Source-network-load-storage integrated multi-time-scale scheduling method and system

Abstract

The invention discloses a source-grid-load-storage integrated multi-time-scale scheduling method and system, and solves the scheduling problem caused by unstable wind power and photovoltaic power generation. Firstly, a probability model of wind power and load is established according to weather prediction and historical data, and a preliminary plan of thermal power starting and stopping, energy storage charging and discharging and user electricity utilization excitation is made one day in advance; then, the unit output and demand response scheme is adjusted in a rolling mode every four hours according to the newest weather and electricity consumption prediction; and finally, power generation and energy storage equipment is finely adjusted in real time according to the power grid state, and quick response load resources are started. The whole process adopts a simplified power grid power flow model for coordinated calculation, and through energy storage adjustment and flexible power utilization of a user side, the stability of a power grid is ensured, and wind energy and solar energy are utilized to the maximum extent. Dispatching of different time scales is linked layer by layer, thermal power is responsible for basic power supply, new energy is preferentially used, energy storage and adjustable loads are matched to fill fluctuation, and finally efficient absorption of clean energy and economical and safe operation of a power grid are achieved.

Description

Background Art

[0002] New energy power generation will reach a higher proportion in the power grid. However, a new power system with a high penetration rate of renewable energy has uncertainties and fluctuations, which brings difficulties to the power system dispatching. In this context, establishing a new multi-time scale dispatching method involving sources, loads, and energy storage driven by new energy is of great significance for promoting the consumption of new energy and improving system economy.

[0003] On the one hand, hydrogen energy storage is suitable for energy regulation and power and energy balance at long time scales (daily, weekly, seasonal levels), and electrical energy storage is more suitable for rapid power smoothing and peak shaving at short time scales (second, minute, hour levels). The dispatching scheme designed in the present invention jointly deploys both hydrogen and electrical energy storage methods, which can not only complement each other on multiple time scales of operation control, but also serve as backups for each other in the case of component failures.

[0004] On the other hand, price-based demand response can shift the load distribution of users and has the effect of peak shaving, while incentive-based demand response cannot shift the load distribution of users, but its load reduction effect during the peak period of users is better than that of price-based demand response. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a multi-time scale dispatching method and system for source-network-load-storage integration in view of the deficiencies in the above-mentioned prior art. By combining the electricity price and incentive mechanisms, it can maximize the dispatching potential of users, provide decision-making references for market players participating in the power market, achieve the maximization of multi-party interests, improve the investment economy and operation flexibility of new energy power generation, electrical energy storage, hydrogen energy storage, and responsive loads in the power system, and solve the technical problems of high wind curtailment rate and high operation cost in the new power system driven by new energy while taking into account the solution speed and accuracy of the power system.

[0006] The present invention adopts the following technical solutions:

[0007] A multi-time scale dispatching method for source-network-load-storage integration includes the following steps:

[0008] Based on the relevant information required for multi-time scale dispatching involving sources, loads, and energy storage driven by new energy, uncertainty modeling is carried out for wind and light power generation and responsive load resources, a wind speed conversion model and an incentive model for responsive load users are established, and considering historical data, real-time meteorological information, load prediction models, and market electricity price factors, the uncertainty data is converted into a probability distribution;

[0009] Based on the obtained probability distribution, a multi-time scale day-ahead dispatching model involving sources, loads, and energy storage based on the DLPF power flow model is constructed under the participation of renewable energy output and demand-side response load;

[0010] Feed the results output by the multi-time scale day-ahead scheduling model and the measured system data under the current state back into the intra-day rolling optimization model, and solve the optimal control sequence in combination with the predicted data of the wind-solar load; use the optimal control sequence as the real-time scheduling instruction, send it to each component in the source-load-storage system, and operate according to the predetermined control strategy;

[0011] Use the multi-time scale day-ahead scheduling model to determine the start-stop plan of thermal power units, the PDR and Class B IDR scheduling plans, use the output of thermal power units and the Class B IDR scheduling plan determined by the intra-day rolling optimization model, and combine the regulation function of the energy storage power station to correct the output of each unit in real time to determine the Class C and Class D IDR scheduling plans; and according to the system operation status, use the PID and IDR types of adjustable load resources to correct the output of thermal power units in real time;

[0012] Solve the multi-time scale day-ahead scheduling model participated by the source-load-storage based on the DLPF power flow model to obtain the optimal output schemes of the wind power generation system, thermal power generation system, energy storage system, and load-side response resources, and realize the multi-time scale scheduling of the source-network-load-storage integration driven by renewable energy.

[0013] Preferably, the relevant information required for the multi-time scale scheduling participated by the source-load-storage driven by new energy includes:

[0014] Equipment parameters, purchase costs, annual operation and maintenance costs, service lives, and operation parameters of the thermal power generation system, wind power generation system, battery energy storage system, hydrogen energy storage system, and adjustable load resources;

[0015] Meteorological parameters, historical fluctuation factors of wind resources around the proposed site;

[0016] Load parameters, historical shiftable load scale in the pilot area of demand response to be carried out, and historical incentive response load in similar regions;

[0017] Economic parameters, annualized interest rate, PDR time-of-use electricity price in the local power grid of the proposed area, IDR compensation cost coefficient, and new energy on-grid electricity price.

[0018] Preferably, uncertainty modeling is carried out on wind power generation and adjustable load resources to obtain a model for stimulating users to participate using preferential policies, specifically as follows:

[0019] P L,t =P f,t +P PDR,t +P IDRA,t +P IDRB,t +P IDRC,t +P IDRD,t

[0020] Among them, P f,t represents the fixed power load in period t; PIDRA,t / P IDRB,t / P IDRC,t / P IDRD,t represents the IDR type corresponding to A, B, C, and D.

[0021] Preferably, the objective function of the multi-time scale day-ahead scheduling model with source-load-storage participation based on the DLPF power flow model is as follows:

[0022]

[0023] where f 1 is the total cost, f G,t is the power generation cost of thermal power units, f ES,t is the operating cost of energy storage power stations, f DG,t is the operation and maintenance cost of wind power generation systems, f L,t is the cost function of user loads.

[0024] Preferably, the power generation cost f G,t of thermal power units, the operating cost f ES,t of energy storage power stations, the operation and maintenance cost f DG,t of wind power generation systems, and the cost function f L,t of user loads are respectively:

[0025]

[0026] where a i b i c i are respectively the power generation cost coefficients of the i-th thermal power unit; P Gi,t is the active power of thermal power unit i; N G is the number of thermal power generator nodes in the power system; S i is the start-up cost of thermal power unit i, f ES,t is the operating cost of energy storage power stations, f DG,t is the operation and maintenance cost of wind power generation systems, k DG is the curtailment cost coefficient, is the predicted wind power output of wind turbine i at time t, f L,t is the cost function of user loads, k IDR is the cost coefficient of IDR; Δ∣P IDR,t ∣ is the IDR call volume at time t.

[0027] Preferably, the network power flow security constraints of the multi-time scale day-ahead scheduling model with source-load-storage participation based on the DLPF power flow model are as follows:

[0028] Power flow constraint of transmission network branches

[0029]

[0030] Among them, P ij is the active power on branch ij; Q ij is the reactive power on branch ij, and B ij ' is the susceptance of branch ij excluding the self-susceptance;

[0031] Voltage security constraint:

[0032] v min ≤v i,t ≤v max

[0033]

[0034] Among them, v max , v min are the upper and lower limits of the node voltage amplitude respectively; are the upper and lower limits of the voltage phase angle difference of line ij respectively;

[0035] Transmission line capacity constraint:

[0036]

[0037] Among them, represents the upper limit of the complex power of line ij;

[0038] Power balance constraint:

[0039]

[0040] Among them, Q Gi,t,s is the reactive power generation of the i-th thermal power unit at time t; Q ESi,t,s is the reactive power output of the energy storage power station i at time t; Q DGi,t,s represents the reactive power output of the i-th distributed unit at time t; Q loss,t,s is the reactive power loss of the load at time t; D fixed is the part of the load that does not change with the electricity price; ΔP PDR,t is the change in the active power of the PDR active load at time t; ΔQ PDR,t is the change in the reactive power of the PDR reactive load at time t; ΔP IDRA,t is the change in the active power of the Type A IDR active load at time t; ΔQ IDRA,t is the change in the reactive power of the Type A reactive IDR load at time t; ΔP IDRB,t,s is the change in the active power of the Type B IDR active load at time t; ΔQ IDRB,t,s is the change in the reactive power of the Type B IDR reactive load at time t;

[0041] The constraints of the energy units include the constraints of the thermal power unit subsystem, the wind farm and the energy storage power station, which are specifically as follows:

[0042] Output constraint of thermal power unit:

[0043]

[0044] Among them, P Gi,t is the active power output of thermal power unit i; are the upper and lower limits of the active power output of node i respectively; u Gi,t is a 0-1 variable that determines the start and stop of the thermal power unit;

[0045] Ramp rate constraint of thermal power unit:

[0046]

[0047] Among them, R Gi,u and R Gi,d are the upper / lower ramp rates of the thermal power unit respectively, and S Gi,u and S Gi,d are the maximum start / stop rates of the thermal power unit respectively;

[0048] Start and stop time constraint of thermal power unit:

[0049]

[0050] Among them, TS and TO are the minimum shutdown / startup times of the thermal power unit respectively;

[0051] Output constraint of wind farm:

[0052] P i w,min ≤P i w ≤P i w,max

[0053] Among them, P i g is the active power output of the wind farm at node i; P i w,min and P i w,max are the maximum and minimum limits of the wind farm output at node i respectively;

[0054] The constraints of the electrochemical energy storage power station include:

[0055] Single operating state constraint:

[0056]

[0057] Charge and discharge power limit constraint:

[0058]

[0059] Among them, are all binary variables, which are the charging and discharging state parameters of the i-th energy storage device at time t, respectively;

[0060] State of charge balance constraint:

[0061]

[0062] Battery capacity constraint:

[0063]

[0064] Among them: dod ESi is the maximum discharge depth of the e-th battery; is the maximum capacity of the n-th energy storage device;

[0065] Initial and final state consistency constraint of the week:

[0066] E ESi,1,s = E ESi,T,s

[0067] The constraints of the hydrogen energy storage power station include:

[0068] Hydrogen energy balance constraint:

[0069]

[0070] Among them, are the masses of hydrogen transferred by fuel cell utilization, hydrogen storage tank discharge, hydrogen storage tank charge, electrolyzer manufacturing, and external hydrogen purchase at time t in region i, respectively;

[0071] Hydrogen fuel cell constraint:

[0072]

[0073] Among them, is the generated electric power of the fuel cell; θ HE is the hydrogen-to-electricity conversion coefficient of the fuel cell; η HE is the hydrogen-to-electricity conversion efficiency of the fuel cell; is the state variable of the fuel cell's on / off;

[0074] Electrolyzer constraint:

[0075]

[0076] Among them, η G / θ EH are the electrolysis hydrogen efficiency and the electricity-to-hydrogen conversion coefficient of the electrolyzer, respectively:

[0077] Hydrogen storage tank constraint:

[0078]

[0079]

[0080] Responsive load resource constraint:

[0081]

[0082]

[0083]

[0084] Among them, and are the lower and upper limits of the call volume of the PDR load respectively; and are the increase / decrease load amounts of Class A IDR respectively; and are the upper limits of the increase / decrease load amounts of Class A IDR load respectively; and are the increase / decrease load amounts of Class B IDR respectively; and are the upper limits of the increase / decrease load amounts of Class B IDR load respectively.

[0085] Preferably, the objective function of the intra-day rolling optimization model is:

[0086]

[0087] Among them, f 2 is the total system operation cost during the intra-day rolling disinfection stage, f G,t is the power generation cost of thermal power units, f L,t is the cost function of user load, f ES,t is the operation cost of energy storage power stations, f DG,t is the operation and maintenance cost of wind power systems.

[0088] Preferably, the cost function f L,t of user load is:

[0089] f L,t = k IDRB Δ∣P IDRB,t ∣ + k IDRC Δ∣P IDRC,t ∣

[0090] Among them, k IDRC is the cost coefficient of Class C IDR; Δ∣P IDRC,t ∣ is the call volume of Class C IDR at time t, f L,tis the cost function of the user load.

[0091] Preferably, the constraint conditions of the intraday rolling optimization model include:

[0092]

[0093] Among them, and are the increased / decreased load amounts of Class C IDR respectively; and are the upper limits of the increased / decreased load amounts of Class C IDR respectively, and are the increased / decreased load amounts of Class B IDR respectively; and are the upper limits of the increased / decreased load amounts of Class B IDR respectively.

[0094] On the second aspect, the embodiments of the present invention provide

[0095] A multi-time scale scheduling system for source-network-load-storage integration, characterized by including:

[0096] A data module, based on the relevant information required for multi-time scale scheduling participated by source-load-storage driven by new energy, models the uncertainties of wind power generation and responsive load resources, establishes a wind speed conversion model and a responsive load user incentive model, and converts the uncertain data into a probability distribution considering historical data, real-time meteorological information, load prediction models, and market electricity price factors;

[0097] A construction module, based on the obtained probability distribution, constructs a multi-time scale day-ahead scheduling model participated by source-load-storage based on the DLPF power flow model under the participation of renewable energy output and demand-side response load;

[0098] A solution module, feeds back the results output by the multi-time scale day-ahead scheduling model and the measured system data in the current state into the intraday rolling optimization model, and solves the optimal control sequence in combination with the predicted data of wind-light load; uses the optimal control sequence as a real-time scheduling instruction, sends it to each component in the source-load-storage system, and operates according to the predetermined control strategy;

[0099] A correction module, determines the start-stop plan of thermal power units, the PDR and Class B IDR scheduling plans by using the multi-time scale day-ahead scheduling model, combines the output of thermal power units and the Class B IDR scheduling plan determined by the intraday rolling optimization model, and corrects the output of each unit in real time by using the regulation function of the energy storage power station to determine the Class C and Class D IDR scheduling plans; and according to the system operation state, uses PID and IDR-class responsive load resources to correct the output of thermal power units in real time;

[0100] The scheduling module solves the multi - time - scale day - ahead scheduling model involving sources, loads, and energy storage based on the DLPF power flow model, obtains the optimal output schemes of the wind power generation system, thermal power generation system, energy storage system, and load - side response resources, and realizes the multi - time - scale scheduling of source - grid - load - storage integration driven by renewable energy.

[0101] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above - mentioned multi - time - scale scheduling method for source - grid - load - storage integration are implemented.

[0102] In a fourth aspect, an embodiment of the present invention provides a computer - readable storage medium including a computer program. When the computer program is executed by a processor, the steps of the above - mentioned multi - time - scale scheduling method for source - grid - load - storage integration are implemented.

[0103] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above - mentioned multi - time - scale scheduling method for source - grid - load - storage integration are implemented.

[0104] In a sixth aspect, an embodiment of the present invention provides an electronic device including a computer program. When the computer program is executed by the electronic device, the steps of the above - mentioned multi - time - scale scheduling method for source - grid - load - storage integration are implemented.

[0105] Compared with the prior art, the present invention has at least the following beneficial effects:

[0106] A multi - time - scale scheduling method for source - grid - load - storage integration can effectively solve the problems of accuracy and speed in power system scheduling, optimize the operation state of the power system on a multi - time - scale, and under the framework of the collaborative scheduling decision - making of sources, loads, and energy storage driven by new energy, a multi - time - scale scheduling optimization model for a new - energy power grid is proposed with the goal of minimizing the sum of the operating costs of thermal power units, the operation and maintenance costs of energy storage systems, the curtailment costs of wind power generation, and the compensation costs for the invocation of demand - response resources; based on the historical data of key system operation parameters, the Weibull probability distribution function is used to simulate the wind speed, and the wind speed is converted into wind power output according to the power conversion characteristic curve of the wind turbine.

[0107] Furthermore, the acquisition of relevant information such as equipment parameters, meteorological parameters, load parameters, and economic parameters can provide data support for the economic scheduling analysis and optimal control of a new - type power system with the participation of new energy.

[0108] Furthermore, the generated fan output includes the wind resource fluctuation factors at different times of a day and the self-use load to be met, which can provide an operating boundary for the economic dispatch analysis and optimal control of a new power system with new energy participation, enabling the dispatch analysis scheme to adapt to different implementations of system operation.

[0109] Furthermore, with the objective function of minimizing the total cost, including the operating cost of thermal power units, the operation and maintenance cost of energy storage systems, the curtailment cost of wind power generation, and the compensation cost for the invocation of demand response resources, the investment and operation economy of the dispatch analysis scheme can be comprehensively considered.

[0110] Furthermore, the setting of the decoupled linearized power flow model constraint conditions can provide a safe operating boundary for power grid power flow analysis and optimal dispatch.

[0111] Furthermore, the setting of the load-side response resource model constraint conditions can adjust the power consumption behavior of users according to system requirements, avoiding problems such as grid overload or voltage instability.

[0112] Furthermore, the setting of the operating constraints of the wind power generation system, the operating constraints of thermal power units, the system power flow security constraints, and the operating constraints of energy storage systems can provide an operating boundary for the output dispatch analysis and optimal control of a renewable energy-driven power system.

[0113] It can be understood that the beneficial effects of the second to sixth aspects above can be referred to the relevant descriptions in the first aspect above, and will not be elaborated here.

[0114] In summary, the present invention helps to improve the economic efficiency and environmental sustainability of the power system, while ensuring the stability and security of power supply.

[0115] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. Obviously, the following described drawings are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0117] Figure 1 It is a schematic diagram of a multi-time scale new dispatch structure with sources, loads, and energy storage participating based on the DLPF model of the present invention; Figure 2 It is a framework diagram of the multi-time scale solution process of the present invention; Figure 3 It is a topological diagram of the regional power grid of the present invention; Figure 4 The day-ahead, intra-day, and real-time prediction curve graphs of the load of the present invention; Figure 5 The day-ahead, intra-day, and real-time prediction curve graphs of the wind power of the present invention; Figure 6 is the scheduling result graph of the present invention, wherein, (a) is the day-ahead power balance, (b) is the intra-day power balance, and (c) is the real-time power balance; Figure 7 The schematic diagram of the computer device provided by an embodiment of the present invention; Figure 8 The block diagram of an electronic device provided by an embodiment of the present invention.

[0118] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access storage unit; 6202. Cache storage unit; 6203. Read-only storage unit; 6204. Program / utilities; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed implementation manners

[0125] The present invention provides a multi-time-scale scheduling method for source-grid-load-storage integration, conducts research on the operating characteristics of new energy power generation systems, electricity, hydrogen energy storage systems, and responsive loads, and based on the DLPF power flow model, solves the problem of high wind curtailment rate in new power systems. On the basis of meeting the system solution speed and accuracy, it optimizes the allocation of power resources, effectively promotes the consumption of new energy, improves the stability and economy of the power system, and promotes the clean and low-carbon transformation of the energy and power system.

[0126] Please refer to Figure 1 , a typical source-load-storage joint scheduling structure includes a power generation system, an electricity-hydrogen hybrid energy storage system, and different types of user loads.

[0127] Among them, the wind power generation system, traditional thermal power units, and battery energy storage systems are connected through the same bus, connect the electrolyzer and other in-station power loads, and interact with the external power grid through a step-up transformer; the hydrogen output port of the electrolyzer is connected to the input port of the hydrogen storage tank and the fuel cell; the output port of the hydrogen storage tank is connected to the input port of the fuel cell; the fuel cell consumes hydrogen energy and converts it into electrical energy to provide energy for the user load through the output port.

[0128] 1) The specific structural features of the source-load-storage integrated dispatching system are as follows:

[0129] (1) The wind power generation system consists of a wind turbine group, an inverter, a collection line, and other auxiliary equipment, and can convert wind energy into electrical energy;

[0130] (2) The thermal power unit consists of main equipment such as a boiler, a steam turbine, a generator, a condenser, a feed water pump, and many auxiliary systems, and can convert the chemical energy of fuel (such as coal, oil, natural gas, etc.) into electrical energy.

[0131] (2) The battery energy storage system consists of a battery pack, a battery management system, a power conversion system, a collection line, and other auxiliary equipment, and is used for the storage, conversion, and release of electrical energy;

[0132] (3) The electrolyzer consists of a cell body, an anode, and a cathode, and can generate hydrogen through the electrolysis reaction of water;

[0133] (4) The hydrogen storage tank is a pressure vessel for storing gaseous hydrogen, stores hydrogen in a high-pressure storage manner, and includes necessary safety accessories and pressure detection and display instruments, and can perform rapid hydrogen charging and discharging at normal temperature to supply hydrogen to the hydrogen fuel cell;

[0134] (5) The hydrogen fuel cell consists of a proton exchange membrane, an anode, a cathode, a catalyst, a bipolar plate, etc., and can convert the chemical energy of hydrogen and oxygen into clean and efficient electrical energy.

[0135] 2) The specific operating characteristics of the source-load-storage integrated dispatching system are as follows:

[0136] (1) The wind power generation system and the thermal power generation system convert wind energy and chemical energy into electrical energy, and use the energy storage system to smooth the output to provide clean, low-carbon emission, continuous, and stable power supply.

[0137] (2) The electrical energy jointly generated by the wind power generation system, the thermal power generation system, and the battery energy storage system is input into the electrolyzer, and water is decomposed into hydrogen and oxygen through the electrolysis reaction; after being pressurized by a compressor, the hydrogen is introduced into the hydrogen storage tank for storage; when there is a load demand at the user end, the hydrogen in the hydrogen storage tank is supplied to the hydrogen fuel cell for power generation to meet the power consumption demand at the user end.

[0138] (3) Under the condition that the industrial and commercial peak-valley electricity price is implemented in the local power grid, the source-load-storage integrated dispatching system can interact with the power grid as a flexible resource on the demand side in a friendly manner. If it is in the valley electricity price period, the source-load-storage integrated dispatching system can purchase electricity from the external power grid, store it in the battery energy storage system or use the electrolyzer to produce hydrogen and store the hydrogen in the hydrogen storage tank; if it is in the peak electricity price period, the source-load-storage integrated dispatching system discharges through the battery energy storage system and sells electricity to the external power grid to obtain economic benefits.

[0139] Example 1

[0140] Please refer to Figure 2 , a multi-time scale scheduling method for source-network-load-storage integration of the present invention includes the following steps:

[0141] S1. Obtain relevant information required for multi-time scale scheduling participated by source, load and storage, specifically including:

[0142] (1) Equipment parameters, such as the purchase cost, annual operation and maintenance cost, service life, operation parameters, etc. of thermal power generation systems, wind power generation systems, battery energy storage systems, hydrogen energy storage systems and responsive load resources;

[0143] (2) Meteorological parameters, such as the historical fluctuation factor of wind resources around the proposed site;

[0144] (3) Load parameters, such as the historical shiftable load scale in the area where demand response pilot is to be carried out, the historical incentive-based response load in similar areas, etc.;

[0145] (4) Economic parameters, such as annualized interest rate, PDR time-of-use electricity price local to the power grid in the proposed area, IDR compensation cost coefficient, and new energy on-grid electricity price, etc.

[0146] S2. Based on the historical wind resource fluctuation factor and the historical self-use load in the power grid in step S1, conduct uncertainty modeling for wind power generation and responsive load resources;

[0147] (1) Wind farm output model

[0148] A wind turbine (abbreviation: wind turbine) is a power generation device that converts wind energy into electrical energy. The output power of the wind turbine is jointly determined by the actual wind speed at its hub height and the power conversion characteristics.

[0149] Generally, it is considered that the wind speed change follows the Weibull distribution, and the analytical formula of the probability density distribution function f is:

[0150]

[0151] where c and k are the scale parameter and shape parameter of the wind speed distribution respectively, and v t is the actual wind speed of the wind farm.

[0152] In addition, before calculating the wind power output, it is necessary to correct the initial simulated wind speed according to the hub height of the wind turbine. The height correction formula for the wind speed is:

[0153]

[0154] where v h is the wind speed at the hub height, v m is the wind speed at the measurement height, m / s; H is the hub height, Hm where m is the measured height; α is the power-law exponent, and its engineering experience value is generally 0.14.

[0155] According to the power conversion characteristic curve of the wind turbine, the corrected wind speed can be converted into wind power output. The typical mathematical expression of the wind power conversion characteristic curve is as follows:

[0156]

[0157] where P W,i (v t ) is the output of the wind turbine corresponding to the wind speed v t ; R W is the rated output of the wind turbine, in kW; v ci , v co and v r are the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine, respectively, in m / s.

[0158] (2) Demand response strategy model

[0159] Demand response (DR) can be divided into price-based demand response (PDR) and incentive-based demand response (IDR) according to different user response mechanisms.

[0160] PDR affects the electricity consumption pattern of users by implementing various pricing strategies, and the response level is directly proportional to the fluctuation of electricity price. This relationship is usually characterized by an elasticity matrix, which quantifies the interaction between the change in electricity price and the corresponding PDR response rate.

[0161]

[0162] where P PDR,t represents the change rate of PDR (price demand elasticity) at time t; Δp t represents the change rate of electricity price at time t; Δp t is the price elasticity coefficient.

[0163] IDR refers to the strategy of using preferential policies to stimulate users to participate. According to the response time of users, IDR can be divided into the following types:

[0164] 1) Type A IDR: Planned response one day in advance.

[0165] 2) Type B IDR: Response time is between 15 minutes and 2 hours.

[0166] 3) Type C IDR: Response time is between 5 and 15 minutes.

[0167] 4) Type D IDR: Real-time response.

[0168] In the present invention, IDR encourages users to participate by providing economic compensation, and the specific mathematical model is as follows:

[0169] P L,t = P f,t + P PDR,t + P IDRA,t + P IDRB,t + P IDRC,t + P IDRD,t (5)

[0170] Among them, P f,t represents the fixed power load in period t; P IDRA,t / P IDRB,t / P IDRC,t / P IDRD,t represents four types of IDR corresponding to A, B, C, and D.

[0171] S3. Under the participation of renewable energy output and demand-side response load, a multi-time scale day-ahead scheduling model involving sources, loads, and energy storage based on the DLPF power flow model is constructed;

[0172] 1) Objective function

[0173] The objective of the multi-time scale new scheduling model involving sources, loads, and energy storage based on the DLPF power flow model is to minimize the total cost incurred, which is expressed as follows:

[0174]

[0175] (1) The total cost incurred f 1 (Equation (4)) includes the power generation cost f G,t of thermal power units, the operating cost f ES,t (including hydrogen energy storage and battery energy storage) of energy storage power stations, the operation and maintenance cost f DG,t of wind power generation systems, and the cost function f L,t of user loads.

[0176] (2) The power generation cost f G,t of thermal power units generally refers to the fuel cost or coal consumption consumed during operation and is related to the output power of the units.

[0177] Among them, a i b i c i are the power generation cost coefficients of the i-th thermal power unit respectively; P Gi,t is the active power magnitude of thermal power unit i; N G is the number of thermal power generator nodes in the power system; S i is the start-up cost of thermal power unit i.

[0178] (3) The operating cost f ES,tThe operation and maintenance costs of the electrochemical energy storage system and the hydrogen energy storage system mainly refer to the battery degradation costs that need to be replaced regularly as the battery performance gradually declines with the increase in the number of charge-discharge cycles, the costs of construction, maintenance, and safety guarantee of the hydrogen storage tank, as well as the daily operation, maintenance, repair, and personnel management costs of the equipment.

[0179] (4) The operation and maintenance cost \(f\) of the wind power generation system DG,t It mainly refers to the normal operation cost of the wind power generation system and the penalty cost generated when the enterprise abandons wind energy and solar energy, which is mainly caused by the overcapacity in the wind power and photovoltaic industries and the lag in the construction of power grid equipment.

[0180] Among them, \(k\) DG is the curtailment cost coefficient; is the predicted wind power output of wind turbine \(i\) at time \(t\).

[0181] (5) The cost function \(f\) of the user load L,t It mainly refers to the inverse relationship function between the input cost and the obtained benefits during the implementation of the demand-side response load. This is mainly caused by the uncertainty of the demand-side response load and the differences in user participation.

[0182] Among them, \(k\) IDR is the cost coefficient of IDR; \(\Delta|P\) IDR,t | is the IDR call volume at time \(t\).

[0183] 2) Power system network power flow constraints

[0184] The network power flow security constraints of the multi-time scale new scheduling model participated by the source-load-storage are as follows:

[0185] (1) Transmission network branch power flow constraints

[0186] The transmission network is a power transmission network that connects power plants, substations, or substations, and mainly undertakes the task of transmitting electric energy. According to different transmission voltages, it can be divided into high-voltage transmission network (110 - 220 kV), extra-high-voltage transmission network (330 - 750 kV), and ultra-high-voltage transmission network (1000 kV and above).

[0187] The active and reactive component expressions of the AC power flow equation are:

[0188]

[0189] Among them, \(P\) i is the active power injection at node \(i\); \(Q\) i is the reactive power injection at node \(i\); \(G\) ij is the sum of all conductances connected to node \(i\) (including the conductance of \(i\) to the ground); \(B\) ijis the sum of all susceptances connected to node i (including the susceptance of i to the ground); V i is the voltage magnitude of node i; V j is the voltage magnitude of node j; θ ij is the electrical phase angle difference between node i and node j; B i 、B ij represent the set of power system nodes and the set of nodes connected to node i, respectively.

[0190] In this scheduling scheme, the active power injection expression is expanded in a first-order Taylor series with respect to V and the higher-order terms are ignored, resulting in the following equation:

[0191]

[0192] Similarly, the expression for the reactive power injected at a node can also be approximated as:

[0193]

[0194] Using the same approximation method, it is easy to derive the expressions for the active power and reactive power of a branch as:

[0195]

[0196] where, P ij is the active power on branch ij; Q ij is the reactive power on branch ij. B ij ' is the susceptance of branch ij excluding the self-susceptance. Relative The difference is that when i = j, the value is 0.

[0197] (2) Voltage security constraint

[0198] Voltage security constraints are conditions or limitations set in power system analysis and operation to ensure that the system voltage is within a safe, stable, and acceptable range:

[0199] v min ≤v i,t ≤v max (13)

[0200]

[0201] Equations (11) and (12) are the node voltage magnitude constraint and the line voltage phase angle difference constraint, respectively;

[0202] where, v max ,v min are the upper and lower limits of the node voltage magnitude, respectively; They are the upper and lower limits of the voltage phase angle difference of line ij respectively.

[0203] (3) Transmission line capacity constraint

[0204] The transmission line capacity constraint refers to the limiting condition for the maximum power that can be transmitted by the transmission lines in the power system:

[0205]

[0206] Among them, represents the upper limit of the complex power of line ij.

[0207] (4) Power balance constraint:

[0208]

[0209] Among them, Q Gi,t,s is the reactive power generation of the i-th thermal power unit at time t; Q ESi,t,s is the reactive power output of the energy storage power station i at time t; Q DGi,t,s represents the reactive power output of the i-th distributed unit at time t; Q loss,t,s is the reactive power loss of the load at time t. D fixed is the part of the load that does not change with the electricity price; ΔP PDR,t is the change in the active power load of PDR at time t; ΔQ PDR,t is the change in the reactive power load of PDR at time t; ΔP IDRA,t is the change in the active power load of type A IDR at time t; ΔQ IDRA,t is the change in the reactive power load of type A IDR at time t; ΔP IDRB,t,s is the change in the active power load of type B IDR at time t; ΔQ IDRB,t,s is the change in the reactive power load of type B IDR at time t.

[0210] 3) Energy unit constraint

[0211] The energy unit constraint includes the thermal power unit subsystem constraint, the wind farm and energy storage power station constraint.

[0212] (1) Thermal power unit constraint

[0213] a. Thermal power unit output constraint

[0214] The thermal power unit output constraint refers to the limiting condition for the power generation output of the thermal power unit

[0215]

[0216] Among them, P Gi,t is the magnitude of the active power output of the thermal power unit i; are the upper and lower limits of the active power output of node i; u Gi,t is a 0-1 variable that determines the start-up and shut-down of thermal power units.

[0217] b. Ramp rate constraint of thermal power units

[0218] The ramp rate constraint of thermal power units refers to the rate limit for increasing or decreasing the output of thermal power units within a unit time.

[0219]

[0220] Among them, R Gi,u and R Gi,d are the upper / lower ramp rates of thermal power units respectively, and S Gi,u and S Gi,d are the maximum start-up / shut-down rates of thermal power units respectively.

[0221] c. Start-up and shut-down time constraint of thermal power units

[0222]

[0223] Among them, TS and TO are the minimum shut-down / start-up times of thermal power units respectively.

[0224] (2) Output constraint of wind farms

[0225] P i w,min ≤P i w ≤P i w,max (21)

[0226] Among them, P i g is the magnitude of the active power output of the wind farm at node i; P i w,min and P i w,max are the maximum and minimum limits of the wind farm output at node i respectively.

[0227] (3) Constraints of electrochemical energy storage power stations

[0228] a. Single operating state constraint

[0229] The battery is an important energy storage component for buffering power supply and load in the power system, with three states: charging, discharging, and idle. By introducing binary state variables and to assist in representation. Taking charging as an example, when it means the battery is in the charging state, it means it is not in the charging state. At this time, if it means it is in the discharging state, if It indicates that the battery is idle. The same applies during discharging. It should be noted that for safety reasons, the battery is not allowed to charge and discharge simultaneously. This constraint is expressed as:

[0230]

[0231] b. Charge and Discharge Power Limit Constraint

[0232] After the battery state is determined, the relationship constraints between the charging power, discharging power, and rated power are expressed as:

[0233]

[0234] Among them, are all binary variables, which are the charging and discharging state parameters of the i-th energy storage device at time t, respectively.

[0235] c. State of Charge Balance Constraint

[0236] During the process of storing energy in the battery, due to chemical reactions and the characteristics of its own materials, a certain degree of self-discharge phenomenon will occur, resulting in a gradual reduction in the battery's power. The self-discharge amount of the battery within a fixed time is related to the current power:

[0237]

[0238] d. Battery Capacity Constraint

[0239] To ensure the service life of the battery, the discharge depth constraint and the capacity upper limit constraint should also be considered:

[0240]

[0241] Among them: dod ESi is the maximum discharge depth of the e-th battery; is the maximum capacity of the n-th energy storage device.

[0242] e. Constraint of Consistent State at the Beginning and End of the Week

[0243] This equation is used to ensure that the state of charge at the initial moment and the end moment of the operation simulation period of the energy storage power station is the same, which is convenient for optimization in the next period.

[0244] E ESi,1,s = E ESi,T,s (26 )

[0245] (3) Hydrogen Energy Storage Power Station Constraint

[0246] In the power system, the hydrogen energy storage system is used to improve energy utilization efficiency, reduce environmental pollution, and enhance the flexibility and stability of the energy system. It mainly includes four aspects: hydrogen production, storage, transmission, and application.

[0247] a. Hydrogen energy balance constraint

[0248] The hydrogen energy balance constraint is established based on the law of mass conservation in the system and is used to describe the input-output relationship of hydrogen in each device of the hydrogen energy storage system. At any moment, the hydrogen consumed by each device in the data center energy system is equal to the mass of hydrogen input into the system. The devices that consume hydrogen mainly include fuel cells and hydrogen storage tanks, and the sources of hydrogen are hydrogen production from electrolyzers and hydrogen release from hydrogen storage tanks.

[0249]

[0250] Among them, are the masses of hydrogen transferred at time t for fuel cell utilization, hydrogen release from the hydrogen storage tank, hydrogen charging into the hydrogen storage tank, hydrogen production by the electrolyzer, and hydrogen purchased from outside in region i, respectively;

[0251] b. Hydrogen fuel cell constraint

[0252] A fuel cell is a device that uses hydrogen and oxygen to generate electrical energy through a chemical reaction. Its principle is to catalyze the reaction of hydrogen and oxygen on the electrodes to produce electrons, protons, and water, and then generate electrical energy and heat. Therefore, first, the hydrogen-electricity conversion equation needs to be obtained according to the conversion efficiency. Then, along with the hydrogen combustion reaction, a part of the chemical energy is converted into heat, which is absorbed by the flowing water and cooled, and converted into hot water for storage and used to supply the absorption chiller. This process is regarded as a constant-pressure heating process, ignoring the transmission heat loss, then there is:

[0253]

[0254] In addition, the fuel cell also has an output power constraint. The hydrogen fuel cell cannot operate at low load for a long time. Therefore, when starting up, the output power has a lower limit greater than 0, and its mathematical expression is as follows:

[0255]

[0256] Among them, is the generated electric power of the fuel cell; θ HE is the hydrogen-to-electricity conversion coefficient of the fuel cell; η HE is the hydrogen-to-electricity conversion efficiency of the fuel cell; is the state variable of the fuel cell's on-off operation.

[0257] c. Electrolyzer constraint

[0258] The electrolyzer for hydrogen production by electrolyzing water consists of two electrodes and an electrolyte solution. The electrolyte solution is usually pure water or water containing a small amount of electrolyte (such as KOH). When an electric current passes through the electrolyzer, water molecules will be ionized to form hydrogen ions and hydroxide ions. The hydrogen ions will accept electrons on the cathode surface and be reduced to hydrogen gas; the hydroxide ions will release electrons on the anode surface and be oxidized to oxygen gas. Considering the losses of the electrolyzer, the following constraints for hydrogen production by the electrolyzer can be established:

[0259]

[0260] where η G / θ EH are the electrolytic hydrogen efficiency and the electricity-to-hydrogen conversion coefficient of the electrolyzer respectively:

[0261] d. Hydrogen storage tank constraints

[0262] For the purpose of making full use of the characteristics of different energy load peaks and valleys, improving the system reliability, and maintaining the safe operation of the system, etc., hydrogen storage equipment is installed in the integrated energy system. Similar to the battery, the hydrogen storage tank also has three states: charging, discharging, and idle. The following are its constraints, and the modeling process will not be elaborated.

[0263]

[0264]

[0265] 3) Responsive load resource constraints

[0266]

[0267] where, and are the lower and upper limits of the call volume of the PDR load respectively; and are the increased / decreased load amounts of Class A IDR respectively; and are the upper limits of the increased / decreased load amounts of Class A IDR load respectively; and are the increased / decreased load amounts of Class B IDR respectively; and are the upper limits of the increased / decreased load amounts of Class B IDR load respectively.

[0268] S4. Feed the results output by the day-ahead scheduling model in step S3 and the measured system data in the current state back into the intra-day rolling optimization model, and combine the predicted data of the wind-solar load with a time scale of 15 minutes within the next 4 hours to solve the optimal control sequence;

[0269] 1) Objective function

[0270] The objective function of intraday rolling optimization is also to minimize the system operation cost. Compared with the day-ahead scheduling model, the only thing changed in the rolling model is the cost of the IDR-type load call volume. Since type A has been determined, the total load cost is the sum of type B and type C IDRs, f G,t 、f ES,t 、f DG,t The same as above.

[0271]

[0272] f L,t =k IDRB Δ∣P IDRB,t ∣+k IDRC Δ∣P IDRC,t ∣ (42)

[0273] Among them, k IDRC is the cost coefficient of type C IDR; Δ∣P IDRC,t ∣ is the call volume of type C IDR at time t.

[0274] 2) Constraints

[0275] Since the start-stop plan of thermal power units has been determined in the day-ahead scheduling model, the intraday rolling model no longer considers the start-stop of thermal power units and removes the ramp constraint. At the same time, the constraint of type A IDR is removed, and the constraint of type C IDR is added.

[0276]

[0277] Among them, and are the increased / decreased load amounts of type C IDR respectively; and are the upper limits of the increased / decreased load amounts of type C IDR respectively.

[0278] S5. First, use the start-stop plan of thermal power units determined by the day-ahead scheduling obtained in step S3, the PID and type A IDR scheduling plans. Secondly, use the optimal outputs of the wind power system, thermal power system, energy storage system and type B IDR determined after the intraday rolling scheduling obtained in step S4. Finally, combine the regulation function of the energy storage power station to determine the type C and type D IDR scheduling plans, and adjust and correct the outputs of each unit in real time.

[0279] 3) Objective function

[0280] The objective function of intraday rolling optimization is also to minimize the system operation cost. Compared with the day-ahead scheduling model and the intraday rolling model, the only thing changed in the real-time scheduling model is the cost of the IDR-type load call volume. Since type A and type B have been determined, the total load cost is the sum of type C and type D IDRs. f G,t 、f ES,t, f DG,t The same as above.

[0281]

[0282] f L,t = k IDRC Δ∣P IDRC,t ∣ + k IDRD Δ∣P IDRD,t ∣ (46)

[0283] where k IDRD is the cost coefficient of D - type IDR; Δ∣P IDRD,t ∣ is the call volume of D - type IDR at time t.

[0284] 4) Constraints

[0285] The intra - day scheduling determines the start - stop status of thermal power units, PDR, and A - type IDR, and the intra - day rolling determines the output of thermal power units, the output of wind power generation systems, and the scheduling volume of B - type IDR. Therefore, only the power balance constraint conditions, energy storage system constraint conditions, and the constraint conditions of C - type and D - type IDR are left here. The system constraint conditions and IDR constraint conditions are basically the same as those before and will not be elaborated here.

[0286] S6. Taking a regional power grid with serious new - energy consumption constraints in East China as an example, solve the multi - time - scale scheduling model involving sources, loads, and energy storage based on the DLPF power - flow model established in steps S3, S4, and S5, and obtain the optimal output schemes of wind power generation systems, thermal power generation systems, energy storage systems, and load - side response resources. The power grid structure diagram is as Figure 3 shown.

[0287] Those skilled in the art of the present invention can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0288] Embodiment 2

[0289] The present invention provides a multi - time - scale scheduling system for source - grid - load - energy - storage integration, which can be used to implement the above - mentioned multi - time - scale scheduling method for source - grid - load - energy - storage integration. Specifically, the multi - time - scale scheduling system for source - grid - load - energy - storage integration includes a data module, a construction module, a solution module, a correction module, and a scheduling module.

[0290] Among them, the data module models the uncertainties of wind power generation and responsive load resources based on the relevant information required for multi-time scale scheduling involving new energy-driven source-load-storage participation, establishes a wind speed conversion model and a user incentive model for responsive load, and converts the uncertain data into a probability distribution considering historical data, real-time meteorological information, load prediction models, and market electricity price factors;

[0291] The construction module constructs a multi-time scale day-ahead scheduling model involving source-load-storage participation based on the DLPF power flow model under the participation of renewable energy output and demand-side response load based on the obtained probability distribution;

[0292] The solution module feeds back the results output by the multi-time scale day-ahead scheduling model and the measured system data in the current state into the intra-day rolling optimization model, and solves the optimal control sequence in combination with the predicted data of wind-solar load; uses the optimal control sequence as a real-time scheduling instruction and sends it to each component in the source-load-storage system to operate according to the predetermined control strategy;

[0293] The correction module determines the start-stop plan of thermal power units, the PDR and Class B IDR scheduling plans using the multi-time scale day-ahead scheduling model, determines the output of thermal power units and the Class B IDR scheduling plan using the intra-day rolling optimization model, and combines the regulation function of the energy storage power station to correct the output of each unit in real time to determine the Class C and Class D IDR scheduling plans; and according to the system operation state, uses PID and IDR-class responsive load resources to correct the output of thermal power units in real time;

[0294] The scheduling module solves the multi-time scale day-ahead scheduling model involving source-load-storage participation based on the DLPF power flow model, obtains the optimal output schemes of the wind power generation system, thermal power generation system, energy storage system, and load-side response resources, and realizes the multi-time scale scheduling of source-network-load-storage integration driven by renewable energy.

[0295] Embodiment 3

[0296] The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiments of the present invention can be used for the operation of the multi-time scale scheduling method of source-network-load-storage integration, including:

[0297] Based on the relevant information required for the multi-time scale scheduling involving new energy-driven source-load-storage participation, uncertainty modeling is carried out for wind-solar power generation and responsive load resources. A wind speed conversion model and an incentive model for responsive load users are established. Considering historical data, real-time meteorological information, load forecasting models, and market electricity price factors, the uncertain data is converted into a probability distribution. Based on the obtained probability distribution, with the participation of renewable energy output and demand-side response load, a multi-time scale day-ahead scheduling model involving source-load-storage participation based on the DLPF power flow model is constructed. The results output by the multi-time scale day-ahead scheduling model and the measured system data in the current state are fed back into the intra-day rolling optimization model, and the optimal control sequence is solved by combining the forecasting data of wind-solar load. The optimal control sequence is used as the real-time scheduling instruction and sent to each component in the source-load-storage system to operate according to the predetermined control strategy. The start-stop plan of thermal power units, PDR, and Class B IDR scheduling plans are determined using the multi-time scale day-ahead scheduling model. The output of thermal power units and Class B IDR scheduling plans determined using the intra-day rolling optimization model, combined with the regulation function of the energy storage power station, are used to real-time correct the output of each unit to determine Class C and Class D IDR scheduling plans. And according to the system operation status, the output of thermal power units is real-time corrected using PID and IDR-class responsive load resources. Solving the multi-time scale day-ahead scheduling model involving source-load-storage participation based on the DLPF power flow model, the optimal output schemes of the wind power generation system, thermal power generation system, energy storage system, and load-side response resources are obtained, realizing the multi-time scale scheduling of source-network-load-storage integration driven by renewable energy.

[0298] Please refer to Figure 7 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and operable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the multi-time scale scheduling method of source-network-load-storage integration in the embodiment. To avoid repetition, it will not be elaborated here one by one. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the multi-time scale scheduling system of source-network-load-storage integration in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0299] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 7 merely examples of the computer device 60, which do not constitute a limitation on the computer device 60. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the computer device may also include input-output devices, network access devices, buses, etc.

[0300] The so-called processor 61 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), 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. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0301] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0302] Furthermore, the memory 62 may also include both the internal storage unit of the computer device 60 and the external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0303] Please refer to Figure 8 , the terminal device is an electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0304] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method part of this specification above. For example, the processing unit 610 can execute the steps as shown in Figure 2 .

[0305] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0306] The storage unit 620 may also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0307] The bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0308] The electronic device 600 may also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication may be carried out through the input / output interface 650. Moreover, the electronic device 600 may also communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0309] Embodiment 4

[0310] The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. And, in this storage space, there are also stored one or more instructions suitable for being loaded and executed by a processor, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disc read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0311] The computer-readable storage medium also includes data signals propagated in a baseband or as part of a carrier wave, which carry the readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0312] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages - such as Java, C++, etc., and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0313] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the multi-time scale scheduling method for source-grid-load-storage integration in the above embodiments; the one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0314] Based on the relevant information required for multi-time scale scheduling involving source-load-storage driven by new energy, perform uncertainty modeling on wind-solar power generation and responsive load resources, establish a wind speed conversion model and a responsive load user incentive model, consider historical data, real-time meteorological information, load prediction models, and market electricity price factors, and convert the uncertainty data into probability distributions; based on the obtained probability distributions, construct a multi-time scale day-ahead scheduling model involving source-load-storage based on the DLPF power flow model with the participation of renewable energy output and demand-side response loads; feedback the results output by the multi-time scale day-ahead scheduling model and the measured system data in the current state into the intra-day rolling optimization model, and solve the optimal control sequence in combination with the predicted data of wind-solar loads; use the optimal control sequence as a real-time scheduling instruction, send it to each component in the source-load-storage system, and operate according to the predetermined control strategy; use the multi-time scale day-ahead scheduling model to determine the start-stop plan of thermal power units, PDR and Class B IDR scheduling plans, use the thermal power unit output and Class B IDR scheduling plans determined by the intra-day rolling optimization model, and in combination with the regulation function of the energy storage power station, real-time correct the output of each unit to determine Class C and Class D IDR scheduling plans; and according to the system operation state, use PID and IDR-type responsive load resources to real-time correct the output of thermal power units; solve the multi-time scale day-ahead scheduling model involving source-load-storage based on the DLPF power flow model to obtain the optimal output schemes of the wind power generation system, thermal power generation system, energy storage system, and load-side response resources, and realize the multi-time scale scheduling of source-grid-load-storage integration driven by renewable energy.

[0315] The databases involved in the embodiments provided in the present application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. The processors involved in the embodiments provided in the present application may be a general-purpose processor, a central processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0316] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0317] Taking a regional power grid in the East China region with severely limited new energy consumption as an example. Relevant information required for the optimal power grid dispatching model involving new energy-driven source-load-storage participation is obtained. Tables 1 to 4 respectively show the main economic parameters of the thermal power generation system, wind power generation system, battery energy storage system, and hydrogen energy storage system. Tables 5 and 6 show the time-of-use electricity price data of the demand-side response load PDR, and Table 6 shows the IDR compensation cost coefficient.

[0318] Table 1 Main economic parameters of the thermal power generation system

[0319]

[0320] Table 2 Main economic parameters of the wind power generation system

[0321]

[0322]

[0323] Table 5 Time-of-use electricity price data of PDR

[0324]

[0325] Table 6 IDR compensation cost coefficient

[0326]

[0327] Based on the measured data of a typical power grid, the predictions of load and wind power are both generated by adding white noise to the measured data (the prediction errors follow a normal distribution). Among them, the time scale of the measured curve is extended from 1 h to 15 min, that is, the 4 data within each hour are the same, all being the data at each hour point, with a total of 96 data points. The day-ahead, intra-day, and real-time prediction errors of the load are 3%, 1%, and 0.5% respectively. The day-ahead, intra-day, and real-time prediction errors of wind power are 5%, 3%, and 1% respectively. The measured and predicted curves of load and wind power are shown in Figure 4 、 5 。

[0328] By solving models (1) to (40), the scheduling result of this embodiment is shown in Figure 6.

[0329] From the scheduling results in the figure, in day-ahead scheduling, the 1-hour time scale cannot accurately cope with the fluctuations in renewable energy output; while in intraday scheduling, the rolling optimization with a 15-minute time scale can adjust the output of each unit in a short time to avoid an increase in the scheduling cost of the power system due to prediction errors. In real-time scheduling, the rolling optimization with a 5-minute time scale can utilize the fast response ability of the energy storage system to quickly respond to the fluctuations in new energy output, improve the consumption of renewable energy, and reduce the operating cost of the system.

[0330] In the test case, the power load of the system is provided by renewable energy, thermal power units, hydrogen fuel cells, and energy storage. In terms of power supply, it is mainly responsible for providing stable base load output to ensure the continuous and stable operation of the power system. However, this dependence on thermal power units may lead to increased energy consumption and environmental pollution. Therefore, during peak electricity consumption periods, by applying demand response and energy storage technologies, the power demand can be shifted to other periods, thereby reducing the dependence on thermal power units. In addition, the power supply of the energy storage system matches the characteristics of wind power during peak load periods, making full use of wind power to reduce the power gap and surplus caused by wind power fluctuations, thereby optimizing the power supply and demand balance and improving the stability and economic benefits of the system.

[0331] During the period from 0:00 to 8:00, the power consumption is low, in the low load stage. The power generation of the power system is greater than the power consumption of the load, and the excess power is stored through the energy storage power station and the hydrogen energy storage station. During the peak electricity consumption period from 10:00 to 14:00, the thermal power units are operating at full load, while fully mobilizing the output of the wind farm, and assisting in meeting the load demand through the discharge of energy storage and the discharge of hydrogen fuel cells. By 16:00, on the basis of meeting the load demand, the power generated by the wind turbines is stored in the energy storage power station with a faster response speed to prepare for the next stage of use. During the period from 18:00 to 24:00, the load demand reaches the peak, and the discharge of energy storage batteries and fuel cells is enabled to meet the load demand and ensure the stability of the system.

[0332] Four scenarios are set to verify the economy of the multi-time scale day-ahead scheduling model involving sources, loads, and storage based on the DLPF power flow model proposed in the present invention.

[0333] Scenario 1: Do not consider the demand-side response load and the energy storage system.

[0334] Scenario 2: Consider the demand-side response load and do not consider the energy storage system.

[0335] Scenario 3: Without considering the demand-side response load, considering the energy storage system.

[0336] Scenario 4: Considering the energy storage system and the demand-side response load.

[0337]

[0338] As can be seen from Table 1, compared with other schemes, the total cost of the proposed scheme is 15,354.1 yuan, which is the most economical among the four schemes. This is because the proposed scheme combines the fast regulation characteristics of the electrochemical energy storage power station with the large-capacity and high-power characteristics of the hydrogen energy storage power station, and uses the value of demand response resources to finely adjust the output, thereby improving the economy of the system and providing a decision-making basis for the multi-time scale day-ahead scheduling model of new energy-driven source-load-storage participation.

[0339] In summary, a multi-time scale scheduling method and system for source-network-load-storage integration of the present invention aims to analyze the interaction between the "source-network-load-storage" links in the power system. On this basis, a multi-time scale scheduling method based on the DPLF model is proposed to deeply explore the scheduling strategies of different generating units. The research results show that compared with the traditional DC / AC model, the DPLF model exhibits faster response speed and higher calculation accuracy. By implementing a multi-time scale scheduling strategy that integrates the energy storage system and demand response, the present invention effectively reduces the uncertainty of renewable energy and load forecasting, not only improves the utilization efficiency of wind energy, but also significantly reduces the operating cost of the system. In addition, the instant regulation characteristics of the electrical energy storage system and the long-term energy storage capacity of the hydrogen energy storage system complement each other, jointly providing a key energy storage solution for the power system during peak power generation periods, while effectively promoting peak shaving during peak power demand periods and valley filling during valley periods, thereby realizing the coordinated optimization of each component of the power system and improving the overall operating efficiency of the power system.

[0340] The above content is only to illustrate the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A multi-time scale scheduling method for integrating source, grid, load and storage, characterized in that: The following steps are involved: Based on the relevant information required for multi-time scale scheduling involving source, load and storage driven by new energy, uncertainty modeling is performed on wind and solar power generation and responsive load resources, and a wind speed conversion model and a responsive load user incentive model are established. Considering historical data, real-time meteorological information, load forecasting models and market electricity price factors, uncertainty data is converted into probability distribution; Based on the obtained probability distribution, a multi-time scale day-ahead dispatch model with the participation of source, load and storage based on the DLPF power flow model is constructed with the participation of renewable energy output and demand-side response load. Feed the output of the multi-time scale day-ahead dispatch model and the measured system data in the current state into the intraday rolling optimization model, and solve the optimal control sequence in combination with the forecast data of wind and solar loads; send the optimal control sequence as a real-time dispatch instruction to each component in the source-load-storage system to operate according to the predetermined control strategy; The multi-time scale day-ahead dispatch model is used to determine the start and stop plan of thermal power units, PDR and Class B IDR dispatch plans. The thermal power unit output and Class B IDR dispatch plan are determined by the intraday rolling optimization model. The output of each unit is corrected in real time in combination with the regulation function of the energy storage power station to determine the Class C and Class D IDR dispatch plans. And according to the system operation status, the output of the thermal power unit can be corrected in real time using PID and IDR type corresponding load resources; Solve the multi-time-scale day-ahead dispatch model involving source, load and storage based on the DLPF power flow model, obtain the optimal output plan of wind power generation system, thermal power generation system, energy storage system and load-side response resources, and realize multi-time-scale dispatch of source, grid, load and storage integration driven by renewable energy.

2. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 1 is characterized in that: The relevant information required for multi-time scale scheduling based on the participation of sources, loads and storage driven by new energy sources includes: Equipment parameters, purchase costs, annual operation and maintenance costs, operating life, and operating parameters of thermal power generation systems, wind power generation systems, battery energy storage systems, hydrogen energy storage systems, and responsive load resources; Meteorological parameters, historical fluctuation factors of wind resources around the proposed site; Load parameters, including the historical load size of the demand response pilot area and the historical incentive response load of similar areas; Economic parameters, annualized interest rate, local PDR time-of-use electricity price of the proposed regional power grid, IDR compensation cost coefficient and renewable energy on-grid electricity price.

3. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 1 is characterized in that: Uncertainty modeling of wind power generation and responsive load resources is performed to obtain a model for the strategy of using preferential policies to stimulate user participation, as follows: P L,t =P f,t +P PDR,t +P IDRA,t +P IDRB,t +P IDRC,t +P IDRD,t Among them, P f,t represents the fixed power load during period t; P IDRA,t / P IDRB,t / P IDRC,t / P IDRD,t Represents the IDR types corresponding to A, B, C, and D.

4. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 1 is characterized in that: The objective function of the multi-time-scale day-ahead scheduling model with source, load and storage participation based on the DLPF power flow model is as follows: Among them, f1 is the total cost, f G,t is the power generation cost of thermal power units, f ES,t is the operating cost of the energy storage power station, f DG,t is the operation and maintenance cost of the wind power generation system, f L,t is the cost function of user load.

5. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 4 is characterized in that: The cost of electricity generation from thermal power plants G,t , the operating cost of the energy storage power station is ES,t , Wind power generation system operation and maintenance costs DG,t , the cost function f of user load L,t They are: Among them, a i , b i 、c i are the power generation cost coefficients of the i-th thermal power unit; P Gi,t is the active power of thermal power unit i; N G is the number of thermal power generator nodes in the power system; S i is the startup cost of thermal power unit i, f ES,t is the operating cost of the energy storage power station, f DG,t is the operation and maintenance cost of the wind power generation system, k DG is the wind curtailment cost coefficient, is the predicted wind power output of wind turbine i at time t, f L,t is the cost function of user load, k IDR is the cost coefficient of IDR; Δ|P IDR,t ∣ is the number of IDR calls at time t.

6. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 4 is characterized in that: The network power flow security constraints of the multi-time scale day-ahead scheduling model with source, load and storage participating based on the DLPF power flow model are as follows: Transmission network branch power flow constraints Among them, P ij is the active power on branch ij; Q ij is the reactive power on branch ij, B ij ' is the susceptance of the branch ij except the self-susceptance; Voltage safety constraints: Among them, v max ,v min are the upper and lower limits of the node voltage amplitude respectively; are the upper and lower limits of the voltage phase angle difference of line ij respectively; Transmission line capacity constraints: in, represents the upper limit of the complex power of line ij; Power balance constraints: Among them, Q Gi,t,s is the reactive power generation of the i-th thermal power unit at time t; Q ESi,t,s is the reactive power output of energy storage power station i at time t; Q DGi,t,s represents the reactive power output of the i-th distributed unit at time t; Q loss,t,s is the reactive power loss of the load at time t; D fixed is the part of the load that does not change with the electricity price; ΔP PDR,t is the change of PDR active load at time t; ΔQ PDR,t is the change in PDR reactive load at time t; ΔP IDRA,t is the change in active load of Class A IDR at time t; ΔQ IDRA,t is the change of Class A reactive IDR load at time t; ΔP IDRB,t,s is the change in active load of Class B IDR at time t; ΔQ IDRB,t,s is the change of Class B IDR reactive load at time t; Energy unit constraints include thermal power unit subsystem constraints, wind farm and energy storage power station constraints, as follows: Output constraints of thermal power units: Among them, P Gi,t is the active output of thermal power unit i; are the upper and lower limits of active output of node i; u Gi,t It is a 0-1 variable that determines the start and stop of thermal power units; Thermal power unit climbing constraints: Among them, R Gi,u , R Gi,d are the up / down ramp rates of the thermal power units, S Gi,u , S Gi,d They are the maximum start / stop rates of thermal power units; Start and stop time constraints of thermal power units: Among them, TS and TO are the minimum shutdown / startup time of thermal power units, respectively; Wind farm output constraints: P i w,min ≤P i w ≤P i w,max Among them, P i g is the active output of the wind farm at node i; P i w,min , P i w,max are the maximum and minimum limits of wind farm output at node i, respectively; Electrochemical energy storage power plant constraints include: Single run state constraints: Charge and discharge power limit constraints: in, are binary variables, which are the charging and discharging state parameters of the i-th energy storage device in period t; State of charge balance constraints: Battery capacity constraints: Among them: dod ESi is the maximum discharge depth of the e-th battery; is the maximum capacity of the nth type of energy storage device; Constraints on consistency of status at the beginning and end of the week: AND ESi,1,s =And ESi,T,s Hydrogen energy storage power station constraints include: Hydrogen energy balance constraints: in, are the masses of hydrogen delivered at time t by fuel cell utilization, hydrogen storage tank discharge, hydrogen storage tank filling, electrolyzer manufacturing and external hydrogen purchase in region i, respectively; Hydrogen fuel cell constraints: in, is the power generated by the fuel cell; θ HE is the hydrogen-to-electricity conversion coefficient of the fuel cell; η HE is the hydrogen-to-electricity efficiency of the fuel cell; is the state variable of the fuel cell on / off; Electrolyzer Constraints: Among them, η G / θ EH They are the electrolytic hydrogen efficiency and the electro-to-hydrogen conversion coefficient of the electrolyzer: Hydrogen storage tank constraints: Responsive load resource constraints: in, and They are the lower and upper limits of the PDR load call volume, respectively; and Increase / decrease the load for Class A IDR respectively; and They are the upper limits of the load increase / decrease for Class A IDR loads; and Increase / decrease the load for Class B IDR respectively; and They are the upper limits of the load increase / decrease for Class B IDR loads.

7. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 1 is characterized in that: The objective function of the intraday rolling optimization model is: Among them, f2 is the total system operation cost during the rolling disinfection stage within a day, f G,t is the power generation cost of thermal power units, f L,t is the cost function of user load, f ES,t is the operating cost of the energy storage power station, f DG,t Operation and maintenance costs of wind power generation systems.

8. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 7 is characterized in that: The cost function f of user load L,t for: f L,t =k IDRB Δ∣P IDRB,t ∣+k IDRC Δ∣P IDRC,t ∣ Among them, k IDRC is the cost coefficient of Class C IDR; Δ|P IDRC,t ∣ is the number of calls of class C IDR at time t, f L,t is the cost function of user load.

9. The multi-time scale scheduling method for source-grid-load-storage integration according to claim 7 is characterized in that: The constraints of the intraday rolling optimization model include: in, and Increase / decrease the load for Class C IDR respectively; and They are the upper limits of the load increase / decrease for Class C IDR loads, and Increase / decrease the load for Class B IDR respectively; and They are the upper limits of the load increase / decrease for Class B IDR loads.

10. A multi-time scale dispatching system integrating source, grid, load and storage, characterized in that: include: The data module, based on the relevant information required for multi-time scale scheduling involving sources, loads and storage driven by new energy, models uncertainty for wind and solar power generation and responsive load resources, establishes a wind speed conversion model and a responsive load user incentive model, considers historical data, real-time meteorological information, load forecasting models and market electricity price factors, and converts uncertainty data into probability distribution; The construction module, based on the obtained probability distribution, constructs a multi-time scale day-ahead dispatch model with the participation of source, load and storage based on the DLPF power flow model under the participation of renewable energy output and demand-side response load; The solution module feeds the output of the multi-time scale day-ahead dispatch model and the measured system data in the current state into the intraday rolling optimization model, and solves the optimal control sequence in combination with the forecast data of wind and solar loads; the optimal control sequence is sent as a real-time dispatch instruction to each component in the source-load-storage system, and operates according to the predetermined control strategy; The correction module uses the multi-time scale day-ahead dispatch model to determine the start and stop plan of the thermal power units, the PDR and Class B IDR dispatch plan, uses the thermal power unit output and Class B IDR dispatch plan determined by the intraday rolling optimization model, and combines the regulation function of the energy storage power station to correct the output of each unit in real time and determine the Class C and Class D IDR dispatch plan; And according to the system operation status, the output of the thermal power unit can be corrected in real time using PID and IDR type corresponding load resources; The scheduling module solves the multi-time-scale day-ahead scheduling model involving sources, loads and storage based on the DLPF power flow model, obtains the optimal output plan for wind power generation systems, thermal power generation systems, energy storage systems, and load-side response resources, and realizes multi-time-scale scheduling based on the integration of sources, grids, loads and storage driven by renewable energy.

Images