New energy power system stable and economic dispatching method cooperating with electric vehicle demand response and space-time flexible load

Abstract

The invention relates to a new energy power system stable economic dispatching method cooperating with electric vehicle demand response and space-time flexible load, comprising the following steps: obtaining output prediction data of new energy, establishing a dispatching model of a supply side generator set and a new energy system, and embedding small signal stability constraints; an electric vehicle demand response model based on the self-adaptive time-of-use electricity price is established on a demand side, so that a charging load is converted into a schedulable resource; meanwhile, a unified space-time flexible load model is established to cooperatively schedule time and space flexible loads; and finally, in the stable economic dispatching process, implementing a two-stage optimization strategy, namely screening a dispatching scheme set meeting the small signal stability constraint in the first stage as a safety feasible region, and in the second stage, iteratively searching a dispatching scheme with an optimal system comprehensive operation index in the safety feasible region by taking the minimum total operation cost as an objective function. According to the invention, economic optimal scheduling under dynamic stability of the power grid is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, and in particular to a stable and economical dispatching method for new energy power systems that coordinates electric vehicle demand response and spatiotemporal flexible loads. Background Technology

[0002] With the increasing penetration of new energy sources in the power system and the rapid popularization of new loads such as electric vehicles, the operation and dispatch of the power system face new challenges. The intermittency and volatility of new energy sources impact the stability of the power grid, while the disorderly charging of a large number of electric vehicles exacerbates the peak-valley load difference, affecting the economic operation and safe and stable operation of the power grid.

[0003] Currently, existing scheduling methods attempt to mitigate fluctuations by utilizing demand-side resources. However, existing scheduling strategies often focus on economic optimization while neglecting the dynamic stability of the system, or failing to effectively coordinate time-transferable or movable loads with spatially dispatchable loads. Traditional economic scheduling models mainly rely on static constraints, such as power balance and line capacity, and cannot capture the broadband oscillation risks caused by the dynamic interaction between power electronic converters and the grid, leading to potential instability threats to the system in scenarios with a high proportion of renewable energy. Furthermore, demand-side resources are often simplified into single-dimensional adjustment measures, lacking a spatiotemporal coordinated scheduling mechanism, which limits their role in solving local stability problems.

[0004] Therefore, there is an urgent need for a unified scheduling method that can coordinate the demand response of electric vehicles and the spatiotemporal flexible loads, so as to achieve optimal economic benefits while ensuring the dynamic stability of the power system. Summary of the Invention

[0005] To address the problems existing in the prior art, the purpose of this invention is to provide a stable and economical dispatch method for new energy power systems that coordinates electric vehicle demand response and spatiotemporally flexible loads, thereby overcoming the shortcomings of existing dispatch methods in terms of dynamic stability assurance and demand-side resource coordination, and improving the operational stability and economy of the power system after a high proportion of new energy is integrated.

[0006] To achieve the above objectives, the present invention provides the following solution: A stable and economical dispatch method for a new energy power system that coordinates electric vehicle demand response and spatiotemporally flexible loads includes: S1. Obtain power output forecast data of new energy sources, establish a scheduling model of supply-side generator units and new energy systems, and embed small-signal stability constraints. S2. Establish an electric vehicle demand response model based on adaptive time-of-use pricing on the demand side to transform electric vehicle charging load into schedulable resources. S3. Establish a unified spatiotemporal flexible load model on the demand side to coordinate the scheduling of load flexibility in time and space dimensions; S4. A two-stage optimization strategy is adopted for the constructed supply and demand model, and an optimization algorithm is used to complete the two-stage solution. Based on the optimization results, the optimal scheduling scheme that takes into account both dynamic stability and economy is output.

[0007] Optionally, the specific steps of the scheduling model for the supply-side generator sets and the new energy system are as follows: First, a basic model for day-ahead economic dispatch is established, which includes conventional generating units and new energy systems. The new energy systems include all-converter wind power generation and all-converter photovoltaic power generation. The model aims to minimize the total operating cost. Its decision variables include the output of each conventional generating unit in each time period, and the constraints include system power balance, upper and lower limits of unit output, and ramp rate. The output of new energy is connected as a parameter based on prediction. To evaluate the system's dynamic stability under any candidate scheduling scheme, it is necessary to perform a check at each scheduling time. Using the power flow solution at that moment as the initial operating point, the external AC power system and the new energy system are linearized respectively to obtain their open-loop state-space models. Then, the new energy system is coupled with the external AC power system as a dynamic element to establish a closed-loop state-space model reflecting the dynamic interaction between the two, which is as follows: ; in, express The state vector of the synchronous generator , , , For the open-loop state-space matrix of the external AC power system, Represents the state variables of the new energy system. , , , This is the open-loop state matrix of the new energy system.

[0008] Optionally, the small-signal stability constraint includes: Eigenvalue analysis and participation factor analysis are performed on the closed-loop state matrix to obtain all electromechanical oscillation modes of the system. From these, the electromechanical oscillation modes with significant participation factors of the new energy system are identified and defined as critical modes. Let the eigenvalues ​​of this critical mode be... ;in, The real part of the eigenvalue. The imaginary part of the eigenvalues; the damping ratio is calculated based on the eigenvalues: ; The dynamic stability assessment process is transformed into constraints on the scheduling model: requiring that, throughout the entire scheduling cycle, all time periods... Critical modal damping ratio All are not lower than the preset minimum stability damping ratio threshold. : ; This constraint is the small-signal stability constraint of the embedded scheduling model, which means that when the optimization algorithm searches for a scheduling scheme, it ensures that the linearized model of the system running point determined by the scheme at each moment satisfies the small-signal stability requirement.

[0009] Optionally, establishing the electric vehicle demand response model based on adaptive time-of-use pricing specifically includes the following steps: The Monte Carlo algorithm is used to simulate the random driving and charging behavior of electric vehicles to determine the baseline charging load before any response. First, it is assumed that the daily driving distance of the electric vehicle follows a normal distribution, and its probability density function is expressed as: ; in, Indicates the distance traveled. and The mean and standard deviation of the distance traveled; Based on driving mileage, the daily charging power of the electric vehicle is generated: ; in, It refers to the charging distance of electric vehicles. This indicates the initial charging state. Represents the charging cycle of an electric vehicle. Indicates the battery capacity of an electric vehicle. and These represent charging power and efficiency, respectively. This indicates the moment the EV leaves the charging station. It is the time it takes for the EV to connect to the charging station. In time The power of electric vehicles, Indicates the first The electricity demand of an electric vehicle This indicates the total number of electric vehicles; In the overall scheduling framework, this model optimizes the changes in charging power of electric vehicle clusters under the adaptive electricity price mechanism as a whole. That is, by using the electricity price adjustment coefficient or the expected load transfer amount as optimization variables, the dispersed charging behavior is aggregated into a set of power commands that can be flexibly adjusted on the time axis, while keeping the total charging energy demand of users unchanged. Ultimately, the optimized solution yields the charging power adjustment amounts for each time period, which constitute dispatchable demand-side resources that can directly participate in grid dispatch and be used to smooth out fluctuations and support stability.

[0010] Optionally, the adaptive time-of-use pricing includes: ; ; in, and These are the times at the peak and the trough, respectively. The tilting block rate, It is time The specified charging threshold; These are the three price values ​​for adaptive time-of-use pricing during peak hours. These are three price values ​​during a downturn. It is time The current total charge amount.

[0011] Optionally, the spatiotemporal flexible load model includes: The spatial flexible load model is used to characterize the schedulability of loads in geographical location. It changes the power flow distribution of the system by redistributing transferable loads in the network among different nodes. The spatial flexible load model uses all spatially transferable loads in the system as optimization variables. The time-flexible load model includes transferable loads and mobile loads; the transferable loads flexibly allocate power consumption within the scheduling cycle while keeping the total energy demand of users unchanged; the mobile loads shift their operating time within an allowed time window while maintaining fixed power and fixed duration, generating a scheduled power vector.

[0012] Optionally, minimizing the total operating cost includes: ; in, This represents a constant, used to delineate stable and unstable regions; This represents the damping ratio of the critical mode in the current scheduling scheme. For the operating cost of generator sets, The cost of subsidies after flexible load scheduling For the damping ratio, The cost of using electric vehicles to meet demand.

[0013] The beneficial effects of this invention are as follows: This invention embeds small-signal stability constraints into the supply-side model and establishes an electric vehicle demand response model and a unified spatiotemporal flexible load model on the demand side. It employs a two-stage hierarchical optimization strategy: first, it selects safe and feasible regions that satisfy the stability constraints; then, within these regions, it performs economic scheduling with the goal of minimizing total operating costs. This optimization logic, which prioritizes stability over economic efficiency, ensures that the scheduling scheme balances dynamic stability and cost-effectiveness.

[0014] This invention employs a two-stage optimization strategy, prioritizing system dynamic stability (first stage) and then co-optimizing electric vehicle demand response and spatiotemporally flexible load resources within a safe and feasible domain that satisfies stability (second stage). This method effectively overcomes the limitations of existing scheduling strategies that prioritize economy while neglecting dynamic stability or failing to effectively coordinate spatiotemporally flexible loads. It achieves economically optimal scheduling while ensuring grid dynamic stability, thereby improving the overall operational efficiency and safety of the system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a method for stable and economical dispatching of a new energy power system that coordinates electric vehicle demand response and spatiotemporally flexible loads according to an embodiment of the present invention. Figure 2 This is a diagram illustrating the stable economic scheduling effect of an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1As shown, this embodiment discloses a stable and economical dispatch method for a new energy power system that coordinates electric vehicle demand response and spatiotemporal flexible loads. The method includes: acquiring output forecast data of the new energy system; using the output forecast data to establish a dispatch model of the supply-side generating units and the new energy system, and embedding stability constraints; establishing an electric vehicle demand response model and a spatiotemporal flexible load model based on adaptive time-of-use pricing on the demand side, and using an objective optimization algorithm to perform stable and economical dispatch on the supply-demand side models, generating an optimal dispatch scheme that balances stability and economy; and implementing a two-stage optimization strategy during the stable and economical dispatch process. In the first stage, a set of dispatch schemes that satisfy small-signal stability constraints is selected as the safe and feasible region. In the second stage, the optimal dispatch scheme with the minimum total operating cost is iteratively searched within the safe and feasible region to find the dispatch scheme with the optimal overall system operating indicators.

[0020] Specifically, this embodiment discloses a stable and economical dispatch method for a new energy power system that coordinates electric vehicle demand response and spatiotemporally flexible loads, including the following steps: S1: Obtain power output forecast data of new energy sources, establish a scheduling model of supply-side generator units and new energy systems, and embed small-signal stability constraints; S2: Establish an electric vehicle demand response model based on adaptive time-of-use pricing on the demand side to transform electric vehicle charging load into schedulable resources; S3: Establish a unified spatiotemporal flexible load model on the demand side to coordinate the scheduling of load flexibility in time and space dimensions; S4: A two-stage optimization strategy is adopted for the constructed supply and demand side model, and an optimization algorithm is used to complete the two-stage solution. Based on the optimization results, the optimal scheduling scheme that takes into account both dynamic stability and economy is output.

[0021] In this embodiment, small-signal stability constraints are embedded in the supply-side model, and an electric vehicle demand response model and a unified spatiotemporal flexible load model are established on the demand side. A two-stage hierarchical optimization strategy is adopted: first, a safe and feasible region that satisfies the stability constraints is selected; then, within this region, economic scheduling is performed with the goal of minimizing total operating costs. This optimization logic, which prioritizes stability over economy, ensures that the scheduling scheme takes into account both dynamic stability and economy.

[0022] In step S1, the specific steps of the scheduling model for the supply-side generator sets and the new energy system are as follows: S1.1: First, establish a basic day-ahead economic dispatch model that includes conventional generating units and new energy systems, where the new energy systems include fully inverter wind power generation and fully inverter photovoltaic power generation. This model aims to minimize total operating costs, and its decision variables include the output of each conventional generating unit at each time period. Constraints include system power balance, upper and lower limits of unit output, and ramp rate. New energy output is incorporated as a prediction-based parameter. S1.2: To evaluate the dynamic stability of the system under any candidate scheduling scheme, it is necessary to perform a test at each scheduling time. Using the power flow solution at that moment as the initial operating point, linearization is performed on both the external AC power system and the renewable energy system to obtain their open-loop state-space models. Then, the renewable energy system is coupled as a dynamic element with the external AC power system to establish a closed-loop state-space model reflecting their dynamic interaction, as follows: ; in, express The state vector of the synchronous generator , , , For the open-loop state-space matrix of the external AC power system, Represents the state variables of the new energy system. , , , This represents the open-loop state matrix of the new energy system. S1.3: Perform eigenvalue analysis and participation factor analysis on the closed-loop state matrix to obtain all electromechanical oscillation modes of the system. Identify the electromechanical oscillation modes with significant participation factors in the new energy system, defining them as critical modes. Let the eigenvalues ​​of this critical mode be... ;in, The real part of the eigenvalue. The imaginary part of the eigenvalues; the damping ratio is calculated based on the eigenvalues: ; S1.4: Transform the above dynamic stability assessment process into constraints on the scheduling model. This requires that throughout the entire scheduling cycle, in all time periods... Critical modal damping ratio All are not lower than the preset minimum stability damping ratio threshold. (For example ): This constraint is the small-signal stability constraint of the embedded scheduling model. It means that when the optimization algorithm searches for a scheduling scheme, it must ensure that the linearized model of the system running point determined by the scheme at each time step satisfies the small-signal stability requirement.

[0023] Specifically, in step S2, constructing the electric vehicle demand response model based on adaptive time-of-use pricing includes the following steps: S2.1: First, a physical model of the daily charging needs of electric vehicle users needs to be performed. The Monte Carlo algorithm is used to simulate the random driving and charging behavior of electric vehicles to determine the baseline charging load before any response. First, it is assumed that the daily driving distance of electric vehicles follows a normal distribution, and its probability density function is expressed as: ; in, Indicates the distance traveled. and The mean and standard deviation of the distance traveled; Based on driving mileage, the daily charging power of the electric vehicle is generated: ; in, It refers to the charging distance of electric vehicles. This indicates the initial charging state. Represents the charging cycle of an electric vehicle. Indicates the battery capacity of an electric vehicle. and These represent charging power and efficiency, respectively. This indicates the moment the EV leaves the charging station. It is the time it takes for the EV to connect to the charging station. In time The power of electric vehicles, Indicates the first The electricity demand of an electric vehicle This indicates the total number of electric vehicles; S2.2: Constructing an Adaptive Time-of-Use Pricing Mechanism. To transform disordered charging loads into dispatchable resources, an adaptive pricing mechanism combining skewed block ratio and time-of-use pricing is established. This mechanism dynamically adjusts the electricity price based on the total charging volume at each moment to reflect the degree of grid congestion. The current electric vehicle charging price is calculated as follows: ; The price of adaptive time-of-use pricing depends on Total amount of charge at any time ,Right now: ; ; in, It is time The current total charge amount; and These are the times at the peak and the trough, respectively. The tilting block rate, It is time The specified charging threshold; These are the three price values ​​for adaptive time-of-use pricing during peak hours. These are three price values ​​during a downturn, defined as: ; in, It is the threshold pricing constant, i.e. , , and The charging threshold at any time t depends on the total load of all electric vehicle users at that time t, defined as... and ,in, It is the threshold limit constant, i.e. and Therefore, at any time The greater the load, the longer the time. The smaller the consumption threshold, the better. Similarly, this invention can obtain... .

[0024] S2.3: Specifically, in step S2, to link the aforementioned electricity price signal with the change in dispatchable charging load power, it is necessary to quantify the user's responsiveness to the electricity price. This involves introducing a demand response elasticity coefficient. To illustrate this relationship; the elasticity coefficient is defined as the ratio of the change in electricity demand to the change in electricity price: ; in, Indicates time Demand response elasticity coefficient express Real-time monitoring of changes in electricity demand before and after demand response. express Real-time monitoring of electricity price changes before and after demand response. and express Implement electricity prices before and after demand response in real time. and express Real-time monitoring of electric vehicle power before and after demand response.

[0025] Through the demand response elasticity coefficient By quantifying user responsiveness, the electric vehicle demand response model transforms abstract price signals into concrete load power changes that can be incorporated into the scheduling plan. This allows the electric vehicle cluster to participate in optimization as a controllable variable in subsequent stable economic scheduling: by solving for the optimal... Or optimize directly While ensuring system stability constraints, the total operating cost, including power generation cost and demand response cost, is minimized, thereby achieving economically optimal scheduling with stability as a prerequisite.

[0026] S2.4: In summary, a complete electric vehicle demand response model is constructed. Within the overall scheduling framework, this model optimizes the changes in charging power of electric vehicle clusters under an adaptive electricity pricing mechanism as a whole. Its core lies in using the electricity price adjustment coefficient or the expected load transfer amount as optimization variables. Under the premise of keeping the total user charging energy demand constant, it establishes a quantitative relationship between electricity price changes and load changes using an elasticity coefficient model, thereby aggregating dispersed charging behaviors into a set of power commands that can be flexibly adjusted along the time axis. Finally, the optimized solution for the charging power adjustment amounts in each time period constitutes a dispatchable demand-side resource that can directly participate in grid scheduling, used to smooth fluctuations and support stability.

[0027] Specifically, in step S3, the spatial flexible load model is used to characterize the schedulability of load in geographical location. By redistributing the transferable load in the network among different nodes, the power flow distribution of the system is changed. The model uses all spatially transferable loads in the system as optimization variables. On the supply side, the output power of each generator The operating parameters directly determine the stability of the system. Therefore, for an external AC power system consisting of N generators, the optimization framework must include the operating parameters of all generators on the power supply side. ; in, This represents the power of the Nth generator at time t.

[0028] On the demand side, the load on each bus also affects system stability. Effective integration of demand-side response requires optimizing all spatially transferable loads, necessitating that optimization variables encompass all transferable loads within the system. ; in, This represents the load transferred from node g to node h at time t.

[0029] Specifically, in step S3, the time-flexible load model includes transferable loads and mobile loads; the transferable loads flexibly allocate power consumption within the scheduling cycle while ensuring that the total energy demand of users remains unchanged; the mobile loads shift their operating time within an allowed time window while maintaining fixed power and fixed duration, thereby generating a scheduled power vector.

[0030] The characteristic of transferable loads is that they can flexibly allocate power consumption within the scheduling cycle while keeping the total energy demand of users constant; their key constraint is energy conservation. ; in, It is the total power of the load before the transferable load. It is the load power after regulation, and its dispatch power constraint is: ; in, and This represents the maximum and minimum values ​​of the transferable load power.

[0031] Mobile loads are characterized by operating at fixed power and for a fixed duration. Assuming a unit scheduling cycle of 1 hour, for mobile loads... The power distribution vector before it participates in scheduling as follows: ; in It refers to the power of the portable load, and the subscript indicates the operating time. These indicate the initial start time and duration of the load, respectively.

[0032] If the allowed scheduling time range is Then the start-up time of the mobile load after scheduling must meet the following requirements. This is to ensure that once started, it can run continuously for a period of time. The scheduled power vector is: .

[0033] Specifically, in step S4, the two-stage optimization strategy employs a hierarchical optimization logic: the first stage filters out safe and feasible regions that satisfy small-signal stability constraints; the second stage searches for the optimal scheduling scheme for the overall system operating coordinates within the safe and feasible regions. Its characteristic is: Phase 1 (Safety Feasibility Domain Screening): This phase directly corresponds to the stability constraints embedded in step S1. The optimization algorithm searches the complete decision space (covering all variables defined in steps S1-S3, including conventional unit output, EV charging load adjustment, and spatiotemporal flexible load scheduling). For each candidate scheduling scheme X, the method described in steps S1.2-S1.3 is called to calculate its corresponding critical modal damping ratio. Filter out all that meet the criteria. The proposed scheme constitutes a dynamic, safe, and feasible domain. The core objective of this stage is to ensure that economic optimization can only be performed within the solution space that guarantees the stability of small signals.

[0034] The second stage (optimization of comprehensive operational indicators): Within the safe and feasible region determined in the first stage, the optimal scheduling scheme for the overall system operational indicators is sought. Economic optimization is performed within the obtained dynamic safe and feasible region. The objective function of this stage is to minimize the total system operating cost. The function is defined by the power generation cost in step S1. Electric vehicle demand response cost defined in step S2 and the flexible load subsidy cost defined in step S3. The optimization process always remains within the safe and feasible region. Its objective function is expressed as: ; in, This represents a constant, used to delineate stable and unstable regions; This represents the damping ratio of the critical mode in the current scheduling scheme, and the operating cost of the generator unit. Represented as: ; in, This indicates the number of time periods for unit scheduling, typically 24 hours. Indicates the total number of units participating in the scheduling; , , This represents the fuel cost coefficient.

[0035] The cost of using electric vehicle demand response is related to the demand response volume, and can be expressed as: ; in, The demand response price per unit power.

[0036] The subsidy cost after flexible load scheduling can be expressed as: ; in, and These are the compensation prices for transferable load and mobile load per unit power, respectively.

[0037] Specifically, in step S4, the optimization algorithm is a metaheuristic algorithm, such as particle swarm optimization or genetic algorithm.

[0038] Figure 2 The iterative convergence curves of the two-stage optimization strategy are shown. The left curve illustrates the iterative process in the first stage, which aims to satisfy stability constraints, while the right curve illustrates the economic iterative process in the second stage, which aims to minimize total operating cost within the safe and feasible region. This figure verifies that the two-stage optimization strategy adopted in this invention can effectively converge, achieving economically optimal scheduling while ensuring the dynamic stability of the power grid by prioritizing stability.

[0039] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for stable and economical dispatch of a new energy power system that coordinates electric vehicle demand response and spatiotemporally flexible loads, characterized in that, include: S1. Obtain power output forecast data of new energy sources, establish a scheduling model of supply-side generator units and new energy systems, and embed small-signal stability constraints. S2. Establish an electric vehicle demand response model based on adaptive time-of-use pricing on the demand side to transform electric vehicle charging load into schedulable resources. S3. Establish a unified spatiotemporal flexible load model on the demand side to coordinate the scheduling of load flexibility in time and space dimensions; S4. A two-stage optimization strategy is adopted for the constructed supply and demand model, and an optimization algorithm is used to complete the two-stage solution. Based on the optimization results, the optimal scheduling scheme that takes into account both dynamic stability and economy is output.

2. The method for stable and economical dispatch of new energy power systems based on coordinated electric vehicle demand response and spatiotemporally flexible loads as described in claim 1, characterized in that, The specific steps of the scheduling model for the supply-side generator sets and new energy systems are as follows: First, a basic model for day-ahead economic dispatch is established, which includes conventional generating units and new energy systems. The new energy systems include all-converter wind power generation and all-converter photovoltaic power generation. The model aims to minimize the total operating cost. Its decision variables include the output of each conventional generating unit in each time period, and the constraints include system power balance, upper and lower limits of unit output, and ramp rate. The output of new energy is connected as a parameter based on prediction. To evaluate the system's dynamic stability under any candidate scheduling scheme, it is necessary to perform a check at each scheduling time. Using the power flow solution at that moment as the initial operating point, the external AC power system and the new energy system are linearized respectively to obtain their open-loop state-space models. Then, the new energy system is coupled with the external AC power system as a dynamic element to establish a closed-loop state-space model reflecting the dynamic interaction between the two, which is as follows: ； in, express The state vector of the synchronous generator , , , For the open-loop state-space matrix of the external AC power system, Represents the state variables of the new energy system. , , , This is the open-loop state matrix of the new energy system.

3. The method for stable and economical dispatch of new energy power systems based on coordinated electric vehicle demand response and spatiotemporally flexible loads as described in claim 2, characterized in that, The small-signal stability constraints include: Eigenvalue analysis and participation factor analysis are performed on the closed-loop state matrix to obtain all electromechanical oscillation modes of the system. From these, the electromechanical oscillation modes with significant participation factors of the new energy system are identified and defined as critical modes. Let the eigenvalues ​​of this critical mode be... ;in, The real part of the eigenvalue. The imaginary part of the eigenvalues; the damping ratio is calculated based on the eigenvalues: ; The dynamic stability assessment process is transformed into constraints on the scheduling model: requiring that, throughout the entire scheduling cycle, all time periods... Critical modal damping ratio All are not lower than the preset minimum stability damping ratio threshold. : ； This constraint is the small-signal stability constraint of the embedded scheduling model, which means that when the optimization algorithm searches for a scheduling scheme, it ensures that the linearized model of the system running point determined by the scheme at each moment satisfies the small-signal stability requirement.

4. The method for stable and economical dispatch of new energy power systems based on coordinated electric vehicle demand response and spatiotemporally flexible loads as described in claim 1, characterized in that, Establishing the electric vehicle demand response model based on adaptive time-of-use pricing specifically includes the following steps: The Monte Carlo algorithm is used to simulate the random driving and charging behavior of electric vehicles to determine the baseline charging load before any response. First, it is assumed that the daily driving distance of the electric vehicle follows a normal distribution, and its probability density function is expressed as: ； in, Indicates the distance traveled. and The mean and standard deviation of the distance traveled; Based on driving mileage, the daily charging power of the electric vehicle is generated: ； in, It refers to the charging distance of electric vehicles. This indicates the initial charging state. Represents the charging cycle of an electric vehicle. Indicates the battery capacity of an electric vehicle. and These represent charging power and efficiency, respectively. This indicates the moment the EV leaves the charging station. It is the time it takes for the EV to connect to the charging station. In time The power of electric vehicles, Indicates the first The electricity demand of an electric vehicle This indicates the total number of electric vehicles; In the overall scheduling framework, this model optimizes the changes in charging power of electric vehicle clusters under the adaptive electricity price mechanism as a whole. That is, by using the electricity price adjustment coefficient or the expected load transfer amount as optimization variables, the dispersed charging behavior is aggregated into a set of power commands that can be flexibly adjusted on the time axis, while keeping the total charging energy demand of users unchanged. Ultimately, the optimized solution yields the charging power adjustment amounts for each time period, which constitute dispatchable demand-side resources that can directly participate in grid dispatch and be used to smooth out fluctuations and support stability.

5. The method for stable and economical dispatch of new energy power systems based on coordinated electric vehicle demand response and spatiotemporally flexible loads as described in claim 1, characterized in that, The adaptive time-of-use pricing includes: ； ； in, and These are the times at the peak and the trough, respectively. The tilting block rate, It is time The specified charging threshold; These are the three price values ​​for adaptive time-of-use pricing during peak hours. These are three price values ​​during a downturn. It is time The current total charge amount.

6. The method for stable and economical dispatch of new energy power systems based on coordinated electric vehicle demand response and spatiotemporally flexible loads as described in claim 1, characterized in that, The spatiotemporal flexible load model includes: The spatial flexible load model is used to characterize the schedulability of loads in geographical location. It changes the power flow distribution of the system by redistributing transferable loads in the network among different nodes. The spatial flexible load model uses all spatially transferable loads in the system as optimization variables. The time-flexible load model includes transferable loads and mobile loads; the transferable loads flexibly allocate power consumption within the scheduling cycle while keeping the total energy demand of users unchanged; the mobile loads shift their operating time within an allowed time window while maintaining fixed power and fixed duration, generating a scheduled power vector.

7. The method for stable and economical dispatch of new energy power systems based on coordinated electric vehicle demand response and spatiotemporally flexible loads as described in claim 2, characterized in that, Minimizing the total operating cost includes: ； in, This represents a constant, used to delineate stable and unstable regions; This represents the damping ratio of the critical mode in the current scheduling scheme. For the operating cost of generator sets, The cost of subsidies after flexible load scheduling For the damping ratio, The cost of using electric vehicles to meet demand.

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