Subnet autonomous capability analysis method and system considering distributed controllable resource collaboration

Abstract

The invention discloses a subnet autonomous capability analysis method and system considering distributed controllable resource collaboration, and the method comprises the steps: building a distributed controllable resource model through considering whether distributed resources are controllable and adjustable and operation constraint conditions; considering operation rules and operation constraint conditions of the micro-grid, and establishing an overall optimization constraint model of the micro-grid; based on the distributed controllable resource model and the microgrid overall optimization constraint model, upper and lower bounds of a demand interval are coupled, and an optimization objective function is constructed by considering the consistency of operation reserve capacities of the microgrid at all moments; and obtaining the upper and lower boundaries of the external autonomous capability of the micro-grid by solving the optimization objective function. The invention provides a subnet autonomous capability analysis method and system considering distributed controllable resource collaboration, which are convenient for better carrying out overall planning and scheduling optimization of the whole region and guaranteeing the safe and stable operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning and dispatching, and more specifically, to a subnet autonomy capability analysis method and system considering the coordination of distributed controllable resources. Background Art

[0002] In recent years, the number of distributed power sources such as photovoltaic, wind power, and energy storage connected to the distribution network has increased rapidly. Their grid-connected voltage level is low, the penetration rate is high, and the grid-connected points are mostly distributed in weak power grids such as rural distribution networks, which brings great challenges to the operation and dispatch of medium and low voltage distribution networks. In order to achieve regional autonomy of distribution networks and reduce the complexity of distribution network operation and dispatch, the regional autonomy of distribution networks, especially the power and energy balance capabilities, are key issues. Due to the time distribution characteristics of high-proportion distributed photovoltaic and load output, the distribution network has different external power characteristics requirements at different times, and the random fluctuation characteristics of photovoltaic and load power make the source-load imbalance power in the distribution network change dynamically. Therefore, studying the analysis method of the autonomy capability of distribution networks that adapt to high-proportion new energy access is of great significance to improving the flexible adjustment capability of distribution networks under high-proportion new energy access. At the same time, as an electrical interconnected system, microgrids undertake the tasks of collecting, using, and managing load-side resources. They can improve the economic and environmental benefits of the system under the premise of ensuring the safety and stability of the main network, and improve the flexibility of the system by smoothing power fluctuations.

[0003] Existing technologies have been used by scholars to conduct a lot of research on the autonomy and flexibility of microgrids in distribution network substations, but most of them are optimized with the goal of optimizing the economic cost of microgrids or distribution-microgrid joint, which is difficult to deal with the difficulty of evaluating the investment and operation costs of various power sources in future microgrids and the construction and operation costs. At the same time, the coordinated dispatching uses the autonomous grid as a subordinate unit, mainly to ensure the balance of power in the distribution network, which is not in line with the future operation mode of high distributed power sources connected to the back-end area.

[0004] Therefore, a technology is needed to achieve subnet autonomy capability analysis considering the coordination of distributed controllable resources. Summary of the invention

[0005] The technical solution of the present invention provides a subnet autonomy capability analysis method and system considering the coordination of distributed controllable resources, so as to solve the problem of how to analyze the subnet autonomy capability considering the coordination of distributed controllable resources.

[0006] In order to solve the above problems, the present invention provides a subnet autonomy capability analysis method considering the coordination of distributed controllable resources, the method comprising:

[0007] Consider whether the distributed resources are controllable, adjustable, and the operating constraints, and establish a distributed controllable resource model;

[0008] Considering the microgrid operation rules and operation constraints, an overall optimization constraint model of the microgrid is established;

[0009] Based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, the upper and lower bounds of the demand interval are coupled, the operating reserve capacity of the microgrid is considered to be consistent at all times, and the optimization objective function is constructed;

[0010] By solving the optimization objective function, the upper and lower boundaries of the external autonomy capability of the microgrid are obtained.

[0011] Preferably, the consideration of whether the distributed resources are controllable, adjustable and operating constraints to establish a distributed controllable resource model includes:

[0012] The distributed power sources in the microgrid mainly consider controllable but unadjustable distributed photovoltaic and distributed wind power. The distributed power generation model and constraints are as follows:

[0013]

[0014] Among them, ON pv (t),ON wd (t) are the number of photovoltaic and wind power stations operating at time t, N pv 、N wd are the number of photovoltaic and wind power installed capacity, n pv 、n wd are the installed capacity of a single photovoltaic and wind power station respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic power and wind power, respectively; pv(t) and wd(t) are the normalized theoretical outputs of photovoltaic power and wind power, respectively;

[0015] The load model mainly considers fixed load, time-shiftable load and interruptible load. The load model and constraints are as follows:

[0016]

[0017] The load at time t (t) is determined by the fixed load load fix (t), time-shiftable load adj (t) and interruptible load int (t) composition, N fix 、N adj,i 、N int,j are the maximum capacities of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; fix(t), adj(t), int(t) are the normalized power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively;

[0018] The energy storage is considered to be daily regulation energy storage, the default capacity is the maximum discharge power*2h, and the energy storage model and constraints are:

[0019]

[0020] in: It is the charging and discharging state variable of the energy storage device; is the maximum value of charge and discharge power; is the charging and discharging power of the energy storage device; S s,t is the capacity of the energy storage device at time t; S s，1 is the capacity of the energy storage device at time 1; S max is the maximum capacity of the energy storage system; S s,T is the capacity of the energy storage system at the end of a typical day; σ is the self-discharge rate of the energy storage device; η c , η d Charging and discharging efficiency of charging equipment.

[0021] Preferably, the microgrid operation rules and operation constraints are considered to establish a microgrid overall optimization constraint model, including:

[0022] The microgrid operation constraints of the microgrid are shown in the formula:

[0023]

[0024] in, They are the external power demand and return power of the microgrid, is positive, is negative, N tra is the maximum load demand of the microgrid;

[0025] The power supply reliability constraint is that the power failure rate cannot exceed the planned upper limit f LPSP :

[0026]

[0027] Where: LPSP is the power failure rate; P loss (t) is the load shedding amount during period t; P load (t) is the net load during period t;

[0028] The renewable energy reduction rate constraint is that the renewable energy reduction rate within a year shall not exceed the planned upper limit LRES max :

[0029]

[0030] Where: P loss,RE (t) is the average power of renewable energy curtailment during period t;

[0031] The renewable energy installed capacity ratio constraint is that the ratio of renewable energy installed capacity to peak load power PR reaches more than 50%:

[0032]

[0033] The annual exchange power ratio constraint is: The annual exchange power ratio AEEP The annual exchange power between the new energy microgrid and the external power grid generally does not exceed 50% of the annual power consumption. The annual exchange power ratio AEEP is:

[0034]

[0035] Preferably, based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, the upper and lower bounds of the demand interval are coupled, taking into account the consistency of the spare capacity of the microgrid at each moment, and constructing an optimization objective function, including:

[0036] Considering that any strategy can be implemented within the demand interval, the upper and lower bounds of the demand interval are coupled by combining the idea of ​​robust optimization and strong duality theory. The upper / lower bounds at time t-1 are used to determine the lower / upper bounds at time t. Constraints are constructed for the independent and mutually coupled states of the upper / lower bounds. Taking into account the objective function of operating spare capacity, the optimization objective is designed as follows:

[0037]

[0038] Among them, P E,up (t), P E,dn (t) are the external power purchases corresponding to the upper and lower boundaries of the strategy at time t; D[P E,up (t)Δt-P E,dn (t)Δt] represents the sample variance of the difference between the upper and lower boundaries at each moment, that is, under the premise of ensuring the maximum controllable area, referring to the principle of equal reserve capacity when the large power grid is in operation, try to ensure that the upward or downward controllable power at each moment is equal.

[0039] Preferably, the distributed resources include wind power, photovoltaics, loads and energy storage.

[0040] Based on another aspect of the present invention, the present invention provides a subnet autonomous capability analysis system considering distributed controllable resource collaboration, the system comprising:

[0041] The first establishing unit is used to establish a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable and operation constraints;

[0042] The second establishment unit is used to consider the microgrid operation rules and operation constraints to establish the overall optimization constraint model of the microgrid;

[0043] A third establishing unit is used to couple the upper and lower bounds of the demand interval based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, taking into account the consistency of the operating reserve capacity of the microgrid at each moment, and constructing an optimization objective function;

[0044] The result unit is used to obtain the upper and lower boundaries of the external autonomy capability of the microgrid by solving the optimization objective function.

[0045] Preferably, the first establishing unit is used to establish a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable and operation constraints, and is also used to:

[0046] The distributed power sources in the microgrid mainly consider controllable but unadjustable distributed photovoltaic and distributed wind power. The distributed power generation model and constraints are as follows:

[0047]

[0048] Among them, ON pv (t),ON wd (t) are the number of photovoltaic and wind power stations operating at time t, N pv 、N wd are the number of photovoltaic and wind power installed capacity, n pv 、n wd are the installed capacity of a single photovoltaic and wind power station respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic power and wind power, respectively; pv(t) and wd(t) are the normalized theoretical outputs of photovoltaic power and wind power, respectively;

[0049] The load model mainly considers fixed load, time-shiftable load and interruptible load. The load model and constraints are as follows:

[0050]

[0051] Among them, the load at time t load(t) By fixed load fix (t), time-shiftable load adj (t) and interruptible load int (t) composition, N fix 、N adj,i 、N int,j are the maximum capacities of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; fix(t), adj(t), int(t) are the normalized power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively;

[0052] The energy storage is considered to be daily regulation energy storage, the default capacity is the maximum discharge power*2h, and the energy storage model and constraints are:

[0053]

[0054] in: It is the charging and discharging state variable of the energy storage device; is the maximum value of charge and discharge power; is the charging and discharging power of the energy storage device; S s,t is the capacity of the energy storage device at time t; S s,1 is the capacity of the energy storage device at time 1; S max is the maximum capacity of the energy storage system; S s,T is the capacity of the energy storage system at the end of a typical day; σ is the self-discharge rate of the energy storage device; η c , η d Charging and discharging efficiency of charging equipment.

[0055] Preferably, the second establishing unit is used to establish the overall optimization constraint model of the microgrid by considering the microgrid operation rules and operation constraints, and is also used to:

[0056] The microgrid operation constraints of the microgrid are shown in the formula:

[0057]

[0058] in, They are the external power demand and return power of the microgrid, is positive, is negative, N tra is the maximum load demand of the microgrid;

[0059] The power supply reliability constraint is that the power failure rate cannot exceed the planned upper limit f LPSP :

[0060]

[0061] Where: LPSP is the power failure rate; P loss (t) is the load shedding amount during period t; P load (t) is the net load during period t;

[0062] The renewable energy reduction rate constraint is that the renewable energy reduction rate within a year shall not exceed the planned upper limit LRES max :

[0063]

[0064] Where: P loss,RE (t) is the average power of renewable energy curtailment during period t;

[0065] The renewable energy installed capacity ratio constraint is that the ratio of renewable energy installed capacity to peak load power PR reaches more than 50%:

[0066]

[0067] The annual exchange power ratio constraint is: The annual exchange power ratio AEEP The annual exchange power between the new energy microgrid and the external power grid generally does not exceed 50% of the annual power consumption. The annual exchange power ratio AEEP is:

[0068]

[0069] Preferably, the third establishing unit is used to couple the upper and lower bounds of the demand interval based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, taking into account the consistency of the spare capacity of the microgrid at each moment, and constructing an optimization objective function, and is also used to:

[0070] Considering that any strategy can be implemented within the demand interval, the upper and lower bounds of the demand interval are coupled by combining the idea of ​​robust optimization and strong duality theory. The upper / lower bounds at time t-1 are used to determine the lower / upper bounds at time t. Constraints are constructed for the independent and mutually coupled states of the upper / lower bounds. Taking into account the objective function of operating spare capacity, the optimization objective is designed as follows:

[0071]

[0072] Among them, P E,up (t), P E,dn (t) are the external power purchases corresponding to the upper and lower boundaries of the strategy at time t; D[P E,up (t)Δt-P E,dn (t)Δt] represents the sample variance of the difference between the upper and lower boundaries at each moment, that is, under the premise of ensuring the maximum controllable area, referring to the principle of equal reserve capacity when the large power grid is in operation, try to ensure that the upward or downward controllable power at each moment is equal.

[0073] Preferably, the distributed resources include wind power, photovoltaics, loads and energy storage.

[0074] The technical solution of the present invention provides a method and system for analyzing the autonomous capability of a subnet considering the coordination of distributed controllable resources, wherein the method includes: considering whether the distributed resources are controllable, adjustable, and the operating constraints, establishing a distributed controllable resource model; considering the operating rules and operating constraints of the microgrid, establishing an overall optimization constraint model of the microgrid; based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, coupling the upper and lower bounds of the demand interval, taking into account the consistency of the operating reserve capacity of the microgrid at all times, and constructing an optimization objective function; by solving the optimization objective function, obtaining the upper and lower bounds of the external autonomous capability of the microgrid. The present invention proposes a method and system for analyzing the autonomous capability of a subnet considering the coordination of distributed controllable resources, starting from analyzing the overall external support and regulation capabilities of the microgrid, focusing on studying the upper and lower bounds of the external characteristics of the microgrid to the upper management agency, so as to better coordinate and optimize the entire region and ensure the safe and stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0076] Figure 1 A flow chart of a subnet autonomy capability analysis method considering distributed controllable resource collaboration according to a preferred embodiment of the present invention;

[0077] Figure 2 A flow chart of a subnet autonomy capability analysis method considering distributed controllable resource collaboration according to a preferred embodiment of the present invention;

[0078] Figure 3 A wind power normalization curve diagram according to a preferred embodiment of the present invention;

[0079] Figure 4 A photovoltaic normalization curve diagram according to a preferred embodiment of the present invention;

[0080] Figure 5 A load normalization curve diagram according to a preferred embodiment of the present invention;

[0081] Figure 6 This is a graph of typical day calculation results under a scenario where the permeability is 200% according to a preferred embodiment of the present invention;

[0082] Figure 7 This is a graph showing calculation results of a typical day calculation under a scenario where the permeability is 400% according to a preferred embodiment of the present invention;

[0083] Figure 8 This is a graph of typical day calculation results in a scenario where the permeability is 600% according to a preferred embodiment of the present invention;

[0084] Fig. 9Graphs showing typical day calculation results in different scenarios according to a preferred embodiment of the present invention;

[0085] Fig.10 A diagram showing the distance between the upper and lower boundaries of a microgrid and a net load at different penetration rates according to a preferred embodiment of the present invention;

[0086] Fig.11 A diagram showing the influence of different permeabilities on the area of ​​the demand interval according to a preferred embodiment of the present invention; and

[0087] Fig.12 A structural diagram of a subnet autonomous capability analysis system considering the coordination of distributed controllable resources according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0088] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0089] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0090] Figure 1 The flowchart is a subnet autonomous capability analysis method considering the coordination of distributed controllable resources according to a preferred embodiment of the present invention.

[0091] The present invention proposes a subnet autonomy capability analysis method that considers the coordination of distributed controllable resources. Starting from analyzing the overall external support and regulation capabilities of the microgrid, the present invention focuses on studying the upper and lower boundaries of the microgrid's external characteristics for the upper management agency, so as to better coordinate and optimize the entire region and ensure the safe and stable operation of the system. To this end, the external characteristics of the microgrid under different distributed power generation installed capacity penetration conditions are analyzed with the maximum external regulation capability of the microgrid and the minimum difference in operating reserve capacity at each moment as the optimization goal, and the essential characteristics of the microgrid's external regulation capability are analyzed. At the same time, the proposed method refers to the principle of equal reserve capacity during the operation of the large power grid to ensure that the external characteristics after measurement have good reserve resources at all times to cope with the upward and downward flexibility adjustment needs of the system at all times.

[0092] like Figure 1As shown, the present invention provides a subnet autonomy capability analysis method considering the coordination of distributed controllable resources, the method comprising:

[0093] Step 101: Consider whether the distributed resources are controllable, adjustable, and the operating constraints, and establish a distributed controllable resource model;

[0094] Preferably, considering whether the distributed resources are controllable, adjustable, and the operating constraints, a distributed controllable resource model is established, including:

[0095] The distributed power sources in the microgrid mainly consider controllable but unadjustable distributed photovoltaic and distributed wind power. The distributed power generation model and constraints are as follows:

[0096]

[0097] Among them, ON pv (t),ON wd (t) are the number of photovoltaic and wind power stations operating at time t, N pv 、N wd are the number of photovoltaic and wind power installed capacity, n pv 、n wd are the installed capacity of a single photovoltaic and wind power station respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic power and wind power, respectively; pv(t) and wd(t) are the normalized theoretical outputs of photovoltaic power and wind power, respectively;

[0098] The load model mainly considers fixed load, time-shiftable load and interruptible load. The load model and constraints are as follows:

[0099]

[0100] The load at time t (t) is determined by the fixed load load fix (t), time-shiftable load adj (t) and interruptible load int (t) composition, N fix 、N adj,i 、N int,j are the maximum capacities of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; fix(t), adj(t), int(t) are the normalized power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively;

[0101] The energy storage is considered to be daily regulation energy storage, the default capacity is the maximum discharge power*2h, and the energy storage model and constraints are:

[0102]

[0103] in: It is the charging and discharging state variable of the energy storage device; is the maximum value of charge and discharge power; is the charging and discharging power of the energy storage device; S s,t is the capacity of the energy storage device at time t; S s,1 is the capacity of the energy storage device at time 1; S max is the maximum capacity of the energy storage system; S s,T is the capacity of the energy storage system at the end of a typical day; σ is the self-discharge rate of the energy storage device; η c , η d Charging and discharging efficiency of charging equipment.

[0104] Preferably, the distributed resources include wind power, photovoltaics, loads and energy storage.

[0105] The present invention considers whether distributed resources such as wind power, photovoltaics, loads, and energy storage are controllable and adjustable, and constructs a distributed controllable resource model in combination with operating constraints.

[0106] 1) Distributed Generation Model

[0107] The distributed power sources in the microgrid mainly consider controllable but unadjustable distributed photovoltaic and distributed wind power. The main models are as follows:

[0108]

[0109] Among them, ON pv (t),ON wd (t) are the number of photovoltaic and wind power stations operating at time t, N pv 、N wd are the number of photovoltaic and wind power installed capacity, n pv 、n wd are the installed capacity of a single photovoltaic and wind power station respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic power and wind power respectively, pv(t) and wd(t) are the normalized theoretical outputs of photovoltaic power and wind power respectively.

[0110] 2) Load model and constraints

[0111] The load model mainly considers fixed load, time-shiftable load and interruptible load. The specific model is as follows:

[0112]

[0113] The load at time t (t) is determined by the fixed load load fix (t), time-shiftable load adj (t) and interruptible loadint (t) composition, N fix 、N adj,i 、N int,j are the maximum capacities of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; fix(t), adj(t), and int(t) are the normalized power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively.

[0114] 3) Energy storage model and constraints

[0115] Energy storage is considered as daily regulation energy storage, and the default capacity is the maximum discharge power*2h. The energy storage model is as follows:

[0116]

[0117] Where: It is the charging and discharging state variable of the energy storage device; is the maximum value of charge and discharge power; Energy storage device charging and discharging power; S s,t The capacity of the energy storage device at time t; S max is the maximum capacity of the energy storage system; S s,T is the capacity of the energy storage system at the end of a typical day; σ is the self-discharge rate of the energy storage device; η c , η d Charging and discharging efficiency of charging equipment.

[0118] Step 102: Considering the microgrid operation rules and operation constraints, establish an overall optimization constraint model of the microgrid;

[0119] Preferably, considering the microgrid operation rules and operation constraints, an overall optimization constraint model of the microgrid is established, including:

[0120] The microgrid operation constraints of the microgrid are shown in the formula:

[0121]

[0122] in, They are the external power demand and return power of the microgrid, is positive, is negative, N tra is the maximum load demand of the microgrid;

[0123] The power supply reliability constraint is that the power failure rate cannot exceed the planned upper limit f LPSP :

[0124]

[0125] Where: LPSP is the power failure rate; P loss(t) is the load shedding amount during period t; P load (t) is the net load during period t;

[0126] The renewable energy reduction rate constraint is that the renewable energy reduction rate within a year shall not exceed the planned upper limit LRES max :

[0127]

[0128] Where: P loss,RE (t) is the average power of renewable energy curtailment during period t;

[0129] The renewable energy installed capacity ratio constraint is that the ratio of renewable energy installed capacity to peak load power PR reaches more than 50%:

[0130]

[0131] The annual exchange power ratio constraint is: The annual exchange power ratio AEEP The annual exchange power between the new energy microgrid and the external power grid generally does not exceed 50% of the annual power consumption. The annual exchange power ratio AEEP is:

[0132]

[0133] The present invention considers the relevant regulations of microgrid operation, power supply reliability, renewable energy reduction rate, renewable energy installed capacity ratio, annual exchange power ratio and other operation constraints, and constructs the overall optimization constraint model of microgrid.

[0134] 1) Microgrid Constraints

[0135] According to the concept of reverse load rate in "DL / T 2041-2019 Guidelines for Assessment of Distributed Generation Access to Grid Carrying Capacity". If the reverse load rate is less than 0, it is defined as green, and distributed photovoltaic access is recommended; if the reverse load rate is greater than 0 and less than 80%, it is defined as yellow, and access to distributed photovoltaics requires investigation. If the reverse load rate is greater than 80%, it is defined as red. For areas marked as red, distributed photovoltaic access is suspended until the grid carrying capacity is improved. (The reverse load rate during special periods such as statutory holidays that cause grid load fluctuations during the assessment period is not taken into account) Therefore, the microgrid operation constraints of the microgrid are as shown in the formula:

[0136]

[0137] In the formula, They are the external power demand and return power of the microgrid, is positive, is negative, N tra is the maximum load demand of the microgrid.

[0138] 2) Power supply reliability constraints

[0139] In order to ensure the reliability of power supply for important loads in the substation, the total amount of load shedding in a year is required to account for the proportion of load power consumption (Loss of Power Supply Probability, LPSP), that is, the power failure rate cannot exceed the planned upper limit value f LPSP .

[0140]

[0141] Where: LPSP is the power failure rate; P loss (t) is the load shedding amount during period t; P load (t) is the net load during period t.

[0142] 3) Constraints on the reduction rate of renewable energy

[0143] The Loss of Renewable Energy Sources (LRES) is used to reflect the utilization of renewable energy generation in the distribution and micro-coordination planning. In order to reduce the amount of renewable energy abandoned, it is required that the renewable energy reduction rate within one year shall not exceed the planned upper limit LRES. max .

[0144]

[0145] Where: P loss,RE (t) is the average power of renewable energy curtailment during period t.

[0146] 4) Proportion of renewable energy installed capacity

[0147] In principle, the proportion of installed renewable energy power to peak load power should reach more than 50%. Diesel engines should be used as cold standby, and their power generation should account for less than 20% of the total power demand.

[0148]

[0149] 5) Annual exchange power ratio

[0150] The annual exchange electricity percentage AEEP (Annual exchange electricity percentage) is the annual exchange electricity between the new energy microgrid and the external power grid, which generally does not exceed 50% of the annual electricity consumption.

[0151]

[0152] Step 103: Based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, the upper and lower bounds of the demand interval are coupled, taking into account the consistency of the operating reserve capacity of the microgrid at each moment, and constructing an optimization objective function;

[0153] Preferably, based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, the upper and lower bounds of the demand interval are coupled, taking into account the consistency of the operating reserve capacity of the microgrid at each moment, and constructing the optimization objective function, including:

[0154] Considering that any strategy can be implemented within the demand interval, the upper and lower bounds of the demand interval are coupled by combining the idea of ​​robust optimization and strong duality theory. The upper / lower bounds at time t-1 are used to determine the lower / upper bounds at time t. Constraints are constructed for the independent and mutually coupled states of the upper / lower bounds. Taking into account the objective function of operating spare capacity, the optimization objective is designed as follows:

[0155]

[0156] Among them, P E,up (t), P E,dn (t) are the external power purchases corresponding to the upper and lower boundaries of the strategy at time t; D[P E,up (t)Δt-P E,dn (t)Δt] represents the sample variance of the difference between the upper and lower boundaries at each moment, that is, under the premise of ensuring the maximum controllable area, referring to the principle of equal reserve capacity when the large power grid is in operation, try to ensure that the upward or downward controllable power at each moment is equal.

[0157] The present invention takes into account that any strategy within the demand interval can be implemented, combines the idea of ​​robust optimization with the strong duality theory to couple the upper and lower bounds of the demand interval, takes into account the consistency of the spare capacity of the microgrid at all times, and constructs an optimization objective function.

[0158] Considering that any strategy can be implemented within the demand interval, the idea of ​​robust optimization and strong duality theory are combined to couple the upper and lower bounds of the demand interval (the upper / lower bounds at time t-1 determine the lower / upper bounds at time t), and the upper / lower bounds are one of several demand strategies that can be executed within the interval. Therefore, it is necessary to construct constraints for both the independent and mutually coupled states of the upper / lower bounds. At the same time, taking into account the objective function of operating spare capacity, the optimization objective is designed as

[0159]

[0160] Among them, in the formula: P E,up (t), P E,dn (t) are the external power purchases corresponding to the upper and lower boundaries of the strategy at time t. E,up (t)Δt-P E,dn(t)Δt] represents the sample variance of the difference between the upper and lower boundaries at each moment, that is, under the condition of ensuring the maximum controllable area, referring to the principle of equal reserve capacity when the large power grid is in operation, try to ensure that the upward or downward controllable power at each moment is equal.

[0161] Step 104: Obtain the upper and lower bounds of the microgrid's external autonomy capability by solving the optimization objective function.

[0162] The present invention outputs the upper and lower boundaries of the external autonomy capability of the microgrid.

[0163] The present invention proposes a subnet autonomy analysis method that considers the coordination of distributed controllable resources. Starting from analyzing the overall external support and regulation capabilities of the microgrid, the upper and lower boundaries of the external characteristics of the cold and hot microgrids for the upper management agencies are studied, so as to better coordinate and optimize the entire region and ensure the safe and stable operation of the system. The method proposed in the present invention refers to the principle of equal spare capacity during the operation of the large power grid, ensuring that the external characteristics after measurement have good spare resources at all times to cope with the upward and downward flexibility adjustment needs of the system at all times.

[0164] The present invention proposes a subnet autonomy capability analysis method that considers the coordination of distributed controllable resources. Starting from analyzing the overall external support and regulation capabilities of the microgrid, the upper and lower boundaries of the external characteristics of the cold and hot microgrids to the upper management agencies are studied, so as to better coordinate and optimize the entire region and ensure the safe and stable operation of the system. At the same time, the proposed method refers to the principle of equal reserve capacity during the operation of the large power grid to ensure that the external characteristics after measurement have good reserve resources at all times to cope with the upward and downward flexibility adjustment needs of the system at all times. After calculation, this patent can establish a microgrid power demand adjustable capability evaluation model based on the source-load configuration of the microgrid, combined with a variety of energy supply equipment models, energy storage system models and load demand models, fully evaluate the adjustable capability of the microgrid, and quantify the fluctuation range of the microgrid's power demand to the upper level.

[0165] The implementation method of the present invention refers to a new energy microgrid, and the peak load of the microgrid is selected as 2MW. The ratio of fixed load, time-shiftable load, and interruptible load is 7:2:1. The distributed photovoltaic / distributed wind power is connected to the grid with a fixed capacity ratio of 1:3. The 8760 data of distributed wind power, distributed photovoltaic, and load are as follows: Figure 3 , Figure 4 , Figure 5 As shown in the figure, the annual utilization hours of wind power, photovoltaic power and load are 2005 hours, 1319 hours and 6259 hours respectively. Energy storage can ensure that important loads can operate off-grid for 2 hours, and important loads account for 10% of the total load.

[0166] The present invention takes into account that the installed capacity ratio of renewable energy is not less than 50%, and analyzes the external characteristics of the microgrid at different ratios of PR from 1.5 to 6.

[0167] In the present invention, the capacity configuration of distributed power sources in the microgrid is the result of comprehensive consideration of safety, economy and operation goals. It is difficult to determine the penetration rate configuration of distributed power sources in each microgrid in the future. Considering that the distributed capacity has a relatively large impact on the operation of the microgrid, setting different distributed power source installed penetration rates is a typical microgrid configuration scenario.

[0168] Table 1-1 Installed capacity penetration and electricity share of distributed generation in different scenarios

[0169]

[0170] The notice (NDRC Energy

[2017] No. 1339) mentioned that "the microgrid has a control system that ensures the independent operation of load power consumption and electrical equipment, and has the ability to self-balance power supply and demand and black start. When operating independently, it can ensure continuous power supply to important loads (not less than 2 hours). The annual exchange of electricity between the microgrid and the external power grid generally does not exceed 50% of the annual electricity consumption." That is, the power of the microgrid's own distributed power source is greater than 50% of the load power consumption in the microgrid. Therefore, the penetration rate of the microgrid's distributed power source is set from 38.2% to 152.8% of the self-generated power, and a total of 10 scenarios are simulated and analyzed. Fig. 9 shown.

[0171] In the present invention, the influence of different permeabilities on the demand interval area (energy demand) is found by calculating typical daily data:

[0172] The upper and lower boundaries of the external demand of the microgrid under different penetration rates can well envelop the net load curve of the microgrid, and the upper and lower boundaries are at different distances from the net load curve;

[0173] As the different penetration rates increase, the net load curve and envelope decrease overall, the return power gradually increases, and the maximum external power demand gradually decreases;

[0174] like Figure 6 , Figure 7 , Figure 8As shown in the figure, under different penetration rates, as the penetration rate of new energy increases, the dispatchable area of ​​the upper boundary of the microgrid increases, while the dispatchable area of ​​the lower boundary does not change much. The fundamental reason is that the dispatchable area of ​​the upper boundary represents the abandonable new energy power of the net load curve, while the lower boundary is the power that can be lost in the microgrid. With the increase in penetration rate, the installed capacity of distributed power sources increases, and the system energy storage capacity increases. According to the 10% new energy abandonment rate, the abandonable power gradually increases, and the distance between the upper boundary and the net load continues to increase. At the same time, with the increase in penetration rate, the capacity of the system load remains unchanged, the amount of load power that can be lost remains unchanged, and considering the influence of the energy storage charging and discharging efficiency, the distance between the lower boundary of the system and the net load remains basically unchanged. As Fig.10 , Fig.11 shown.

[0175] With the increase in the penetration rate of distributed power sources, the total area of ​​the microgrid's typical daily adjustable area gradually increases. Since the overall power of distributed power sources has increased linearly, the theoretically discardable power of distributed power sources has also increased linearly, so the adjustable area at the upper boundary shows a linear growth trend. At the same time, the load power remains unchanged, the discardable load power remains unchanged, and the adjustable area at the lower boundary remains basically unchanged.

[0176] Fig.12 A structural diagram of a subnet autonomous capability analysis system considering the coordination of distributed controllable resources according to a preferred embodiment of the present invention.

[0177] like Fig.12 As shown, the present invention provides a subnet autonomy capability analysis system considering the coordination of distributed controllable resources, the system comprising:

[0178] The first establishing unit 1201 is used to establish a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable, and operation constraints;

[0179] Preferably, the first establishing unit 1201 is used to consider whether the distributed resources are controllable, adjustable and the operation constraints, establish a distributed controllable resource model, and is also used to:

[0180] The distributed power sources in the microgrid mainly consider controllable but unadjustable distributed photovoltaic and distributed wind power. The distributed power generation model and constraints are as follows:

[0181]

[0182] Among them, ON pv (t),ON wd (t) are the number of photovoltaic and wind power stations operating at time t, N pv 、N wd are the number of photovoltaic and wind power installed capacity, n pv 、n wdare the installed capacity of a single photovoltaic and wind power station respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic power and wind power, respectively; pv(t) and wd(t) are the normalized theoretical outputs of photovoltaic power and wind power, respectively;

[0183] The load model mainly considers fixed load, time-shiftable load and interruptible load. The load model and constraints are as follows:

[0184]

[0185] The load at time t (t) is determined by the fixed load load fix (t), time-shiftable load adj (t) and interruptible load int (t) composition, N fix 、N adj,i 、N int,j are the maximum capacities of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; fix(t), adj(t), int(t) are the normalized power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively;

[0186] The energy storage is considered to be daily regulation energy storage, the default capacity is the maximum discharge power*2h, and the energy storage model and constraints are:

[0187]

[0188] in: It is the charging and discharging state variable of the energy storage device; is the maximum value of charge and discharge power; is the charging and discharging power of the energy storage device; S s,t is the capacity of the energy storage device at time t; S s,1 is the capacity of the energy storage device at time 1; S max is the maximum capacity of the energy storage system; S s,T is the capacity of the energy storage system at the end of a typical day; σ is the self-discharge rate of the energy storage device; η c , η d Charging and discharging efficiency of charging equipment.

[0189] Preferably, the distributed resources include wind power, photovoltaics, loads and energy storage.

[0190] The second establishing unit 1202 is used to establish an overall optimization constraint model of the microgrid by considering the microgrid operation rules and operation constraints;

[0191] Preferably, the second establishing unit 1202 is used to establish the overall optimization constraint model of the microgrid by considering the microgrid operation rules and operation constraints, and is also used to:

[0192] The microgrid operation constraints of the microgrid are shown in the formula:

[0193]

[0194] in, They are the external power demand and return power of the microgrid, is positive, is negative, N tra is the maximum load demand of the microgrid;

[0195] The power supply reliability constraint is that the power failure rate cannot exceed the planned upper limit f LPSP :

[0196]

[0197] Where: LPSP is the power failure rate; P loss (t) is the load shedding amount during period t; P load (t) is the net load during period t;

[0198] The renewable energy reduction rate constraint is that the renewable energy reduction rate within a year shall not exceed the planned upper limit LRES max :

[0199]

[0200] Where: P loss,RE (t) is the average power of renewable energy curtailment during period t;

[0201] The renewable energy installed capacity ratio constraint is that the ratio of renewable energy installed capacity to peak load power PR reaches more than 50%:

[0202]

[0203] The annual exchange power ratio constraint is: The annual exchange power ratio AEEP The annual exchange power between the new energy microgrid and the external power grid generally does not exceed 50% of the annual power consumption. The annual exchange power ratio AEEP is:

[0204] .

[0206] The third establishing unit 1203 is used to couple the upper and lower bounds of the demand interval based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, taking into account the consistency of the operating reserve capacity of the microgrid at each moment, and constructing an optimization objective function;

[0207] Preferably, the third establishing unit 1203 is used to couple the upper and lower bounds of the demand interval based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, taking into account the consistency of the spare capacity of the microgrid at each moment, and constructing the optimization objective function, and is also used to:

[0208] Considering that any strategy can be implemented within the demand interval, the upper and lower bounds of the demand interval are coupled by combining the idea of ​​robust optimization and strong duality theory. The upper / lower bounds at time t-1 are used to determine the lower / upper bounds at time t. Constraints are constructed for the independent and mutually coupled states of the upper / lower bounds. Taking into account the objective function of operating spare capacity, the optimization objective is designed as follows:

[0209]

[0210] Among them, P E,up (t), P E,dn (t) are the external power purchases corresponding to the upper and lower boundaries of the strategy at time t; D[P E,up (t)Δt-P E,dn (t)Δt] represents the sample variance of the difference between the upper and lower boundaries at each moment, that is, under the premise of ensuring the maximum controllable area, referring to the principle of equal reserve capacity when the large power grid is in operation, try to ensure that the upward or downward controllable power at each moment is equal.

[0211] The result unit 1204 is used to obtain the upper and lower boundaries of the external autonomy capability of the microgrid by solving the optimization objective function.

[0212] A subnet autonomous capability analysis system considering distributed controllable resource collaboration in a preferred embodiment of the present invention corresponds to a subnet autonomous capability analysis method considering distributed controllable resource collaboration in another preferred embodiment of the present invention, which will not be described in detail here.

[0213] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0214] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0215] 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.

[0216] 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.

[0217] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0218] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0219] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0220] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a / said / the [means, components, etc.]" are to be openly interpreted as at least one instance of the means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

Claims

1. A subnet autonomy capability analysis method considering distributed controllable resource collaboration, the method comprising: Consider whether the distributed resources are controllable, adjustable, and the operating constraints, and establish a distributed controllable resource model; Considering the microgrid operation rules and operation constraints, an overall optimization constraint model of the microgrid is established; Based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, the upper and lower bounds of the demand interval are coupled, the operating reserve capacity of the microgrid is considered to be consistent at all times, and the optimization objective function is constructed; By solving the optimization objective function, the upper and lower boundaries of the external autonomy capability of the microgrid are obtained.

2. According to the method of claim 1, the establishment of a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable and operating constraints includes: The distributed power sources in the microgrid mainly consider controllable but unadjustable distributed photovoltaic and distributed wind power. The distributed power generation model and constraints are as follows: Among them, ON pv (t),ON wd (t) are the number of photovoltaic and wind power stations operating at time t, N pv 、N wd are the number of photovoltaic and wind power installed capacity, n pv 、n wd are the installed capacity of a single photovoltaic and wind power station respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic power and wind power, respectively; pv(t) and wd(t) are the normalized theoretical outputs of photovoltaic power and wind power, respectively; The load model mainly considers fixed load, time-shiftable load and interruptible load. The load model and constraints are as follows: The load at time t (t) is determined by the fixed load load fix (t), time-shiftable load adj (t) and interruptible load int (t) composition, N fix 、N adj,i 、N int,j are the maximum capacities of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; fix(t), adj(t), int(t) are the normalized power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; The energy storage is considered to be daily regulation energy storage, the default capacity is the maximum discharge power*2h, and the energy storage model and constraints are: in: It is the charging and discharging state variable of the energy storage device; is the maximum value of charge and discharge power; is the charging and discharging power of the energy storage device; S s,t is the capacity of the energy storage device at time t; S s，1 is the capacity of the energy storage device at time 1; S max is the maximum capacity of the energy storage system; S s,T is the capacity of the energy storage system at the end of a typical day; σ is the self-discharge rate of the energy storage device; η c , η d Charging and discharging efficiency of charging equipment.

3. According to the method of claim 1, the step of considering the microgrid operation rules and operation constraints to establish the microgrid overall optimization constraint model comprises: The microgrid operation constraints of the microgrid are shown in the formula: in, They are the external power demand and return power of the microgrid, is positive, is negative, N tra is the maximum load demand of the microgrid; The power supply reliability constraint is that the power failure rate cannot exceed the planned upper limit f LPSP : Where: LPSP is the power failure rate; P loss (t) is the load shedding amount during period t; P load (t) is the net load during period t; The renewable energy reduction rate constraint is that the renewable energy reduction rate within a year shall not exceed the planned upper limit LRES max : Where: P loss,RE (t) is the average power of renewable energy curtailment during period t; The renewable energy installed capacity ratio constraint is that the ratio of renewable energy installed capacity to peak load power PR reaches more than 50%: The annual exchange power ratio constraint is: The annual exchange power ratio AEEP The annual exchange power between the new energy microgrid and the external power grid generally does not exceed 50% of the annual power consumption. The annual exchange power ratio AEEP is:

4. The method according to claim 1, wherein based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, the upper and lower bounds of the demand interval are coupled, the consistent operation reserve capacity of the microgrid at each moment is taken into account, and the optimization objective function is constructed, including: Considering that any strategy can be implemented within the demand interval, the upper and lower bounds of the demand interval are coupled by combining the idea of ​​robust optimization and strong duality theory. The upper / lower bounds at time t-1 are used to determine the lower / upper bounds at time t. Constraints are constructed for the independent and mutually coupled states of the upper / lower bounds. Taking into account the objective function of operating spare capacity, the optimization objective is designed as follows: Among them, P E,up (t), P E,dn (t) are the external power purchases corresponding to the upper and lower boundaries of the strategy at time t; D[P E,up (t)Δt-P E,dn (t)Δt] represents the sample variance of the difference between the upper and lower boundaries at each moment, that is, under the premise of ensuring the maximum controllable area, referring to the principle of equal reserve capacity when the large power grid is in operation, try to ensure that the upward or downward controllable power at each moment is equal. 5 . The method according to claim 1 , wherein the distributed resources include wind power, photovoltaic power, load and energy storage.

6. A subnet autonomous capability analysis system considering distributed controllable resource collaboration, the system comprising: The first establishing unit is used to establish a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable and operation constraints; The second establishment unit is used to consider the microgrid operation rules and operation constraints to establish the microgrid overall optimization constraint model; A third establishing unit is used to couple the upper and lower bounds of the demand interval based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, taking into account the consistency of the operating reserve capacity of the microgrid at each moment, and constructing an optimization objective function; The result unit is used to obtain the upper and lower boundaries of the external autonomy capability of the microgrid by solving the optimization objective function.

7. The system according to claim 6, wherein the first establishing unit is used to establish a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable, and operation constraints, and is also used to: The distributed power sources in the microgrid mainly consider controllable but unadjustable distributed photovoltaic and distributed wind power. The distributed power generation model and constraints are as follows: in, ON pv (t),ON wd (t) are the number of photovoltaic and wind power stations operating at time t, N pv 、N wd are the number of photovoltaic and wind power installed capacity, n pv 、n wd are the installed capacity of a single photovoltaic and wind power station respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic power and wind power, respectively; pv(t) and wd(t) are the normalized theoretical outputs of photovoltaic power and wind power, respectively; The load model mainly considers fixed load, time-shiftable load and interruptible load. The load model and constraints are as follows: The load at time t (t) is determined by the fixed load load fix (t), time-shiftable load adj (t) and interruptible load int (t) composition, N fix 、N adj,i 、N int,j are the maximum capacities of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; fix(t), adj(t), int(t) are the normalized power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively; The energy storage is considered to be daily regulation energy storage, the default capacity is the maximum discharge power*2h, and the energy storage model and constraints are: in: It is the charging and discharging state variable of the energy storage device; is the maximum value of charge and discharge power; is the charging and discharging power of the energy storage device; S s,t is the capacity of the energy storage device at time t; S s，1 is the capacity of the energy storage device at time 1; S max is the maximum capacity of the energy storage system; S s,T is the capacity of the energy storage system at the end of a typical day; σ is the self-discharge rate of the energy storage device; η c , η d Charging and discharging efficiency of charging equipment.

8. The system according to claim 6, wherein the second establishing unit is used to establish the overall optimization constraint model of the microgrid by considering the microgrid operation rules and operation constraints, and is also used to: The microgrid operation constraints of the microgrid are shown in the formula: in, They are the external power demand and return power of the microgrid, is positive, is negative, N tra is the maximum load demand of the microgrid; The power supply reliability constraint is that the power failure rate cannot exceed the planned upper limit f LPSP : Where: LPSP is the power failure rate; P loss (t) is the load shedding amount during period t; P load (t) is the net load during period t; The renewable energy reduction rate constraint is that the renewable energy reduction rate within a year shall not exceed the planned upper limit LRES max : Where: P loss,RE (t) is the average power of renewable energy curtailment during period t; The renewable energy installed capacity ratio constraint is that the ratio of renewable energy installed capacity to peak load power PR reaches more than 50%: The annual exchange power ratio constraint is: The annual exchange power ratio AEEP The annual exchange power between the new energy microgrid and the external power grid generally does not exceed 50% of the annual power consumption. The annual exchange power ratio AEEP is:

9. The system according to claim 6, wherein the third establishing unit is used to couple the upper and lower bounds of the demand interval based on the distributed controllable resource model and the overall optimization constraint model of the microgrid, taking into account the consistency of the spare capacity of the microgrid at each moment, and constructing an optimization objective function, and is also used to: Considering that any strategy can be implemented within the demand interval, the upper and lower bounds of the demand interval are coupled by combining the idea of ​​robust optimization and strong duality theory. The upper / lower bounds at time t-1 are used to determine the lower / upper bounds at time t. Constraints are constructed for the independent and mutually coupled states of the upper / lower bounds. Taking into account the objective function of operating spare capacity, the optimization objective is designed as follows: in, P E,up (t), P E,dn (t) are the external power purchases corresponding to the upper and lower boundaries of the strategy at time t; D[P E,up (t)Δt-P E,dn (t)Δt] represents the sample variance of the difference between the upper and lower boundaries at each moment, that is, under the premise of ensuring the maximum controllable area, referring to the principle of equal reserve capacity when the large power grid is in operation, try to ensure that the upward or downward controllable power at each moment is equal.

10. The system according to claim 6, wherein the distributed resources include wind power, photovoltaic power, load and energy storage.

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