Power system inertia analysis method considering new energy uncertainty

Abstract

The invention relates to a power system inertia analysis method considering new energy uncertainty. The method comprises the steps of determining a new energy output uncertainty interval model of various new energy generator sets in a target power system based on new energy prediction data of the target power system; determining a first deterministic constraint condition for the target power system and a constraint condition considering the wind and light uncertainty, converting the constraint condition considering the wind and light uncertainty into a second deterministic constraint condition based on the new energy output uncertainty interval model and the interval possibility, and constructing a deterministic inertia interval evaluation model; and splitting the deterministic inertia interval evaluation model into an optimal sub-model and a worst sub-model, and performing inertia calculation by using the optimal sub-model and the worst sub-model based on the first deterministic constraint condition and the second deterministic constraint condition to obtain a system inertia interval. By adopting the method, an accurate system inertia interval can be obtained, so that the system inertia trend and the inertia adequacy can be analyzed.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method for analyzing inertia of power systems taking into account the uncertainty of new energy sources. Background Art

[0002] In recent years, with the significant increase in the proportion of installed renewable energy generation capacity and DC feed-in capacity, the operating capacity of traditional generator sets has continued to decline, resulting in a downward trend in the power system's rotational inertia, posing severe challenges to the safe and stable operation of the power grid. Furthermore, the inherent random fluctuations and intermittent nature of renewable energy generation not only increase uncertainty on the power supply side, but also increase the risk of low-inertia operation in the power system.

[0003] Inertia assessment in power systems with a high proportion of renewable energy is a current hot research topic. Most inertia assessment work focuses on using frequency-active power measurement data after a disturbance fault to estimate the inertia level offline or online using system identification or fitting algorithms. Currently, research analyzing the changing trends of system inertia levels over short-term, medium-term, and long-term timescales is relatively rare. Under normal conditions, the inertia level of a power system is affected by multiple factors, including changes in load demand, unit startup and shutdown, renewable energy penetration, and tie-line power adjustments. Therefore, it is necessary to comprehensively consider the coupling of these factors to study the evolution of system inertia.

[0004] Research on system inertia prediction and analysis focuses on analyzing the correlation between various influencing factors and the system's equivalent inertia level, thereby deducing the trend of system inertia changes. However, this inertia trend assessment still lacks explicit consideration of the impact of renewable energy output uncertainty. Deterministic assessment frameworks fail to capture the impact of the random fluctuations of renewable energy on unit startup and shutdown, potentially causing inertia trend assessment results to deviate from actual operating conditions, leading to the risk of misjudging inertia adequacy. Summary of the Invention

[0005] Based on this, it is necessary to provide a power system inertia analysis method that takes into account the uncertainty of new energy sources and can obtain accurate system inertia range to address the above technical problems.

[0006] A method for analyzing power system inertia considering the uncertainty of new energy sources, the method comprising:

[0007] Determining, based on the renewable energy forecast data of the target power system, a renewable energy output uncertainty interval model for each renewable energy generator set in the target power system;

[0008] Determining a first deterministic constraint condition for the target power system and a constraint condition considering wind and solar uncertainty, and converting the constraint condition considering wind and solar uncertainty into a second deterministic constraint condition based on the renewable energy output uncertainty interval model and interval possibility, and constructing a deterministic inertia interval assessment model;

[0009] The deterministic inertia interval evaluation model is split into an optimal sub-model and a worst sub-model, and based on the first deterministic constraint condition and the second deterministic constraint condition, the optimal sub-model and the worst sub-model are used to perform inertia calculation to obtain a system inertia interval.

[0010] In one embodiment, the objective function of the deterministic inertia interval evaluation model is:

[0011]

[0012] in, Indicates the inertia of the target power system when all types of synchronous units are in the minimum startup state; Indicates the inertia of the target power system when all types of synchronous units are in the maximum startup state; H i is the inertia constant of the i-th thermal power unit, is the rated active capacity of the i-th thermal power unit; H j is the inertia constant of the j-th hydropower unit, is the rated active capacity of the jth hydropower unit; x i,t is the switch state of the i-th thermal power unit at time t, x i,t ∈{0,1};x j,t is the switch status of the j-th hydropower unit at time t.

[0013] In one embodiment, the expression of the optimal sub-model is:

[0014]

[0015] The expression of the worst sub-model is:

[0016]

[0017] in, It represents the minimum value of the objective function F when the power generation of wind turbines and photovoltaic units is the maximum. It represents the maximum value of the objective function F when the power generation of wind turbines and photovoltaic units is the maximum; It represents the minimum value of the objective function F when the power generation of wind turbines and photovoltaic units is the minimum. It represents the maximum value of the objective function F when the power generation of wind turbines and photovoltaic units is minimum; is the power generation range of the wind turbine w, is the power generation range of the photovoltaic unit v; is the maximum power generation of the wind turbine w, is the minimum power generation of the wind turbine w, is the minimum power generation power of photovoltaic unit v, is the maximum power generation power of photovoltaic unit v.

[0018] In one embodiment, the expression of the new energy output uncertainty interval model is:

[0019]

[0020] Among them, P n is the actual power generation value of the nth unit in the target power system, is the predicted value of the generated power of the nth unit, e n is the prediction error of the power generation of the nth unit; is the minimum prediction error of the nth unit, is the maximum prediction error of the nth unit; Indicates the power generation range of the nth unit.

[0021] In one embodiment, the constraint condition considering the uncertainty of wind and solar power is an active power balance constraint, and the first deterministic constraint condition includes at least one of a thermal power unit output constraint, a hydropower unit output constraint, a new energy output constraint, an AC tie line power constraint, a spinning reserve constraint, a thermal power unit ramp constraint, a unit start-stop constraint, and a base state and N-1 state line flow constraint;

[0022] The expression of the active power balance constraint is:

[0023]

[0024] Among them, P i,t is the power generation capacity of the i-th thermal power unit at time t, N i is the number of thermal power units; P j,t is the generating power of the jth hydropower unit at time t, N j is the number of hydropower units; P w,t is the total power of the w-th wind turbine at time t, N w is the number of wind turbines; P v,t is the total output power of the vth photovoltaic unit at time t, N v is the number of photovoltaic units; P D,t is the total load of the target power system at time t; P al,t is the transmission power of the DC transmission line al between the target power system and the connected power system at time t, N AL is the number of DC transmission lines; P dl,t is the transmission power of the AC tie line dl between the target power system and the connected power system at time t, NDL is the number of communication lines;

[0025] The expression of the output constraint of the thermal power unit is:

[0026] x i,t ·P i,min ≤P i,t ≤x i,t ·P i,max ,i∈N i

[0027] Among them, P i,min is the minimum power generation capacity of the i-th thermal power unit, P i,max is the maximum power generation of the i-th thermal power unit, x i,t is the switching state of the i-th thermal power unit at time t;

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

[0029] P j,t =cQ j,t / δ j

[0030] x j,t ·P j,min ≤P j,t ≤x j,t ·P j,max ,j∈N j

[0031]

[0032] Among them, δ j is the water consumption rate of the jth hydropower unit during power generation, c is the power generation efficiency of the hydropower unit, Q j,t is the power generation flow of the jth hydropower unit at time t; P j,min is the minimum generating power of the jth hydropower unit, P j,max is the maximum generating power of the jth hydropower unit, x j,t is the switch state of the jth hydropower unit at time t; V h,t is the water storage capacity of the hth hydropower station at time t, V h,t+1 represents the water storage capacity of the hth hydropower station at time t+1, is the runoff flowing into the h-th hydropower station at time t, Δt is the time step, Q j,t is the power generation flow of the j-th hydropower unit at time t, is the maximum power generation flow of the jth hydropower unit at time t; represents the minimum water storage capacity of the h-th hydropower station at time t, represents the maximum water storage capacity of the h-th hydropower station at time t;

[0033] The expression of the new energy output constraint is:

[0034]

[0035] in, is the predicted power generation value of the w-th wind turbine generator at time t; is the predicted power generation value of the vth photovoltaic unit at time t;

[0036] The expression of the AC tie line power constraint is:

[0037] P al,min ≤P al,t ≤P al,max ,al∈N AL

[0038] Among them, P al,min is the minimum transmission power of the AC tie line al, P al,max Indicates the maximum transmission power of the AC tie line al;

[0039] The expression of the spinning reserve constraint is:

[0040]

[0041] Among them, x n Indicates the switch status of the nth unit, represents the maximum generating power of the nth unit, Indicates the minimum power generation of the nth unit, P n represents the actual value of the generated power of the nth unit, R1 represents the upward spinning reserve capacity required by the target power system, and R2 represents the downward spinning reserve capacity required by the target power system;

[0042] The expression of the thermal power unit climbing constraint is:

[0043] -r d,i ΔT≤P i,t -P i,t-1 ≤r u,i ΔT

[0044] Among them, r d,i is the downward ramp rate of the i-th thermal power unit, r u,i is the ramp rate of the i-th thermal power unit, ΔT represents the ramp time, P i,t-1 is the power generation capacity of the i-th thermal power unit at time t-1;

[0045] The expression of the unit start-stop constraint is:

[0046]

[0047] Among them, T 1 Indicates the minimum continuous operation time of the nth unit, T 0 represents the minimum downtime of the nth unit, x n,t Indicates the switch status of the nth unit at time t, S n,t is the start-up flag of the nth unit at time t, d n,t is the shutdown sign of the nth unit at time t;

[0048] The expressions of the line power flow constraints in the base state and the N-1 state are:

[0049]

[0050] Among them, P l,t represents the transmission power of the transmission line l in the target power system at time t when the target power system is in the base state; represents the maximum transmission power of transmission line l; It represents the transmission power of transmission line l at time t in the N-1 state; N is the number of components in the target power system, and the N-1 state represents the state in which any component in the target power system is damaged.

[0051] In one embodiment, the step of converting the constraint condition considering wind and solar power uncertainty into a second deterministic constraint condition based on the new energy output uncertainty interval model and interval possibility includes:

[0052] Substitute the power generation range of the wind turbine and the power generation range of the photovoltaic unit into the expression of the active power balance constraint, and perform term shifting on the substituted expression to obtain the shifted expression, which is:

[0053]

[0054] Combine the active power balance constraint condition with the AC tie line power constraint P al,min ≤P al,t ≤P al,max , using the interval possibility to transform into the second deterministic constraint condition, the expression of the second deterministic constraint condition is:

[0055]

[0056] The combined expression is converted into a deterministic probability constraint condition, and the expression of the deterministic probability constraint condition is:

[0057] P(f(x,u)≤P al,max )≥λ1,P(P al,min ≤f(x,u))≥λ2

[0058] Among them, P al,min is the minimum transmission power of the DC transmission line al between the target power system and the connected power system, P al,max is the maximum transmission power of the DC transmission line al between the target power system and the connected power system, represents the power generation range of the w-th wind turbine at time t, represents the power generation range of the vth photovoltaic unit at time t; λ1∈[0,1], λ2∈[0,1], λ2 and λ1 are both preset probability levels; P(f(x,u)≤P al,max ) represents the probability that the function f(x,u) is less than or equal to the maximum transmission power of the AC / DC tie line al, P(P al,min ≤f(x,u)) represents the probability that the function f(x,u) is greater than or equal to the minimum transmission power of the AC / DC tie line al.

[0059] The above-mentioned power system inertia analysis method considering the uncertainty of new energy determines the new energy output uncertainty interval model of various new energy generators in the target power system based on the new energy forecast data of the target power system, determines the first deterministic constraint condition and the constraint condition considering wind and solar uncertainty for the target power system, and converts the constraint condition considering wind and solar uncertainty into a second deterministic constraint condition based on the new energy output uncertainty interval model and interval possibility, constructs a deterministic inertia interval evaluation model, splits the deterministic inertia interval evaluation model into an optimal sub-model and a worst sub-model, and based on the first deterministic constraint condition and the second deterministic constraint condition, uses the optimal sub-model and the worst sub-model to perform inertia calculation to obtain the system inertia interval. This application introduces uncertainty modeling, which can avoid the inertia estimation deviation caused by ignoring volatility, and obtain an accurate system inertia interval, thereby analyzing the system inertia trend and inertia margin. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 FIG. 1 is an application environment diagram of a power system inertia analysis method considering the uncertainty of new energy sources in one embodiment;

[0061] Figure 2 1 is a flow chart of a method for analyzing inertia of a power system considering the uncertainty of new energy sources in one embodiment;

[0062] Figure 3 Schematic diagram of the overall process of a method for analyzing inertia of a power system considering the uncertainty of new energy sources in one embodiment;

[0063] Figure 4 This is a graph showing the inertia interval analysis results when the average proportion of new energy output is 40% in an embodiment;

[0064] Figure 5 This is a schematic diagram of analyzing the system inertia range and the number of startup units when the average proportion of renewable energy output is 40% in one embodiment;

[0065] Figure 6 This is a schematic diagram of analyzing the system inertia range and the number of startup units when the average proportion of new energy output is 40% in another embodiment;

[0066] Figure 7 A schematic diagram of the relationship between the new energy penetration rate and the lower boundary of the system inertia range in one embodiment;

[0067] Figure 8 A structural block diagram of a power system inertia analysis device considering the uncertainty of new energy sources in one embodiment;

[0068] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0070] The power system inertia analysis method considering the uncertainty of new energy provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 interacts with the server 104 via a wired / wireless channel. The data storage system can store data that the server 104 needs to process. Based on the target power system's renewable energy forecast data, the server 104 determines a new energy output uncertainty interval model for each type of renewable energy generator set in the target power system. The server 104 determines a first deterministic constraint and a constraint that considers wind and solar power uncertainty for the target power system. Based on the renewable energy output uncertainty interval model and interval probability, the constraint that considers wind and solar power uncertainty is converted into a second deterministic constraint, thereby constructing a deterministic inertia interval assessment model. The server 104 splits the deterministic inertia interval assessment model into an optimal sub-model and a worst-case sub-model. Based on the first and second deterministic constraints, the server 104 uses the optimal and worst-case sub-models to calculate inertia and obtain a system inertia interval. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, etc. The server 104 can be a single server, a server cluster consisting of multiple servers, or a cloud computing center consisting of multiple servers.

[0071] In one embodiment, Figure 2 As shown in the figure, a method for analyzing the inertia of a power system considering the uncertainty of new energy is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0072] S202, based on the new energy forecast data of the target power system, determining the new energy output uncertainty interval model of various new energy generating units in the target power system.

[0073] Among them, new energy prediction data includes but is not limited to new energy power generation prediction data and new energy power generation prediction error.

[0074] New energy generator sets are equipment that use renewable or clean energy to generate electricity. New energy generator sets include but are not limited to wind turbines, photovoltaic turbines, and tidal turbines.

[0075] The renewable energy output uncertainty interval model is used to describe the relationship between the power generated by renewable energy generators and renewable energy forecast data. Specifically, the renewable energy output uncertainty interval model can be used to describe the relationship between the power generated by wind turbines and wind power generation forecast data, and the renewable energy output uncertainty interval model can be used to describe the relationship between the power generated by photovoltaic generators and photovoltaic power generation forecast data.

[0076] S204, determine the first deterministic constraint conditions for the target power system and the constraint conditions considering wind and solar uncertainty, and convert the constraint conditions considering wind and solar uncertainty into the second deterministic constraint conditions based on the new energy output uncertainty interval model and interval possibility, and construct a deterministic inertia interval evaluation model.

[0077] The target power system also includes hydropower and thermal power units. The renewable energy output uncertainty interval model, combined with the operating data of hydropower and thermal power units, derives first-order deterministic constraints for renewable energy and traditional power units in the target power system, as well as constraints that account for wind and solar power uncertainties. Traditional power units refer to units that generate electricity using traditional methods, such as hydropower and thermal power units.

[0078] The first deterministic constraint, the constraint that considers wind and solar uncertainties, and the deterministic inertia interval assessment model are an inertia interval assessment framework that considers optimal unit output distribution, startup and shutdown, and frequency security. The deterministic inertia interval assessment model is constructed based on the objective functions of maximizing and minimizing the total rotational kinetic energy of the target power system.

[0079] The method for optimizing unit output allocation and startup and shutdown considers the following: The upper inertia bound represents the maximum equivalent inertia level that the power system can support under specific operating conditions. Its evaluation principle is to optimize the unit combination strategy, prioritize the dispatch of high-inertia synchronous generators and maintain their grid-connected operation while ensuring power balance, reserve capacity, base state, and N-1 state power flow security, while coordinating the matching relationship between renewable energy fluctuations and load demand, thereby maximizing the total kinetic energy reserve of rotating equipment within the system. The core of the method lies in establishing a mapping model between unit startup and shutdown and inertia contribution. By screening online combination schemes for high-inertia units, the optimal operating mode that maximizes system inertia is determined within the feasible domain. The lower inertia bound represents the minimum equivalent inertia level that the system can achieve while satisfying operating constraints. The core of the method is to optimize the unit combination strategy, prioritize the shutdown of high-inertia units and maximize renewable energy output substitution while ensuring power balance, reserve capacity, base state, and N-1 state power flow security, thereby minimizing the kinetic energy reserve of the system's synchronous units. Specifically, by constructing a unit combination optimization model, screening the operating mode of giving priority to grid connection to low-inertia units, and at the same time coupling the maximum output scenario of new energy to reduce the demand for synchronous unit commissioning, we finally determine the critical operating state for minimizing system inertia.

[0080] S206 , splitting the deterministic inertia interval evaluation model into an optimal sub-model and a worst sub-model, and performing inertia calculation using the optimal sub-model and the worst sub-model based on the first deterministic constraint condition and the second deterministic constraint condition to obtain a system inertia interval.

[0081] The optimal and worst submodels are submodels of the deterministic inertia interval assessment model at two extremes. By finding the optimal and worst submodels, we can obtain the upper and lower limits of the system inertia, that is, the system inertia interval.

[0082] When using the optimal sub-model and the worst sub-model for inertia calculation, the Gurobi optimization solver in MATLAB can be used to solve the optimal sub-model and the worst sub-model. By obtaining the optimal solution of the optimal sub-model and the optimal solution of the worst sub-model, and combining the optimal solution of the optimal sub-model and the optimal solution of the worst sub-model, the system inertia range can be obtained. Specifically, by using the Gurobi optimization solver in MATLAB to solve the optimal sub-model, the minimum value of the optimal sub-model when the power generation of the new energy unit is maximum, and the maximum value of the optimal sub-model when the power generation of the new energy unit is maximum can be obtained. By using the Gurobi optimization solver in MATLAB to solve the worst sub-model, the maximum value of the worst sub-model when the power generation of the new energy unit is minimum, and the minimum value of the worst sub-model when the power generation of the new energy unit is minimum can be obtained. Combining the minimum value of the optimal sub-model when the power generation of the new energy unit is maximum and the minimum value of the worst sub-model when the power generation of the new energy unit is minimum, the upper boundary of the system inertia can be obtained. Combining the maximum value of the optimal sub-model when the power generation of the new energy unit is maximum and the maximum value of the worst sub-model when the power generation of the new energy unit is minimum, the lower boundary of the system inertia can be obtained. The upper bound of system inertia analyzes the maximum inertia the power system can provide under certain operating conditions. This refers to the upper limit of the system's inertia capacity when synchronous generator resources are fully utilized. If the actual operating inertia of the power system approaches the upper bound, it indicates a high risk of inertia shortage in the event of a disturbance. The lower bound of system inertia analyzes the minimum inertia allowed for the power system to operate under certain operating conditions while meeting various constraints, such as power balance. If the actual operating inertia of the power system approaches the lower bound, the power system is operating in a low-inertia state, and if a disturbance occurs, the system's inertia support may be insufficient. When calculating inertia using the optimal and worst submodels, the upper bound of system inertia is the first interval formed by the minimum value of the optimal submodel when the renewable energy generator power is maximum and the minimum value of the worst submodel when the renewable energy generator power is minimum. The lower bound of system inertia is the second interval formed by the maximum value of the optimal submodel when the renewable energy generator power is maximum and the maximum value of the worst submodel when the renewable energy generator power is minimum.

[0083] In the above-mentioned power system inertia analysis method considering the uncertainty of new energy, the new energy output uncertainty interval model of various new energy generating units in the target power system is determined based on the new energy forecast data of the target power system, the first deterministic constraint condition for the target power system and the constraint condition considering wind and solar uncertainty are determined, and the constraint condition considering wind and solar uncertainty is converted into a second deterministic constraint condition based on the new energy output uncertainty interval model and the interval possibility, and a deterministic inertia interval evaluation model is constructed. The deterministic inertia interval evaluation model is split into an optimal sub-model and a worst sub-model, and based on the first deterministic constraint condition and the second deterministic constraint condition, the optimal sub-model and the worst sub-model are used to calculate the inertia to obtain the system inertia interval. This application introduces uncertainty modeling, which can avoid the inertia estimation deviation caused by ignoring volatility, and obtain an accurate system inertia interval, thereby analyzing the system inertia trend and inertia margin.

[0084] In one embodiment, the objective function of the deterministic inertia interval evaluation model is:

[0085]

[0086] in, Indicates the inertia of the target power system when all types of synchronous units are in the minimum startup state; Indicates the inertia of the target power system when all types of synchronous units are in the maximum startup state; H i is the inertia constant of the i-th thermal power unit, is the rated active capacity of the i-th thermal power unit; H j is the inertia constant of the j-th hydropower unit, is the rated active capacity of the jth hydropower unit; x i,t is the switch state of the i-th thermal power unit at time t, x i,t ∈{0,1};x j,t is the switch state of the j-th hydropower unit at time t. Specifically, at x i,t = 0, it means that the i-th thermal power unit is in shutdown state at time t. i,t =1, it means that the i-th thermal power unit is in the startup state at time t. j,t = 0, it means that the jth hydropower unit is in shutdown state at time t. j,t =1, it means that the jth hydropower unit is in the startup state at time t.

[0087] Minimum operation is when the minimum number of generators required to meet basic power demand is running. This means running as few generators as possible to save fuel costs and reduce emissions, while also ensuring system frequency stability and power supply reliability. Maximum operation, on the other hand, involves running all available and necessary generators to meet peak loads or extreme conditions.

[0088] In the objective function of the deterministic inertia interval evaluation model, x i,t and x j,t is a variable, and the rest of the parameters are constants. Equivalent to calculating at different x i,t and x j,t The maximum inertia that can be obtained under the value, Equivalent to calculating at different x i,t and x j,t The maximum inertia that can be obtained under the given value.

[0089] In this embodiment, the system inertia interval is determined by maximizing and minimizing the system inertia, thereby providing a quantitative indicator for the stability and reliability of the power system.

[0090] In one embodiment, the expression of the optimal sub-model is:

[0091]

[0092] The expression of the worst sub-model is:

[0093]

[0094] in, It represents the minimum value of the objective function F when the power generation of wind turbines and photovoltaic units is the maximum. It represents the minimum value of the objective function F when the power generation of wind turbines and photovoltaic units is the minimum. It indicates the maximum value of the objective function F when the power generation of wind turbines and photovoltaic units is the maximum. It represents the maximum value of the objective function F when the power generation of wind turbines and photovoltaic units is minimum; is the power generation range of the wind turbine w, is the power generation range of the photovoltaic unit v; is the maximum power generation of the wind turbine w, is the minimum power generation of the wind turbine w, is the minimum power generation power of photovoltaic unit v, is the maximum power generation power of photovoltaic unit v.

[0095] By combining the solutions of the optimal sub-model and the worst sub-model, we can get the upper bound of the system inertia and the lower bound of system inertia

[0096] In this embodiment, by splitting the deterministic inertia interval assessment model into an optimal sub-model and a worst-case sub-model, and performing system inertia calculation, the extreme cases of various types of generator sets under different conditions can be fully covered, making the analysis of system inertia more comprehensive, thereby obtaining an accurate system inertia interval.

[0097] In one embodiment, the expression of the new energy output uncertainty interval model is:

[0098]

[0099] Among them, P n is the actual power generation value of the nth unit in the target power system, is the predicted value of the generated power of the nth unit, e n is the prediction error of the power generation of the nth unit; is the minimum prediction error of the nth unit, is the maximum prediction error of the nth unit; Indicates the power generation range of the nth unit.

[0100] Furthermore, according to the actual value of the generated power of the n-th unit, a prediction algorithm is used to perform data prediction to obtain a predicted value of the generated power of the n-th unit.

[0101] Furthermore, based on the actual value of the wind turbine power generation P w and power generation forecast By formula The prediction error e of the wind turbine can be obtained w , based on the prediction error e of the wind turbine w , we can get the power generation range of the wind turbine Based on the actual value of the photovoltaic power generation power P v and power generation forecast By formula The prediction error e of the photovoltaic unit can be obtained v , based on the prediction error e of the photovoltaic unit v , we can get the power generation range of the photovoltaic unit in, is the maximum power generation of the wind turbine w, is the minimum power generation of the wind turbine w, is the minimum power generation power of photovoltaic unit v, is the maximum power generation power of photovoltaic unit v.

[0102] In this embodiment, the formula The forecast deviation of renewable energy output can be determined by The prediction error can be limited to a reasonable range, thereby avoiding the impact of extreme situations on the system inertia analysis and improving the robustness and reliability of the model.

[0103] In one embodiment, the constraint condition considering the uncertainty of wind and solar power is a power balance constraint, and the first deterministic constraint condition includes at least one of a thermal power unit output constraint, a hydropower unit output constraint, a renewable energy output constraint, an AC tie line power constraint, a spinning reserve constraint, a thermal power unit ramping constraint, a unit start-stop constraint, and a base state and N-1 state line power flow constraint.

[0104] The expression of active power balance constraint is:

[0105]

[0106] Among them, P i,t is the power generation capacity of the i-th thermal power unit at time t, N i is the number of thermal power units; P j,t is the generating power of the jth hydropower unit at time t, N j is the number of hydropower units; P w,t is the total power of the w-th wind turbine at time t, N w is the number of wind turbines; P v,t is the total output power of the vth photovoltaic unit at time t, N v is the number of photovoltaic units; P D,t is the total load of the target power system at time t; P al,t is the transmission power of the DC transmission line al between the target power system and the connected power system at time t, N AL is the number of DC transmission lines; P dl,t is the transmission power of the AC tie line dl between the target power system and the connected power system at time t, N DL is the number of communication lines;

[0107] The expression of thermal power unit output constraint is:

[0108] x i,t ·P i,min ≤P i,t ≤x i,t ·P i,max ,i∈N i

[0109] Among them, P i,min is the minimum power generation capacity of the i-th thermal power unit, Pi,max is the maximum power generation of the i-th thermal power unit, x i,t is the switching state of the i-th thermal power unit at time t. Specifically, at x i,t = 0, it means that the i-th thermal power unit is in shutdown state at time t. i,t =1, it means that the i-th thermal power unit is in the startup state at time t.

[0110] The expression of the hydropower unit output constraint is:

[0111] P j,t =cQ j,t / δ j

[0112] x j,t ·P j,min ≤P j,t ≤x j,t ·P j,max ,j∈N j

[0113]

[0114] Among them, δ j is the water consumption rate of the jth hydropower unit during power generation, c is the power generation efficiency of the hydropower unit, Q j,t is the power generation flow of the jth hydropower unit at time t; P j,min is the minimum generating power of the jth hydropower unit, P j,max is the maximum generating power of the jth hydropower unit, x j,t is the switch state of the jth hydropower unit at time t; V h,t is the water storage capacity of the hth hydropower station at time t, V h,t+1 represents the water storage capacity of the hth hydropower station at time t+1, is the runoff flowing into the h-th hydropower station at time t, Δt is the time step, Q j,t is the power generation flow of the j-th hydropower unit at time t, is the maximum power generation flow of the jth hydropower unit at time t; represents the minimum water storage capacity of the h-th hydropower station at time t, represents the maximum water storage capacity of the h-th hydropower station at time t;

[0115] The expression of new energy output constraint is:

[0116]

[0117] in, is the predicted power generation value of the w-th wind turbine generator at time t; is the predicted power generation value of the vth photovoltaic unit at time t;

[0118] The expression of AC tie line power constraint is:

[0119] P al,min ≤P al,t ≤P al,max ,al∈N AL

[0120] Among them, P al,min is the minimum transmission power of the AC tie line al, P al,max Indicates the maximum transmission power of the AC tie line a1. The minimum transmission power of the AC tie line a1 and the maximum transmission power of the AC tie line a1 can be preset.

[0121] The expression of the spinning reserve constraint is:

[0122]

[0123] Among them, x n Indicates the switch status of the nth unit, represents the maximum generating power of the nth unit, Indicates the minimum power generation of the nth unit, P n represents the actual value of the generated power of the nth unit, R1 represents the upward spinning reserve capacity required by the target power system, and R2 represents the downward spinning reserve capacity required by the target power system;

[0124] The expression of the thermal power unit climbing constraint is:

[0125] -r d,i ΔT≤P i,t -P i,t-1 ≤r u,i ΔT

[0126] Among them, r d,i is the downward ramp rate of the i-th thermal power unit, r u,i is the ramp rate of the i-th thermal power unit, ΔT represents the ramp time, P i,t-1 is the power generation capacity of the i-th thermal power unit at time t-1;

[0127] The expression of the unit start-stop constraint is:

[0128]

[0129] Among them, T 1 Indicates the minimum continuous operation time of the nth unit, T 0 Indicates the minimum downtime of the nth unit; x n,t Indicates the switch status of the nth unit at time t; S n,tis the start-up flag of the nth unit at time t, S n,t Indicates whether the nth unit is switched from shutdown to startup at time t. If the nth unit is started at time t, then S n,t =1, otherwise S n,t =0;d n,t is the shutdown mark of the nth unit at time t, d n,t Indicates whether the nth unit changes from the on state to the off state at time t. If the nth unit stops at time t, then d n,t =1, otherwise d n,t = 0. The minimum continuous running time after startup of each unit is consistent, and the minimum downtime after shutdown of each unit is consistent.

[0130] The expressions of line power flow constraints in the base state and N-1 state are:

[0131]

[0132] Among them, P l,t represents the transmission power of the transmission line l in the target power system at time t when the target power system is in the base state; represents the maximum transmission power of transmission line l; represents the transmission power of transmission line l at time t under the N-1 state; N is the number of components in the target power system. The N-1 state indicates that any component in the target power system is damaged, but the target power system still operates normally. The base state refers to the state of the target power system under normal operating conditions, with all equipment in the target power system operating as expected and no faults or abnormalities occurring.

[0133] In this embodiment, by using at least one of active power balance constraints, thermal power unit output constraints, hydropower unit output constraints, renewable energy output constraints, AC interconnection line power constraints, spinning reserve constraints, thermal power unit ramping constraints, unit start / stop constraints, and base state and N-1 state line flow constraints, a system inertia interval for safe and stable operation of the power system can be obtained, making the obtained system inertia interval reliable.

[0134] In one embodiment, based on the uncertainty interval model of renewable energy output and the interval probability, the constraint condition considering the uncertainty of wind and solar power is converted into a second deterministic constraint condition, including:

[0135] Substitute the power generation range of the wind turbine and the power generation range of the photovoltaic unit into the expression of the active power balance constraint, and perform term shifting on the substituted expression to obtain the shifted expression, which is:

[0136]

[0137] in, is the power generation range of the wind turbine, is the power generation range of the photovoltaic system. and When P in the expression of active power balance constraint is w,t Replace with P v,t Replace with When moving items, it is P D,t and After the item is moved, the active power balance constraint condition is combined with the AC tie line power constraint P al,min ≤P al,t ≤P al,max , using the interval possibility to transform into the second deterministic constraint condition, the expression of the second deterministic constraint condition is:

[0138]

[0139] Therefore, it can be transformed into the following two determination constraints:

[0140] P(f(x,u)≤P al,max )≥λ1,P(P al,min ≤f(x,u))≥λ2

[0141] Among them, P al,min is the minimum transmission power of the DC transmission line al between the target power system and the connected power system, P al,max is the maximum transmission power of the DC transmission line al between the target power system and the connected power system, represents the power generation range of the w-th wind turbine at time t, represents the power generation range of the vth photovoltaic unit at time t; λ1∈[0,1], λ2∈[0,1], λ2 and λ1 are both preset probability levels; P(f(x,u)≤P al,max ) represents the probability that the function f(x,u) is less than or equal to the maximum transmission power of the AC / DC tie line al, P(P al,min ≤f(x,u)) represents the probability that the function f(x,u) is greater than or equal to the minimum transmission power of the AC / DC tie line al.

[0142] In this embodiment, by converting the active power balance constraint containing the interval number into a deterministic constraint condition based on the interval possibility, and combining it with the AC interconnection line power constraint, it is possible to accurately analyze the inertia trend of the power system and optimize the scheduling. By setting appropriate preset possibility levels λ2 and λ1, it is possible to ensure that the power system meets the predetermined operating requirements in most cases, thereby improving the reliability and stability of the power system.

[0143] In a specific application, the overall flow chart of the system inertia interval analysis method is as follows: Figure 3 As shown. Specifically, obtain new energy forecast data, load demand forecast data, rated power and rated capacity of synchronous generator sets, AC / DC interconnection data, establish unit start-stop constraints, base state and N-1 state line flow constraints, active power balance constraints, thermal power unit output constraints, thermal power unit climbing constraints, rotating reserve constraints, hydropower unit output constraints, new energy output constraints, AC interconnection line power constraints, and convert the active power balance constraints into the second deterministic constraint based on the interval possibility. Split the deterministic inertia interval evaluation model into the optimal sub-model and the worst sub-model. Based on the first deterministic constraint and the second deterministic constraint, the maximum total rotational kinetic energy of the target power system and the minimum total rotational kinetic energy of the target power system are used as the optimization goals. Use the optimal sub-model and the worst sub-model to calculate the inertia and obtain the system inertia interval. The inertia interval analysis results with an average proportion of new energy output of 40% are shown in the figure below. Figure 4 As shown, the horizontal axis is time and the vertical axis is system inertia. When the average proportion of renewable energy output is 40%, the system inertia range and the number of startups are analyzed as shown in the figure below. Figure 5 and Figure 6 The relationship between different new energy penetration rates and the lower boundary of the system inertia range is shown in Figure 7 shown.

[0144] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0145] Based on the same inventive concept, an embodiment of the present application further provides a device for analyzing inertia of a power system considering the uncertainty of renewable energy, which is used to implement the aforementioned method for analyzing inertia of a power system considering the uncertainty of renewable energy. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for analyzing inertia of a power system considering the uncertainty of renewable energy provided below can be found in the above-mentioned limitations of the method for analyzing inertia of a power system considering the uncertainty of renewable energy, and will not be repeated here.

[0146] In one embodiment, Figure 8 As shown, a power system inertia analysis device considering the uncertainty of new energy sources is provided, comprising:

[0147] The first model building module 802 is used to determine the new energy output uncertainty interval model of various new energy generators in the target power system based on the new energy forecast data of the target power system;

[0148] The second model construction module 804 is used to determine the first deterministic constraint condition for the target power system and the constraint condition considering the uncertainty of wind and solar power. Based on the new energy output uncertainty interval model and interval possibility, the constraint condition considering the uncertainty of wind and solar power is converted into the second deterministic constraint condition, and a deterministic inertia interval assessment model is constructed.

[0149] The system inertia acquisition module 806 is used to split the deterministic inertia interval evaluation model into an optimal sub-model and a worst sub-model, and use the optimal sub-model and the worst sub-model to perform inertia calculation based on the first deterministic constraint condition and the second deterministic constraint condition to obtain the system inertia interval.

[0150] Each module in the aforementioned power system inertia analysis device considering the uncertainty of renewable energy sources can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0151] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store new energy forecast data, a new energy output uncertainty interval model, a first deterministic constraint condition, a constraint condition considering wind and solar uncertainty, a second deterministic constraint condition, a deterministic inertia interval evaluation model, an optimal sub-model, a worst sub-model, and a system inertia interval. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a power system inertia analysis method considering new energy uncertainty.

[0152] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0153] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0154] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0155] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0156] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0157] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0158] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0159] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for analyzing the inertia of a power system considering the uncertainty of new energy sources, characterized in that: The method comprises: Determining, based on the renewable energy forecast data of the target power system, a renewable energy output uncertainty interval model for each renewable energy generator set in the target power system; Determining a first deterministic constraint condition for the target power system and a constraint condition considering wind and solar uncertainty, and converting the constraint condition considering wind and solar uncertainty into a second deterministic constraint condition based on the renewable energy output uncertainty interval model and interval possibility, and constructing a deterministic inertia interval assessment model; The deterministic inertia interval evaluation model is split into an optimal sub-model and a worst sub-model, and based on the first deterministic constraint condition and the second deterministic constraint condition, the optimal sub-model and the worst sub-model are used to perform inertia calculation to obtain a system inertia interval.

2. The method according to claim 1, characterized in that The objective function of the deterministic inertia interval evaluation model is: in, Indicates the inertia of the target power system when all types of synchronous units are in the minimum startup state; Indicates the inertia of the target power system when all types of synchronous units are in the maximum startup state; H i is the inertia constant of the i-th thermal power unit, P i max is the rated active capacity of the i-th thermal power unit; H j is the inertia constant of the j-th hydropower unit, is the rated active capacity of the jth hydropower unit; x i,t is the switch state of the i-th thermal power unit at time t, x i,t ∈{0,1};x j,t is the switch status of the j-th hydropower unit at time t.

3. The method according to claim 2, characterized in that The expression of the optimal sub-model is: The expression of the worst sub-model is: in, It represents the minimum value of the objective function F when the power generation of wind turbines and photovoltaic units is the maximum. It represents the maximum value of the objective function F when the power generation of wind turbines and photovoltaic units is the maximum; It represents the minimum value of the objective function F when the power generation of wind turbines and photovoltaic units is the minimum. It represents the maximum value of the objective function F when the power generation of wind turbines and photovoltaic units is minimum; is the power generation range of the wind turbine w, is the power generation range of the photovoltaic unit v; is the maximum power generation of the wind turbine w, is the minimum power generation of the wind turbine w, is the minimum power generation power of photovoltaic unit v, is the maximum power generation power of photovoltaic unit v.

4. The method according to claim 1, wherein The expression of the new energy output uncertainty interval model is: Among them, P n is the actual power generation value of the nth unit in the target power system, is the predicted value of the generated power of the nth unit, e n is the prediction error of the power generation of the nth unit; is the minimum prediction error of the nth unit, is the maximum prediction error of the nth unit; Indicates the power generation range of the nth unit.

5. The method according to claim 1, wherein The constraint condition considering the uncertainty of wind and solar power is an active power balance constraint, and the first deterministic constraint condition includes at least one of a thermal power unit output constraint, a hydropower unit output constraint, a renewable energy output constraint, an AC tie line power constraint, a spinning reserve constraint, a thermal power unit ramp constraint, a unit start-stop constraint, and a base state and N-1 state line flow constraint; The expression of the active power balance constraint is: Among them, P i,t is the power generation capacity of the i-th thermal power unit at time t, N i is the number of thermal power units; P j,t is the generating power of the jth hydropower unit at time t, N j is the number of hydropower units; P w,t is the total power of the w-th wind turbine at time t, N w is the number of wind turbines; P v,t is the total output power of the vth photovoltaic unit at time t, N v is the number of photovoltaic units; P D,t is the total load of the target power system at time t; P al,t is the transmission power of the DC transmission line al between the target power system and the connected power system at time t, N AL is the number of DC transmission lines; P dl,t is the transmission power of the AC tie line dl between the target power system and the connected power system at time t, N DL is the number of communication lines; The expression of the output constraint of the thermal power unit is: x i,t ·P i,min ≤P i,t ≤x i,t ·P i,max ,i∈N i Among them, P i,min is the minimum power generation capacity of the i-th thermal power unit, P i,max is the maximum power generation of the i-th thermal power unit, x i,t is the switching state of the i-th thermal power unit at time t; The expression of the hydropower unit output constraint is: P.S j,t (cQ j,t / δ j x j,t ·P j,min ≤P j,t ≤x j,t ·P j,max ,j∈N j Among them, δ j is the water consumption rate of the jth hydropower unit during power generation, c is the power generation efficiency of the hydropower unit, Q j,t is the power generation flow of the jth hydropower unit at time t; P j,min is the minimum generating power of the jth hydropower unit, P j,max is the maximum generating power of the jth hydropower unit, x j,t is the switch state of the jth hydropower unit at time t; V h,t is the water storage capacity of the hth hydropower station at time t, V h,t+1 represents the water storage capacity of the hth hydropower station at time t+1, is the runoff flowing into the h-th hydropower station at time t, Δt is the time step, Q j,t is the power generation flow of the j-th hydropower unit at time t, is the maximum power generation flow of the jth hydropower unit at time t; represents the minimum water storage capacity of the h-th hydropower station at time t, represents the maximum water storage capacity of the h-th hydropower station at time t; The expression of the new energy output constraint is: in, is the predicted power generation value of the w-th wind turbine generator at time t; is the predicted power generation value of the vth photovoltaic unit at time t; The expression of the AC tie line power constraint is: P al,min ≤P al,t ≤P al,max ,al∈N AL Among them, P al,min is the minimum transmission power of the AC tie line al, P al,max Indicates the maximum transmission power of the AC tie line al; The expression of the spinning reserve constraint is: Among them, x n Indicates the switch status of the nth unit, represents the maximum generating power of the nth unit, Indicates the minimum power generation of the nth unit, P n represents the actual value of the generated power of the nth unit, R1 represents the upward spinning reserve capacity required by the target power system, and R2 represents the downward spinning reserve capacity required by the target power system; The expression of the thermal power unit climbing constraint is: -r d,i ΔT≤P i,t -P i,t-1 ≤r u,i ΔT Among them, r d,i is the downward ramp rate of the i-th thermal power unit, r u,i is the ramp rate of the i-th thermal power unit, ΔT represents the ramp time, P i,t-1 is the power generation capacity of the i-th thermal power unit at time t-1; The expression of the unit start-stop constraint is: Among them, T 1 Indicates the minimum continuous operation time of the nth unit, T 0 represents the minimum downtime of the nth unit, x n,t Indicates the switch status of the nth unit at time t, S n,t is the start-up flag of the nth unit at time t, d n,t is the shutdown sign of the nth unit at time t; The expressions of the line power flow constraints in the base state and the N-1 state are: -P l max ≤P l,t ≤P l max Among them, P l,t P represents the transmission power of the target power system at the time t under the base state of the target power system; l max represents the maximum transmission power of transmission line l; It represents the transmission power of transmission line l at time t in the N-1 state; N is the number of components in the target power system, and the N-1 state represents the state in which any component in the target power system is damaged.

6. The method according to claim 5, characterized in that The step of converting the constraint condition considering wind and solar power uncertainty into a second deterministic constraint condition based on the new energy output uncertainty interval model and interval possibility includes: Substitute the power generation range of the wind turbine and the power generation range of the photovoltaic unit into the expression of the active power balance constraint, and perform term shifting on the substituted expression to obtain the shifted expression, which is: Combine the active power balance constraint condition with the AC tie line output constraint P al,min ≤P al,t ≤P al,max , using the interval possibility to transform into the second deterministic constraint condition, the expression of the second deterministic constraint condition is: P al,min ≤f(x,u)≤P al,max The combined expression is converted into a deterministic probability constraint condition, and the expression of the deterministic probability constraint condition is: P(f(x,u)≤P al,max )≥λ1,P(P al,min ≤f(x,u))≥λ2 Among them, P al,min is the minimum transmission power of the DC transmission line al between the target power system and the connected power system, P al,max is the maximum transmission power of the DC transmission line al between the target power system and the connected power system, represents the power generation range of the w-th wind turbine at time t, represents the power generation range of the vth photovoltaic unit at time t; λ1∈[0,1], λ2∈[0,1], λ2 and λ1 are both preset probability levels; P(f(x,u)≤P al,max ) represents the probability that the function f(x,u) is less than or equal to the maximum transmission power of the AC / DC tie line al, P(P al,min ≤f(x,u)) represents the probability that the function f(x,u) is greater than or equal to the minimum transmission power of the AC / DC tie line al.

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