Power grid dispatching method and system based on multi-resource space-time coupling characteristics

Abstract

The application discloses a power grid dispatching method and system based on multi-resource space-time coupling characteristics, constructs a multi-resource coordinated optimization dispatching model based on a coal-fired unit model, an extra-high voltage direct current coupling model and an electric hydrogen production load cluster dispatching aggregation model; optimizes the multi-resource coordinated optimization dispatching model based on coal-fired unit operation constraints, extra-high voltage direct current operation constraints and hydrogen production load operation constraints, and obtains unit start-stop arrangement, extra-high voltage direct current transmission planning and multi-resource power generation and consumption planning based on the optimized multi-resource coordinated optimization dispatching model. The application fully considers new power system construction requirements, constructs a multi-resource multi-objective optimization dispatching model considering coal-fired unit deep regulation characteristics, electric hydrogen production new resources and extra-high voltage direct current regulation capacity, realizes the promotion of system regulation capacity through multi-resource collaborative operation, relieves new energy consumption and power supply pressure, and adapts to the development and application of a new power system.

Description

Technical Field

[0001] This invention belongs to the field of power grid operation scheduling and control, and specifically relates to a power grid scheduling method and system based on the spatiotemporal coupling characteristics of multiple resources. Background Technology

[0002] With the increasing proportion of renewable energy, the equivalent load of the system exhibits a valley-shaped curve characteristic. The mismatch between power generation and load in time and space, coupled with a lack of adjustment resources, highlights the contradiction between the difficulty in absorbing renewable energy during periods of high generation and the difficulty in ensuring power supply during periods of low renewable energy generation. Renewable energy has a low minimum guaranteed output level, making power supply assurance difficult and requiring coordination with the main power grid, backup power sources, and other types of flexible adjustment resources to achieve power supply security. During periods of low renewable energy output, the power system needs conventional power sources and other non-renewable energy units to achieve power balance. Prolonged high renewable energy output poses a significant challenge to system absorption.

[0003] The rapid and unexpected development of new energy sources has presented the power grid dispatch with a significant challenge in balancing the integration and supply of these sources. Simultaneously, system regulation resources are continuously increasing, and the potential for flexible system regulation is enormous, necessitating the full utilization of resources such as flexible coal-fired power, new energy storage, ultra-high-voltage direct current (UHVDC), and adjustable loads. Currently, the SCUC (Security-Constrained Unit Commitment) model is used in power systems to determine unit output plans and energy storage station charging and discharging plans. This aims to meet load demand within specific time periods while minimizing costs and ensuring the safe and stable operation of the power system. However, existing methods do not consider the impact of new energy generation on the overall power grid system at different times, leading to untimely and inflexible grid dispatch. This is particularly problematic in real-time dispatch, where rapid dispatch results are crucial, placing higher demands on computational efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a power grid dispatching method and system based on the spatiotemporal coupling characteristics of multiple resources, so as to overcome the problem that existing methods do not consider the impact of new energy power generation on the entire power grid system at different times, resulting in untimely power grid dispatching and poor flexibility.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a power grid scheduling method based on the spatiotemporal coupling characteristics of multiple resources, comprising the following steps:

[0007] A multi-resource coordinated optimization scheduling model is constructed based on a coal-fired power unit model, an ultra-high voltage DC coupling model, and an electric hydrogen production load cluster scheduling aggregation model.

[0008] The multi-resource coordinated optimization scheduling model is optimized based on the operating constraints of coal-fired power units, ultra-high voltage direct current (UHVDC) power transmission, and hydrogen production load. Based on the optimized multi-resource coordinated optimization scheduling model, the unit start-up and shutdown arrangements, UHVDC power transmission plans, and multi-resource power generation and consumption plans are obtained.

[0009] Preferably, the optimization objectives for optimizing the multi-resource coordinated scheduling model include operating costs within the scheduling cycle, renewable energy curtailment, and system carbon emissions.

[0010] Preferably, the operating costs during the scheduling cycle include power generation costs, adjustable load adjustment costs, hydrogen production adjustment costs, and energy storage charging and discharging compensation costs.

[0011] Preferably, optimizing the multi-resource coordinated optimization scheduling model specifically involves converting the abandoned renewable energy and system carbon emissions into costs, jointly optimizing them with the operating costs within the scheduling cycle, and solving the objective function of the multi-resource coordinated optimization scheduling model using the CPLEX method to obtain the optimized unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans.

[0012] Preferably, the operating constraints of the coal-fired power unit include cost constraints, unit load increase / decrease rate constraints, minimum operating time constraints for deep peak shaving of the unit, and upper and lower limits constraints for unit output.

[0013] Preferably, the UHVDC operation constraints include transmission power limit constraints, transmission power adjustment rate constraints, constraints that power cannot be adjusted in reverse during adjacent time periods, constraints on the number of UHVDC adjustment times, constraints on the power already traded, and constraints on the relationship between the number of coal-fired power plants in operation and transmission power limit constraints.

[0014] Preferably, the hydrogen production load operation constraints include upper and lower limits of hydrogen production load power, load increase / decrease rate constraints, and hydrogen storage constraints.

[0015] In a second aspect, the present invention provides a power grid dispatching system based on the spatiotemporal coupling characteristics of multiple resources, comprising a model optimization module and a power grid dispatching module.

[0016] The model optimization module is used to construct a multi-resource coordinated optimization scheduling model based on a coal-fired power unit model, an ultra-high voltage direct current coupling model, and an electric hydrogen production load cluster scheduling aggregation model; and to optimize the multi-resource coordinated optimization scheduling model based on the operating constraints of coal-fired power units, ultra-high voltage direct current operating constraints, and hydrogen production load operating constraints.

[0017] The power grid dispatching module is used to obtain unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans based on the optimized multi-resource coordinated optimization dispatching model.

[0018] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described power grid scheduling method based on the spatiotemporal coupling characteristics of multiple resources.

[0020] Compared with the prior art, the present invention has the following beneficial technical effects:

[0021] This invention provides a power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources. A multi-resource coordinated optimization dispatching model is constructed based on a coal-fired power unit model, an ultra-high voltage direct current (UHVDC) coupling model, and an electric hydrogen production load cluster dispatching aggregation model. The multi-resource coordinated optimization dispatching model is optimized based on the operating constraints of coal-fired power units, UHVDC operating constraints, and hydrogen production load operating constraints. Based on the optimized multi-resource coordinated optimization dispatching model, the unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans are obtained. This invention fully considers the construction requirements of new power systems, constructing a multi-resource, multi-objective optimized dispatching model that considers the deep regulation characteristics of coal-fired power units, the new resource of electric hydrogen production, and the regulation capacity of UHVDC. Through the coordinated operation of multiple resources, the system's regulation capacity is improved, alleviating the pressure on new energy consumption and power supply, and adapting to the development and application of new power systems. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the power grid scheduling method based on the spatiotemporal coupling characteristics of multiple resources in an embodiment of the present invention.

[0023] Figure 2 This is a linearization diagram of the non-monotonic incremental cost increase stage of the unit in an embodiment of the present invention.

[0024] Figure 3 This is a block diagram of a power grid dispatching system based on the spatiotemporal coupling characteristics of multiple resources in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] like Figure 1 As shown, this invention provides a power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources. This method coordinates various flexible adjustment resources on the generation and load sides, aiming to improve the renewable energy absorption level and overall regulation capability of the power system—a direction for power grid dispatching development. The method specifically includes the following steps:

[0028] S1. Based on the coal-fired power unit deep adjustment characteristics model, the UHVDC coupling model considering the coal-fired power unit start-up mode and the UHVDC power transmission, and the hydrogen production load cluster scheduling aggregation model, a multi-resource coordinated optimization scheduling model is constructed. The multi-resource coordinated optimization scheduling model is optimized based on the coal-fired power unit operation constraints, UHVDC operation constraints and hydrogen production load operation constraints.

[0029] S2, based on the optimized multi-resource coordination and scheduling model, obtains the unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans.

[0030] Specifically, in the specific embodiments of this application, the multiple resources include power sources, power grids, adjustable loads in the system, new energy storage and hydrogen production loads in the system; power sources include conventional units and new energy units.

[0031] The optimization objectives in the multi-resource coordinated optimization scheduling model include operating costs, renewable energy curtailment, and system carbon emissions within the scheduling cycle.

[0032] The operating costs during the scheduling cycle include power generation costs, adjustable load adjustment costs, hydrogen production adjustment costs, and energy storage charging and discharging compensation costs.

[0033] In the specific implementation of this application, the abandoned electricity from new energy sources and the carbon emissions of the system are converted into costs and jointly optimized with the operating costs within the scheduling cycle.

[0034] The objective function F of the constructed multi-resource coordination and optimization scheduling model is:

[0035] F=F1+k2F2+k3F3+F4+F5+F6(1)

[0036] Where: F1 represents the cost of power generation, which specifically includes the cost of power generation operation and the cost of startup;

[0037]

[0038] F2 represents the amount of renewable energy curtailed, specifically including the amount of wind and solar power curtailed:

[0039]

[0040] F3 indicates the system's carbon emissions:

[0041]

[0042] F4 indicates the adjustable load adjustment cost:

[0043]

[0044] F5 indicates the cost of adjusting for hydrogen production via electro-hydrogen:

[0045]

[0046] F6 represents the cost of energy storage charging and discharging compensation:

[0047]

[0048] In the objective function above, k2 represents the cost conversion factor for wind and solar curtailment penalties, and k3 represents the system carbon emission cost conversion factor. By setting different coefficient values, a balance is achieved between system operating costs, renewable energy consumption, and carbon emissions. In the above formula: T is the number of time periods during system scheduling; I is the number of thermal power units in the system; p i (t) represents the active power of unit i at time t; x i (t) represents the continuous start-up and shutdown time of unit i at time t, x i (t) > 0 indicates the continuous power-on time, x i (t) < 0 indicates continuous downtime; u i (t) represents the state of unit i at time t, u i (t) = 1 indicates that the device is powered on, u i (t) = 0 indicates a shutdown; C i (p i (t) represents the operating cost (expense) of unit i at time t; S i (x i (t-1),u i(t) represents the start-up cost (expense) of unit i from time period t-1 to time period t when the state changes; it can be expressed as a linear function; W represents the number of wind farms in the system, and V represents the number of photovoltaic power stations in the system; P w (t) represents the predicted value of wind farm w at time t, P v (t) represents the predicted value of v of the photovoltaic power station at time t, λ w (t) represents the curtailment rate of wind farm w at time t, λ v (t) represents the curtailment rate of the photovoltaic power plant at time t; δ i Represents the carbon emission factor of unit i; L is the system adjustable load number; C l (t) represents the adjustment cost of load l at time t, Δp l (t) represents the adjustment amount of load l at time t; H is the number of hydrogen production stations in the system; C h (t) represents the adjustment cost of electro-hydrogen production h at time t, Δp h (t) represents the adjustment amount of the electro-hydrogen production h at time t; S is the number of energy storage stations in the system; This represents the charging compensation cost and discharging compensation cost of energy storage station s at time t. These represent the charging power and discharging power of energy storage station s at time t, respectively.

[0049] The multi-resource coordinated optimization scheduling model is optimized based on the operating constraints of coal-fired power units, ultra-high voltage direct current (UHVDC) operation constraints, and hydrogen production load operation constraints. The operating constraints of coal-fired power units include cost constraints, unit load increase / decrease rate constraints, minimum operating time constraints for deep peak shaving of units, and upper and lower limits constraints for unit output.

[0050] A coal-fired power unit model based on the deep adjustment characteristics of coal-fired power units was constructed: After the flexibility of the coal-fired power units was modified, their low-load operation capability, rapid ramping capability, and rapid start-up capability were improved. The marginal cost curve characteristics, ramping capability, and landslide rate of the constructed coal-fired power unit model also changed significantly.

[0051] A coal-fired power unit model supporting non-monotonic increasing marginal cost curves is constructed. Specifically, a non-convex cost curve linearization method is used for linearization, including the following methods:

[0052] Piecewise linearization constraint for non-convex cost functions (for linearization of non-monotonic incremental cost stages):

[0053] like Figure 2 As shown, F total (P) is the cost function; P is the unit's output, satisfying the following constraints:

[0054]

[0055]

[0056]

[0057]

[0058] F n =F total (P n (12)

[0059]

[0060]

[0061]

[0062] (P n+1 -P n )·δ n+1 ≤q n ≤(P n+1 -P n )·δ n , n=1,2,…,N-1 (16)

[0063] 0≤q N ≤(P N+1 -P N )·δ N (17)

[0064] δ n ={0,1}(n=1,2,…N)(18)

[0065] Unit load increase / decrease rate constraints:

[0066] The rate of load increase / decrease is related to whether the unit is in deep peak shaving. If it is currently in normal operation, i.e., the output p i (t) is equal to or greater than P i peak Then the maximum value of the output force that can be added or subtracted is If we are currently in a deep peak shaving phase, i.e., the output p i (t) is less than P i peak Then the maximum value of the output force that can be added or subtracted is

[0067]

[0068] Minimum operating time constraints for deep peak shaving of generating units:

[0069] Once the generator set enters the deep peak-shaving range, it needs to run for a certain period of time to avoid damage to the generator set.

[0070]

[0071] In the formula: This is the minimum operating time for deep peak shaving of the generating unit.

[0072] UHVDC operation constraints:

[0073] Ultra-high voltage direct current (UHVDC) is a flexible regulation resource with certain regulation capabilities. Its flexible regulation capabilities are constrained by the minimum operating power of the DC, the number of power regulation times, the amount of electricity to be traded, and the demand factors of the end grid. Specifically, it includes constraints such as transmission power limit constraints, transmission power adjustment rate constraints, constraints that power cannot be adjusted in reverse between adjacent time periods, constraints on the number of UHVDC adjustment times, constraints on the amount of electricity already traded, and constraints on the relationship between the number of coal-fired power plants in operation and the transmission power limit to construct an UHVDC dispatch model.

[0074] The power transmission limit constraint:

[0075]

[0076] In the formula: P tl (t), These represent the lower and upper limits of the transmission power of UHVDC tl at time t, respectively.

[0077] The power transmission rate adjustment constraint:

[0078]

[0079]

[0080]

[0081] In the formula: Δ tl This indicates the maximum value of the UHVDC TL power adjustment rate; These represent the upward and downward power adjustment states of UHVDC tl at time t, respectively. This indicates an upward adjustment.

[0082] This indicates a downward adjustment.

[0083] The power constraint cannot be adjusted in reverse during adjacent time periods:

[0084]

[0085]

[0086] The constraint on the number of adjustments for ultra-high voltage direct current:

[0087]

[0088] wherein: X max represents the maximum value of the UHV DC adjustment times.

[0089] The concluded transaction power constraint:

[0090]

[0091] wherein: P tl (t), respectively represent the concluded transaction power and the maximum power transmission power of the UHV DC tl at time t.

[0092] The constraint on the relationship between the number of operating coal-fired generating units and the transmission power limit (taking the non-linear relationship between the number of operating units and the transmission capacity as an example):

[0093]

[0094]

[0095]

[0096]

[0097] wherein: M represents the number of coal-fired generating units supporting the UHV DC tl; P i represents the transmission capacity when i coal-fired generating units supporting the UHV DC tl are started.

[0098] The hydrogen production load operation constraint:

[0099] The hydrogen production load operation constraint includes the hydrogen production load power upper and lower limit constraint, the load increase and decrease rate constraint, and the hydrogen storage constraint.

[0100] The hydrogen production power upper and lower limit constraint:

[0101] p h (t)=P h (t)+Δp h (t) (34)

[0102]

[0103] wherein: P h (t) represents the baseline hydrogen production power of the electric hydrogen production station h at time t, Δp h (t) represents the adjustment amount of the electric hydrogen production station h at time t; p h respectively represent the upper and lower limits of the hydrogen production power of the electric hydrogen production station h; u h (t) represents whether hydrogen production is started, u h (t)=1 represents hydrogen production, u h (t)=0 represents shutdown.

[0104] The ramp rate constraint:

[0105] 20-Δ h ≤p h (t)-p h (t-1)≤△ h (36)

[0106] In the formula: △ h This indicates the maximum value of the added or subtracted power of the hydrogen production station h.

[0107] The hydrogen storage constraint:

[0108] E h (t)=E h (t-1)+η h p h (t)-O h (t) (37)

[0109]

[0110] In the formula: E h (t) represents the hydrogen storage capacity of the hydrogen production station h at time t; η h Indicates hydrogen production conversion rate; O h (t) represents the hydrogen usage plan of the electric hydrogen production station h at time t; E h , These represent the lower and upper limits of the energy storage capacity h at the hydrogen production station, respectively.

[0111] In specific embodiments of this application, the hydrogen production load operation constraints also include wind and solar operation constraints, adjustable load operation constraints, energy storage operation constraints, system operation constraints, and grid security constraints.

[0112] In the specific embodiments of this application, the wind and solar operation constraints specifically refer to the upper and lower limits of power generation constraints:

[0113] p w (t)=P w (t)(1-λ w (t)) (39)

[0114] 0≤λ w (t)≤1 (40)

[0115] p v (t)=P v (t)(1-λ v (t)) (41)

[0116] 0≤λ v (t)≤1 (42)

[0117] Adjustable load operation constraints include upper and lower limits for power consumption, ramp rate constraints, and daily power consumption constraints.

[0118] Specifically, the upper and lower limits of power consumption constraints are as follows:

[0119] p l (t)=P l (t)+Δp l (t) (43)

[0120]

[0121] In the formula: P l (t) represents the baseline electrical power consumption of the adjustable load l at time t, Δp l (t) represents the adjustment amount of the adjustable load l at time t; p l These represent the upper and lower limits of the hydrogen production power of the adjustable load l, respectively.

[0122] The load increase / decrease rate constraints are as follows:

[0123] -△ l ≤p h (t)-p h (t-1)≤△ l (45)

[0124] In the formula: △ l This indicates the maximum value of the adjustable load l's power increase or decrease.

[0125] Daily electricity consumption is constrained, and the daily electricity consumption remains the same before and after adjustable load optimization:

[0126]

[0127] In the formula: T1 is the number of time periods during the first day of scheduling in the system, and so on for subsequent days.

[0128] The energy storage operation constraints include charge / discharge state constraints, charging power constraints, discharging power constraints, state of charge (SOC) constraints, charge / discharge quantity constraints, and charge / discharge cycle constraints.

[0129] The charge / discharge state constraints are as follows:

[0130]

[0131] In the formula: These represent the charging and discharging states of energy storage s at time t, respectively. Indicates charging. This indicates a discharge.

[0132] Charging power constraints:

[0133]

[0134] In the formula: These represent the upper and lower limits of the charging power of the energy storage device s, respectively.

[0135] The discharge power constraint:

[0136]

[0137] In the formula: These represent the upper and lower limits of the charging power of the energy storage device s, respectively.

[0138] The state of charge (SOC) constraint:

[0139]

[0140]

[0141] In the formula: These represent the charging conversion efficiency and discharging conversion efficiency of energy storage, respectively; C s This indicates the capacity of energy storage s; S s , These represent the lower and upper limits of the state of charge of the energy storage s, respectively.

[0142] The charge / discharge quantity constraint:

[0143] To ensure the continuity of energy storage deployment, it is agreed that the end-of-day charge state remains unchanged.

[0144]

[0145] In the formula: T1 is the number of time periods during the first day of scheduling in the system, and so on for subsequent days.

[0146] The charge / discharge cycle constraint:

[0147]

[0148]

[0149] Where: N c N d These represent the daily charging limit, the daily discharging limit, and so on for subsequent days.

[0150] The system operation constraints include load balancing constraints and spinning reserve constraints.

[0151] The load balance constraint:

[0152]

[0153] In the formula: P d (t) represents the system load at time t; p tl (t) represents the output power of the tie line tl at time t.

[0154] The rotational spare constraint:

[0155]

[0156]

[0157] In the formula: The upward rotational backup provided for unit i at time t; The system's rotational backup requirement at time t is adjusted upwards. r i (t) represents the reduced rotational reserve provided by unit i at time t; p r (t) represents the reduced spin-off reserve requirement of the system at time t.

[0158] The power grid security constraints include branch power flow constraints and tie line section power flow constraints.

[0159] The branch power flow constraints are as follows:

[0160]

[0161] In the formula: p ij , These represent the power flow and upper limit of branches i and j, respectively.

[0162] The power flow constraint of the connecting line section:

[0163]

[0164] In the formula: P ij , These represent the power flow and upper limit of tie line sections i and j, respectively.

[0165] In the specific implementation of this application, the abandoned renewable energy and system carbon emissions are converted into costs and jointly optimized with the operating costs within the scheduling cycle. Then, the objective optimization solution is obtained by using the CPLEX method based on the objective function of the multi-resource coordinated optimization scheduling model. This yields the optimized unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans, thereby achieving coordinated and optimized operation of multiple types of active resources, including coal-fired power units, hydrogen production by electricity, UHVDC, renewable energy, and energy storage.

[0166] See Figure 3 In another embodiment of the present invention, a power grid dispatching system based on the spatiotemporal coupling characteristics of multiple resources is provided, including a model optimization module and a power grid dispatching module;

[0167] The model optimization module constructs a multi-resource coordinated optimization scheduling model based on a coal-fired power unit model, an ultra-high voltage direct current coupling model, and an electric hydrogen production load cluster scheduling aggregation model; it then optimizes the multi-resource coordinated optimization scheduling model based on the operating constraints of coal-fired power units, ultra-high voltage direct current operation constraints, and hydrogen production load operation constraints.

[0168] The power grid dispatching module obtains unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans based on the optimized multi-resource coordination and optimization dispatching model.

[0169] In a more specific implementation, the multi-resource coordination and optimization scheduling model needs to be optimized, and the optimization objectives include operating costs, renewable energy curtailment, and system carbon emissions within the scheduling cycle.

[0170] The operating costs during the scheduling cycle include power generation costs, adjustable load adjustment costs, hydrogen production adjustment costs, and energy storage charging and discharging compensation costs.

[0171] The optimization of the multi-resource coordinated optimization scheduling model specifically involves converting the abandoned renewable energy and system carbon emissions into costs, and jointly optimizing them with the operating costs within the scheduling cycle. By using the CPLEX method to solve the objective function of the multi-resource coordinated optimization scheduling model, the optimized unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans can be obtained.

[0172] The operating constraints of the coal-fired power units include cost constraints, unit load increase / decrease rate constraints, minimum operating time constraints for deep peak shaving constraints, and upper and lower limits constraints for unit output.

[0173] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment of the present invention can be used for the operation of power grid dispatching methods based on the spatiotemporal coupling characteristics of multiple resources.

[0174] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the power grid scheduling method based on the spatiotemporal coupling characteristics of multiple resources in the above embodiments.

[0175] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0177] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources, characterized in that, Includes the following steps: A multi-resource coordinated optimization scheduling model is constructed based on a coal-fired power unit model, an ultra-high voltage direct current coupling model, and an electric hydrogen production load cluster scheduling aggregation model. The multi-resource coordinated optimization scheduling model is then optimized based on the operating constraints of coal-fired power units, ultra-high voltage direct current operation constraints, and hydrogen production load operation constraints. The unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans are determined based on the optimized multi-resource coordination and optimization scheduling model. The objective function F of the constructed multi-resource coordination and optimization scheduling model is: （1） in: This indicates the cost of power generation, specifically including output operating costs and start-up costs; （2） This refers to the amount of renewable energy curtailed, specifically including the amount of wind and solar power curtailed: （3） Indicates the system's carbon emissions: （4） Indicates the cost of adjustable load adjustment: （5） Indicates the cost adjustment of hydrogen production by electricity: （6） Indicates the cost of energy storage charging and discharging compensation: （7） In the above objective function This represents the factor for calculating the penalty costs of wind and solar power curtailment. This represents the carbon emission cost conversion factor for the system. By setting different factor values, a balance can be achieved between system operating costs, renewable energy consumption, and carbon emissions. In the above formula: This represents the number of time periods during system scheduling. This refers to the number of thermal power units in the system. For the unit exist Active power at that time; For the unit exist The continuous start-up and shutdown time. Indicates continuous power-on time. Indicates the duration of continuous downtime; For the unit exist The state at that time, Indicates that the device is powered on. Indicates that the machine is stopped; For the unit exist Operating costs at that time; For the unit When there is a state change, from Time's up The startup cost for a given period can be expressed as a linear function. For the number of wind farms in the system, This refers to the number of photovoltaic power plants in the system. Indicates wind farm exist Predicted value at time Indicates photovoltaic power station exist Predicted value at time Indicates wind farm exist curtailment rate Indicates photovoltaic power station exist curtailment rate; Indicates the unit Carbon emission factors; The number of adjustable loads in the system; Indicates load exist Adjustment costs at the time Indicates load exist Adjustment amount at time; The number of hydrogen production stations in the system; Indicates electro-hydrogen production exist Adjustment costs at the time Indicates electro-hydrogen production exist Adjustment amount at time; This refers to the number of energy storage stations in the system. , Indicates energy storage station exist Charging compensation cost and discharging compensation cost at the time , They represent energy storage stations exist The charging power and discharging power at that time.

2. The power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources according to claim 1, characterized in that, In the step of optimizing the multi-resource coordinated optimization scheduling model based on the operating constraints of coal-fired power units, ultra-high voltage direct current operation constraints, and hydrogen production load operation constraints, the optimization objectives for optimizing the multi-resource coordinated optimization scheduling model include operating costs, renewable energy curtailment, and system carbon emissions within the scheduling cycle.

3. The power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources according to claim 2, characterized in that, The operating costs during the scheduling cycle include power generation costs, adjustable load adjustment costs, hydrogen production adjustment costs, and energy storage charging and discharging compensation costs.

4. The power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources according to claim 2, characterized in that, In the step of optimizing the multi-resource coordinated optimization scheduling model based on the operating constraints of coal-fired power units, ultra-high voltage direct current (UHVDC) power transmission, and hydrogen production load, the optimization of the multi-resource coordinated optimization scheduling model specifically involves converting the abandoned renewable energy and system carbon emissions into costs, jointly optimizing them with the operating costs within the scheduling cycle, and solving the objective function of the multi-resource coordinated optimization scheduling model using the CPLEX method to obtain the optimized unit start-up and shutdown arrangements, UHVDC power transmission plan, and multi-resource power generation and consumption plan.

5. The power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources according to claim 2, characterized in that, The operating constraints of the coal-fired power units include cost constraints, unit load increase / decrease rate constraints, minimum operating time constraints for deep peak shaving constraints, and upper and lower limits constraints for unit output.

6. The power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources according to claim 2, characterized in that, The constraints on ultra-high voltage direct current (UHVDC) operation include transmission power limit constraints, transmission power adjustment rate constraints, constraints that power cannot be adjusted in reverse during adjacent time periods, constraints on the number of UHVDC adjustment times, constraints on the power traded in transactions, and constraints on the relationship between the number of coal-fired power plants in operation and transmission power limit constraints.

7. The power grid dispatching method based on the spatiotemporal coupling characteristics of multiple resources according to claim 2, characterized in that, The constraints on hydrogen production load operation include upper and lower limits of hydrogen production load power, load increase / decrease rate constraints, and hydrogen storage constraints.

8. A power grid dispatching system based on the spatiotemporal coupling characteristics of multiple resources, characterized in that, Includes a model optimization module and a power grid dispatching module; The model optimization module is used to construct a multi-resource coordinated optimization scheduling model based on a coal-fired power unit model, an ultra-high voltage direct current coupling model, and an electric hydrogen production load cluster scheduling aggregation model; and to optimize the multi-resource coordinated optimization scheduling model based on the operating constraints of coal-fired power units, ultra-high voltage direct current operating constraints, and hydrogen production load operating constraints. The power grid dispatching module is used to determine the unit start-up and shutdown arrangements, UHVDC transmission plans, and multi-resource power generation and consumption plans based on the optimized multi-resource coordination and optimization dispatching model. The objective function F of the constructed multi-resource coordination and optimization scheduling model is: （1） in: This indicates the cost of power generation, specifically including output operating costs and start-up costs; （2） This refers to the amount of renewable energy curtailed, specifically including the amount of wind and solar power curtailed: （3） Indicates the system's carbon emissions: （4） Indicates the cost of adjustable load adjustment: （5） Indicates the cost adjustment of hydrogen production by electricity: （6） Indicates the cost of energy storage charging and discharging compensation: （7） In the above objective function This represents the factor for calculating the penalty costs of wind and solar power curtailment. This represents the carbon emission cost conversion factor of the system. By setting different factor values, a balance can be achieved between system operating costs, renewable energy consumption, and carbon emissions. In the above formula: This represents the number of time periods during system scheduling. This refers to the number of thermal power units in the system. For the unit exist Active power at that time; For the unit exist The continuous start-up and shutdown time. Indicates continuous power-on time. Indicates the duration of continuous downtime; For the unit exist The state at that time, Indicates that the device is powered on. Indicates that the machine is stopped; For the unit exist Operating costs at that time; For the unit When there is a state change, from Time's up The startup cost for a given period can be expressed as a linear function. For the number of wind farms in the system, This refers to the number of photovoltaic power plants in the system. Indicates wind farm exist Predicted value at time Indicates photovoltaic power station exist Predicted value at time Indicates wind farm exist curtailment rate Indicates photovoltaic power station exist curtailment rate; Indicates the unit Carbon emission factors; The number of adjustable loads in the system; Indicates load exist Adjustment costs at the time Indicates load exist Adjustment amount at time; The number of hydrogen production stations in the system; Indicates electro-hydrogen production exist Adjustment costs at the time Indicates electro-hydrogen production exist Adjustment amount at time; This refers to the number of energy storage stations in the system. , Indicates energy storage station exist Charging compensation cost and discharging compensation cost at the time , They represent energy storage stations exist The charging power and discharging power at that time.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power grid scheduling method based on the spatiotemporal coupling characteristics of multiple resources as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the power grid scheduling method based on the spatiotemporal coupling characteristics of multiple resources as described in any one of claims 1 to 7.

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