A method and device for evaluating the flexibility of a distribution network using distributed source-load-storage resources

Abstract

The present invention discloses a method and device for evaluating the flexibility of a distribution network using distributed source-load-storage resources. The method obtains the external flexibility supply index of the energy storage system through the energy storage system flexibility supply submodel; obtains the flexibility supply index provided by the temperature control load through the temperature control load flexibility supply submodel; obtains the electric vehicle flexibility supply index through the electric vehicle flexibility supply submodel; obtains the flexibility supply index of the distributed intelligent distribution network to the external distribution network through the distributed intelligent distribution network flexibility supply submodel; solves and optimizes the distributed intelligent distribution network optimization scheduling model, simulates the operation of the distributed intelligent distribution network, specifies the flexibility resource output according to the set time period obtained by the solution, calculates the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period, and completes the distributed intelligent distribution network flexibility evaluation. The present invention solves the problem that the existing evaluation method is not applicable to the distributed intelligent distribution network and the wind and solar power consumption is insufficient.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network flexibility assessment, and specifically relates to a method and device for assessing the flexibility of a distribution network using distributed source-load-storage resources. Background Art

[0002] Distributed smart distribution networks are characterized by accepting a high proportion of distributed new energy and adapting to bidirectional power flows. While consuming energy locally, they interact with external power grids to help further promote the construction of a new power system. The core of distributed smart distribution networks lies in the coordination and optimization of sources (new energy), loads (loads), and storage (energy storage). Clarifying its characteristics and evaluating its flexibility will help to better leverage the value of distributed smart distribution networks and promote their development.

[0003] At present, the value of the distributed smart distribution network as a flexible value resource to interact with other distribution networks is still unclear. Therefore, clarifying the characteristics of the distributed smart distribution network and evaluating the flexibility of the distributed smart distribution network will be conducive to the development of the distributed smart distribution network and enable the distributed smart distribution network to better play its own value. The research objects of traditional flexibility assessment are all traditional power systems, which take the fluctuation of wind and solar load as flexibility demand and evaluate the ability of the power system itself to meet such fluctuations. Distributed smart distribution networks are different from them. They can absorb wind and solar power locally and participate in the market externally. Therefore, traditional technical solutions are not suitable for the evaluation of the flexibility of distributed smart distribution networks. Summary of the invention

[0004] To this end, the present invention provides a method and device for evaluating the flexibility of distribution networks using distributed source, load and storage resources, which solves the current problem of lack of flexibility evaluation methods for distributed intelligent distribution networks and the problem that existing evaluation methods do not consider wind and solar power consumption, and emphasizes the role of flexibility resources such as energy storage systems, temperature control loads, and electric vehicles in improving the flexibility of distributed intelligent distribution networks.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a method for evaluating the flexibility of a distribution network using distributed source-load-storage resources, comprising:

[0006] Establish a distributed intelligent distribution network model, which includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; simulate the wind speed through the wind turbine sub-model and combine the wind turbine power characteristics to simulate the wind turbine output characteristics; simulate the output characteristics of the photovoltaic unit through the photovoltaic sub-model; simulate the energy storage system operation characteristics through the energy storage system sub-model; simulate the temperature control load operation characteristics through the temperature control load sub-model; and simulate the electric vehicle flexibility output characteristics through the electric vehicle sub-model;

[0007] Based on the distributed intelligent distribution network model, a distributed intelligent distribution network flexibility supply model is constructed; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply sub-model; the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load are obtained through the temperature control load flexibility supply sub-model; the electric vehicle flexibility supply index is obtained through the electric vehicle flexibility supply sub-model; the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model;

[0008] Based on the distributed intelligent distribution network model, a distributed intelligent distribution network optimization scheduling model is constructed. The distributed intelligent distribution network optimization scheduling model takes the lowest operating cost of the distributed intelligent distribution network as the objective function. The constraints of the distributed intelligent distribution network optimization scheduling model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints and power balance constraints that meet the full absorption of wind and solar power in the distributed intelligent distribution network. By solving and optimizing the distributed intelligent distribution network optimization scheduling model, the operation of the distributed intelligent distribution network is simulated, and the flexibility resource output is specified according to the set time period obtained by the solution, and the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated to complete the flexibility evaluation of the distributed intelligent distribution network.

[0009] As a preferred method for evaluating the flexibility of the distribution network by distributed source-load-storage resources, the wind turbine sub-model simulates the wind speed and combines the wind turbine power characteristics to simulate the wind turbine output characteristics. The expression is:

[0010] ;

[0011] In the formula, A, B, and C are the parameters of the wind turbine power characteristic curve; v is the actual wind speed; is the rated power of the wind turbine; is the cut-in wind speed; is the rated wind speed; Cut out wind speed;

[0012] The expression for simulating the output characteristics of the photovoltaic unit through the photovoltaic sub-model is:

[0013] ;

[0014] In the formula, The actual output of the photovoltaic unit; is the rated output of the photovoltaic unit; is the actual irradiance; is the power temperature coefficient of the photovoltaic unit; is the temperature of the device in the photovoltaic system; is the standard temperature; is the irradiance under standard test conditions; The temperature is under standard test conditions.

[0015] As a preferred method for evaluating the flexibility of the distribution network using distributed source-load-storage resources, the expression for simulating the operating characteristics of the energy storage system through the energy storage system submodel is:

[0016] ;

[0017] In the formula, For energy storage systems t Discharge power at the moment; is the maximum discharge power of the energy storage system; For energy storage systems t Charging power at the moment; is the maximum charging power of the energy storage system; are two binary integer variables used to represent the charge and discharge status; For energy storage systems t Capacity at the moment; represents the capacity of the energy storage system in the previous period; τ represents the time interval; are the charging and discharging efficiency coefficients of the energy storage system respectively; They are the lower and upper limits of the energy storage system capacity respectively.

[0018] As a preferred solution for the flexibility evaluation method of the distributed source-load-storage resources for the distribution network, the expression for simulating the operating characteristics of the temperature-controlled load through the temperature-controlled load sub-model is:

[0019] ;

[0020] In the formula, The temperature control load is t Indoor temperature and ambient temperature at the moment, are the thermal resistance and thermal capacity of the temperature control load respectively. is the cooling / heating coefficient of the temperature control load, is the coefficient related to the time constant, is the time constant; is the product coefficient of thermal resistance and cooling / heating coefficient, For temperature control load t The power of the moment, is the maximum power of the temperature control load, The temperature set by the user, The tolerable temperature fluctuation value set by the user;

[0021] The expression for simulating the flexible output characteristics of electric vehicles through the electric vehicle sub-model is:

[0022] ;

[0023] In the formula, Respectively i Electric cars in t The charging and discharging power of the time period, , Respectively i The upper limit of charging and discharging power of electric vehicles. is a binary integer variable, For the i Electric vehicles in t The battery capacity of the time period, are the time when electric vehicles enter and leave the grid, are the charging and discharging efficiency coefficients of electric vehicles, and They are the minimum and maximum capacity of electric vehicle batteries, the off-grid capacity required for car owners to travel, and the expected off-grid capacity set by users.

[0024] As a preferred solution for the flexibility evaluation method of distributed source-load-storage resources for distribution networks, the expression of the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply sub-model:

[0025] ;

[0026] In the formula, The energy storage system is t Upward and downward flexibility provided at all times;

[0027] The expressions of the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load obtained by the temperature control load flexibility supply sub-model are:

[0028] ;

[0029] In the formula, For temperature control load t Upward and downward flexibility provided at all times.

[0030] The expression of the electric vehicle flexibility supply index obtained by the electric vehicle flexibility supply sub-model is:

[0031] ;

[0032] In the formula, For the i Electric cars in t The upward and downward flexibility of the time period is provided. t ≠lea means online time, t =lea indicates the time of leaving the grid;

[0033] The expressions of the upward and downward flexibility supply indicators of the distributed smart distribution network to the external distribution network obtained by the distributed smart distribution network flexibility supply sub-model are as follows:

[0034] ;

[0035] In the formula, Provides upward and downward flexibility for distributed intelligent distribution networks to external distribution networks.

[0036] As a preferred solution for the flexibility evaluation method of the distributed source-load-storage resources for the distribution network, the objective function expression of the distributed intelligent distribution network optimization scheduling model is:

[0037] ;

[0038] In the formula, is the operating cost of the energy storage system, is the operating cost of the temperature control load, The operating cost of electric vehicles, is the network loss cost, is the unit operating cost of the energy storage system, is the unit operating cost of the temperature control load, is the unit operating cost of electric vehicles, For energy storage systems t- Discharge power at moment 1; For energy storage systems t- Charging power at 1 moment, For temperature control load t- Power at 1 moment;

[0039] The constraint expression of the distributed intelligent distribution network optimization dispatching model is:

[0040] ;

[0041] In the formula, It is the generator output sequence of the distributed intelligent distribution network. They are the photovoltaic and wind turbine output sequences, The charging and discharging power sequence of the energy storage system. For the i The charging and discharging power sequence of an electric vehicle, is the temperature control load power sequence, It is the load sequence of distributed smart distribution network.

[0042] The present invention also provides a device for evaluating the flexibility of a distribution network using distributed source-load-storage resources, comprising:

[0043] A distributed intelligent distribution network model analysis module is used to establish a distributed intelligent distribution network model, wherein the distributed intelligent distribution network model includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; the wind turbine sub-model simulates the wind speed and combines the wind turbine power characteristics to simulate the wind turbine output characteristics; the photovoltaic sub-model simulates the output characteristics of the photovoltaic unit; the energy storage system sub-model simulates the energy storage system operation characteristics; the temperature control load sub-model simulates the temperature control load operation characteristics; the electric vehicle sub-model simulates the electric vehicle flexibility output characteristics;

[0044] A distribution network flexibility supply index analysis module is used to construct a distributed intelligent distribution network flexibility supply model based on the distributed intelligent distribution network model; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply sub-model; the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load are obtained through the temperature control load flexibility supply sub-model; the electric vehicle flexibility supply index is obtained through the electric vehicle flexibility supply sub-model; the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model;

[0045] The distribution network optimization and scheduling simulation analysis module is used to construct a distributed intelligent distribution network optimization and scheduling model based on the distributed intelligent distribution network model. The distributed intelligent distribution network optimization and scheduling model takes the lowest operating cost of the distributed intelligent distribution network as the objective function. The constraints of the distributed intelligent distribution network optimization and scheduling model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints and power balance constraints that meet the full absorption of wind and solar power in the distributed intelligent distribution network. By solving and optimizing the distributed intelligent distribution network optimization and scheduling model, the operation of the distributed intelligent distribution network is simulated, and the flexibility resource output is specified according to the set time period obtained by the solution, and the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated to complete the flexibility evaluation of the distributed intelligent distribution network.

[0046] As a preferred solution for the flexibility evaluation device of the distributed source-load-storage resource for the distribution network, in the distributed intelligent distribution network model analysis module, the wind speed is simulated by the wind turbine sub-model and combined with the wind turbine power characteristics to simulate the wind turbine output characteristics. The expression is:

[0047] ;

[0048] In the formula, A, B, and C are the parameters of the wind turbine power characteristic curve; v is the actual wind speed; is the rated power of the wind turbine; is the cut-in wind speed; is the rated wind speed; Cut out wind speed;

[0049] In the distributed intelligent distribution network model analysis module, the expression for simulating the output characteristics of the photovoltaic unit through the photovoltaic sub-model is:

[0050] ;

[0051] In the formula, The actual output of the photovoltaic unit; is the rated output of the photovoltaic unit; is the actual irradiance; is the power temperature coefficient of the photovoltaic unit; is the temperature of the device in the photovoltaic system; is the standard temperature; is the irradiance under standard test conditions; is the temperature under standard test conditions;

[0052] In the distributed intelligent distribution network model analysis module, the expression for simulating the operating characteristics of the energy storage system through the energy storage system submodel is:

[0053] ;

[0054] In the formula, For energy storage systems t Discharge power at the moment; is the maximum discharge power of the energy storage system; For energy storage systems t Charging power at the moment; is the maximum charging power of the energy storage system; are two binary integer variables used to represent the charge and discharge status; For energy storage systems t Capacity at the moment; represents the capacity of the energy storage system in the previous period; τ represents the time interval; are the charging and discharging efficiency coefficients of the energy storage system respectively; They are the lower and upper limits of the energy storage system capacity respectively;

[0055] In the distributed intelligent distribution network model analysis module, the expression for simulating the operating characteristics of the temperature control load through the temperature control load sub-model is:

[0056] ;

[0057] In the formula, The temperature control load is t Indoor temperature and ambient temperature at the moment, are the thermal resistance and thermal capacity of the temperature control load respectively. is the cooling / heating coefficient of the temperature control load, is the coefficient related to the time constant, is the time constant; is the product coefficient of thermal resistance and cooling / heating coefficient, For temperature control load t The power of the moment, is the maximum power of the temperature control load, The temperature set by the user, The tolerable temperature fluctuation value set by the user;

[0058] In the distributed intelligent distribution network model analysis module, the expression for simulating the flexible output characteristics of electric vehicles through the electric vehicle sub-model is:

[0059] ;

[0060] In the formula, Respectively i Electric cars in t The charging and discharging power of the time period, , Respectively i The upper limit of charging and discharging power of electric vehicles. is a binary integer variable, For the i Electric vehicles in t The battery capacity of the time period, are the time when electric vehicles enter and leave the grid, are the charging and discharging efficiency coefficients of electric vehicles, and They are the minimum and maximum capacity of electric vehicle batteries, the off-grid capacity required for car owners to travel, and the expected off-grid capacity set by users.

[0061] As a preferred solution of the distributed source-load-storage resource flexibility evaluation device for the distribution network, in the distribution network flexibility supply index analysis module, the expression of the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply submodel:

[0062] ;

[0063] In the formula, The energy storage system is t Upward and downward flexibility provided at all times;

[0064] In the distribution network flexibility supply index analysis module, the expressions of the upward flexibility supply index and the downward flexibility supply index provided by the temperature-controlled load are obtained through the temperature-controlled load flexibility supply sub-model:

[0065] ;

[0066] In the formula, For temperature control load t Upward and downward flexibility provided at all times.

[0067] In the distribution network flexibility supply index analysis module, the expression of the electric vehicle flexibility supply index obtained by the electric vehicle flexibility supply sub-model is:

[0068] ;

[0069] In the formula, For the i Electric cars in t The upward and downward flexibility of the time period is provided. t ≠lea means online time, t =lea indicates the time of leaving the grid;

[0070] In the distribution network flexibility supply index analysis module, the expressions of the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model:

[0071] ;

[0072] In the formula, Provides upward and downward flexibility for distributed intelligent distribution networks to external distribution networks.

[0073] As a preferred solution for the distributed source-load-storage resource flexibility evaluation device for the distribution network, in the distribution network optimization scheduling simulation analysis module, the objective function expression of the distributed intelligent distribution network optimization scheduling model is:

[0074] ;

[0075] In the formula, is the operating cost of the energy storage system, is the operating cost of the temperature control load, The operating cost of electric vehicles, is the network loss cost, is the unit operating cost of the energy storage system, is the unit operating cost of the temperature control load, is the unit operating cost of electric vehicles, For energy storage systems t- Discharge power at moment 1; For energy storage systems t- Charging power at 1 moment, For temperature control load t- Power at 1 moment;

[0076] In the distribution network optimization dispatch simulation analysis module, the constraint expression of the distributed intelligent distribution network optimization dispatch model is:

[0077] ;

[0078] In the formula, It is the generator output sequence of the distributed intelligent distribution network. They are the photovoltaic and wind turbine output sequences, The charging and discharging power sequence of the energy storage system. For the i The charging and discharging power sequence of an electric vehicle, is the temperature control load power sequence, It is the load sequence of distributed smart distribution network.

[0079] The present invention has the following advantages: establishing a distributed intelligent distribution network model, the distributed intelligent distribution network model includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; simulating the wind speed through the wind turbine sub-model and combining the wind turbine power characteristics to simulate the wind turbine output characteristics; simulating the output characteristics of the photovoltaic unit through the photovoltaic sub-model; simulating the energy storage system operation characteristics through the energy storage system sub-model; simulating the temperature control load operation characteristics through the temperature control load sub-model; simulating the electric vehicle flexibility output characteristics through the electric vehicle sub-model; constructing a distributed intelligent distribution network flexibility supply model based on the distributed intelligent distribution network model; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; obtaining the external flexibility supply index of the energy storage system through the energy storage system flexibility supply sub-model; obtaining the external flexibility supply index of the energy storage system through the temperature control load flexibility supply sub-model The upward flexibility supply index and the downward flexibility supply index provided to the temperature-controlled load are obtained; the electric vehicle flexibility supply index is obtained through the electric vehicle flexibility supply sub-model; the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model; based on the distributed intelligent distribution network model, a distributed intelligent distribution network optimization scheduling model is constructed, the distributed intelligent distribution network optimization scheduling model takes the lowest operating cost of the distributed intelligent distribution network as the objective function, and the constraints of the distributed intelligent distribution network optimization scheduling model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints and power balance constraints that meet the full absorption of wind and solar power in the distributed intelligent distribution network; by solving and optimizing the distributed intelligent distribution network optimization scheduling model, the distributed intelligent distribution network operation is simulated, the flexibility resource output is specified according to the set time period obtained by the solution, the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated, and the flexibility evaluation of the distributed intelligent distribution network is completed. Under the premise of achieving complete local consumption, the distributed intelligent distribution network has the potential to provide a certain degree of flexibility, thereby effectively solving the problem that the existing evaluation technology is not suitable for distributed intelligent distribution networks and the problem of insufficient wind and solar power consumption, which is conducive to the development of distributed intelligent distribution networks and enables distributed intelligent distribution networks to better play their own value. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0081] Figure 1 A schematic flow chart of a method for evaluating the flexibility of a distribution network using distributed source-load-storage resources provided in an embodiment of the present invention;

[0082] Figure 2 A schematic diagram of the flexibility evaluation results of a distributed smart distribution network provided in an embodiment of the present invention;

[0083] Figure 3 This is the evaluation result of the distributed smart distribution network under 70% renewable energy penetration rate provided in the embodiment of the present invention;

[0084] Figure 4 This is an architecture diagram of a device for evaluating the flexibility of a distribution network using distributed source-load-storage resources provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] Example 1

[0087] See also Figure 1 The embodiment of the present invention provides a method for evaluating the flexibility of a distribution network using distributed source-load-storage resources, comprising the following steps:

[0088] S1. Establish a distributed intelligent distribution network model, which includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; simulate the wind speed through the wind turbine sub-model and combine the wind turbine power characteristics to simulate the wind turbine output characteristics; simulate the output characteristics of the photovoltaic unit through the photovoltaic sub-model; simulate the energy storage system operation characteristics through the energy storage system sub-model; simulate the temperature control load operation characteristics through the temperature control load sub-model; and simulate the electric vehicle flexibility output characteristics through the electric vehicle sub-model;

[0089] S2. Based on the distributed intelligent distribution network model, a distributed intelligent distribution network flexibility supply model is constructed; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; the energy storage system flexibility supply sub-model is used to obtain the external flexibility supply index of the energy storage system; the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load are obtained through the temperature control load flexibility supply sub-model; the electric vehicle flexibility supply sub-model is used to obtain the electric vehicle flexibility supply index; the distributed intelligent distribution network flexibility supply sub-model is used to obtain the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network;

[0090] S3. Based on the distributed intelligent distribution network model, a distributed intelligent distribution network optimization scheduling model is constructed. The distributed intelligent distribution network optimization scheduling model takes the lowest operating cost of the distributed intelligent distribution network as the objective function. The constraints of the distributed intelligent distribution network optimization scheduling model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints and power balance constraints that meet the full absorption of wind and solar power in the distributed intelligent distribution network. By solving and optimizing the distributed intelligent distribution network optimization scheduling model, the operation of the distributed intelligent distribution network is simulated, and the flexibility resource output is specified according to the set time period obtained by the solution, and the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated to complete the flexibility evaluation of the distributed intelligent distribution network.

[0091] Although a large number of distributed energy sources are connected to the distributed intelligent distribution network, it also includes flexible resources such as energy storage systems, temperature control loads, and electric vehicles, which can be consumed locally, so that it can interact with the upper power grid. The operating characteristics of the distributed intelligent distribution network are mainly composed of the wind and solar output characteristics and the operating characteristics of different flexible resources. Therefore, the present invention constructs a distributed intelligent distribution network model based on the wind and solar output characteristics and the operating characteristics of flexible resources.

[0092] In this embodiment, in step S1, the wind power output itself has the characteristics of intermittent, fluctuating and random. When describing these characteristics, the auto-regressive moving average (ARMA) model is used to simulate the wind speed. Based on the simulated wind speed, combined with the wind turbine power characteristics, the wind turbine output characteristics can be obtained. The wind turbine output characteristics are simulated by simulating the wind speed through the wind turbine sub-model and combining the wind turbine power characteristics. The expression is:

[0093] ;

[0094] In the formula, A, B, and C are the parameters of the wind turbine power characteristic curve, which vary according to different wind turbines and can be expressed by formula (2); v is the actual wind speed; is the rated power of the wind turbine; is the cut-in wind speed; is the rated wind speed; Cut out wind speed.

[0095] In step S1, since the output of the photovoltaic system is mainly affected by irradiance and temperature, the expression for simulating the output characteristics of the photovoltaic system through the photovoltaic sub-model is:

[0096] ;

[0097] In the formula, The actual output of the photovoltaic unit; is the rated output of the photovoltaic unit; is the actual irradiance; is the power temperature coefficient of the photovoltaic unit; is the temperature of the device in the photovoltaic system; is the standard temperature; is the irradiance under standard test conditions; The temperature is under standard test conditions.

[0098] During operation, the power system will have flexibility demands for up and down adjustments to adapt to fluctuations on the power generation side and the load side. The upward flexibility generally refers to the need for the system to increase power generation to meet the load demand when the load demand of the power system suddenly increases or the output of renewable energy is insufficient. The downward flexibility generally refers to the need for the system to reduce power generation to avoid energy waste when the load demand of the power system decreases or there is excess power generation from renewable energy. Various types of flexibility resources in the distributed intelligent distribution network will meet its flexibility demands for up and down adjustments. The energy storage system is different from traditional power generation and consumption equipment, and it has charging and discharging characteristics. When the power system needs to adjust its flexibility upward, the energy storage system discharges or reduces the charging power to meet the upward fluctuation of the net load; when the power system needs to adjust its flexibility downward, the energy storage system charges or reduces the discharge power to meet the downward fluctuation of the net load. Therefore, in this embodiment, in step S1, the expression for simulating the operating characteristics of the energy storage system through the energy storage system submodel is:

[0099] ;

[0100] In the formula, For energy storage systems t Discharge power at the moment; is the maximum discharge power of the energy storage system; For energy storage systems t Charging power at the moment; is the maximum charging power of the energy storage system; are two binary integer variables used to represent the charge and discharge status; For energy storage systems t Capacity at the moment; represents the capacity of the energy storage system in the previous period; τ represents the time interval; are the charging and discharging efficiency coefficients of the energy storage system respectively; They are the lower and upper limits of the energy storage system capacity respectively.

[0101] Temperature control loads, such as air conditioners and refrigerators, are different from energy storage systems. They are flexible resources with one-way energy exchange and are widely considered to have great potential to support the operation of power systems. Temperature control loads are usually adjusted within the range allowed by load users. Temperature control loads are called to provide flexibility without affecting the user's comfort. Therefore, in this embodiment, in step S1, the expression for simulating the operating characteristics of the temperature control load through the temperature control load submodel is:

[0102] ;

[0103] In the formula, The temperature control load is t Indoor temperature and ambient temperature at the moment, are the thermal resistance and thermal capacity of the temperature control load respectively. is the cooling / heating coefficient of the temperature control load, is the coefficient related to the time constant, is the time constant; is the product coefficient of thermal resistance and cooling / heating coefficient, For temperature control load t The power of the moment, is the maximum power of the temperature control load, The temperature set by the user, The tolerable temperature fluctuation value set by the user.

[0104] Since the dual carbon goals were proposed, electric vehicles have developed rapidly due to their environmental protection and energy-saving characteristics. At the same time, existing studies have shown that the number of electric vehicles has a trend of increasing. A large number of electric vehicles connected to the power grid can become part of the new power system. At the same time, due to the popularization of V2G technology, it is possible for electric vehicles to become a new type of flexible resource. In terms of energy storage form, electric vehicles and energy storage equipment are similar, but electric vehicles are a means of transportation, and their access and off-grid time are closely related to the travel needs of the owner. At the same time, the battery capacity of electric vehicles is also constrained by the subjective behavior of the owner, and the electric vehicle needs to be charged and discharged on the premise of meeting the owner's travel needs.

[0105] To simplify the problem, in this embodiment, in step S1, it is assumed that the owner sets the scheduled departure time and the expected off-grid power before charging, and the owner does not intervene in the electric vehicle during the charging and discharging process of the electric vehicle. The expression for simulating the flexible output characteristics of the electric vehicle through the electric vehicle sub-model is:

[0106] ;

[0107] In the formula, Respectively i Electric cars in t The charging and discharging power of the time period, , Respectively i The upper limit of charging and discharging power of electric vehicles. is a binary integer variable, For the i Electric vehicles in t The battery capacity of the time period, are the time when electric vehicles enter and leave the grid, are the charging and discharging efficiency coefficients of electric vehicles, and They are the minimum and maximum capacity of electric vehicle batteries, the off-grid capacity required for car owners to travel, and the expected off-grid capacity set by users.

[0108] In this embodiment, in step S2, the energy storage system performs flexibility supply by changing the charging and discharging power. After the distributed intelligent distribution network itself is running, the change in the charging and discharging power is used as the external flexibility supply of the energy storage system. The expression of the external flexibility supply index of the energy storage system obtained by the energy storage system flexibility supply submodel is:

[0109] ;

[0110] In the formula, The energy storage system is t Upward and downward flexibility provided at all times;

[0111] Specifically, in step S2, the temperature control load provides upward flexibility by reducing the load amount, and provides downward flexibility by increasing the load amount. When calculating the upward and downward flexibility, the temperature change range set by the user needs to be considered. The expressions of the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load are obtained through the temperature control load flexibility supply submodel:

[0112] ;

[0113] ;

[0114] In the formula, For temperature control load t Upward and downward flexibility provided at all times.

[0115] In step S2, electric vehicles are similar to energy storage systems, and flexibility is provided by changing the charging and discharging power. However, while considering the time when electric vehicles are connected to and disconnected from the grid, the travel needs of electric vehicle users must also be met. The expression of the electric vehicle flexibility supply index obtained through the electric vehicle flexibility supply submodel is:

[0116] ;

[0117] In the formula, For the i Electric cars in t The upward and downward flexibility of the time period is provided. t ≠lea means online time, t =lea represents the off-grid moment; when an electric vehicle is off-grid, the off-grid capacity set by the electric vehicle user must be considered when calculating the upward flexibility supply.

[0118] Specifically, in step S2, after the distributed smart distribution network itself is running, the output that can still be provided by the flexible resources such as the energy storage system, temperature control load, and electric vehicles in the distributed smart distribution network constitutes the flexibility provided by the distributed smart distribution network to the upper main network. The expressions of the upward and downward flexibility supply indicators of the distributed smart distribution network to the external distribution network are obtained through the distributed smart distribution network flexibility supply sub-model:

[0119] ;

[0120] In the formula, Provides upward and downward flexibility for distributed intelligent distribution networks to external distribution networks.

[0121] In this embodiment, in step S3, the distributed intelligent distribution network optimization scheduling model is constructed with the lowest operating cost of the distributed intelligent distribution network as the objective function. The objective function of the distributed intelligent distribution network optimization scheduling model is shown in formula (26). The constraints include the above-mentioned operating constraints of the flexibility resources and the power balance constraint that satisfies the complete absorption of wind and solar power in the distributed intelligent distribution network. Among them, the operation of the distributed intelligent distribution network is simulated by solving the optimization scheduling model, and the output of the flexibility resources is specified according to the set time period obtained by the solution, and the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated to complete the flexibility evaluation of the distributed intelligent distribution network. According to the output of each flexibility resource in each time period obtained by the solution, the upward and downward flexibility potential of the distributed intelligent distribution network in each time period is calculated to complete the flexibility evaluation of the distributed intelligent distribution network. The above-mentioned optimization problem is solved by the CPLEX solver after the second-order cone transformation to complete the operation status simulation process of the distributed intelligent distribution network, and then the calculation is completed in combination with the flexibility supply index of step S2 to realize the flexibility evaluation of the distributed intelligent distribution network.

[0122] Specifically, the objective function expression of the distributed intelligent distribution network optimization scheduling model is:

[0123] ;

[0124] In the formula, is the operating cost of the energy storage system, is the operating cost of the temperature control load, The operating cost of electric vehicles, is the network loss cost, is the unit operating cost of the energy storage system, is the unit operating cost of the temperature control load, is the unit operating cost of electric vehicles, For energy storage systems t- Discharge power at moment 1; For energy storage systems t- Charging power at 1 moment, For temperature control load t- Power at 1 moment;

[0125] Among them, power balance constraints are added to ensure local consumption of wind and solar power and actively meet the operation requirements of the power grid. The constraint expression of the distributed intelligent distribution network optimization scheduling model is:

[0126] (27);

[0127] In the formula, It is the generator output sequence of the distributed intelligent distribution network. They are the photovoltaic and wind turbine output sequences, The charging and discharging power sequence of the energy storage system. For the i The charging and discharging power sequence of an electric vehicle, is the temperature control load power sequence, It is the load sequence of distributed smart distribution network.

[0128] The effects of the present invention are described below by examples:

[0129] The present invention sets the new energy penetration rate of the distributed intelligent distribution network to 40%, and simulates the operation of the distributed intelligent distribution network under this condition. According to the set time period obtained by solving, the flexibility resource output is specified, the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated, the flexibility evaluation of the distributed intelligent distribution network is completed, and the flexibility supply of the system is calculated. The upward and downward flexibility supply of each time period is obtained. Figure 2 shown.

[0130] Under the premise of meeting the local consumption of wind and solar power, the upward flexibility of the system is mainly concentrated in 0:00-7:00 and 17:00-22:00, and the upward flexibility is mainly provided by the energy storage system, and the overall flexibility supply output ranges from 0.1MW to 0.6MW. This is because during the night and evening periods, the output of new energy decreases, the net load level increases, and the flexibility resources are mostly in the external discharge state, so upward flexibility supply is provided to the outside at this time. During the period of 0:00-3:00, the overall load level is low, and electric vehicles can enter the charging state to provide downward flexibility and meet the discharge needs after the net load increases in the next period. From the perspective of downward flexibility, the downward flexibility of the system is mainly concentrated in 9:00-16:00, and the downward flexibility is mainly provided by the temperature control load and the energy storage system. The overall flexibility supply output ranges from 0.05MW to 0.5MW. This is because the net load level decreases and the flexibility resources enter the charging state to meet the needs at night, so at this time, the distributed intelligent distribution network mostly provides downward flexibility.

[0131] If the penetration rate of renewable energy increases to 70%, the evaluation results will be as follows: Figure 3As shown in the figure, the upward and downward flexibility supply of the distributed smart distribution network in each period has decreased, and the highest upward flexibility has decreased from 0.6MW to less than 0.4MW, with a decrease of more than 30%. At the same time, the output volatility of each flexible resource has increased. From 11:00 to 15:00, in order to absorb the output of renewable energy, the downward flexibility supply of temperature control load and energy storage system has decreased. At the same time, from 17:00 to 18:00, the downward flexibility supply almost disappeared. This is because the output of renewable energy is at a low level during this period, and more flexible resources are required to participate in the discharge, resulting in a decrease in downward flexibility supply. It can be seen that in the distributed smart distribution network characterized by a high proportion of renewable energy access, the size of its external participation in the power market flexibility supply is greatly affected by the penetration rate of renewable energy. Although wind and solar abandonment can be used as a means of flexibility adjustment, the effect is limited and seriously affects the flexibility evaluation results. Therefore, the distributed smart distribution network requires the participation of multiple flexibility resources to improve its external flexibility supply.

[0132] In summary, the present invention establishes a distributed intelligent distribution network model, which includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; the wind turbine sub-model simulates the wind speed and combines the wind turbine power characteristics to simulate the wind turbine output characteristics; the photovoltaic sub-model simulates the output characteristics of the photovoltaic unit; the energy storage system sub-model simulates the energy storage system operation characteristics; the temperature control load sub-model simulates the temperature control load operation characteristics; the electric vehicle sub-model simulates the electric vehicle flexibility output characteristics; based on the distributed intelligent distribution network model, a distributed intelligent distribution network flexibility supply model is constructed; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; the energy storage system flexibility supply sub-model is used to obtain the external flexibility supply index of the energy storage system; the temperature control load flexibility supply sub-model is used to obtain The upward flexibility supply index and the downward flexibility supply index provided by the temperature-controlled load; the electric vehicle flexibility supply index is obtained through the electric vehicle flexibility supply sub-model; the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model; based on the distributed intelligent distribution network model, a distributed intelligent distribution network optimization scheduling model is constructed, the distributed intelligent distribution network optimization scheduling model takes the lowest operating cost of the distributed intelligent distribution network as the objective function, and the constraints of the distributed intelligent distribution network optimization scheduling model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints and power balance constraints that meet the full absorption of wind and solar power in the distributed intelligent distribution network; by solving and optimizing the distributed intelligent distribution network optimization scheduling model, the distributed intelligent distribution network operation is simulated, the flexibility resource output is specified according to the set time period obtained by the solution, the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated, and the flexibility evaluation of the distributed intelligent distribution network is completed. Under the premise of achieving complete local consumption, the distributed intelligent distribution network has the potential to provide a certain degree of flexibility, thereby effectively solving the problem that the existing evaluation technology is not suitable for distributed intelligent distribution networks and the problem of insufficient wind and solar power consumption, which is conducive to the development of distributed intelligent distribution networks and enables distributed intelligent distribution networks to better play their own value.

[0133] Example 2

[0134] See also Figure 4 Embodiment 2 of the invention further provides a device for evaluating the flexibility of a distribution network using distributed source-load-storage resources, including:

[0135] The distributed intelligent distribution network model analysis module 100 is used to establish a distributed intelligent distribution network model, wherein the distributed intelligent distribution network model includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; the wind turbine sub-model simulates the wind speed and combines the wind turbine power characteristics to simulate the wind turbine output characteristics; the photovoltaic sub-model simulates the output characteristics of the photovoltaic unit; the energy storage system sub-model simulates the energy storage system operation characteristics; the temperature control load sub-model simulates the temperature control load operation characteristics; the electric vehicle sub-model simulates the electric vehicle flexibility output characteristics;

[0136] The distribution network flexibility supply index analysis module 200 is used to construct a distributed intelligent distribution network flexibility supply model based on the distributed intelligent distribution network model; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply sub-model; the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load are obtained through the temperature control load flexibility supply sub-model; the electric vehicle flexibility supply index is obtained through the electric vehicle flexibility supply sub-model; the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model;

[0137] The distribution network optimization scheduling simulation analysis module 300 is used to construct a distributed intelligent distribution network optimization scheduling model based on the distributed intelligent distribution network model. The distributed intelligent distribution network optimization scheduling model takes the lowest operating cost of the distributed intelligent distribution network as the objective function. The constraints of the distributed intelligent distribution network optimization scheduling model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints and power balance constraints that meet the full absorption of wind and solar power in the distributed intelligent distribution network; by solving and optimizing the distributed intelligent distribution network optimization scheduling model, the distributed intelligent distribution network operation situation is simulated, and the flexibility resource output is specified according to the set time period obtained by the solution, and the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated to complete the distributed intelligent distribution network flexibility evaluation.

[0138] In this embodiment, in the distributed intelligent distribution network model analysis module 100, the wind speed is simulated by the wind turbine sub-model and combined with the wind turbine power characteristics to simulate the wind turbine output characteristics. The expression is:

[0139] ;

[0140] In the formula, A, B, and C are the parameters of the wind turbine power characteristic curve;v is the actual wind speed; is the rated power of the wind turbine; is the cut-in wind speed; is the rated wind speed; Cut out wind speed;

[0141] In the distributed intelligent distribution network model analysis module 100, the expression for simulating the output characteristics of the photovoltaic unit through the photovoltaic sub-model is:

[0142] ;

[0143] In the formula, The actual output of the photovoltaic unit; is the rated output of the photovoltaic unit; is the actual irradiance; is the power temperature coefficient of the photovoltaic unit; is the temperature of the device in the photovoltaic system; is the standard temperature; is the irradiance under standard test conditions; is the temperature under standard test conditions;

[0144] In the distributed intelligent distribution network model analysis module 100, the expression for simulating the operating characteristics of the energy storage system through the energy storage system submodel is:

[0145] ;

[0146] In the formula, For energy storage systems t Discharge power at the moment; is the maximum discharge power of the energy storage system; For energy storage systems t Charging power at the moment; is the maximum charging power of the energy storage system; are two binary integer variables used to represent the charge and discharge status; For energy storage systems t Capacity at the moment; represents the capacity of the energy storage system in the previous period; τ represents the time interval; are the charging and discharging efficiency coefficients of the energy storage system respectively; They are the lower and upper limits of the energy storage system capacity respectively;

[0147] In the distributed intelligent distribution network model analysis module 100, the expression for simulating the operating characteristics of the temperature control load through the temperature control load sub-model is:

[0148] ;

[0149] In the formula, The temperature control load is t Indoor temperature and ambient temperature at the moment, are the thermal resistance and thermal capacity of the temperature control load respectively. is the cooling / heating coefficient of the temperature control load, is the coefficient related to the time constant, is the time constant; is the product coefficient of thermal resistance and cooling / heating coefficient, For temperature control load t The power of the moment, is the maximum power of the temperature control load, The temperature set by the user, The tolerable temperature fluctuation value set by the user;

[0150] In the distributed intelligent distribution network model analysis module 100, the expression for simulating the flexible output characteristics of electric vehicles through the electric vehicle sub-model is:

[0151] ;

[0152] In the formula, Respectively i Electric cars in t The charging and discharging power of the time period, , Respectively i The upper limit of charging and discharging power of electric vehicles. is a binary integer variable, For the i Electric vehicles in t The battery capacity of the time period, are the time when electric vehicles enter and leave the grid, are the charging and discharging efficiency coefficients of electric vehicles, and They are the minimum and maximum capacity of electric vehicle batteries, the off-grid capacity required for car owners to travel, and the expected off-grid capacity set by users.

[0153] In this embodiment, in the distribution network flexibility supply index analysis module 200, the expression of the external flexibility supply index of the energy storage system obtained by the energy storage system flexibility supply sub-model is:

[0154] ;

[0155] In the formula, The energy storage system is t Upward and downward flexibility provided at all times;

[0156] In the distribution network flexibility supply index analysis module 200, the expressions of the upward flexibility supply index and the downward flexibility supply index provided by the temperature-controlled load are obtained through the temperature-controlled load flexibility supply sub-model:

[0157] ;

[0158] In the formula, For temperature control load t Upward and downward flexibility provided at all times.

[0159] In the distribution network flexibility supply index analysis module 200, the expression of the electric vehicle flexibility supply index obtained by the electric vehicle flexibility supply sub-model is:

[0160] ;

[0161] In the formula, For the i Electric cars in t The upward and downward flexibility of the time period is provided. t ≠lea means online time, t =lea indicates the time of leaving the grid;

[0162] In the distribution network flexibility supply index analysis module 200, the expressions of the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model:

[0163] ;

[0164] In the formula, Provides upward and downward flexibility for distributed intelligent distribution networks to external distribution networks.

[0165] In this embodiment, in the distribution network optimization scheduling simulation analysis module 300, the objective function expression of the distributed intelligent distribution network optimization scheduling model is:

[0166] ;

[0167] In the formula, is the operating cost of the energy storage system, is the operating cost of the temperature control load, The operating cost of electric vehicles, is the network loss cost, is the unit operating cost of the energy storage system, is the unit operating cost of the temperature control load, is the unit operating cost of electric vehicles, For energy storage systems t-Discharge power at moment 1; For energy storage systems t- Charging power at 1 moment, For temperature control load t- Power at 1 moment;

[0168] In the distribution network optimization scheduling simulation analysis module 300, the constraint expression of the distributed intelligent distribution network optimization scheduling model is:

[0169] ;

[0170] In the formula, It is the generator output sequence of the distributed intelligent distribution network. They are the photovoltaic and wind turbine output sequences, The charging and discharging power sequence of the energy storage system. For the i The charging and discharging power sequence of an electric vehicle, is the temperature control load power sequence, It is the load sequence of distributed smart distribution network.

[0171] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0172] Example 3

[0173] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a method for evaluating the flexibility of a distribution network using distributed source, load and storage resources is stored. The program code includes instructions for executing embodiment 1 or any possible implementation thereof, a method for evaluating the flexibility of a distribution network using distributed source, load and storage resources.

[0174] Computer-readable storage media can be any available media that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives, SSDs), etc.

[0175] Example 4

[0176] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0177] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method for evaluating the flexibility of a distribution network using distributed source-load-storage resources in accordance with Example 1 or any possible implementation thereof.

[0178] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0179] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0180] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0181] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for evaluating the flexibility of a distribution network using distributed source, load and storage resources, characterized in that: include: Establish a distributed intelligent distribution network model, which includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; simulate the wind speed through the wind turbine sub-model and combine the wind turbine power characteristics to simulate the wind turbine output characteristics; simulate the output characteristics of the photovoltaic unit through the photovoltaic sub-model; simulate the energy storage system operation characteristics through the energy storage system sub-model; simulate the temperature control load operation characteristics through the temperature control load sub-model; and simulate the electric vehicle flexibility output characteristics through the electric vehicle sub-model; Based on the distributed intelligent distribution network model, a distributed intelligent distribution network flexibility supply model is constructed; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply sub-model; the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load are obtained through the temperature control load flexibility supply sub-model; the electric vehicle flexibility supply index is obtained through the electric vehicle flexibility supply sub-model; the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model; Based on the distributed smart distribution network model, a distributed smart distribution network optimization dispatching model is constructed. The distributed smart distribution network optimization dispatching model takes the lowest operating cost of the distributed smart distribution network as the objective function. The constraints of the distributed smart distribution network optimization dispatching model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints, and power balance constraints that meet the full absorption of wind and solar power in the distributed smart distribution network. By solving and optimizing the distributed smart distribution network optimization dispatching model, the operation of the distributed smart distribution network is simulated, and the flexibility resource output is specified according to the set time period obtained by the solution, and the upward and downward flexibility potential of the distributed smart distribution network in the set time period is calculated, so as to complete the flexibility evaluation of the distributed smart distribution network. The wind speed is simulated by the wind turbine sub-model and combined with the wind turbine power characteristics to simulate the wind turbine output characteristics P W The expression is: Where A, B, C are the parameters of the wind turbine power characteristic curve; v is the actual wind speed; P r is the rated power of the wind turbine; ν ci is the cut-in wind speed; r is the rated wind speed; ν co Cut out wind speed; The expression for simulating the output characteristics of the photovoltaic unit through the photovoltaic sub-model is: Where P PV is the actual output of the photovoltaic unit; P N is the rated output of the photovoltaic unit; G is the actual irradiance; α P is the power temperature coefficient of the photovoltaic unit; T C is the temperature of the device in the photovoltaic unit; T0 is the standard temperature; G N is the irradiance under standard test conditions; T STC is the temperature under standard test conditions; The expression for simulating the operating characteristics of the energy storage system through the energy storage system submodel is: In the formula, is the discharge power of the energy storage system at time t; is the maximum discharge power of the energy storage system; is the charging power of the energy storage system at time t; is the maximum charging power of the energy storage system; are two binary integer variables used to represent the charge and discharge status; is the capacity of the energy storage system at time t; represents the capacity of the energy storage system in the previous period; τ represents the time interval; k c , k d are the charging and discharging efficiency coefficients of the energy storage system respectively; They are the lower and upper limits of the energy storage system capacity respectively; The expression for simulating the operating characteristics of the temperature control load by the temperature control load sub-model is: In the formula, are the indoor temperature and ambient temperature of the temperature control load at time t, are the thermal resistance and thermal capacity of the temperature control load, η W is the cooling / heating coefficient of the temperature control load, a w is the coefficient related to the time constant, τ is the time constant; b w P is the product coefficient of thermal resistance and cooling / heating coefficient, t W is the power of the temperature control load at time t, is the maximum power of the temperature control load, is the temperature set by the user, Δθ W The tolerable temperature fluctuation value set by the user; The expression for simulating the flexible output characteristics of electric vehicles through the electric vehicle sub-model is: In the formula, are the charging and discharging powers of the ith electric vehicle at the tth period, are the upper limits of charging and discharging power of the i-th electric vehicle, is a binary integer variable, is the battery capacity of the i-th electric vehicle at time t, are the time when electric vehicles enter and leave the grid, are the charging and discharging efficiency coefficients of electric vehicles, and They are the minimum and maximum battery capacities of electric vehicles, the off-grid capacity required for vehicle owners to travel, and the expected off-grid capacity set by users; The expression of the external flexibility supply index of the energy storage system obtained by the energy storage system flexibility supply sub-model is: In the formula, They are the upward and downward flexibility supplies provided by the energy storage system at time t respectively; The expressions of the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load obtained by the temperature control load flexibility supply sub-model are: In the formula, The upward and downward flexibility supply provided for the temperature control load at time t; The expression of the electric vehicle flexibility supply index obtained by the electric vehicle flexibility supply sub-model is: In the formula, is the upward and downward flexibility supply of the ith electric vehicle in the tth period, t≠lea represents the on-grid time, and t=lea represents the off-grid time; The expressions of the upward and downward flexibility supply indicators of the distributed smart distribution network to the external distribution network obtained by the distributed smart distribution network flexibility supply sub-model are as follows: In the formula, Provide upward and downward flexibility for the distributed smart distribution network to the external distribution network; The objective function expression of the distributed intelligent distribution network optimization scheduling model is: In the formula, is the operating cost of the energy storage system, is the operating cost of the temperature control load, is the operating cost of electric vehicles, C loss is the network loss cost, c E is the unit operating cost of the energy storage system, c W is the unit operating cost of temperature control load, c V is the unit operating cost of electric vehicles, is the discharge power of the energy storage system at time t-1; is the charging power of the energy storage system at time t-1, is the power of the temperature control load at time t-1; The constraint expression of the distributed intelligent distribution network optimization dispatching model is: Where P g is the output sequence of the distributed intelligent distribution network generator, P PV , P W are the photovoltaic and wind turbine output sequences, P c E , is the charging and discharging power sequence of the energy storage system, P c V,i , is the charging and discharging power sequence of the i-th electric vehicle, P w is the temperature control load power sequence, P load It is the load sequence of distributed intelligent distribution network.

2. A device for evaluating the flexibility of a distribution network using distributed source, load and storage resources, characterized in that: include: A distributed intelligent distribution network model analysis module is used to establish a distributed intelligent distribution network model, wherein the distributed intelligent distribution network model includes a wind turbine sub-model, a photovoltaic sub-model, an energy storage system sub-model, a temperature control load sub-model and an electric vehicle sub-model; the wind turbine sub-model simulates the wind speed and combines the wind turbine power characteristics to simulate the wind turbine output characteristics; the photovoltaic sub-model simulates the output characteristics of the photovoltaic unit; the energy storage system sub-model simulates the energy storage system operation characteristics; the temperature control load sub-model simulates the temperature control load operation characteristics; the electric vehicle sub-model simulates the electric vehicle flexibility output characteristics; A distribution network flexibility supply index analysis module is used to construct a distributed intelligent distribution network flexibility supply model based on the distributed intelligent distribution network model; the distributed intelligent distribution network flexibility supply model includes an energy storage system flexibility supply sub-model, a temperature control load flexibility supply sub-model, an electric vehicle flexibility supply sub-model and a distributed intelligent distribution network flexibility supply sub-model; the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply sub-model; the upward flexibility supply index and the downward flexibility supply index provided by the temperature control load are obtained through the temperature control load flexibility supply sub-model; the electric vehicle flexibility supply index is obtained through the electric vehicle flexibility supply sub-model; the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model; The distribution network optimization scheduling simulation analysis module is used to construct a distributed intelligent distribution network optimization scheduling model based on the distributed intelligent distribution network model. The distributed intelligent distribution network optimization scheduling model takes the lowest operating cost of the distributed intelligent distribution network as the objective function. The constraints of the distributed intelligent distribution network optimization scheduling model include energy storage system operation constraints, temperature control load operation constraints, electric vehicle operation constraints, and power balance constraints that meet the full absorption of wind and solar power in the distributed intelligent distribution network. By solving and optimizing the distributed intelligent distribution network optimization scheduling model, the distributed intelligent distribution network operation is simulated, and the flexibility resource output is specified according to the set time period obtained by the solution, and the upward and downward flexibility potential of the distributed intelligent distribution network in the set time period is calculated to complete the distributed intelligent distribution network flexibility evaluation; In the distributed intelligent distribution network model analysis module, the wind speed is simulated by the wind turbine sub-model and combined with the wind turbine power characteristics to simulate the wind turbine output characteristics P W The expression is: Where A, B, C are the parameters of the wind turbine power characteristic curve; v is the actual wind speed; P r is the rated power of the wind turbine; ν ci is the cut-in wind speed; r is the rated wind speed; ν co Cut out wind speed; In the distributed intelligent distribution network model analysis module, the expression for simulating the output characteristics of the photovoltaic unit through the photovoltaic sub-model is: Where P PV is the actual output of the photovoltaic unit; P N is the rated output of the photovoltaic unit; G is the actual irradiance; α P is the power temperature coefficient of the photovoltaic unit; T C is the temperature of the device in the photovoltaic unit; T0 is the standard temperature; G N is the irradiance under standard test conditions; T STC is the temperature under standard test conditions; In the distributed intelligent distribution network model analysis module, the expression for simulating the operating characteristics of the energy storage system through the energy storage system submodel is: In the formula, is the discharge power of the energy storage system at time t; is the maximum discharge power of the energy storage system; is the charging power of the energy storage system at time t; is the maximum charging power of the energy storage system; are two binary integer variables used to represent the charge and discharge status; is the capacity of the energy storage system at time t; represents the capacity of the energy storage system in the previous period; τ represents the time interval; k c , k d are the charging and discharging efficiency coefficients of the energy storage system respectively; They are the lower and upper limits of the energy storage system capacity respectively; In the distributed intelligent distribution network model analysis module, the expression for simulating the operating characteristics of the temperature control load through the temperature control load sub-model is: In the formula, θ t W , are the indoor temperature and ambient temperature of the temperature control load at time t, are the thermal resistance and thermal capacity of the temperature control load, η W is the cooling / heating coefficient of the temperature control load, a w is the coefficient related to the time constant, τ is the time constant; b w P is the product coefficient of thermal resistance and cooling / heating coefficient, t W is the power of the temperature control load at time t, is the maximum power of the temperature control load, is the temperature set by the user, Δθ W The tolerable temperature fluctuation value set by the user; In the distributed intelligent distribution network model analysis module, the expression for simulating the flexible output characteristics of electric vehicles through the electric vehicle sub-model is: In the formula, are the charging and discharging powers of the ith electric vehicle at the tth period, are the upper limits of charging and discharging power of the i-th electric vehicle, is a binary integer variable, is the battery capacity of the i-th electric vehicle at time t, are the time when electric vehicles enter and leave the grid, , are the charging and discharging efficiency coefficients of electric vehicles, and They are the minimum and maximum battery capacities of electric vehicles, the off-grid capacity required for vehicle owners to travel, and the expected off-grid capacity set by users; In the distribution network flexibility supply index analysis module, the expression of the external flexibility supply index of the energy storage system is obtained through the energy storage system flexibility supply sub-model: In the formula, They are the upward and downward flexibility supplies provided by the energy storage system at time t respectively; In the distribution network flexibility supply index analysis module, the expressions of the upward flexibility supply index and the downward flexibility supply index provided by the temperature-controlled load are obtained through the temperature-controlled load flexibility supply sub-model: In the formula, The upward and downward flexibility supply provided for the temperature control load at time t; In the distribution network flexibility supply index analysis module, the expression of the electric vehicle flexibility supply index obtained by the electric vehicle flexibility supply sub-model is: In the formula, is the upward and downward flexibility supply of the ith electric vehicle in the tth period, t≠lea represents the on-grid time, and t=lea represents the off-grid time; In the distribution network flexibility supply index analysis module, the expressions of the upward and downward flexibility supply indexes of the distributed intelligent distribution network to the external distribution network are obtained through the distributed intelligent distribution network flexibility supply sub-model: In the formula, Provide upward and downward flexibility for the distributed smart distribution network to the external distribution network; In the distribution network optimization scheduling simulation analysis module, the objective function expression of the distributed intelligent distribution network optimization scheduling model is: In the formula, is the operating cost of the energy storage system, is the operating cost of the temperature control load, is the operating cost of electric vehicles, C loss is the network loss cost, c E is the unit operating cost of the energy storage system, c W is the unit operating cost of temperature control load, c V is the unit operating cost of electric vehicles, is the discharge power of the energy storage system at time t-1; is the charging power of the energy storage system at time t-1, is the power of the temperature control load at time t-1; In the distribution network optimization dispatch simulation analysis module, the constraint expression of the distributed intelligent distribution network optimization dispatch model is: P g +P PV +P W +P d E +P d V,i =P c E +P c V,i +P w +P load Where P g is the output sequence of the distributed intelligent distribution network generator, P PV , P W are the photovoltaic and wind turbine output sequences, P c E , P d E is the charging and discharging power sequence of the energy storage system, P c V,i , P d V,i is the charging and discharging power sequence of the i-th electric vehicle, P w is the temperature control load power sequence, P load It is the load sequence of distributed intelligent distribution network.

Images