Dispatching method, device, computer equipment and storage medium for isolated island microgrid

Abstract

The present application relates to a method, device, computer equipment, storage medium and computer program product for scheduling an isolated island microgrid. The method includes: obtaining the day-ahead predicted power of renewable energy power generation equipment; sampling to obtain renewable energy output curve scenarios based on the day-ahead predicted power and the power prediction error probability distribution function; constructing a day-ahead scheduling objective function based on the renewable energy output curve scenarios, and establishing constraints for the day-ahead scheduling objective function; rolling optimization of the operating costs of hybrid energy storage equipment within the forward-looking period to construct an intraday scheduling objective function; solving the day-ahead scheduling objective function, and using the solution results to construct constraints for the intraday scheduling objective function; solving the intraday scheduling objective function, and scheduling the isolated island microgrid based on the solution results. The use of this method can improve the accuracy of the isolated island microgrid scheduling method and reduce the operating costs of the isolated island microgrid.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a scheduling method, device, computer equipment, storage medium and computer program product for an isolated island microgrid. Background Art

[0002] With the development of power technology, the technology of predicting renewable energy power generation has emerged. The current island microgrid dispatching technology often uses the combination of predicted renewable energy power generation and building heat storage system to realize the dispatching of island microgrids. However, most existing studies assume that the prediction error of renewable energy power generation at all times in the forward-looking period is exactly the same, which leads to relatively ideal decision-making of island microgrid dispatching and greatly reduces the actual application effect. Summary of the invention

[0003] Based on this, it is necessary to provide an island microgrid scheduling method, device, computer equipment, computer readable storage medium and computer program product to address the above technical problems.

[0004] In a first aspect, the present application provides a scheduling method for an isolated island microgrid, wherein the isolated island microgrid includes renewable energy power generation equipment, a diesel engine, and a hybrid energy storage device. The method includes:

[0005] Obtain the day-ahead predicted power of renewable energy power generation equipment;

[0006] Sampling is performed according to the day-ahead predicted power and the power prediction error probability distribution function to obtain a preset number of renewable energy output curve scenarios;

[0007] Constructing a day-ahead dispatch objective function with the goal of minimizing the operating costs of the diesel engine and the hybrid energy storage device according to the preset number of renewable energy output curve scenarios, and establishing constraints of the day-ahead dispatch objective function according to the operating costs of the diesel engine and the hybrid energy storage device and the operating cost discount rate;

[0008] The model predictive control strategy is used to rollingly optimize the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period, and an intraday scheduling objective function is constructed with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period;

[0009] Solving the day-ahead scheduling objective function, and using the solution result to construct the constraint conditions of the intraday scheduling objective function;

[0010] Solve the intraday dispatch objective function, and dispatch the isolated island microgrid according to the solution result.

[0011] In one embodiment, the renewable energy power generation equipment includes a wind power generation equipment and a photovoltaic power generation equipment, and the step of obtaining the day-ahead predicted power of the renewable energy power generation equipment includes:

[0012] Obtain future wind data, future sunshine intensity data, wind turbine rated power, maximum test power output of photovoltaic cells under standard test conditions, sunshine intensity under standard test conditions, power temperature coefficient, solar panel operating temperature and reference temperature from the isolated island microgrid dispatching center;

[0013] Determine the day-ahead predicted power of the wind power generation equipment according to future wind power data and the rated power of the wind turbine;

[0014] The day-ahead predicted power of the photovoltaic power generation equipment is determined based on the maximum test power output by the photovoltaic cell under the standard test conditions, the sunshine intensity data in the future, the sunshine intensity under the standard test conditions, the power temperature coefficient, the operating temperature of the battery panel and the reference temperature.

[0015] In one embodiment, the hybrid energy storage device includes a battery and a phase change thermal storage system.

[0016] In one embodiment, the step of establishing the constraint condition of the day-ahead scheduling objective function according to the operating cost parameters of the diesel engine and the hybrid energy storage device and the operating cost discount rate includes:

[0017] Obtain the dispatch cycle, discount factor, diesel engine operating cost parameters and battery operating cost parameters corresponding to different renewable energy output curve scenarios;

[0018] The constraint conditions of the day-ahead scheduling objective function are constructed according to the scheduling period, the discount factor, the diesel engine operating cost parameters corresponding to the different renewable energy output curve scenarios, and the battery operating cost parameters.

[0019] In one embodiment, the step of constructing a daily scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device in the forward-looking period comprises:

[0020] Obtain the duration of the forward-looking period and the diesel engine operating cost parameters, battery operating cost parameters, and discount coefficient corresponding to the forward-looking period;

[0021] According to the look-ahead period duration and the diesel engine operating cost parameters, battery operating cost parameters and discount coefficient corresponding to the look-ahead period, a daily scheduling objective function is constructed with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device during the look-ahead period.

[0022] In one of the embodiments, the solution result includes the thermal load state of the phase change thermal storage system and the battery charge state of the battery corresponding to the day-ahead scheduling objective function.

[0023] In a second aspect, the present application further provides an island microgrid scheduling device, the device comprising:

[0024] A power acquisition module, used to obtain the day-ahead predicted power of renewable energy power generation equipment;

[0025] A scenario acquisition module, used to perform sampling according to the day-ahead predicted power and the power prediction error probability distribution function to obtain a preset number of renewable energy output curve scenarios;

[0026] A first objective function construction module is used to construct a day-ahead scheduling objective function with the goal of minimizing the operating costs of the diesel engine and the hybrid energy storage device according to the preset number of renewable energy output curve scenarios, and to establish constraint conditions of the day-ahead scheduling objective function according to the operating costs of the diesel engine and the hybrid energy storage device and the operating cost discount rate;

[0027] The second objective function construction module is used to use the model predictive control strategy to rollingly optimize the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period, and to construct an intraday scheduling objective function with the minimum sum of the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period as the goal;

[0028] A constraint construction module, used to solve the day-ahead scheduling objective function and use the solution result to construct the constraint conditions of the intra-day scheduling objective function;

[0029] The scheduling solution module is used to solve the intra-day scheduling objective function and schedule the isolated island microgrid according to the solution result.

[0030] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0032] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0033] The above-mentioned island microgrid scheduling method, device, computer equipment, storage medium and computer program product obtain the day-ahead predicted power of the renewable energy equipment, and obtain the renewable energy output curve scenes corresponding to the preset number by sampling according to the day-ahead predicted power and the power prediction error probability distribution function. According to the renewable energy output curve scenes corresponding to the preset number, a day-ahead scheduling objective function with the lowest operating cost of the diesel engine and the hybrid energy storage device as the goal is constructed, and the constraints of the day-ahead scheduling objective function are established according to the operating cost parameters and operating cost discount rate of the diesel engine and the hybrid energy storage device. The operating cost of the hybrid energy storage device in the forward-looking period is rolled optimized to construct an intraday scheduling objective function with the minimum sum of the operating costs of the diesel engine and the hybrid energy storage device in the forward-looking period as the goal; the operating costs of the diesel engine and the hybrid energy storage device are less different from the actual results after rolling optimization in the forward-looking period. Solve the day-ahead scheduling objective function, and use the solution result to construct the constraints of the intraday scheduling objective function; solve the intraday scheduling objective function, and schedule the island microgrid according to the solution result. This application takes into account the different prediction errors in different forward-looking periods. By rolling and optimizing the scheduling after discounting the operating costs at the corresponding moments, the adverse effects of prediction errors on the isolated island microgrid can be minimized. At the same time, a two-layer scheduling strategy of day-ahead and day-intraday is adopted to improve the operating economy of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 An application environment diagram of an isolated island microgrid scheduling method in an embodiment;

[0035] Figure 2 A schematic diagram of a flow chart of an isolated island microgrid scheduling method in one embodiment;

[0036] Figure 3 A schematic diagram of the power balance relationship among a diesel engine, a battery, a heat pump and a net load in one embodiment;

[0037] Figure 4 A graph showing the charge and discharge power and SOC curve of a battery in another embodiment;

[0038] Figure 5 It is a graph showing the operating power and SOC curve of a phase change thermal energy storage system in one embodiment;

[0039] Figure 6 A schematic diagram of a flow chart of steps for obtaining a day-ahead predicted power of a renewable energy power generation device in one embodiment;

[0040] Figure 7 A schematic flow chart of the steps of establishing the constraint conditions of the day-ahead scheduling objective function according to the operating cost parameters and the operating cost discount rate of the hybrid energy storage device in one embodiment;

[0041] Figure 8 A schematic flow chart of steps for constructing an intraday scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device in a forward-looking period in an embodiment;

[0042] Fig. 9 is a structural block diagram of an isolated island microgrid dispatching device in one embodiment;

[0043] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] The isolated island microgrid scheduling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The server 102 communicates with the isolated island microgrid 104 through the network, the server 102 obtains information from the isolated island microgrid 104, and sends the scheduling strategy to the isolated island microgrid 104. The server 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The server 102 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0046] In one embodiment, Figure 2 As shown, a scheduling method for an isolated island microgrid is provided, wherein the isolated island microgrid includes a renewable energy power generation device, a diesel generator and a hybrid energy storage device, and the method is applied to Figure 1 Taking the isolated island microgrid 104 in the example as an example, the method includes the following steps:

[0047] Step 202: Obtain the day-ahead predicted power of the renewable energy power generation equipment.

[0048] The day-ahead predicted power is the power generation power of renewable energy power generation equipment predicted within the next 24 hours. For example, if the power generation power of renewable energy power generation equipment is predicted from 0:00 to 24:00 tomorrow, the power generation power obtained is the day-ahead predicted power of renewable energy power generation equipment.

[0049] Step 204 , sampling is performed according to the day-ahead predicted power and the power prediction error probability distribution function to obtain a preset number of renewable energy output curve scenarios.

[0050] The day-ahead power prediction of renewable energy generation equipment includes the day-ahead power prediction of wind power generation equipment and photovoltaic power generation equipment, and their day-ahead power prediction errors obey the beta distribution function and the normal distribution function respectively; the renewable energy output curve scenario is a matrix representing the day-ahead power prediction of renewable energy at different times in the island microgrid scheduling. The power prediction error in the following embodiments refers to the day-ahead power prediction error.

[0051] In one embodiment, the Beta distribution function obeyed by the probability distribution of the day-ahead power forecast error of the wind power generation equipment can be expressed as:

[0052]

[0053] The parameters in the formula are α=4.2、β=9.8;P t WT The day-ahead power forecast for wind power generation equipment (such as wind turbines); It is the rated power of wind power generation equipment (such as wind turbine).

[0054] In one embodiment, the normal distribution function obeyed by the probability distribution of the day-ahead power prediction error of the photovoltaic power generation equipment can be expressed as:

[0055]

[0056] The parameter μ in the formula PV =0,σ PV =0.3; P t PV Day-ahead power forecast for photovoltaic power generation equipment.

[0057] In one embodiment, based on the day-ahead predicted power of wind power generation equipment and photovoltaic power generation equipment and the beta distribution function and normal distribution function obeyed by the day-ahead predicted power error, a Monte Carlo sampling technique is used to generate renewable energy output curve scenarios that may occur within a preset number S (e.g., S=5) days, such as a 5-dimensional matrix:

[0058]

[0059] in: It represents the sum of the power generation of the photovoltaic power generation equipment and the wind power generation equipment at time t in scenario s (s=1,2,3,4,5).

[0060] Step 206, constructing a day-ahead scheduling objective function with the goal of minimizing the operating costs of the diesel engine and the hybrid energy storage device based on the preset number of renewable energy output curve scenarios, and establishing constraints of the day-ahead scheduling objective function based on the operating costs of the diesel engine and the hybrid energy storage device and the operating cost discount rate.

[0061] The day-ahead scheduling objective function is the scheduling objective function predicted 24 hours in advance; the operating costs in this application include but are not limited to time and resources.

[0062] In one embodiment, based on the renewable energy output curve scenarios that may occur within a preset number S (such as S=5) days, a day-ahead scheduling objective function is constructed with the diesel engine and the hybrid energy storage device as the goal of minimizing the operating cost, and the constraints of the day-ahead scheduling objective function are established based on the operating costs of the diesel engine and the hybrid energy storage device and the operating cost discount rate.

[0063] The day-ahead scheduling objective function can be expressed as:

[0064]

[0065] In the formula, S is the scenario; T is the look-ahead period; t is the look-ahead duration value; and are the diesel generator power generation cost and battery operation cost under scenario s (s=1,2,…,S); γ t is the discount factor that takes into account the long-term increasing effect of forecast errors.

[0066] Step 208, using a model predictive control strategy to rollingly optimize the operating cost of the hybrid energy storage device within the forward-looking period, and constructing a daily scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period.

[0067] Among them, the forward-looking period is the unit time length taken in the process of predicting the next 24 hours, such as 3 hours or 4 hours as a forward-looking period. In traditional technology, the longer the forward-looking period, the greater the difference between the predicted parameters and the actual parameters. For example, the operating cost of the hybrid energy storage equipment in the next 24 hours is predicted with a forward-looking period of 6 hours and 3 hours respectively. The final result shows that the error of the predicted parameters with a forward-looking period of 6 hours is greater than the predicted parameters with a forward-looking period of 3 hours. The intraday scheduling objective function is the scheduling objective function within the predicted 24 hours.

[0068] In one embodiment, a model predictive control strategy is used to perform rolling optimization on the operating costs of the diesel engine and the hybrid energy storage device within the next 24 hours with a forward-looking cycle (e.g., 4 hours as a forward-looking cycle), and the operation cost of the hybrid energy storage device within the next 4 hours is minimized.

[0069] In one embodiment, assuming that today is 0:00 on September 1, a model predictive control strategy is used to perform rolling optimization on the operating costs of the diesel engine and the hybrid energy storage device from 0:00 to 24:00 on September 1 with a 3-hour forward-looking cycle. The specific rolling optimization steps are as follows: at 0:00, an operation plan for 0:00-3:00 is formulated, and the operating costs of the diesel engine and the hybrid energy storage device from 0:00 to 1:00 are determined according to the operation plan, and the operation plan is abandoned after determination; at 1:00, an operation plan for 1:00-4:00 is formulated, and the operating costs of the diesel engine and the hybrid energy storage device from 1:00 to 2:00 are determined according to the operation plan, and the operation plan is abandoned after determination; at 2:00, a plan for 2:00-5:00 is formulated, and the operating costs of the diesel engine and the hybrid energy storage device from 2:00 to 3:00 are determined according to the operation plan, and the operation plan is abandoned after determination... and so on, at 23:00 When the operation plan is formulated from 23:00 on September 1 to 2:00 the next day, the operating costs of the diesel engine and the hybrid energy storage equipment from 23:00 to 24:00 are determined based on the operation plan. After the determination, the operation plan is abandoned. At this time, the rolling optimization of the operating costs of the diesel engine and the hybrid energy storage equipment on September 1 is completed. The forward-looking period of each rolling optimization has an overlapping part, which makes the final scheduling strategy more accurate.

[0070] In one embodiment, the intraday scheduling objective function constructed with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device in the next 4 hours can be expressed as:

[0071]

[0072] Where M is the look-ahead period of the model predictive control strategy; t is the look-ahead duration; τ is the numerical variable of the look-ahead duration introduced for the convenience of calculation; γ τ They respectively represent the diesel engine operating cost, battery operating cost and discount factor corresponding to a certain forward-looking period (such as 3 hours).

[0073] Step 210, solving the day-ahead scheduling objective function, and using the solution result to construct the constraint conditions of the intra-day scheduling objective function.

[0074] In one embodiment, yalmip software is used to call the commercial solver CPLEX to solve the day-ahead scheduling objective function, and the solution result is used to construct the constraint conditions of the intraday scheduling objective function.

[0075] Among them, when solving the day-ahead dispatch objective function, the linearization technology is first used to transform the nonlinear constraint (4) into (6)~(13), and then the MATALB platform is used to encode and characterize the day-ahead dispatch objective function. The yalmip software is used to call the commercial solver CPLEX to solve the day-ahead dispatch objective function, and the thermal load state of the phase change heat storage system and the battery charge state of the battery are obtained. (6)~(7) indicate that the actual operating power of the diesel engine and the power generation cost can be represented by the linear combination of fixed power points and the corresponding costs, (8) indicates that the actual operating power of the diesel engine can only fall within a single power range, (9) indicates that when the linear combination of fixed power points is used to represent the actual operating power, the sum of the corresponding fixed power point weights is equal to 1, and (10)~(13) restrict that only adjacent fixed power points can be allowed to participate in the linear combination.

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] In the formula, for The diesel generator power generation cost at time t under scenario s (s = 1, 2, …, S); r is the beginning of the linearization segment; R is the end of the linearization segment; R-1 is the number of linearization segments; Pr is the power of the corresponding breakpoint; is the weight corresponding to the beginning r of the linearization segment number in scenario s (s = 1, 2, ..., S); is the weight corresponding to the end R of the linearization segment number in scenario s (s = 1, 2, ..., S); is the selected segment; The weight corresponding to time t in scenario s (s = 1, 2, ..., S) is 1; C G (P r ) indicates that the diesel engine power generation is equal to P r operating costs.

[0085] Among them, the constraints of the intraday scheduling objective function constructed by using the heat load state of the phase change heat storage system and the battery charge state of the battery are expressed as (14) to (15):

[0086]

[0087]

[0088] In the formula, and They respectively represent the heat load state of the phase change heat storage system and the battery charge state of the battery obtained by the day-ahead optimization; and They respectively represent the thermal load state of the phase change heat storage system and the battery charge state of the battery in the daily stage; t is the look-ahead time; M is the look-ahead period of the model predictive control strategy (such as 24 hours); H represents the phase change heat storage system; B represents the battery.

[0089] Step 212, solving the intra-day dispatch objective function, and dispatching the isolated island microgrid according to the solution result.

[0090] In one embodiment, the yalmip software is used to call the commercial solver CPLEX to solve the intraday scheduling objective function, output the operating power and energy storage heat load status of the diesel engine, battery and phase change heat storage system respectively, and schedule the isolated island microgrid. For details on the output operating power and energy storage charge status of the diesel engine, battery and phase change heat storage system, please refer to Figure 3 (Schematic diagram of power balance between diesel engine, battery, heat pump and net load), Figure 4 (Battery charge and discharge power and SOC curve) and Figure 5 (Phase change thermal energy storage system operating power and SOC curve).

[0091] In one embodiment, the dispatch result of the isolated microgrid is as follows: the daily operating cost of the isolated microgrid is 10,955 yuan, including the diesel generator cost of 10,648 yuan and the battery operating cost of 307 yuan. Figure 3 It can be seen that the diesel engine, battery and heat pump in the phase change heat storage system work together to maintain the power balance of the isolated power grid and realize the operation of the isolated power grid with the lowest operating cost of the hybrid energy storage equipment; Figure 4 middle Figure 5 The SOC of the battery and phase change heat storage system fluctuates between 0 and 1, achieving a higher utilization rate of renewable energy in the isolated island microgrid.

[0092] In the above-mentioned dispatching method of the isolated microgrid, the day-ahead predicted power of the renewable energy equipment is obtained, and the renewable energy output curve scenes corresponding to the preset number are obtained by sampling according to the day-ahead predicted power and the probability distribution function of the power forecast error. A day-ahead dispatching objective function with the lowest operating cost of the hybrid energy storage equipment as the goal is constructed according to the renewable energy output curve scenes corresponding to the preset number, and the constraint conditions of the day-ahead dispatching objective function are established according to the operating cost parameters and the operating cost discount rate of the hybrid energy storage equipment. The operating cost of the hybrid energy storage equipment in the forward-looking period is rolled optimized to construct an intraday dispatching objective function with the minimum sum of the operating costs of the hybrid energy storage equipment in the forward-looking period as the goal; the operating cost of the hybrid energy storage equipment is less different from the actual result after rolling optimization in the forward-looking period. The yalmip software is used to call the commercial solver CPLEX to solve the day-ahead dispatching objective function, and the solution results are used to construct the constraint conditions of the intraday dispatching objective function; the yalmip software is used to call the commercial solver CPLEX to solve the intraday dispatching objective function, output the operating power of the hybrid energy storage equipment, and dispatch the isolated microgrid. This application takes into account the different prediction errors in different forward-looking periods. By rolling and optimizing the scheduling after discounting the operating costs at the corresponding moments, the adverse effects of prediction errors on the isolated island microgrid can be minimized. At the same time, a two-layer scheduling strategy of day-ahead and day-intraday is adopted to improve the operating economy of the power grid.

[0093] In one embodiment, Figure 6 As shown, the renewable energy power generation equipment includes wind power generation equipment and photovoltaic power generation equipment, and the step of obtaining the day-ahead predicted power of the renewable energy power generation equipment includes:

[0094] Step 602, obtaining future wind data, future sunshine intensity data, rated power of wind turbines, maximum test power output of photovoltaic cells under standard test conditions, sunshine intensity under standard test conditions, power temperature coefficient, operating temperature of solar panels and reference temperature from the isolated island microgrid dispatching center.

[0095] In one embodiment, the island microgrid dispatching center in the island microgrid will predict and save future wind data, future sunshine intensity data, and save the rated power of the wind turbine, the maximum test power output of the photovoltaic cell under standard test conditions, the sunshine intensity under standard test conditions, the power temperature coefficient, the operating temperature of the solar panel and the reference temperature. The server 102 can obtain the above data directly from the island microgrid dispatching center.

[0096] Step 604: determine the day-ahead predicted power of the wind power generation equipment according to future wind power data and the rated power of the wind turbine.

[0097] Among them, the future refers to the next 24 hours after the start of the forecast.

[0098] In one embodiment, the server 102 determines the day-ahead predicted power of the wind power generation equipment (such as a wind turbine with a capacity of 450 kW) according to the wind power data of the next day and the rated power of the wind power generation equipment (such as a wind turbine with a capacity of 450 kW):

[0099]

[0100] Among them, P t WT is the day-ahead power forecast for wind power generation equipment; t is the wind speed at time t; r and They are cut-in wind speed, rated wind speed and cut-out wind speed; is the rated power of the fan.

[0101] Step 606, determining the day-ahead predicted power of the photovoltaic power generation equipment according to the maximum test power output by the photovoltaic cell under the standard test conditions, the sunshine intensity data in the future, the sunshine intensity under the standard test conditions, the power temperature coefficient, the operating temperature of the solar panel and the reference temperature.

[0102] In one embodiment, the server 102 determines the day-ahead predicted power of the photovoltaic power generation equipment (e.g., a photovoltaic installation with a capacity of 750 kW) according to the maximum test power output of the photovoltaic cell under the standard test conditions, the future sunshine intensity data, the sunshine intensity under the standard test conditions, the power temperature coefficient, the operating temperature of the solar panel, and the reference temperature:

[0103] P tPV =P STC G AC,t [1+ε(T c,t -T r )] / G STC (17)

[0104] Among them, P t PV is the day-ahead predicted power of the photovoltaic power generation equipment; P STC G is the maximum test power output of the photovoltaic cell under standard test conditions; AC,t , G STC are the sunshine intensity at time t and under standard test conditions; ε is the power temperature coefficient; T c,t is the operating temperature of the solar panel at time t; T r is the reference temperature.

[0105] In this embodiment, the server 102 obtains the future wind data, the future sunshine intensity data, the rated power of the wind turbine, the maximum test power output of the photovoltaic cell under standard test conditions, the sunshine intensity under standard test conditions, the power temperature coefficient, the working temperature of the battery panel and the reference temperature from the isolated island microgrid dispatching center, and determines the day-ahead predicted power of the wind turbine according to the future wind data and the rated power of the wind turbine; and then determines the day-ahead predicted power of the photovoltaic power generation equipment according to the maximum test power output of the photovoltaic cell under standard test conditions, the future sunshine intensity data, the sunshine intensity under standard test conditions, the power temperature coefficient, the working temperature of the battery panel and the reference temperature. By obtaining the relevant data such as the wind data and sunshine intensity of the next day, the day-ahead predicted power of the wind power generation equipment and the photovoltaic power generation equipment is made more accurate.

[0106] In one embodiment, the hybrid energy storage device includes a battery and a phase change thermal storage system.

[0107] In one embodiment, the phase change heat storage system mainly utilizes the phase change process of phase change materials to store or release heat, wherein heat storage can be achieved by controlling the heat pump electric heat conversion process; wherein, phase change materials are a new type of heat storage medium, which has significant advantages such as large latent heat absorption / release, low cost, and long service life. The diesel engine, battery, and phase change heat storage system must meet certain operating constraints during operation. The diesel engine operating constraints can be expressed as (18) to (19). Among them, formula (18) indicates that the diesel engine operating power cannot be greater than the rated operating power, and formula (19) indicates that the diesel engine power generation power change cannot be greater than the ramp rate.

[0108]

[0109]

[0110] In the formula, P is the start-up sign of the diesel engine at t hours; t G and are the operating power and rated power of the diesel engine at time t respectively; is the operating power of the diesel engine at time t-1; ΔP G It is the maximum operating climbing rate of the diesel engine.

[0111] The battery operation constraints can be expressed as (20) to (25), where (20) to (22) indicate that the battery cannot be charged and discharged at the same time, and the charging or discharging power cannot be greater than the rated power. (23) indicates that the battery SOC at time t is determined by the SOC at the previous time and the charging and discharging power at time t. The SOC (Stage of Charge) indicates the battery state of charge or the thermal load state of the energy storage system. (24) indicates the battery energy storage limit. (25) requires that the battery energy storage should return to the initial value at the end of the scheduling.

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] In the formula, and They are the battery charging and discharging signs; P t Bc and P t Bd are the charging and discharging power of the battery at time t respectively; and are the rated charging power and discharging power of the battery respectively; is the battery SOC at time t; is the rated capacity of the battery (e.g. 2000kW); η B is the battery charge and discharge efficiency (e.g. 0.9); and They are the lower limit and upper limit of the battery SOC, such that the lower limit and upper limit are 0.1 and 1.0 respectively; and and are the battery SOC at the beginning and end of the look-ahead period, respectively.

[0119] The operating constraints of the phase change heat storage system can be expressed as (26) to (30). Among them, (26) indicates that the actual energy storage of the phase change heat storage system is related to the specific heat capacity, (27) defines the SOC of the phase change heat storage system, (28) indicates that the SOC of the phase change heat storage system at time t is determined by the SOC at the previous time and the heat pump operating power and load at time t, (29) indicates the energy storage limit of the phase change heat storage system, and (30) requires that the energy storage of the phase change heat storage system should return to the initial value at the end of the scheduling:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] In the formula, represents the heat storage of the phase change heat storage system at time t; m H is the mass of the phase change material; and They are the specific heat capacity of the phase change material at time t and the minimum heat capacity of the phase change process; represents the heat storage state of the phase change heat storage system at time t; is the maximum heat storage capacity of the phase change heat storage system; and η pump are the power of the heat pump at time t and the electric-to-heat conversion efficiency; is the building cooling demand; η H is the heat leakage rate caused by the temperature difference between the outdoor environment and the phase change material; and They are the minimum and maximum values ​​of SOC of the phase change thermal storage system; and and are the SOC at the beginning and end of the look-ahead period T, respectively.

[0126] In this embodiment, the hybrid energy storage device includes a battery and a phase change heat storage system, wherein the phase change heat storage system contains phase change materials, which can improve the flexible adjustment capability of the phase change heat storage system, thereby improving the combined heat and power dispatching effect of the microgrid.

[0127] In one embodiment, Figure 7 As shown, the step of establishing the constraint condition of the day-ahead scheduling objective function according to the operating cost parameters of the diesel engine and the hybrid energy storage device and the operating cost discount rate includes:

[0128] Step 702, obtaining the dispatching period, discount factor, diesel engine operating cost parameters and battery operating cost parameters corresponding to different renewable energy output curve scenarios.

[0129] In one embodiment, within a scheduling cycle (such as 24 hours), the scheduling cycle, discount factor, diesel engine operating cost parameters and battery operating cost parameters corresponding to different renewable energy output curve scenarios are determined by the dispatcher and saved in a designated location of the server 102.

[0130] Step 704 , constructing the constraint conditions of the day-ahead scheduling objective function according to the scheduling period, the discount factor, the diesel engine operating cost parameters corresponding to the different renewable energy output curve scenarios, and the battery operating cost parameters.

[0131] Among them, the diesel engine operating cost parameters refer to the accounting parameters in the process of calculating the diesel engine operating cost; the battery operating cost parameters refer to the accounting parameters in the process of calculating the battery operating cost.

[0132] In one embodiment, the constraints of the day-ahead dispatch objective function are shown in (31) to (34). Among them, (31) to (32) represent the calculation formulas for diesel generator power generation cost and battery charging and discharging cost, (33) represents the operating cost discount rate taking into account the long-term increasing effect of prediction error, and (34) represents the power balance constraint of the isolated island microgrid.

[0133]

[0134]

[0135]

[0136]

[0137] In the formula, and They represent the diesel generator power generation cost and the battery operating cost under scenario s (s=1, 2, …, S) respectively; a, b and c are parameters for measuring the operating cost of the diesel generator power generation. In one embodiment, a can be parameter 9; b can be parameter 0.52575 / kW; c can be parameter 0.0018025 / kW 2 ;p B It is the unit charge and discharge power cost of the battery. For example, the unit charge and discharge power cost parameter of the battery is 0.3 / kWh; and are the charging and discharging power of the battery at time t under scenario s (s=1, 2,…, S); and P t load are the heat pump power and electrical load power at time t under scenario s (s = 1, 2, ..., S); t is the look-ahead duration; γ t and r are the discount coefficient and discount rate respectively, which take into account the long-term increasing effect of forecast error.

[0138] In this embodiment, the dispatching personnel will determine the dispatching period, discount factor, diesel engine operating cost parameters and battery operating cost parameters corresponding to different renewable energy output curve scenarios in advance according to the actual situation, and save them in the designated location of server 102. Server 102 obtains the dispatching period, discount factor, diesel engine operating cost parameters and battery operating cost parameters corresponding to different renewable energy output curve scenarios, and constructs the constraint conditions of the day-ahead dispatching objective function according to the battery operating cost parameters. By calculating the diesel engine power generation cost and battery charging and discharging cost at different times under different scenarios, the calculated diesel engine power generation cost and battery operating cost are more accurate and more in line with the actual situation.

[0139] In one embodiment, Figure 8 As shown, the steps of constructing a daily scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device in the forward-looking period include:

[0140] Step 802, obtaining the duration of the forward-looking period and the diesel engine operating cost parameter, battery operating cost parameter, and discount factor corresponding to the forward-looking period.

[0141] In one embodiment, the dispatcher sets the forward-looking period (such as 4 hours as one forward-looking period), the diesel engine operating cost parameters corresponding to the forward-looking duration, the battery operating cost parameters, and the discount factor, and saves them in a designated location on the server 102 .

[0142] Step 804, constructing a daily scheduling objective function with the goal of minimizing the operating cost parameters of the diesel engine and the hybrid energy storage device during the look-ahead period according to the look-ahead period duration and the diesel engine operating cost parameters, battery operating cost parameters, and discount coefficient corresponding to the look-ahead period duration.

[0143] In one embodiment, the server 102 obtains and constructs a daily scheduling objective function for the diesel engine and the hybrid energy storage device with the minimum operating cost parameter in the forward-looking period as the goal based on the forward-looking period duration (e.g., 4 hours) and the diesel engine operating cost parameter, battery operating cost parameter, and discount factor corresponding to the forward-looking period. The daily scheduling objective function is shown in (35):

[0144]

[0145] Where M is the look-ahead period of the model predictive control strategy; t is the value of the look-ahead duration; τ is the numerical variable of the look-ahead duration introduced for the convenience of calculation; γ τ ” are respectively the diesel engine operating cost, battery operating cost and discount factor corresponding to a certain forward-looking time (such as 4 hours);.

[0146] In this embodiment, the 24 hours of a day are divided into a certain forward-looking period (such as 4 hours), that is, taking into account the different effects of different forward-looking periods on the diesel engine operating cost, the battery operating cost, and the discount factor, and by comparing the diesel engine operating cost and the battery operating cost corresponding to different forward-looking periods, the intraday scheduling objective function is made more accurate, thereby reducing the operating cost of the isolated island microgrid.

[0147] In one embodiment, the solution result includes the thermal load state of the phase change thermal storage system and the battery state of charge of the battery corresponding to the day-ahead scheduling objective function.

[0148] In one embodiment, linearization technology is first used to convert nonlinear constraints into linear constraints, and then the MATALB platform is used to encode and characterize the day-ahead scheduling objective function. The yalmip software is used to call the commercial solver CPLEX to solve the day-ahead scheduling objective function, and the SOC of the phase change heat storage system and the battery corresponding to the day-ahead scheduling objective function is output, where the SOC of the phase change heat storage system and the battery floats between 0 and 1, jointly maintaining the power balance and stable operation of the isolated island microgrid.

[0149] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0150] Based on the same inventive concept, the embodiment of the present application also provides a dispatching device for an isolated microgrid for implementing the dispatching method for an isolated microgrid involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the dispatching device for one or more isolated microgrids provided below can refer to the limitations of the dispatching method for an isolated microgrid above, and will not be repeated here.

[0151] In one embodiment, Fig. 9 As shown, an island microgrid scheduling device is provided, including: a power acquisition module 902, a scene acquisition module 904, a first objective function construction module 906, a second objective function construction module 908, a constraint construction module 910 and a scheduling solution module 912, wherein:

[0152] A power acquisition module 902 is used to acquire the day-ahead predicted power of the renewable energy power generation equipment;

[0153] A scenario acquisition module 904 is used to perform sampling according to the day-ahead predicted power and the power prediction error probability distribution function to obtain a preset number of renewable energy output curve scenarios;

[0154] A first objective function construction module 906 is used to construct a day-ahead scheduling objective function with the goal of minimizing the operating costs of the diesel engine and the hybrid energy storage device according to the preset number of renewable energy output curve scenarios, and to establish constraints of the day-ahead scheduling objective function according to the operating costs of the diesel engine and the hybrid energy storage device and the operating cost discount rate;

[0155] The second objective function construction module 908 is used to use the model predictive control strategy to rollingly optimize the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period, and to construct an intraday scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period;

[0156] The constraint construction module 910 is used to solve the day-ahead scheduling objective function and construct the constraint conditions of the intra-day scheduling objective function using the solution result;

[0157] The scheduling solution module 912 is used to solve the intra-day scheduling objective function and schedule the isolated island microgrid according to the solution result.

[0158] In one embodiment, the renewable energy power generation equipment includes wind power generation equipment, diesel engines and photovoltaic power generation equipment, and the step of obtaining the day-ahead predicted power of the renewable energy power generation equipment includes: the server 102 obtains future wind data, future sunshine intensity data, the rated power of the wind turbine, the maximum test power output of the photovoltaic cell under standard test conditions, the sunshine intensity under standard test conditions, the power temperature coefficient, the operating temperature of the battery panel and the reference temperature from the isolated island microgrid dispatching center; the power acquisition module 902 determines the day-ahead predicted power of the wind power generation equipment according to the future wind data and the rated power of the wind turbine; the power acquisition module 902 determines the day-ahead predicted power of the photovoltaic power generation equipment according to the maximum test power output of the photovoltaic cell under standard test conditions, the future sunshine intensity data, the sunshine intensity under standard test conditions, the power temperature coefficient, the operating temperature of the battery panel and the reference temperature.

[0159] In one embodiment, the steps of establishing the constraint conditions of the day-ahead scheduling objective function according to the operating cost parameters of the diesel engine and the hybrid energy storage device and the operating cost discount rate include: the server 102 obtains the scheduling period, the discount factor, and the corresponding diesel engine operating cost parameters and battery operating cost parameters under different renewable energy output curve scenarios; the first objective function construction module 906 constructs the constraint conditions of the day-ahead scheduling objective function according to the scheduling period, the discount factor, and the corresponding diesel engine operating cost parameters and battery operating cost parameters under different renewable energy output curve scenarios.

[0160] In one embodiment, the steps of constructing a daily scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device in the forward-looking period include: the server 102 obtains the forward-looking period duration and the diesel engine operating cost parameters, battery operating cost parameters, and discount factors corresponding to the forward-looking period; the second objective function construction module 908 constructs the daily scheduling objective function with the goal of minimizing the sum of the operating costs of the hybrid energy storage device in the forward-looking period according to the forward-looking period duration and the diesel engine operating cost parameters, battery operating cost parameters, and discount factors corresponding to the forward-looking period.

[0161] In one embodiment, the scheduling solution module 912 is used to solve the intra-day scheduling objective function, and the solution result includes the thermal load state of the phase change thermal storage system and the battery charge state of the battery corresponding to the day-ahead scheduling objective function.

[0162] Each module in the above-mentioned isolated microgrid dispatching device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0163] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.10As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a scheduling method for an isolated microgrid is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0164] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0165] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0166] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0167] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0168] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0169] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A scheduling method for an isolated island microgrid, characterized in that: The isolated island microgrid includes renewable energy power generation equipment, a diesel engine and a hybrid energy storage device, and the method includes: Obtaining the day-ahead predicted power of the renewable energy power generation equipment; the day-ahead predicted power of the renewable energy power generation equipment includes the day-ahead power prediction of the wind power generation equipment and the photovoltaic power generation equipment, and the day-ahead power prediction errors of the wind power generation equipment and the photovoltaic power generation equipment obey the beta distribution function and the normal distribution function respectively; Sampling is performed according to the day-ahead predicted power and the power prediction error probability distribution function to obtain a preset number of renewable energy output curve scenarios; the renewable energy output curve scenario represents a matrix of the day-ahead predicted power of renewable energy at different times in the island microgrid scheduling; A day-ahead scheduling objective function is constructed based on the preset number of renewable energy output curve scenarios with the lowest operating cost of the diesel engine and the hybrid energy storage device as the goal, and a constraint condition of the day-ahead scheduling objective function is established based on the operating cost parameters of the diesel engine and the hybrid energy storage device and the operating cost discount rate; the day-ahead scheduling objective function is expressed as: , where S For the scene; T For the forward-looking period; t is the value of the look-ahead duration; and Respectively for scenes s(s=1,2,…,S) Diesel power generation costs and battery operating costs under The discount factor to take into account the long-term increasing effect of forecast errors; The model predictive control strategy is used to rollingly optimize the operating costs of the diesel engine and the hybrid energy storage device in the forward-looking period, and an intraday scheduling objective function is constructed with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device in the forward-looking period; the intraday scheduling objective function is expressed as: , where M is the look-ahead period of the model predictive control strategy; t For the forward-looking duration; is a numerical variable for the look-ahead duration; , They represent the diesel engine operating cost, battery operating cost, and discount factor corresponding to a certain forward-looking period respectively; Solving the day-ahead scheduling objective function, and using the solution result to construct the constraint conditions of the intraday scheduling objective function; Solve the intraday dispatch objective function, and dispatch the isolated island microgrid according to the solution result.

2. The method according to claim 1, characterized in that The renewable energy power generation equipment includes wind power generation equipment and photovoltaic power generation equipment, and the step of obtaining the day-ahead predicted power of the renewable energy power generation equipment includes: Obtain future wind data, future sunshine intensity data, wind turbine rated power, maximum test power output of photovoltaic cells under standard test conditions, sunshine intensity under standard test conditions, power temperature coefficient, solar panel operating temperature and reference temperature from the isolated island microgrid dispatching center; Determine the day-ahead predicted power of the wind power generation equipment according to future wind power data and the rated power of the wind turbine; The day-ahead predicted power of the photovoltaic power generation equipment is determined according to the maximum test power output by the photovoltaic cell under the standard test conditions, the sunshine intensity data in the future, the sunshine intensity under the standard test conditions, the power temperature coefficient, the operating temperature of the solar panel and the reference temperature.

3. The method according to claim 1, characterized in that The hybrid energy storage device includes a battery and a phase change heat storage system.

4. The method according to claim 3, characterized in that The step of establishing the constraint condition of the day-ahead scheduling objective function according to the operating cost parameters of the diesel engine and the hybrid energy storage device and the operating cost discount rate comprises: Obtain the dispatch cycle, discount factor, diesel engine operating cost parameters and battery operating cost parameters corresponding to different renewable energy output curve scenarios; The constraint conditions of the day-ahead scheduling objective function are constructed according to the scheduling period, the discount factor, the diesel engine operating cost parameters corresponding to the different renewable energy output curve scenarios, and the battery operating cost parameters.

5. The method according to claim 1, characterized in that The solution result includes the heat load state of the phase change heat storage system and the battery charge state of the battery corresponding to the day-ahead scheduling objective function.

6. A dispatching device for an isolated island microgrid, characterized in that: The device comprises: A power acquisition module, used to acquire the day-ahead predicted power of the renewable energy power generation equipment; the day-ahead predicted power of the renewable energy power generation equipment includes the day-ahead power prediction of the wind power generation equipment and the photovoltaic power generation equipment, and the day-ahead power prediction errors of the wind power generation equipment and the photovoltaic power generation equipment obey the beta distribution function and the normal distribution function respectively; A scenario acquisition module, used to obtain a preset number of renewable energy output curve scenarios by sampling according to the day-ahead predicted power and the power prediction error probability distribution function; the renewable energy output curve scenario represents a matrix of the day-ahead predicted power of renewable energy at different times in the island microgrid scheduling; The first objective function construction module is used to construct a day-ahead scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device according to the preset number of renewable energy output curve scenarios, and to establish the constraint conditions of the day-ahead scheduling objective function according to the operating cost parameters of the diesel engine and the hybrid energy storage device and the operating cost discount rate; the day-ahead scheduling objective function is expressed as: , where S For the scene; T For the forward-looking period; t is the value of the look-ahead duration; and Respectively for scenes s(s=1,2,…,S) Diesel power generation costs and battery operating costs under The discount factor to take into account the long-term increasing effect of forecast errors; The second objective function construction module is used to use the model predictive control strategy to rollingly optimize the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period, and to construct an intraday scheduling objective function with the goal of minimizing the sum of the operating costs of the diesel engine and the hybrid energy storage device within the forward-looking period; the intraday scheduling objective function is expressed as: , where M is the look-ahead period of the model predictive control strategy; t For the forward-looking duration; is a numerical variable for the look-ahead duration; , They represent the diesel engine operating cost, battery operating cost, and discount factor corresponding to a certain forward-looking period respectively; A constraint construction module, used to solve the day-ahead scheduling objective function and use the solution result to construct the constraint conditions of the intra-day scheduling objective function; The scheduling solution module is used to solve the intra-day scheduling objective function and schedule the isolated island microgrid according to the solution result.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images