A method and device for optimizing energy dispatching of island microgrid

Abstract

The present invention discloses an island microgrid energy optimization scheduling method, including: taking the island economy as the goal, constructing an island microgrid energy optimization scheduling model; formulating a day-ahead scheduling plan according to the characteristics of ocean energy generation and island load; comparing the day-ahead scheduling plan with the intra-day real-time rolling calculation result, correcting the day-ahead scheduling plan according to the comparison result and the intra-day real-time flow calculation result, and obtaining the island microgrid energy scheduling optimization plan for the day. The problem that the existing microgrid energy optimization scheduling method is not applicable to the island microgrid system is solved.

Description

Technical Field

[0001] The present application relates to the field of island microgrid energy optimization, and specifically to an island microgrid energy optimization scheduling method, and also to an island microgrid energy optimization scheduling device. Background Art

[0002] Islands far from land are mostly powered by independent microgrids. Currently, research on microgrid optimization and scheduling is mainly carried out on inland microgrids. Inland microgrids do not include ocean energy generation and do not involve island load characteristics. Therefore, the proposed microgrid energy optimization scheduling method is not applicable to island microgrid systems. Summary of the invention

[0003] In order to solve the above problems, the present application provides an island microgrid energy optimization scheduling method, comprising:

[0004] Taking island economy as the goal, an island microgrid energy optimization dispatch model is constructed;

[0005] Formulate day-ahead dispatching plans based on the characteristics of ocean energy power generation and island loads;

[0006] According to the day-ahead dispatch plan and the intraday real-time dispatch plan, the island microgrid energy optimization dispatch model is solved to obtain the island microgrid energy dispatch optimization plan for the day.

[0007] Preferably, with the island economy as the goal, an island microgrid energy optimization dispatching model is constructed, including:

[0008] Taking the economic efficiency of island system operation as the goal, three optimization sub-objective functions are selected: daily operation cost, power outage cost and system renewable energy utilization rate;

[0009] The three optimization sub-objective functions constitute the island microgrid energy optimization scheduling model.

[0010] Preferably, the island microgrid energy optimization scheduling model is composed of the three optimization sub-objective functions, including:

[0011] The linear weighted summation method is used to define the weight coefficients of the three optimization sub-objective functions of daily operating cost, power outage cost and renewable energy utilization rate as m1, m2 and m3 respectively;

[0012] The island microgrid energy optimization scheduling model composed of the three optimization sub-objective functions is:

[0013] min f=m1g1+m2g2-m3g3

[0014] g1 is the daily operating cost, g2 is the power outage cost, and g3 is the renewable energy utilization rate.

[0015] Preferably, the daily operating cost optimization sub-objective function is composed of the daily operating costs of each major power generation unit, and the daily operating cost is,

[0016] min g1=C W +C PV +C WE +C TE +C DE +C SB

[0017] In the formula, g1 is the daily operating cost, and C W , C PV , C WE , C TE , C DE , C SB They are the daily operating costs of wind turbines, photovoltaics, wave energy, tidal energy, batteries and diesel engines in the microgrid.

[0018] Preferably, the outage cost optimization sub-objective function uses the interruption load compensation fee to represent the outage cost, and the outage cost is,

[0019] min g2=C IL

[0020]

[0021] Where g2 is the power outage cost, C IL is the interruptible load compensation fee, M is the number of users, and N is the total number of dispatch periods; C IL,price (i) is the interruptible load quotation of the i-th user; U IL (i, t) is a 0-1 variable, indicating whether the i-th user is selected in time period t. Its value 1 indicates that the interruptible load is called in this time period; S(i, t) is the interruption capacity of the i-th user in time period t.

[0022] Preferably, the renewable energy utilization rate is the ratio of the system's renewable energy generation to the total load, max g3 = U RES

[0023]

[0024] In the formula, g3 is the utilization rate of renewable energy, U RES is the ratio of renewable energy generation to total load in the system, P load (t) is the load demand of the microgrid in period t (including interruptible load), P water (t) is the output of the seawater desalination unit in period t, P DE (t) is the output of the diesel generator, U WT(t) is a 0-1 variable, indicating whether the desalination unit is selected in time period t. Its value is 1, indicating that the interruptible load is called in this time period, and N is the total number of scheduling periods.

[0025] Preferably, a day-ahead dispatching plan is formulated based on the characteristics of ocean energy power generation and island load, including:

[0026] Based on the next day's wind, light and ocean hydrological forecast data, the renewable energy power generation model is used to predict the output of each micro-source;

[0027] Combined with load demand forecast data, predict the state of charge of the reservoir and the amount of fresh water stored;

[0028] Develop a dispatch plan for the next 24 hours for slow-responding and high-power loads.

[0029] Preferably, according to the day-ahead scheduling plan and the intraday real-time scheduling plan, the island microgrid energy optimization scheduling model is solved to obtain the island microgrid energy scheduling optimization plan for the day, including:

[0030] According to the day-ahead dispatch plan, the current load, battery charge status and fresh water reserve are called up. Based on the weather and ocean forecast data for the next hour of the day, the island's source, load and storage are predicted for the next hour to obtain a real-time dispatch plan for the day.

[0031] According to the intraday real-time dispatch plan, the island microgrid energy optimization dispatch model is solved to obtain the intraday real-time rolling calculation results, the calculation results are compared with the corresponding time period of the day-ahead dispatch plan, and the day-ahead dispatch plan is corrected according to the comparison results and the intraday real-time flow calculation results to obtain the island microgrid energy dispatch optimization plan for the day.

[0032] The present application also provides an island microgrid energy optimization scheduling device, including:

[0033] The model building unit builds an island microgrid energy optimization dispatch model with the island economy as the goal;

[0034] The dispatch plan formulation unit formulates the day-ahead dispatch plan based on the characteristics of ocean energy power generation and island load;

[0035] The optimization scheme acquisition unit solves the island microgrid energy optimization scheduling model according to the day-ahead scheduling plan and the intra-day real-time scheduling plan to obtain the island microgrid energy scheduling optimization plan for the day.

[0036] Preferably, the optimization scheme acquisition unit includes:

[0037] The prediction subunit, based on the day-ahead dispatch plan, calls the current load, battery charge status and fresh water reserve, and predicts the island's source, load and storage for the next hour based on the weather and ocean forecast data for the next hour of the day, to obtain a real-time dispatch plan for the day;

[0038] The optimization scheme acquisition subunit solves the island microgrid energy optimization scheduling model according to the intraday real-time scheduling plan, obtains the intraday real-time flow calculation results, and compares the calculation results with the corresponding time period of the day-ahead scheduling plan. According to the comparison results and the intraday real-time flow calculation results, the day-ahead scheduling plan is corrected to obtain the island microgrid energy scheduling optimization plan for the day. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of an island microgrid energy optimization scheduling method provided by the present application;

[0040] Figure 2 is a day-ahead scheduling flow chart involved in this application;

[0041] Figure 3 It is the intraday scheduling flow chart involved in this application;

[0042] Figure 4 It is a typical daily load diagram of the island involved in this application;

[0043] Figure 5 It is a prediction curve diagram of the output of distributed renewable energy on the island involved in this application;

[0044] Figure 6 This is a typical day source-load dispatch result diagram of the island involved in this application;

[0045] Figure 7 This is the unbalanced power comparison diagram without considering source load removal involved in this application;

[0046] Figure 8 This is a schematic diagram of an island microgrid energy optimization scheduling device provided in this application. DETAILED DESCRIPTION

[0047] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0048] Figure 1 This is a flow chart of an island microgrid energy optimization scheduling method provided by this application. Figure 1 The method provided in this application is described in detail.

[0049] Step S101, taking the island economy as the goal, constructing an island microgrid energy optimization scheduling model.

[0050] This application targets the unique characteristics of distributed power sources and loads in island microgrids, takes the economic efficiency of island system operation as the goal, considers the factors of renewable energy consumption, selects three optimization sub-objective functions: daily operation cost, power outage cost, and system renewable energy utilization rate; the three optimization sub-objective functions form an island microgrid energy optimization scheduling model, which is as follows:

[0051] According to the different needs of different island scenarios, three optimization sub-objective functions are selected, namely daily operating cost, power outage cost and system renewable energy utilization rate. The importance of each optimization target in each island scenario is different, so the present invention uses the linear weighted summation method to convert the multi-objective optimization problem into a single-objective optimization problem.

[0052] The linear weighted summation method is used to define the weight coefficients of the three optimization sub-objective functions of daily operating costs, power outage costs and renewable energy utilization rate, which are m1, m2, and m3 respectively; the weight coefficients of the objective function can be adjusted according to actual scheduling needs.

[0053] The island microgrid energy optimization scheduling model composed of the three optimization sub-objective functions is:

[0054] min f=m1g1+m2g2-m3g3 (1)

[0055] g1 is the daily operating cost, g2 is the power outage cost, and g3 is the renewable energy utilization rate.

[0056] (1) Daily operating costs: The daily operating cost optimization sub-objective function is composed of the daily operating costs of each power generation unit. The daily operating costs of each power generation unit include operation and maintenance costs, depreciation costs, and installation costs.

[0057] min g1=C W +C PV +C WE +C TE +C DE +C SB (2)

[0058] In the formula, g1 is the daily operating cost, and C W , C PV , C WE , C TE , C DE , C SB They are the daily operating costs of wind turbines, photovoltaics, wave energy, tidal energy, batteries and diesel engines in the microgrid.

[0059] The expressions for the daily operating costs of each power generation unit are as follows:

[0060]

[0061] In the formula, C W,DP (t), C W,OM (t) are the wind turbine investment depreciation cost and operation and maintenance cost respectively. t is the dispatch period number, and N is the total number of dispatch periods.

[0062]

[0063] Where C PV,DP (t), C PV,OM (t) are the photovoltaic investment depreciation cost and operation and maintenance cost respectively.

[0064]

[0065] Where C WE,DP (t), C WE,OM (t) are the depreciation cost of wave energy investment and operation and maintenance cost respectively.

[0066]

[0067] Where C TE,DP (t), C TE,OM (t) are the depreciation cost of tidal energy investment and operation and maintenance cost respectively.

[0068]

[0069] Where C DE,DP (t), C DE,OM (t), C DE,fuel (t), C E (t) They are respectively the diesel engine investment depreciation cost, operation and maintenance cost, fuel cost, environmental cost, and the start-up and shutdown cost of the diesel engine unit. DE It is the start / stop flag of diesel generator set m in period t, which is a 0-1 variable. When its value is 1, it means that diesel generator set m is started in period t.

[0070]

[0071] Where C SB,DP (t), C SB,OM (t) are the battery investment depreciation cost and operation and maintenance cost respectively.

[0072] (2) Power outage cost: The power outage cost optimization sub-objective function uses the interrupted load compensation fee to represent the power outage cost, which indirectly reflects the system reliability.

[0073] min g2=C IL (9)

[0074]

[0075] Where g2 is the power outage cost, C IL is the interruptible load compensation fee, M is the number of users, and N is the total number of dispatch periods; C IL,price (i) is the interruptible load quotation of the i-th user; U IL (i, t) is a 0-1 variable, indicating whether the i-th user is selected in time period t. Its value 1 indicates that the interruptible load is called in this time period; S(i, t) is the interruption capacity of the i-th user in time period t.

[0076] (3) Renewable energy utilization rate: This application uses the renewable energy utilization rate to characterize environmental protection. The ratio of the system's renewable energy generation to the total load is defined as the renewable energy utilization rate, as shown below:

[0077] max g3=U RES (11)

[0078]

[0079] In the formula, g3 is the utilization rate of renewable energy, U RES is the ratio of renewable energy generation to total load in the system, P load (t) is the load demand of the microgrid in period t (including interruptible load), P water (t) is the output of the seawater desalination unit in period t, P DE (t) is the output of the diesel generator. WT (t) is a 0-1 variable, indicating whether the desalination unit is selected in time period t. Its value is 1, indicating that the interruptible load is called in this time period, and N is the total number of scheduling periods.

[0080] The optimization objective function of the day-ahead and intraday optimization scheduling models is consistent. Due to the different time scales, the intraday day-ahead optimization scheduling models differ only in the constraints. Compared with the day-ahead, the intraday time scale is small, and its constraints need to consider the diesel generator ramp rate constraint. At the same time, in order to cope with the impact of the sudden severe convective weather source load forecast at sea, it is necessary to consider adding the load loss power P in the load balance constraint in the intraday scheduling. loss (t).

[0081] Day-ahead constraints:

[0082] (1) Power balance constraints

[0083] At any time, it is necessary to ensure that the output power of each power generation unit and energy storage device meets the load demand.

[0084] P load (t)-P IL (t) = P W (t)+P PV (t)+P WE (t)+P TE (t)+P DE (t)+P SB (t) (13)

[0085]

[0086] Where P IL (t) is the actual load demand of the interruptible load at time t. W (t), P PV (t), P WE (t), P TE (t), P DE (t) and P SB (t) are the outputs of wind turbine, photovoltaic, wave energy, tidal energy, battery and diesel generator at time t.

[0087] (2) Upper and lower limits of output of each micro-source in the microgrid

[0088] P min ≤P x ≤P max (15)

[0089] Where P x Represents the contribution of each micro-source.

[0090] (3) Battery-related constraints

[0091] a. Battery state of charge constraints

[0092] SOC min ≤SOC(t)≤SOC max (16)

[0093] b. Battery charging and discharging power limit

[0094] P SBmin ≤P(t)≤P SBmax (17)

[0095] c. Maximum battery charging rate limit

[0096] -P SB (t)≤[SOC max -SOC(t)]α c (18)

[0097] Where α cis the maximum charging power of the battery, SOC(t) is the state of charge of the battery, SOC max It is the maximum state of charge of the battery.

[0098] (4) Interruptible load-related constraints

[0099] a. Interruptible load capacity constraints

[0100] S(i,t)≤S max (i) (19)

[0101] Where S max (i) is the upper limit of the interruption capacity allowed for user i.

[0102] b. Constraints on interruption duration of interruptible load

[0103] T d (i)≤T dmax (i) (20)

[0104] Where T dmax (i) is the upper limit of the duration of each interruption allowed for the i-th user.

[0105] c. Constraints on the total number of interruptible load cutoffs

[0106]

[0107] Where ΔT is the unit scheduling time, y i The number of interruptions allowed per day for user i.

[0108] d. Interruptible load interruption time interval constraints

[0109]

[0110] Where n(i) is the minimum time interval between two interruptions required by user i.

[0111] (5) Constraints related to seawater desalination systems

[0112] a. Upper and lower limits of seawater desalination unit output

[0113] P water-min ≤P water (t)≤P water-max (twenty three)

[0114] b. Seawater desalination tank capacity constraints

[0115] C water-min ≤C water (t)≤C water-max (twenty four)

[0116] In the formula, Cwater (t) is the capacity of the reservoir at time t.

[0117] Intraday constraints:

[0118] (1) Load balancing constraints

[0119] P load (t)-P IL (t) = P W (t)+P PV (t)+P WE (t)+P TE (t)+P DE (t)+P SB (t)+P loss (t)(25)

[0120] (2) Diesel generator ramp rate constraints

[0121] r down ≤|P DE (t)-P DE (t-1)|≤r up (26)

[0122] Where r down 、r up They are the upper and lower climbing rate limits of the diesel engine respectively.

[0123] Step S102, formulate a day-ahead dispatching plan based on the characteristics of ocean energy power generation and island load.

[0124] This application takes into account the continuity of state variables such as the capacity of batteries and desalination system reservoirs. In order to avoid the influence of the calculation deviation of state variables at a certain moment on the global optimal scheduling calculation result of the day, the scheduling plan is divided into two parts: a 24-hour scheduling plan for the day before and a 1-hour rolling scheduling plan within the day. The day-ahead scheduling plan is compared with the real-time rolling deduction results within the day, and the day-ahead scheduling plan is corrected according to the rolling calculation results within the day. The scheduling model is solved by this method to achieve the global optimal energy scheduling of the microgrid on the day.

[0125] Among them, the day-ahead dispatch plan uses the renewable energy generation model to predict the output of each micro-source based on the next day's wind, light and ocean hydrological forecast data, and combines the load demand forecast data to predict the charge state of the reservoir and the freshwater storage capacity, and formulates a dispatch plan for the next 24 hours for high-power loads with slow response speed and action. Figure 3 shown.

[0126] Step S103, solving the island microgrid energy optimization scheduling model according to the day-ahead scheduling plan and the intra-day real-time scheduling plan to obtain the island microgrid energy scheduling optimization plan for the day.

[0127] According to the day-ahead dispatch plan, the current load, battery charge status and fresh water reserve are called, and the source, load and storage on the island in the next hour are predicted based on the weather and ocean forecast data for the next hour of the day to obtain the intraday real-time dispatch plan. CPLEX is used for iterative calculations. According to the intraday real-time dispatch plan, the island microgrid energy optimization dispatch model is solved to obtain the intraday real-time flow calculation results, and the calculation results are compared with the corresponding time period of the day-ahead dispatch plan. At the same time, in order to avoid falling into the local optimum, the remaining time period is predicted and deduced, and a second comparison is made with the day-ahead dispatch results. According to the comparison results and the intraday real-time flow calculation results, the day-ahead dispatch plan is revised to obtain the day's island microgrid energy dispatch optimization plan. The process is as follows: Figure 4 shown.

[0128] The best implementation examples of the application are as follows:

[0129] The research object is an independent island microgrid system on the eastern coast. The system includes photovoltaic, wind turbine, wave and tidal power generation devices, diesel generators and batteries. The source and load configuration is shown in Appendix 1. The typical daily load curve on the island is shown in Appendix Figure 4 The output prediction curve of distributed renewable energy on islands is shown in the attached figure. Figure 5 The typical daily load data are used in both day-ahead and intraday dispatch calculations, which basically reflects the production and living rules of the island residents.

[0130] Appendix 1 Source and load configuration of island microgrid system

[0131]

[0132] The optimization scheduling method proposed in the present invention is used to optimize the energy scheduling of the above island microgrid system. The optimization scheduling method flow is shown in the attached figure. Figure 2-3 shown.

[0133] The above-mentioned optimization scheduling method is used to solve the optimization scheduling model established by the present invention. The scheduling results are shown in the attached figure. Figure 6-7 shown.

[0134] Combined with the island's source and load data (attached Figure 4-5 ), analyze the typical day source-load dispatch results and unbalanced power comparison results (Appendix Figure 6-7 ) It can be found that when there is excess new energy, new energy is only cut off in the 4-5h period, and during the peak load period, a small amount of load is only cut off in the 22-23h period. In the rest of the time period, the island microgrid system can maintain power balance through the regulation of batteries, diesel generators and seawater desalination systems, fully ensuring the stability of the system.

[0135] Based on the same inventive concept, the present application also provides an island microgrid energy optimization scheduling device 800, such as Figure 8 As shown, including:

[0136] The model building unit 810 builds an island microgrid energy optimization scheduling model with the island economy as the goal;

[0137] The scheduling plan formulation unit 820 formulates a day-ahead scheduling plan according to the characteristics of ocean energy power generation and island load;

[0138] The optimization scheme acquisition unit 830 solves the island microgrid energy optimization scheduling model according to the day-ahead scheduling plan and the intra-day real-time scheduling scheme to obtain the island microgrid energy scheduling optimization scheme for the day.

[0139] Preferably, the optimization scheme acquisition unit includes:

[0140] The prediction subunit, based on the day-ahead dispatch plan, calls the current load, battery charge status and fresh water reserve, and predicts the source, load and storage on the island in the next hour based on the weather and ocean forecast data for the next hour of the day;

[0141] The optimization scheme acquisition subunit solves the island microgrid energy optimization scheduling model according to the intraday real-time scheduling plan, obtains the intraday real-time flow calculation results, and compares the calculation results with the corresponding time period of the day-ahead scheduling plan. According to the comparison results and the intraday real-time flow calculation results, the day-ahead scheduling plan is corrected to obtain the island microgrid energy scheduling optimization plan for the day.

[0142] The present invention discloses an island microgrid energy optimization scheduling method, which solves the problem that the existing microgrid energy optimization scheduling method is not applicable to the island microgrid system. At the same time, in view of the fact that the current optimization scheduling solution process does not consider the influence of the continuity of state variables in the model on the global optimal result, a day-ahead scheduling is combined with intra-day real-time rolling calculation, and an island microgrid energy scheduling method is proposed to achieve the global optimality of the microgrid energy scheduling on the same day.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalents that do not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing energy scheduling of an island microgrid, characterized in that: include: With the island economy as the goal, an island microgrid energy optimization dispatch model is constructed, including: Taking the economic efficiency of island system operation as the goal, three optimization sub-objective functions are selected: daily operation cost, power outage cost and system renewable energy utilization rate; The island microgrid energy optimization dispatch model is composed of the three optimization sub-objective functions, including: The linear weighted summation method is used to define the weight coefficients of the three optimization sub-objective functions of daily operating cost, power outage cost and renewable energy utilization rate as m1, m2 and m3 respectively; The island microgrid energy optimization scheduling model composed of the three optimization sub-objective functions is: min f=m1g1+m2g2-m3g3 g1 is the daily operating cost, g2 is the power outage cost, and g3 is the renewable energy utilization rate; The daily operating cost optimization sub-objective function is composed of the daily operating costs of each major power generation unit. The daily operating cost is, min g1=C W +C PV +C WE +C TE +C DE +C SB In the formula, g1 is the daily operating cost, and C W , C PV , C WE , C TE , C DE , C SB They are the daily operating costs of wind turbines, photovoltaics, wave energy, tidal energy, batteries, and diesel engines in the microgrid; The outage cost optimization sub-objective function uses the interrupted load compensation fee to represent the outage cost. The outage cost is, min g2=C IL Where g2 is the outage cost, C IL is the interruptible load compensation fee, M is the number of users, and N is the total number of dispatch periods; C IL,price (i) is the interruptible load quotation of the i-th user; U IL (i, t) is a 0-1 variable, indicating whether the i-th user is selected in the t period. Its value is 1, indicating that the interruptible load is called in the period. S(i, t) is the interruption capacity of the i-th user in the t period. The utilization rate of renewable energy is the ratio of the system's renewable energy generation to the total load. max g3=U RES In the formula, g3 is the utilization rate of renewable energy, U RES is the ratio of renewable energy generation to total load in the system, P load (t) is the load demand of the microgrid in period t (including interruptible load), P water (t) is the output of the seawater desalination unit in period t, P DE (t) is the output of the diesel generator, U WT (t) is a 0-1 variable, indicating whether the desalination unit is selected in time period t. Its value of 1 indicates that the interruptible load is called in this time period, and N is the total number of scheduling periods; Formulate day-ahead dispatching plans based on the characteristics of ocean energy power generation and island loads; According to the day-ahead dispatch plan and the intraday real-time dispatch plan, the island microgrid energy optimization dispatch model is solved to obtain the island microgrid energy dispatch optimization plan for the day.

2. The method according to claim 1, characterized in that According to the characteristics of ocean energy power generation and island load, formulate a day-ahead dispatch plan, including: Based on the next day's wind, light and ocean hydrological forecast data, the renewable energy power generation model is used to predict the output of each micro-source; Combined with load demand forecast data, predict the state of charge of the reservoir and the amount of fresh water stored; Develop a dispatch plan for the next 24 hours for slow-responding and high-power loads.

3. The method according to claim 1, characterized in that According to the day-ahead dispatch plan and the intraday real-time dispatch plan, the island microgrid energy optimization dispatch model is solved to obtain the island microgrid energy dispatch optimization plan for the day, including: According to the day-ahead dispatch plan, the current load, battery charge status and fresh water reserve are called up. Based on the weather and ocean forecast data for the next hour of the day, the island's source, load and storage are predicted for the next hour to obtain a real-time dispatch plan for the day. According to the intraday real-time dispatch plan, the island microgrid energy optimization dispatch model is solved to obtain the intraday real-time rolling calculation results, the calculation results are compared with the corresponding time period of the day-ahead dispatch plan, and the day-ahead dispatch plan is corrected according to the comparison results and the intraday real-time flow calculation results to obtain the island microgrid energy dispatch optimization plan for the day.

4. An island microgrid energy optimization scheduling device, characterized in that: include: The model building unit takes the island economy as the goal and builds an island microgrid energy optimization dispatch model, including: Taking the economic efficiency of island system operation as the goal, three optimization sub-objective functions are selected: daily operation cost, power outage cost and system renewable energy utilization rate; The island microgrid energy optimization dispatch model is composed of the three optimization sub-objective functions, including: The linear weighted summation method is used to define the weight coefficients of the three optimization sub-objective functions of daily operating cost, power outage cost and renewable energy utilization rate as m1, m2 and m3 respectively; The island microgrid energy optimization scheduling model composed of the three optimization sub-objective functions is: min f=m1g1+m2g2-m3g3 g1 is the daily operating cost, g2 is the power outage cost, and g3 is the renewable energy utilization rate; The daily operating cost optimization sub-objective function is composed of the daily operating costs of each major power generation unit. The daily operating cost is, min g1=C W +C PV +C WE +C TE +C DE +C SB In the formula, g1 is the daily operating cost, and C W , C PV , C WE , C TE , C DE , C SB They are the daily operating costs of wind turbines, photovoltaics, wave energy, tidal energy, batteries, and diesel engines in the microgrid; The outage cost optimization sub-objective function uses the interrupted load compensation fee to represent the outage cost. The outage cost is, min g2=C IL Where g2 is the outage cost, C IL is the interruptible load compensation fee, M is the number of users, and N is the total number of dispatch periods; C IL,price (i) is the interruptible load quotation of the i-th user; U IL (i, t) is a 0-1 variable, indicating whether the i-th user is selected in the t period. Its value is 1, indicating that the interruptible load is called in the period. S(i, t) is the interruption capacity of the i-th user in the t period. The utilization rate of renewable energy is the ratio of the system's renewable energy generation to the total load. max g3=U RES In the formula, g3 is the utilization rate of renewable energy, U RES is the ratio of renewable energy generation to total load in the system, P load (t) is the load demand of the microgrid in period t (including interruptible load), P water (t) is the output of the seawater desalination unit in period t, P DE (t) is the output of the diesel generator, U WT (t) is a 0-1 variable, indicating whether the desalination unit is selected in time period t. Its value of 1 indicates that the interruptible load is called in this time period, and N is the total number of scheduling periods; The dispatch plan formulation unit formulates the day-ahead dispatch plan based on the characteristics of ocean energy power generation and island load; The optimization scheme acquisition unit solves the island microgrid energy optimization scheduling model according to the day-ahead scheduling plan and the intra-day real-time scheduling plan to obtain the island microgrid energy scheduling optimization plan for the day.

5. The device according to claim 4, characterized in that The optimization solution acquisition unit includes: The prediction subunit, based on the day-ahead dispatch plan, calls the current load, battery charge status and fresh water reserve, and predicts the island's source, load and storage for the next hour based on the weather and ocean forecast data for the next hour of the day, to obtain a real-time dispatch plan for the day; The optimization scheme acquisition subunit solves the island microgrid energy optimization scheduling model according to the intraday real-time scheduling plan, obtains the intraday real-time flow calculation results, and compares the calculation results with the corresponding time period of the day-ahead scheduling plan. According to the comparison results and the intraday real-time flow calculation results, the day-ahead scheduling plan is corrected to obtain the island microgrid energy scheduling optimization plan for the day.

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