New energy power grid optimization scheduling method and related device

Abstract

The invention belongs to a power grid dispatching method, and provides a new energy power grid optimal dispatching method and a related device aiming at the technical problem that a solution for realizing cooperative work of self-balancing units and an upper-layer power grid is lacked at present, the power grid is divided into a plurality of self-balancing units, and models of the corresponding self-balancing units are respectively established; and determining a flexible range of each self-balancing unit, formulating a global scheduling scheme by combining an optimization target of the energy system, and allocating a corresponding scheduling target to each self-balancing unit according to the global scheduling scheme. According to the optimal scheduling method, the real prediction model can be continuously optimized and improved according to the actual situation of each day, so that the prediction precision and the scheduling effect of the system are gradually improved in a dynamic environment, and the efficient operation of a power grid is ensured.

Description

Technical Field

[0001] The present application relates to a power grid dispatching method, and specifically to a new energy power grid optimization dispatching method and related devices. Background Art

[0002] Hydrogen energy is gradually gaining attention as a clean energy. Hydrogen energy not only has great potential in the field of power generation, but also shows a growing momentum in the promotion of hydrogen-powered vehicles. With the development of the hydrogen energy industry, the proportion of hydrogen energy in the energy structure will continue to increase.

[0003] Large factories and data centers have huge demand for electricity. As electricity prices rise, these high-energy-consuming enterprises are more sensitive to electricity costs. In order to reduce costs, more and more enterprises are willing to participate in grid dispatching. This flexible electricity management not only helps save costs, but also relieves the pressure on the grid, reduces peak loads and fills valleys, and improves energy efficiency. In this context, how to maximize the absorption of new energy while ensuring the stability of the grid has become a key issue. Reducing the proportion of thermal power and reducing carbon emissions are important ways to achieve carbon neutrality. However, with the access of a large number of flexible loads and distributed energy to the grid, the traditional dispatching model faces the challenges of complex data processing and decision-making. Large-scale dispatching brings huge amounts of data, making grid management more difficult. To this end, the concept of self-balancing units is proposed, which divides the grid into multiple relatively independent small areas, and each area achieves autonomy and balance. The upper grid only needs to coordinate the dispatch between its own balancing units. This hierarchical dispatching structure reduces complexity, improves response speed and the absorption capacity of new energy. However, how the self-balancing unit works in coordination with the upper grid is still an unresolved issue. Summary of the invention

[0004] In order to solve the technical problem that there is currently a lack of a solution to achieve collaborative working between a self-balancing unit and an upper power grid, the present application provides a new energy power grid optimization scheduling method and related devices.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions: In the first aspect, the present application proposes a new energy power grid optimization dispatching method, comprising: Determine the self-balancing units of the power grid and build models of the respective balancing units; Determine the flexibility range of each self-balancing unit in combination with the models of the respective balancing units; Determine the optimization target of the energy system and formulate a global dispatch plan based on the flexibility range of all self-balancing units; According to the global scheduling scheme, assign corresponding scheduling targets to respective balancing units; Enable each balancing unit to perform target optimization according to the assigned scheduling objectives.

[0006] In the second aspect, the present application proposes a new energy power grid optimization dispatching system, comprising: A sub-model building module is used to determine the self-balancing units of the power grid and build models of the respective balancing units; A flexibility range determination module, for determining the flexibility range of each self-balancing unit in combination with the models of the respective balancing units; The global module is used to determine the optimization target of the energy system and formulate a global dispatch plan based on the flexibility range of all self-balancing units; An allocation module, configured to allocate corresponding scheduling targets to respective balancing units according to the global scheduling scheme; The optimization module is used to optimize the targets of the respective balancing units according to the assigned scheduling targets.

[0007] In the third aspect, the present application proposes an electronic device, comprising: a memory and one or more processors; the memory is coupled to the processor; wherein the memory stores computer program code, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the above-mentioned new energy power grid optimization scheduling method.

[0008] In a fourth aspect, the present application proposes a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned new energy power grid optimization scheduling method are implemented.

[0009] Compared with the prior art, this application has the following beneficial effects: The present application proposes a new energy grid optimization dispatching method, which divides the grid into multiple self-balancing units, establishes the corresponding self-balancing unit models respectively, determines the flexible range of each self-balancing unit, combines the optimization target of the energy system, formulates a global dispatching scheme, and then allocates the corresponding dispatching target to each balancing unit according to the global dispatching scheme. Compared with the traditional single energy dispatching, the self-balancing unit model of the new energy grid considering the hydrogen energy system constructed by the present invention can fully consider the complementary characteristics between thermal power generation, new energy power generation, hydrogen energy network and various flexible power loads, and has a more comprehensive monitoring of the operation characteristics of the grid, so as to improve the optimization dispatching effect of the self-balancing unit, and then improve the economy and stability of the self-balancing unit. On this basis, the optimization dispatching method is more flexible, and the day can be used as the optimization cycle, and the optimization scale can be set to one hour, which can avoid excessive data volume while maintaining the sensitive response of the system, reduce the system calculation burden, and better meet the needs of actual grid operation. At the same time, the forecast data of the day can be used for optimization dispatching before the beginning of each day, which improves the forward-looking nature of dispatching and enables the self-balancing unit to have more sufficient preparation time, thereby improving the adequacy of dispatching work. The optimization scheduling method of the present application can also continuously optimize and improve the entire prediction model according to the actual situation of each day, so that the system can gradually improve the prediction accuracy and scheduling effect in a dynamic environment, ensuring the efficient operation of the power grid.

[0010] The present application also proposes a new energy power grid optimization dispatching system, an electronic device and a computer storage medium, which have all the advantages of the above-mentioned new energy power grid optimization dispatching method. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 This is a first flow chart of the method for optimizing the dispatching of new energy power grids in this application; Figure 2 This is a second flow chart of the new energy grid optimization dispatching method for this application; Figure 3 A schematic diagram of the implementation principle of a method for optimizing the dispatching of new energy power grids; Figure 4 A schematic diagram of a new energy grid self-balancing unit in an embodiment of the present application; Figure 5 This is a schematic diagram of the new energy power grid optimization dispatching system for this application. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0014] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0015] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0016] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0017] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0018] In the description of the embodiments of the present application, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0019] In order to reduce dependence on fossil energy, the development of renewable energy is being actively promoted, especially wind power and photovoltaic power generation. However, the volatility of renewable energy also poses a challenge to the stability of the power grid, especially the output fluctuation of photovoltaic power generation at night or on cloudy days. In addition, hydrogen energy, as a clean energy, is gradually gaining attention. It has potential in the field of power generation and is also showing growth momentum in the promotion of hydrogen-powered vehicles. It will become an important direction for future transportation energy.

[0020] As electricity prices rise, high-energy-consuming enterprises such as large factories and data centers are more sensitive to electricity costs. In order to reduce costs, these enterprises are increasingly willing to participate in grid dispatching, saving costs and alleviating grid pressure through flexible electricity management. The key issues and technical challenges faced mainly include: (1) New energy consumption problem: How to maximize the consumption of new energy while ensuring the stability of the grid is a key issue in the current energy transformation. Reducing the proportion of thermal power and reducing carbon emissions are important ways to achieve carbon neutrality, but the access of a large number of flexible loads and distributed energy sources increases the complexity of grid dispatching. (2) Data processing and decision-making challenges: Traditional dispatching models face challenges in processing large amounts of data and making decisions. Large-scale dispatching brings massive amounts of data, which significantly increases the difficulty of grid management.

[0021] Therefore, it is proposed to divide the power grid into multiple relatively independent small areas (self-balancing units), each of which is autonomous and balanced. The upper power grid only needs to coordinate the dispatch between the balancing units, thereby reducing complexity and improving response speed and the ability to absorb new energy.

[0022] At present, most virtual power plants are operated by optimizing scheduling schemes based on reinforcement learning, and the aggregate output and power purchase and sales decisions of various units are obtained through learning. The distributed renewable energy and small hydropower groups are studied for regulation, and different virtual power plant aggregation schemes and operation strategies are set to evaluate the regulation capabilities under extremely high renewable energy proportions. Alternatively, an efficient aggregation method for wide-area distributed resources is proposed and applied to the optimization regulation in virtual power plant scenarios.

[0023] Although there have been many studies on virtual power plants and distributed energy regulation, there is still a lack of research on the participation of self-balancing units in grid dispatching. Therefore, the development of technologies and methods that can achieve efficient collaboration between balancing units and upper-level grids is an urgent problem to be solved. This mainly includes studying the collaborative working mechanism between balancing units and upper-level grids, optimizing dispatching algorithms, and data processing technologies.

[0024] Based on the above situation, the present application proposes a new energy power grid optimization scheduling method and related devices, and the present application is described in detail below in conjunction with embodiments and drawings.

[0025] like Figure 1As shown, it is a flow chart of the new energy grid optimization dispatching method of the present application, which may include: S101, determining the self-balancing units of the power grid and constructing models of the respective balancing units.

[0026] It should be noted that the self-balancing unit is an area or node in the power grid that can balance energy supply and demand relatively independently. It may be a microgrid, a distributed energy system, or a power grid area with self-regulation capabilities. In practical applications, detailed data on energy production (such as solar energy, wind energy, energy storage, etc.), consumption (user load), and network structure (lines, transformers, etc.) in the self-balancing unit can be collected. Based on the collected data, mathematical models (such as state space models, optimization models, etc.) are used to describe the energy flow, supply and demand relationships, and possible constraints (such as voltage stability, frequency control, etc.) in the self-balancing unit. The model parameters are then calibrated using historical data or actual operating data to ensure that the model can accurately reflect the actual operating conditions of the self-balancing unit.

[0027] S102, determining the flexibility range of each self-balancing unit in combination with the models of the respective balancing units.

[0028] It should be noted that the flexibility range refers to the ability of the self-balancing unit to flexibly adjust its energy production, consumption or energy storage without affecting the stable operation of the power grid. In practical applications, the response ability of each element in the self-balancing unit (such as generators, loads, energy storage, etc.) to changes in external conditions (such as weather changes, electricity price fluctuations, etc.) can be evaluated through model analysis. Considering the physical constraints of the power grid (such as line capacity, voltage level, etc.) and operational constraints (such as safety regulations, economic costs, etc.), the maximum adjustable range of the self-balancing unit under given conditions can be determined. Then, based on the above analysis, the flexibility range of each self-balancing unit in different time scales (such as hours, days, weeks, etc.) is determined.

[0029] S103, determine the optimization target of the energy system and formulate a global scheduling plan based on the flexibility range of all self-balancing units.

[0030] In practical applications, the optimization objectives may include minimizing energy costs, maximizing the utilization of new energy, improving grid stability, etc., which can be determined according to the optimization scheduling requirements. In practical applications, the objective function can be constructed according to the optimization objectives. The function should be able to reflect the economy and sustainability of the global energy system, and the flexibility range of each balancing unit should be integrated into the optimization problem as a constraint to ensure that the scheduling scheme is within the feasible domain. Then, an appropriate optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) is used to solve the global optimization problem and obtain the optimal or suboptimal scheduling scheme.

[0031] S104: Allocate corresponding scheduling targets to respective balancing units according to the global scheduling solution.

[0032] In practical applications, the overall goal of the global scheduling plan is decomposed into specific scheduling goals for each self-balancing unit, such as specific power generation plans, energy storage charging and discharging strategies, etc., and then the scheduling goals are sent to the control center or smart device of each balancing unit through the communication system.

[0033] S105, enabling each balancing unit to perform target optimization according to the assigned scheduling target.

[0034] Each balancing unit formulates and implements specific control strategies based on the received dispatch targets and local actual conditions (such as real-time meteorological data, equipment status, etc.). During the execution process, the self-balancing unit should monitor and feedback the operating data in real time so as to dynamically adjust and optimize the dispatch targets according to the actual conditions. Collaborative optimization may be required between the balancing units to ensure the realization of the global dispatch targets and improve the efficiency and stability of the entire energy system.

[0035] The optimization scheduling method of the present application can achieve overall balance and balance after scheduling inside the self-balancing unit, thereby improving the degree of new energy consumption and reducing the impact of new energy volatility on the system while meeting all electricity loads, ensuring stable and safe operation of the system.

[0036] like Figure 2 As shown, it is a second flow chart of the new energy grid optimization dispatching method of this application, taking the energy system including hydrogen energy as an example. Figure 3 As shown in FIG. 1 , a schematic diagram of an implementation principle of a new energy grid optimization scheduling method is shown. The specific optimization scheduling method may include: S201, establish a new energy grid self-balancing unit model and constraints considering the hydrogen energy system.

[0037] Specifically, based on the actual physical characteristics of thermal, wind and photovoltaic power generation units, including but not limited to flexible and adjustable loads such as large data centers and large process plants, and considering the hydrogen network composed of electrolyzers, hydrogen fuel cells and hydrogen storage tanks, a hydrogen network is constructed. Figure 4 The new energy grid self-balancing unit shown simultaneously characterizes the system supply and demand balance relationship, the spatiotemporal coupling characteristics of the grid and hydrogen network, and the physical characteristics of photovoltaic power generation units, thermal power units, wind power units, flexible loads and hydrogen network related equipment.

[0038] In this model, the constraints of wind turbines include but are not limited to the cut-in wind speed and cut-out wind speed constraints, the physical model of wind turbine power generation, and the constraints of wind turbine output. The constraints of photovoltaic generators include the physical model of photovoltaic panels and the constraints of photovoltaic output. The constraints of thermal power generators include the constraints of upper and lower limits of power generation, the constraints of ramp rate, and the constraints of the start-stop process. The constraints of hydrogen vehicles involve the corresponding relationship model between mileage and hydrogen consumption. The constraints of data centers include the physical models of CPUs, servers, and data centers, as well as the constraints of CPU frequency, upper and lower limits of working voltage, and CPU utilization. The constraints of air conditioning systems include the physical model of heat dissipation and the constraints of upper and lower limits of temperature. The constraints of process plants include the operating model and the constraints of the workload throughout the day. The constraints of electrolyzers and fuel cells mainly involve the constraints of upper and lower limits of power output and the constraints of ramp rate. The constraints of hydrogen storage tanks include the upper and lower limits of input and output power, the constraints of hydrogen storage capacity, and the physical model of hydrogen input and output. In addition, this model also includes constraints on hydrogen network balance.

[0039] In some embodiments of the present application, specific constraints may include: The model corresponding constraints of the wind turbine generator set include:

[0040]

[0041]

[0042] in, is the power output of the wind turbine generator set, is the rated power of the wind turbine generator set, is the rated wind speed of the wind turbine generator set, Cut out wind speed for wind turbines, is the cut-in wind speed of the wind turbine, is the rated wind speed of the wind turbine generator set, is the current wind speed, and are the first and second parameters of the wind turbine generator set, For wind turbines In the period of efforts, For wind turbines In the period The power generation capacity; The model corresponding constraints of the photovoltaic power generation group include:

[0043] in, is the power generated by the photovoltaic module, For photovoltaic generators in the period contribution; The model corresponding constraints of the thermal power generating set include:

[0044]

[0045]

[0046]

[0047] in, For thermal power units In the period of efforts, and Thermal power units In the period The upper and lower limits of output, and Thermal power units The upper and lower limits of the climbing slope, For the period the number of hours covered; and Thermal power units The minimum on time and minimum off time, For thermal power units In the period working status, is the index variable; The model of the hydrogen car corresponds to constraints including

[0048]

[0049] in, For hydrogen cars In the period The remaining hydrogen state, For hydrogen cars The hydrogen storage tank capacity, and Hydrogen-powered vehicles In the period The hydrogenation quality and hydrogen consumption quality, For hydrogen cars In the period The hydrogen consumption of air conditioner, For hydrogen vehicles In the period Mileage is the conversion factor between mileage and hydrogen consumption; The model corresponding constraints of the data center include:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] in, For Data Center In the period The heat dissipation power, The heat absorbed by the air conditioning equipment in the data center. and Data Center In the period Indoor and outdoor temperatures, is the thermal resistance of the data center building, is the building volume of the data center, is the building area of ​​the data center, is the air density, is the specific heat capacity of air, The power conversion efficiency of the data center air conditioner is is the maximum cooling rate of the data center, is the maximum temperature rise rate of the data center, For Data Center In the period The minimum outdoor temperature, For Data Center In the period The maximum outdoor temperature, CPU In the period The power, CPU In the period The operating frequency, CPU In the period Chip utilization, CPU In the period The working voltage, It is a parameter constant, which is related to the physical properties of the CPU when it leaves the factory. For Server In the period The power, For other modules of the server except CPU The fixed power For Data Center In the period The power, is the power utilization rate of the data center, CPU In the period The actual processing volume, CPU In the period The maximum amount of tasks that can be processed, is the total amount of tasks that need to be completed within the current optimization scheduling time interval, is the current operating frequency of the CPU, is the CPU available frequency range, The lowest adjustable voltage for the CPU. The maximum adjustable voltage for the CPU; The model corresponding constraints of the process plant include:

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] in, is the number of cache units in the process plant, is the number of work units in the process factory, For work unit In the period The number of processed workpieces, For work unit In the period The maximum number of workpieces to be processed, Cache Unit In the period The number of cached artifacts, Cache Unit In the period The maximum number of cached artifacts, To optimize the overall scheduling of the target production volume within the time period, is the power consumption of a single working unit, is the power consumption of a single cache unit. For process factories in the period Total electrical power consumption; The model of the electrolytic cell corresponds to constraints including:

[0070]

[0071]

[0072] in, For the electrolytic cell in the period The power consumption, For the electrolytic cell in the period The upper limit of power consumption, For the electrolytic cell in the period The amount of hydrogen produced, is the electric-to-hydrogen conversion coefficient, is the lower limit of the climbing slope of the electrolytic cell, is the upper limit of the climbing of the electrolytic cell; The fuel cell model corresponds to constraints including:

[0073]

[0074]

[0075] in, For fuel cells during the period The hydrogen consumption, For fuel cells during the period The upper limit of hydrogen consumption power, For fuel cells during the period The power generation capacity, is the hydrogen-to-electricity conversion coefficient, is the lower limit of fuel cell climbing, is the upper limit of the fuel cell's climbing capacity; The model corresponding constraints of the hydrogen storage tank include:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] in, For hydrogen storage tanks during the period The hydrogen storage quality, For hydrogen storage tanks during the period The upper limit of hydrogen storage mass, and The hydrogen storage tanks are The input power and output power of For hydrogen storage tanks during the period The upper limit of input power, For hydrogen storage tanks during the period The upper limit of the output power, is the conversion efficiency of electric hydrogen input into the hydrogen storage tank, The output conversion efficiency of the hydrogen storage tank is and The hydrogen storage tanks are The input hydrogen mass and output hydrogen mass; The model corresponding to the hydrogen network balance includes:

[0082] .

[0083] S202, the power balance analysis module calculates the hourly power / load flexibility upper limit of the self-balancing unit with the goal of minimum energy consumption; and calculates the hourly power / load flexibility lower limit of the self-balancing unit with the goal of optimal performance; the self-balancing unit reports the power / load flexibility range of the module output to the upper-level energy system.

[0084] In this embodiment, each self-balancing unit is provided with a corresponding power balance analysis module, and receives a corresponding power dispatch target issued by the upper energy system.

[0085] The first step of the power balance analysis module is to minimize energy consumption. Its objective function for:

[0086]

[0087]

[0088] in, For the self-balancing unit in the time period The difference in power generation capacity.

[0089] This step calculates the hourly power / load flexibility lower limit of the self-balancing unit .

[0090] The second step of the power balance analysis module aims at the optimal performance, and its objective function for:

[0091]

[0092] This step calculates the hourly power / load flexibility upper limit of the self-balancing unit. .

[0093] Finally, the hourly power / load flexibility range of the self-balancing unit is obtained and reported to the upper-level energy dispatching system:

[0094] S203, through the power optimization dispatch module, obtains its own flexible dispatch target from the upper energy system, and uses the multi-objective optimization method to take the minimum cost and new energy consumption rate as the objective function to obtain the flexible dispatch target allocated by the upper system. As a constraint, the final optimal scheduling plan inside the self-balancing unit is obtained.

[0095] Specifically, according to S202, within the dispatch framework of the upper energy system, each self-balancing unit first transmits its own flexibility range to the upper energy system. These flexibility ranges represent the ability of each self-balancing unit to flexibly adjust in power dispatch. The upper energy system formulates a global dispatch plan based on the data transmitted by all self-balancing units, comprehensively considers the optimization goals and constraints of the overall system, and assigns a specific dispatch goal to each self-balancing unit.

[0096] S204: The self-balancing unit performs multi-objective optimization according to the received scheduling target.

[0097] Specifically, the scheduling objective function for:

[0098] in, For minimum cost, is the new energy consumption rate, The optimization goal of the energy system.

[0099] The multi-objective optimization process uses the AUGMECON algorithm to solve the Pareto frontier. The specific steps are as follows: First, according to the lexicographic optimization principle, a profit table is generated. The purpose of this step is to perform preliminary optimization on each objective function to determine the ideal value of each objective.

[0100]

[0101]

[0102] in, for Minimum The value of for Minimum The value of for Minimum The value of for Minimum The value of .

[0103] Next, the decision space is meshed, which aims to improve the accuracy of the Pareto front solution by meshing, thus providing a basis for subsequent optimization calculations.

[0104]

[0105]

[0106]

[0107] Among them, k represents the kth target, and its value range is , is the value of the kth target at the lth grid point, is the optimization range of the kth objective value, is the number of grids divided for the kth target, and l is the grid point number.

[0108] Then, model transformation is performed to transform the original multi-objective optimization problem into a single-objective problem.

[0109]

[0110] in, To get a large number, is the slack variable.

[0111] Finally, the converted model is solved using the CPLEX solver to calculate and obtain the Pareto front.

[0112] After the Pareto front solution is completed, continue to perform sensitivity decision analysis. This analysis includes the following steps: First, construct a comprehensive objective function

[0113]

[0114] in, and are the weighting coefficients of the two objectives respectively.

[0115] Then, the interaction sensitivity calculation is performed. Each target is normalized separately, and then the partial derivative of each target of the comprehensive objective function is calculated to calculate the impact factor between targets. and .

[0116]

[0117]

[0118] Based on the results of the sensitivity analysis, the problem is transformed into a new optimization form:

[0119] Finally, the weighting coefficient is determined by optimizing the solution. and .

[0120] Substitute the weighted coefficients back into the comprehensive objective function, transform the multi-objective optimization into a single-objective optimization, and use the CPLEX solver with the comprehensive objective function as the goal to obtain the final optimization scheduling result of the self-balancing unit.

[0121] Through the above steps, it is ensured that the self-balancing unit can fully utilize its own flexible range and achieve optimized operation under the premise of meeting the scheduling objectives of the upper energy system.

[0122] The ionization balance analysis module in the present application mainly performs two steps of analysis and calculation: the first step is to calculate the hourly power / load flexibility upper limit of the self-balancing unit with the goal of minimum energy consumption, so as to obtain how much electric energy the self-balancing unit can provide to other self-balancing units under the jurisdiction of the upper energy system at most after meeting the basic requirements of all power demands, if there is power redundancy. If the power cannot meet the needs of this unit, this step can be used to understand how much electricity is needed from other units at least; the second step is to calculate the hourly power / load flexibility lower limit of the self-balancing unit with the goal of optimal performance, so as to understand how much electric energy the self-balancing unit can provide to other units at least, or how much electricity it can absorb from other units at most; the self-balancing unit reports the power / load flexibility range output by the module to the upper energy system. Through this method, the upper energy system only needs to process the flexibility range data reported by each balancing unit, without considering the specific power generation, power consumption and power dispatching conditions within each self-balancing unit, and the data is cleaner and more concise, which can greatly improve efficiency.

[0123] In addition, the power optimization scheduling module in the present application can obtain its own flexible scheduling objectives from the upper energy system, convert them into constraints, and obtain the final optimization scheduling plan within the self-balancing unit through multi-objective optimization methods. Most existing studies only consider multi-objective optimization to solve power grid scheduling problems, and do not consider their own flexible scheduling objectives. However, the power optimization scheduling module constructed in this application has the function of scheduling within its own balancing area in combination with the scheduling results of the upper energy system, that is, it takes into account the scheduling of the two-layer power energy system, making the power optimization scheduling module more comprehensive.

[0124] In practical applications, the above optimization scheduling method can be executed in a set scheduling period according to scheduling needs, such as a scheduling period of 1 day and a time scale of 1 hour.

[0125] In the above embodiment, the calculation and scheduling method of the flexible range of the self-balancing unit of the new energy power grid of the hydrogen network is considered. The regional self-balancing unit adopted to cope with power grid fluctuations cannot always achieve self-balancing within the unit. For this reason, it is proposed that when participating in the upper-level regulation, the flexible upper limit of the self-balancing unit at each moment is first calculated by the method of minimum energy consumption, and then the flexible lower limit of the self-balancing unit is calculated with the optimal performance as the goal. After uploading the flexible range data to the upper-level energy system, the upper-level energy system feeds back the specific flexible scheduling target, and optimizes the optimized scheduling scheme inside the self-balancing unit through the multi-objective optimization method. The present invention enables the self-balancing unit to meet all power loads after the upper-level scheduling, and assists the upper-level power grid to perform optimized scheduling, thereby improving the economy and stability of the overall energy system.

[0126] like Figure 5 As shown, it is a schematic diagram of the new energy grid optimization dispatching system of the present application, which may include: A sub-model building module is used to determine the self-balancing units of the power grid and build models of the respective balancing units; A flexibility range determination module, for determining the flexibility range of each self-balancing unit in combination with the models of the respective balancing units; The global module is used to determine the optimization target of the energy system and formulate a global dispatch plan based on the flexibility range of all self-balancing units; An allocation module, configured to allocate corresponding scheduling targets to respective balancing units according to the global scheduling scheme; The optimization module is used to optimize the targets of the respective balancing units according to the assigned scheduling targets.

[0127] In some embodiments of the new energy power grid optimization and dispatching system of the present application, the self-balancing units of the power grid may include wind turbines, photovoltaic turbines, thermal turbines, hydrogen vehicles, data centers, air-conditioning systems, process plants, electrolytic cells and fuel cells, hydrogen storage tanks and hydrogen network balance.

[0128] In some embodiments of the new energy grid optimization scheduling system of the present application, the model corresponding constraints of each respective balancing unit can adopt the specific embodiments of the aforementioned new energy grid optimization scheduling method.

[0129] In some embodiments of the new energy grid optimization dispatching system of the present application, determining the flexibility range of each self-balancing unit may include: Flexible range of each self-balancing unit ;

[0130]

[0131]

[0132] in, For the self-balancing unit in the period The power generation difference;

[0133] .

[0134] In some embodiments of the new energy grid optimization dispatching system of the present application, the optimization objectives of the energy system may include:

[0135]

[0136]

[0137]

[0138] in, For minimum cost, is the new energy consumption rate, To optimize the energy system, is the penalty power factor, For the self-balancing unit in the period The scheduling goal is For the self-balancing unit in the period The error between the scheduling target and the actual situation.

[0139] In some embodiments of the new energy grid optimization dispatching system of the present application, the optimization objectives of the energy system may include: minimum cost and maximum system energy consumption rate; the flexibility range of all self-balancing units is used as a constraint for formulating a global dispatching plan.

[0140] In some embodiments of the new energy grid optimization dispatching system of the present application, the allocating corresponding dispatching targets to respective balancing units according to the global dispatching scheme may include: The AUGMECON algorithm is used to solve the Pareto frontier of the global scheduling scheme; Based on the obtained Pareto front, sensitivity decision analysis is performed to transform the global scheduling scheme, and the scheduling targets of each balancing unit are obtained after solution.

[0141] In some embodiments of the new energy grid optimization dispatching system of the present application, the use of the AUGMECON algorithm to solve the Pareto frontier of the global dispatching scheme may include: Based on the lexicographic optimization principle, a profit table is generated to determine the ideal value of each objective function in the global scheduling scheme; Based on the ideal values ​​of each objective function in the global scheduling scheme, the decision space is gridded; Combined with the grid division results of the decision space, the multi-objective optimization problem corresponding to each objective function in the global scheduling scheme is converted into a single-objective problem; The converted single-objective problem is solved using the CPLEX solver to obtain the Pareto front.

[0142] In some embodiments of the new energy grid optimization dispatching system of the present application, the sensitivity decision analysis is performed based on the Pareto front obtained by solution, the global dispatching scheme is converted, and the dispatching target of each balancing unit is obtained after solution, which may include: Construct a comprehensive objective function based on the optimization goal of the energy system; Normalize the optimization objectives of the energy system respectively, then find the partial derivative of each optimization objective in the comprehensive objective function, calculate the importance of each optimization objective, and convert the global scheduling plan into a new optimization form; By optimizing and solving the new optimization form, the weighted coefficients of each optimization target in the comprehensive objective function are determined; The weighted coefficients of each optimization objective are substituted into the comprehensive objective function, and the problem of the global scheduling solution is transformed into a single-objective optimization problem. The transformed comprehensive objective function is used as the goal, and the CPLEX solver is used to obtain the scheduling objectives of each balancing unit after solving.

[0143] It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of each module is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The module described as a separate component may or may not be physically separated. The component displayed as a module may be a physical unit or multiple physical units, that is, it may be located in one place, or it may be distributed in multiple different places. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0144] In addition, each module in each embodiment of the present invention may be integrated into a processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0145] An embodiment of the present application also provides an electronic device, which may include one or more processors, a memory, and a communication interface.

[0146] The memory, the communication interface and the processor are coupled, for example, the memory, the communication interface and the processor may be coupled together via a bus.

[0147] The communication interface is used for data transmission with other devices. The memory stores computer program code. The computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the above-mentioned new energy grid optimization scheduling method.

[0148] Wherein, the processor can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the present disclosure. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, and the like. The processor can be used to support electronic devices to execute the method steps provided in the above embodiments.

[0149] The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The above bus may be divided into an address bus, a data bus, a control bus, etc.

[0150] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned new energy power grid optimization scheduling method are implemented.

[0151] The computer-readable storage medium involved in the present application includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field.

[0152] For the description of the relevant parts of the new energy grid optimization dispatching system, electronic device and computer-readable storage medium provided in the embodiments of the present application, please refer to the detailed description of the corresponding parts in the new energy grid optimization dispatching method provided in the embodiments of the present application, which will not be repeated here. In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0153] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A new energy power grid optimization scheduling method, characterized in that: include: Determine the self-balancing units of the power grid and build models of the respective balancing units; Determine the flexibility range of each self-balancing unit in combination with the models of the respective balancing units; Determine the optimization target of the energy system and formulate a global dispatch plan based on the flexibility range of all self-balancing units; According to the global scheduling scheme, assign corresponding scheduling targets to respective balancing units; Enable each balancing unit to perform target optimization according to the assigned scheduling objectives.

2. The new energy grid optimization dispatching method according to claim 1 is characterized in that: The self-balancing units of the power grid include: wind turbines, photovoltaic turbines, thermal turbines, hydrogen vehicles, data centers, air conditioning systems, process plants, electrolytic cells and fuel cells, hydrogen storage tanks and hydrogen network balance.

3. The new energy grid optimization dispatching method according to claim 2 is characterized in that: The model corresponding constraints of the wind turbine generator set include: in, is the power output of the wind turbine generator set, is the rated power of the wind turbine generator set, is the rated wind speed of the wind turbine generator set, Cut out wind speed for wind turbines, is the cut-in wind speed of the wind turbine, is the rated wind speed of the wind turbine generator set, is the current wind speed, and are the first and second parameters of the wind turbine generator set, For wind turbines In the period of effort, For wind turbines In the period The power generation capacity; The model corresponding constraints of the photovoltaic power generation group include: in, is the power generated by the photovoltaic module, For photovoltaic generators in the period contribution; The model corresponding constraints of the thermal power generating set include: in, For thermal power units In the period of effort, and Thermal power units In the period The upper and lower limits of output, and Thermal power units The upper and lower limits of the climbing slope, For the period the number of hours covered; and Thermal power units The minimum on time and minimum off time, For thermal power units In the period working status, Index variable The model of the hydrogen car corresponds to the constraints, including in, For hydrogen vehicles In the period The remaining hydrogen state, For hydrogen vehicles The hydrogen storage tank capacity, and Hydrogen-powered vehicles In the period The hydrogenation quality and hydrogen consumption quality, For hydrogen vehicles In the period The hydrogen consumption of air conditioner, For hydrogen vehicles In the period Mileage is the conversion factor between mileage and hydrogen consumption; The model corresponding constraints of the data center include: in, For Data Center In the period The heat dissipation power, The heat absorbed by the air conditioning equipment in the data center. and Data Center In the period Indoor and outdoor temperatures, is the thermal resistance of the data center building, is the building volume of the data center, is the building area of ​​the data center, is the air density, is the specific heat capacity of air, The power conversion efficiency of the data center air conditioner is is the maximum cooling rate of the data center, is the maximum temperature rise rate of the data center, For Data Center In the period The minimum outdoor temperature, For Data Center In the period The maximum outdoor temperature, CPU In the period The power, CPU In the period The operating frequency, CPU In the period Chip utilization, CPU In the period The working voltage, It is a parameter constant, which is related to the physical properties of the CPU when it leaves the factory. For Server In the period The power, For other modules of the server except CPU The fixed power, For Data Center In the period The power, is the power utilization rate of the data center, CPU In the period The actual processing volume, CPU In the period The maximum amount of tasks that can be processed, is the total amount of tasks that need to be completed within the current optimization scheduling time interval, is the current operating frequency of the CPU, is the CPU available frequency range, The lowest adjustable voltage for the CPU. The maximum adjustable voltage for the CPU; The model corresponding constraints of the process plant include: in, is the number of cache units in the process plant, is the number of work units in the process factory, For work units In the period The number of processed workpieces, For work unit In the period The maximum number of workpieces to be processed, Cache Unit In the period The number of cached artifacts, Cache Unit In the period The maximum number of cached artifacts, To optimize the overall scheduling of the target production volume within the time period, is the power consumption of a single working unit, is the power consumption of a single cache unit, For process factories in the period Total electrical power consumption; The model of the electrolytic cell corresponds to constraints including: in, For the electrolytic cell in the period The power consumption, For the electrolytic cell in the period The upper limit of power consumption, For the electrolytic cell in the period The amount of hydrogen produced, is the electric-to-hydrogen conversion coefficient, is the lower limit of the climbing slope of the electrolytic cell, is the upper limit of the climbing of the electrolytic cell; The fuel cell model corresponds to constraints including: in, For fuel cells during the period The hydrogen consumption, For fuel cells during the period The upper limit of hydrogen consumption power, For fuel cells during the period The power generation capacity, is the hydrogen-to-electricity conversion coefficient, is the lower limit of fuel cell climbing, is the upper limit of the fuel cell's climbing capacity; The model corresponding constraints of the hydrogen storage tank include: in, For hydrogen storage tanks during the period The hydrogen storage quality, For hydrogen storage tanks during the period The upper limit of hydrogen storage mass, and The hydrogen storage tanks are The input power and output power of For hydrogen storage tanks during the period The upper limit of input power, For hydrogen storage tanks during the period The upper limit of the output power, is the conversion efficiency of electric hydrogen input into the hydrogen storage tank, is the conversion efficiency of hydrogen storage tank electric hydrogen output, and The hydrogen storage tanks are The input hydrogen mass and output hydrogen mass; The model corresponding to the hydrogen network balance includes: 。 4. The new energy grid optimization dispatching method according to claim 3 is characterized in that: Determining the flexibility range of each self-balancing unit and reporting it to the upper energy dispatching system includes: Flexible range of each self-balancing unit ; in, For the self-balancing unit in the period The power generation difference, For the period base load of self-balancing units; 。 5. The new energy grid optimization dispatching method according to claim 4 is characterized in that: The optimization objectives of the energy system include: in, For minimum cost, is the new energy consumption rate, To optimize the energy system, is the penalty power factor, For the self-balancing unit in the period The scheduling goal is For the self-balancing unit in the period The error between the scheduling target and the actual situation.

6. The new energy grid optimization dispatching method according to claim 5 is characterized in that: The optimization objectives of the energy system include: minimum cost and maximum system energy consumption rate; The flexibility range of all self-balancing units is used as a constraint for formulating the global scheduling plan.

7. The new energy grid optimization dispatching method according to claim 6 is characterized in that: The allocating corresponding scheduling targets to respective balancing units according to the global scheduling scheme includes: The AUGMECON algorithm is used to solve the Pareto frontier of the global scheduling scheme; Based on the obtained Pareto front, sensitivity decision analysis is performed to transform the global scheduling scheme, and the scheduling targets of each balancing unit are obtained after solution.

8. The new energy grid optimization dispatching method according to claim 7 is characterized in that: The AUGMECON algorithm is used to solve the Pareto frontier of the global scheduling scheme, including: Based on the lexicographic optimization principle, a profit table is generated to determine the ideal value of each objective function in the global scheduling scheme; Based on the ideal values ​​of each objective function in the global scheduling scheme, the decision space is gridded; Combined with the grid division results of the decision space, the multi-objective optimization problem corresponding to each objective function in the global scheduling scheme is converted into a single-objective problem; The converted single-objective problem is solved using the CPLEX solver to obtain the Pareto front.

9. The new energy grid optimization dispatching method according to claim 7 is characterized in that: Based on the obtained Pareto frontier, sensitivity decision analysis is performed to transform the global scheduling scheme, and the scheduling targets of each balancing unit are obtained after the solution, including: Construct a comprehensive objective function based on the optimization goal of the energy system; Normalize the optimization objectives of the energy system respectively, then find the partial derivative of each optimization objective in the comprehensive objective function, calculate the importance of each optimization objective, and convert the global scheduling plan into a new optimization form; By optimizing and solving the new optimization form, the weighted coefficients of each optimization target in the comprehensive objective function are determined; The weighted coefficients of each optimization objective are substituted into the comprehensive objective function, and the problem of the global scheduling solution is transformed into a single-objective optimization problem. The transformed comprehensive objective function is used as the goal, and the CPLEX solver is used to obtain the scheduling objectives of each balancing unit after solving.

10. A new energy power grid optimization dispatching system, characterized in that: include: A sub-model building module is used to determine the self-balancing units of the power grid and build models of the respective balancing units; A flexibility range determination module, for determining the flexibility range of each self-balancing unit in combination with the models of the respective balancing units; The global module is used to determine the optimization target of the energy system and formulate a global dispatch plan based on the flexibility range of all self-balancing units; An allocation module, configured to allocate corresponding scheduling targets to respective balancing units according to the global scheduling scheme; The optimization module is used to optimize the targets of the respective balancing units according to the assigned scheduling targets.

11. An electronic device, characterized in that: include: A memory and one or more processors; the memory is coupled to the processor; wherein the memory stores computer program code, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the new energy grid optimization scheduling method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the new energy power grid optimization scheduling method as described in any one of claims 1 to 9 are implemented.

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