Consider distributed controllable resource coordination subnet autonomous capability analysis method and system

Abstract

The application discloses a kind of subnet autonomous ability analysis method and system considering distributed controllable resource cooperation, wherein method includes: considering whether distributed resource is controllable, whether it is adjustable and operating constraint condition, establishes distributed controllable resource model;Considering microgrid operation rules and operating constraint condition, establish microgrid overall optimization constraint model;Based on the distributed controllable resource model and the microgrid overall optimization constraint model, the upper and lower bounds of demand interval are coupled, the consistent reserve capacity of microgrid at each time is considered, and the optimization objective function is constructed;By solving the optimization objective function, the upper and lower bounds of the external autonomy of microgrid are obtained.The application proposes a subnet autonomous ability analysis method and system considering distributed controllable resource cooperation, which is convenient for better overall scheduling optimization of the whole area and ensures the safe and stable operation of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network planning and dispatching, and more particularly to a sub-network autonomous capability analysis method and system considering coordination of distributed controllable resources. BACKGROUND

[0002] In recent years, the number of grid-connected distributed power sources such as photovoltaic, wind power and energy storage in the distribution network has grown rapidly. The grid-connected voltage level is low, the penetration rate is high, and the grid-connected points are mainly distributed in rural distribution networks and other weak power grids, which brings great challenges to the operation and dispatching of medium and low voltage distribution networks. To realize regional autonomy of the distribution network and reduce the complexity of the operation and dispatching of the distribution network, the regional autonomous capability of the distribution network, especially the power and energy balance capability, is a key problem. Due to the time distribution characteristics of high proportion of distributed photovoltaic and load output, the distribution network has different external power characteristics requirements in different periods. In addition, the random fluctuation characteristics of photovoltaic and load power make the source and load imbalance power in the distribution network dynamically change. Therefore, it is of great significance to study the autonomous capability analysis method of the distribution network with high proportion of new energy access to improve the flexible adjustment capability of the distribution network under high proportion of new energy access. At the same time, as an electrical interconnection system, the microgrid undertakes the task of collecting, using and managing load side resources, and can improve the economic and environmental benefits of the system and the flexibility level of the system by suppressing power fluctuations under the premise of ensuring the safety and stability of the main grid.

[0003] Existing technologies have many studies on the autonomous capability and flexibility of microgrids in distribution network areas, but most of them optimize the joint economic cost of microgrids or distribution-microgrid systems, which is difficult to solve the problem of investment operation and construction operation cost evaluation of various power sources in the future microgrid. At the same time, the coordination and dispatching take the autonomous grid as a subordinate unit, mainly to ensure the power and energy balance of the distribution network, which does not conform to the operation mode of the future distribution network with high proportion of distributed power access.

[0004] Therefore, a technology is needed to analyze the autonomous capability of the sub-network considering the coordination of distributed controllable resources. SUMMARY

[0005] The technical scheme of the present application provides a sub-network autonomous capability analysis method and system considering the coordination of distributed controllable resources to solve the problem of how to analyze the autonomous capability of the sub-network considering the coordination of distributed controllable resources.

[0006] To solve the above problems, the present application provides a sub-network autonomous capability analysis method considering the coordination of distributed controllable resources, which comprises:

[0007] A distributed controllable resource model is established considering whether the distributed resources are controllable, adjustable and operating constraints;

[0008] A micro-grid overall optimization constraint model is established by considering micro-grid operation rules and operation constraints;

[0009] Based on the distributed controllable resource model and the micro-grid overall optimization constraint model, the upper and lower bounds of the demand interval are coupled, the consistent operation reserve capacity of the micro-grid at each time is taken into account, and an optimization objective function is constructed;

[0010] The upper and lower bounds of the autonomous ability of the micro-grid to the outside are obtained by solving the optimization objective function.

[0011] Preferably, the distributed controllable resource model is established by considering whether the distributed resource is controllable, adjustable and operation constraints, and comprises:

[0012] The distributed power supply in the micro-grid mainly considers controllable and non-adjustable distributed photovoltaic and distributed wind power, and the distributed power generation model and constraint conditions are:

[0013]

[0014] Wherein, ON pv (t) and ON wd (t) are the numbers of the photovoltaic and wind power stations operating at t, N pv and N wd are the installed capacities of the photovoltaic and wind power stations, n pv and n wd are the installed capacities of a single photovoltaic and wind power station; P pv (t) and P wd (t) are the theoretical outputs of the photovoltaic and wind power, and pv(t) and wd(t) are the normalized theoretical outputs of the photovoltaic and wind power.

[0015] The load model mainly considers fixed load, time-shiftable load and interruptible load, and the load model and constraint conditions are:

[0016]

[0017] Wherein, the load load(t) at t is composed of the fixed load load fix (t), the time-shiftable load load adj (t) and the interruptible load load int (t), N fix , N adj,i and 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 demands of the fixed load, the i-th time-shiftable load and the j-th interruptible load, respectively.

[0018] The energy storage considers the daily regulation energy storage, and the default capacity is the maximum discharge power*2h. The energy storage model and constraint conditions are as follows:

[0019]

[0020] Wherein: is the charge and discharge state variable of the energy storage device; is the maximum value of the charge and discharge power; is the charge and discharge power of the energy storage device; s,t is the capacity of the energy storage device at t moment; s，1 is the capacity of the energy storage device at 1 moment; max is the maximum capacity of the energy storage system; s,T is the capacity of the energy storage system at the end of a typical day; c is the self-discharge rate of the energy storage device; d is the charge and discharge efficiency of the energy storage device.

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

[0022] The micro-grid operation constraints are as shown in the formula:

[0023]

[0024] Wherein, are the power demand and returned power of the micro-grid to the outside, respectively, is positive, is negative, tra is the maximum load demand of the micro-grid;

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

[0026]

[0027] Wherein: LPSP is the outage rate; P loss (t) is the load shedding amount at t period; P load (t) is the net load at t period;

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

[0029]

[0030] Wherein: P loss,RE (t) is the renewable energy curtailment amount average power at t period;

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

[0032]

[0033] The annual exchange capacity ratio constraint is that the annual exchange capacity ratio AEEP of the new energy micro-grid and the external grid is generally less than 50% of the annual electricity consumption, and the annual exchange capacity ratio AEEP is:

[0034]

[0035] Preferably, the demand interval upper and lower bounds are coupled based on the distributed controllable resource model and the micro-grid overall optimization constraint model, the operation reserve capacity of the micro-grid at each time is considered, an optimization objective function is constructed, and the optimization objective function comprises:

[0036] Considering that any strategy in the demand interval can be realized, the demand interval upper and lower bounds are coupled based on the idea of robust optimization and the strong duality theory, the upper / lower bound at t-1 time is used to determine the lower / upper bound at t time, the independent and mutual coupling states of the upper / lower bound are constructed as constraint conditions, the operation reserve capacity is considered as a target function, and the optimization objective is

[0037]

[0038] Wherein, P E,up (t) and P E,dn (t) are the external power purchase powers of the upper and lower boundary corresponding strategies at t time; and 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 time, that is, under the condition of ensuring the largest controllable area, the upward or downward controllable power at each time is as equal as possible according to the principle of equal reserve capacity in the reference large grid operation.

[0039] Preferably, the distributed resource comprises wind power, photovoltaic, load and energy storage.

[0040] According to another aspect of the present application, the present application provides a sub-grid autonomy capability analysis system considering the coordination of distributed controllable resources, and the system comprises:

[0041] A first establishing unit is configured to establish a distributed controllable resource model by considering whether the distributed resource is controllable, adjustable and operation constraint conditions;

[0042] A second establishing unit is configured to establish a micro-grid overall optimization constraint model by considering micro-grid operation rules and operation constraint conditions;

[0043] A third establishing unit is configured to couple upper and lower bounds of the demand interval based on the distributed controllable resource model and the micro-grid overall optimization constraint model, take into account consistent reserve capacity of the micro-grid at each time, and construct an optimization objective function.

[0044] A result unit is configured to obtain upper and lower bounds of the autonomous capability of the micro-grid by solving the optimization objective function.

[0045] Preferably, the first establishing unit is configured to establish the distributed controllable resource model by considering whether the distributed resource is controllable, adjustable and operating constraint conditions, and is further configured to:

[0046] The distributed power supply in the micro-grid mainly considers controllable and non-adjustable distributed photovoltaic and distributed wind power, and the distributed power generation model and constraint conditions are:

[0047]

[0048] wherein, ON pv (t) and ON wd (t) are the number of operating photovoltaic and wind power stations at time t, N pv and N wd are the installed capacities of the photovoltaic and wind power stations, n pv and n wd are the installed capacities of a single photovoltaic and wind power station, P pv (t) and P wd (t) are the theoretical outputs of the photovoltaic and wind power, and pv(t) and wd(t) are the normalized theoretical outputs of the photovoltaic and wind power.

[0049] The load model mainly considers fixed load, time-shiftable load and interruptible load, and the load model and constraint conditions are:

[0050]

[0051] wherein, the load load(t) at time t is composed of the fixed load load fix (t), the time-shiftable load load adj (t) and the interruptible load load int (t), N fix , N adj,i and N int,j are the maximum capacities of the fixed load, the ith time-shiftable load and the jth interruptible load, respectively, fix(t), adj(t) and int(t) are the normalized power demands of the fixed load, the ith time-shiftable load and the jth interruptible load, respectively.

[0052] The energy storage is considered as a daily regulation energy storage, and the default capacity is the maximum discharge power*2h, and the energy storage model and constraint conditions are:

[0053]

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

[0055] Preferably, the second establishing unit is configured to establish a micro-grid overall optimization constraint model by considering micro-grid operation rules and operation constraints, and is further configured to:

[0056] The micro-grid operation constraint is shown in the following formula:

[0057]

[0058] wherein, are the power demand and the power return of the micro-grid, respectively, is positive, is negative, tra is the maximum load demand of the micro-grid;

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

[0060]

[0061] wherein: LPSP is the outage rate; P loss (t) is the load shedding amount at time t; P load (t) is the net load at time t;

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

[0063]

[0064] wherein: P loss,RE (t) is the average power of renewable energy curtailment at time t;

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

[0066]

[0067] The annual exchange capacity ratio constraint is that the annual exchange capacity ratio AEEP of the new energy micro-grid and the external grid is generally less than 50% of the annual electricity consumption, and the annual exchange capacity ratio AEEP is:

[0068]

[0069] Preferably, the third establishing unit is configured to couple the upper and lower bounds of the demand interval, taking into account the consistent reserve capacity of the micro-grid at each time, and construct an optimization objective function based on the distributed controllable resource model and the micro-grid overall optimization constraint model, and is further configured to:

[0070] Considering that any strategy within the demand interval can be achieved, the upper and lower bounds of the demand interval are coupled by combining the idea of robust optimization and the strong duality theory, the upper / lower bound at time t-1 is used to determine the lower / upper bound at time t, and constraint conditions are constructed for the independent and mutual coupling states of the upper / lower bound, taking into account the objective function of the operating reserve capacity, and the optimization objective is designed as

[0071]

[0072] wherein, P E,up (t) and P E,dn (t) are the external power purchase power of the upper and lower boundary corresponding strategies at time t, respectively; 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 time, that is, under the condition of ensuring the largest controllable area, the principle of equal reserve capacity when the reference large grid is running is considered, and the upward or downward controllable power at each time is as equal as possible.

[0073] Preferably, the distributed resource includes wind power, photovoltaic, load and energy storage.

[0074] The technical scheme of the present application provides a subnet autonomous capability analysis method and system considering distributed controllable resource cooperation, wherein the method comprises the following steps: establishing a distributed controllable resource model by considering whether the distributed resource is controllable, adjustable and operating constraint conditions; establishing a micro-grid overall optimization constraint model by considering micro-grid operation rules and operating constraint conditions; coupling the upper and lower bounds of the demand interval based on the distributed controllable resource model and the micro-grid overall optimization constraint model, taking into account the consistent micro-grid operating reserve capacity at each time, and constructing an optimization objective function; and obtaining the upper and lower bounds of the external autonomous capability of the micro-grid by solving the optimization objective function. The present application provides a subnet autonomous capability analysis method and system considering distributed controllable resource cooperation, which starts from analyzing the overall external support and adjustment capability of the micro-grid, focuses on the upper and lower bounds of the external characteristics of the micro-grid for the upper management mechanism, so as to better perform overall scheduling optimization of the entire region and ensure the safe and stable operation of the system. BRIEF DESCRIPTION OF DRAWINGS

[0075] The exemplary embodiments of the present application can be more completely understood in reference to the following drawings:

[0076] Figure 1 A subnet autonomous capability analysis method considering distributed controllable resource cooperation according to a preferred embodiment of the present application is shown in the flow chart;

[0077] Figure 2 A subnet autonomous capability analysis method considering distributed controllable resource cooperation according to a preferred embodiment of the present application is shown in the flow chart;

[0078] Figure 3 A wind power standardization curve according to a preferred embodiment of the present application is shown in the graph;

[0079] Figure 4 A photovoltaic standardization curve according to a preferred embodiment of the present application is shown in the graph;

[0080] Figure 5 A load standardization curve according to a preferred embodiment of the present application is shown in the graph;

[0081] Figure 6 A typical day calculation result graph under a scenario of 200% penetration according to a preferred embodiment of the present application is shown in the graph;

[0082] Figure 7 A typical day calculation result graph under a scenario of 400% penetration according to a preferred embodiment of the present application is shown in the graph;

[0083] Figure 8 A typical day calculation result graph under a scenario of 600% penetration according to a preferred embodiment of the present application is shown in the graph;

[0084] Figure 9Figures of typical day calculation results in different scenarios according to the preferred embodiments of the present application;

[0085] Figure 10 Figures of upper and lower boundaries of microgrid and distance to net load in different penetration rates according to the preferred embodiments of the present application;

[0086] Figure 11 Figures of influence of different penetration rates on the area of demand interval according to the preferred embodiments of the present application; and

[0087] Figure 12 Structure diagram of a subgrid autonomous capability analysis system considering coordination of distributed controllable resources according to the preferred embodiments of the present application. DETAILED DESCRIPTION

[0088] Reference will now be made to the drawings to describe the exemplary embodiments of the present application in detail. The present application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and fully convey the scope of the application to those skilled in the art. Like reference numerals refer to like elements throughout the specification. It will be understood that when an element is referred to as being "on" another element, it can be directly on the element or intervening elements can also be present. In addition, terms such as first and second are used herein when claiming certain embodiments of the present application and should not be construed as limiting the scope of the present application unless otherwise stated.

[0089] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0090] Figure 1 Flow chart of a subgrid autonomous capability analysis method considering coordination of distributed controllable resources according to the preferred embodiments of the present application.

[0091] The present application proposes a subgrid autonomous capability analysis method considering coordination of distributed controllable resources. The present application analyzes the support and adjustment capability of a microgrid as a whole, and focuses on the upper and lower boundaries of the external characteristics of the microgrid to the upper management mechanism, so as to better perform overall scheduling optimization of the entire region and guarantee safe and stable operation of the system. For this purpose, the present application analyzes the external characteristics of the microgrid under different distributed power installation penetration rates, taking the maximum external adjustment capability of the microgrid and the minimum difference of the standby capacity at each time as the optimization objectives, and analyzes the essential characteristics of the external adjustment capability of the microgrid. Meanwhile, the method proposed herein refers to the principle of equal standby capacity in the operation of a large power grid, and ensures that the external characteristics after measurement have good standby resources at each time to cope with the upward and downward flexibility adjustment requirements of the system at each time.

[0092] As Figure 1As shown, the present application provides a subnet autonomous capability analysis method considering distributed controllable resource cooperation, the method comprising:

[0093] Step 101: considering whether the distributed resource is controllable, adjustable and running constraint condition, establishing a distributed controllable resource model;

[0094] Preferably, considering whether the distributed resource is controllable, adjustable and running constraint condition, establishing a distributed controllable resource model, comprising:

[0095] The distributed power supply in the microgrid mainly considers controllable and non-adjustable distributed photovoltaic and distributed wind power, and the distributed power generation model and constraint condition are:

[0096]

[0097] Wherein, ON pv (t) and ON wd (t) are the number of running photovoltaic and wind power stations at t time, N pv and N wd are the installed capacity of photovoltaic and wind power, n pv and n wd are the installed capacity of single photovoltaic and wind power station; P pv (t) and P wd (t) are the theoretical output of photovoltaic and wind power, and pv(t) and wd(t) are the normalized theoretical output of photovoltaic and wind power;

[0098] The load model mainly considers fixed load, time-shiftable load and interruptible load, and the load model and constraint condition are:

[0099]

[0100] Wherein, the load load(t) at t time is composed of fixed load load fix (t), time-shiftable load load adj (t) and interruptible load load int (t), N fix , N adj,i and N int,j are the maximum capacity of fixed load, the i th time-shiftable load and the j th interruptible load; fix(t), adj(t) and int(t) are the normalized power demand of fixed load, the i th time-shiftable load and the j th interruptible load, respectively;

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

[0102]

[0103] wherein: is the state of charge of the energy storage device; is the maximum charge and discharge power; is the charge and discharge power of the energy storage device; s,t is the capacity of the energy storage device at time t; s,1 is the capacity of the energy storage device at time t; max is the maximum capacity of the energy storage system; 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 is the charge and discharge efficiency of the energy storage device.

[0104] Preferably, the distributed resources include wind power, photovoltaic, load and energy storage.

[0105] The present application considers whether the distributed resources such as wind power, photovoltaic, load 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 and non-adjustable distributed photovoltaic and distributed wind power, and the main model is as follows:

[0108]

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

[0110] 2) Load model and constraints

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

[0112]

[0113] wherein, the load at time t load(t) is composed of fixed load load fix (t), time-shiftable load load adj (t) and interruptible load loadint (t) constitute, N fix , N adj,i , N int,j respectively are the maximum capacity of fixed load, ith time-shiftable load, jth interruptible load; fix(t), adj(t), int(t) are respectively the normalized power demand of fixed load, ith time-shiftable load, jth interruptible load.

[0114] 3) Energy storage model and constraints

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

[0116]

[0117] In the formula: is the charge and discharge state variable of the energy storage device; is the maximum value of charge and discharge power; is the charge and discharge power of the energy storage device; S s,t is 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 is the charge and discharge efficiency of the energy storage device.

[0118] Step 102: considering the microgrid operation rules and operation constraints, a microgrid overall optimization constraint model is established;

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

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

[0121]

[0122] Among them, are respectively the power demand and the returned power of the microgrid to the outside, is positive, is negative, N tra is the maximum load demand of the microgrid;

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

[0124]

[0125] Among them: LPSP is the outage rate; P loss(t) is the t period cut load amount; P load (t) is the t period net load;

[0126] The renewable energy reduction rate constraint is that the renewable energy reduction rate in a year cannot exceed the upper limit value LRES of planning max :

[0127]

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

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

[0130]

[0131] The annual exchange capacity ratio constraint is that the annual exchange capacity ratio AEEP of the new energy micro-grid and the external grid is generally not more than 50% of the annual electricity consumption, and the annual exchange capacity ratio AEEP is:

[0132]

[0133] The present application considers the micro-grid operation related regulations, power supply reliability, renewable energy reduction rate, renewable energy installed capacity ratio, annual exchange capacity ratio and other operation constraint requirements, and constructs a micro-grid overall optimization constraint model

[0134] 1) Micro-grid constraint

[0135] According to the concept of reverse load rate in the DL / T 2041-2019 Distributed Power Access to Power Grid Carrying Capacity Evaluation Guide. 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 distributed photovoltaic access needs to be investigated. If the reverse load rate is greater than 80%, it is defined as red. For the areas marked as red, distributed photovoltaic access is suspended before the power grid carrying capacity is improved. (The reverse load rate during the evaluation period of statutory holidays and other special periods causing power grid load fluctuation is not considered) Therefore, the micro-grid operation constraint of the micro-grid is as shown in the formula:

[0136]

[0137] In the formula, P is the power demand and return power of the micro-grid to the outside, is positive, is negative, N tra is the maximum load demand of the micro-grid.

[0138] 2) Power supply reliability constraints

[0139] To ensure the power supply reliability of important loads in the transformer area, the total amount of load shedding within a year should not exceed the planned upper limit value f of the Loss of Power Supply Probability (LPSP), i.e., the power failure rate LPSP .

[0140]

[0141] wherein: LPSP is the power failure rate; P loss (t) is the amount of load shedding at time t; P load (t) is the net load at time t.

[0142] 3) Renewable energy reduction rate constraints

[0143] The Loss of Renewable Energy Sources (LRES) is used to reflect the utilization of renewable energy generation in the micro-grid collaborative planning. In order to reduce the amount of renewable energy curtailment, the renewable energy reduction rate within a year should not exceed the planned upper limit value LRES max .

[0144]

[0145] wherein: P loss,RE (t) is the average power of renewable energy curtailment at time t.

[0146] 4) Proportion of installed renewable energy

[0147] The ratio of installed renewable energy power to peak load power PR (The proportion of installed renewable energy) should in principle reach more than 50%, and diesel engines should be used as cold standby, with their power generation accounting for less than 20% of the total electricity demand.

[0148]

[0149] 5) Annual exchange electricity percentage

[0150] The Annual exchange electricity percentage AEEP (Annual exchange electricity percentage) between the new energy micro-grid and the external grid should generally 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 micro-grid, the upper and lower bounds of the demand interval are coupled, the consistent reserve capacity of the micro-grid at each time is considered, and an optimization objective function is constructed;

[0153] Preferably, based on the distributed controllable resource model and the overall optimization constraint model of the micro-grid, the upper and lower bounds of the demand interval are coupled, the consistent reserve capacity of the micro-grid at each time is considered, and an optimization objective function is constructed, including:

[0154] Considering that any strategy in the demand interval can be realized, 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 boundary at t-1 time determines the lower / upper boundary at t time, constraint conditions are constructed for the independent and mutual coupling state of the upper / lower boundary, the target function of the operation reserve capacity is considered, and the optimization target is designed as

[0155]

[0156] Wherein, P E,up (t) and P E,dn (t) are external power purchase powers of the upper and lower boundary corresponding strategies at t time; and D[P E,up (t)Δt-P E,dn (t)Δt] represents a sample variance of the difference between the upper and lower boundaries at each time, that is, under the condition of ensuring the largest controllable area, the principle of equal reserve capacity when the large power grid is running is referred to, and the upward or downward controllable power at each time is preferably ensured to be equal.

[0157] The present application considers that any strategy in the demand interval can be realized, the upper and lower bounds of the demand interval are coupled by combining the idea of robust optimization and strong duality theory, the consistent reserve capacity of the micro-grid at each time is considered, and an optimization objective function is constructed.

[0158] Considering that any strategy in the demand interval can be realized, 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 boundary at t-1 time determines the lower / upper boundary at t time), and the upper / lower boundary is one of a plurality of demand strategies that can be executed in the interval. Therefore, constraint conditions are constructed for the independent and mutual coupling state of the upper / lower boundary. Meanwhile, the target function of the operation reserve capacity is considered, and the optimization target is designed as

[0159]

[0160] Wherein, in the formula, P E,up (t) and P E,dn (t) are external power purchase powers of the upper and lower boundary corresponding strategies at t time. D[P E,up (t)Δt-P E,dn(t)Δt] represents the variance of the difference between the upper and lower boundaries at each time, that is, in the case of ensuring the largest adjustable area, the principle of equal reserve capacity when the reference large power grid is running, and the upward or downward adjustable power at each time is as equal as possible.

[0161] Step 104: obtaining the upper and lower boundaries of the autonomy of the micro-grid to the outside by solving the optimization objective function.

[0162] The present application outputs the upper and lower boundaries of the autonomy of the micro-grid to the outside.

[0163] The present application proposes a sub-grid autonomy analysis method considering the cooperation of distributed controllable resources, which starts from analyzing the overall support and adjustment capability of the micro-grid to the outside, studies the upper and lower boundaries of the cold and heat micro-grid to the external characteristics of the upper management mechanism, so as to better carry out overall scheduling optimization of the entire region and guarantee the safe and stable operation of the system. The method proposed in the present application refers to the equal reserve capacity principle when the large power grid is running, and ensures that the measured external characteristics have good reserve resources at each time to meet the upward and downward flexibility adjustment demand of the system at each time.

[0164] The present application proposes a sub-grid autonomy analysis method considering the cooperation of distributed controllable resources, which starts from analyzing the overall support and adjustment capability of the micro-grid to the outside, studies the upper and lower boundaries of the cold and heat micro-grid to the external characteristics of the upper management mechanism, so as to better carry out overall scheduling optimization of the entire region and guarantee the safe and stable operation of the system. At the same time, the method proposed in the present application refers to the equal reserve capacity principle when the large power grid is running, and ensures that the measured external characteristics have good reserve resources at each time to meet the upward and downward flexibility adjustment demand of the system at each time. Through calculation, the present application can establish a micro-grid power demand adjustable capability evaluation model based on the source and load configuration of the micro-grid, combined with a variety of energy supply equipment models, energy storage system models and load demand models, fully evaluate the adjustable capability of the micro-grid, and quantify the fluctuation range of the micro-grid to the upper power demand.

[0165] The embodiment of the present application refers to a certain new energy micro-grid, selects the peak load of the micro-grid as 2MW, and the proportion of fixed load, time-shiftable load and interruptible load is 7:2:1. The fixed capacity ratio of distributed photovoltaic / distributed wind power is 1:3 and is connected to the grid. The 8760 data of the distributed wind power, the distributed photovoltaic and the load are as shown in Figure 3 、 Figure 4 、 Figure 5 The annual utilization hours of the wind power, the photovoltaic and the load are 2005 hours, 1319 hours and 6259 hours respectively. The energy storage can guarantee the off-grid operation of the important load for 2 hours, and the important load accounts for 10% of the total load.

[0166] The application considers that the renewable energy installed capacity ratio PR is not less than 50%, and the external characteristics of the micro-grid under different ratios of PR from 1.5 to 6 are analyzed respectively.

[0167] In the application, the capacity configuration of the distributed power supply in the micro-grid is the result of comprehensive consideration of safety, economy and operation targets, and the penetration rate of the distributed power supply in each micro-grid in the future is difficult to determine. Considering that the distributed capacity has a greater impact on the operation of the micro-grid, different distributed power supply installed penetration rates are set as the configuration scenarios of the typical micro-grid.

[0168] Table 1-1 Distributed power supply installed penetration rate and power consumption ratio under different scenarios

[0169]

[0170] In the Notice (Energy Development and Reform Energy

[2017] 1339), it is mentioned that the micro-grid has a control system to ensure the independent operation of load power consumption and electrical equipment, has the ability of self-balancing operation and black start, and can ensure continuous power supply (not less than 2 hours) for important loads when operating independently. The annual exchange power between the micro-grid and the external grid is generally not more than 50% of the annual power consumption. That is, the power of the distributed power supply in the micro-grid is greater than 50% of the load power consumption in the micro-grid, so the penetration rate of the distributed power supply in the micro-grid is set from 38.2% to 152.8% of the self-generation power, a total of 10 scenarios for simulation analysis. As shown in Figure 9 .

[0171] In the application, the influence of different penetration rates on the demand interval area (energy demand) is calculated by typical daily data:

[0172] The upper and lower boundaries of the external demand of the micro-grid under different penetration rates can well envelope the net load curve of the micro-grid, and the upper and lower boundaries are different distances from the net load curve;

[0173] With the increase of different penetration rates, the net load curve and the envelope curve as a whole decrease, the return power gradually increases, and the maximum demand for external power gradually decreases;

[0174] As shown in Figure 6 , Figure 7 , Figure 8As shown in the different permeability, with the increase of new energy penetration, the upper boundary of the micro-grid schedulable area increases, while the lower boundary schedulable area changes little, the fundamental reason is that the schedulable area of the upper boundary represents the abandoned new energy of the net load curve, and the lower boundary is the schedulable area of the micro-grid. With the increase of the penetration, the distributed power capacity increases, the system energy storage capacity increases, according to the 10% of the new energy abandoned electricity rate, the schedulable area gradually increases, the distance between the upper boundary and the net load increases. At the same time, with the increase of the penetration, the capacity of the system load is unchanged, the schedulable area of the load is unchanged, considering the influence of the energy storage charging and discharging efficiency, the distance between the lower boundary of the system and the net load is basically unchanged. As shown in the figure. Figure 10 、 Figure 11

[0175] With the increase of the penetration of the distributed power, the total schedulable area of the micro-grid typical day gradually increases, since the total power of the distributed power increases linearly, the schedulable area of the distributed power is also linearly increased, so the schedulable area of the upper boundary presents a linear growth trend. At the same time, the load capacity is unchanged, the schedulable area of the load is unchanged, and the schedulable area of the lower boundary is basically unchanged.

[0176] Figure 12 A sub-network autonomous capability analysis system considering the coordination of distributed controllable resources according to the preferred embodiment of the application is shown in the figure.

[0177] As shown in the figure, Figure 12 The application provides a sub-network autonomous capability analysis system considering the coordination of distributed controllable resources, the system comprises:

[0178] The first establishment unit 1201 is used for establishing a distributed controllable resource model by considering whether the distributed resource is controllable, schedulable and the operation constraint condition;

[0179] Preferably, the first establishment unit 1201 is used for establishing a distributed controllable resource model by considering whether the distributed resource is controllable, schedulable and the operation constraint condition, and is also used for:

[0180] The distributed power in the micro-grid mainly considers the controllable and non-schedulable distributed photovoltaic and distributed wind power, and the distributed power generation model and constraint condition are:

[0181]

[0182] Wherein, ON pv (t), ON wd (t) are the number of photovoltaic and wind power stations running at t, N pv , N wd are the number of photovoltaic and wind power stations, n pv , n wd ​P pv (t), P wd (t) are the theoretical output of photovoltaic and wind power respectively, pv(t), wd(t) are the normalized theoretical output of photovoltaic and wind power respectively;

[0183] The load model mainly considers fixed load, time-shiftable load and interruptible load, and the load model and constraint conditions are as follows:

[0184]

[0185] Wherein, the load load(t) at time t is composed of fixed load load fix (t), time-shiftable load load adj (t) and interruptible load load int (t), N fix , N adj,i , N int,j are the maximum capacity of 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 demand of fixed load, the i-th time-shiftable load and the j-th interruptible load respectively.

[0186] The energy storage is considered as daily regulation energy storage, and the default capacity is the maximum discharge power*2h, and the energy storage model and constraint conditions are as follows:

[0187]

[0188] Wherein: is the charge and discharge state variable of the energy storage device; is the maximum value of charge and discharge power; is the charge and discharge 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 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 is the charge and discharge efficiency of the energy storage device.

[0189] Preferably, the distributed resources include wind power, photovoltaic, load and energy storage.

[0190] The second establishment unit 1202 is configured to consider the micro-grid operation rules and operation constraints, and establish a micro-grid overall optimization constraint model;

[0191] Preferably, the second establishing unit 1202 is configured to establish a micro-grid overall optimization constraint model by considering micro-grid operation rules and operation constraints, and is further configured to:

[0192] The micro-grid operation constraint of the micro-grid is shown in the following formula:

[0193]

[0194] wherein, Pout and Preturn are respectively the power demand and the returned power of the micro-grid, is positive, is negative, and N tra is the maximum load demand of the micro-grid;

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

[0196]

[0197] wherein: LPSP is the outage rate; P loss (t) is the load shedding amount at the t period; P load (t) is the net load at the t period;

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

[0199]

[0200] wherein: P loss,RE (t) is the renewable energy curtailment amount average power at the t period;

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

[0202]

[0203] The annual exchange power ratio constraint is that the annual exchange power ratio AEEP of the new energy micro-grid and the external grid is generally not more than 50% of the annual electricity consumption, and the annual exchange power ratio AEEP is:

[0204]

[0205] The third establishing unit 1203 is configured to couple the upper and lower bounds of the demand interval based on the distributed controllable resource model and the micro-grid overall optimization constraint model, take into account the consistent operation standby capacity of the micro-grid at each time, and construct an optimization objective function.

[0206] Preferably, the third establishing unit 1203 is configured to couple the upper and lower bounds of the demand interval, taking into account the consistent reserve capacity of the micro-grid at each time, based on the distributed controllable resource model and the overall optimization constraint model of the micro-grid, and construct an optimization objective function, and is further configured to:

[0207] Considering that any strategy in the demand interval can be implemented, the upper and lower bounds of the demand interval are coupled by combining the idea of robust optimization and the strong duality theory, the upper / lower bound at time t-1 is used to determine the lower / upper bound at time t, constraint conditions are constructed for the independent and mutual coupling states of the upper / lower bound, the objective function of the operation reserve capacity is taken into account, and the optimization objective is designed as

[0208]

[0209] Wherein, P E,up (t) and P E,dn (t) are the external power purchase powers of the upper and lower bounds at time t, respectively; and D[P E,up (t)Δt-P E,dn (t)Δt] represents the sample variance of the difference between the upper and lower bounds at each time, that is, under the condition of ensuring the largest controllable area, the principle of equal reserve capacity when the large power grid is running is referred to, and the upward or downward controllable power at each time is as equal as possible.

[0210] The result unit 1204 is configured to obtain the upper and lower bounds of the autonomy of the micro-grid to the outside by solving the optimization objective function.

[0211] The sub-network autonomy analysis system considering the cooperation of distributed controllable resources in the preferred embodiment of the present application corresponds to the sub-network autonomy analysis method considering the cooperation of distributed controllable resources in another preferred embodiment of the present application, and will not be described here.

[0212] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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 code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

[0213] The present application is described in reference to the flowchart and / or block diagram of the method, apparatus (system) and computer program product according to an embodiment of the present application. It will be understood that each block of the flowchart and / or block diagram, and combinations of 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, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0214] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0215] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0216] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to cover all such modifications and variations as fall within the true spirit and scope of the application.

[0217] It is apparent that a person skilled in the art can make various changes and modifications to the application without departing from the spirit and scope thereof. Thus, if these modifications and variations fall within the scope of the patent claims and their equivalents, they are intended to be included therein.

[0218] The application has been described with reference to a limited number of embodiments. However, other embodiments known to those of ordinary skill in the art, and particularly in the field of the application, as well as equivalents thereof, are intended to be included within the scope of the application as defined by the appended claims.

[0219] 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" or "an" means "at least one" unless otherwise clearly indicated by the context of the only language "one or more". The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

Claims

1. A method for analyzing autonomous capability of a sub-network considering coordination of distributed controllable resources, the method comprising: establishing a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable and operating constraints; establishing a micro-grid overall optimization constraint model by considering micro-grid operation rules and operating constraints; coupling upper and lower bounds of a demand interval based on the distributed controllable resource model and the micro-grid overall optimization constraint model, taking into account consistency of operating reserve capacity of the micro-grid at each time, and constructing an optimization objective function, including: coupling the upper and lower bounds of the demand interval by considering that any strategy within the demand interval can be implemented, combining the idea of robust optimization and strong duality theory, and using the upper / lower bound of t-1 time to determine the lower / upper bound of t time, constructing constraint conditions for independent and mutual coupling states of the upper / lower bound, taking into account the objective function of operating reserve capacity, and designing the optimization objective as: solving the optimization objective function to obtain upper and lower bounds of autonomous capability of the micro-grid. 2.The method of claim 1, wherein the distributed controllable resource model is established by considering whether the distributed resources are controllable, adjustable and operating constraints, including: a distributed power generation model and constraint conditions are: a load model and constraint conditions are: energy storage is considered as daily regulation energy storage, and the default capacity is maximum discharge power*2h, an energy storage model and constraint conditions are: 3.The method of claim 1, wherein the micro-grid overall optimization constraint model is established by considering micro-grid operation rules and operating constraints, including: micro-grid micro-grid operation constraints are as shown in the formula: a renewable energy installed capacity ratio constraint is that a ratio PR of renewable energy installed power to peak load power is greater than or equal to 50%: an annual exchange electricity capacity ratio constraint is that an annual exchange electricity capacity ratio AEEP is: 4.The method of claim 1, wherein the distributed resources include wind power, photovoltaic, load and energy storage. 5.A system for analyzing autonomous capability of a sub-network considering coordination of distributed controllable resources, the system comprising: a first establishing unit configured to establish a distributed controllable resource model by considering whether the distributed resources are controllable, adjustable and operating constraints; a second establishing unit configured to establish a micro-grid overall optimization constraint model by considering micro-grid operation rules and operating constraints; a third establishing unit configured to couple upper and lower bounds of a demand interval based on the distributed controllable resource model and the micro-grid overall optimization constraint model, taking into account consistency of operating reserve capacity of the micro-grid at each time, and constructing an optimization objective function, and further configured to: couple the upper and lower bounds of the demand interval by considering that any strategy within the demand interval can be implemented, combining the idea of robust optimization and strong duality theory, and using the upper / lower bound of t-1 time to determine the lower / upper bound of t time, constructing constraint conditions for independent and mutual coupling states of the upper / lower bound, taking into account the objective function of operating reserve capacity, and designing the optimization objective as: a result unit configured to solve the optimization objective function to obtain upper and lower bounds of autonomous capability of the micro-grid. wherein P E,up (t) and P E,dn (t) are the external power purchase of the upper and lower boundary corresponding strategies at time t, respectively; 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 time, i.e. under the condition of ensuring the largest controllable area, the principle of equal reserve capacity when the reference large power grid is running is ensured, and the upward or downward controllable power at each time is as equal as possible; ​ ​ ​ where, ON pv (t), ON wd (t) are the number of operating photovoltaic and wind power plants at time t, respectively, N pv , N wd are the installed capacities of photovoltaic and wind power plants, respectively, n pv , n wd are the installed capacities of a single photovoltaic and wind power plant, respectively; P pv (t), P wd (t) are the theoretical outputs of photovoltaic and wind power, respectively, pv(t), wd(t) are the normalized theoretical outputs of photovoltaic and wind power, respectively. ​ wherein the load at time t, load(t), is composed of a fixed load, loadfix(t), a time- shiftable load, loadadj(t), and an interruptible load, loadint(t), N fix , N adj , N int , N fix , N adj,i , N int,j fix(t), adj(t), int(t) are the normalized power demand of the fixed load, the i-th time- shiftable load, and the j-th interruptible load, respectively. ​ wherein: is the state of charge of the energy storage device; is the maximum charge and discharge power; is the charge and discharge power of the energy storage device; s,t is the capacity of the energy storage device at time t; s,1 is the capacity of the energy storage device at time t; max is the maximum capacity of the energy storage system; s,T is the capacity of the energy storage system at the end of a typical day; c is the self-discharge rate of the energy storage device; d is the charge and discharge efficiency of the energy storage device. ​ ​ wherein, Pd and Pf are the power demand and the power feed-in of the microgrid, respectively, is positive, is negative, N tra is the maximum load demand of the microgrid; The power supply reliability constraint is that the outage rate cannot exceed the planned upper limit value f LPSP : Wherein: LPSP is the power failure rate; P loss (t) is the t period cut load; P load (t) is the t period net load; The renewable energy reduction rate constraint is that the renewable energy reduction rate in a year cannot exceed the planned upper limit value LRES max : Where: P loss,RE (t) represents the average power of renewable energy curtailment during time period t; ​ wherein N pv , N wd are the number of photovoltaic and wind power installations, respectively, n pv , n wd are the installed capacity of a single photovoltaic and wind power installation, respectively; load(t) is the load at time t. ​ ​ ​ ​ ​ ​ ​ P E,up (t) and P E,dn (t) are the external power purchased by the upper and lower boundary corresponding strategies at time t, respectively; 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 time, that is, under the condition of ensuring the largest controllable area, the principle of equal reserve capacity when the reference large power grid is running is ensured, and the upward or downward controllable power at each time is as equal as possible. ​ 6. The system of claim 5, wherein the first establishing unit is configured to establish a distributed controllable resource model by considering whether a distributed resource is controllable, adjustable, and operating constraints, and further configured to: The distributed generation model and constraints are as follows: wherein ON pv (t), ON wd (t) are the number of operating photovoltaic and wind power plants at time t, N pv , N wd are the installed capacity of photovoltaic and wind power, n pv , n wd are the installed capacity of a single photovoltaic and wind power plant; P pv (t), P wd (t) are the theoretical output of photovoltaic and wind power, pv(t), wd(t) are the normalized theoretical output of photovoltaic and wind power, respectively. The load model and constraints are as follows: Wherein, the load at time t is determined by the fixed load load. fix (t) Time-shiftable load adj (t) and interruptible load int (t) constitutes, N fix N adj,i N int,j Let be the maximum capacity 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 per-unit power requirements of the fixed load, the i-th time-shiftable load, and the j-th interruptible load, respectively. The storage is considered as a daily regulation storage, and the default capacity is the maximum discharge power*2h. The storage model and constraints are as follows: wherein: is the state of charge of the energy storage device; is the maximum charge and discharge power; is the charge and discharge 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 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 is the charge and discharge efficiency of the energy storage device.

7. The system of claim 5, wherein the second establishing unit is configured to establish a micro-grid overall optimization constraint model by considering micro-grid operation rules and operating constraints, and further configured to: The micro-grid operation constraints are as follows: wherein Pout and Pin are the power demand and the power feed-in of the microgrid, respectively, Pout > 0, Pout < 0, N tra Pmax is the maximum load demand of the microgrid. The power supply reliability constraint is that the outage rate cannot exceed the planned upper limit value f LPSP : Wherein: LPSP is the power failure rate; P loss (t) is the t period cut load; P load (t) is the t period net load; The renewable energy reduction rate constraint is that the renewable energy reduction rate in a year cannot exceed the planned upper limit value LRES max : Where: P loss,RE (t) represents the average power of renewable energy curtailment during time period t; The renewable energy installed capacity ratio constraint is that the ratio PR of renewable energy installed capacity to peak load power is greater than 50%: wherein N pv , N wd are the number of photovoltaic and wind power installations, respectively, n pv , n wd are the installed capacity of a single photovoltaic and wind power installation, respectively; load(t) is the load at time t. The annual exchange electricity capacity ratio constraint is that the annual exchange electricity capacity ratio AEEP is:

8. The system of claim 5, wherein the distributed resources include wind power, photovoltaic, load, and storage.

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