Multi-resource collaborative power system flexibility evaluation method and device, terminal equipment and storage medium

Abstract

The invention discloses a multi-resource collaborative power system flexibility evaluation method and device, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the following steps: acquiring historical output data, power transaction cost, resource operation data and power grid power flow data of a to-be-evaluated power system; constructing a power system time sequence model and model operation constraints according to typical daily source load output data, power transaction cost, resource operation data and power grid power flow data obtained according to historical output data; solving the power system time sequence model under the model operation constraint to obtain the output of each resource at each moment and the charging and discharging state of the energy storage equipment; and solving the constructed flexibility aggregation evaluation model according to the output of each resource at each moment and the charging and discharging state of the energy storage equipment to obtain geometrical characteristic parameters corresponding to the multi-resource coupling feasible region, and evaluating the to-be-evaluated power system to obtain a flexibility evaluation result. By implementing the method, the problem of low flexibility evaluation accuracy in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to a method, device, terminal device and storage medium for evaluating the flexibility of a power system with multi-resource collaboration. Background Art

[0002] Driven by the global energy transformation and the "dual carbon" goal, with the large-scale application of high-proportion renewable energy access and new loads, the uncertainty of power system operation increases, and the demand for flexibility supply capacity becomes more urgent. The traditional mode relying on a single power source is difficult to adapt to the complex scenarios of multi-source-network-load-storage multi-resource interaction. It is necessary to evaluate the flexibility of the power system under the multi-resource collaboration of the source, network, load and storage to support the safe and stable operation and efficient optimal dispatching of the power system.

[0003] The existing power system flexibility evaluation methods mainly focus on the regulation capabilities of single-type resources and use static or simplified models for independent analysis. For example, the flexibility of power sources such as thermal power and photovoltaic is often quantified separately by linear indicators such as ramp rate and output range. However, there are multi-dimensional coupling relationships among resources such as power sources, loads and energy storage in the actual operation process. Without considering the physical coupling constraints among multi-resources, and because simple linear weighting cannot accurately describe the true boundary of the feasible region, the accuracy of power system flexibility evaluation is low. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, terminal device and storage medium for evaluating the flexibility of a power system with multi-resource collaboration, which can effectively solve the problem that the existing technology does not consider the physical coupling constraints among multi-resources, and because simple linear weighting cannot accurately describe the true boundary of the feasible region, resulting in low accuracy of power system flexibility evaluation.

[0005] An embodiment of the present invention provides a method for evaluating the flexibility of a power system with multi-resource collaboration, including:

[0006] Obtaining historical output data, power trading costs, resource operation data and grid power flow data of the power system to be evaluated;

[0007] Predicting based on the historical output data to obtain typical day source-load output data;

[0008] Constructing a power system time series model and model operation constraints corresponding to the power system time series model according to the typical day source-load output data, the power trading costs, the resource operation data and the grid power flow data;

[0009] Based on the typical daily source-load output data, the electricity trading cost, the resource operation data, and the power grid power flow data, with the goal of minimizing the operation cost, under the operation constraints of the model, solve the power system time-series model to obtain the output of each resource at each moment and the charge and discharge states of the energy storage devices;

[0010] Construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of the energy storage devices;

[0011] Solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of the energy storage devices to obtain the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model;

[0012] Evaluate the power system to be evaluated according to the geometric characteristic parameters corresponding to the multi-resource coupling feasible region to obtain the flexibility evaluation result of the power system to be evaluated.

[0013] Further, the resource operation data includes: the number of electric vehicles, the number of energy storage devices, the number of photovoltaic units, the number of thermal power units, the number of hydropower units, the charging power of electric vehicles, the charging efficiency of electric vehicles, the discharging efficiency of electric vehicles, the discharging power of electric vehicles, the state of charge of electric vehicles, the maximum power of electric vehicles, the minimum power of electric vehicles, the charging power of energy storage devices, the discharging power of energy storage devices, the charging efficiency of energy storage devices, the discharging efficiency of energy storage devices, the state of charge of energy storage devices, the minimum power of energy storage devices, the maximum power of energy storage devices, the upper limit of the output of thermal power units, the lower limit of the output of thermal power units, the upper limit of the ramp rate of thermal power units, the lower limit of the ramp rate of thermal power units, the upper limit of the output of photovoltaic units, and the upper limit of the output of hydropower units; the power grid power flow data includes: the number of nodes, the upper limit of the node voltage amplitude, the lower limit of the node voltage amplitude, the active power injection of nodes, the reactive power injection of nodes, the node admittance matrix, the number of branches, the phase angle difference of branches, the minimum active power of branches, and the maximum active power of branches; the typical daily source-load output data includes: the number of typical daily moments, the original electrical load, and the electrical load after response;

[0014] The operation constraints of the model include: power balance constraint, electric vehicle operation constraint, thermal power unit operation constraint, photovoltaic unit output constraint, hydropower unit output constraint, energy storage operation constraint, power flow constraint, and demand response constraint;

[0015] The power balance constraint is:

[0016]

[0017] where P e,buy,t is the purchased power at time t; N ev is the number of electric vehicles; is the discharging efficiency of the i-th electric vehicle at time t; is the charging efficiency of the $i$-th electric vehicle at time $t$; $N$ es is the energy storage quantity; is the discharging power of the $j$-th energy storage at time $t$; is the charging power of the $j$-th energy storage at time $t$; $N$ pv is the number of photovoltaic units; is the predicted output of the $m$-th photovoltaic unit at time $t$; $N$ g is the number of thermal power units; $P$ g,k,t is the power of the $k$-th thermal power at time $t$; $N$ hy is the number of hydroelectric units; is the predicted output of the $n$-th hydroelectric unit at time $t$; $P$ load,t is the load after demand response at time $t$; $P$ e,out,t is the external power transmission at time $t$, $P$ e,cut,t is the curtailed power at time $t$;

[0018] The operating constraints of the electric vehicle are:

[0019]

[0020] Among them, is the discharging state of the $i$-th electric vehicle at time $t$; is the charging state of the $i$-th electric vehicle at time $t$; $P$ ev,i,max1 is the maximum discharging power of the $i$-th electric vehicle; $P$ ev,i,max2 is the maximum charging power of the $i$-th electric vehicle; $SOC$ i,t-1 is the state of charge of the $i$-th electric vehicle at $t - 1$; $SOC$ min is the minimum battery level of the electric vehicle; $SOC$ max is the maximum battery level of the electric vehicle; is the charging efficiency of the $i$-th electric vehicle; is the discharging efficiency of the $i$-th electric vehicle;

[0021] The operating constraints of the thermal power unit are:

[0022] $P$ g,k,b $\leq P$ g,k,t $\leq P$ g,k,max ;

[0023]

[0024] Among them, $P$ g,k,b is the lower limit of the output of the $k$-th thermal power unit; $P$ g,k,max is the upper limit of the output of the $k$-th thermal power unit, $P$ g,k,t is the output of the $k$-th thermal power unit at time $t$; $P$ g,k,t-1 is the output of the $k$-th thermal power unit at $t - 1$; is the ramp - up limit of the k - th thermal power unit; is the ramp - down limit of the k - th thermal power unit;

[0025] The output constraint of the photovoltaic unit is:

[0026]

[0027] where P pv,m,t is the optimized output of the m - th photovoltaic unit at time t; is the upper limit of the photovoltaic unit output;

[0028] The output constraint of the hydropower unit is:

[0029]

[0030] where P hy,n,t is the optimized output of the n - th photovoltaic unit at time t; is the upper limit of the hydropower unit output;

[0031] The operating constraint of the energy storage is:

[0032]

[0033] where is the discharge power of the j - th energy storage at time t; is the charge power of the j - th energy storage at time t; P es,j,max1 is the maximum discharge power of the j - th energy storage; P es,j,max2 is the maximum charge power of the j - th energy storage; U es,j,t is the charge - discharge state of the j - th energy storage unit at time t; is the charge efficiency of the j - th energy storage unit; is the discharge efficiency of the j - th energy storage unit; SOC j,t-1 represents the state of charge of the j - th energy storage unit at time t - 1; SOC es,min minimum energy storage capacity; SOC es,max is the maximum energy storage capacity;

[0034] The power flow constraint is:

[0035]

[0036] where P i,t is the active power injection at node i at time t; V i,t is the voltage magnitude of node i at time t; V j,t is the voltage magnitude of node j at time t; n Node is the number of nodes; N bus is the number of branches; Q i,tReactive power injection power of node i at time t; G ij,t Real part of the (i, j)-th element of the nodal admittance matrix; B ij,t Imaginary part of the (i, j)-th element of the nodal admittance matrix; θ ij,t Phase angle difference between both ends of branch ij at time t; Minimum active power allowed to flow through branch ij; Maximum active power allowed to flow through branch ij; V i max Upper limit of the voltage magnitude of node i; V i min Lower limit of the voltage magnitude of node i; P ij,t Active power flowing from node i to node j at time t;

[0037] The demand response constraint is:

[0038]

[0039] Among them, P sl,t,0 Maximum electrical load of the transferable response; T day Number of time instants of a typical day; P sl,t Transferable electrical load; P al,t Reducible electrical load; P al,t,0 Maximum electrical load of the reducible response; Original electrical load; P load,t Electrical load after response.

[0040] Furthermore, the electricity trading cost includes: unit power purchase cost and unit power abandonment cost;

[0041] The power system time-sequential model is:

[0042]

[0043] Among them, f1 is the operating cost; N T A scheduling period; c e,buy Unit power purchase cost; P e,buy,t Power purchase power at time t; P e,out,t Power transmitted out at time t; P e,cut,t Power abandonment power at time t; c e,cut Unit power abandonment cost.

[0044] Furthermore, according to the output of each resource at each time instant and the charge and discharge state of the energy storage device, a flexibility aggregation evaluation model is constructed, including:

[0045] According to the output of each resource at each moment, a flexibility calculation model for thermal power units, a flexibility calculation model for photovoltaic units, a flexibility calculation model for hydroelectric units, a flexibility calculation model for electric vehicles, a flexibility calculation model for energy storage, a flexibility calculation model for the capacity of external transmission channels, and a flexibility calculation model for demand response are respectively constructed;

[0046] Constrain the power changes corresponding to the flexibility calculation model for thermal power units, the flexibility calculation model for photovoltaic units, the flexibility calculation model for hydroelectric units, the flexibility calculation model for electric vehicles, the flexibility calculation model for energy storage, the flexibility calculation model for the capacity of external transmission channels, and the flexibility calculation model for demand response within a feasible region to generate a flexibility aggregation evaluation model.

[0047] Furthermore, the flexibility calculation model for thermal power units is as follows:

[0048]

[0049] where ΔP g,k,t is the output of the thermal power unit at each moment; ΔP g,t is the adjustable power of the thermal power unit at time t;

[0050] The flexibility calculation model for photovoltaic units is as follows:

[0051]

[0052] where ΔP pv,m,t is the output of the photovoltaic unit at each moment; ΔP pv,t is the adjustable power of the photovoltaic unit at time t;

[0053] The flexibility calculation model for hydroelectric units is as follows:

[0054]

[0055] where ΔP hy,n,t is the output of the hydroelectric unit at each moment; ΔP hy,t is the adjustable power of the hydroelectric unit at time t;

[0056] The flexibility calculation model for electric vehicles is as follows:

[0057]

[0058] where ΔP ev,j,t is the output of the electric vehicle at each moment; ΔP ev,t is the adjustable power of the electric vehicle at time t;

[0059] The flexibility calculation model for energy storage is as follows:

[0060]

[0061] Among them, ΔP es,j,t is the output of the energy storage at each moment; ΔP es,t is the adjustable power of the energy storage at time t;

[0062] The calculation model for the flexibility of the outbound channel quota is as follows:

[0063]

[0064] Among them, is the upper limit of the ramp of the outbound power per unit time; is the lower limit of the ramp of the outbound power per unit time; is the upper limit of the outbound at each moment; is the lower limit of the outbound at each moment; ΔP e,out,t is the adjustment amount of the outbound power at time t; is the minimum quota of the outbound channel;

[0065] The calculation model for the flexibility of demand response is as follows:

[0066]

[0067] 0 ≤ P al,t + ΔP al,t ≤ P al,t,0 ;

[0068] Among them, ΔP sl,t is the adjustment amount of the transferable load at time t; is the load transfer rate limit; Δt is the unit time period; ΔP al,t is the adjustment amount of the load that can be curtailed at time t.

[0069] Furthermore, according to the output of each resource at each moment and the charge-discharge state of the energy storage device, the flexibility aggregation evaluation model is solved to obtain the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model, including:

[0070] According to the output of each resource at each moment and the charge-discharge state of the energy storage device, the flexibility aggregation evaluation model is solved to obtain the polyhedron form of the flexibility aggregation evaluation model;

[0071] According to the polyhedron form of the flexibility aggregation evaluation model and the preset resource contribution weights, the multi-resource coupling feasible region of the flexibility aggregation evaluation model is solved;

[0072] According to the boundary area and the number of boundaries corresponding to the multi-resource coupling feasible region, the surface area of the feasible region is calculated;

[0073] According to the volume and the surface area of the feasible region corresponding to the multi-resource coupling feasible region, the surface-to-volume ratio of the feasible region is calculated;

[0074] Calculate the feasibility region roundness based on the dimension corresponding to the multi-resource coupling feasibility region, the surface ratio of the dimension sphere body surface, and the surface ratio of the feasibility region.

[0075] Take the surface area of the feasibility region, the surface ratio of the feasibility region, and the roundness of the feasibility region as the geometric feature parameters corresponding to the multi-resource coupling feasibility region.

[0076] Furthermore, evaluate the power system to be evaluated based on the geometric feature parameters corresponding to the multi-resource coupling feasibility region, and obtain the flexibility evaluation result of the power system to be evaluated, including:

[0077] Evaluate the power regulation range of the power system to be evaluated according to the surface area of the feasibility region and the surface ratio of the feasibility region.

[0078] Evaluate the resource constraints of the power system to be evaluated according to the surface ratio of the feasibility region.

[0079] Evaluate the resource regulation ability of the power system to be evaluated according to the roundness of the feasibility region, and obtain the flexibility evaluation result of the power system to be evaluated.

[0080] As an improvement of the above solution, another embodiment of the present invention correspondingly provides a flexibility evaluation device for a multi-resource collaborative power system, including:

[0081] A power system data acquisition module, configured to acquire historical output data, power trading costs, resource operation data, and grid power flow data of the power system to be evaluated.

[0082] A typical daily output data prediction module, configured to predict based on the historical output data to obtain typical daily source-load output data.

[0083] A first model construction module, configured to construct a power system time series model and the corresponding model operation constraints of the power system time series model according to the typical daily source-load output data, the power trading cost, the resource operation data, and the grid power flow data.

[0084] A first model solving module, configured to solve the power system time series model under the model operation constraints with the goal of minimizing the operation cost according to the typical daily source-load output data, the power trading cost, the resource operation data, and the grid power flow data, and obtain the output of each resource at each moment and the charge and discharge states of energy storage devices.

[0085] A second model construction module, configured to construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of energy storage devices.

[0086] A second model solving module, configured to solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge state of the energy storage device, so as to obtain the geometric feature parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model;

[0087] A flexibility evaluation module, configured to evaluate the power system to be evaluated according to the geometric feature parameters corresponding to the multi-resource coupling feasible region, so as to obtain the flexibility evaluation result of the power system to be evaluated.

[0088] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for evaluating the flexibility of a multi-resource collaborative power system as described in the above embodiment.

[0089] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a method for evaluating the flexibility of a multi-resource collaborative power system as described in the above embodiment.

[0090] By implementing the present invention, at least the following beneficial effects are achieved:

[0091] The present invention provides a method, device, terminal device, and storage medium for evaluating the flexibility of a multi-resource collaborative power system. The method can obtain the historical output data, power trading cost, resource operation data, and power grid power flow data of the power system to be evaluated; make a prediction according to the historical output data to obtain the source-load output data of a typical day; construct a power system time series model and the model operation constraints corresponding to the power system time series model according to the source-load output data of the typical day, the power trading cost, the resource operation data, and the power grid power flow data; solve the power system time series model under the model operation constraints with the goal of minimizing the operation cost according to the source-load output data of the typical day, the power trading cost, the resource operation data, and the power grid power flow data, so as to obtain the output of each resource at each moment and the charge and discharge state of the energy storage device; construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge state of the energy storage device; solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge state of the energy storage device, so as to obtain the geometric feature parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model; evaluate the power system to be evaluated according to the geometric feature parameters corresponding to the multi-resource coupling feasible region, so as to obtain the flexibility evaluation result of the power system to be evaluated.

[0092] Based on the resource operation data and power grid power flow data of each resource during actual operation, a time-series model of the power system and the corresponding model operation constraints of the time-series model of the power system are constructed. The typical daily source-load output data, power trading costs, resource operation data, and power grid power flow data are comprehensively considered in the model operation constraints, reflecting the physical coupling relationship between resources. The power grid power flow data is also used to construct relevant constraints, reflecting the power transmission relationship between each node and line in the power grid, thus considering the spatial coupling of resources. Therefore, while considering the physical coupling constraints of multiple resources, the output of each resource at each moment and the charge and discharge states of energy storage devices are obtained by solving, and the operation states of all resources are integrated to construct a flexibility aggregation evaluation model; by solving the flexibility aggregation evaluation model, the geometric characteristic parameters corresponding to the multi-resource coupling feasible region are obtained. The geometric characteristic parameters are calculated based on actual physical constraints and resource operation states, rather than simply using a linear weighting method. Through these geometric characteristic parameters, the shape and size of the feasible region can be more accurately described, avoiding the boundary distortion problem caused by simple linear weighting. Therefore, by comprehensively considering the physical coupling constraints between multiple resources and accurately describing the true boundary of the feasible region, the flexibility evaluation result of the power system is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 is a schematic flowchart of a method for evaluating the flexibility of a multi-resource collaborative power system provided by an embodiment of the present invention;

[0094] Figure 2 is a schematic structural diagram of a device for evaluating the flexibility of a multi-resource collaborative power system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0095] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0096] See Figure 1 , to solve the problem in the prior art that the physical coupling constraints between multiple resources are not considered, and because simple linear weighting cannot accurately describe the true boundary of the feasible region, resulting in low accuracy of the flexibility evaluation of the power system, an embodiment of the present invention provides a schematic flowchart of a method for evaluating the flexibility of a multi-resource collaborative power system, including:

[0097] S1. Obtain the historical output data, power trading costs, resource operation data, and power grid power flow data of the power system to be evaluated;

[0098] Specifically, the resources of the power system to be evaluated include electric vehicles, thermal power units, photovoltaic units, hydroelectric power units, and energy storage.

[0099] The resource operation data includes: the number of electric vehicles, the number of energy storage, the number of photovoltaic units, the number of thermal power units, the number of hydroelectric power units, the charging power of electric vehicles, the charging efficiency of electric vehicles, the discharging efficiency of electric vehicles, the discharging power of electric vehicles, the state of charge of electric vehicles, the maximum power of electric vehicles, the minimum power of electric vehicles, the charging power of energy storage, the discharging power of energy storage, the charging efficiency of energy storage, the discharging efficiency of energy storage, the state of charge of energy storage, the minimum power of energy storage, the maximum power of energy storage, the upper limit of the output of thermal power units, the lower limit of the output of thermal power units, the upper limit of the ramping rate of thermal power units, the lower limit of the ramping rate of thermal power units, the upper limit of the output of photovoltaic units, and the upper limit of the output of hydroelectric power units; The power grid power flow data includes: the number of nodes, the upper limit of the node voltage amplitude, the lower limit of the node voltage amplitude, the active power injection of the node, the reactive power injection of the node, the node admittance matrix, the number of branches, the phase angle difference of the branches, the minimum active power of the branches, and the maximum active power of the branches; The typical daily source-load output data includes: the number of typical daily time points, the original electrical load, and the electrical load after response. The power trading cost includes: the unit power purchase cost and the unit power abandonment cost.

[0100] S2. Perform prediction based on the historical output data to obtain the typical daily source-load output data;

[0101] Specifically, the typical daily source-load output data represents the typical curves that can reflect the variation of the power system's power output (such as photovoltaic, wind power, thermal power, etc.) and load demand (such as industrial, residential, and commercial loads) over time within a specific time scale (such as daily, weekly, monthly). Prediction based on the historical output data to obtain the typical daily source-load output data can be performed according to the existing technology of data preprocessing, feature extraction, clustering analysis, and typical day screening. The present invention does not make specific limitations.

[0102] In a preferred embodiment of the present invention, the historical output data can be first subjected to data cleaning and integration, processing missing values and outliers, and performing time alignment, then extracting the core features affecting the source-load output, such as time features, seasonal features, and load characteristics, and performing load prediction after clustering according to the core features to obtain the typical daily source-load output data. Among them, the typical daily source-load output data includes the predicted values of the output of each resource and the original load.

[0103] S3. Construct a power system time series model and the corresponding model operation constraints of the power system time series model according to the typical daily source-load output data, the power trading cost, the resource operation data, and the power grid power flow data;

[0104] Specifically, the model operation constraints include: power balance constraint, electric vehicle operation constraint, thermal power unit operation constraint, photovoltaic unit output constraint, hydropower unit output constraint, energy storage operation constraint, power flow constraint, and demand response constraint;

[0105] The power balance constraint is:

[0106]

[0107] wherein, P e,buy,t is the power purchase at time t; N ev is the number of electric vehicles; is the discharge efficiency of the i-th electric vehicle at time t; is the charging efficiency of the i-th electric vehicle at time t; N es is the number of energy storages; is the discharge power of the j-th energy storage at time t; is the charging power of the j-th energy storage at time t; N pv is the number of photovoltaic units; is the predicted output of the m-th photovoltaic unit at time t; N g is the number of thermal power units; P g,k,t is the power of the k-th thermal power at time t; N hy is the number of hydropower units; is the predicted output of the n-th hydropower unit at time t; P load,t is the load after demand response at time t; P e,out,t is the external power transmission at time t, P e,cut,t is the curtailed power at time t;

[0108] The electric vehicle operation constraint is:

[0109]

[0110] wherein, is the discharge state of the i-th electric vehicle at time t; is the charging state of the i-th electric vehicle at time t; P ev,i,max1 is the maximum discharge power of the i-th electric vehicle; P ev,i,max2 is the maximum charging power of the i-th electric vehicle; SOC i,t-1 is the state of charge of the i-th electric vehicle at time t-1; SOC min is the minimum battery level of the electric vehicle; SOC max is the maximum battery level of the electric vehicle; is the charging efficiency of the i-th electric vehicle; is the discharge efficiency of the i-th electric vehicle;

[0111] and is a 0-1 variable, indicating that the electric vehicle is in the discharging state, indicating that the electric vehicle is in the charging state when the vehicle is off-grid

[0112] The operating constraints of the thermal power unit are as follows:

[0113] P g,k,b ≤P g,k,t ≤P g,k,max ;

[0114]

[0115] where P g,k,b is the lower limit of the output of the k-th thermal power unit; P g,k,max is the upper limit of the output of the k-th thermal power unit, P g,k,t is the output of the k-th thermal power unit at time t; P g,k,t-1 is the output of the k-th thermal power unit at time t-1; is the upper limit of the ramp of the k-th thermal power unit; is the lower limit of the ramp of the k-th thermal power unit;

[0116] The output constraints of the photovoltaic unit are as follows:

[0117]

[0118] where P pv,m,t is the optimized output of the m-th photovoltaic unit at time t; is the upper limit of the output of the photovoltaic unit;

[0119] The output constraints of the hydropower unit are as follows:

[0120]

[0121] where P hy,n,t is the optimized output of the n-th photovoltaic unit at time t; is the upper limit of the output of the hydropower unit;

[0122] The operating constraints of the energy storage are as follows:

[0123]

[0124] where, is the discharging power of the j-th energy storage at time t; is the charging power of the j-th energy storage at time t; P es,j,max1 is the maximum discharging power of the j-th energy storage; P es,j,max2 is the maximum charging power of the j-th energy storage; U es,j,tis the charge and discharge state of the j-th energy storage unit at time t; is the charging efficiency of the j-th energy storage unit; is the discharging efficiency of the j-th energy storage unit; SOC j,t-1 represents the state of charge of the j-th energy storage unit at time t-1; SOC es,min minimum energy storage level; SOC es,max is the maximum energy storage level;

[0125] U es,j,t is the charge and discharge state of the j-th energy storage unit at time t, a 0-1 variable, U es,j,t When U = 0, the energy storage is in the charging state, U es,j,t When U = 1, the energy storage is in the discharging state.

[0126] The power flow constraint is:

[0127]

[0128] where P i,t is the active power injection at node i at time t; V i,t is the voltage magnitude of node i at time t; V j,t is the voltage magnitude of node j at time t; n Node is the number of nodes; N bus is the number of branches; Q i,t is the reactive power injection at node i at time t; G ij,t is the real part of the (i,j)-th element of the nodal admittance matrix; B ij,t is the imaginary part of the (i,j)-th element of the nodal admittance matrix; θ ij,t is the phase angle difference between the two ends of branch ij at time t; is the minimum active power allowed to flow through branch ij; is the maximum active power allowed to flow through branch ij; V i max is the upper limit of the voltage magnitude of node i; V i min is the lower limit of the voltage magnitude of node i; P ij,t is the active power flowing from node i to node j at time t;

[0129] The demand response constraint is:

[0130]

[0131] where P sl,t,0 is the maximum electrical load of the transferable response; T day is the number of time periods in a typical day; P sl,t is the transferable electrical load; P al,t is the curtailable electrical load; Pal,t,0 is the maximum electrical load for the reducible response; is the original electrical load; P load,t is the electrical load after response.

[0132] Specifically, the demand response constraint mainly optimizes the response amount to change the values of the original load at each moment in the power balance constraint. Since the demand response is an important regulation resource, it needs to be considered in the time series simulation.

[0133] In a preferred embodiment of the present invention, the power system time series model performs time series operation simulation on the power system to be evaluated, in order to obtain the actual output of each device of the power system to be evaluated at each moment to calculate the flexibility supply capacity of each device.

[0134] S4. According to the typical daily source-load output data, the power trading cost, the resource operation data, and the power grid power flow data, with the goal of minimizing the operation cost, under the model operation constraints, solve the power system time series model to obtain the output of each resource at each moment and the charge and discharge states of the energy storage devices;

[0135] Preferably, the power system time series model is:

[0136]

[0137] wherein, f1 is the operation cost; N T is a scheduling period; c e,buy is the unit power purchase cost; P e,buy,t is the power purchase power at time t; P e,out,t is the power transmitted out at time t; P e,cut,t is the curtailed power at time t; c e,cut is the unit curtailed power cost.

[0138] In a preferred embodiment of the present invention, with the goal of economy, taking the minimum operation cost as the goal, solve the power system time series model. According to the output of each resource at each moment and the model operation constraints of each resource, according to the typical daily source-load output data, the power trading cost, the resource operation data, and the power grid power flow data, obtain the output of each resource at each moment and the charge and discharge states of the energy storage devices. The charge and discharge states of the energy storage devices include the charge and discharge states of the energy storage and the charge and discharge states of the electric vehicles.

[0139] S5. Construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of the energy storage devices;

[0140] Preferably, constructing a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of the energy storage devices includes:

[0141] According to the output of each resource at each moment, respectively construct the flexibility calculation models of thermal power units, photovoltaic units, hydropower units, electric vehicles, energy storage, the flexibility calculation model of the external transmission channel quota, and the flexibility calculation model of demand response;

[0142] Constrain the power changes corresponding to the flexibility calculation models of thermal power units, photovoltaic units, hydropower units, electric vehicles, energy storage, the flexibility calculation model of the external transmission channel quota, and the flexibility calculation model of demand response within a feasible region to generate a flexibility aggregation evaluation model.

[0143] Specifically, the flexibility aggregation evaluation model represents an aggregation model of the flexibility supply capabilities of various types of flexibility resources in the power system to be evaluated. The flexibility calculation model of the thermal power unit is:

[0144]

[0145] Among them, ΔP g,k,t is the output of the thermal power unit at each moment; ΔP g,t is the adjustable power of the thermal power unit at time t;

[0146] The flexibility calculation model of the photovoltaic unit is:

[0147]

[0148] Among them, ΔP pv,m,t is the output of the photovoltaic unit at each moment; ΔP pv,t is the adjustable power of the photovoltaic unit at time t;

[0149] The flexibility calculation model of the hydropower unit is:

[0150]

[0151] Among them, ΔP hy,n,t is the output of the hydropower unit at each moment; ΔP hy,t is the adjustable power of the hydropower unit at time t;

[0152] The flexibility calculation model of the electric vehicle is:

[0153]

[0154] Among them, ΔP ev,j,t is the output of the electric vehicle at each moment; ΔP ev,t is the adjustable power of the electric vehicle at time t;

[0155] The flexibility calculation model of the energy storage is:

[0156]

[0157] Among them, ΔP es,j,t is the output of the energy storage at each moment; ΔP es,t is the adjustable power of the energy storage at time t;

[0158] The calculation model for the flexibility of the external transmission channel quota is as follows:

[0159]

[0160] Among them, is the upper limit of the ramp of the external transmission power per unit time; is the lower limit of the ramp of the external transmission power per unit time; is the upper limit of the external transmission at each moment; is the lower limit of the external transmission at each moment; ΔP e,out,t is the adjustment amount of the external transmission power at time t; is the minimum quota of the external transmission channel;

[0161] The calculation model for the flexibility of demand response is as follows:

[0162]

[0163] 0 ≤ P al,t + ΔP al,t ≤ P al,t,0 ;

[0164] Among them, ΔP sl,t is the adjustable amount of the transferable load at time t; is the load transfer rate limit; Δt is the unit time period; ΔP al,t is the adjustable amount of the curtailable load at time t.

[0165] In a preferred embodiment of the present invention, the adjustment ability of the external transmission power is restricted by the dynamic thermal stability limit and the protocol of the transmission channel. The adjustment amount of the external transmission power at time t is defined as ΔP e,out,t . Among them, is for upward adjustment, is for downward adjustment.

[0166] In a preferred embodiment of the present invention, the power change constraints corresponding to the flexibility calculation models of thermal power units, photovoltaic units, hydroelectric units, electric vehicles, energy storage, the flexibility calculation model of the external transmission channel quota, and the flexibility calculation model of demand response are placed within a feasible region to generate a flexibility aggregation evaluation model:

[0167] Among them, p is the power state of various flexibility resources, that is, the output of each resource at each moment; y is the adjustable output range of thermal power units; Δw is the aggregated adjustable capacity; A, B, C, and b are corresponding preset coefficient matrices.

[0168] S6. Solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge state of the energy storage device, and obtain the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model.

[0169] Specifically, solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge state of the energy storage device, and obtain the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model, including:

[0170] Solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge state of the energy storage device, and obtain the polyhedron form of the flexibility aggregation evaluation model.

[0171] Solve the multi-resource coupling feasible region of the flexibility aggregation evaluation model according to the polyhedron form of the flexibility aggregation evaluation model and the preset resource contribution weights.

[0172] Calculate the surface area of the feasible region according to the boundary area and the number of boundaries corresponding to the multi-resource coupling feasible region.

[0173] Calculate the surface-to-volume ratio of the feasible region according to the volume and the surface area of the feasible region corresponding to the multi-resource coupling feasible region.

[0174] Calculate the roundness of the feasible region according to the dimension, the surface-to-volume ratio of the dimensional sphere, and the surface-to-volume ratio of the feasible region corresponding to the multi-resource coupling feasible region.

[0175] Take the surface area of the feasible region, the surface-to-volume ratio of the feasible region, and the roundness of the feasible region as the geometric characteristic parameters corresponding to the multi-resource coupling feasible region.

[0176] In a preferred embodiment of the present invention, the flexibility aggregation evaluation model has the following polyhedron form:

[0177]

[0178] U = {u|B T u = 0, -1 ≤ u ≤ 0};

[0179] Among them, A describes the influence of the flexibility resource power state on the constraints; B is associated with the constraints of the thermal power unit regulation ability such as the ramp rate; C is the weight of the flexibility contribution of different resources, that is, the preset resource contribution weight; b is the system operation boundary constant term, u is the slack variable of the duality principle, U is the set of slack variables, B TIt is the transpose of the coefficient matrix B. u is the Lagrange multiplier introduced in the dual problem, which is used to transform the inequality constraint Ap + By + CΔw ≤ b in the original problem into the extreme value condition in the dual space.

[0180] In the polyhedron form of the flexibility aggregation evaluation model, that is, the geometric characteristics of the multi - resource coupling feasible region are used to evaluate the supply capacity of the power system. The surface area of the feasible region, the surface - to - volume ratio of the feasible region, and the roundness of the feasible region, as the geometric characteristic parameters corresponding to the multi - resource coupling feasible region, are used as evaluation indicators. The surface area S of the feasible region M and the surface - to - volume ratio L of the feasible region M have the following calculation formulas:

[0181]

[0182] In the formula, P is the number of boundaries corresponding to the multi - resource coupling feasible region, S sei is the area of the boundary p of the multi - resource coupling feasible region, and V M is the volume of the multi - resource coupling feasible region.

[0183] The roundness I of the feasible region M has the following calculation formula:

[0184]

[0185] In the formula, a is the dimension corresponding to the multi - resource coupling feasible region, and L o is the surface - to - volume ratio of the a - dimensional sphere, that is, the surface - to - volume ratio of the dimensional sphere.

[0186] S7. According to the geometric characteristic parameters corresponding to the multi - resource coupling feasible region, evaluate the power system to be evaluated, and obtain the flexibility evaluation result of the power system to be evaluated.

[0187] Preferably, according to the geometric characteristic parameters corresponding to the multi - resource coupling feasible region, evaluate the power system to be evaluated, and obtain the flexibility evaluation result of the power system to be evaluated, including:

[0188] Evaluate the power adjustment range of the power system to be evaluated according to the surface area of the feasible region and the surface - to - volume ratio of the feasible region;

[0189] Evaluate the resource constraints of the power system to be evaluated according to the surface - to - volume ratio of the feasible region;

[0190] Evaluate the resource adjustment ability of the power system to be evaluated according to the roundness of the feasible region, and obtain the flexibility evaluation result of the power system to be evaluated.

[0191] In a preferred embodiment of the present invention, the larger the volume of the feasible region, the wider the power regulation range that the power system to be evaluated can provide. A high surface-to-volume ratio of the feasible region indicates that the constraint conditions of the power system to be evaluated are mutually compatible. For example, there is no conflict between the energy storage charging and discharging rate and the thermal power ramp rate, and the power system to be evaluated can achieve the maximum regulation capacity at the minimum constraint cost. A power system with a high surface-to-volume ratio of the feasible region requires a lower reserve capacity under the same flexibility requirements. A high roundness of the feasible region indicates that the power system to be evaluated has uniform capabilities in the regulation directions of various resources. For example, the regulation capabilities in the scenarios of sudden drops in wind and solar power output and sudden increases in load are symmetric.

[0192] The prior art mainly focuses on the regulation capabilities of single-type resources and conducts independent analyses using static or simplified models. For example, the prediction of electric vehicle charging and discharging loads is mostly based on fixed charging demand distributions or historical averages, ignoring the impact of charging and discharging behavior decisions on electric vehicle loads. The flexibility of power sources such as thermal power and photovoltaic is often quantified separately through linear indicators such as ramp rates and output ranges, without considering the physical coupling constraints among multiple resources. In terms of flexibility aggregation, existing methods usually linearly weight each resource, and the evaluation indicators focus on a single dimension such as the maximum regulation capacity and ramp time. It is difficult to consider new grid-connected subjects such as electric vehicle clusters. At the same time, when aggregating the adjustable capabilities, each resource is linearly weighted, or the feasible region is simplified into an independent hypercube or rectangular region, and the system-level regulation capacity is generated through linear superposition, resulting in distortion of the feasible region boundary. The evaluation indicators focus on a single dimension such as the maximum regulation capacity and ramp time, lacking quantitative analysis of the geometric characteristics (such as volume and surface complexity) of the flexibility feasible region. In this embodiment, a polyhedron-based aggregation model is constructed, and constraints such as the thermal power output range and energy storage state are embedded in the coefficient matrix to accurately represent the multi-resource coupling feasible region. The volume of the feasible region is introduced to reflect the regulation capacity, the surface-to-volume ratio is used to quantify the constraint complexity, and the roundness is used to evaluate the degree to which the feasible region approaches an ideal sphere, comprehensively supporting the robustness analysis of dispatching strategies. At the same time, through the coefficient matrix and dual slack variables, the multi-resource constraints are uniformly expressed in polyhedron form to solve the mathematical compatibility problem of heterogeneous constraints. The volume, surface-to-volume ratio, and roundness are defined as evaluation indicators, breaking through the limitations of traditional single-capacity indicators and quantifying the geometry of the feasible region.

[0193] By implementing this embodiment, according to the resource operation data of each resource during the actual operation process and the power grid power flow data, a power system time series model and the corresponding model operation constraints of the power system time series model are constructed. The typical daily source-load output data, power trading costs, resource operation data, and power grid power flow data are comprehensively considered in the model operation constraints, reflecting the physical coupling relationship between resources. The power grid power flow data is also used to construct relevant constraints, reflecting the power transmission relationship between each node and line in the power grid, thus considering the spatial coupling of resources. Therefore, while considering the physical coupling constraints of multiple resources, the output of each resource at each moment and the charge and discharge state of the energy storage device are obtained by solving, and the operation states of all resources are integrated together to construct a flexibility aggregation evaluation model; by solving the flexibility aggregation evaluation model, the geometric characteristic parameters corresponding to the multi-resource coupling feasible region are obtained. The geometric characteristic parameters are calculated based on the actual physical constraints and resource operation states, rather than simply using a linear weighting method. Through these geometric characteristic parameters, the shape and size of the feasible region can be more accurately described, avoiding the boundary distortion problem caused by simple linear weighting. Thus, by comprehensively considering the physical coupling constraints between multiple resources and accurately describing the true boundary of the feasible region, the power system flexibility evaluation result is more accurate.

[0194] See Figure 2 , which is a schematic structural diagram of a power system flexibility evaluation device for multi-resource collaboration provided by an embodiment of the present invention, including:

[0195] A power system data acquisition module, configured to acquire the historical output data, power trading costs, resource operation data, and power grid power flow data of the power system to be evaluated;

[0196] A typical daily output data prediction module, configured to perform prediction based on the historical output data to obtain typical daily source-load output data;

[0197] A first model construction module, configured to construct a power system time series model and the corresponding model operation constraints of the power system time series model according to the typical daily source-load output data, the power trading costs, the resource operation data, and the power grid power flow data;

[0198] A first model solving module, configured to solve the power system time series model under the model operation constraints with the goal of minimizing the operation cost according to the typical daily source-load output data, the power trading costs, the resource operation data, and the power grid power flow data, to obtain the output of each resource at each moment and the charge and discharge state of the energy storage device;

[0199] A second model construction module, configured to construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge state of the energy storage device;

[0200] The second model solving module is configured to solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of energy storage devices, and obtain the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model;

[0201] The flexibility evaluation module is configured to evaluate the power system to be evaluated according to the geometric characteristic parameters corresponding to the multi-resource coupling feasible region, and obtain the flexibility evaluation result of the power system to be evaluated.

[0202] The present invention provides a power system flexibility evaluation device for multi-resource collaboration. The power system data acquisition module acquires the historical output data, power trading cost, resource operation data, and grid power flow data of the power system to be evaluated; the typical daily output data prediction module predicts based on the historical output data to obtain the typical daily source-load output data; the first model construction module constructs a power system time series model and the corresponding model operation constraints of the power system time series model according to the typical daily source-load output data, the power trading cost, the resource operation data, and the grid power flow data; the first model solving module solves the power system time series model under the model operation constraints with the goal of minimizing the operation cost according to the typical daily source-load output data, the power trading cost, the resource operation data, and the grid power flow data, and obtains the output of each resource at each moment and the charge and discharge states of energy storage devices; the second model construction module constructs a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of energy storage devices; the second model solving module solves the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of energy storage devices, and obtains the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model; finally, the flexibility evaluation module evaluates the power system to be evaluated according to the geometric characteristic parameters corresponding to the multi-resource coupling feasible region, and obtains the flexibility evaluation result of the power system to be evaluated.

[0203] Based on the resource operation data and power grid power flow data of each resource during actual operation, a time-series model of the power system and the corresponding model operation constraints of the power system time-series model are constructed. The typical daily source-load output data, power trading costs, resource operation data, and power grid power flow data are comprehensively considered in the model operation constraints, which reflects the physical coupling relationship between resources. The power grid power flow data is also used to construct relevant constraints, reflecting the power transmission relationship between each node and line in the power grid, thus considering the spatial coupling of resources. Therefore, while considering the physical coupling constraints of multiple resources, the output of each resource at each moment and the charge-discharge state of the energy storage device are obtained through solution. The operation states of all resources are integrated together to construct a flexibility aggregation evaluation model; by solving the flexibility aggregation evaluation model, the geometric characteristic parameters corresponding to the multi-resource coupling feasible region are obtained. The geometric characteristic parameters are calculated based on the actual physical constraints and resource operation states, rather than simply using a linear weighting method. Through these geometric characteristic parameters, the shape and size of the feasible region can be more accurately described, avoiding the boundary distortion problem caused by simple linear weighting. Thus, by comprehensively considering the physical coupling constraints between multiple resources and accurately describing the true boundary of the feasible region, the flexibility evaluation result of the power system is made more accurate.

[0204] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0205] Those skilled in the art can clearly understand that for the convenience and conciseness, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0206] Another embodiment of the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for evaluating the flexibility of a multi-resource collaborative power system as described in the above embodiment. The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0207] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device and connects various parts of the entire terminal device using various interfaces and lines.

[0208] The memory can be used to store the computer program. The processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include high-speed random access memory and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0209] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for evaluating the flexibility of a multi-resource collaborative power system described in the above embodiment.

[0210] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0211] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art of the present technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A flexibility assessment method for a power system with multi-resource collaboration, characterized in that, Including: Obtain the historical output data, power trading costs, resource operation data, and grid power flow data of the power system to be evaluated; Perform prediction based on the historical output data to obtain the typical day source-load output data; Construct a power system time series model and the corresponding model operation constraints for the power system time series model according to the typical day source-load output data, the power trading costs, the resource operation data, and the grid power flow data; Taking the minimum operation cost as the goal, solve the power system time series model under the model operation constraints according to the typical day source-load output data, the power trading costs, the resource operation data, and the grid power flow data to obtain the output of each resource at each moment and the charge and discharge states of the energy storage device; Construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of the energy storage device; Solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of the energy storage device to obtain the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model; Evaluate the power system to be evaluated according to the geometric characteristic parameters corresponding to the multi-resource coupling feasible region to obtain the flexibility evaluation result of the power system to be evaluated.

2. The flexibility evaluation method of a multi-resource collaborative power system according to claim 1, characterized in that The resource operation data includes: the number of electric vehicles, the number of energy storage devices, the number of photovoltaic units, the number of thermal power units, the number of hydropower units, the charging power of electric vehicles, the charging efficiency of electric vehicles, the discharging efficiency of electric vehicles, the discharging power of electric vehicles, the state of charge of electric vehicles, the maximum power of electric vehicles, the minimum power of electric vehicles, the charging power of energy storage devices, the discharging power of energy storage devices, the charging efficiency of energy storage devices, the discharging efficiency of energy storage devices, the state of charge of energy storage devices, the minimum power of energy storage devices, the maximum power of energy storage devices, the upper limit of the output of thermal power units, the lower limit of the output of thermal power units, the upper limit of the ramping of thermal power units, the lower limit of the ramping of thermal power units, the upper limit of the output of photovoltaic units, and the upper limit of the output of hydropower units; The grid power flow data includes: the number of nodes, the upper limit of the node voltage amplitude, the lower limit of the node voltage amplitude, the active power injection of the node, the reactive power injection of the node, the node admittance matrix, the number of branches, the phase angle difference of the branches, the minimum active power of the branches, and the maximum active power of the branches; The typical day source-load output data includes: the number of typical day moments, the original electrical load, and the electrical load after response; The model operation constraints include: power balance constraints, electric vehicle operation constraints, thermal power unit operation constraints, photovoltaic unit output constraints, hydropower unit output constraints, energy storage operation constraints, power flow constraints, and demand response constraints; The power balance constraint is: Among them, P e,buy,t is the purchased power at time t; N ev is the number of electric vehicles; is the discharge efficiency of the i-th electric vehicle at time t; is the charging efficiency of the i-th electric vehicle at time t; N es is the quantity of energy storage; is the discharge power of the j-th energy storage at time t; is the charging power of the j-th energy storage at time t; N pv is the number of photovoltaic units; is the predicted output of the m-th photovoltaic unit at time t; N g is the number of thermal power units; P g,k,t is the power of the k-th thermal power at time t; N hy is the number of hydroelectric units; is the predicted output of the n-th hydroelectric unit at time t; P load,t is the load after demand response at time t; P e,out,t is the external power transmission at time t, P e,cut,t is the curtailed power at time t; The electric vehicle operation constraint is: wherein, is the discharging state of the i-th electric vehicle at time t; is the charging state of the i-th electric vehicle at time t; P ev,i,max1 is the maximum discharging power of the i-th electric vehicle; P ev,i,max2 is the maximum charging power of the i-th electric vehicle; SOC i,t-1 is the state of charge of the i-th electric vehicle at time t-1; SOC min is the minimum battery level of the electric vehicle; SOC max is the maximum battery level of the electric vehicle; is the charging efficiency of the i-th electric vehicle; is the discharging efficiency of the i-th electric vehicle; The thermal power unit operation constraint is: P g,k,b ≤P g,k,t ≤P g,k,max ; Among them, P g,k,b is the lower limit of the output of the k-th thermal power unit; P g,k,max is the upper limit of the output of the k-th thermal power unit, P g,k,t is the output of the k-th thermal power unit at time t; P g,k,t-1 is the output of the k-th thermal power unit at time t - 1; is the upper limit of the ramp of the k-th thermal power unit; is the lower limit of the ramp of the k-th thermal power unit; The photovoltaic unit output constraint is: Among them, P pv,m,t is the optimized output of the m-th photovoltaic unit at time t; is the upper limit of the output of the photovoltaic unit; The hydropower unit output constraint is: Among them, P hy,n,t is the optimized output of the nth photovoltaic unit at time t; is the upper limit of the output of the hydropower unit; The energy storage operation constraint is: Among them, is the discharge power of the j-th energy storage at time t; is the charging power of the j-th energy storage at time t; P es,j,max1 is the maximum discharge power of the j-th energy storage; P es,j,max2 is the maximum charging power of the j-th energy storage; U es,j,t is the charge-discharge state of the j-th energy storage unit at time t; is the charging efficiency of the j-th energy storage unit; is the discharge efficiency of the j-th energy storage unit; SOC j,t-1 represents the state of charge of the j-th energy storage unit at time t-1; SOC es,min minimum energy storage power; SOC es,max is the maximum energy storage power; The power flow constraint is: Among them, P i,t is the active injection power of node i at time t; V i,t is the voltage amplitude of node i at time t; V j,t is the voltage amplitude of node j at time t; n Node is the number of nodes; N bus is the number of branches; Q i,t is the reactive injection power of node i at time t; G ij,t is the real part of the i-th row and j-th column in the node admittance matrix; B ij,t is the imaginary part of the i-th row and j-th column in the node admittance matrix; θ ij,t is the phase angle difference between both ends of branch ij at time t; is the minimum active power allowed to flow through branch ij; is the maximum active power allowed to flow through branch ij; V i max is the upper limit of the voltage amplitude of node i; V i min is the lower limit of the voltage amplitude of node i; P ij,t is the active power flowing from node i to node j at time t; The demand response constraint is: Among them, P sl,t,0 is the maximum electrical load of the transferable response; T day is the number of time points of a typical day; P sl,t is the transferable electrical load; P al,t is the curtailable electrical load; P al,t,0 is the maximum electrical load of the curtailable response; is the original electrical load; P load,t is the electrical load after response.

3. A method for evaluating the flexibility of a multi-resource collaborative power system according to claim 1, characterized in that, The power trading costs include: the unit power purchase cost and the unit power abandonment cost; The power system time series model is: Among them, f1 is the operating cost; N T is a scheduling period; c e,buy is the unit power purchase cost; P e,buy,t is the power purchase power at time t; P e,out,t is the power transmitted out at time t; P e,cut,t is the curtailed power at time t; c e,cut is the unit curtailed power cost.

4. A flexibility evaluation method for a multi-resource collaborative power system according to claim 1, characterized in that Construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge and discharge states of the energy storage device, including: According to the output of each resource at each moment, a flexibility calculation model for thermal power units, a flexibility calculation model for photovoltaic units, a flexibility calculation model for hydropower units, a flexibility calculation model for electric vehicles, a flexibility calculation model for energy storage, a flexibility calculation model for the transmission channel quota, and a flexibility calculation model for demand response are respectively constructed; The power change constraints corresponding to the flexibility calculation model for thermal power units, the flexibility calculation model for photovoltaic units, the flexibility calculation model for hydropower units, the flexibility calculation model for electric vehicles, the flexibility calculation model for energy storage, the flexibility calculation model for the transmission channel quota, and the flexibility calculation model for demand response are constrained within a feasible region to generate a flexibility aggregation evaluation model.

5. The flexibility assessment method of a multi-resource collaborative power system according to claim 4, characterized in that The flexibility calculation model for thermal power units is: Among them, ΔP g,k,t is the output of the thermal power unit at each moment; ΔP g,t is the adjustable power of the thermal power unit at time t; The flexibility calculation model for photovoltaic units is: Among them, ΔP pv,m,t is the output of each photovoltaic unit at each moment; ΔP pv,t is the adjustable power of the photovoltaic unit at time t; The flexibility calculation model for hydropower units is: Among them, ΔP hy,n,t is the output of the hydropower unit at each moment; ΔP hy,t is the adjustable power of the hydropower unit at time t; The flexibility calculation model for electric vehicles is: where, ΔP ev,j,t is the output of the electric vehicle at each moment; ΔP ev,t is the adjustable power of the electric vehicle at time t; The flexibility calculation model for energy storage is: Among them, ΔP es,j,t is the output of the energy storage at each moment; ΔP es,t is the adjustable power of the energy storage at time t; The flexibility calculation model for the transmission channel quota is: Among them, is the upper limit of the ramp rate of the external transmission power per unit time; is the lower limit of the ramp rate of the external transmission power per unit time; is the upper limit of the external transmission at each moment; is the lower limit of the external transmission at each moment; ΔP e,out,t is the adjustment amount of the external transmission power at time t; is the minimum amount of the external transmission channel; The flexibility calculation model for demand response is: 0 ≤ P al,t + ΔP al,t ≤ P al,t,0 ; where, ΔP sl,t is the adjustable transferable load at time t; is the load transfer rate limit; Δt is the unit time period; ΔP al,t is the adjustable curtailable load at time t.

6. The flexibility evaluation method of a multi-resource collaborative power system according to claim 1, characterized in that According to the output of each resource at each moment and the charge and discharge state of the energy storage device, the flexibility aggregation evaluation model is solved to obtain the geometric characteristic parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model, including: According to the output of each resource at each moment and the charge and discharge state of the energy storage device, the flexibility aggregation evaluation model is solved to obtain the polyhedron form of the flexibility aggregation evaluation model; According to the polyhedron form of the flexibility aggregation evaluation model and the preset resource contribution weights, the multi-resource coupling feasible region of the flexibility aggregation evaluation model is solved; According to the boundary area and the number of boundaries corresponding to the multi-resource coupling feasible region, the surface area of the feasible region is calculated; According to the volume and the surface area of the feasible region corresponding to the multi-resource coupling feasible region, the surface-to-volume ratio of the feasible region is calculated; According to the dimension, the surface-to-volume ratio of the dimensional sphere, and the surface-to-volume ratio of the feasible region corresponding to the multi-resource coupling feasible region, the roundness of the feasible region is calculated; The surface area of the feasible region, the surface-to-volume ratio of the feasible region, and the roundness of the feasible region are used as the geometric characteristic parameters corresponding to the multi-resource coupling feasible region.

7. The method for evaluating the flexibility of a multi - resource collaborative power system according to claim 6, wherein, According to the geometric characteristic parameters corresponding to the multi-resource coupling feasible region, the power system to be evaluated is evaluated to obtain the flexibility evaluation result of the power system to be evaluated, including: According to the surface area of the feasible region and the surface-to-volume ratio of the feasible region, the power adjustment range of the power system to be evaluated is evaluated; According to the surface-to-volume ratio of the feasible region, the resource constraints of the power system to be evaluated are evaluated; According to the roundness of the feasible region, the resource adjustment ability of the power system to be evaluated is evaluated to obtain the flexibility evaluation result of the power system to be evaluated.

8. A flexibility evaluation device for a power system with multi-resource collaboration, characterized in that, Including: A power system data acquisition module for acquiring the historical output data, power trading costs, resource operation data, and grid power flow data of the power system to be evaluated; A typical daily output data prediction module for predicting based on the historical output data to obtain the typical daily source-load output data; The first model construction module is used to construct a power system time-series model and the corresponding model operation constraints of the power system time-series model according to the typical daily source-load output data, the power trading cost, the resource operation data, and the power grid power flow data; The first model solving module is used to solve the power system time-series model under the model operation constraints with the goal of minimizing the operation cost according to the typical daily source-load output data, the power trading cost, the resource operation data, and the power grid power flow data, so as to obtain the output of each resource at each moment and the charge-discharge state of the energy storage device; The second model construction module is used to construct a flexibility aggregation evaluation model according to the output of each resource at each moment and the charge-discharge state of the energy storage device; The second model solving module is used to solve the flexibility aggregation evaluation model according to the output of each resource at each moment and the charge-discharge state of the energy storage device, so as to obtain the geometric feature parameters corresponding to the multi-resource coupling feasible region of the flexibility aggregation evaluation model; The flexibility evaluation module is used to evaluate the power system to be evaluated according to the geometric feature parameters corresponding to the multi-resource coupling feasible region, so as to obtain the flexibility evaluation result of the power system to be evaluated.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for evaluating the flexibility of a multi-resource collaborative power system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a method for evaluating the flexibility of a multi-resource collaborative power system according to any one of claims 1 to 7.

Images