset periodic-real-time two-layer control structure, combined with the actual operating parameters of the power system, the standard node wiring diagram is selected, and the appropriate time scale is selected as the control period of the periodic control layer and the emergency control layer. At the same time, the corresponding input and output variables are determined, and the control objectives of the two levels are clarified. Ultimately, the AGC and AVC dynamic coordinated optimization control system can be realized.

[0211] Step 2: Figure 4 As shown, the adjustment parameters of multiple types of energy storage units are uploaded to form an aggregated hybrid energy storage system model. According to the power output models of battery energy storage, flywheel energy storage and gravity energy storage, a hybrid energy storage system is established by aggregation. The aggregated parameters include active adjustment capacity, reactive adjustment capacity, and system remaining storage capacity.

[0212] When receiving the control command, Figure 5 The droop coefficient adjustment method based on the preset is shown to distribute instructions. The droop coefficient adjustment method is used in the discharge process of multiple types of energy storage. Its notable feature is that the output of energy storage changes with the change of frequency deviation. The focus is on solving the prominent problems of small frequency deviation but large change rate in the early stage of load disturbance and large frequency deviation but small change rate in the later stage. According to the droop coefficient k of different types of energy storage units i , the droop coefficient is expressed in the form of per-unit value, as shown in formula (23).

[0213] By generalizing the above conclusions, we can get the power distribution of multiple units as follows:

[0214]

[0215] Step 3: Based on the preset cycle-real-time two-layer control structure, the established mathematical model of AGC and AVC coordinated optimization control at the minute level is used to calculate the active and reactive reference power, and at the same time, active and reactive adjustment instructions are issued to achieve economic operation of the power grid.

[0216] In periodic control, the optimal power flow model is established based on the real-time monitoring parameters of the system operation, the network load forecast value and the calculated output model of each unit. The objective function is established with the lowest economic cost and safe and stable operation, and the upper and lower limits of the active output, reactive output, node voltage, branch power, tie line power, and converter capacity of the generator are used as inequality constraints to form a nonlinear mathematical programming model. Considering that the main control target of AGC, the tie line power, is as close to the planned value as possible, it can be equivalent to a generator set and introduced into the optimization control model as a state variable. Therefore, the expression of the coordinated optimization control model of AGC and AVC considering the tie line power control is shown in formula (34).

[0217] Step 4: Based on the preset periodic-real-time two-layer control structure, according to the emergency control level AGC and AVC coordinated control strategy, calculate the active and reactive adjustment amounts, and send the adjustment instructions to the active and reactive adjustment devices to achieve real-time correction control of the active and reactive power of the power grid.

[0218] At the emergency control layer, the event-driven module is first established to determine whether the event is triggered, and then the node voltage deviation and tie line deviation are obtained according to the real-time monitoring parameters of the system operation as instructions to the control module. The control module establishes independent control models for AGC and AVC based on active and reactive control targets. Finally, the following is adopted: Figure 2 The cross-iterative control method shown is used to solve the calculation.

[0219] An event module is established according to the main control objectives. In order to distinguish the triggering conditions for the start of AGC and AVC control, the AGC control event with the tie line exchange power as the triggering condition and the AVC control event with the dominant node voltage exceeding the limit as the triggering condition are defined respectively, as shown in Equation (42) and Equation (43), respectively.

[0220] When the upper layer event is triggered, the real-time control system will ΔP T and ΔV P It is sent to the control layer for coordinated optimization of the AGC and AVC adjustment modules to reduce the deviation to within the threshold range.

[0221] The AGC optimization control model is established based on the additional output of the units involved in regulation and control, the impact of output on network losses, and regional control deviation (as shown in Equation (44)). The constraints include: the upper and lower limits of active output of traditional units, the upper and lower limits of output of hybrid energy storage systems, the command balance equation, and the upper and lower limits of active power flow of the line.

[0222] The AVC optimization control model (as shown in formula (49)) is established based on the additional power generation of the AVC control units involved in regulation, node voltage changes, and reactive power balance in the control area. The constraints include: the upper and lower limits of reactive power output of traditional units, the upper and lower limits of reactive power output of the hybrid energy storage system, the voltage deviation of the central node, the terminal voltage of the units involved in regulation, and the upper and lower limits of the converter capacity of the energy storage.

[0223] In summary, the embodiments of the present application have the following beneficial effects:

[0224] (1) By utilizing the hybrid control concept based on modern control theory, a time-based and event-driven combination mechanism is adopted for optimal control, and a hierarchical control system is used to allocate control tasks layer by layer, ultimately achieving a multi-objective optimal control effect. This effectively reduces the control burden of each layer, accelerates the control process, and improves the reliability of the emergency control system.

[0225] (2) By utilizing the frequency and voltage regulation technical advantages of energy storage power stations, frequency regulation has faster and more stable frequency regulation capabilities than traditional frequency regulation methods; during voltage regulation, the energy storage emits / absorbs reactive power and only occupies the inverter capacity, which does not affect the charge state of the energy storage system. Therefore, when there is surplus inverter capacity, energy storage is used to participate in grid voltage regulation, which weakens the coupling between frequency and voltage control.

[0226] (3) Through the proposed hybrid energy storage model that aggregates multiple types of energy storage, the traditional units are combined to participate in the coordinated control of AGC and AVC. After the hybrid energy storage receives the control command, the command redistribution process among multiple types is carried out. This can effectively improve the ability of distributed stations or distributed energy storage to participate in the coordinated control of large power grids in scenarios where the scale of installed capacity is small and the types are complex.

[0227] According to the AGC and AVC dynamic collaborative control method proposed in the embodiment of the present application, based on the active and reactive output characteristics of multiple types of energy storage, an active and reactive output model of multiple types of energy storage can be established; based on the active and reactive output model, a hybrid energy storage system model is established, and based on the hybrid energy storage system model and the preset unit, the AGC and AVC collaborative control strategy is determined; based on the minute-level prediction results of load demand and real-time network data, a periodic control model of the power system is established using a periodic-real-time double-layer control structure; based on preset key control indicators, an emergency control model is established using a preset cross-iteration strategy, and based on the periodic control model, the emergency control model and the AGC and AVC collaborative control strategy, AGC and AVC are dynamically collaboratively controlled. In this way, the mutual influence between AGC and AVC is avoided, and the dynamic optimization control of the time scale is realized, thereby improving the stability and efficiency of the entire power system.

[0228] Next, the AGC and AVC dynamic cooperative control device proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0229] Figure 6 It is a block diagram of an AGC and AVC dynamic cooperative control device according to an embodiment of the present application.

[0230] like Figure 6 As shown, the AGC and AVC dynamic cooperative control device 10 includes: a first establishing module 100 , a determining module 200 , a second establishing module 300 and a control module 400 .

[0231] The first establishing module 100 is used to establish active and reactive output models of multiple types of energy storage based on active output characteristics and reactive output characteristics of multiple types of energy storage;

[0232] A determination module 200 is used to establish a hybrid energy storage system model based on active and reactive output models of multiple types of energy storage, and determine an AGC and AVC collaborative control strategy based on the hybrid energy storage system model and a preset unit;

[0233] The second building module 300 is used to build a periodic control model of the power system based on the minute-level prediction results of load demand and real-time network data using a preset periodic-real-time double-layer control structure;

[0234] The control module 400 is used to establish an emergency control model based on preset key control indicators and using a preset cross-iteration strategy, and to dynamically coordinate control of the AGC and AVC based on the periodic control model, the emergency control model and the AGC and AVC coordinated control strategy.

[0235] Furthermore, in some embodiments, the first establishing module 100 is specifically configured to:

[0236] Obtaining a first output characteristic and a first energy storage state of charge of battery energy storage participating in power system control, a second output characteristic and a second energy storage state of charge of flywheel energy storage participating in power system control, and a third output characteristic and a third energy storage state of charge of gravity energy storage participating in power system control among multiple types of energy storage;

[0237] A first physical constraint model is obtained based on the first output characteristic and the first energy storage state of charge, a second physical constraint model is obtained based on the second output characteristic and the second energy storage state of charge, and a third physical constraint model is obtained based on the third output characteristic and the third energy storage state of charge;

[0238] According to the first physical constraint model, the second physical constraint model and the third physical constraint model, active and reactive output models of multiple types of energy storage are established.

[0239] Further, in some embodiments, the determination module 200 is specifically configured to:

[0240] Obtain unit control instructions;

[0241] Determine the droop coefficient corresponding to each energy storage unit in the hybrid control unit by using a preset droop coefficient adjustment method;

[0242] Based on the droop coefficient corresponding to each energy storage unit, the unit control instruction is distributed to each energy storage unit in the hybrid control unit to obtain the control instruction of each energy storage unit, wherein the hybrid control unit is obtained by the hybrid energy storage system model and the preset unit;

[0243] The AGC and AVC collaborative control strategy is determined according to the control instructions of each energy storage unit.

[0244] Further, in some embodiments, the second establishing module 300 is specifically configured to:

[0245] Determine the economic optimal objective function and safety objective function based on minute-level load demand forecast results and real-time network data;

[0246] Based on the economic optimal objective function and the safety objective function, a periodic control model of the power system is obtained by using a preset periodic-real-time double-layer control structure.

[0247] Furthermore, in some embodiments, the preset periodic-real-time two-layer control structure includes a periodic control layer and a real-time control layer, wherein the periodic control layer coordinates and controls the power system according to a preset period based on a preset coordinated control optimization index, and the real-time control layer utilizes a preset driving mechanism to perform cross-iterative control of the AGC or AVC in the power system.

[0248] Furthermore, in some embodiments, the control module 400 is specifically configured to:

[0249] Based on the preset key control indicators, an AGC event module and an AVC event module are established, and a first control event and a first event triggering condition corresponding to the AGC event module, and a second control event and a second event triggering condition corresponding to the AVC event module are determined;

[0250] Construct AGC optimization control model and AVC optimization control model;

[0251] Based on the first control event, the first event triggering condition, the second control event, the second event triggering condition, the AGC optimization control model and the AVC optimization control model, an emergency control model is obtained by using a preset cross iteration strategy.

[0252] It should be noted that the above explanation of the embodiment of the AGC and AVC dynamic collaborative control method is also applicable to the AGC and AVC dynamic collaborative control device of this embodiment, and will not be repeated here.

[0253] According to the AGC and AVC dynamic collaborative control device proposed in the embodiment of the present application, based on the active and reactive output characteristics of multiple types of energy storage, an active and reactive output model of multiple types of energy storage can be established; based on the active and reactive output model, a hybrid energy storage system model is established, and based on the hybrid energy storage system model and the preset unit, the AGC and AVC collaborative control strategy is determined; based on the minute-level prediction results of load demand and real-time network data, a periodic control model of the power system is established using a periodic-real-time double-layer control structure; based on preset key control indicators, an emergency control model is established using a preset cross-iteration strategy, and based on the periodic control model, the emergency control model and the AGC and AVC collaborative control strategy, AGC and AVC are dynamically collaboratively controlled. In this way, the mutual influence between AGC and AVC is avoided, and the dynamic optimization control of the time scale is realized, thereby improving the stability and efficiency of the entire power system.

[0254] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0255] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0256] When the processor 702 executes the program, the AGC and AVC dynamic cooperative control method provided in the above embodiment is implemented.

[0257] Furthermore, the electronic device further comprises:

[0258] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0259] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0260] The memory 701 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0261] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0262] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0263] The processor 702 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0264] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned AGC and AVC dynamic collaborative control method.

[0265] An embodiment of the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the above-mentioned AGC and AVC dynamic collaborative control method.

[0266] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0267] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0268] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for dynamic cooperative control of AGC and AVC, characterized in that: The following steps are involved: Based on the active and reactive output characteristics of multiple types of energy storage, active and reactive output models of multiple types of energy storage are established; Based on the active and reactive output models of the multi-type energy storage, a hybrid energy storage system model is established, and based on the hybrid energy storage system model and the preset units, an AGC and AVC collaborative control strategy is determined; Based on the minute-level forecast results of load demand and real-time network data, a periodic control model of the power system is established using the preset periodic-real-time double-layer control structure; Based on preset key control indicators and using a preset cross-iteration strategy, an emergency control model is established, and based on the periodic control model, the emergency control model and the AGC and AVC collaborative control strategy, the AGC and the AVC are dynamically collaboratively controlled.

2. The method according to claim 1, characterized in that The active and reactive output models of the multi-type energy storage are established based on the active output characteristics and reactive output characteristics of the multi-type energy storage, including: Obtaining a first output characteristic and a first energy storage state of charge of battery energy storage participating in power system control, a second output characteristic and a second energy storage state of charge of flywheel energy storage participating in power system control, and a third output characteristic and a third energy storage state of charge of gravity energy storage participating in power system control among the multiple types of energy storage; Obtaining a first physical constraint model based on the first output characteristic and the first energy storage state of charge, obtaining a second physical constraint model based on the second output characteristic and the second energy storage state of charge, and obtaining a third physical constraint model based on the third output characteristic and the third energy storage state of charge; According to the first physical constraint model, the second physical constraint model and the third physical constraint model, an active and reactive output model of the multi-type energy storage is established.

3. The method according to claim 1, characterized in that The determining of the AGC and AVC collaborative control strategy based on the hybrid energy storage system model and the preset unit includes: Obtain unit control instructions; Determine the droop coefficient corresponding to each energy storage unit in the hybrid control unit by using a preset droop coefficient adjustment method; Based on the droop coefficient corresponding to each energy storage unit, the unit control instruction is distributed to each energy storage unit in the hybrid control unit to obtain the control instruction of each energy storage unit, wherein the hybrid control unit is obtained by the hybrid energy storage system model and the preset unit; The AGC and AVC collaborative control strategy is determined according to the control instructions of each energy storage unit.

4. The method according to claim 1, characterized in that: The periodic control model of the power system is established by using the preset periodic-real-time double-layer control structure based on the minute-level prediction results of load demand and real-time network data, including: Determining an economic optimal objective function and a safety objective function based on the minute-level forecast result of the load demand and the real-time network data; Based on the economic optimal objective function and the safety objective function, a periodic control model of the power system is obtained by utilizing a preset periodic-real-time double-layer control structure.

5. The method according to claim 1 or 4, characterized in that: The preset periodic-real-time two-layer control structure includes a periodic control layer and a real-time control layer, wherein the periodic control layer coordinates the power system according to a preset period based on a preset coordinated control optimization index, and the real-time control layer uses a preset driving mechanism to cross-iteratively control the AGC or AVC in the power system.

6. The method according to claim 1, characterized in that The emergency control model is established based on the preset key control indicators and using the preset cross-iteration strategy, including: Based on the preset key control indicators, an AGC event module and an AVC event module are established, and a first control event and a first event triggering condition corresponding to the AGC event module, and a second control event and a second event triggering condition corresponding to the AVC event module are determined; Construct AGC optimization control model and AVC optimization control model; Based on the first control event, the first event triggering condition, the second control event, the second event triggering condition, the AGC optimization control model and the AVC optimization control model, the emergency control model is obtained by using the preset cross-iteration strategy.

7. An AGC and AVC dynamic cooperative control device, characterized in that: include: A first building module is used to build active and reactive output models of multiple types of energy storage based on active output characteristics and reactive output characteristics of multiple types of energy storage; A determination module, used to establish a hybrid energy storage system model based on the active and reactive output models of the multi-type energy storage, and determine the AGC and AVC collaborative control strategy based on the hybrid energy storage system model and the preset units; The second establishment module is used to establish a periodic control model of the power system based on the minute-level forecast results of load demand and real-time network data using a preset periodic-real-time double-layer control structure; The control module is used to establish an emergency control model based on preset key control indicators and using a preset cross-iteration strategy, and to dynamically coordinate control of the AGC and the AVC based on the periodic control model, the emergency control model and the AGC and AVC coordinated control strategy.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the AGC and AVC dynamic collaborative control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the AGC and AVC dynamic collaborative control method as described in any one of claims 1-6.

10. A computer program product, characterized in that It includes a computer program, which, when executed by a processor, is used to implement the AGC and AVC dynamic collaborative control method as described in any one of claims 1-6.

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