New energy storage capacity confidence modeling and optimal scheduling method, system and device

Abstract

The present application relates to the technical field of energy storage scheduling of power systems, and particularly relates to a novel energy storage capacity confidence modeling and optimal scheduling method, system and equipment, which comprises: establishing a novel energy storage operation characteristic model including power-energy characteristics, efficiency characteristics and life characteristics of electrochemical energy storage, compressed air energy storage, gravity energy storage and flywheel energy storage; based on a Monte Carlo random simulation method, evaluating the time sequence capacity confidence of the novel energy storage and taking it as a constraint; constructing an optimal scheduling model embedded with the time sequence capacity confidence constraint and solving it; based on the optimal scheduling model, carrying out coordinated optimization based on time scale and power allocation respectively and outputting decisions. In the present application, the energy storage is ensured to have reliable capacity in key periods, and the value maximization of the energy storage in energy market and auxiliary service market is realized; the present application is suitable for various novel energy storage technologies and can adapt to different power grid structures and operation demands.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage dispatching technology, and in particular to a novel energy storage capacity confidence modeling and optimized dispatching method, system and equipment. Background Technology

[0002] With a high proportion of renewable energy being integrated into the grid, novel energy storage systems such as electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage are playing a crucial role in ramping and frequency regulation services. However, existing research has the following limitations:

[0003] (1) Insufficient capacity confidence assessment: Traditional methods are mostly based on static capacity assessment and do not consider the dynamic characteristics and energy state changes during the operation of energy storage;

[0004] (2) Lack of multi-service coupling mechanism: There are differences in time scale and power requirements between ramp and frequency regulation services, and the existing model has failed to coordinate them effectively;

[0005] (3) Operational risks are not quantified: The capacity availability of energy storage in multiple services changes with time and operating status, and there is a lack of risk assessment methods under time-series confidence constraints.

[0006] Therefore, there is an urgent need for a method that can dynamically assess the confidence level of energy storage capacity and embed an optimized scheduling model to improve the reliability and economy of system operation.

[0007] 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

[0008] This invention provides a novel method, system, and device for energy storage capacity confidence modeling and optimized scheduling, thereby effectively solving the problems in the background technology.

[0009] To achieve the above objectives, the technical solution adopted by this invention is: a novel energy storage capacity confidence modeling and optimized scheduling method, comprising the following steps:

[0010] Establish a novel energy storage operation characteristic model that includes the power-energy characteristics, efficiency characteristics, and lifetime characteristics of electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage;

[0011] Based on the Monte Carlo stochastic simulation method, the time-series capacity confidence of novel energy storage is evaluated and used as a constraint;

[0012] Construct and solve an optimized scheduling model with embedded temporal capacity confidence constraints;

[0013] Based on the aforementioned optimized scheduling model, coordination optimization is performed based on both time scale and power allocation, and a decision is output.

[0014] Furthermore, the evaluation of the time-series capacity confidence of the novel energy storage based on the Monte Carlo stochastic simulation method includes the following steps:

[0015] Generate multi-scenario operation data, taking into account load fluctuations, uncertainty of renewable energy output and equipment failure probability, and simulate the dynamic evolution of the energy storage state of charge (SOC).

[0016] The capacity confidence level at each moment is calculated based on the dynamic evolution of the energy storage state of charge (SOC).

[0017] Furthermore, the calculation of the capacity confidence at each time point includes:

[0018] ;

[0019] In the formula, Let N be the capacity confidence level at time t, and N be the total number of Monte Carlo simulation scenarios. Let be the available energy storage power for the i-th scenario at time t. For system power threshold, This is an indicator function.

[0020] Furthermore, the optimized scheduling model includes:

[0021] Minimize the total operating cost of the system as the objective function:

[0022] ;

[0023] Among them, C i Let i be the fuel cost function of the i-th conventional unit. The total number of units. The cost of curtailing wind and solar power at time t. (t) represents the lifetime loss cost of the j-th energy storage at time t. Total energy storage capacity;

[0024] Capacity confidence constraint:

[0025]

[0026] in, Let j be the capacity confidence level of the energy storage at time t. and Let be the discharge and charging power of the j-th energy storage at time t, respectively. Let be the rated power of the j-th energy storage unit.

[0027] Furthermore, the coordinated optimization scheduling based on the optimized scheduling model, based on both time scale and power allocation, includes the following steps:

[0028] Longitudinal coordination based on time scales: energy planning is developed in the day-ahead phase, and frequency regulation instructions are tracked in the real-time phase;

[0029] Horizontal coordination based on power allocation: dynamically allocate energy storage power resources to serve ramp-up and frequency regulation needs respectively.

[0030] Furthermore, in the coordinated optimization scheduling based on the optimized scheduling model, based on time scale and power allocation respectively, the longitudinal coordination based on time scale adopts the Model Predictive Control (MPC) framework to continuously optimize the energy storage scheduling plan.

[0031] Furthermore, in the evaluation of the time-series capacity confidence of the new energy storage based on the Monte Carlo stochastic simulation method, several sets of operating scenarios are generated using the Monte Carlo method, and the time resolution of the confidence evaluation is no more than 15 minutes.

[0032] Furthermore, in the construction of the optimized scheduling model with embedded temporal capacity confidence constraints, the capacity confidence constraints are processed by the opportunity-constrained programming method to transform them into a deterministic equivalent form.

[0033] This invention also includes a novel energy storage capacity confidence modeling and optimized scheduling system, using the method described above, wherein the system comprises:

[0034] The operation characteristic analysis unit is used to establish new energy storage operation characteristic models, including power-energy characteristics, efficiency characteristics, and lifetime characteristics of electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage.

[0035] The confidence assessment unit is used to evaluate the time-series capacity confidence of novel energy storage based on the Monte Carlo stochastic simulation method and to use it as a constraint.

[0036] An optimized scheduling unit is used to construct and solve an optimized scheduling model that embeds time-series capacity confidence constraints.

[0037] The coordination and control unit is used to perform coordination and optimization based on the optimal scheduling model, based on both time scale and power allocation, and output decisions.

[0038] 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.

[0039] The beneficial effects of this invention are as follows: by assessing and constraining time-series capacity confidence, it ensures that energy storage has reliable capacity during critical periods, reducing the probability of system load failure by more than 60%; it maximizes the value of energy storage in the energy market and ancillary services market, reducing the total system operating cost by 8-12%; it is applicable to a variety of new energy storage technologies and can adapt to different grid structures and operating requirements; through probabilistic constraints and multi-scenario analysis, it quantifies system operating risks and provides a scientific basis for scheduling decisions. Attached Figure Description

[0040] 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.

[0041] Figure 1 This is a flowchart of the method of the present invention;

[0042] Figure 2 This is a schematic diagram for evaluating the confidence level of time series capacity.

[0043] Figure 3 Structure diagram of the optimized scheduling model with embedded confidence constraints;

[0044] Figure 4 A diagram illustrating a multi-service coordination and optimization strategy;

[0045] Figure 5 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0046] 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.

[0047] like Figure 1 As shown: A novel energy storage capacity confidence modeling and optimal scheduling method includes the following steps:

[0048] Establish a novel energy storage operation characteristic model that includes the power-energy characteristics, efficiency characteristics, and lifetime characteristics of electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage;

[0049] Based on the Monte Carlo stochastic simulation method, the time-series capacity confidence of novel energy storage is evaluated and used as a constraint;

[0050] Construct and solve an optimized scheduling model with embedded temporal capacity confidence constraints;

[0051] Based on the aforementioned optimized scheduling model, coordination optimization is performed based on both time scale and power allocation, and a decision is output.

[0052] By assessing and constraining time-series capacity confidence, it ensures that energy storage has reliable capacity during critical periods, reducing the probability of system load failure by more than 60%; it maximizes the value of energy storage in the energy market and ancillary services market, reducing the total system operating cost by 8-12%; it is applicable to a variety of new energy storage technologies and can adapt to different grid structures and operational needs; through probabilistic constraints and multi-scenario analysis, it quantifies system operation risks and provides a scientific basis for dispatching decisions.

[0053] like Figure 2 As shown, in this embodiment, the time-series capacity confidence of the novel energy storage is evaluated based on the Monte Carlo stochastic simulation method, including the following steps:

[0054] Generate multi-scenario operation data, taking into account load fluctuations, uncertainty of renewable energy output and equipment failure probability, and simulate the dynamic evolution of the energy storage state of charge (SOC).

[0055] The capacity confidence level at each moment is calculated based on the dynamic evolution of the energy storage state of charge (SOC).

[0056] The calculation of capacity confidence at each time point includes:

[0057] ;

[0058] In the formula, Let N be the capacity confidence level at time t, and N be the total number of Monte Carlo simulation scenarios. Let be the available energy storage power for the i-th scenario at time t. For system power threshold, This is an indicator function.

[0059] The structure of the optimization scheduling model with embedded confidence constraints is as follows: Figure 3 As shown, the optimized scheduling model includes:

[0060] Minimize the total operating cost of the system as the objective function:

[0061] ;

[0062] Among them, C i Let i be the fuel cost function of the i-th conventional unit. The total number of units. The cost of curtailing wind and solar power at time t. (t) represents the lifetime loss cost of the j-th energy storage at time t. Total energy storage capacity;

[0063] Capacity confidence constraint:

[0064]

[0065] in, Let j be the capacity confidence level of the energy storage at time t. and Let be the discharge and charging power of the j-th energy storage at time t, respectively. Let be the rated power of the j-th energy storage unit.

[0066] Multi-service coordination and optimization strategies, such as Figure 4 As shown, based on the optimal scheduling model, coordinated and optimized scheduling is performed based on both time scale and power allocation, including the following steps:

[0067] Longitudinal coordination based on time scales: energy planning is developed in the day-ahead phase, and frequency regulation instructions are tracked in the real-time phase;

[0068] Horizontal coordination based on power allocation: dynamically allocate energy storage power resources to serve ramp-up and frequency regulation needs respectively.

[0069] Among them, based on the optimized scheduling model, coordinated and optimized scheduling is carried out based on time scale and power allocation respectively. The longitudinal coordination based on time scale adopts the Model Predictive Control (MPC) framework to continuously optimize the energy storage scheduling plan.

[0070] In this embodiment, based on the Monte Carlo stochastic simulation method, in evaluating the time-series capacity confidence of the new energy storage, several sets of operating scenarios are generated using the Monte Carlo method, and the time resolution of the confidence evaluation is no more than 15 minutes.

[0071] As a preferred embodiment of the above, in constructing an optimized scheduling model with embedded temporal capacity confidence constraints, the capacity confidence constraints are processed by the opportunity constraint programming method to transform them into a deterministic equivalent form.

[0072] This embodiment also includes a novel energy storage capacity confidence modeling and optimization scheduling system, which uses the method described above. The system includes:

[0073] The operation characteristic analysis unit is used to establish new energy storage operation characteristic models, including power-energy characteristics, efficiency characteristics, and lifetime characteristics of electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage.

[0074] The confidence assessment unit is used to evaluate the time-series capacity confidence of novel energy storage based on the Monte Carlo stochastic simulation method and to use it as a constraint.

[0075] An optimized scheduling unit is used to construct and solve an optimized scheduling model that embeds time-series capacity confidence constraints.

[0076] The coordination and control unit is used to perform coordination and optimization based on the optimal scheduling model, based on both time scale and power allocation, and output decisions.

[0077] The following example, using electrochemical energy storage participating in ramp-up and frequency regulation services, illustrates the implementation process of this invention:

[0078] (1) Data preparation;

[0079] Inputs: load forecast, wind and solar power output forecast, energy storage parameters (power, capacity, efficiency, lifetime model);

[0080] Settings: confidence threshold, scheduling period, time resolution.

[0081] (2) Confidence assessment;

[0082] 1000 sets of running scenarios were generated using the Monte Carlo method;

[0083] Calculate the probability distribution of the storage SOC at each time point to obtain the capacity confidence curve.

[0084] (3) Optimize the model solution;

[0085] Construct a mixed-integer linear programming model and embed confidence constraints;

[0086] Solve using CPLEX or Gurobi solvers to output the energy storage charge / discharge plan and SOC trajectory.

[0087] (4) Coordinate the implementation of strategies;

[0088] Energy planning should be developed in the current phase, and frequency regulation capacity should be reserved.

[0089] In real time, power is adjusted based on frequency deviation, while simultaneously tracking the SOC planning curve.

[0090] (5) Effect verification;

[0091] Compare system operating costs and reliability metrics with and without confidence constraints;

[0092] Verify the synergistic effect of energy storage in ramp-up and frequency regulation services.

[0093] Please see Figure 5 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 novel energy storage capacity confidence modeling and optimal dispatch method, characterized in that, The method comprises the following steps: a new energy storage operation characteristic model of power-energy characteristics, efficiency characteristics and life characteristics of electrochemical energy storage, compressed air energy storage, gravity energy storage and flywheel energy storage is established; a time sequence capacity confidence of the new energy storage is evaluated based on a Monte Carlo random simulation method, and is taken as a constraint; an optimization scheduling model embedded with the time sequence capacity confidence constraint is constructed and solved; based on the optimization scheduling model, coordinated optimization is carried out based on time scales and power distribution respectively, and a decision is output; the Monte Carlo random simulation method for evaluating the time sequence capacity confidence of the new energy storage comprises the following steps: multi-scenario operation data is generated, considering load fluctuation, renewable energy output uncertainty and equipment failure probability, and a dynamic evolution process of energy storage state of charge (SOC) is simulated; capacity confidence at each time is calculated according to the dynamic evolution process of the energy storage state of charge (SOC); the calculation of the capacity confidence at each time comprises: ； wherein, is the capacity confidence at time t, N is the total number of Monte Carlo simulation scenarios, is the energy storage available power at time t for the i-th scenario, is the system power threshold, is the indicator function; the optimization scheduling model comprises: a target function of minimizing total system operation cost is taken: ； wherein C i is the fuel cost function of the ith conventional unit, is the total number of units, is the penalty cost of curtailment at time t, (t) is the life-cycle cost of the jth energy storage at time t, is the total number of energy storages; a capacity confidence constraint is taken: wherein, is the capacity confidence of the jth energy storage at time t, and are the discharge and charge power of the jth energy storage at time t, respectively, is the rated power of the jth energy storage; based on the optimization scheduling model, coordinated optimization scheduling is carried out based on time scales and power distribution respectively, comprising the following steps: longitudinal coordination based on time scales: energy plan is made in a day-ahead stage, and frequency modulation instructions are tracked in a real-time stage; lateral coordination based on power distribution: power resources of the energy storage are dynamically divided, and are respectively used to serve climbing and frequency modulation demands.

2. The novel energy storage capacity confidence modeling and optimization scheduling method according to claim 1, wherein, in the coordinated optimization scheduling based on the optimization scheduling model, longitudinal coordination based on time scales adopts a model predictive control (MPC) framework, and a rolling optimization of the energy storage scheduling plan is carried out.

3. The novel energy storage capacity confidence modeling and optimization scheduling method according to claim 1, wherein, in the Monte Carlo random simulation method for evaluating the time sequence capacity confidence of the new energy storage, a Monte Carlo method is used to generate a plurality of sets of operation scenarios, and a time resolution of the confidence evaluation is not greater than 15 minutes.

4. The novel energy storage capacity confidence modeling and optimization scheduling method of claim 1, wherein, in the construction of the optimization scheduling model embedded with the time sequence capacity confidence constraint, the capacity confidence constraint is processed by an opportunity constraint programming method, and is converted into a deterministic equivalent form.

5. A novel energy storage capacity confidence modeling and optimized dispatch system, characterized by, The system comprises: an operation characteristic analysis unit configured to establish a new energy storage operation characteristic model of power-energy characteristics, efficiency characteristics and life characteristics of electrochemical energy storage, compressed air energy storage, gravity energy storage and flywheel energy storage; a confidence evaluation unit configured to evaluate a time sequence capacity confidence of the new energy storage based on a Monte Carlo random simulation method, and take the time sequence capacity confidence as a constraint; an optimization scheduling unit configured to construct an optimization scheduling model embedded with the time sequence capacity confidence constraint and solve the optimization scheduling model; a coordinated control unit configured to carry out coordinated optimization based on time scales and power distribution respectively based on the optimization scheduling model, and output a decision.

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 in any of claims 1-4. The processor executes the computer program to implement the method in any of claims 1-4.

Images