A method and system for coordinated optimization of ramp-frequency regulation considering energy storage capacity uncertainty

Abstract

This invention relates to the field of power system automation technology, and particularly to a method and system for coordinated optimization of ramp-frequency regulation considering the uncertainty of energy storage capacity. The method includes: establishing an opportunity constraint model of the energy storage system based on capacity uncertainty to describe the reliable feasible region under energy storage power deviation; establishing a linearized model of the energy storage system considering unified power constraints for ramp-frequency regulation, realizing the power coupling and shared constraint description of the two types of regulation services; establishing a piecewise linearization model of ramp capacity using a piecewise linearization method to address the nonlinear characteristics of the ramp capacity of thermal power units; establishing a joint ramp-frequency regulation coordination optimization model of thermal power and energy storage, and solving the optimization model using a quadratic programming method to obtain the ramp power and frequency regulation reserve capacity of the thermal power units and energy storage. This invention can dynamically reflect the actual available range of the energy storage system capacity, avoid the risk of power exceeding limits due to energy storage capacity uncertainty, and improve the reliability of dispatch results.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, and in particular to a ramp-frequency regulation coordination optimization method and system that considers the uncertainty of energy storage capacity. Background Technology

[0002] As an important flexible regulation resource in the power system, energy storage systems play a crucial role in promoting the consumption of renewable energy, smoothing power fluctuations, and improving system operational stability. Due to their rapid response and bidirectional charging and discharging capabilities, energy storage systems can participate in net load ramping regulation by absorbing or releasing electrical energy to reduce load fluctuations. Simultaneously, by reserving a certain capacity, they can participate in system frequency regulation and backup services, providing frequency support and regulation capabilities to the power grid, thereby enhancing the overall flexibility and reliability of the system.

[0003] However, existing optimized scheduling strategies for energy storage to participate in ramping or frequency regulation generally have two shortcomings:

[0004] First, most research or engineering applications typically assume that the energy storage system's capacity and power are fixed values, failing to adequately consider the uncertainty of energy storage capacity. In reality, the usable capacity of an energy storage system is affected by various factors such as battery aging, temperature changes, fluctuations in charge and discharge efficiency, and errors in SOC estimation. This leads to deviations between the actual adjustable capacity and the theoretical value, potentially causing risks such as exceeding energy storage capacity limits or service failure.

[0005] Secondly, traditional models often model the "ramp-up" and "frequency regulation" processes of energy storage independently, without considering the power coupling relationship between the two. In fact, the ramp-up power and frequency regulation reserve capacity of an energy storage system often share the same power limit during the same period, and there is a competition and mutual exclusion relationship between the two types of services. Ignoring this coupling characteristic will lead to unreasonable allocation of energy storage resources, underutilization of potential, and even, in extreme cases, problems such as power exceeding limits or insufficient reserve.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] This invention provides a ramp-frequency regulation coordination optimization method and system that considers the uncertainty of energy storage capacity, thereby effectively solving the problems in the background technology.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: a ramp-frequency regulation coordination optimization method and system considering the uncertainty of energy storage capacity, comprising the following steps:

[0009] An opportunity constraint model for an energy storage system is established based on capacity uncertainty to describe the reliable feasible region under the deviation of energy storage capacity.

[0010] Based on the opportunity constraint model, a linearized model of the energy storage system considering the unified power constraint of ramp-frequency regulation is established to realize the power coupling and sharing constraint description of the two types of regulation services, ramp-frequency regulation.

[0011] To address the nonlinear characteristics of the climbing ability of thermal power units, a piecewise linearization model of the climbing ability is established using a piecewise linearization method.

[0012] Based on the linearized model of the energy storage system and the piecewise linearized model of the ramping capability, a joint ramping-frequency regulation coordination optimization model of thermal power-energy storage is established, and the ramping power and frequency regulation reserve capacity of thermal power units and energy storage are obtained by solving the optimization model using the quadratic programming method.

[0013] Furthermore, the opportunity constraint model for establishing an energy storage system based on capacity uncertainty includes:

[0014] ;

[0015] In the formula, and The maximum and minimum allowable capacity of energy storage are determined by the rated capacity and SOC of the energy storage. Let be the probability distribution function of a normal distribution. To satisfy the confidence level of the capacity constraint, it means that the probability that the e-th energy storage system will not exceed the limit at time t is not less than [a certain percentage]. ; Nominal power, i.e., actual power consumption. The measured or estimated value; The probability is a probability distribution.

[0016] Furthermore, the establishment of a linearized model for the energy storage system considering the unified constraint of ramp-frequency regulation power includes:

[0017] ;

[0018] ;

[0019] In the formula, and These refer to the power allocated for energy storage to participate in ramp-up and frequency regulation, respectively. , Let be the uphill and downhill variables for energy storage, respectively. , These are the upper limits of energy storage output power and energy storage output power, respectively.

[0020] Furthermore, the step of establishing a piecewise linearized model of climbing ability using a piecewise linearization method includes:

[0021] The rate of change of a thermal power unit's power output is defined as the rate of change of its power output, expressed by the formula:

[0022] ;

[0023] In the formula, Δt represents the power output of the thermal power unit, and Δt represents the optimized time interval length.

[0024] The unified constraint for thermal power units participating in ramp-frequency regulation is expressed as:

[0025] ;

[0026] In the formula, the middle and These represent the power of thermal power unit i participating in ramp-up and frequency regulation at time t, respectively. Let represent the climbing ability of thermal power unit i at time t. This is the upper limit of the output of thermal power unit i;

[0027] Climbing ability Using a quadratic function for fitting, it can be expressed as:

[0028] ;

[0029] In the formula, Let be the power of thermal power unit i at time t. , , These are the parameters of the quadratic function;

[0030] Uphill variables Downhill variables They respectively satisfy:

[0031] ;

[0032] Linearization is reduced to the form of two inequality constraints:

[0033] ;

[0034] Piecewise linearization of the climbing ability function: Assume that the output range of the thermal power unit is divided into n equal parts. k The maximum and minimum power of each segment are denoted as _____. and Climbing ability at each end point They are respectively denoted as and The slope is denoted as Then, by utilizing the unified constraint of thermal power units participating in ramp-frequency regulation, we can obtain... and The expression:

[0035] ;

[0036] slope The calculation is as follows:

[0037] ;

[0038] Introducing binary variables Indicates whether thermal power unit i is in segment k at time t, a real variable. Indicates the offset within the segment. and It also needs to meet the following constraints:

[0039] ;

[0040] In the formula, Let i represent the state of thermal power unit i at time t, where 1 indicates that the unit is in the on state and 0 indicates that the unit is in the off state.

[0041] thermal power unit power and and The relationship between them is represented as follows:

[0042] ;

[0043] use and linearized The expression:

[0044] .

[0045] Furthermore, the establishment of the thermal power-energy storage joint ramp-frequency regulation coordination optimization model includes:

[0046] To minimize the total system cost, the objective function is established as follows:

[0047] ;

[0048] In the formula , , The costs are electricity cost, ramp-up cost, and frequency regulation cost, respectively, and are expressed as follows:

[0049] ;

[0050] ;

[0051] ;

[0052] In the formula, The number of generators. The number of wind farms. The number of energy storage units. To optimize the number of time periods, , These represent the start-up and shutdown actions of thermal power unit i at time t. , These represent the single start-up and shutdown costs of thermal power unit i, respectively. The penalty price for abandoning wind power. For wind curtailment power, , , and The unit price for providing ramp-up and frequency regulation ancillary services for thermal power units and energy storage respectively; electricity cost. Covers the costs of power generation, start-up, shutdown, and wind curtailment for thermal power units; ramp-up costs. and frequency modulation costs These respectively reflect the costs of ancillary services provided by thermal power units and energy storage for ramping and frequency regulation;

[0053] Establish the following constraints:

[0054] The active power balance constraint can be expressed as:

[0055] ;

[0056] In the formula , These are the predicted values ​​for wind power and load, respectively. For energy storage electrical power, Number of load nodes

[0057] Taking into account the uncertainties of wind power and load forecasting, the frequency regulation reserve constraint can be expressed as:

[0058] ;

[0059] In the formula The confidence level that satisfies the constraints for frequency modulation backup. This represents the inverse function of the cumulative distribution function. and These are the standard deviations of the forecast errors for wind power and load, respectively.

[0060] The charge-power coupling constraint can be expressed as:

[0061] ;

[0062] In the formula Let e ​​be the amount of electricity in energy storage unit e at time t. , These are the charging and discharging powers, It is time-granular and satisfies the following constraints:

[0063] ;

[0064] Energy storage power Represented as:

[0065] ;

[0066] The initial charge must equal the final charge; the constraint is expressed as follows:

[0067] ;

[0068] The upper and lower limits of the output power of thermal power units can be expressed as:

[0069] ;

[0070] in , These are the upper and lower limits of the output of thermal power unit i, respectively; Let i represent the state of thermal power unit i at time t, where 1 indicates that the unit is in the on state and 0 indicates that the unit is in the off state.

[0071] Start-up and shutdown status of thermal power units It also needs to meet the start / stop logic constraints:

[0072] ;

[0073] in , These represent the start-up and shutdown actions of thermal power unit i at time t.

[0074] Furthermore, the constraints also include:

[0075] Opportunity constraints for establishing energy storage systems based on capacity uncertainty;

[0076] Unified constraints for energy storage system ramp-up and frequency regulation;

[0077] Unified constraint for thermal power unit ramp-frequency regulation.

[0078] Furthermore, the step of using quadratic programming to solve the optimization model to obtain the ramp-up power and frequency regulation reserve capacity of thermal power units and energy storage includes the following steps:

[0079] The optimization model is solved using a mixed-integer quadratic programming algorithm;

[0080] The power of energy storage used for ramping and frequency regulation at different times is obtained. and And the power of the generator used for hill climbing and frequency regulation. and .

[0081] The present invention also includes a ramp-frequency regulation coordination optimization system considering energy storage capacity uncertainty, using the method described above, the system comprising:

[0082] Opportunity constraint modeling unit, used to establish opportunity constraint model of energy storage system based on capacity uncertainty, to describe reliable feasible region under energy storage power deviation;

[0083] The power unified constraint construction unit is used to establish a linearized model of the energy storage system that considers the unified power constraint of ramping and frequency regulation based on the opportunity constraint model, so as to realize the power coupling and shared constraint description of the two types of regulation services, ramping and frequency regulation.

[0084] The unit for constructing the climbing capacity of thermal power units is used to establish a piecewise linearized model of the climbing capacity by adopting a piecewise linearization method to address the nonlinear characteristics of the climbing capacity of thermal power units.

[0085] The optimization solution unit is used to establish a joint climbing-frequency regulation coordination optimization model of thermal power-energy storage based on the linearized model of the energy storage system and the piecewise linearized model of the climbing capacity, and to solve the optimization model using the quadratic programming method to obtain the climbing power and frequency regulation reserve capacity of thermal power units and energy storage.

[0086] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0087] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0088] The beneficial effects of this invention are as follows: By introducing an energy storage capacity uncertainty model and a chance constraint mechanism, the actual available range of energy storage system capacity can be dynamically reflected in scheduling optimization, avoiding the risk of exceeding power limits due to energy storage capacity uncertainty, and improving the reliability and robustness of scheduling results. The originally independent ramping power and frequency regulation reserve capacity are collaboratively modeled to achieve power sharing and complementary regulation, enabling the energy storage system and thermal power units to collaboratively allocate regulation capacity under the same constraint framework, thereby more fully exploring the ramping and frequency regulation potential of energy storage and thermal power units and improving response efficiency. The piecewise linearization modeling method is used to describe the nonlinear characteristics of the ramping capacity of thermal power units as a function of current power, accurately reflecting the actual ramping law of the units limited by mechanical inertia and the fuel regulation system, thereby improving the accuracy and feasibility of the scheduling model. Attached Figure Description

[0089] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0090] Figure 1 Flowchart of a ramp-frequency regulation coordination optimization method that takes into account the capacity uncertainty of energy storage systems;

[0091] Figure 2 A schematic diagram illustrating the energy constraint modeling that takes into account capacity uncertainty;

[0092] Figure 3 A schematic diagram of piecewise linearization of the climbing ability (characteristics) of thermal power units;

[0093] Figure 4 This is a schematic diagram of the structure of a computer device. Detailed Implementation

[0094] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0095] like Figure 1 As shown: A ramp-frequency regulation coordinated optimization method considering the uncertainty of energy storage capacity includes the following steps:

[0096] The first step is to model the power constraints that take into account capacity uncertainty;

[0097] Without considering capacity uncertainty, the capacity (electricity) constraint of an energy storage system can be expressed as:

[0098]

[0099] in and The maximum and minimum allowable capacity of energy storage are determined by the rated capacity and SOC of the energy storage:

[0100]

[0101] in and These represent the upper and lower limits of the SOC of energy storage unit e, respectively.

[0102] When capacity uncertainty is taken into account, the capacity constraint of the energy storage system It can be transformed into the form of opportunity constraints:

[0103]

[0104] in To satisfy the confidence level of the capacity constraint, it means that the probability that the e-th energy storage system will not exceed the limit at time t is not less than [a certain percentage]. .

[0105] Assuming that due to the uncertainty of the SOC of the energy storage system, its actual power output... Following a certain probability distribution, taking the normal distribution as an example, it can be expressed as:

[0106]

[0107] in This refers to the "nominal power consumption," which is the actual power consumption. The measured or estimated value.

[0108] According to the distribution function Opportunity constraints It can be further transformed into:

[0109]

[0110] in Let be the probability distribution function of a normal distribution.

[0111] After simplification, the energy constraint can be further transformed into equation [equation missing]. Listed formats:

[0112]

[0113] Due to the symmetry of the normal distribution, the following condition is satisfied:

[0114]

[0115] Power constraints This can be further simplified to:

[0116]

[0117] In summary, the constraints This constitutes a power constraint model that takes into account capacity uncertainty.

[0118] The second step is to model the unified constraints of energy storage system ramp-frequency regulation.

[0119] Since energy storage systems are jointly constrained by the upper limit of energy storage output power when participating in ramp-frequency regulation, the unified constraint of ramp-frequency regulation for energy storage systems can be expressed as:

[0120]

[0121] in and These represent the power allocated for energy storage to participate in ramp-up and frequency regulation, respectively.

[0122] In order to constrain conditions Transform it into a linear form and introduce the climbing variable. , They respectively satisfy:

[0123]

[0124] Then the unified constraint of ramp-frequency regulation for energy storage system It can be converted into:

[0125]

[0126] In summary, the constraints - This constitutes a linearized unified constraint model for the ramp-frequency regulation of energy storage systems.

[0127] The third step is to model the unified constraints of thermal power unit ramp-frequency regulation.

[0128] The rate of change of a thermal power unit's power output is defined as the rate of change of its power output, which can be expressed by the formula:

[0129]

[0130] in Δt represents the power output of the thermal power unit, and Δt represents the optimized time interval length.

[0131] The power of thermal power units participating in ramp-frequency regulation is also constrained by the upper limit of output power. The unified constraint for their participation in ramp-frequency regulation can be expressed as:

[0132]

[0133] in and These represent the power of thermal power unit i participating in ramp-up and frequency regulation at time t. Let represent the climbing ability of thermal power unit i at time t. This is the upper limit of the output of thermal power unit i.

[0134] Because the climbing ability of thermal power units is limited by mechanical inertia and fuel regulation systems, it typically exhibits non-linear characteristics with the current power output. Climbing ability Using a quadratic function for fitting, it can be expressed as:

[0135]

[0136] , , These are the parameters of the quadratic function;

[0137] Because the thermal power unit model includes absolute values ​​and min functions (constraints) ) and quadratic functions (Constraints) need to be linearized to facilitate solving the optimization problem.

[0138] First, let's look at the variables related to the hill climb. Downhill variables They respectively satisfy:

[0139]

[0140] but It can be linearized into the form of two inequality constraints:

[0141]

[0142] Next, we examine the climbing ability function. Piecewise linearization is performed. Assume the output range of the thermal power unit is equally divided into n parts. k The maximum and minimum power of each segment are denoted as _____. and Climbing ability at each end point They are respectively denoted as and The slope is denoted as Then utilize available and The expression:

[0143]

[0144] slope It can be calculated as:

[0145]

[0146] Based on this, binary variables are introduced. Indicates whether thermal power unit i is in segment k at time t, a real variable. Indicates the offset within the segment. and It also needs to meet the following constraints:

[0147]

[0148] In the formula, Let i represent the state of thermal power unit i at time t, where 1 indicates that the unit is in the on state and 0 indicates that the unit is in the off state.

[0149] thermal power unit power and and The relationship between them can be represented as:

[0150]

[0151] Based on this, utilize and Linearization can be obtained The expression:

[0152]

[0153] In summary, the constraints , - , - This constitutes a linearized unified constraint model for the ramp-frequency regulation of thermal power units.

[0154] The fourth step is to perform coordinated optimization modeling of thermal power-energy storage joint ramp-frequency regulation.

[0155] (1) Establish optimization objectives

[0156] The optimization objective is to minimize the total system cost, mathematically expressed as:

[0157]

[0158] in , , The costs of electricity, ramp-up, and frequency regulation, respectively, can be expressed as:

[0159]

[0160]

[0161]

[0162] in, The number of generators. The number of wind farms. The number of energy storage units. To optimize the number of time periods, , These represent the start-up and shutdown actions of thermal power unit i at time t. , These represent the single start-up and shutdown costs of thermal power unit i, respectively. The penalty price for abandoning wind power. For wind curtailment power, , , and The unit price for providing ramp-up and frequency regulation ancillary services for thermal power units and energy storage, respectively. Electricity cost. Covers the costs of power generation, start-up, shutdown, and wind curtailment for thermal power units; ramp-up costs. and frequency modulation costs These reflect the costs of ancillary services provided by thermal power units and energy storage for ramping and frequency regulation, respectively.

[0163] (2) Establish constraints:

[0164] The active power balance constraint can be expressed as:

[0165]

[0166] in , These are the predicted values ​​for wind power and load, respectively. For energy storage electrical power, This represents the number of load nodes.

[0167] Taking into account the uncertainties of wind power and load forecasting, the frequency regulation reserve constraint can be expressed as:

[0168]

[0169] in The confidence level that satisfies the constraints for frequency modulation backup. This represents the inverse function of the cumulative distribution function. and These are the standard deviations of the prediction errors for wind power and load, respectively.

[0170] The charge-power coupling constraint can be expressed as:

[0171]

[0172] in Let e ​​be the amount of electricity in energy storage unit e at time t. , These are the charging and discharging powers, It is time-granular and satisfies the following constraints:

[0173]

[0174] Energy storage power Represented as:

[0175]

[0176] The initial charge must equal the final charge; the constraint is expressed as follows:

[0177]

[0178] The upper and lower limits of the output power of thermal power units can be expressed as:

[0179]

[0180] in , These are the upper and lower limits of the output of thermal power unit i, respectively; This represents the state of thermal power unit i at time t (1 indicates that the unit is in the on state, and 0 indicates that the unit is in the off state).

[0181] Start-up and shutdown status of thermal power units It also needs to meet the start / stop logic constraints:

[0182]

[0183] in , These represent the start-up and shutdown actions of thermal power unit i at time t.

[0184] In addition, the constraints also include those established in steps one through three, specifically including:

[0185] The first step establishes the energy constraint (constraint condition) for the energy storage system, taking into account capacity uncertainty. ).

[0186] The second step establishes a unified constraint for the energy storage system's ramp-up and frequency regulation (constraint conditions). - ).

[0187] The third step establishes a unified constraint on the ramp-frequency regulation of thermal power units (constraint conditions). , - , - ).

[0188] The fifth step is to optimize the solution of the model;

[0189] In the thermal power-energy storage joint ramp-frequency regulation coordination optimization model established in the fourth step, all constraints are linear except for the optimization objective, which contains a quadratic function. The mixed integer quadratic programming algorithm can be used to solve it.

[0190] By solving the optimization model, the power of energy storage participating in ramp-up and frequency regulation in each time period is finally obtained. and And the power of the generator used for hill climbing and frequency regulation. and This enables the coordinated optimization of energy storage and thermal power units participating in ramp-frequency regulation.

[0191] Specifically, Figure 1 The main flowchart of the method provided in this embodiment is given.

[0192] The first step is to establish a power constraint model that takes into account capacity uncertainty, such as Figure 2 As shown. In the traditional energy storage system's capacity (electricity) constraint model (constraint conditions)... Based on this, and considering capacity uncertainty, an opportunity-constrained model (constraints) is established. Assuming the energy storage system's charge follows a normal distribution (formula...), After simplification, a simplified form of the energy constraint considering capacity uncertainty is obtained (constraint condition). ).

[0193] The second step is to establish a unified constraint model for the energy storage system's ramp-up and frequency regulation. This considers that the energy storage system is subject to the constraint of the upper limit of its energy storage output power when participating in ramp-up and frequency regulation (constraint conditions). After linearization, a linearized unified constraint model for energy storage system ramp-frequency regulation is obtained (constraint conditions). - ).

[0194] The third step is to establish a unified constraint model for the ramp-up and frequency regulation of thermal power units. The ramp-up of a thermal power unit is defined as the rate of change of its power (constraint condition). Considering that the power of thermal power units participating in ramp-frequency regulation is simultaneously constrained by the upper limit of output power, constraint conditions can be established. and Among them, the constraints After linearization, the constraints are obtained. - The climbing ability function adopts... This can then be further piecewise linearized to obtain the linearized constraint form (constraint conditions). - ), as shown in the diagram Figure 3 As shown. The final linearized unified constraint model for thermal power unit ramp-frequency regulation is obtained (constraint conditions). , - , - ).

[0195] The fourth step is to establish a coordinated optimization model for the combined thermal power-energy storage ramp-frequency regulation system. This includes establishing the optimization objective (equation). - ), and constraints (including constraints) - The first step establishes the energy constraint of the energy storage system that takes into account capacity uncertainty. The second step establishes a unified constraint for the energy storage system's ramp-up and frequency regulation. - The unified constraint for thermal power unit ramp-frequency regulation established in the third step , - , - ).

[0196] The fifth step is to solve the optimization model. An integer quadratic programming algorithm is used to solve the optimization model established in the fourth step to obtain the energy storage power used for ramp-up and frequency regulation in each time period. and And the power of the generator used for hill climbing and frequency regulation. and This will ultimately achieve synergistic optimization of energy storage and thermal power units participating in ramp-frequency regulation.

[0197] This embodiment also includes a ramp-frequency regulation coordination optimization system that considers the uncertainty of energy storage capacity. Using the method described above, the system includes:

[0198] Opportunity constraint modeling unit, used to establish opportunity constraint model of energy storage system based on capacity uncertainty, to describe reliable feasible region under energy storage power deviation;

[0199] The unified power constraint building unit is used to establish a linearized model of an energy storage system that considers the unified power constraint of ramp-frequency regulation, and realizes the power coupling and shared constraint description of the two types of regulation services.

[0200] The unit for constructing the climbing capacity of thermal power units is used to establish a piecewise linearized model of the climbing capacity by adopting a piecewise linearization method to address the nonlinear characteristics of the climbing capacity of thermal power units.

[0201] An optimization unit is used to establish a joint ramp-frequency regulation coordination optimization model for thermal power and energy storage, and a quadratic programming method is used to solve the optimization model to obtain the ramp power and frequency regulation reserve capacity of thermal power units and energy storage.

[0202] Compared with existing optimization methods that involve energy storage in ramping or frequency regulation, the coordination optimization method in this embodiment has the following main advantages:

[0203] 1) It can fully consider the uncertainty of energy storage capacity. By introducing an energy storage capacity uncertainty model and opportunity constraint mechanism, it can dynamically reflect the actual available range of energy storage system capacity in scheduling optimization, avoid the risk of exceeding the power limit caused by energy storage capacity uncertainty, and improve the reliability and robustness of scheduling results.

[0204] 2) It enables coordinated optimization of ramping and frequency regulation. By co-modeling the originally independent ramping power and frequency regulation reserve capacity, power sharing and complementary regulation can be achieved. This allows energy storage systems and thermal power units to coordinately allocate regulation capabilities under the same constraint framework, thereby more fully tapping the ramping and frequency regulation potential of energy storage and thermal power units and improving response efficiency.

[0205] 3) It can take into account the nonlinear characteristics of the climbing ability of thermal power units. The piecewise linearization modeling method is used to describe the nonlinear characteristics of the climbing ability of thermal power units as the current power changes. This can accurately reflect the actual climbing law of the unit under the constraints of mechanical inertia and fuel regulation system, thereby improving the accuracy and feasibility of the scheduling model.

[0206] Please see Figure 4 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0207] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0208] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0209] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0210] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0211] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0212] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0213] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0214] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0215] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0216] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A ramp-rate-frequency-coordinated optimization method considering uncertainty of energy storage capacity, characterized in that, The method comprises the following steps: An opportunity constraint model of the energy storage system is established based on capacity uncertainty, to describe a reliable feasible region under energy storage power deviation; A linearization model of the energy storage system considering unified constraints of ramping and frequency modulation is established based on the opportunity constraint model, to realize power coupling and sharing constraint description of the two types of regulation services of ramping and frequency modulation; A segmented linearization model of ramping capacity is established by using a segmented linearization method, in view of the nonlinear characteristics of the ramping capacity of the thermal power unit; A thermal power- energy storage joint ramping and frequency modulation coordination optimization model is established based on the linearization model of the energy storage system and the segmented linearization model of the ramping capacity, and a quadratic programming method is used to solve the optimization model to obtain the ramping power and frequency modulation reserve capacity of the thermal power unit and the energy storage; The opportunity constraint model of the energy storage system based on capacity uncertainty comprises: ； In the formula, and are the maximum and minimum power allowed by the energy storage, determined by the rated power of the energy storage and the SOC; is a probability distribution function of normal distribution, is the confidence level that meets the capacity constraint, which means that the probability that the e-th energy storage system does not exceed the limit at time t is not less than ; is the nominal power, which is the measured value or estimated value of the real power ; is the probability of the probability distribution; The linearization model of the energy storage system considering unified constraints of ramping and frequency modulation comprises: ； ； In the formula, and are the calling power of the energy storage participating in climbing and frequency modulation, respectively, , are the upper and lower climbing variables of the energy storage, respectively, , are the energy storage output power and the upper limit of the energy storage output power, respectively. The segmented linearization model of the ramping capacity established by using the segmented linearization method comprises: The ramping of the thermal power unit is defined as the rate of change of its power, which is expressed by a formula as follows: ； In the formula, P is the power of the thermal power unit, and Δt is the length of the optimized time interval. The unified constraint of the thermal power unit participating in ramping and frequency modulation is expressed as: ； In the formula, mid and is the calling power of the thermal power unit i participating in climbing and frequency modulation at time t, is the climbing ability of the thermal power unit i at time t, is the upper limit of the output of the thermal power unit i; Climbing ability Using a quadratic function fit, expressed as: ； In the formula, Pti is the power of the thermal power unit i at time t, , , are parameters of the quadratic function, respectively. define an upper ramping variable of the thermal power generating unit i , a lower ramping variable , respectively ； Linearization is in the form of two inequality constraints: ； Piecewise linearization of the climbing ability function: Assume that the output range of the thermal power unit is divided into n equal parts. k The maximum and minimum power of each segment are denoted as _____. and Climbing ability at each end point They are respectively denoted as and The slope is denoted as Then, by utilizing the unified constraint of thermal power units participating in ramp-frequency regulation, we can obtain... and The expression: ； Slope Calculated as: ； Introduce binary variable denotes whether the thermal power unit i is in the kth segment at time t, real variable denotes the offset within the segment, and It also needs to satisfy the following constraints: ； In the formula, is the state of the thermal power unit i at time t, 1 indicates that the unit is in an open state, and 0 indicates that the unit is in a closed state; thermal power unit power and and The relationship between them is represented as follows: ； Utilizing and obtaining a linearized expression of ； The thermal power- energy storage joint ramping and frequency modulation coordination optimization model is established by taking the minimization of the total system cost as an objective, and a target function is established as follows: ； wherein , , are the electrical energy cost, the ramping cost and the frequency regulation cost, respectively, expressed as: ； ； ； In the formula, N is the number of generators, N is the number of wind farms, N is the number of energy storages, N is the number of optimization periods, , respectively, the start-up and shut-down actions of thermal power unit i at time t, , respectively, the single start-up and shut-down costs of thermal power unit i, is the penalty price of curtailment, is the curtailment power, , , and respectively, the unit prices of auxiliary services of ramping and frequency modulation provided by thermal power units and energy storages; the cost of electric energy includes the costs of power generation, start-up and shut-down, and curtailment of thermal power units; the cost of ramping and the cost of frequency modulation respectively, the auxiliary service fees of ramping and frequency modulation provided by thermal power units and energy storages.

2. The ramp-rate and frequency modulation coordinated optimization method considering uncertainty of energy storage capacity according to claim 1, wherein, The thermal power- energy storage joint ramping and frequency modulation coordination optimization model further comprises: The following constraint conditions are established: The active power balance constraint can be expressed as: ； wherein, , are the predicted values of wind power and load, respectively, is the electrical power of the energy storage, is the number of load nodes; The frequency modulation reserve constraint can be expressed as: ； wherein the confidence of the reserve to meet the frequency constraint, denotes the inverse function of the cumulative distribution function, and are the standard deviations of the forecast errors of wind power and load, respectively. The power- energy coupling constraint can be expressed as: ； In the formula is the electric quantity of the energy storage unit e at time t, , are the charging and discharging power, respectively, is the time granularity, and satisfies the constraint: ； Energy storage electric power is represented as: ； The starting energy needs to be equal to the ending energy, and the constraint condition is expressed as: ； The upper and lower limits of the output power of the thermal power unit can be expressed as: ； wherein , are the upper and lower bounds of the power output of thermal power unit i, respectively; is the state of thermal power unit i at time t, with 1 indicating that the unit is on and 0 indicating that the unit is off. Start-stop state of thermal power generating unit Start-stop logic constraints also need to be met: ； wherein , respectively the start-up and shut-down actions of the thermal power unit i at time t.

3. The ramp-rate and frequency modulation coordinated optimization method considering uncertainty of energy storage capacity according to claim 2, characterized in that, The constraint condition further comprises: An opportunity constraint of the energy storage system is established based on capacity uncertainty; A unified constraint of ramping and frequency modulation of the energy storage system; A unified constraint of ramping and frequency modulation of the thermal power unit.

4. The ramp-rate and frequency modulation coordinated optimization method considering uncertainty of energy storage capacity according to claim 1, wherein, The quadratic programming method is used to solve the optimization model to obtain the ramping power and frequency modulation reserve capacity of the thermal power unit and the energy storage, which comprises the following steps: A mixed integer quadratic programming algorithm is used to solve the optimization model; get the called power of energy storage participating in ramping and frequency modulation in each period and and the called power of generator participating in ramping and frequency modulation and .

5. A coordinated optimization system of ramping-frequency modulation considering uncertainty of energy storage capacity, characterized in that, The system comprises the method as claimed in any one of claims 1 to 4, and the system comprises: An opportunity constraint model building unit is configured to establish an opportunity constraint model of the energy storage system based on capacity uncertainty, to describe a reliable feasible region under energy storage power deviation; A power unified constraint construction unit is configured to establish a linearization model of the energy storage system considering unified constraints of ramping and frequency modulation based on the opportunity constraint model, to realize power coupling and sharing constraint description of the two types of regulation services of ramping and frequency modulation; A thermal power unit ramping capacity construction unit is configured to establish a segmented linearization model of ramping capacity by using a segmented linearization method, in view of the nonlinear characteristics of the ramping capacity of the thermal power unit; An optimization solving unit is configured to establish a coordinated optimization model of the thermal power-ES joint ramping-frequency modulation based on the linearization model of the ES and the piecewise linearization model of the ramping capability, and solve the optimization model by using a quadratic programming method to obtain the ramping power and the frequency modulation reserve capacity of the thermal power unit and the ES.

6. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method of any one of claims 1-4.

7. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the method of any one of claims 1-4.

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