Scheduling Method, Device and Medium Based on Power Energy Harmony Index of Grid-Connected Main Equipment

Abstract

The present invention relates to a scheduling method, device, and medium based on the power harmony index of grid-connected main equipment. The method comprises the following steps: establishing mathematical models of the main grid-connected equipment and obtaining operating data, wherein the main grid-connected equipment includes thermal power units, wind power units, photovoltaic systems, and energy storage systems; constructing a power harmony index based on the mathematical model and operating data, wherein the power harmony index is the normalized weighted average of at least two of the following indices: a grid access security coordination capability index, a grid access security coordination performance index, a green environmental protection index, a low-carbon index, and an energy-saving index; constructing a multi-objective optimization model, whose objective function includes maximizing the system's total power harmony index and minimizing total operating costs; and solving the multi-objective optimization model to obtain an optimal scheduling solution for the main grid-connected equipment. Compared with the prior art, the present invention can ensure that the equipment connected to the grid meets both low-carbon and environmental protection requirements and grid harmony and safety requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected dispatching, and in particular to a dispatching method, equipment and medium based on the electric energy harmony index of grid-connected main equipment. Background Art

[0002] With the global emphasis on environmental protection and sustainable development, renewable energy generation technologies such as wind power and photovoltaics have experienced rapid development. These renewable energy sources offer the advantages of cleanliness and sustainability, effectively reducing greenhouse gas emissions and meeting low-carbon environmental requirements. However, since wind and photovoltaic power generation are intermittent and uncertain due to natural conditions (wind speed, sunlight intensity), current forecasting technologies are primarily used to estimate their output in advance. During real-time dispatch, the grid dispatch center adjusts the output of conventional generators based on actual wind and photovoltaic output and load fluctuations. At the same time, backup capacity must be rationally allocated to address fluctuations in wind and photovoltaic output. For example, if wind power output suddenly drops, backup thermal power units or other flexible power sources can be quickly activated to ensure grid power balance. The optimal dispatch of distribution networks incorporating renewable energy requires consideration of their specific characteristics. This computational process is complex, time-consuming, and lacks adaptability. Existing technologies offer a variety of solutions to this problem, such as multi-objective optimization algorithms, fuzzy logic control, and neural network control. Among these methods, multi-objective optimization algorithms have significant advantages, especially when dealing with complex scheduling problems, they can comprehensively consider multiple objectives and constraints, thereby achieving a better scheduling solution. For example, CN114567006A discloses a multi-objective optimization operation method and system for a distribution network, which solves the problem that the existing technology does not fully consider the adjustment rate constraints of power regulation resources, resulting in the optimization results being unable to be implemented in the power regulation of adjustable resources in actual projects. It can meet the maximum adjustment rate constraint, thereby making the optimization results more reasonable and in line with the actual constraints of the industry. It has the advantages of making the simulation calculation of distribution network optimization scheduling more accurate and meeting the actual constraint requirements of the equipment involved in the optimization in the project. In dealing with the problem of adjustable resources participating in the optimization operation of the distribution network, this method can set different optimization objective function expressions and different decision variables and constraints according to different optimization scenarios, flexibly adopt numerical optimization algorithms and intelligent algorithms, and the solution is simple, practical, and indeed feasible. However, this method only considers operating costs and energy efficiency for optimization, and does not consider environmental protection and grid operation safety. Summary of the Invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a scheduling method, equipment and medium based on the power harmony index of the main grid-connected equipment to ensure that the equipment connected to the grid meets the green requirements of low carbon and environmental protection while also meeting the requirements of grid harmony and safety.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] According to a first aspect of the present invention, a scheduling method based on the power harmony index of grid-connected main equipment is provided, the method comprising the following steps:

[0006] Establishing mathematical models of main grid-connected equipment and obtaining operating data, respectively, wherein the main grid-connected equipment includes thermal power units, wind power units, photovoltaic systems, and energy storage systems;

[0007] constructing an electric energy harmony index based on the mathematical model and the operating data, wherein the electric energy harmony index is a normalized weighted average of at least two of a network access security coordination capability index, a network access security coordination performance index, a green environmental protection index, a low carbon index, and an energy-saving index;

[0008] Constructing a multi-objective optimization model, wherein the objective function of the multi-objective optimization model includes maximizing the system total power energy harmony index and minimizing the total operating cost;

[0009] Solve the multi-objective optimization model to obtain the optimal scheduling solution for the main grid-connected equipment.

[0010] As a preferred technical solution, the grid access safety coordination capability index is determined based on the primary frequency regulation capability, AGC capability, AVC capability, phase leading capability, phase lagging capability, black start capability, deep peak regulation capability, rapid load shedding capability and unit steady-state performance of the grid-connected entity, wherein,

[0011] When the primary frequency regulation performance index of the grid-connected generator set meets the preset requirements, the corresponding primary frequency regulation capability index is 1; if it does not meet the requirements, the primary frequency regulation capability index is 0;

[0012] When the AGC performance index of the grid-connected generator set meets the preset requirements, the corresponding AGC capability index is 1; if not, the AGC capability index is 0;

[0013] The calculation method of AVC capability index is: ,in, is the AVC capability indicator, is the AVC performance qualification factor, is the monthly commissioning rate of AVC. When AVC can be adjusted to the target range according to the instruction requirements within the preset time, The value is 1, otherwise it is 0;

[0014] The calculation method of the phase-advancing capability index is:

[0015] ,

[0016] in, is the phase-advancing capability indicator, It is the actual phase advance depth of the unit under the working condition of 100% rated power. is the rated power of the unit;

[0017] The calculation method of the hysteresis capability index is:

[0018] ,

[0019] in, is the hysteresis capability index, It is the actual lag degree of the unit under the working condition of 100% rated power. is the rated active power of the unit;

[0020] The black start capability is determined by whether the unit has the black start capability. If the unit has the black start capability, the black start capability index is 1, otherwise it is 0;

[0021] When the minimum power generation capacity of the generator set is less than or equal to 30% The deep peak load regulation capability index is 1, which is greater than 30%. and less than or equal to 40% The deep peak regulation capability index is 0.5, which is greater than 40%. and less than or equal to 50% The deep peak regulation capability index is 0.2, which is greater than or equal to 50%. The deep peak-shaving capability index is 0;

[0022] The rapid load shedding capability is determined by whether the unit has the capability of rapid load shedding. If the unit has the capability of rapid load shedding, the rapid load shedding capability index is 1, otherwise it is 0.

[0023] The calculation method of the unit steady-state performance index is:

[0024] ,

[0025] in, and They are the stable operation time of the unit and the total operation time of the unit respectively.

[0026] As a preferred technical solution, the network access safety coordination performance index is determined based on the primary frequency regulation performance, AGC performance, AVC performance, low-frequency regulation response performance, unit non-stop performance, and unit technical management performance, wherein:

[0027] Primary frequency modulation performance indicators The calculation method is:

[0028] ,

[0029] in, and They are the number of qualified actions for one frequency modulation and the number of assessments for one frequency modulation action;

[0030] AGC Performance Indicators The calculation method is:

[0031] ,

[0032] in, and They are the AGC performance qualified time and the total AGC operation time;

[0033] AVC performance indicators The calculation method is:

[0034] ,

[0035] in, and They are AVC response qualified time and AVC total operation time;

[0036] The calculation method of low-frequency regulation response performance index is as follows: the number of low-frequency regulation responses that the unit should participate in is calculated, and the number of low-frequency regulation responses that the unit participates in each adjustment is calculated. , and are the actual action value and the theoretical action value respectively. If it is greater than 1, it is assigned a value of 1. , To evaluate the number of low-frequency regulation responses that the unit should participate in during the time period, It is a performance indicator for low frequency regulation response;

[0037] The calculation method of the unit non-stop performance index is:

[0038] ,

[0039] in, and They are the maximum number of tripping times of the unit and the maximum number of tripping times of all units in the entire network respectively;

[0040] The unit technical management performance indicator is the technical supervision quality evaluation score of each unit.

[0041] As a preferred technical solution, the green environmental index is based on NO X Emission indicators , SO2 emission indicators , smoke emission indicators and solid waste emission indicators The calculation method of each indicator is as follows:

[0042] ,

[0043] ,

[0044] ,

[0045] ,

[0046] in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, 、 、 、 Respectively Actual NO of units x , SO2, smoke and solid waste performance emission rates.

[0047] As a preferred technical solution, the low carbon index is based on the carbon emission index of the power plant and carbon emission reduction indicators Determine, among which the calculation method of each indicator is:

[0048] ,

[0049] ,

[0050] in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, For the Actual CO2 performance emission rate of each unit, For the Carbon emissions reduction of power generation units, is the total carbon emission reduction of regional power generation.

[0051] As a preferred technical solution, the energy saving index is based on the fossil energy consumption index , factory electricity consumption indicators and water consumption indicators Determine, among which the calculation method of each indicator is:

[0052] ,

[0053] ,

[0054] ,

[0055] in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, For the Coal consumption for power supply at 80% load rate of each unit, For the The power consumption of the plant corresponding to each unit is For the Water consumption of power supply of each unit.

[0056] As a preferred technical solution, the objective function of the multi-objective optimization model is expressed as:

[0057] ,

[0058] ,

[0059] in, T is the total number of time periods in the scheduling cycle, N is the total number of main grid-connected devices, For equipment i exist t The power harmony index of the time period, For equipment i exist t Output during the period, 、 、 、 are the operating costs of thermal power units, wind turbines, photovoltaic systems and energy storage systems at time t respectively.

[0060] As a preferred technical solution, the constraints of the multi-objective optimization model include power balance constraints, thermal power unit ramp constraints, energy storage SOC constraints and power grid security constraints.

[0061] According to a second aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method when executing the program.

[0062] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] (1) The present invention designs an electric energy harmony index for scheduling scenarios that target both traditional grid-connected thermal power units and new energy grid-connected main equipment such as wind power and photovoltaic power. The electric energy harmony index is taken as one of the core optimization objectives, while taking into account the operating cost of the system. Through mathematical modeling and optimization algorithms, the optimal scheduling scheme that meets the conditions for safe operation of the power grid is found. Specifically, by optimizing scheduling, the present invention can reduce dependence on traditional thermal power units, thereby reducing carbon emissions, achieving clean energy utilization and low-carbon operation, and avoiding the impact of the intermittent and uncertain nature of new energy on the power grid, thereby improving safety. In addition, by considering the system operating cost in the objective function, the output of different power sources can be reasonably allocated, reducing the overall operating cost of the system.

[0065] (2) The power harmony index of the present invention can be constructed by selecting multiple different sub-indicators according to the needs of actual application scenarios, deeply considering the tendency of different scenarios to scheduling schemes, and realizing accurate and effective scheduling in different scenarios, which has wide applicability.

[0066] (3) The present invention can adapt to different grid structures and operating conditions and has high flexibility and adaptability. Whether in a grid with a high proportion of renewable energy or a grid dominated by traditional thermal power units, it can effectively achieve optimized scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0069] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0070] Example 1

[0071] This embodiment provides a scheduling method based on the power harmony index of the main grid-connected equipment. Figure 1 As shown, the method includes the following steps:

[0072] S1, respectively establish mathematical models of the main grid-connected equipment and obtain operating data. The main grid-connected equipment includes thermal power units, wind turbines, photovoltaic systems and energy storage systems.

[0073] The mathematical models of thermal power units, wind turbines, photovoltaic systems and energy storage systems can refer to the construction methods in the existing technology. This embodiment only provides a possible implementation method and does not limit the specific construction of the mathematical models of each device.

[0074] In one embodiment, the relationship between the fuel consumption and output power of a thermal power unit is generally expressed by a quadratic function:

[0075] ,

[0076] in, is the output power of the thermal power unit (MW), F is the fuel consumption, a 、 b 、 c It is the characteristic parameter of the unit.

[0077] In this embodiment, the obtained operating parameters of the thermal power unit include but are not limited to: thermal power unit output, unit coal cost, unit ramp cost, cumulative operating time, thermal power unit start-up and shutdown cost, number of thermal power units started and stopped, pollutant gas emissions, thermal power unit load power time curve and thermal power unit combustion power time curve.

[0078] In one embodiment, the output power model of the wind turbine generator set may be:

[0079] ,

[0080] in, is the air density, is the wind wheel swept area, is the power coefficient, and the tip speed ratio and pitch angle related, is the wind speed.

[0081] In this embodiment, the operating parameters of the wind turbine generator set include, but are not limited to: real-time wind speed, unit cost of wind curtailment, wind turbine generator set power generation, and actual grid-connected wind turbine generator set output.

[0082] In one embodiment, the output power model of the photovoltaic system may be:

[0083] ,

[0084] in, is the light intensity (W / m²), is the photovoltaic panel area, is the conversion efficiency, is the battery temperature, is the reference temperature, is the temperature coefficient.

[0085] In this embodiment, the operating parameters of the photovoltaic system include, but are not limited to: light intensity, unit cost of abandoned light, photovoltaic power station power generation and actual grid-connected photovoltaic power station output.

[0086] In one embodiment, the mathematical model of the energy storage system mainly includes an SOC dynamic model:

[0087] ,

[0088] in, is the SOC at time t, is the charge and discharge current at time t, is the time step, is the nominal capacity of the battery, It is the battery charge and discharge efficiency.

[0089] In this embodiment, the operating parameters of the energy storage system include but are not limited to the current SOC, charge and discharge power, number of cycles, health status, and temperature.

[0090] S2, constructs the power harmony index based on mathematical models and operating data.

[0091] In this embodiment, the electric energy harmony index is the normalized weighted average of at least two of the following indices: the network access security coordination capability index, the network access security coordination performance index, the green environmental protection index, the low carbon index, and the energy saving index. Among them, the network access security coordination capability index is used to evaluate the dynamic adjustment capability of the power system, including the grid connection adjustment capability, stability, fault recovery speed, etc. of the power grid. The network access security coordination performance index is used to reflect the response performance of the power system in actual operation, such as response speed, response effect, etc. The green environmental protection index is used to measure the impact of the power system on the environment during energy utilization, such as pollutant emissions. The low carbon index is used to evaluate the carbon emissions and carbon emission reductions generated by the power system during operation, and promote the development of a low-carbon economy. The energy saving index is used to reflect the efficiency of the power system in the process of energy conversion and transmission, and reduce energy waste.

[0092] The flexibility of this embodiment lies in the ability to select different index combinations based on different actual usage scenarios. For example, in a power grid primarily based on renewable energy, the green environmental protection index and low-carbon index can be prioritized to promote the use of renewable energy and reduce carbon emissions. In an industrial power grid, the energy conservation index and the network access security coordination capability index can be prioritized to improve energy efficiency and grid stability. In a residential power grid, the network access security coordination performance index and energy conservation index can be comprehensively considered to ensure power supply security and reduce residential electricity costs.

[0093] S3, build a multi-objective optimization model.

[0094] In this embodiment, the objective function of the multi-objective optimization model includes maximizing the total power harmony index of the system and minimizing the total operating cost, which can be expressed as:

[0095] ,

[0096] ,

[0097] in, T is the total number of time periods in the scheduling cycle, N is the total number of main grid-connected devices, For equipment i exist t The power harmony index of the time period, For equipment i exist t Output during the period, 、 、 、 are the operating costs of thermal power units, wind turbines, photovoltaic systems and energy storage systems at time t respectively.

[0098] The constraints of the multi-objective optimization model include:

[0099] 1) Power balance constraints

[0100] ,

[0101] in, is the output of thermal power unit i in period t, is the output of the wind turbine in period t, is the photovoltaic power output in period t, is the discharge power of the energy storage system in period t, is the charging power of the energy storage system in period t, is the flexible load demand during period t.

[0102] 2) Thermal power unit ramp constraints

[0103] ,

[0104] in, is the output of thermal power unit i in period t, is the maximum ramp rate of thermal power unit i.

[0105] 3) Energy storage SOC constraints

[0106] ,

[0107] in, is the state of charge of the energy storage system in period t, 、 They are the minimum state of charge and maximum state of charge allowed for energy storage.

[0108] 4) Grid security constraints

[0109] 41) Node voltage constraints

[0110] ,

[0111] in, is the voltage amplitude of node n in period t, 1 is the reference voltage.

[0112] 42) Flow Constraints

[0113] ,

[0114] in, is the transmission power of line l in period t, is the maximum allowed transmission capacity of line l.

[0115] S4, solve the multi-objective optimization model to obtain the optimal scheduling plan for the main grid-connected equipment.

[0116] The solution methods of the multi-objective optimization model include but are not limited to: traditional mathematical programming methods (weight coefficient method, ε-constraint method), evolutionary algorithms (non-dominated sorting genetic algorithm, decomposition-based multi-objective evolutionary algorithm), decomposition-based methods (goal programming, hierarchical optimization), intelligent optimization algorithms (multi-objective particle swarm optimization, multi-objective differential evolution), etc. This embodiment does not limit the specific solution method adopted, and the difference in the solution method adopted does not affect the realization of the purpose of the invention.

[0117] In one embodiment, the solution steps are described by taking the non-dominated sorting genetic algorithm (NSGA-II) as an example.

[0118] Step 1) Initialize the population: Generate an initial population P0 with a population size of N. Each individual represents a possible solution, usually represented by a vector.

[0119] Step 2) Non-dominated sorting: Perform non-dominated sorting on the individuals in the population, dividing the population into multiple non-dominated layers (Front). Each individual is assigned a crowding distance, which is used to measure the distribution density of the individual in the target space.

[0120] Step 3) Selection: Use tournament selection to select individuals from the current population for reproduction. When selecting, prioritize individuals with lower non-dominated layers. If the non-dominated layers are the same, select individuals with a larger crowding distance.

[0121] Step 4) Crossover and mutation: Perform crossover and mutation operations on the selected individuals to generate a new offspring population Q t .

[0122] Step 5) Merge populations: merge the parent population P t and the offspring population Q t Merge into a temporary population R t , of size 2N.

[0123] Step 6) Environment selection: select the merged population R t Perform non-dominated sorting and crowding calculation, and select the first N individuals as the new parent population P t+1 .

[0124] Step 7) Check whether the termination condition (such as reaching the maximum number of iterations or convergence condition) is met. If so, terminate the algorithm and output the final non-dominated solution set (Pareto Front). Otherwise, return to step 2).

[0125] Example 2

[0126] This embodiment, based on the first embodiment, describes in detail the composition of the electric energy harmony index.

[0127] (1) Network security coordination capability index

[0128] The grid access safety coordination capability index is determined based on the grid-connected entity's primary frequency regulation capability, AGC (Automatic Generation Control) capability, AVC (Automatic Voltage Control) capability, phase-leading capability, phase-lagging capability, black start capability, deep peak regulation capability, rapid load shedding capability, and unit steady-state performance. , specifically:

[0129] (11) Primary frequency regulation capability index

[0130] When the primary frequency regulation performance index of the grid-connected generator set meets the requirements of GB / T 31464 5.4.2.3.1-k, the corresponding primary frequency regulation capability index When it is 1, the primary frequency regulation capability index is not satisfied. is 0.

[0131] (12) AGC capability indicators

[0132] When the AGC performance index of the grid-connected generator set meets the requirements of GB / T 31464 5.4.2.3.1-1, the corresponding AGC capability index When it is 1, the AGC capability index is not satisfied. is 0.

[0133] (13) AVC capability indicators

[0134] The calculation method of AVC capability index is: ,in, is the AVC capability indicator, is the AVC performance qualification factor, is the monthly commissioning rate of AVC. When AVC can be adjusted to the target range according to the instruction requirements within the preset time, The value is 1, otherwise it is 0.

[0135] (14) Phase-advancing capability index

[0136] The calculation method of the phase-advancing capability index is:

[0137] ,

[0138] in, is the phase-advancing capability indicator, It is the actual phase advance depth of the unit under the working condition of 100% rated power. is the rated power of the unit.

[0139] (15) Hysteresis capability index

[0140] The calculation method of the hysteresis capability index is:

[0141] ,

[0142] in, is the hysteresis capability index, It is the actual lag degree of the unit under the working condition of 100% rated power. is the rated active power of the unit.

[0143] (16) Black start capability indicators

[0144] The black start capability is determined by whether the unit has the black start capability. If it has the black start capability, the black start capability index is 1, otherwise is 0.

[0145] (17) Deep peak load regulation capability index

[0146] When the minimum power generation capacity of the generator set is less than or equal to 30% Time and depth peak regulation capability index 1, greater than 30% and less than or equal to 40% Time and depth peak regulation capability index 0.5, greater than 40% and less than or equal to 50% Time and depth peak regulation capability index 0.2, greater than or equal to 50% Time and depth peak regulation capability index is 0.

[0147] (18) Rapid load shedding capability index

[0148] The rapid load shedding capability is determined by whether the unit has the capability of rapid load shedding. If the unit has the capability of rapid load shedding, the rapid load shedding capability index Is 1, if not is 0.

[0149] (19) Unit steady-state performance indicators

[0150] The calculation method of the unit steady-state performance index is:

[0151] ,

[0152] in, and They are the stable operation time of the unit and the total operation time of the unit respectively.

[0153] (2) Network security coordination performance index

[0154] The network access safety coordination performance index is determined based on the primary frequency regulation performance, AGC performance, AVC performance, low-frequency regulation response performance, unit non-stop performance, and unit technical management performance. , specifically:

[0155] (21) Primary frequency modulation performance indicators

[0156] ,

[0157] in, and They are the number of qualified frequency modulation actions and the number of frequency modulation action assessments respectively.

[0158] (22) AGC performance indicators

[0159] ,

[0160] in, and They are the AGC performance qualification time and the total AGC operation time.

[0161] (23) AVC performance indicators

[0162] ,

[0163] in, and The AVC response qualification time and the total AVC operation time are respectively. The criteria for AVC response qualification are: the AVC operation rate should reach above 98%, and the adjustment speed should meet the requirements of adjusting to the target range within 2 minutes according to the instructions.

[0164] (24) Low-frequency regulation response performance indicators

[0165] Calculated based on the number of low-frequency regulation responses that the unit should participate in. The number of low-frequency regulation responses that the unit participates in each adjustment is , and are the actual action value and the theoretical action value respectively. If it is greater than 1, it is assigned a value of 1. , To evaluate the number of low-frequency regulation responses that the unit should participate in during the time period, It is a performance indicator for low frequency regulation response;

[0166] (25) Unit non-stop performance indicators

[0167] The calculation method of the unit non-stop performance index is:

[0168] ,

[0169] in, and They are the maximum number of tripping times of the unit and the maximum number of tripping times of all units in the entire network respectively;

[0170] (26) Unit technical management performance indicators

[0171] The unit technical management performance indicator is the technical supervision quality evaluation score of each unit.

[0172] (3) Green Environmental Protection Index

[0173] Green environmental index is based on NO X Emission indicators , SO2 emission indicators , smoke emission indicators and solid waste emission indicators Sure, , the calculation method of each indicator is:

[0174] ,

[0175] ,

[0176] ,

[0177] ,

[0178] in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, 、 、 、 Respectively Actual NO of units x , SO2, smoke and solid waste performance emission rates.

[0179] (4) Low carbon index

[0180] The low carbon index is based on the carbon emission indicators of power plants and carbon emission reduction indicators Sure, , where the calculation method of each indicator is:

[0181] ,

[0182] ,

[0183] in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, For the Actual CO2 performance emission rate of each unit, For the Carbon emissions reduction of power generation units, is the total carbon emission reduction of regional power generation.

[0184] (5) Energy saving index

[0185] Energy saving index based on fossil energy consumption index , factory electricity consumption indicators and water consumption indicators Sure, , where the calculation method of each indicator is:

[0186] ,

[0187] ,

[0188] ,

[0189] in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, For the Coal consumption for power supply at 80% load rate of each unit, For the The power consumption of the plant corresponding to each unit is For the Water consumption of power supply of each unit.

[0190] Example 3

[0191] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0192] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0193] The processing unit performs the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S4 described above can be performed. Alternatively, in other embodiments, the CPU can be configured to execute methods S1 to S4 by any other appropriate means (for example, by means of firmware).

[0194] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0195] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0196] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0197] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A scheduling method based on the power harmony index of grid-connected main equipment, characterized in that: The method comprises the following steps: Establishing mathematical models of main grid-connected equipment and obtaining operating data, respectively, wherein the main grid-connected equipment includes thermal power units, wind power units, photovoltaic systems, and energy storage systems; constructing an electric energy harmony index based on the mathematical model and the operating data, wherein the electric energy harmony index is a normalized weighted average of at least two of a network access security coordination capability index, a network access security coordination performance index, a green environmental protection index, a low carbon index, and an energy-saving index; Constructing a multi-objective optimization model, wherein the objective function of the multi-objective optimization model includes maximizing the system total power energy harmony index and minimizing the total operating cost; Solving the multi-objective optimization model to obtain the optimal scheduling solution for the main grid-connected equipment; The grid access safety coordination capability index is determined based on the primary frequency regulation capability, AGC capability, AVC capability, phase leading capability, phase lagging capability, black start capability, deep peak regulation capability, rapid load shedding capability and unit steady-state performance of the grid-connected entity, wherein: When the primary frequency regulation performance index of the grid-connected generator set meets the preset requirements, the corresponding primary frequency regulation capability index is 1; if it does not meet the requirements, the primary frequency regulation capability index is 0; When the AGC performance index of the grid-connected generator set meets the preset requirements, the corresponding AGC capability index is 1; if not, the AGC capability index is 0; The calculation method of AVC capability index is: ,in, is the AVC capability indicator, is the AVC performance qualification factor, is the monthly commissioning rate of AVC. When AVC can be adjusted to the target range according to the instruction requirements within the preset time, The value is 1, otherwise it is 0; The calculation method of the phase-advancing capability index is: ， in, is the phase-advancing capability indicator, It is the actual phase advance depth of the unit under the working condition of 100% rated power. is the rated power of the unit; The calculation method of the hysteresis capability index is: ， in, is the hysteresis capability index, It is the actual lag degree of the unit under the working condition of 100% rated power. is the rated active power of the unit; The black start capability is determined by whether the unit has the black start capability. If the unit has the black start capability, the black start capability index is 1, otherwise it is 0; When the minimum power generation capacity of the generator set is less than or equal to 30% The deep peak load regulation capability index is 1, which is greater than 30%. and less than or equal to 40% The deep peak regulation capability index is 0.5, which is greater than 40%. and less than or equal to 50% The deep peak regulation capability index is 0.2, which is greater than or equal to 50%. The deep peak regulation capability index is 0; The rapid load shedding capability is determined by whether the unit has the capability of rapid load shedding. If the unit has the capability of rapid load shedding, the rapid load shedding capability index is 1, otherwise it is 0. The calculation method of the unit steady-state performance index is: ， in, and They are the stable operation time of the unit and the total operation time of the unit; The network access safety coordination performance index is determined based on the primary frequency regulation performance, AGC performance, AVC performance, low-frequency regulation response performance, unit non-stop performance, and unit technical management performance, among which: Primary frequency modulation performance indicators The calculation method is: ， in, and They are the number of qualified actions for one frequency modulation and the number of assessments for one frequency modulation action; AGC Performance Indicators The calculation method is: ， in, and They are the AGC performance qualified time and the total AGC operation time; AVC performance indicators The calculation method is: ， in, and They are AVC response qualified time and AVC total operation time; The calculation method of low-frequency regulation response performance index is as follows: the number of low-frequency regulation responses that the unit should participate in is calculated, and the number of low-frequency regulation responses that the unit participates in each adjustment is calculated. , and are the actual action value and the theoretical action value respectively. If it is greater than 1, it is assigned a value of 1. , To evaluate the number of low-frequency regulation responses that the unit should participate in during the time period, It is a performance indicator for low frequency regulation response; The calculation method of the unit non-stop performance index is: ， in, and They are the maximum number of tripping times of the unit and the maximum number of tripping times of all units in the entire network respectively; The unit technical management performance indicator is the technical supervision quality evaluation score of each unit; The green environmental index is based on NO X Emission indicators , SO2 emission indicators , smoke emission indicators and solid waste emission indicators The calculation method of each indicator is as follows: ， ， ， ， in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, 、 、 、 Respectively Actual NO of units x , SO2, smoke, and solid waste performance emission rates; The low carbon index is based on the carbon emission indicators of power plants and carbon emission reduction indicators Determine, among which the calculation method of each indicator is: ， ， in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, For the The actual CO2 performance emission rate of each unit, For the Carbon emissions reduction of power generation units, is the total carbon emission reduction of regional power generation; The energy saving index is based on the fossil energy consumption index , factory electricity consumption indicators and water consumption indicators Determine, among which the calculation method of each indicator is: ， ， ， in, is the serial number of the thermal power unit, is the total number of thermal power units in the metropolitan area, For the The proportion of the power generation of the unit to the total power generation of the thermal power units in the whole network, For the Coal consumption for power supply at 80% load rate of each unit, For the The power consumption of the plant corresponding to each unit is For the Water consumption of power supply of each unit.

2. The scheduling method based on the power harmony index of the main grid-connected equipment according to claim 1 is characterized in that: The objective function of the multi-objective optimization model is expressed as: ， ， in, T is the total number of time periods in the scheduling cycle, N is the total number of main grid-connected devices, For devices i exist t The power harmony index of the time period, For devices i exist t Output during the period, 、 、 、 are the operating costs of thermal power units, wind turbines, photovoltaic systems and energy storage systems at time t respectively.

3. The scheduling method based on the power harmony index of the main grid-connected equipment according to claim 1 is characterized in that: The constraints of the multi-objective optimization model include power balance constraints, thermal power unit ramp constraints, energy storage SOC constraints and power grid security constraints.

4. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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