Extensible data architecture of a power system and dynamic optimization method and system thereof

Abstract

The application discloses an extensible data architecture of a power system and a dynamic optimization method and system thereof. The method comprises the following steps: S110, collecting basic data of the power system; S120, constructing the extensible data architecture according to the basic data; S130, setting a dynamic optimization target and a constraint condition of the extensible data architecture; and S140, establishing a dynamic optimization model according to the dynamic optimization target and the constraint condition. The application establishes the extensible data architecture and the dynamic optimization model, thereby providing guarantee for stable operation of the power system.

Description

TECHNICAL FIELD

[0001] The present application relates to target optimization and dynamic programming technology, and particularly relates to an extensible data architecture of a power system and a dynamic optimization method and system thereof. BACKGROUND

[0002] Based on the dynamic optimization requirements of modern power systems, it is necessary to focus on solving the coordination problem between distributed energy, energy storage devices and load fluctuations. With the widespread access of renewable energy (such as wind and solar energy), the operation of the power system presents a high degree of volatility and uncertainty, which puts higher requirements on the stability and economy of the system. To meet this challenge, dynamic optimization technology combines power generation power distribution, energy storage device scheduling and load management through mathematical modeling and intelligent algorithms, gradually improving the economic benefit and operation efficiency of the power system. This case introduces the Lagrange multiplier method and dynamic programming technology, aiming to provide an efficient and reliable optimization solution for complex power systems with multiple stages and multiple constraints. SUMMARY

[0003] The present application provides an extensible data architecture of a power system and a dynamic optimization method thereof, comprising:

[0004] S110, collecting basic data of the power system;

[0005] S120, constructing an extensible data architecture according to the basic data;

[0006] S130, setting dynamic optimization targets and constraint conditions of the extensible data architecture;

[0007] S140, establishing a dynamic optimization model according to the dynamic optimization targets and constraint conditions.

[0008] The extensible data architecture of the power system and the dynamic optimization method thereof as described above, wherein the basic data of the power system includes generator data, substation data, user load data and energy storage device data.

[0009] The extensible data architecture of the power system and the dynamic optimization method thereof as described above, wherein the generator data includes power output, fuel consumption rate, operating state and power upper and lower limits, the substation data includes transformer state, switch device state, line capacity and fault alarm, the user load data includes real-time load, load type, power consumption mode and peak-valley characteristics, and the energy storage device data includes energy storage power, energy storage state, power upper and lower limits, energy upper and lower limits and charge-discharge efficiency.

[0010] The extensible data architecture of the power system and the dynamic optimization method thereof as described above, wherein the method of setting dynamic optimization targets and constraint conditions of the extensible data architecture specifically comprises the following sub-steps:

[0011] define an objective function of the scalable data architecture;

[0012] set constraints.

[0013] The scalable data architecture of the power system and the dynamic optimization method thereof as described above, wherein the method for establishing a dynamic optimization model according to the dynamic optimization objective and the constraints specifically comprises the following sub-steps:

[0014] determine optimization phase and construction phase objectives;

[0015] introduce constraints through the Lagrange multiplier method;

[0016] perform dynamic programming.

[0017] The present application also provides a scalable data architecture of a power system and a dynamic optimization system thereof, comprising: a data collection module, a scalable data architecture module, an optimization objective and constraint condition module, and a dynamic optimization module.

[0018] The data collection module collects basic data of the power system.

[0019] The scalable data architecture module constructs a scalable data architecture according to the basic data.

[0020] The optimization objective and constraint condition module sets a dynamic optimization objective and constraints of the scalable data architecture.

[0021] The dynamic optimization module establishes a dynamic optimization model according to the dynamic optimization objective and the constraints.

[0022] The scalable data architecture of the power system and the dynamic optimization system thereof as described above, wherein the basic data of the power system comprises generator data, substation data, user load data, and energy storage device data.

[0023] The scalable data architecture of the power system and the dynamic optimization system thereof as described above, wherein the generator data comprises power output, fuel consumption rate, operating state, and power upper and lower limits, the substation data comprises transformer state, switch device state, line capacity, and fault alarm, the user load data comprises real-time load, load type, power consumption mode, and peak-valley characteristics, and the energy storage device data comprises energy storage power, energy storage state, power upper and lower limits, energy upper and lower limits, and charge and discharge efficiency.

[0024] The scalable data architecture of the power system and the dynamic optimization system thereof as described above, wherein the optimization objective and constraint condition module is specifically configured to define an objective function of the scalable data architecture and set constraints.

[0025] The above describes a scalable data architecture for a power system and its dynamic optimization system, wherein the dynamic optimization module is specifically used to determine the objectives of the optimization and construction phases; introduce constraints using the Lagrange multiplier method; and perform dynamic programming.

[0026] The beneficial effects achieved by this invention are as follows: This application establishes a scalable data architecture and its dynamic optimization model, which provides a guarantee for the stable operation of the power system. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0028] Figure 1 This is a flowchart of a scalable data architecture for a power system and its dynamic optimization method provided in Embodiment 1 of this application;

[0029] Figure 2 This is a schematic diagram of a scalable data architecture and dynamic optimization system for a power system provided in Embodiment 2 of the present invention. Detailed Implementation

[0030] 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, not all, of the embodiments of the present invention. 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.

[0031] Example 1

[0032] like Figure 1 As shown, Embodiment 1 of this application provides a scalable data architecture for a power system and its dynamic optimization method.

[0033] Step S110: Collect basic data of the power system;

[0034] Data collection in power systems is the foundation for establishing and dynamically optimizing scalable data architectures. Its purpose is to obtain real-time operating status and historical data of generators, substations, user loads, and energy storage devices in the system.

[0035] The generator data includes power output, fuel consumption rate, operating state and power upper and lower limits, the generator state and power output are obtained in real time by using the data acquisition and monitoring system of power dispatching, the operating parameters are collected by using the power meter, temperature sensor and vibration monitor installed in the generator, the power generation curve and fuel consumption historical data are extracted by accessing the management database of the power plant, and the real-time state is obtained from the power plant dispatching center through the communication protocol.

[0036] The substation data includes transformer state, switch device state, line capacity and fault alarm, the state information is collected by using intelligent equipment such as intelligent circuit breaker and intelligent transformer, the fault and alarm data are collected by the relay protection device, the local data is processed and reported to the central control room by arranging edge computing terminals in the substation, and the data is transmitted through optical fiber network and wireless communication.

[0037] The user load data includes real-time load, load type, power consumption mode and peak-valley characteristics, the user real-time power consumption data is collected by the smart meter, the load current and voltage are recorded by installing load monitoring devices at the user side, the smart meter data is collected centrally, large-scale load statistics and analysis are carried out, and the historical power consumption behavior is analyzed in combination with the user's electricity bill and use mode.

[0038] The energy storage device data includes energy storage power, energy storage state, power upper and lower limits, energy upper and lower limits and charge-discharge efficiency, the SOC, voltage, current and other data of the energy storage device are collected in real time by the energy storage management system, the temperature and battery aging state are monitored by the sensors installed on the energy storage device, and the charge-discharge plan and historical data are obtained from the centralized control center of the energy storage cluster.

[0039] Step S120, constructing an extensible data architecture according to the basic data;

[0040] The extensible data architecture includes a data layer, a processing layer and an application layer, the data layer is used to collect real-time data of generators, substations, user loads and energy storage devices, the processing layer adopts a combination of edge computing and cloud computing to perform distributed processing of data, and the application layer is used to perform real-time display and feedback control on the optimization results. Specifically, the formula is: D = G + L + S + T, wherein D represents the data architecture, G represents a data set of generator groups, L represents user load data, S represents energy storage device state, and T is a timestamp sequence to ensure real-time data.

[0041] Step S130, setting dynamic optimization targets and constraint conditions of the extensible data architecture;

[0042] The objective of dynamic optimization is to meet the system load demand at the lowest cost and maximize energy utilization efficiency. The method of setting the dynamic optimization objective and constraints of the scalable data architecture includes the following sub-steps:

[0043] Step S131, define the objective function of the scalable data architecture;

[0044] The core idea of setting the objective function is to balance the economy and operation efficiency of the power system, to meet the load demand of the system at the lowest operation cost, and to ensure the safety of the equipment and the maximization of energy utilization. The objective function usually includes the generation cost, the charging and discharging cost of energy storage devices, and considers the overall operation benefit of the system. According to the cost function (such as quadratic cost model) of the generator set and the charging and discharging efficiency of the energy storage device, the algorithm of the total cost is constructed, and the objective function needs to combine the power balance, device capacity limit and other constraint conditions to ensure the practical feasibility and stability of the optimization result. Specifically, the formula is:

[0045] The objective function is represented by formula (1), where represents the cost function of the i-th generator, represents the generator, i takes the value of 1-n, represents the charging and discharging cost of the j-th energy storage device, represents the energy storage device, j takes the value of 1-P.

[0046] Step S132, set the constraint condition;

[0047] The design of constraint condition is based on the physical characteristics and operation rules of power system, to ensure that the optimization scheme is technically feasible and meets the actual operation demand, mainly including power balance constraint: to ensure that the power generation, energy storage power and load demand match; device capacity constraint: to limit the power output of generator and energy storage device within the safe range; energy storage state constraint: to control the energy level of energy storage device within a reasonable range; line capacity constraint: to prevent the overload of power transmission line.

[0048] Specifically, the formula is: The power balance constraint is represented by formula (2), where represents the output power of the i-th generator, represents the charging and discharging power of the j-th energy storage device, the positive value is the discharging power and the negative value is the charging power, represents the real-time load demand of the k-th user, N, P and M are the number of generators, the number of energy storage devices and the number of users respectively. The formula is:

[0049] The device capacity constraint is represented by formula (3), where represents the minimum output power of the i-th generator, P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows: P i represents the maximum output power of the i th generator. The formula is as follows:

[0050] Step S140, a dynamic optimization model is established according to a dynamic optimization target and a constraint condition;

[0051] According to the target algorithm and the constraint condition established in the above steps, a dynamic optimization model is established. The constraint condition can be converted into an unconstrained optimization problem by introducing a Lagrange multiplier. Dynamic programming divides the overall optimization problem into a series of stage problems, and solves them step by step by using a recursive method. The specific steps include the following sub-steps:

[0052] Step S141, determining an optimization stage and a stage target;

[0053] In the power system, the optimization stage is divided according to time, and the problem is divided into T time stages, each stage corresponding to a time point t. The decision of each stage is to control the output power of the generator and the energy storage device. The state variable of each stage is the residual energy state of the energy storage device, and the decision variable is the power output of the generator and the energy storage device. The stage target is to minimize the operation cost at each time point, including the generation cost and the charging and discharging cost of the energy storage device, while satisfying the power balance constraint.

[0054] Step S142, introducing constraints by the Lagrange multiplier method;

[0055] The constraint condition is added to the objective function by the Lagrange multiplier method to convert it into an unconstrained problem. Specifically, the formula is as follows: The constraint condition is added to the objective function by the Lagrange multiplier method to convert it into an unconstrained problem. Specifically, the formula is as follows: L represents the Lagrange function, f represents the objective function, C represents the constraint condition set, λ represents the Lagrange multiplier.

[0056] Step S143, dynamic programming;

[0057] Dynamic programming decomposes multi-stage decision problems into a series of recursively solved subproblems. In power system optimization, the recursive formula is designed around a value function, representing the minimum cost of the energy storage state at the current moment. By optimizing the decision variables of the current stage (such as generation power and energy storage power), the operating cost of the current stage and the optimal value of the next stage are calculated, gradually constructing the overall optimal solution. The recursive formula iterates backward from the last stage, while combining state transition formulas to ensure that the decisions at each stage satisfy system operating constraints and physical laws, ultimately achieving global optimum. Specifically, the formula is:

[0058] This represents a recursive formula, where Let represent the value function, indicating the energy storage state at time t. Minimum cost at that time This represents the optimal decision value at the current stage, including the cost of the current stage and the optimal value for the next stage. Represent the Lagrange function, , Let t represent the generator and energy storage device at time t. The recursive solution starts from the final stage t=T and gradually backtracks to the initial stage t=1.

[0059] Step S144: Obtain the global optimal solution based on the dynamic programming results;

[0060] Once optimization for all time periods is complete, the globally optimal solution is obtained. In a power system, the optimal solution represents the achievement of the system's operational objective (lowest total cost or maximum benefit) by optimizing generator power output and energy storage charging and discharging strategies, while satisfying constraints such as supply and demand balance, equipment capacity limitations, and line transmission capacity. The optimal solution not only ensures the system's economy and efficiency during dynamic changes but also guarantees the rational scheduling of energy storage devices, the efficient operation of power generation equipment, and the real-time fulfillment of load demands, thereby achieving global optimality within technically feasible limits.

[0061] Example 2

[0062] like Figure 2 As shown, Embodiment 1 of this application provides a scalable data architecture for a power system and its dynamic optimization system.

[0063] Data collection module 21: Collects basic data of the power system;

[0064] Data collection in power systems is the foundation for establishing and dynamically optimizing scalable data architectures. Its purpose is to obtain real-time operating status and historical data of generators, substations, user loads, and energy storage devices in the system.

[0065] The generator data includes power output, fuel consumption rate, operating state and power upper and lower limits, the generator state and power output are obtained in real time by using the data acquisition and monitoring system of power dispatching, the operating parameters are collected by using the power meter, temperature sensor and vibration monitor installed in the generator, the power generation curve and fuel consumption historical data are extracted by accessing the management database of the power plant, and the real-time state is obtained from the power plant dispatching center through the communication protocol.

[0066] The substation data includes transformer state, switchgear state, line capacity and fault alarm, the state information is collected by using intelligent equipment such as intelligent circuit breaker and intelligent transformer, the fault and alarm data are collected by the relay protection device, the local data is processed and reported to the central control room by arranging edge computing terminals in the substation, and the data is transmitted through the optical fiber network and wireless communication.

[0067] The user load data includes real-time load, load type, power consumption mode and peak-valley characteristics, the user real-time power consumption data is collected by the smart meter, the load current and voltage are recorded by installing load monitoring devices at the user side, the smart meter data is collected centrally, large-scale load statistics and analysis are carried out, and the historical power consumption behavior is analyzed in combination with the user's electricity bill and use mode.

[0068] The energy storage device data includes energy storage power, energy storage state, power upper and lower limits, energy upper and lower limits and charge-discharge efficiency, the SOC, voltage, current and other data of the energy storage device are collected in real time by the energy storage management system, the temperature and battery aging state are monitored by the sensors installed on the energy storage device, and the charge-discharge plan and historical data are obtained from the centralized control center of the energy storage cluster.

[0069] The expandable data architecture module 22 constructs an expandable data architecture according to the basic data;

[0070] The expandable data architecture includes a data layer, a processing layer and an application layer, the data layer is used to collect real-time data of generators, substations, user loads and energy storage devices, the processing layer adopts a combination of edge computing and cloud computing to perform distributed processing of data, and the application layer is used to display and feedback control the optimization results in real time. Specifically, the formula is: The data architecture is represented by D, G represents the data set of the generator group, L represents the user load data, S represents the energy storage device state, and T is a timestamp sequence to ensure the real-time nature of the data.

[0071] The optimization target and constraint condition module 23 sets the dynamic optimization target and constraint condition of the expandable data architecture;

[0072] The objective of dynamic optimization is to meet the system load demand at the lowest cost and maximize energy utilization efficiency. The method of setting dynamic optimization objectives and constraints for scalable data architecture includes the following sub-steps:

[0073] Objective function module: define the objective function of the scalable data architecture;

[0074] The core idea of setting the objective function is to balance the economy and operation efficiency of the power system, to meet the load demand of the system at the lowest operating cost, and to ensure the safety of the equipment and the maximization of energy utilization. The objective function usually includes the generation cost, the charging and discharging cost of energy storage devices, and considers the overall operation benefit of the system. According to the cost function of the generator set (such as the quadratic cost model) and the charging and discharging efficiency of the energy storage device, the algorithm of the total cost is constructed, and the objective function needs to combine the power balance, device capacity constraints and other constraints to ensure the practical feasibility and stability of the optimization results. Specifically, the formula is:

[0075] represents the objective function, where represents the cost function of the i-th generator, represents the generator, i takes the value of 1-n, represents the charging and discharging cost of the j-th energy storage device, represents the energy storage device, j takes the value of 1-P.

[0076] Constraint condition module: set constraints;

[0077] The design of constraints is based on the physical characteristics and operation rules of the power system, to ensure that the optimization scheme is technically feasible and meets the actual operation demand, mainly including power balance constraints: to ensure that the power generation, energy storage power and load demand match; device capacity constraints: to limit the power output of the generator and energy storage device within the safe range; energy storage state constraints: to control the energy level of the energy storage device within a reasonable range; line capacity constraints: to prevent the overload of transmission lines.

[0078] Specifically, the formula is: represents the power balance constraint, where represents the output power of the i-th generator, represents the charging and discharging power of the j-th energy storage device, the positive value is the discharging power and the negative value is the charging power, represents the real-time load demand of the k-th user, N, P, M are the number of generators, the number of energy storage devices and the number of users respectively. The formula is:

[0079] represents the device capacity constraint, where represents the minimum output power of the i-th generator, Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows: Pmax,i represents the maximum output power of the i-th generator. The formula is as follows:

[0080] Dynamic optimization module 24: establishing a dynamic optimization model according to dynamic optimization objectives and constraint conditions;

[0081] According to the objective algorithm and constraint conditions established in the above steps, a dynamic optimization model is established. The constraint conditions can be converted into an unconstrained optimization problem by introducing a Lagrange multiplier. Dynamic programming divides the overall optimization problem into a series of stage problems, and uses a recursive method to solve them step by step. The specific steps include the following sub-steps:

[0082] Stage target module: determining the optimization stage and constructing the stage target;

[0083] In the power system, the optimization stage is divided into T time stages according to time, each stage corresponds to a time point t, the decision of each stage is to control the output power of the generator and the energy storage device, and the state variable of each stage is the remaining energy state of the energy storage device. The decision variable is the power output of the generator and the energy storage device. The stage target is to minimize the operation cost at each time point, including the generation cost and the charging and discharging cost of the energy storage device, while satisfying the power balance constraint.

[0084] Constraint introduction module: introducing constraints through the Lagrange multiplier method;

[0085] The constraint conditions are added to the objective function through the Lagrange multiplier method to convert them into an unconstrained problem. Specifically, the formula is as follows: The constraint conditions are added to the objective function through the Lagrange multiplier method to convert them into an unconstrained problem. Specifically, the formula is as follows: L represents the Lagrange function, f represents the objective function, C represents the constraint condition set, λ represents the Lagrange multiplier.

[0086] Dynamic programming module: performing dynamic programming;

[0087] Dynamic programming decomposes multi-stage decision problems into a series of recursively solved subproblems. In power system optimization, the recursive formula is designed around a value function, representing the minimum cost of the energy storage state at the current moment. By optimizing the decision variables of the current stage (such as generation power and energy storage power), the operating cost of the current stage and the optimal value of the next stage are calculated, gradually constructing the overall optimal solution. The recursive formula iterates backward from the last stage, while combining state transition formulas to ensure that the decisions at each stage satisfy system operating constraints and physical laws, ultimately achieving global optimum. Specifically, the formula is:

[0088] This represents a recursive formula, where Let represent the value function, indicating the energy storage state at time t. Minimum cost at that time This represents the optimal decision value at the current stage, including the cost of the current stage and the optimal value for the next stage. Represent the Lagrange function, , Let t represent the generator and energy storage device at time t. The recursive solution starts from the final stage t=T and gradually backtracks to the initial stage t=1.

[0089] Optimal Solution Module: Obtains the global optimal solution based on the dynamic programming results;

[0090] Once optimization for all time periods is complete, the globally optimal solution is obtained. In a power system, the optimal solution represents the achievement of the system's operational objective (lowest total cost or maximum benefit) by optimizing generator power output and energy storage charging and discharging strategies, while satisfying constraints such as supply and demand balance, equipment capacity limitations, and line transmission capacity. The optimal solution not only ensures the system's economy and efficiency during dynamic changes but also guarantees the rational scheduling of energy storage devices, the efficient operation of power generation equipment, and the real-time fulfillment of load demands, thereby achieving global optimality within technically feasible limits.

[0091] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A scalable data architecture for a power system and its dynamic optimization method, characterized in that, include: S110. Collect basic data of the power system; S120. Build a scalable data architecture based on basic data; The scalable data architecture includes a data layer, a processing layer, and an application layer. The data layer is used to collect real-time data from generators, substations, user loads, and energy storage devices. The processing layer uses a combination of edge computing and cloud computing to perform distributed data processing. The application layer is used to display and provide feedback control on the optimization results in real time. S130. Set dynamic optimization goals and constraints for the scalable data architecture; specifically including the following sub-steps: Step S131: Define the objective function of the scalable data architecture; Formula used: Let represent the objective function, where Let i represent the cost function of the i-th generator. This represents a generator, where i takes values ​​from 1 to n. This represents the charging and discharging cost of the j-th energy storage device. This represents an energy storage device, where j takes the value 1-P; Step S132: Set constraints; The main constraints include power balance constraints: ensuring that the power generation and energy storage power match the load demand; equipment capacity constraints: limiting the power output of generators and energy storage devices within a safe range; and energy storage status constraints: controlling the energy level of energy storage devices within a reasonable range. Line capacity constraints: to prevent transmission lines from overloading; Formula used: Represents the power balance constraint, where This represents the output power of the i-th generator. This represents the charging and discharging power of the j-th energy storage device, with positive values ​​indicating discharge power and negative values ​​indicating charging power. This represents the real-time load demand of the kth user, where N, P, and M represent the number of generators, the number of energy storage devices, and the number of users, respectively. Using formula This indicates the equipment capacity constraint, where Let represent the minimum output power of the i-th generator. This represents the maximum output power of the i-th generator; the formula is: Represents the energy storage state constraints, where This represents the energy storage capacity of the j-th energy storage device. , Represents the minimum and maximum energy storage capacities of the j-th energy storage device. This represents the remaining energy of the j-th energy storage device at time t. Let represent the charging and discharging efficiency of the j-th energy storage device. Indicates a time interval; Formula used: This indicates the line capacity constraint, where This indicates the maximum permissible transmission power of the transmission line; S140. Establish a dynamic optimization model based on the dynamic optimization objective and constraints; this includes the following sub-steps: Step S141: Determine the goals for the optimization and construction phases; Step S142: Introduce constraints using the Lagrange multiplier method; Formula used: Introduce constraints, where Represent the Lagrange function, Describe the objective function. Represents a set of constraints. Represents the Lagrange multipliers; Step S143: Perform dynamic programming; Formula used: This represents a recursive formula, where Let represent the value function, indicating the energy storage state at time t. Minimum cost at that time This represents the optimal decision value at the current stage, including the cost of the current stage and the optimal value for the next stage. Represent the Lagrange function, , Let t represent the generator and energy storage device at time t. The recursive solution starts from the final stage t=T and gradually backtracks to the initial stage t=1. Step S144: Obtain the global optimal solution based on the dynamic programming results; once the optimization for all time periods is complete, the global optimal solution is obtained. .

2. The scalable data architecture and dynamic optimization method for a power system as described in claim 1, characterized in that, The basic data of the power system includes generator data, substation data, user load data, and energy storage device data.

3. The scalable data architecture for a power system and its dynamic optimization method as described in claim 2, characterized in that, Generator data includes power output, fuel consumption rate, operating status, and power upper and lower limits; substation data includes transformer status, switchgear status, line capacity, and fault alarms; user load data includes real-time load, load type, power consumption mode, and peak-valley characteristics; and energy storage device data includes energy storage power, energy storage status, power upper and lower limits, energy upper and lower limits, and charge / discharge efficiency.

4. A scalable data architecture for a power system and its dynamic optimization system, characterized in that, include: Data collection module (21): Collects basic data of the power system; Scalable Data Architecture Module (22): Builds a scalable data architecture based on the underlying data; The scalable data architecture includes a data layer, a processing layer, and an application layer. The data layer is used to collect real-time data from generators, substations, user loads, and energy storage devices. The processing layer uses a combination of edge computing and cloud computing to perform distributed data processing. The application layer is used to display and provide feedback control on the optimization results in real time. Optimization Objectives and Constraints Module (23): Set dynamic optimization objectives and constraints for scalable data architecture; The optimization objective and constraint module (23) includes: an objective function module and a constraint module; Objective function module: Defines the objective function for a scalable data architecture; uses the following formula: Let represent the objective function, where Let i represent the cost function of the i-th generator. This represents a generator, where i takes values ​​from 1 to n. This represents the charging and discharging cost of the j-th energy storage device. This represents an energy storage device, where j takes the value 1-P; Constraints module: Set constraints; use formulas: Represents the power balance constraint, where This represents the output power of the i-th generator. This represents the charging and discharging power of the j-th energy storage device, with positive values ​​indicating discharge power and negative values ​​indicating charging power. This represents the real-time load demand of the k-th user, where N, P, and M represent the number of generators, energy storage devices, and users, respectively; the formula is used. This indicates the equipment capacity constraint, where Let represent the minimum output power of the i-th generator. This represents the maximum output power of the i-th generator; the formula is: Represents the energy storage state constraints, where This represents the energy storage capacity of the j-th energy storage device. , Represents the minimum and maximum energy storage capacities of the j-th energy storage device. This represents the remaining energy of the j-th energy storage device at time t. Let represent the charging and discharging efficiency of the j-th energy storage device. To represent a time interval; use the formula: This indicates the line capacity constraint, where This indicates the maximum permissible transmission power of the transmission line; Dynamic optimization module (24): Establish a dynamic optimization model based on the dynamic optimization objective and constraints; The dynamic optimization module (24) includes a stage target module and a constraint introduction module; Phase Goals Module: Define the goals for the optimization phase and the construction phase; Constraint introduction module: Constraints are introduced using the Lagrange multiplier method; formulas used: Introduce constraints, where Represent the Lagrange function, Describe the objective function. Represents a set of constraints. Represents the Lagrange multipliers; Dynamic Programming Module: Performs dynamic programming; uses the following formula: This represents a recursive formula, where Let represent the value function, indicating the energy storage state at time t. Minimum cost at that time This represents the optimal decision value at the current stage, including the cost of the current stage and the optimal value for the next stage. Represent the Lagrange function, , Let t represent the generator and energy storage device at time t. The recursive solution starts from the final stage t=T and gradually backtracks to the initial stage t=1. Optimal Solution Module: Obtains the global optimal solution based on the dynamic programming results; the global optimal solution is obtained when optimization for all time periods is complete. .

5. The scalable data architecture and dynamic optimization system for a power system as described in claim 4, characterized in that, The basic data of the power system includes generator data, substation data, user load data, and energy storage device data.

6. The scalable data architecture and dynamic optimization system for a power system as described in claim 5, characterized in that, Generator data includes power output, fuel consumption rate, operating status, and power upper and lower limits; substation data includes transformer status, switchgear status, line capacity, and fault alarms; user load data includes real-time load, load type, power consumption mode, and peak-valley characteristics; and energy storage device data includes energy storage power, energy storage status, power upper and lower limits, energy upper and lower limits, and charge / discharge efficiency.

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