Island microgrid multi-target scheduling optimization method considering flexibility

Abstract

The invention discloses an island microgrid multi-target scheduling optimization method considering flexibility. Constructing a day-ahead optimization scheduling model, and constructing an objective function of the day-ahead optimization scheduling model according to the flexible supply and demand matching index; obtaining prediction uncertainty data of each type of source load, inputting the prediction uncertainty data into the day-ahead optimization scheduling model, and solving to obtain a day-ahead optimization result; constructing an intra-day optimization scheduling model, obtaining real-time state information of the unit, taking the real-time state information of the unit, the day-ahead optimization result and the real-time optimization scheduling result of the previous time period as input of the intra-day optimization scheduling model, and solving to obtain a real-time optimization scheduling result of the current time period; and updating the input of the intra-day optimization scheduling model, and circulating until the whole intra-day scheduling period is completed. According to the method, the flexibility margin level of the system can be properly evaluated through the flexibility supply and demand matching indexes, and sufficient flexibility reserve is ensured.

Description

Technical Field

[0001] This invention relates to the field of nano-microgrid group planning, and in particular to a multi-objective scheduling optimization method for islanded microgrids that takes flexibility into account. Background Technology

[0002] In islanded microgrids, renewable energy sources such as wind and solar power are intermittent and fluctuating. To ensure a reliable power supply, islanded microgrids need to have sufficient flexibility margins at all times.

[0003] Existing technologies only provide day-ahead scheduling and cannot guarantee sufficient instantaneous flexibility. Current technologies typically employ multi-objective particle swarm optimization algorithms or Pareto optimal solution sets to solve for the objective. However, these models only consider the steady-state mathematical model of the power system, neglecting transient processes. This results in low modeling accuracy and difficulty in ensuring constraint compliance during transient processes. Furthermore, most methods only consider day-ahead scheduling, and long-term predictions during the day-ahead phase have inherent errors, making it difficult to guarantee sufficient flexibility over smaller timescales. Summary of the Invention

[0004] This invention addresses the problems in the background technology by modeling different components of an islanded microgrid, realizing a real-time optimized scheduling method for islanded microgrids that takes flexibility into account, and conducting simulation experiments of the entire process from scheduling to control. This provides an autonomous operation strategy for islanded microgrids that offers both flexibility and economic advantages.

[0005] The technical solution adopted in this invention is: S1. Construct a day-ahead optimization scheduling model, and construct the objective function of the day-ahead optimization scheduling model based on the flexibility supply and demand matching index; S2. Obtain the prediction uncertainty data of various source loads and use them as input to the day-ahead optimization scheduling model. Solve the day-ahead optimization scheduling model to obtain the day-ahead optimization results. S3. Construct an intraday optimized scheduling model, and construct the objective function of the intraday optimized scheduling model based on the flexibility supply and demand matching index for each time period; S4. Obtain the real-time status information of the unit, and use the real-time status information of the unit, the day-ahead optimization results and the real-time optimization scheduling results of the previous period as input to the intraday optimization scheduling model. Solve the intraday optimization scheduling model to obtain the real-time optimization scheduling results for the current period. S5. Update the input of the intraday optimization scheduling model based on the real-time optimization scheduling results of the current time period, and return to step S4 until the entire intraday scheduling cycle is completed.

[0006] The constraints of the day-ahead optimization scheduling model include one or more combinations of the following constraints: a) At any given time, the sum of the gas turbine output, the energy storage battery output, and the photovoltaic output equals the load power; b) The minimum and maximum values ​​of the battery's energy storage ratio, the minimum and maximum values ​​of the battery's maximum energy dissipation power as a percentage of the device's total capacity, and the minimum and maximum values ​​of the battery's maximum energy charging power as a percentage of the device's total capacity. c) Maximum and minimum output of the gas turbine, minimum and maximum uphill power coefficient of the gas turbine, and minimum and maximum downhill power coefficient of the gas turbine.

[0007] The flexibility supply and demand matching indicators include the degree of matching between uplink flexibility supply and demand and the degree of matching between downlink flexibility supply and demand. Specifically, the degree of matching between uplink / downlink flexibility supply and demand is the ratio of the distribution network's uplink / downlink flexibility demand to the system's uplink / downlink flexibility supply capacity. The upward / downward flexibility requirements of the distribution network are obtained by processing the upper / lower limits of the net load fluctuation. The system's upward / downward flexibility supply capacity is the sum of the battery's upward / downward adjustment capacity and the gas turbine's upward / downward adjustment capacity.

[0008] The battery's upward adjustment capability is obtained by processing the battery's maximum discharge power, the current discharge / charge power, the battery's minimum energy storage capacity, the current energy storage capacity, and the battery's discharge efficiency. The battery's downward adjustment capability is obtained by processing the battery's maximum charging power, the current discharge / charge power, the battery's maximum energy storage capacity, and the battery's current energy storage capacity. The upward adjustment capability of the gas turbine is obtained by processing the upward ramping constraint of the gas turbine and the maximum and minimum output constraints of the gas turbine. The downward adjustment capability of the gas turbine is obtained by processing the downward ramp constraint of the gas turbine and the maximum and minimum output constraints of the gas turbine.

[0009] The objective function of the day-ahead optimization scheduling model is set according to the following formula: in, This represents the total operating cost. This indicates the flexibility of the system and the degree of supply-demand matching. , These represent the weighting coefficients for total operating cost and the degree of supply-demand matching for system flexibility, respectively. , , and These represent the operating costs of the gas turbine, photovoltaic power source, electrochemical energy storage, and load shedding, respectively. and These represent the degree of supply-demand matching for upward and downward flexibility, respectively. This is the weighting coefficient for the upswing flexibility insufficiency rate of the microgrid system within one hour. and These represent the upward and downward flexibility requirements of the distribution network, respectively. and These represent the system's upward and downward flexibility in supplying resources, respectively.

[0010] The objective function of the intraday optimized scheduling model is specifically set according to the following formula: Where N represents the number of time periods included in the prediction period of the intraday optimized scheduling model, i represents the time index, and k represents the current sampling time. Indicates at time The calculated value for the future The predicted output of the step, This represents the reference output trajectory from the previous day's scheduling. Indicates the first Step control input, This indicates the control increment. This represents the square of the weighted L2 norm. This represents the weight matrix of the output tracking error. The weight matrix represents the magnitude of the control quantity. The weight matrix represents the control increment. This indicates the control increment penalty coefficient.

[0011] The prediction uncertainty data for various sources and loads includes historical and predicted data for photovoltaics and loads in isolated microgrids; The day-ahead optimization results include the gas turbine output power plan, energy storage charging and discharging power plan and corresponding SOC trajectory for each day-ahead scheduling period, load shedding and curtailment plans, and reference trajectories for the supply and demand matching degree of uplink / downlink flexibility for each period.

[0012] The real-time status information of the unit includes the current output power of the gas turbine, the upper and lower limits of the gas turbine output, the gas turbine ramping ability information, the current SOC and current charge and discharge power of the energy storage, the charge and discharge constraint boundary information, and the load power and photovoltaic power generation power. The real-time optimized scheduling results include the gas turbine power command, energy storage charging and discharging power command, load shedding / curtailment command, and the output trajectory of the flexibility supply and demand matching degree at the current moment.

[0013] The beneficial effects of this invention are: To address the problem that existing technologies only provide day-ahead scheduling and cannot guarantee sufficient instantaneous flexibility, this invention implements a multi-timescale optimized scheduling strategy. By establishing a flexibility resource model and a flexibility demand model, and introducing a flexibility supply-demand matching index, this invention can appropriately assess the system's flexibility margin level. During the day-ahead scheduling phase, a multi-objective optimization method is employed to simultaneously optimize economic and flexibility objectives, ensuring that the scheduling scheme meets both economic requirements and guarantees sufficient flexibility reserves. Based on this, model predictive control is used to achieve real-time intraday optimized scheduling, tracking the day-ahead scheduling flexibility supply-demand matching index. This optimized scheduling method can effectively cope with short-term prediction errors, providing an autonomous operation strategy with both high flexibility and economy for isolated microgrids.

[0014] To address the shortcomings of existing technologies that only consider steady-state mathematical models, this invention constructs a complete hierarchical control architecture encompassing first-, second-, and third-level control. This multi-timescale hierarchical control model not only considers steady-state processes but, more importantly, can accurately simulate the dynamic characteristics during transient processes. Therefore, this invention ensures that the microgrid meets operational constraints under various operating conditions, including transient processes such as load surges and fluctuations in renewable energy output, significantly improving modeling accuracy and the reliability of simulation results. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of protection of this invention.

[0018] like Figure 1 As shown, this embodiment includes the following steps: S1. Construct a day-ahead optimization scheduling model, and construct the objective function of the day-ahead optimization scheduling model based on the flexibility supply and demand matching index; S2. Obtain the prediction uncertainty data of various source loads and use them as input to the day-ahead optimization scheduling model. Solve the day-ahead optimization scheduling model to obtain the day-ahead optimization results. S3. Construct an intraday optimized scheduling model, and construct the objective function of the intraday optimized scheduling model based on the flexibility supply and demand matching index for each time period; S4. Obtain the real-time status information of the unit, and use the real-time status information of the unit, the day-ahead optimization results and the real-time optimization scheduling results of the previous period as input to the intraday optimization scheduling model. Solve the intraday optimization scheduling model to obtain the real-time optimization scheduling results for the current period. S5. Update the input of the intraday optimization scheduling model based on the real-time optimization scheduling results of the current time period, return to step S4, until the entire intraday scheduling cycle is completed, and summarize the real-time optimization scheduling results of each time period in the entire intraday scheduling cycle as the intraday optimization scheduling output.

[0019] The constraints currently used to optimize the scheduling model include one or more combinations of the following constraints: a) At any given time, the sum of the gas turbine output, the energy storage battery output, and the photovoltaic output equals the load power; b) The minimum and maximum values ​​of the battery's energy storage ratio, the minimum and maximum values ​​of the battery's maximum energy dissipation power as a percentage of the device's total capacity, and the minimum and maximum values ​​of the battery's maximum energy charging power as a percentage of the device's total capacity. c) Maximum and minimum output of the gas turbine, minimum and maximum uphill power coefficient of the gas turbine, and minimum and maximum downhill power coefficient of the gas turbine.

[0020] Specifically, the constraints include power balance constraints, energy storage battery charge / discharge constraints, and gas turbine output constraints: Power balance constraints: Energy storage battery charge and discharge constraints: Gas turbine output constraints: The flexibility supply and demand matching indicators include the degree of supply and demand matching of uplink flexibility and the degree of supply and demand matching of downlink flexibility. Specifically, the degree of supply and demand matching of uplink / downlink flexibility is the ratio of the distribution network's uplink / downlink flexibility demand to the system's uplink / downlink flexibility supply capacity. The upward / downward flexibility requirements of the distribution network are obtained by processing the upper / lower limits of net load fluctuations; The system's up / down flexibility supply capacity is the sum of the battery's up / down adjustment capacity and the gas turbine's up / down adjustment capacity.

[0021] The battery's up-adjustment capability is obtained by processing the battery's maximum discharge power, the current discharge / charge power, the battery's minimum energy storage capacity, the current energy storage capacity, and the battery's discharge efficiency. The battery's downward adjustment capability is obtained by processing the battery's maximum charging power, the current discharge / charge power, the battery's maximum energy storage capacity, and the battery's current energy storage capacity. The upward adjustment capability of the gas turbine is obtained by processing the upward ramping constraint of the gas turbine and the maximum and minimum output constraints of the gas turbine. The downward adjustment capability of the gas turbine is obtained by processing the downward ramp constraint of the gas turbine and the maximum and minimum output constraints of the gas turbine.

[0022] Upward adjustment capability refers to the maximum power that can be increased at the current moment, while downward adjustment capability refers to the maximum power that can be decreased at the current moment.

[0023] Specifically, the upward / downward flexibility requirements of the distribution network are as follows: Flexibility demand refers to the active power regulation required due to the volatility and uncertainty of net load. In this islanded microgrid, flexibility demand is mainly generated by photovoltaic power generation and the volatility and uncertainty of load. More specifically, it refers to the fluctuation of the net load curve in each time period. In addition to the uncertainty of possible fluctuations, The resulting power change represents the flexibility requirements of the microgrid.

[0024] in Net load, load and photovoltaic power generation during the period , , They are respectively Fluctuation of net load over a given period, upper limit of fluctuation, and lower limit of fluctuation; Confidence levels The corresponding left and right quantiles of the standard normal distribution. for Standard deviation of net load fluctuation over a period of time; , These refer to the upward and downward flexibility requirements of the distribution network.

[0025] The ratio of the system's upward / downward flexibility supply capacity is as follows: Microgrid flexibility resources are primarily provided by batteries and gas turbines.

[0026] (1) Energy storage flexibility resource model In equations (3)-(6): , , These are the battery's maximum discharge power, maximum charging power, and charging / discharging power during time period t, respectively. , , These represent the battery's maximum energy storage capacity, minimum energy storage capacity, and energy storage capacity during time period t, respectively. , These are the battery's energy storage efficiency and energy release efficiency, respectively. , These are the maximum charge and discharge coefficients of the battery, representing the proportion of the battery's maximum charge and discharge power to the total capacity of the device. , These represent the minimum and maximum energy storage ratios of the battery, respectively.

[0027] (2) Gas turbine flexibility resource model In the formula: , These are the minimum and maximum output coefficients of the gas turbine, respectively. This refers to the output electrical power of the gas turbine. , These are the power limit coefficients for the gas turbine's downhill and uphill ramps, respectively.

[0028] (3) System flexibility resource model In the formula: , These refer to the system's upward and downward flexibility supply capabilities, i.e., flexibility resources; , These refer to the upward and downward flexibility supply capabilities of the battery, respectively. , These refer to the upward and downward flexibility supply capabilities of the gas turbine, respectively. The flexibility supply and demand matching index is set according to the following formula: in, This indicates the degree of supply-demand matching in terms of upward flexibility. This refers to the degree of supply and demand matching for downward flexibility.

[0029] The objective function for optimizing the scheduling model is set according to the following formula: in, This represents the total operating cost. This indicates the flexibility of the system and the degree of supply-demand matching. , These represent the weighting coefficients for total operating cost and the degree of supply-demand matching for system flexibility, respectively. , , and These represent the operating costs of the gas turbine, photovoltaic power source, electrochemical energy storage, and load shedding, respectively. and These represent the degree of supply-demand matching for upward and downward flexibility, respectively. This is the weighting coefficient for the upswing flexibility insufficiency rate of the microgrid system within one hour. and These represent the upward and downward flexibility requirements of the distribution network, respectively. and These represent the system's upward and downward flexibility supply capabilities, respectively. The system's upward flexibility supply capability is equal to the sum of the battery's upward adjustment capability and the gas turbine's upward adjustment capability.

[0030] The constraints of the intraday optimal scheduling model are consistent with those of the day-ahead optimal scheduling model.

[0031] Intraday real-time optimization and scheduling phase: Building a prediction model Establish an intraday real-time optimization scheduling model based on model predictive control using a state-space model: Take discharge as positive. : 2) Indicators representing supply and demand matching flexibility 3) Construct the intraday real-time optimization objective function (MPC cost function) In the intraday real-time optimization phase, the degree of flexibility in supply and demand matching in the day-ahead scheduling phase is taken as the expected output, and the control objective is to make the predicted output track the expected output in the day-ahead scheduling. The objective function of the intraday optimization scheduling model is set according to the following formula: Where N represents the number of time periods included in the prediction period of the intraday optimized scheduling model, i.e., the prediction time domain, i represents the time index, and k represents the current sampling time. Indicates at time The calculated value for the future The predicted output of the step, This represents the reference output trajectory from the previous day's scheduling. Indicates the first Step control input, This indicates the control increment (the amount of change in the control input). The weighted L2 norm square (usually defined as) represents the square of the weighted L2 norm. = ), The weight matrix representing the output tracking error (for...) (the severity of the punishment) The weight matrix representing the magnitude of the control quantity (for) (The punishment is based on the "force applied" itself). The weight matrix representing the control increment (for) The punishment emphasizes smoothness. This indicates the control increment penalty coefficient.

[0032] The day-ahead optimization results include the gas turbine output power plan, energy storage charging and discharging power plan and corresponding SOC trajectory for each day-ahead scheduling period, the load shedding and curtailment plan when necessary, and the reference trajectory of the supply and demand matching degree of uplink / downlink flexibility for each period. The real-time status information of the unit includes the current output power of the gas turbine, the upper and lower limits of the gas turbine output, the gas turbine ramping capability information, the current SOC and current charging and discharging power of the energy storage, the charging and discharging constraint boundary information, and the real-time / updated predicted load power and photovoltaic power generation power used to calculate net load and flexibility requirements. The real-time optimized scheduling results include the control input sequence obtained in the prediction time domain based on model predictive control (including at least the gas turbine power command, energy storage charging and discharging power command, and load shedding / curtailment command if necessary), and the output trajectory of the flexibility supply and demand matching degree predicted by the control input (used to track the day-ahead reference value).

[0033] This invention achieves multi-objective real-time optimal scheduling of islanded microgrids with flexibility and economy as objectives based on model predictive control, ensuring sufficient instantaneous flexibility during real-time scheduling. Model predictive control is also used to track the flexibility supply-demand matching index in the day-ahead optimization plan during intraday real-time optimal scheduling. Electromagnetic transient verification of this optimization strategy was implemented using Matlab / Simulink.

[0034] This embodiment constructs a complete hierarchical control architecture including primary, secondary, and tertiary control levels. Primary control includes a photovoltaic model, an energy storage model, and a micro gas turbine model. The photovoltaic array main circuit topology consists of photovoltaic cells, a DC-DC converter, and a DC-AC inverter. The DC-DC converter adopts a current-reversible chopper circuit topology and uses MPPT or PQ control depending on the microgrid requirements. The DC-AC inverter adopts a three-phase two-level topology and uses PI control to maintain the DC bus voltage to achieve constant power output.

[0035] The main circuit topology of the energy storage unit consists of an electrochemical energy storage battery, a DC-DC converter, and a DC-AC inverter. The DC-DC converter adopts a current reversible chopper circuit topology and is responsible for stabilizing the DC bus voltage; the DC-AC inverter uses a three-phase two-level topology and employs a droop control strategy to maintain the microgrid frequency stability.

[0036] Since this method primarily considers the operating state of the micro gas turbine and does not take into account its start-up and shutdown states, the gas turbine and its turbine-side inverter are simplified to a stable DC voltage source. The main circuit topology of the gas turbine model consists of a DC voltage source and a three-phase two-level inverter, which employs droop control.

[0037] In microgrids, energy storage and gas turbine units employ droop control as a primary control strategy, which is inherently flawed. Secondary control is needed to compensate for voltage and frequency deviations to achieve error-free regulation. This secondary control utilizes centralized compensation, where the microgrid central controller collects voltage and frequency signals from each node and calculates the deviation values ​​for each generation unit based on voltage and frequency droop formulas, obtaining new active and reactive power reference values ​​for each unit. By adjusting these reference values, secondary control corrects deviations in the primary control, thereby achieving precise voltage and frequency regulation across the entire system and ensuring stable system operation without long-term deviations.

[0038] Based on primary and secondary control, the microgrid has achieved basic power balance. The microgrid central controller, through the microgrid optimal scheduling model introduced in section II, achieves multi-objective optimal scheduling with flexibility and economy as its goals. Firstly... Before the designated time period, pre-day optimized scheduling is implemented. During the intraday phase, real-time optimized scheduling determines the scheduling scheme for the next real-time scheduling period.

[0039] The above detailed embodiments illustrate the technical solution and beneficial effects of the present invention. It should be understood that the above description is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-objective scheduling optimization method for islanded microgrids that considers flexibility, characterized in that, The method includes the following steps: S1. Construct a day-ahead optimization scheduling model, and construct the objective function of the day-ahead optimization scheduling model based on the flexibility supply and demand matching index; S2. Obtain the prediction uncertainty data of various source loads and use them as input to the day-ahead optimization scheduling model. Solve the day-ahead optimization scheduling model to obtain the day-ahead optimization results. S3. Construct an intraday optimized scheduling model, and construct the objective function of the intraday optimized scheduling model based on the flexibility supply and demand matching index for each time period; S4. Obtain the real-time status information of the unit, and use the real-time status information of the unit, the day-ahead optimization results and the real-time optimization scheduling results of the previous period as input to the intraday optimization scheduling model. Solve the intraday optimization scheduling model to obtain the real-time optimization scheduling results for the current period. S5. Update the input of the intraday optimization scheduling model based on the real-time optimization scheduling results of the current time period, and return to step S4 until the entire intraday scheduling cycle is completed.

2. The multi-objective scheduling optimization method for islanded microgrids considering flexibility according to claim 1, characterized in that: The constraints of the day-ahead optimization scheduling model include one or more combinations of the following constraints: a) At any given time, the sum of the gas turbine output, the energy storage battery output, and the photovoltaic output equals the load power; b) The minimum and maximum values ​​of the battery's energy storage ratio, the minimum and maximum values ​​of the battery's maximum energy dissipation power as a percentage of the device's total capacity, and the minimum and maximum values ​​of the battery's maximum energy charging power as a percentage of the device's total capacity. c) Maximum and minimum output of the gas turbine, minimum and maximum uphill power coefficient of the gas turbine, and minimum and maximum downhill power coefficient of the gas turbine.

3. The multi-objective scheduling optimization method for islanded microgrids considering flexibility according to claim 2, characterized in that: The flexibility supply and demand matching indicators include the degree of matching between uplink flexibility supply and demand and the degree of matching between downlink flexibility supply and demand. Specifically, the degree of matching between uplink / downlink flexibility supply and demand is the ratio of the distribution network's uplink / downlink flexibility demand to the system's uplink / downlink flexibility supply capacity. The upward / downward flexibility requirements of the distribution network are obtained by processing the upper / lower limits of the net load fluctuation. The system's upward / downward flexibility supply capacity is the sum of the battery's upward / downward adjustment capacity and the gas turbine's upward / downward adjustment capacity.

4. The multi-objective scheduling optimization method for islanded microgrids considering flexibility according to claim 1, characterized in that: The battery's upward adjustment capability is obtained by processing the battery's maximum discharge power, the current discharge / charge power, the battery's minimum energy storage capacity, the current energy storage capacity, and the battery's discharge efficiency. The battery's downward adjustment capability is obtained by processing the battery's maximum charging power, the current discharge / charge power, the battery's maximum energy storage capacity, and the battery's current energy storage capacity. The upward adjustment capability of the gas turbine is obtained by processing the upward ramping constraint of the gas turbine and the maximum and minimum output constraints of the gas turbine. The downward adjustment capability of the gas turbine is obtained by processing the downward ramp constraint of the gas turbine and the maximum and minimum output constraints of the gas turbine.

5. The multi-objective scheduling optimization method for islanded microgrids considering flexibility according to claim 1, characterized in that: The objective function of the day-ahead optimization scheduling model is set according to the following formula: in, This represents the total operating cost. This indicates the flexibility of the system and the degree of supply-demand matching. , These represent the weighting coefficients for total operating cost and the degree of supply-demand matching for system flexibility, respectively. , , and These represent the operating costs of the gas turbine, photovoltaic power source, electrochemical energy storage, and load shedding, respectively. and These represent the degree of supply-demand matching for upward and downward flexibility, respectively. This is the weighting coefficient for the upswing flexibility insufficiency rate of the microgrid system within one hour. and These represent the upward and downward flexibility requirements of the distribution network, respectively. and These represent the system's upward and downward flexibility in supplying resources, respectively.

6. The multi-objective scheduling optimization method for islanded microgrids considering flexibility according to claim 1, characterized in that: The objective function of the intraday optimized scheduling model is specifically set according to the following formula: Where N represents the number of time periods included in the prediction period of the intraday optimized scheduling model, i represents the time index, and k represents the current sampling time. Indicates at time The calculated value for the future The predicted output of the step, This represents the reference output trajectory from the previous day's scheduling. Indicates the first Step control input, This indicates the control increment. This represents the square of the weighted L2 norm. This represents the weight matrix of the output tracking error. The weight matrix represents the magnitude of the control quantity. The weight matrix represents the control increment. This indicates the control increment penalty coefficient.

7. The multi-objective scheduling optimization method for islanded microgrids considering flexibility according to claim 1, characterized in that: The prediction uncertainty data for various sources and loads includes historical and predicted data for photovoltaics and loads in isolated microgrids; The day-ahead optimization results include the gas turbine output power plan, energy storage charging and discharging power plan and corresponding SOC trajectory for each day-ahead scheduling period, load shedding and curtailment plans, and reference trajectories for the supply and demand matching degree of uplink / downlink flexibility for each period.

8. The multi-objective scheduling optimization method for islanded microgrids considering flexibility according to claim 1, characterized in that: The real-time status information of the unit includes the current output power of the gas turbine, the upper and lower limits of the gas turbine output, the gas turbine ramping ability information, the current SOC and current charge and discharge power of the energy storage, the charge and discharge constraint boundary information, and the load power and photovoltaic power generation power. The real-time optimized scheduling results include the gas turbine power command, energy storage charging and discharging power command, load shedding / curtailment command, and the output trajectory of the flexibility supply and demand matching degree at the current moment.

Images