Access measurement and calculation method considering distributed power supply, equipment capacity and power distribution network multi-element electric energy replacement load

Abstract

The invention discloses an access measurement and calculation method considering a distributed power supply, equipment capacity and a power distribution network multi-element electric energy substitution load, and the method comprises the steps: determining multi-element load aggregation power through employing a pre-constructed multi-element load aggregation model based on the power data of n types of loads in the multi-element electric energy substitution load of a to-be-evaluated power grid, and n is a positive integer; based on the multi-element load aggregation power and a distributed power supply model, source load matching is carried out by using a pre-constructed source load matching model to obtain a source load matching result, and the distributed power supply model is used for simulating the output power of a distributed power supply; and based on the source load matching result and the equipment capacity model, evaluating the distributed power supply access capability of the to-be-evaluated power grid, thereby being capable of adapting to the change of equipment capacity limitation in the power grid in real time, and improving the evaluation accuracy of the access capability of the distributed power supply in the power grid by combining with the space-time characteristics of the multivariate electric energy replacement load.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method for calculating the access of distributed power sources, equipment capacity, and multiple electric energy replacement loads in a distribution network. Background Art

[0002] In modern power systems, the access of multiple electric energy replacement loads poses new challenges to the stability and reliability of the power grid. These loads usually have significant temporal and spatial distribution characteristics. Their integration with distributed power sources (such as solar photovoltaics) can easily lead to tidal fluctuations in power grid load and power flow, thus impacting the stable operation of the power grid. Therefore, the distributed power sources that the power grid can access are not unlimited.

[0003] In the prior art, the future distributed power access capacity of the power grid is generally evaluated by analyzing past load patterns and power grid operation data based on historical data. However, this method is difficult to adapt to real-time changes in the power grid in a timely manner. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present application propose a method, system, computer-readable storage medium and computer equipment for access calculation that takes into account distributed power sources, equipment capacity and multiple electric energy alternative loads in the distribution network. The method can adapt to changes in equipment capacity limitations in the power grid in real time, and combine the temporal and spatial characteristics of the multiple electric energy alternative loads to improve the accuracy of the assessment of the access capacity of distributed power sources in the power grid.

[0005] In a first aspect, an embodiment of the present application provides a method for calculating access to a distributed power source, equipment capacity, and a multi-energy alternative load of a distribution network, including:

[0006] Based on the power data of n types of loads in the multi-type electric energy replacement loads of the power grid to be evaluated, the multi-type load aggregation power is determined by using a pre-constructed multi-type load aggregation model, where n is a positive integer;

[0007] Based on the multi-load aggregated power and the distributed power source model, a pre-built source-load matching model is used to perform source-load matching to obtain a source-load matching result, wherein the distributed power source model is used to simulate the output power of the distributed power source;

[0008] Based on the source-load matching result and the equipment capacity model, the distributed power access capability of the power grid to be evaluated is evaluated, wherein the equipment capacity model is used to simulate the operating limit of the power grid equipment of the power grid to be evaluated.

[0009] Optionally, the multi-load aggregation model is constructed according to a load demand function, a first objective function and a first constraint condition, wherein the first objective function is used to indicate a goal of minimizing total cost, and the first constraint condition includes a power balance constraint;

[0010] The method further comprises:

[0011] Obtaining grid demand power and grid supply power;

[0012] The method of determining the aggregated power of multiple loads based on the power data of each of the n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated by using a pre-built multiple load aggregation model comprises:

[0013] Taking the first objective function as the target and the first constraint condition as the restriction, based on the power data of each type of load, using the load demand function to perform weighted sum calculation on the rigid load power and the flexible load power in the power data of each type of load, so as to calculate the power of each type of load;

[0014] The difference between the power demanded by the power grid and the power supplied by the power grid is multiplied by the set adjustment coefficient of each type of load to obtain the power adjustment amount of each type of load;

[0015] Based on the power and power regulation amount of each of the n types of loads, the aggregate power of multiple loads is calculated.

[0016] Optionally, the total cost is the sum of the rigid load cost and the flexible load cost, the rigid load cost refers to the sum of the first costs of the n types of loads, the first cost is the product of the rigid load power corresponding to any type of load and the rigid load unit cost, the flexible load cost refers to the sum of the second costs of the n types of loads, the second cost is the product of the flexible load power corresponding to any type of load, the flexible load unit cost and the flexible load weight factor;

[0017] The power balance constraint is used to indicate that the sum of the rigid load powers of the n types of loads and the sum of the flexible load powers of the n types of loads are equal to the total required power when added together, wherein the flexible load power of each type of load is within the preset flexible load maximum range corresponding to the type of load.

[0018] Optionally, the distributed power supply model includes at least one of the following: a probability density function of photovoltaic power generation output power, a wind power generation output power function;

[0019] The probability density function f(P) of the photovoltaic power generation output power is determined by the following formula:

[0020]

[0021] Among them, P max is the maximum output power of photovoltaic power generation, α is the first shape parameter of Beta distribution, β is the second shape parameter of Beta distribution, P is the actual output power of photovoltaic power generation, Γ() represents the gamma function, Represents the standardized output power of photovoltaic power generation;

[0022] The wind power output power function P m Determined by the following formula:

[0023]

[0024] Among them, C p is the wind energy utilization coefficient, ρ is the air density, V is the wind speed, A is the blade sweep area of ​​the wind turbine, P e is the rated power of the wind turbine, V in is the cut-in wind speed of the wind turbine, V e is the rated wind speed of the wind turbine, V out is the cut-out wind speed of the wind turbine.

[0025] Optionally, the source-load matching model is constructed according to the second objective function and the second constraint condition;

[0026] The second objective function is used to indicate the goal of minimizing the operating cost, wherein the operating cost is the electricity purchase cost minus the electricity selling cost, wherein the electricity purchase cost is determined based on the electricity purchased from the main power grid and its price, and the electricity selling cost is determined based on the electricity sold to the main power grid and its price;

[0027] The second constraint condition includes at least one of the following: supply and demand balance constraint, photovoltaic output constraint, electricity selling power constraint, and grid operation constraint.

[0028] Optionally, performing source-load matching based on the multi-load aggregated power and distributed power source model using a pre-built source-load matching model to obtain a source-load matching result includes:

[0029] Taking the second objective function as the target and the second constraint condition as the restriction, an optimization algorithm is used to perform calculations based on the aggregated power of the multiple loads and the distributed power generation power indicated by the distributed power model to obtain a source-load matching result indicating a power grid operation plan, wherein the power grid operation plan includes a power purchase plan, a power sale plan and a distributed power generation plan.

[0030] Optionally, the equipment capacity model includes a transformer capacity model and / or a transmission line capacity model;

[0031] The transformer capacity model is constructed based on the rated capacity, efficiency, loss and corresponding constraints of the transformer, and the constraints of the transformer include load capacity constraints, temperature rise limit constraints and / or voltage change constraints;

[0032] The transmission line capacity model is constructed based on the line parameters of the transmission line and corresponding constraints, and the constraints of the transmission line include power transmission capacity constraints and / or voltage drop constraints.

[0033] Optionally, the distributed power access capability of the power grid to be evaluated is obtained by evaluating using the following formula:

[0034] P a =min(minC d (t),P m (t))

[0035] Among them, C d (t) = C r *μ(t) is the dynamic capacity of the equipment at time t, C r is the rated capacity of the equipment indicated by the equipment capacity model, μ(t) is the equipment efficiency and operating status coefficient at time t, and P m (t) is the source-load matching power at time t indicated by the source-load matching result, P a is the maximum access power that the power grid to be evaluated can accept at time t.

[0036] Optionally, the method further comprises:

[0037] Identify the power usage mode of each of the n types of loads, wherein the power usage mode includes at least one of the following: an industrial load power usage mode, an agricultural load power usage mode, and a building load power usage mode;

[0038] Determine the adjustable potential information of the n types of loads, wherein the adjustable potential information is used to characterize at least one of the following: a single adjustable potential of each type of load, and a collaborative adjustable potential between the n types of loads;

[0039] The multi-element load aggregation model is adjusted based on the power usage pattern and / or the adjustable potential information.

[0040] In a second aspect, the embodiment of the present application provides an access measurement system taking into account distributed power sources, equipment capacity, and multiple energy replacement loads of a distribution network, including:

[0041] A power aggregation module, used for determining the aggregated power of multiple loads based on the power data of each of n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated, using a pre-built multiple load aggregation model, wherein n is a positive integer;

[0042] A source-load matching module is used to perform source-load matching based on the multi-load aggregated power and the distributed power model using a pre-built source-load matching model to obtain a source-load matching result, wherein the distributed power model is used to simulate the output power of the distributed power;

[0043] An access assessment module is used to assess the access capability of the distributed power source of the power grid to be assessed based on the source-load matching result and the equipment capacity model, wherein the equipment capacity model is used to simulate the operating limit of the power grid equipment of the power grid to be assessed.

[0044] Optionally, the multi-load aggregation model is constructed according to a load demand function, a first objective function and a first constraint condition, wherein the first objective function is used to indicate a goal of minimizing total cost, and the first constraint condition includes a power balance constraint;

[0045] The system further comprises:

[0046] A power grid information acquisition module is used to obtain the power demand and power supply of the power grid;

[0047] The method of determining the aggregated power of multiple loads based on the power data of each of the n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated by using a pre-built multiple load aggregation model comprises:

[0048] Taking the first objective function as the target and the first constraint condition as the restriction, based on the power data of each type of load, using the load demand function to perform weighted sum calculation on the rigid load power and the flexible load power in the power data of each type of load, so as to calculate the power of each type of load;

[0049] The difference between the power demanded by the power grid and the power supplied by the power grid is multiplied by the set adjustment coefficient of each type of load to obtain the power adjustment amount of each type of load;

[0050] Based on the power and power regulation amount of each of the n types of loads, the aggregate power of multiple loads is calculated.

[0051] Optionally, the total cost is the sum of the rigid load cost and the flexible load cost, the rigid load cost refers to the sum of the first costs of the n types of loads, the first cost is the product of the rigid load power corresponding to any type of load and the rigid load unit cost, the flexible load cost refers to the sum of the second costs of the n types of loads, the second cost is the product of the flexible load power corresponding to any type of load, the flexible load unit cost and the flexible load weight factor;

[0052] The power balance constraint is used to indicate that the sum of the rigid load powers of the n types of loads and the sum of the flexible load powers of the n types of loads are equal to the total required power when added together, wherein the flexible load power of each type of load is within the preset flexible load maximum range corresponding to the type of load.

[0053] Optionally, the distributed power supply model includes at least one of the following: a probability density function of photovoltaic power generation output power, a wind power generation output power function;

[0054] The probability density function f(P) of the photovoltaic power generation output power is determined by the following formula:

[0055]

[0056] Among them, P max is the maximum output power of photovoltaic power generation, α is the first shape parameter of Beta distribution, β is the second shape parameter of Beta distribution, P is the actual output power of photovoltaic power generation, Γ() represents the gamma function, Represents the standardized output power of photovoltaic power generation;

[0057] The wind power output power function P m Determined by the following formula:

[0058]

[0059] Among them, C p is the wind energy utilization coefficient, ρ is the air density, V is the wind speed, A is the blade sweep area of ​​the wind turbine, P e is the rated power of the wind turbine, V in is the cut-in wind speed of the wind turbine, V e is the rated wind speed of the wind turbine, V out is the cut-out wind speed of the wind turbine.

[0060] Optionally, the source-load matching model is constructed according to the second objective function and the second constraint condition;

[0061] The second objective function is used to indicate the goal of minimizing the operating cost, wherein the operating cost is the electricity purchase cost minus the electricity selling cost, wherein the electricity purchase cost is determined based on the electricity purchased from the main power grid and its price, and the electricity selling cost is determined based on the electricity sold to the main power grid and its price;

[0062] The second constraint condition includes at least one of the following: supply and demand balance constraint, photovoltaic output constraint, electricity selling power constraint, and grid operation constraint.

[0063] Optionally, performing source-load matching based on the multi-load aggregated power and distributed power source model using a pre-built source-load matching model to obtain a source-load matching result includes:

[0064] Taking the second objective function as the target and the second constraint condition as the restriction, an optimization algorithm is used to perform calculations based on the aggregated power of the multiple loads and the distributed power generation power indicated by the distributed power model to obtain a source-load matching result indicating a power grid operation plan, wherein the power grid operation plan includes a power purchase plan, a power sale plan and a distributed power generation plan.

[0065] Optionally, the equipment capacity model includes a transformer capacity model and / or a transmission line capacity model;

[0066] The transformer capacity model is constructed based on the rated capacity, efficiency, loss and corresponding constraints of the transformer, and the constraints of the transformer include load capacity constraints, temperature rise limit constraints and / or voltage change constraints;

[0067] The transmission line capacity model is constructed based on the line parameters of the transmission line and corresponding constraints, and the constraints of the transmission line include power transmission capacity constraints and / or voltage drop constraints.

[0068] Optionally, the distributed power access capability of the power grid to be evaluated is obtained by evaluating using the following formula:

[0069] P a =min(minC d (t),P m (t))

[0070] Among them, C d (t) = C r *μ(t) is the dynamic capacity of the equipment at time t, C r is the rated capacity of the equipment indicated by the equipment capacity model, μ(t) is the equipment efficiency and operating status coefficient at time t, and P m (t) is the source-load matching power at time t indicated by the source-load matching result, P a is the maximum access power that the power grid to be evaluated can accommodate at time t.

[0071] Optionally, the system further comprises:

[0072] A power usage pattern recognition module, used to recognize the power usage patterns of the n types of loads, wherein the power usage patterns include at least one of the following: industrial load power usage pattern, agricultural load power usage pattern, and building load power usage pattern;

[0073] An adjustable potential determination module, used to determine the adjustable potential information of the n types of loads, wherein the adjustable potential information is used to characterize at least one of the following: a single adjustable potential of each type of load, and a collaborative adjustable potential between the n types of loads;

[0074] An aggregation model adjustment module is used to adjust the multi-load aggregation model based on the power consumption pattern and / or the adjustable potential information.

[0075] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0076] In a fourth aspect, an embodiment of the present application provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any of the methods described above when executing the computer program.

[0077] In summary, the embodiments of the present application have at least the following beneficial effects:

[0078] According to an embodiment of the present application, the power data of each of n types of loads in the multi-energy replacement load of the power grid to be evaluated is used to determine the multi-load aggregate power by using a pre-constructed multi-load aggregation model, wherein n is a positive integer; based on the multi-load aggregate power and the distributed power supply model, source-load matching is performed using a pre-constructed source-load matching model to obtain a source-load matching result, wherein the distributed power supply model is used to simulate the output power of the distributed power supply; based on the source-load matching result and the equipment capacity model, the distributed power supply access capability of the power grid to be evaluated is evaluated, wherein the equipment capacity model is used to simulate the operating limit of the power grid equipment of the power grid to be evaluated, so as to adapt to the changes in the equipment capacity limit in the power grid in real time, and improve the evaluation accuracy of the access capability of the distributed power supply in the power grid in combination with the spatiotemporal characteristics of the multi-energy replacement load. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flow chart of a method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of a distribution network provided in an embodiment of the present application;

[0080] Figure 2 It is a structural diagram of an access measurement system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of a distribution network provided in an embodiment of the present application;

[0081] Figure 3 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0083] In the description of the present application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more. In the description of the present application, the term "including" and its variations are open inclusions, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "according to" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments".

[0084] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0085] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0086] First, see Figure 1 , shows a flow chart of a method for calculating the access of a distributed power source, equipment capacity and multiple electric energy replacement loads of a distribution network provided in an embodiment of the present application, the method comprising steps S101-S103, which are as follows:

[0087] S101, based on the power data of n types of loads in the multi-type electric energy replacement load of the power grid to be evaluated, determine the multi-type load aggregation power by using a pre-constructed multi-type load aggregation model, where n is a positive integer;

[0088] It should be noted that electric energy replacement load generally refers to various applications that directly use electricity to replace traditional energy (such as fossil fuels such as coal, oil and natural gas).

[0089] S102, based on the multi-load aggregated power and the distributed power model, using a pre-built source-load matching model to perform source-load matching to obtain a source-load matching result, wherein the distributed power model is used to simulate the output power of the distributed power;

[0090] In one example, the distributed power source may include photovoltaic power generation equipment, wind power generation equipment, and other new energy power generation devices.

[0091] S103, based on the source-load matching result and the equipment capacity model, evaluating the distributed power access capability of the power grid to be evaluated, wherein the equipment capacity model is used to simulate the operation limit of the power grid equipment of the power grid to be evaluated.

[0092] It should be noted that the distributed power access capability in this embodiment can be used to characterize information such as the number and power generation capacity of distributed power sources that can be connected to the power grid to be evaluated.

[0093] In an optional implementation, the multi-load aggregation model is constructed according to a load demand function, a first objective function and a first constraint condition, wherein the first objective function is used to indicate a goal of minimizing total cost, and the first constraint condition includes a power balance constraint;

[0094] The method further comprises:

[0095] Obtaining grid demand power and grid supply power;

[0096] The method of determining the aggregated power of multiple loads based on the power data of each of the n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated by using a pre-built multiple load aggregation model comprises:

[0097] Taking the first objective function as the target and the first constraint condition as the restriction, based on the power data of each type of load, using the load demand function to perform weighted sum calculation on the rigid load power and the flexible load power in the power data of each type of load, so as to calculate the power of each type of load;

[0098] The difference between the power demanded by the power grid and the power supplied by the power grid is multiplied by the set adjustment coefficient of each type of load to obtain the power adjustment amount of each type of load;

[0099] Based on the power and power regulation amount of each of the n types of loads, the aggregate power of multiple loads is calculated.

[0100] The following is an explanation of how to construct and apply a multivariate load aggregation model using some specific examples.

[0101] (1) Load characteristics analysis: load power Where P i is the power of the i-th load.

[0102] (2) Load demand function: rigid load Flexible load Among them PG i and PF i are the rigid and flexible parts of the i-th type of load, respectively. Based on the original load characteristic analysis, this embodiment introduces a dynamic identification mechanism to continuously update and optimize the load characteristics through real-time data feedback. This not only includes the traditional rigid load and flexible load analysis, but also adds the prediction of load change trends to achieve more accurate load management. i (t) = PG i (t)+PF i (t)*β(t), where P i (t) is the output power of the i-th type of load at time t, and β(t) is the adjustment coefficient of the flexible load at time t, which can be dynamically adjusted according to the grid demand.

[0103] (3) Optimizing the objective function: In combination with the dynamic identification of load characteristics, this embodiment further optimizes the objective function to minimize the total cost and meet the real-time needs of the power grid. The total cost minimization satisfies the following formula: where c i (t) is the unit cost of the i-th type of load at time t, λ is the weight factor of flexible load regulation, cF i (t) is the unit cost of flexible regulation of the i-th load at time t.

[0104] (4) Constraints: Power balance constraints: Where D(t) is the total demand power at time t. Flexible load regulation range constraint: in and is the minimum and maximum adjustment range of the i-th type flexible load.

[0105] (5) This embodiment also proposes a real-time load regulation and response mechanism, which can dynamically adjust the load output according to the real-time operation status and forecast data of the power grid to meet the stability and economic requirements of the power grid. The expression is as follows. i(t) = α*(P d (t)-P s (t)), where ΔP i (t) is the regulation amount of the i-th type of load at time t, P d (t) is the grid power demand at time t, P s (t) is the grid supplied power at time t.

[0106] (6) Multi-element load aggregation model: Based on the above load characteristics, optimization objectives and constraints, this embodiment can construct a multi-element load aggregation model. This model achieves optimal load aggregation through real-time data and dynamic adjustment mechanism. Its expression example is as follows. Where P agg (t) is the aggregate power at time t.

[0107] In an optional implementation, the total cost is the sum of the rigid load cost and the flexible load cost, the rigid load cost refers to the sum of the first costs of the n types of loads, the first cost is the product of the rigid load power corresponding to any type of load and the rigid load unit cost, the flexible load cost refers to the sum of the second costs of the n types of loads, the second cost is the product of the flexible load power corresponding to any type of load, the flexible load unit cost and the flexible load weight factor;

[0108] The power balance constraint is used to indicate that the sum of the rigid load powers of the n types of loads and the sum of the flexible load powers of the n types of loads are equal to the total required power when added together, wherein the flexible load power of each type of load is within the preset flexible load maximum range corresponding to the type of load.

[0109] In an optional implementation, the distributed power supply model includes at least one of the following: a probability density function of photovoltaic power generation output power, a wind power generation output power function;

[0110] The probability density function f(P) of the photovoltaic power generation output power is determined by the following formula:

[0111]

[0112] Among them, P max is the maximum output power of photovoltaic power generation, α is the first shape parameter of Beta distribution, β is the second shape parameter of Beta distribution, P is the actual output power of photovoltaic power generation, Γ() represents the gamma function, Represents the standardized output power of photovoltaic power generation.

[0113] A specific example is given below to explain the probability density function of the photovoltaic power output power.

[0114] Photovoltaic power generation converts solar energy directly into electrical energy. Its principle is to use the photovoltaic effect of solar panels, and ultimately the maximum power tracking algorithm can be used to control its electrical energy output. Assuming a given solar cell array, N represents the number of photovoltaic arrays. If the area and photoelectric conversion efficiency of each array are the same, they are recorded as S and η respectively. The efficiency of general solar cells is around 10%-13%, or about 12%-14%. Assuming the temperature at time P is Tp, the conversion efficiency of the photovoltaic cell can be expressed by the following formula:

[0115] η=η0[1-r T (Tp-Tr)]

[0116] Where: Tr is the reference temperature (298K), η0 is the conversion efficiency of the photovoltaic cell at the reference temperature; r T is the temperature coefficient of the photovoltaic cell, and its value is usually 0.005.

[0117] Since the output power of the photovoltaic system is proportional to the solar irradiance r, the total output power of the photovoltaic system is:

[0118] P=rNSη

[0119] In this way, the probability density function of the output power of the solar cell array can be obtained from the probability density function of the light intensity.

[0120] The wind power output power function P m Determined by the following formula:

[0121]

[0122] Among them, C p is the wind energy utilization coefficient, ρ is the air density, V is the wind speed, A is the blade sweep area of ​​the wind turbine, P e is the rated power of the wind turbine, V in is the cut-in wind speed of the wind turbine, V e is the rated wind speed of the wind turbine, V out is the cut-out wind speed of the wind turbine.

[0123] A specific example is given below to explain the wind power output power function.

[0124] The basic working principle of a wind power generation system is to convert wind energy into electrical energy. Its main component is a wind turbine. The output power of a wind turbine depends on the wind speed, the size and shape of the wind turbine. Its physical model is:

[0125]

[0126] Where: P m is the output power of the wind turbine; C p is the wind energy utilization coefficient; ρ is the air density; V is the wind speed; A is the blade swept area.

[0127] The wind turbine has a minimum working wind speed V when it is working. in (i.e. cut-in wind speed), when the wind speed is lower than V in The wind turbine will stop working; there is also a maximum wind speed V out (i.e. cut-out wind speed), once the wind speed exceeds V out When , the wind turbine should stop working, so the wind power output function can be obtained.

[0128] In an optional implementation, the source-load matching model is constructed according to the second objective function and the second constraint condition;

[0129] The second objective function is used to indicate the goal of minimizing the operating cost, wherein the operating cost is the electricity purchase cost minus the electricity selling cost, wherein the electricity purchase cost is determined based on the electricity purchased from the main power grid and its price, and the electricity selling cost is determined based on the electricity sold to the main power grid and its price;

[0130] In one example, the goal of the second objective function is to minimize the operating cost, including the cost of purchasing electricity and the operating cost of distributed power sources (here and in subsequent embodiments, the distributed photovoltaic system is used as an example unless otherwise specified), while taking into account the possible income from selling electricity. Its expression is as follows:

[0131]

[0132] Where: T is the total number of time periods considered, C grid,t is the price of electricity purchased from the main grid during time period t, P grid,t is the electricity purchased from the main grid during time period t, R feed-in,t is the price of electricity sold in time period t, P feed-in,t is the electricity sold back to the grid during time period t.

[0133] The second constraint condition includes at least one of the following: supply and demand balance constraint, photovoltaic output constraint, electricity selling power constraint, and grid operation constraint.

[0134] In an example:

[0135] Supply and demand balance constraint: At any time period, the sum of purchased electricity, the output of the photovoltaic system and the electricity sold back to the grid must meet the load demand, which is expressed by the following formula:

[0136] P grid,t +P pv,t -P feed-in,t=L t

[0137] Photovoltaic output constraints: The output of the photovoltaic system is limited by the actual sunshine conditions, which can be obtained through the prediction model:

[0138]

[0139] Power selling power constraint: The power sold back to the grid cannot exceed the PV output and a preset maximum power selling power, which is expressed as follows:

[0140]

[0141] Grid operation constraints: When considering the actual situation of grid operation, additional constraints may need to be met, such as power factor requirements, line transmission restrictions, etc.

[0142] In an optional implementation, the performing source-load matching based on the multi-load aggregated power and distributed power source model using a pre-built source-load matching model to obtain a source-load matching result includes:

[0143] Taking the second objective function as the target and the second constraint condition as the restriction, based on the aggregated power of the multi-loads and the distributed power generation power indicated by the distributed power model, an optimization algorithm is used to calculate to obtain a source-load matching result for indicating a power grid operation plan, wherein the power grid operation plan includes a power purchase plan (the optimal power purchase plan P for each time period) grid,t ), electricity selling plan (the optimal electricity selling plan P for each time period feed-in,t ) and distributed generation plan (the output P of the photovoltaic system after adjustment in each time period pv,t ).

[0144] In one example, the optimization algorithm may include any of the following: linear programming, mixed integer linear programming, heuristic algorithms (eg, genetic algorithms, particle swarm optimization).

[0145] In an optional implementation, the equipment capacity model includes a transformer capacity model and / or a transmission line capacity model;

[0146] The transformer capacity model is constructed based on the rated capacity, efficiency, loss and corresponding constraints of the transformer, and the constraints of the transformer include load capacity constraints, temperature rise limit constraints and / or voltage change constraints;

[0147] in,

[0148] Rated capacity: The rated capacity of the transformer C rating Indicates its maximum output power under rated operating conditions.

[0149] Efficiency and losses: The efficiency η and losses ΔP of a transformer affect its actual transmission capacity.

[0150] Transformer constraints:

[0151] Load capacity: Actual load P load Must be less than or equal to the rated capacity. load ≤C rating

[0152] Temperature rise limit: The operating temperature shall not exceed the design limit to protect the insulation material.

[0153] Voltage variation: The output voltage is maintained within the specified range.

[0154] The transmission line capacity model is constructed based on the line parameters of the transmission line and corresponding constraints, and the constraints of the transmission line include power transmission capacity constraints and / or voltage drop constraints.

[0155] in,

[0156] Line parameters: including resistance R, reactance X, and maximum current I of the line max .

[0157] Transmission line constraints:

[0158] Power transmission capacity: the maximum power P transmitted by the line line Limited by thermal limits and voltage drop.

[0159] P line =I max *V≤ P thermal

[0160] Voltage drop: Ensure that the voltage drop ΔV of the line is within the allowable range. ΔV=I line *R*2≤ΔV max

[0161] In an optional implementation, the distributed power access capability of the power grid to be evaluated is obtained by evaluating using the following formula:

[0162] P a =min(minC d (t),P m (t))

[0163] Among them, C d (t) = C r *μ(t) is the dynamic capacity of the equipment at time t, C r is the rated capacity of the equipment indicated by the equipment capacity model, μ(t) is the equipment efficiency and operating status coefficient at time t, and P m(t) is the source-load matching power at time t indicated by the source-load matching result, P a is the maximum access power that the power grid to be evaluated can accommodate at time t.

[0164] A specific example of the evaluation is given below.

[0165] This embodiment mainly focuses on how to reasonably connect distributed power sources (such as photovoltaics) to the distribution network while ensuring the stability and economy of the power grid. The following are the detailed steps of this embodiment:

[0166] (1) Determine the load characteristics of the distribution network

[0167] Collect historical load data of the target distribution network, including total load, peak load time, load growth trend, etc.; analyze the daily / monthly / yearly variation pattern of load and determine load characteristics, including load density, load concurrency rate, etc.

[0168] (2) Analysis of the production capacity characteristics of distributed power sources (such as photovoltaics)

[0169] A photovoltaic capacity model is established based on the efficiency, installation angle, geographical location and climatic conditions of photovoltaic panels; the power generation of the photovoltaic system is predicted using historical meteorological data and the photovoltaic capacity model.

[0170] (3) Source-load matching analysis

[0171] Match the photovoltaic capacity with the load characteristics, and analyze the source-load balance in different time periods; divide the high-matching period and low-matching period according to the source-load matching results.

[0172] (4) Equipment capacity assessment

[0173] In order to more accurately evaluate the access capability of power grid equipment, this embodiment introduces a dynamic evaluation method for equipment capacity. This embodiment dynamically adjusts the operating limit of the equipment by real-time monitoring of equipment status and environmental factors to ensure that the equipment operates within a safe range. d (t) = C r *μ(t), where C d (t) is the dynamic capacity of the equipment at time t, C r is the rated capacity of the equipment, μ(t) is the equipment efficiency and operating status coefficient at time t.

[0174] (5) Access Capacity Calculation

[0175] The access capacity evaluation method of this embodiment combines the source-load matching model and the dynamic evaluation of equipment capacity, and provides a new access capacity calculation framework. This method achieves the maximum access and utilization of new energy while ensuring the stability and economy of the power grid through the optimization algorithm. a=min(minC d (t),P m (t)), where P a is the maximum access power that the grid can accept at time t, P m (t) is the source-to-load matching power at time t.

[0176] Through the above steps, the photovoltaic access capacity of the distribution network based on source-load matching and equipment capacity can be systematically evaluated and calculated, providing a scientific basis for the planning of the distribution network and the rational layout of the photovoltaic system.

[0177] In an optional embodiment, the method further includes:

[0178] Identify the power usage mode of each of the n types of loads, wherein the power usage mode includes at least one of the following: an industrial load power usage mode, an agricultural load power usage mode, and a building load power usage mode;

[0179] It should be noted that:

[0180] (1) Industrial load power consumption mode

[0181] Industrial load is an important part of the power system, and its power consumption pattern is usually characterized by high load, stable and continuous power demand. Mechanical processing, material handling, product assembly and other links involved in the industrial production process all require a large amount of power support. The power consumption pattern identification of industrial load is mainly based on the following aspects:

[0182] 1. Production process and equipment characteristics: Analyze the specific process of industrial production and identify the power consumption characteristics of key equipment such as machine tools, conveyor belts, furnaces, etc., including starting current, operating power and periodic fluctuations.

[0183] 2. Shifts and working modes: Consider the shift arrangements in industrial production (such as three shifts or two shifts), as well as the working mode of the equipment (continuous operation or intermittent operation).

[0184] 3. Seasonality and market influences: Industrial load is affected by market demand and seasonal changes. For example, the electricity demand of manufacturing industry will increase significantly during the peak season.

[0185] (2) Agricultural load electricity consumption pattern

[0186] Agricultural loads mainly involve farmland irrigation, agricultural product processing, animal husbandry and other activities, and their electricity consumption patterns have obvious seasonality and periodicity. The identification of agricultural load electricity consumption patterns considers the following factors:

[0187] 1. Crop planting cycle: Identify the planting cycle and irrigation requirements of different crops, such as the irrigation season and water consumption of crops such as rice and wheat.

[0188] 2. Agricultural mechanization level: Analyze the electricity consumption characteristics of agricultural mechanized equipment such as tractors and harvesters, as well as the frequency of use of these equipment in different agricultural activities.

[0189] 3. Climate and weather conditions: Agricultural electricity consumption is affected by climate and weather conditions. For example, irrigation electricity consumption may increase during the dry season.

[0190] (3) Building load electricity consumption mode

[0191] Building loads include residential, commercial and public facilities, and their electricity consumption patterns are affected by factors such as living habits, business activities and seasonal changes. Building load electricity consumption pattern identification includes:

[0192] 1. Residential electricity consumption pattern: Analyze residents’ daily electricity consumption, such as lighting, household appliances, air conditioning and heating, etc., and the time and intensity of electricity consumption.

[0193] 2. Commercial and service electricity consumption: Identify electricity consumption patterns in commercial areas such as shopping malls, office buildings and hotels, including the impact of business hours and special events on electricity demand.

[0194] 3. Seasonal changes: Building electricity consumption is affected by seasonal changes, such as increased electricity demand for heating in winter and air conditioning in summer.

[0195] Through the identification of the above-mentioned power consumption patterns, the power consumption patterns of different types of loads can be more accurately identified and classified, providing a scientific basis for power grid planning and operation, optimizing the allocation and utilization of power resources, and improving the stability and economy of the power grid.

[0196] Determine the adjustable potential information of the n types of loads, wherein the adjustable potential information is used to characterize at least one of the following: a single adjustable potential of each type of load, and a collaborative adjustable potential between the n types of loads;

[0197] It should be noted that, in the adjustable potential information of this embodiment:

[0198] (1) Single adjustable potential analysis

[0199] Industrial load: By adjusting production schedules, increasing production when grid demand is low and reducing production during peak hours.

[0200] Agricultural load: Optimize irrigation systems and adjust greenhouse operation hours to match grid load demands.

[0201] Commercial and residential loads: Implement demand response strategies, such as adjusting store opening hours and household electricity usage habits.

[0202] (2) Multivariate combination potential (i.e., the potential for collaboration and adjustment between n types of loads)

[0203] In combination with the multi-type energy demand of modern planting, breeding, facility agriculture and characteristic industries, the following multi-load regulation potential is analyzed in this embodiment:

[0204] Complementarity of industrial and agricultural loads: During the off-peak period of industrial production, agricultural water use is increased, and vice versa.

[0205] Coordination of commercial and residential loads: Real-time regulation of commercial and residential loads can be achieved through smart grid technology.

[0206] The multi-element load aggregation model is adjusted based on the power usage pattern and / or the adjustable potential information.

[0207] On the second aspect, accordingly, an embodiment of the present application also provides an access calculation system that takes into account distributed power sources, equipment capacity and multiple electric energy alternative loads in the distribution network, which can implement all processes of the access calculation method that takes into account distributed power sources, equipment capacity and multiple electric energy alternative loads in the distribution network provided in the above embodiment.

[0208] See also Figure 2 , shows a schematic diagram of the structure of an access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of a distribution network provided in an embodiment of the present application, and the access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of a distribution network includes:

[0209] The power aggregation module 201 is used to determine the aggregated power of multiple loads based on the power data of each of n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated, using a pre-constructed multiple load aggregation model, where n is a positive integer;

[0210] The source-load matching module 202 is used to perform source-load matching based on the multi-load aggregated power and the distributed power model using a pre-built source-load matching model to obtain a source-load matching result, wherein the distributed power model is used to simulate the output power of the distributed power;

[0211] The access assessment module 203 is used to assess the access capability of the distributed power source of the power grid to be assessed based on the source-load matching result and the equipment capacity model, wherein the equipment capacity model is used to simulate the operating limit of the power grid equipment of the power grid to be assessed.

[0212] In an optional implementation, the multi-load aggregation model is constructed according to a load demand function, a first objective function and a first constraint condition, wherein the first objective function is used to indicate a goal of minimizing total cost, and the first constraint condition includes a power balance constraint;

[0213] The system further comprises:

[0214] A power grid information acquisition module is used to obtain the power demand and power supply of the power grid;

[0215] The method of determining the aggregated power of multiple loads based on the power data of each of the n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated by using a pre-built multiple load aggregation model comprises:

[0216] Taking the first objective function as the target and the first constraint condition as the restriction, based on the power data of each type of load, using the load demand function to perform weighted sum calculation on the rigid load power and the flexible load power in the power data of each type of load, so as to calculate the power of each type of load;

[0217] The difference between the power demanded by the power grid and the power supplied by the power grid is multiplied by the set adjustment coefficient of each type of load to obtain the power adjustment amount of each type of load;

[0218] Based on the power and power regulation amount of each of the n types of loads, the aggregate power of multiple loads is calculated.

[0219] In an optional implementation, the total cost is the sum of the rigid load cost and the flexible load cost, the rigid load cost refers to the sum of the first costs of the n types of loads, the first cost is the product of the rigid load power corresponding to any type of load and the rigid load unit cost, the flexible load cost refers to the sum of the second costs of the n types of loads, the second cost is the product of the flexible load power corresponding to any type of load, the flexible load unit cost and the flexible load weight factor;

[0220] The power balance constraint is used to indicate that the sum of the rigid load powers of the n types of loads and the sum of the flexible load powers of the n types of loads are equal to the total required power when added together, wherein the flexible load power of each type of load is within the preset flexible load maximum range corresponding to the type of load.

[0221] In an optional implementation, the distributed power supply model includes at least one of the following: a probability density function of photovoltaic power generation output power, a wind power generation output power function;

[0222] The probability density function f(P) of the photovoltaic power generation output power is determined by the following formula:

[0223]

[0224] Among them, P max is the maximum output power of photovoltaic power generation, α is the first shape parameter of Beta distribution, β is the second shape parameter of Beta distribution, P is the actual output power of photovoltaic power generation, Γ() represents the gamma function, Represents the standardized output power of photovoltaic power generation;

[0225] The wind power output power function P m Determined by the following formula:

[0226]

[0227] Among them, C p is the wind energy utilization coefficient, ρ is the air density, V is the wind speed, A is the blade sweep area of ​​the wind turbine, P e is the rated power of the wind turbine, V in is the cut-in wind speed of the wind turbine, V e is the rated wind speed of the wind turbine, V out is the cut-out wind speed of the wind turbine.

[0228] In an optional implementation, the source-load matching model is constructed according to the second objective function and the second constraint condition;

[0229] The second objective function is used to indicate the goal of minimizing the operating cost, wherein the operating cost is the electricity purchase cost minus the electricity selling cost, wherein the electricity purchase cost is determined based on the electricity purchased from the main power grid and its price, and the electricity selling cost is determined based on the electricity sold to the main power grid and its price;

[0230] The second constraint condition includes at least one of the following: supply and demand balance constraint, photovoltaic output constraint, electricity selling power constraint, and grid operation constraint.

[0231] In an optional implementation, the performing source-load matching based on the multi-load aggregated power and distributed power source model using a pre-built source-load matching model to obtain a source-load matching result includes:

[0232] Taking the second objective function as the target and the second constraint condition as the restriction, an optimization algorithm is used to perform calculations based on the aggregated power of the multiple loads and the distributed power generation power indicated by the distributed power model to obtain a source-load matching result indicating a power grid operation plan, wherein the power grid operation plan includes a power purchase plan, a power sale plan and a distributed power generation plan.

[0233] In an optional implementation, the equipment capacity model includes a transformer capacity model and / or a transmission line capacity model;

[0234] The transformer capacity model is constructed based on the rated capacity, efficiency, loss and corresponding constraints of the transformer, and the constraints of the transformer include load capacity constraints, temperature rise limit constraints and / or voltage change constraints;

[0235] The transmission line capacity model is constructed based on the line parameters of the transmission line and corresponding constraints, and the constraints of the transmission line include power transmission capacity constraints and / or voltage drop constraints.

[0236] In an optional implementation, the distributed power access capability of the power grid to be evaluated is obtained by evaluating using the following formula:

[0237] P a =min(minC d (t),P m (t))

[0238] Among them, C d (t) = C r *μ(t) is the dynamic capacity of the equipment at time t, C r is the rated capacity of the equipment indicated by the equipment capacity model, μ(t) is the equipment efficiency and operating status coefficient at time t, and P m (t) is the source-load matching power at time t indicated by the source-load matching result, P a is the maximum access power that the power grid to be evaluated can accept at time t.

[0239] In an optional embodiment, the system further includes:

[0240] A power usage pattern recognition module, used to recognize the power usage patterns of the n types of loads, wherein the power usage patterns include at least one of the following: industrial load power usage pattern, agricultural load power usage pattern, and building load power usage pattern;

[0241] An adjustable potential determination module, used to determine the adjustable potential information of the n types of loads, wherein the adjustable potential information is used to characterize at least one of the following: a single adjustable potential of each type of load, and a collaborative adjustable potential between the n types of loads;

[0242] An aggregation model adjustment module is used to adjust the multi-load aggregation model based on the power consumption pattern and / or the adjustable potential information.

[0243] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0244] In a fourth aspect, an embodiment of the present application provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any of the methods described above when executing the computer program.

[0245] See also Figure 3The computer device of this embodiment includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301, such as an access estimation program taking into account distributed power sources, equipment capacity, and multiple electric energy alternative loads of a distribution network. When the processor 301 executes the computer program, the steps in the above-mentioned access estimation method embodiments taking into account distributed power sources, equipment capacity, and multiple electric energy alternative loads of a distribution network are implemented, such as Figure 1 Steps S101-S103 are shown.

[0246] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0247] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0248] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor 301 may also be any conventional processor, etc. The processor 301 is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0249] The memory 302 can be used to store the computer program and / or module. The processor 301 implements various functions of the computer device by running or executing the computer program and / or module stored in the memory 302 and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0250] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 301. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0251] In summary, the embodiments of the present application have at least the following beneficial effects:

[0252] According to an embodiment of the present application, the power data of each of n types of loads in the multi-energy replacement load of the power grid to be evaluated is used to determine the multi-load aggregate power by using a pre-constructed multi-load aggregation model, wherein n is a positive integer; based on the multi-load aggregate power and the distributed power supply model, source-load matching is performed using a pre-constructed source-load matching model to obtain a source-load matching result, wherein the distributed power supply model is used to simulate the output power of the distributed power supply; based on the source-load matching result and the equipment capacity model, the distributed power supply access capability of the power grid to be evaluated is evaluated, wherein the equipment capacity model is used to simulate the operating limit of the power grid equipment of the power grid to be evaluated, so as to adapt to the changes in the equipment capacity limit in the power grid in real time, and improve the evaluation accuracy of the access capability of the distributed power supply in the power grid in combination with the spatiotemporal characteristics of the multi-energy replacement load.

[0253] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary hardware platform, and of course, it can also be implemented entirely by hardware. Based on such an understanding, all or part of the contribution of the technical solution of the present application to the background technology can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0254] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. A method for calculating the access of distributed power sources, equipment capacity and multiple energy replacement loads of distribution networks, characterized in that: include: Based on the power data of n types of loads in the multi-type electric energy replacement loads of the power grid to be evaluated, the multi-type load aggregation power is determined by using a pre-constructed multi-type load aggregation model, where n is a positive integer; Based on the multi-load aggregated power and the distributed power source model, a pre-built source-load matching model is used to perform source-load matching to obtain a source-load matching result, wherein the distributed power source model is used to simulate the output power of the distributed power source; Based on the source-load matching result and the equipment capacity model, the distributed power access capability of the power grid to be evaluated is evaluated, wherein the equipment capacity model is used to simulate the operating limit of the power grid equipment of the power grid to be evaluated.

2. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of the distribution network as claimed in claim 1, characterized in that: The multi-load aggregation model is constructed according to the load demand function, the first objective function and the first constraint condition, the first objective function is used to indicate the goal of minimizing the total cost, and the first constraint condition includes a power balance constraint; The method further comprises: Obtain the power demand and power supply of the power grid; The method of determining the aggregated power of multiple loads based on the power data of each of the n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated by using a pre-built multiple load aggregation model comprises: Taking the first objective function as the target and the first constraint condition as the restriction, based on the power data of each type of load, using the load demand function to perform weighted sum calculation on the rigid load power and the flexible load power in the power data of each type of load, so as to calculate the power of each type of load; The difference between the power demanded by the power grid and the power supplied by the power grid is multiplied by the set adjustment coefficient of each type of load to obtain the power adjustment amount of each type of load; Based on the power and power regulation amount of each of the n types of loads, the aggregate power of multiple loads is calculated.

3. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of the distribution network as claimed in claim 2, characterized in that: The total cost is the sum of the rigid load cost and the flexible load cost, the rigid load cost refers to the sum of the first costs of the n types of loads, the first cost is the product of the rigid load power corresponding to any type of load and the rigid load unit cost, the flexible load cost refers to the sum of the second costs of the n types of loads, the second cost is the product of the flexible load power corresponding to any type of load, the flexible load unit cost and the flexible load weight factor; The power balance constraint is used to indicate that the sum of the rigid load powers of the n types of loads and the sum of the flexible load powers of the n types of loads are equal to the total required power when added together, wherein the flexible load power of each type of load is within the preset flexible load maximum range corresponding to the type of load.

4. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of the distribution network according to claim 1, characterized in that: The distributed power supply model includes at least one of the following: a probability density function of photovoltaic power generation output power, a wind power generation output power function; The probability density function f(P) of the photovoltaic power generation output power is determined by the following formula: Among them, P max is the maximum output power of photovoltaic power generation, α is the first shape parameter of Beta distribution, β is the second shape parameter of Beta distribution, P is the actual output power of photovoltaic power generation, Γ() represents the gamma function, Represents the standardized output power of photovoltaic power generation; The wind power output power function P m Determined by the following formula: Among them, C p is the wind energy utilization coefficient, ρ is the air density, V is the wind speed, A is the blade sweep area of ​​the wind turbine, P e is the rated power of the wind turbine, V in is the cut-in wind speed of the wind turbine, V e is the rated wind speed of the wind turbine, V out is the cut-out wind speed of the wind turbine.

5. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of the distribution network according to claim 1, characterized in that: The source-load matching model is constructed according to the second objective function and the second constraint condition; The second objective function is used to indicate the goal of minimizing the operating cost, wherein the operating cost is the electricity purchase cost minus the electricity selling cost, wherein the electricity purchase cost is determined based on the electricity purchased from the main power grid and its price, and the electricity selling cost is determined based on the electricity sold to the main power grid and its price; The second constraint condition includes at least one of the following: supply and demand balance constraint, photovoltaic output constraint, electricity selling power constraint, and grid operation constraint.

6. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of the distribution network as claimed in claim 5, characterized in that: The method of performing source-load matching based on the multi-load aggregated power and distributed power source model using a pre-built source-load matching model to obtain a source-load matching result includes: Taking the second objective function as the target and the second constraint condition as the restriction, an optimization algorithm is used to perform calculations based on the aggregated power of the multiple loads and the distributed power generation power indicated by the distributed power model to obtain a source-load matching result indicating a power grid operation plan, wherein the power grid operation plan includes a power purchase plan, a power sale plan and a distributed power generation plan.

7. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of the distribution network according to claim 1, characterized in that: The equipment capacity model includes a transformer capacity model and / or a transmission line capacity model; The transformer capacity model is constructed based on the rated capacity, efficiency, loss and corresponding constraints of the transformer, and the constraints of the transformer include load capacity constraints, temperature rise limit constraints and / or voltage change constraints; The transmission line capacity model is constructed based on the line parameters of the transmission line and corresponding constraints, and the constraints of the transmission line include power transmission capacity constraints and / or voltage drop constraints.

8. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network according to claim 1, characterized in that: The distributed power access capability of the power grid to be evaluated is obtained by evaluating the following formula: P a =min(minC d (t),P m (t)) Among them, C d (t) = C r *μ(t) is the dynamic capacity of the equipment at time t, C r is the rated capacity of the equipment indicated by the equipment capacity model, μ(t) is the equipment efficiency and operating status coefficient at time t, and P m (t) is the source-load matching power at time t indicated by the source-load matching result, P a is the maximum access power that the power grid to be evaluated can accept at time t.

9. The method for calculating the access of distributed power sources, equipment capacity and multiple electric energy replacement loads of the distribution network according to claim 1, characterized in that: The method further comprises: Identify the power usage mode of each of the n types of loads, wherein the power usage mode includes at least one of the following: an industrial load power usage mode, an agricultural load power usage mode, and a building load power usage mode; Determine the adjustable potential information of the n types of loads, wherein the adjustable potential information is used to characterize at least one of the following: a single adjustable potential of each type of load, and a collaborative adjustable potential between the n types of loads; The multi-element load aggregation model is adjusted based on the power usage pattern and / or the adjustable potential information.

10. A system for calculating access to distributed power sources, equipment capacity and multiple energy alternative loads of distribution networks, characterized in that: include: A power aggregation module, used for determining the aggregated power of multiple loads based on the power data of each of n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated, using a pre-built multiple load aggregation model, wherein n is a positive integer; A source-load matching module is used to perform source-load matching based on the multi-load aggregated power and the distributed power model using a pre-built source-load matching model to obtain a source-load matching result, wherein the distributed power model is used to simulate the output power of the distributed power; An access assessment module is used to assess the access capability of the distributed power source of the power grid to be assessed based on the source-load matching result and the equipment capacity model, wherein the equipment capacity model is used to simulate the operating limit of the power grid equipment of the power grid to be assessed.

11. The access calculation system taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 10, characterized in that: The multi-load aggregation model is constructed according to the load demand function, the first objective function and the first constraint condition, the first objective function is used to indicate the goal of minimizing the total cost, and the first constraint condition includes a power balance constraint; The system further comprises: A power grid information acquisition module is used to obtain the power demand and power supply of the power grid; The method of determining the aggregated power of multiple loads based on the power data of each of the n types of loads in the multiple electric energy replacement loads of the power grid to be evaluated by using a pre-built multiple load aggregation model comprises: Taking the first objective function as the target and the first constraint condition as the restriction, based on the power data of each type of load, using the load demand function to perform weighted sum calculation on the rigid load power and the flexible load power in the power data of each type of load, so as to calculate the power of each type of load; The difference between the power demanded by the power grid and the power supplied by the power grid is multiplied by the set adjustment coefficient of each type of load to obtain the power adjustment amount of each type of load; Based on the power and power regulation amount of each of the n types of loads, the aggregate power of multiple loads is calculated.

12. The access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 11, characterized in that: The total cost is the sum of the rigid load cost and the flexible load cost, the rigid load cost refers to the sum of the first costs of the n types of loads, the first cost is the product of the rigid load power corresponding to any type of load and the rigid load unit cost, the flexible load cost refers to the sum of the second costs of the n types of loads, the second cost is the product of the flexible load power corresponding to any type of load, the flexible load unit cost and the flexible load weight factor; The power balance constraint is used to indicate that the sum of the rigid load powers of the n types of loads and the sum of the flexible load powers of the n types of loads are equal to the total required power when added together, wherein the flexible load power of each type of load is within the preset flexible load maximum range corresponding to the type of load.

13. The access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 10, characterized in that: The distributed power supply model includes at least one of the following: a probability density function of photovoltaic power generation output power, a wind power generation output power function; The probability density function f(P) of the photovoltaic power generation output power is determined by the following formula: Among them, P max is the maximum output power of photovoltaic power generation, α is the first shape parameter of Beta distribution, β is the second shape parameter of Beta distribution, P is the actual output power of photovoltaic power generation, Γ() represents the gamma function, Represents the standardized output power of photovoltaic power generation; The wind power output power function P m Determined by the following formula: Among them, C p is the wind energy utilization coefficient, ρ is the air density, V is the wind speed, A is the blade sweep area of ​​the wind turbine, P e is the rated power of the wind turbine, V in is the cut-in wind speed of the wind turbine, V e is the rated wind speed of the wind turbine, V out is the cut-out wind speed of the wind turbine.

14. The access calculation system taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 10, characterized in that: The source-load matching model is constructed according to the second objective function and the second constraint condition; The second objective function is used to indicate the goal of minimizing the operating cost, wherein the operating cost is the electricity purchase cost minus the electricity selling cost, wherein the electricity purchase cost is determined based on the electricity purchased from the main power grid and its price, and the electricity selling cost is determined based on the electricity sold to the main power grid and its price; The second constraint condition includes at least one of the following: supply and demand balance constraint, photovoltaic output constraint, electricity selling power constraint, and grid operation constraint.

15. The access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 14, characterized in that: The method of performing source-load matching based on the multi-load aggregated power and distributed power source model using a pre-built source-load matching model to obtain a source-load matching result includes: Taking the second objective function as the target and the second constraint condition as the restriction, an optimization algorithm is used to perform calculations based on the aggregated power of the multiple loads and the distributed power generation power indicated by the distributed power model to obtain a source-load matching result indicating a power grid operation plan, wherein the power grid operation plan includes a power purchase plan, a power sale plan and a distributed power generation plan.

16. The access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 10, characterized in that: The equipment capacity model includes a transformer capacity model and / or a transmission line capacity model; The transformer capacity model is constructed based on the rated capacity, efficiency, loss and corresponding constraints of the transformer, and the constraints of the transformer include load capacity constraints, temperature rise limit constraints and / or voltage change constraints; The transmission line capacity model is constructed based on the line parameters of the transmission line and corresponding constraints, and the constraints of the transmission line include power transmission capacity constraints and / or voltage drop constraints.

17. The access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 10, characterized in that: The distributed power access capability of the power grid to be evaluated is obtained by evaluating the following formula: P a =min(minC d (t),P m (t)) Among them, C d (t) = C r *μ(t) is the dynamic capacity of the equipment at time t, C r is the rated capacity of the equipment indicated by the equipment capacity model, μ(t) is the equipment efficiency and operating status coefficient at time t, and P m (t) is the source-load matching power at time t indicated by the source-load matching result, P a is the maximum access power that the power grid to be evaluated can accept at time t.

18. The access calculation system for taking into account distributed power sources, equipment capacity and multiple electric energy replacement loads of distribution network as claimed in claim 10, characterized in that: The system further comprises: A power usage pattern recognition module, used to recognize the power usage patterns of the n types of loads, wherein the power usage patterns include at least one of the following: industrial load power usage pattern, agricultural load power usage pattern, and building load power usage pattern; An adjustable potential determination module, used to determine the adjustable potential information of the n types of loads, wherein the adjustable potential information is used to characterize at least one of the following: a single adjustable potential of each type of load, and a collaborative adjustable potential between the n types of loads; An aggregation model adjustment module is used to adjust the multi-load aggregation model based on the power consumption pattern and / or the adjustable potential information.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

20. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method according to any one of claims 1 to 9 when executing the computer program.

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