Thermal power and energy storage combined deep peak shaving method with inertia supporting function

Abstract

The invention discloses a thermal power and energy storage combined deep peak shaving method with an inertia supporting function. The method comprises the steps that firstly, related system parameters of a thermal power generating unit and energy storage are collected, and corresponding models are established according to the characteristics of inertia resources provided by an energy storage system and the thermal power generating unit; secondly, collecting frequency parameters of the system and output data of renewable energy sources, and establishing a model in which the system participates in deep peak regulation and performs inertia response; then, integrating the inertia standby model of the energy storage and thermal power generating unit and the deep peak regulation and inertia response model of the system into system scheduling to form an energy storage-containing power system scheduling model integrated with deep peak regulation and inertia response; and finally, based on system parameters, solving the scheduling model to obtain an optimized system scheduling strategy. According to the method, the performance of the energy storage-containing power system in deep peak regulation and inertia response can be more accurately considered, so that an optimal scheduling decision which better conforms to the actual situation is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage scheduling, and in particular to a thermal power energy storage combined deep peak regulation method with inertia support function. Background Art

[0002] As the proportion of renewable energy access to the grid continues to increase, a large number of traditional synchronous units have been relatively replaced on the source side, resulting in a significant reduction in the grid's rotational inertia resources, which poses a challenge to the stability and security of the grid. In order to solve the inertia reduction problem caused by a high proportion of renewable energy, a new type of virtual inertia resource based on energy storage is introduced, and the inertia response characteristics of the system are taken into consideration during the scheduling stage to achieve inertia support for the system.

[0003] For the research on energy storage to assist wind power in providing inertia support, there have been rich research results at home and abroad for many years, such as reference 1 "Optimal dispatch of wind, solar, thermal and energy storage combined system considering minimum inertia constraints" (Sun Liang, Wang Weiqi, Wang Yifei). Journal of Electric Power Systems and Automation, 2024, 36(08): 30-38.) The minimum inertia requirement of the system is calculated by two frequency indicators, and the frequency stability constraint is linearized and added to the dispatch model. The virtual inertia resource provided by wind storage is considered, and the impact of source and load uncertainty on the combined system is expressed by using opportunity constraints with fuzzy parameters. The system optimization dispatch model is established, and the economy and safety of the model are verified through examples. Reference 2 "Energy Storage Virtual Inertia Active Support and Frequency Regulation State Transfer Control" (Fu Yuan, Wan Yi, Zhang Xiangyu). Proceedings of the CSEE, 2024, 44(07): 2628-2641.) analyzes and compares the inherent inertia of conventional units, the virtual inertia of wind turbines, and the inertia support characteristics of energy storage. It quantifies the virtual inertia of energy storage according to the energy storage capacity configuration, proposes an energy storage active support strategy combining constant frequency and frequency regulation state transfer control, and verifies the effect of energy storage on improving active support performance under the corresponding strategy through simulation system. Reference 3 "Inertia pricing in stochastic electricity markets" (Liang, Z., Mieth, R., and Dvorkin, Y. IEEE Transactions on Power Systems, 2022, 1-14) incorporates the inertia response of the system into the dispatching model. Starting from the physical characteristics of inertia, it analyzes the power characteristics and energy characteristics of inertia. Based on the analysis results, it integrates the system inertia characteristics into the dispatching model to limit the initial frequency oscillation of the system.

[0004] According to current research results, there have been a variety of patent applications involving system inertia response and virtual inertia technology. For example, Patent 1 "A method for combining units with inertia demand constraints considering the inertia supply capacity on both sides of the source and load" (Li Zhaohui, Zhou Ming. CN115065046A, 2022-09-16.) proposes a method for combining units with inertia demand constraints considering the inertia supply capacity on both sides of the source and load. Regarding the source side inertia, conventional synchronous machines and fans equipped with virtual synchronous machine technology are comprehensively considered; regarding the load side inertia, the partitioned inertia evaluation method is used to accurately quantify the system inertia size, and the corresponding load side inertia size can be obtained through the load forecast value at any time. The inertia demand constraint is constructed according to the inertia supply on both sides of the source and load and the system inertia response process, and finally a unit combination model considering inertia demand is established. Patent 2 "Method for realizing frequency modulation and virtual inertia response using integrated wind power unit with energy storage" (Wang Xin, Chen Gang, Wang Chenqi, Liu Zonghao, Zhang Rongrong, Zhang Shuang, Zheng Wenze, Liu Pinyi. CN114172199A, 2022-03-11.) proposes a method for realizing frequency modulation and virtual inertia response using integrated wind power unit with energy storage. Considering an integrated wind power unit with energy storage composed of a single wind turbine and an energy storage system, the energy storage system collects the frequency and voltage signals on the AC side of the grid-connected inverter of the wind turbine, calculates the frequency change rate of the power grid through the frequency signal of the AC bus, and dispatches the active output of the energy storage system according to the preset virtual inertia response active power change formula when the frequency change rate exceeds the limit, so as to realize the virtual inertia response of the energy storage system.

[0005] However, the above existing methods have the following defects:

[0006] 1. There is a lack of accurate energy and power modeling in the process of energy storage providing virtual inertia services, and the function of energy storage in providing virtual inertia cannot be accurately characterized.

[0007] 2. The deep peak-shaving and virtual inertia functions of energy storage are not integrated into the system scheduling model at the same time, which is not conducive to the full utilization of energy storage by the scheduling strategy.

[0008] In view of this, a deep peak-shaving method combining thermal power and energy storage with inertia support function is needed. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a method for combining deep peak-shaving of thermal power and energy storage with inertia support function, which can provide a method for combining deep peak-shaving of thermal power and energy storage with inertia support function to obtain the optimal decision for energy storage scheduling. The specific technical solution is as follows:

[0010] A thermal power energy storage combined deep peak regulation method with inertia support function, comprising:

[0011] Import thermal power unit system parameters, energy storage system parameters, system frequency parameters and renewable energy output data;

[0012] Establish a model for energy storage systems and thermal power units to provide inertia resources;

[0013] Establish system deep peak regulation model, total inertia resource measurement model and system frequency security model;

[0014] Supplement the energy storage system operation model and thermal power unit operation model;

[0015] Establish an optimization objective function and build a power system dispatch model with energy storage that integrates deep peak regulation and inertia response functions;

[0016] The power system dispatch model is solved to obtain the dispatch strategy of thermal power units and energy storage dispatch strategy.

[0017] Preferably, the thermal power unit system parameters include the maximum output of the unit, the minimum output of the unit, the maximum climbing power, the minimum online / offline time, the output cost, the startup cost and the inertia coefficient;

[0018] The energy storage device parameters include energy storage device capacity, maximum output, charging efficiency, discharging efficiency and maximum inertia coefficient.

[0019] Preferably, the model of establishing the inertia resource provided by the energy storage system and the thermal power unit includes an inertia resource model of the unit and an inertia resource model of the energy storage, and the inertia resource model of the unit and the inertia resource model of the energy storage embody model optimization through constraint conditions.

[0020] Preferably, the constraint condition of the inertia resource model of the unit is the inertia reserve constraint of the thermal power unit;

[0021] The constraints of the energy storage inertia resource model include: energy storage equipment inertia power constraint, energy storage equipment inertia energy constraint, energy storage equipment inertia coefficient upper limit constraint, energy storage equipment inertia power upper and lower limit constraints and energy storage equipment energy reserve lower limit constraint.

[0022] Preferably, the system frequency parameters include the system rated frequency and the maximum frequency change rate, and the output data of the renewable energy include the new energy output curve and the unit output cost.

[0023] Preferably, the established system deep peak-shaving model, total inertia resource metering model and system frequency safety model reflect model optimization through constraint conditions.

[0024] Preferably, the constraint conditions of the system deep peak-shaving model include deep peak-shaving output balance constraints;

[0025] The constraint conditions of the total inertia resource measurement model include the total inertia constraint of thermal power units and the total inertia constraint of energy storage;

[0026] The constraints of the system frequency security model include a maximum frequency change rate constraint.

[0027] Preferably, the constraints of the energy storage system operation model include charging power constraints, discharging power constraints, state of charge timing constraints, state of charge upper and lower limit constraints, and initial and final charge consistency constraints;

[0028] The constraints of the thermal power unit operation model include unit active output constraints, unit ramp constraints, unit minimum online and offline time constraints, and unit switch action and online state logic constraints.

[0029] Preferably, the optimization objective function is the sum of the new energy station output cost and the unit operation cost.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. In the present invention, the deep peak load regulation and inertia support functions are integrated into the system scheduling model, which strengthens the consideration of system flexibility, meets the inertia requirements of the system, and improves safety and reliability.

[0032] 2. In the present invention, a more accurate modeling of the process of energy storage participating in deep peak regulation and providing virtual inertia services is carried out, which is conducive to incorporating it into system scheduling considerations.

[0033] 3. In the present invention, the combined deep peak-shaving method of thermal power storage with inertia support function is established as a mixed integer programming model, which can be easily solved using a solver. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0035] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0038] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0039] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0040] Example

[0041] See also Figure 1 , a method for combining thermal power and energy storage with inertia support function for deep peak regulation, comprising the following steps:

[0042] Import thermal power unit system parameters, energy storage system parameters, system frequency parameters and renewable energy output data;

[0043] Establish a model for energy storage systems and thermal power units to provide inertia resources;

[0044] Establish system deep peak regulation model, total inertia resource measurement model and system frequency security model;

[0045] Supplement the energy storage system operation model and thermal power unit operation model;

[0046] Establish an optimization objective function and build a power system dispatch model with energy storage that integrates deep peak regulation and inertia response functions;

[0047] The power system dispatch model is solved to obtain the dispatch strategy of thermal power units and energy storage dispatch strategy.

[0048] As a preferred embodiment, the thermal power unit system parameters include the maximum output of the unit, the minimum output of the unit, the maximum climbing power, the minimum online / offline time, the output cost, the startup cost and the inertia coefficient;

[0049] The energy storage device parameters include energy storage device capacity, maximum output, charging efficiency, discharging efficiency and maximum inertia coefficient. See Table 1 for details:

[0050] Table 1 System parameters of thermal power units and energy storage that need to be obtained

[0051]

[0052]

[0053] As a preferred embodiment, the model of establishing the inertia resource provided by the energy storage system and the thermal power unit includes an inertia resource model of the unit and an inertia resource model of the energy storage. The inertia resource model of the unit and the inertia resource model of the energy storage reflect model optimization through constraint conditions.

[0054] The constraints of the inertia resource model of the unit are:

[0055] Inertia reserve constraints of thermal power units:

[0056] in, is a 0-1 variable, indicating the operating status of thermal power unit u at time t, 1 indicates operation, 0 indicates shutdown, represents the fixed inertia coefficient of the thermal power unit u, Indicates the maximum active output of thermal power unit u.

[0057] The constraints of the inertia resource model of energy storage include:

[0058] Energy storage equipment inertia power constraints:

[0059] Inertia energy constraints of energy storage equipment:

[0060] Upper limit constraint on inertia coefficient of energy storage equipment:

[0061] The upper and lower limits of inertia power of energy storage equipment:

[0062] as well as

[0063] Energy storage equipment energy reserve lower limit constraint:

[0064] in, Indicates that the energy storage device s is used to provide power backup for virtual inertia, Indicates that the energy storage device s is used to provide energy backup for virtual inertia, represents the variable inertia coefficient of the energy storage device s, f0 represents the rated frequency of the system, ROCOF max Indicates the maximum frequency change rate of the system, Δf max represents the maximum frequency deviation of the system, represents the maximum inertia coefficient that the energy storage device s can achieve, and They represent the charging power, discharging power and upper power limit of the energy storage device s respectively, represents the energy level of energy storage device s at time t, Represents the minimum energy capacity of the energy storage device s.

[0065] As a preferred embodiment, the system frequency parameters include the system rated frequency and the maximum frequency change rate, and the output data of the renewable energy include the new energy output curve and the unit output cost, as shown in Table 2 below:

[0066] Table 2 System frequency parameters and renewable energy output data that need to be obtained

[0067] System components Specific parameters System frequency parameters System rated frequency, maximum frequency change rate Renewable energy output data New energy output curve, unit output cost

[0068] It is also noted that the constraints of the energy storage power system dispatch model that integrates deep peak regulation and inertia response functions include the corresponding model and the energy storage system operation model, the thermal power unit operation model, and the system line related constraints. The model is reflected as a constraint in the optimization model.

[0069] As a preferred embodiment, the established system deep peak-shaving model, total inertia resource metering model and system frequency safety model reflect model optimization through constraint conditions.

[0070] The constraints of the system deep peak-shaving model include:

[0071] Deep peak load regulation output balance constraints:

[0072] In the formula, represents the actual output of the kth renewable energy at time t, represents the charging power received by the sth energy storage from the new energy source at time t, is the amount of new energy power abandoned at the kth moment, Predict the output of the kth renewable energy source at time t.

[0073] The constraints of the total inertia resource measurement model include:

[0074] Total inertia constraints of thermal power units:

[0075] Constraints on total energy storage inertia:

[0076] in, Represents the total inertia resource provided by the thermal power generation units. Indicates the operation status of thermal power unit u, represents the inertia coefficient of thermal power unit u, represents the installed capacity of thermal power unit u; Represents the total inertia resource provided by the energy storage device collection.

[0077] The constraints of the system frequency security model include:

[0078] Maximum frequency change rate constraint:

[0079] in, Indicates the load power shortage of the system, f0 indicates the rated frequency of the system, ROCOF max Indicates the maximum frequency change rate of the system.

[0080] As a preferred embodiment, the constraints of the energy storage system operation model include:

[0081] Charging power constraints:

[0082] Discharge power constraint:

[0083] State of Charge Timing Constraints:

[0084] State of charge upper and lower limit constraints:

[0085] And the charge consistency constraint:

[0086] Among them, the charging power constraint stipulates the upper and lower limits of the energy storage charging and discharging power. They represent the charging and discharging power of the energy storage element s at time t, They represent the upper limit of the charge and discharge power of the energy storage element s respectively; the state of charge timing constraint reflects the relationship between the state of charge of the energy storage during the charge and discharge process and the charge and discharge power as well as the state of charge at the previous moment, and is affected by the charge and discharge efficiency. s,t represents the charge state of the selected energy storage element s at time t, η c , η d They represent the charging efficiency and discharging efficiency of energy storage respectively; the upper and lower limits of the state of charge restrict the state of charge to a reasonable range. Soc s They represent the upper and lower limits of the state of charge of the energy storage element s respectively; the initial and final charge consistency constraint is a constraint set for the sustainable utilization of energy storage in each scheduling cycle. Soc s,T They respectively represent the charge state of the energy storage element at the beginning and end of the scheduling cycle.

[0087] The constraints of the thermal power unit operation model include:

[0088] Unit active output constraints:

[0089] Unit climbing constraints:

[0090] Minimum online and offline time constraints for units:

[0091] Logical constraints on unit switch action and online status:

[0092] in, They represent the upper and lower limits of the active output of unit u at time t, They represent the upper and lower climbing power limits of unit u respectively. It is a 0-1 variable, indicating the status of the unit in use: 1 means in use, 0 means out of use, It is a 0-1 variable, which indicates the start and shutdown of the unit respectively: 1 indicates the action occurs, It is the minimum online time and minimum offline time of the thermal power unit.

[0093] The system line related constraints can select the appropriate line power flow model according to the characteristics of the selected line.

[0094] As a preferred embodiment, the optimization objective function is the sum of the output cost of the new energy station and the unit operation cost. The specific formula is as follows:

[0095]

[0096] Among them, Ω k and Ω G Respectively represent the collection of new energy stations and thermal power units, and They represent the predicted output and power abandonment of the new energy station k at time t, c w represents the unit cost of renewable energy output, represents the output of unit u at time t, and Respectively represent the unit output cost, no-load cost and startup cost of thermal power units, It is a 0-1 variable, indicating the operating status of the unit, 1 means running, 0 means shutdown, It is a 0-1 variable, indicating the power-on status of the unit, 1 means power-on.

[0097] In this embodiment, the obtained parameters of the relevant equipment and systems are substituted into the established power system scheduling model with energy storage that integrates deep peak regulation and inertia response functions, and the mixed integer programming model is solved to obtain an optimized system scheduling strategy for deep peak regulation and inertia response functions, including an optimized thermal power unit scheduling strategy and an optimized energy storage device scheduling strategy.

[0098] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0099] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for combining thermal power and energy storage with inertia support function for deep peak regulation, characterized in that: include: Import thermal power unit system parameters, energy storage system parameters, system frequency parameters and renewable energy output data; Establish a model for energy storage systems and thermal power units to provide inertia resources; Establish system deep peak regulation model, total inertia resource measurement model and system frequency security model; Supplement the energy storage system operation model and thermal power unit operation model; Establish an optimization objective function and build a power system dispatch model with energy storage that integrates deep peak regulation and inertia response functions; The power system dispatch model is solved to obtain the dispatch strategy of thermal power units and energy storage dispatch strategy.

2. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 1 is characterized in that: The thermal power unit system parameters include the maximum output of the unit, the minimum output of the unit, the maximum climbing power, the minimum online / offline time, the output cost, the startup cost and the inertia coefficient; The energy storage device parameters include energy storage device capacity, maximum output, charging efficiency, discharging efficiency and maximum inertia coefficient.

3. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 1 is characterized in that: The model of establishing the inertia resource provided by the energy storage system and the thermal power unit includes the inertia resource model of the unit and the inertia resource model of the energy storage. The inertia resource model of the unit and the inertia resource model of the energy storage reflect model optimization through constraint conditions.

4. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 3 is characterized in that: The constraint condition of the inertia resource model of the unit is the inertia reserve constraint of the thermal power unit; The constraints of the energy storage inertia resource model include: energy storage equipment inertia power constraint, energy storage equipment inertia energy constraint, energy storage equipment inertia coefficient upper limit constraint, energy storage equipment inertia power upper and lower limit constraints and energy storage equipment energy reserve lower limit constraint.

5. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 1 is characterized in that: The system frequency parameters include the system rated frequency and the maximum frequency change rate, and the output data of the renewable energy include the new energy output curve and the unit output cost.

6. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 1 is characterized in that: The system deep peak regulation model, total inertia resource measurement model and system frequency safety model established reflect model optimization through constraint conditions.

7. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 6 is characterized in that: The constraint conditions of the system deep peak-shaving model include deep peak-shaving output balance constraint; The constraint conditions of the total inertia resource measurement model include the total inertia constraint of thermal power units and the total inertia constraint of energy storage; The constraints of the system frequency security model include a maximum frequency change rate constraint.

8. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 1 is characterized in that: The constraints of the energy storage system operation model include charging power constraints, discharging power constraints, state of charge timing constraints, state of charge upper and lower limit constraints, and initial and final charge consistency constraints; The constraints of the thermal power unit operation model include unit active output constraints, unit ramp constraints, unit minimum online and offline time constraints, and unit switch action and online state logic constraints.

9. The method for combined deep peak regulation of thermal power and energy storage with inertia support function according to claim 1 is characterized in that: The optimization objective function is the sum of the output cost of the new energy station and the operating cost of the unit.

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