Power system dispatching method with fire-storage combined deep peak regulation

Abstract

The invention provides an electric power system scheduling method containing fire-storage combined deep peak regulation, and belongs to the technical field of energy storage scheduling, and the method comprises the steps: obtaining the technical parameters of a conventional thermal power generating unit and an electric power system, and constructing a basic scheduling model of the electric power system; acquiring technical parameters of the energy storage system, and constructing an energy storage system operation model; constructing a deep peak regulation model of the combination of the traditional thermal power generating unit and the energy storage system; embedding the electric power system deep peak regulation model and the traditional thermal power generating unit and energy storage system operation model into system scheduling, and constructing an electric power system scheduling model containing thermal power and energy storage combined deep peak regulation; a power system scheduling model containing thermal power storage combined deep peak regulation is established as mixed integer linear programming, commercial software is used for solving, and a system scheduling strategy that energy storage and a traditional thermal power generating unit participate in deep peak regulation can be obtained. According to the invention, a technical scheme can be provided for bringing energy storage into deep peak regulation of the power system so as to promote combined operation of the power system and traditional power generation resources.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage dispatching, and in particular to a method for dispatching a power system containing thermal energy storage combined with deep peak regulation. Background Art

[0002] With the continuous growth of global energy demand and the large-scale access of renewable energy, the peak load regulation problem of power system has become increasingly prominent. As an important means to improve the flexibility and stability of power grid, deep peak load regulation technology has received extensive attention in recent years.

[0003] Regarding the technical theories and applications related to deep peak load regulation, many research results have been produced at home and abroad, such as reference 1 "Two-layer Optimal Dispatch of Power System Considering Demand Response and Thermal-Storage Deep Peak Load Regulation Pricing Strategy" (Cui Yang, Xiu Zhijian, Liu Chuang, Zhao Yuting, Tang Yaohua, Chai Xuzheng. Proceedings of the CSEE, 2021, 41(13): 4403-4415.) Firstly, based on the priced-based demand response (PBDR) model, users are guided to actively optimize loads to achieve deep peak load regulation. Then, considering the cost characteristics of thermal power and energy storage, a thermal-storage deep peak load regulation pricing strategy is formulated using the fuzzy analytical hierarchy process (FAHP), and a two-layer optimization model based on demand response and deep peak load regulation pricing strategy is constructed. The feasibility and effectiveness of this method are verified in the improved IEEE 30-bus system. Reference 2 "Comprehensive Economic Analysis of Deep Peak Shaving in Thermal-Storage Coupling System" (Zhang Chengfeng, Zhu Yilin, Hu Dongzi, Fu Zhengyang, Xu Yujie, Shen Guoqing, Wang Liang, Chen Haisheng. Energy Storage Science and Technology, 2024, 13(10): 3693-3705.) Based on the consideration of unit variable operating conditions and energy storage / release dynamic processes, a coupled system variable operating condition simulation model was established through EBSILON, and the impact of deep peak shaving on carbon emissions and boiler life loss was analyzed, thereby establishing a dispatching and operation economic model for deep peak shaving in thermal-storage coupling system, providing a reference for the benefits of deep peak shaving. Reference 3 "Economic Analysis of Multi-type Energy Storage Considering Deep Peak Regulation of Thermal Power" (Zhang Songyan, Miao Shihong, Yin Binxin, Yao Fuxing, Wang Tingtao. Electric Power Construction, 2022, 43(01):132-142.) established operation models for pumped storage, compressed air energy storage, and lithium-ion battery energy storage respectively. Based on the deep peak regulation characteristics of thermal power units, the operation model and economic model of thermal power units were established. The full life cycle cost of multi-type energy storage was analyzed, and a day-ahead economic dispatch model of multi-type energy storage systems considering deep peak regulation of thermal power units was constructed, thereby providing recommendations on energy storage configuration capacity based on the economic benefits of various types of energy storage power stations.

[0004] Now, there are many patents on deep peak regulation. For example, Patent 1 "A multi-time scale distributed energy storage aggregation peak regulation method" (Cai Xiaoling, Zhou Jian, Li Ningkai, Xu Xianjing, Luo Xiangmei, Wang Liwei, Hu Wen. CN118589555A, 2024-09-03.) introduces a multi-time scale distributed energy storage aggregation peak regulation method, which uses the historical data of the distributed energy storage system under its management scope to predict and analyze the behavioral characteristics, adopts the step quotation to formulate the optimal strategy for the day-ahead quotation, establishes the day-ahead dispatch optimization model with the goal of minimizing the peak regulation dispatch cost, introduces the chaos strategy to improve the locust optimization algorithm, solves the optimal solution of the dispatch optimization model, and achieves the effect of energy storage aggregation participating in the peak regulation of the power grid under the goal of minimizing the peak regulation cost. Patent 2 "A method for energy storage capacity configuration and economic optimization of coal-fired power plants under deep peak regulation" (Shao Wencai, Yan Xing, Zhou Xiaohong. CN117996812A, 2024-05-07.) provides a method for energy storage capacity configuration of coal-fired power plants that takes into account deep peak regulation and economy. Based on the power balance limit when the coal-fired power unit and the energy storage system are jointly powered and the state of charge (SOC) constraint of the energy storage system, the NSGA-II algorithm is used to solve the minimum value of the objective function of minimizing the megawatt-hour power generation cost of the coal-fired power unit combined with the energy storage system and the maximum value of the objective function of maximizing the annual net profit of the energy storage system during its life cycle, and obtain the Pareto optimal solution set, thereby optimizing the energy storage configuration and unit operation and reducing the deep peak regulation loss of the unit.

[0005] However, the traditional method has the following inherent defects:

[0006] 1. There is no detailed description of the system scheduling model in the deep peak-shaving process, and there is a lack of a model that coordinates and unifies operation scheduling and deep peak-shaving.

[0007] 2. There is no accurate modeling of the deep peak load regulation involving energy storage and thermal power, and there is a lack of detailed description of the operating status restrictions of energy storage and thermal power units.

[0008] In view of this, a power system dispatching method with combined thermal power and storage for deep peak regulation is needed. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a method for dispatching a power system with deep peak regulation combined with thermal energy storage, which can provide a technical solution for incorporating energy storage into deep peak regulation of the power system, thereby promoting its joint operation with traditional power generation resources. The specific technical solution is as follows:

[0010] A method for dispatching a power system with combined deep peak regulation of thermal power and energy storage, comprising:

[0011] S100, construct a basic dispatching model for the power system;

[0012] S200, constructing an energy storage system operation model;

[0013] S300: Construct a deep peak-shaving model combining traditional thermal power units with energy storage systems;

[0014] S400, construct a power system dispatching model with combined thermal and storage deep peak regulation;

[0015] S500, solving a power system dispatching model including thermal power generation and storage combined deep peak regulation, and obtaining a dispatching strategy.

[0016] Preferably, the objective function of the basic dispatching model of the power system is to minimize the sum of the operating costs of the units.

[0017] Preferably, the constraints of the basic dispatching model of the power system include a unit operation model and a line flow model;

[0018] The unit operation model includes unit active output constraints, unit ramp constraints, unit minimum online and offline time constraints, and unit switch action and online state logic constraints;

[0019] The line power flow model includes node phase angle constraints, DC power flow constraints, transmission line capacity constraints and node power balance constraints.

[0020] Preferably, the energy storage system operation model includes 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.

[0021] Preferably, the deep peak-shaving model combining the traditional thermal power units and the energy storage system includes a wind-fire deep peak-shaving output balance constraint and a wind-storage deep peak-shaving output balance constraint.

[0022] Preferably, the power system dispatching model containing combined thermal energy storage and deep peak regulation is a unit combination model that incorporates the energy storage system operation model and the deep peak regulation model combining traditional thermal power units and the energy storage system into the basic dispatching model of the power system.

[0023] Preferably, the objective function of the power system dispatching model containing thermal power generation and storage combined deep peak regulation is to minimize the sum of wind power operation cost and unit operation cost.

[0024] Preferably, the constraints of the power system dispatching model containing thermal energy storage combined with deep peak regulation are the models described in S100, S200 and S300.

[0025] Preferably, the power system dispatch model containing deep peak regulation combined with thermal energy storage belongs to mixed integer linear programming. By substituting relevant system data into the model and using a commercial solver to solve and calculate, a system dispatch strategy for deep peak regulation involving energy storage and traditional thermal power units can be obtained.

[0026] Preferably, the scheduling strategy includes a unit scheduling strategy and an energy storage scheduling strategy.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The present invention adds a deep peak-shaving balance constraint to the traditional system scheduling model, so that the system can significantly improve the peak-shaving capability by optimizing the configuration of flexible resources, thereby enhancing the security and reliability of the system, which has obvious advantages over the traditional scheduling model.

[0029] 2. The present invention has carried out a more accurate modeling of the role of energy storage equipment in the deep peak regulation process, so that the system scheduling can fully consider the flexibility of the energy storage equipment, thereby achieving more efficient and reasonable resource scheduling.

[0030] 3. The present invention transforms the power system dispatching problem involving deep peak regulation of thermal power and energy storage into a mixed integer programming model. This modeling method is not only concise and clear, but also can quickly and accurately solve the optimal solution to the problem, providing strong mathematical support for power system dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] 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.

[0032] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] 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.

[0035] 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, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0036] 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.

[0037] Example:

[0038] See also Figure 1 The present invention provides a method for dispatching a power system with combined deep peak regulation of thermal power and storage, comprising the following steps:

[0039] S100, builds a basic dispatching model for the power system, specifically:

[0040] In this embodiment, the basic dispatching model of the power system is a unit combination model that optimizes the operation of conventional units; the objective function of the basic dispatching model of the power system is to minimize the sum of the unit operation costs:

[0041]

[0042] In the formula, It represents the active output of unit u at time t; and They represent the unit output cost, no-load cost and startup cost of the unit respectively; Indicates the operating status of the unit, 1 indicates operation, and 0 indicates shutdown; Indicates the power-on status of the unit, 1 means power-on.

[0043] In this embodiment, the constraints of the basic dispatching model of the power system include a unit operation model and a line flow model; wherein the unit operation model includes:

[0044] Unit active output constraints:

[0045] Unit climbing constraint: P u,t -P u,t-1 ≤RU u ,u∈Ω G , -P u,t +P u,t-1 ≤RD u ,u∈Ω G ;

[0046] Minimum online and offline time constraints for units:

[0047] Logical constraints on unit switch action and online status: Among them, Ω G Represents a collection of system units; Respectively represent the upper and lower limits of the active output of unit u at time t; RU u , RD u They represent the upper and lower power limits of the ramping 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, TU u , TD u Indicates the minimum online time and minimum offline time of the thermal power unit.

[0048] In this embodiment, the power system adopts DC power flow modeling, and the line power flow model includes:

[0049] Node phase angle constraint: -π≤θ n,t ≤π,n∈Ω N ;

[0050] DC power flow constraint: pf l,t =(θ e,t -θ s,t )b l ,l∈Ω L ;

[0051] Transmission line capacity constraints:

[0052] Node power balance constraints: Among them, Ω N represents the set of system nodes, θ n,t represents the phase angle of node n at time t; Ω L represents the set of transmission lines in the system, pf l,t represents the active power transmitted by line l at time t, θ e,t ,θ s,t They represent the phase angles of the end and start nodes of line l at time t, and b l represents the admittance of line l; A represents the upper limit of the transmission capacity of line l; n,i represents the node-unit association matrix, B n,l represents the line-node association matrix, L n,t represents the load of node n at time t.

[0053] S200, build the energy storage system operation model; specifically:

[0054] In this embodiment, the energy storage device operation model includes:

[0055] Energy storage equipment charging power constraints:

[0056] Discharge power constraints of energy storage equipment:

[0057] The charging power constraint defines the power range of the energy storage device during charging and discharging. Specifically, and Represent the charging power and discharging power of the energy storage element s at time t, respectively, and and They represent the upper limit of charging power and the upper limit of discharging power of energy storage element s respectively.

[0058] State of Charge Timing Constraints:

[0059] The state of charge timing constraint reveals the relationship between the state of charge of the energy storage device and the charge and discharge power as well as the state of charge at the previous moment during the charge and discharge process, and this state is also affected by the charging efficiency and discharge efficiency. s,t represents the charge state of the energy storage element s at time t, and η c and η d They represent the charging efficiency and discharging efficiency of energy storage respectively.

[0060] State of charge upper and lower limit constraints:

[0061] The upper and lower limits of the state of charge ensure that the state of charge of the energy storage device remains within a reasonable range. and Soc s They represent the upper and lower limits of the state of charge of the energy storage element s respectively;

[0062] Consistent charge control:

[0063] The charge consistency constraint is set to ensure that the energy storage device can be continuously used in each scheduling cycle; this constraint requires that the charge state of the energy storage element at the beginning and end of the scheduling cycle remain consistent, where and Soc s,T They represent the charge state of the energy storage element s at the beginning and end of the scheduling period respectively.

[0064] S300, builds a deep peak-shaving model combining traditional thermal power units and energy storage systems; specifically:

[0065] In this embodiment, the deep peak-shaving model of the traditional thermal power unit combined with the energy storage system includes the wind-fire deep peak-shaving output balance constraint and the wind-storage deep peak-shaving output balance constraint, which are as follows:

[0066] Wind and fire deep peak load regulation output balance constraints:

[0067] Wind storage depth peak load balance constraints:

[0068] in, To provide wind power to the grid, To configure the deep peak load benchmark output of wind turbines for thermal power generation, To configure the actual output of wind turbines for thermal power generation, For abandoned air volume, For maximum wind power output, The charging power received by the energy storage from wind power.

[0069] S400, builds a power system dispatching model with combined thermal and storage deep peak regulation; specifically:

[0070] In this embodiment, the power system dispatching model with combined thermal energy storage and deep peak regulation is a unit combination model that incorporates the energy storage system operation model and the deep peak regulation model of the traditional thermal power units and the energy storage system into the basic dispatching model of the power system.

[0071] The objective function of the power system dispatching model with combined thermal power storage and deep peak regulation is to minimize the sum of wind power operation cost and unit operation cost:

[0072]

[0073] In the formula, and They represent the maximum output and wind abandonment of wind power w at time t, c w Represents the unit cost of wind power.

[0074] In addition, the constraints of the power system dispatching model containing thermal energy storage combined with deep peak regulation in this embodiment include the models described in S100, S200, and S300 above.

[0075] Furthermore, the system node power balance constraint of the above power system dispatching model with combined thermal energy storage and deep peak regulation needs to consider the impact of energy storage and wind power, and needs to be changed to:

[0076]

[0077] Among them, O n,w represents the node-wind farm association matrix, M n,sRepresents the node-storage association matrix

[0078] S500 solves the power system dispatch model with thermal power storage combined deep peak regulation and obtains the dispatch strategy. Specifically:

[0079] The power system dispatch model with combined thermal energy storage and deep peak regulation belongs to mixed integer linear programming. By substituting relevant system data into the model and solving it using commercial software, we can obtain the system dispatch strategy for deep peak regulation involving energy storage and traditional thermal power units. The dispatch strategy includes unit dispatch strategy and energy storage dispatch strategy.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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 dispatching a power system with combined deep peak load regulation of thermal power and storage, characterized in that: include: S100, construct a basic dispatching model for the power system; S200, constructing an energy storage system operation model; S300: Construct a deep peak-shaving model combining traditional thermal power units with energy storage systems; S400, construct a power system dispatching model with combined thermal and storage deep peak regulation; S500, solving a power system dispatching model including thermal power generation and storage combined deep peak regulation, and obtaining a dispatching strategy.

2. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 1, characterized in that: The objective function of the basic dispatching model of the power system is to minimize the sum of the operating costs of the units.

3. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 1, characterized in that: The constraints of the basic dispatching model of the power system include a unit operation model and a line flow model; The unit operation model includes unit active output constraints, unit ramp constraints, unit minimum online and offline time constraints, and unit switch action and online state logic constraints; The line power flow model includes node phase angle constraints, DC power flow constraints, transmission line capacity constraints and node power balance constraints.

4. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 1, characterized in that: The energy storage system operation model includes 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.

5. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 1, characterized in that: The deep peak-shaving model combining the traditional thermal power unit and the energy storage system includes a wind-fire deep peak-shaving output balance constraint and a wind-storage deep peak-shaving output balance constraint.

6. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 1, characterized in that: The power system dispatching model with combined deep peak regulation of thermal power units and energy storage is a unit combination model that incorporates the energy storage system operation model and the deep peak regulation model of the traditional thermal power units and energy storage system into the basic dispatching model of the power system.

7. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 7, characterized in that: The objective function of the power system dispatching model with combined thermal power storage and deep peak regulation is to minimize the sum of wind power operation cost and unit operation cost.

8. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 1, characterized in that: The constraints of the power system dispatching model containing thermal energy and storage combined deep peak regulation are the models described in S100, S200 and S300.

9. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 1, characterized in that: The power system dispatch model containing deep peak regulation combined with thermal energy storage belongs to mixed integer linear programming. By substituting relevant system data into the model and using a commercial solver to solve and calculate, a system dispatch strategy for deep peak regulation involving energy storage and traditional thermal power units can be obtained.

10. The method for dispatching a power system with combined deep peak load regulation of thermal power and energy storage according to claim 9, characterized in that: The dispatching strategy includes a unit dispatching strategy and an energy storage dispatching strategy.

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