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

Abstract

The invention discloses an extensible data architecture of a power system and a dynamic optimization method and system of the extensible data architecture. The extensible data architecture of the power system and the dynamic optimization method thereof comprise the following steps: step S110, collecting basic data of the power system; step S120, constructing an extensible data architecture according to the basic data; step S130, setting a dynamic optimization target and a constraint condition of the extensible data architecture; step S140, establishing a dynamic optimization model according to the dynamic optimization target and the constraint condition; according to the invention, an extensible data architecture and a dynamic optimization model thereof are established, and a guarantee is provided for stable operation of a power system.

Description

Technical Field

[0001] The present invention relates to target optimization and dynamic programming technology, and in particular to an extensible data architecture of an electric power system and a dynamic optimization method and system thereof. Background Art

[0002] Based on the dynamic optimization needs of modern power systems, it is necessary to focus on solving the coordination problems between distributed energy, energy storage equipment and load fluctuations. With the widespread access to renewable energy (such as wind power and solar energy), the operation of the power system has shown a high degree of volatility and uncertainty, which has put forward higher requirements on the stability and economy of the system. To meet this challenge, dynamic optimization technology combines power generation distribution, energy storage equipment scheduling and load management through mathematical modeling and intelligent algorithms, and gradually improves the economic benefits and operating efficiency of the power system. This case introduces the Lagrange multiplier method and dynamic programming technology, aiming to provide efficient and reliable optimization solutions for complex power systems with multiple stages and multiple constraints. Summary of the invention

[0003] The present invention provides an extensible data architecture of a power system and a dynamic optimization method thereof, comprising: S110, collecting basic data of the power system; S120. Build an extensible data architecture based on basic data; S130, setting dynamic optimization goals and constraints of the scalable data architecture; S140. Establish a dynamic optimization model according to the dynamic optimization objectives and constraints.

[0004] As described above, a scalable data architecture of an electric power system and a dynamic optimization method thereof, wherein the basic data of the electric power system includes generator data, substation data, user load data and energy storage device data.

[0005] A scalable data architecture of an electric power system and a dynamic optimization method thereof as described above, wherein 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 alarm, user load data includes real-time load, load type, power consumption mode and peak and 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 charging and discharging efficiency.

[0006] As described above, a scalable data architecture of a power system and a dynamic optimization method thereof, wherein the method for setting dynamic optimization objectives and constraints of the scalable data architecture specifically comprises the following sub-steps: Define the objective function of the scalable data architecture; Set constraints.

[0007] As described above, a scalable data architecture of a power system and a dynamic optimization method thereof, wherein the method of establishing a dynamic optimization model according to dynamic optimization objectives and constraints specifically comprises the following sub-steps: Determine optimization and construction phase goals; Introducing constraints through the Lagrange multiplier method; Perform dynamic planning.

[0008] The present invention also provides an extensible data architecture of an electric power system and a dynamic optimization system thereof, comprising: a data collection module, an extensible data architecture module, an optimization target and constraint condition module, and a dynamic optimization module.

[0009] Data collection module: collects basic data of the power system; Scalable data architecture module: build scalable data architecture based on basic data; Optimization goal and constraint module: set dynamic optimization goals and constraints for scalable data architecture; Dynamic optimization module: Establish a dynamic optimization model based on dynamic optimization objectives and constraints.

[0010] As described above, a scalable data architecture of an electric power system and a dynamic optimization system thereof, wherein the basic data of the electric power system includes generator data, substation data, user load data and energy storage device data.

[0011] A scalable data architecture of an electric power system and its dynamic optimization system as described above, wherein 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 alarm, user load data includes real-time load, load type, power consumption mode and peak and 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 charging and discharging efficiency.

[0012] As described above, a scalable data architecture of an electric power system and a dynamic optimization system thereof, wherein the optimization target and constraint condition module is specifically used to: define the objective function of the scalable data architecture; and set constraints.

[0013] As described above, a scalable data architecture of an electric power system and its dynamic optimization system, wherein the dynamic optimization module is specifically used to determine the optimization phase and construction phase objectives; introduce constraints through the Lagrange multiplier method; and perform dynamic programming.

[0014] The beneficial effects achieved by the present 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 This is a flow chart of a scalable data architecture of a power system and a dynamic optimization method thereof provided in Embodiment 1 of the present application; Figure 2 This is a schematic diagram of a scalable data architecture of an electric power system and its dynamic optimization system provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0017] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1 like Figure 1 As shown, the present application embodiment 1 provides a scalable data architecture of a power system and a dynamic optimization method thereof Step S110, collecting basic data of the power system; Data collection in the power system is the basis for the establishment and dynamic optimization of a scalable data architecture, with the goal of obtaining real-time operating status and historical data of generators, substations, user loads, and energy storage equipment in the system.

[0019] Generator data includes power output, fuel consumption rate, operating status and upper and lower power limits. The data acquisition and monitoring system of power dispatch is used to obtain the generator status and power output in real time. The power meter, temperature sensor and vibration monitor installed inside the generator are used to collect operating parameters. By accessing the power plant's management database, the power generation curve and fuel consumption history data are extracted, and the real-time status is obtained from the power plant dispatch center through the communication protocol.

[0020] Substation data includes transformer status, switchgear status, line capacity and fault alarms. Intelligent devices (such as smart circuit breakers and smart transformers) are used to collect status information, and fault and alarm data are collected through relay protection devices. Edge computing terminals are deployed in substations to process local data and report it to the central control room. Data is transmitted through fiber optic networks and wireless communications.

[0021] User load data includes real-time load, load type, power consumption pattern and peak-valley characteristics. Real-time power consumption data of users is collected through smart meters. Load monitoring devices are installed on the user side to record load current and voltage. Smart meter data is collected centrally to conduct large-scale load statistics and analysis. Historical power consumption behavior is analyzed in combination with users' electricity bills and usage patterns.

[0022] Energy storage equipment data includes energy storage power, energy storage status, power upper and lower limits, energy upper and lower limits, and charging and discharging efficiency. The charging and discharging efficiency collects the SOC, voltage, current and other data of the energy storage equipment in real time through the energy storage management system. Sensors installed on the energy storage equipment are used to monitor temperature, battery aging status, etc., and charge and discharge plans and historical data are obtained from the centralized control center of the energy storage cluster.

[0023] Step S120: construct an extensible data architecture based on basic data; The scalable data architecture includes the data layer, processing layer and application layer. The data layer is used to collect real-time data of 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 the optimization results in real time and provide feedback control. Specifically, the formula is used: Represents the data architecture, where D represents the data architecture, G represents the data set representing the generator set, L represents the user load data, S represents the energy storage device status, and T is the timestamp sequence to ensure the real-time nature of the data.

[0024] Step S130, setting dynamic optimization goals and constraints of the scalable data architecture; The goal of dynamic optimization is to meet the system load requirements at the lowest cost and maximize energy efficiency. The method of setting dynamic optimization goals and constraints for scalable data architecture specifically includes the following sub-steps: Step S131, defining the objective function of the scalable data architecture; The core idea of ​​setting the objective function is to balance the economy and operating efficiency of the power system, meet the load demand of the system with the lowest operating cost, and ensure the safety of the equipment and maximize energy utilization. The objective function usually includes the cost of power generation, the charging and discharging cost of the energy storage equipment, and considers the overall operating benefits 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 equipment, the total cost algorithm is constructed. The objective function needs to be combined with constraints such as power balance and equipment capacity limitations to ensure the practical feasibility and stability of the optimization results. Specifically, the formula is used: represents the objective function, where represents the cost function of the ith generator, represents the generator, i is 1-n, represents the charging and discharging cost of the jth energy storage device, Represents energy storage equipment, and the value of j is 1-P.

[0025] Step S132, setting constraints; The design of constraints is based on the physical characteristics and operating rules of the power system to ensure that the optimization plan is technically feasible and meets actual operating needs. It mainly includes power balance constraints: ensuring that the power of power generation and energy storage matches the load demand; equipment capacity constraints: limiting the power output of generators and energy storage equipment within a safe range; energy storage state constraints: controlling the energy level of energy storage equipment within a reasonable range; line capacity constraints: preventing transmission line overload.

[0026] Specifically, the formula is used: represents the power balance constraint, where represents the output power of the i-th generator, Represents the charging and discharging power of the jth energy storage device. Positive values ​​are discharge power, and negative values ​​are charging power. represents the real-time load demand of the kth user, N, P, and M are the number of generators, the number of energy storage devices, and the number of users, respectively. represents the equipment capacity constraint, where represents the minimum output power of the i-th generator, Represents the maximum output power of the i-th generator. The formula used is: represents the energy storage state constraint, where represents the energy storage capacity of the jth energy storage device, , represents the minimum and maximum energy storage capacity of the j-th energy storage device, represents the remaining energy of the jth energy storage device at time t, represents the charging and discharging efficiency of the jth energy storage device, Represents a time interval. Using the formula: represents the line capacity constraint, where Indicates the maximum allowable transmission power of the transmission line.

[0027] Step S140: establishing a dynamic optimization model according to the dynamic optimization target and constraint conditions; According to the target algorithm and constraints established in the above steps, a dynamic optimization model is established. The constraints can be transformed into an unconstrained optimization problem by introducing Lagrange multipliers. Dynamic programming decomposes the overall optimization problem into a series of staged problems and solves them step by step using a recursive method. Specifically, it includes the following sub-steps: Step S141, determining optimization phase and construction phase goals; 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. The state variable of each stage is the remaining 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 goal is to minimize the operating cost at each time point, including the power generation cost and the charging and discharging cost of the energy storage device, while satisfying the power balance constraint.

[0028] Step S142, introducing constraints by Lagrange multiplier method; The constraints are added to the objective function through the Lagrange multiplier method and transformed into an unconstrained problem. Specifically, the formula is used: Introduce constraints, where represents the Lagrangian function, represents the objective function, represents a set of constraints, represents the Lagrange multiplier.

[0029] Step S143, performing dynamic planning; Dynamic programming is to decompose multi-stage decision-making problems into a series of recursively solved sub-problems. In power system optimization, the design of recursive formulas is centered on the value function, which represents the minimum cost at the current energy storage state. By optimizing the decision variables of the current stage (such as power generation power and energy storage power), the operating cost of the current stage and the optimal value of the next stage are calculated, and the overall optimal solution is gradually constructed. The recursive formula iterates forward from the last stage as the starting point, and combines the state transfer formula to ensure that the decisions at each stage meet the system operation constraints and physical laws, and ultimately achieve global optimization. Specifically, the formula is used: represents a recursive formula, where Represents the value function, indicating that the energy storage state at time t is The minimum cost when Represents the optimal decision value of the current stage, including the cost of the current stage and the optimal value of the next stage. represents the Lagrangian function, , Represents the generator and energy storage device at time t. The recursive solution starts from the last stage t=T and gradually traces back to the initial stage t=1.

[0030] Step S144, obtaining a global optimal solution according to the dynamic programming result; When the optimization of all time periods is completed, the global optimal solution is obtained The optimal solution means that in the power system, by optimizing the power output of the generator and the charging and discharging strategy of the energy storage equipment, the system's operating objectives (minimum total cost or maximum benefit) can be achieved under the premise of satisfying the constraints of supply and demand balance, equipment capacity limitation, line transmission capacity, etc. The optimal solution can not only ensure the economy and efficiency of the system in dynamic changes, but also ensure the reasonable scheduling of energy storage equipment, the efficient operation of power generation equipment, and the real-time satisfaction of load demand, thereby achieving the global optimum within the technically feasible range.

[0031] Embodiment 2 like Figure 2 As shown, the first embodiment of the present application provides a scalable data architecture of a power system and a dynamic optimization system thereof Data collection module 21: collects basic data of the power system; Data collection in the power system is the basis for the establishment and dynamic optimization of a scalable data architecture, with the goal of obtaining real-time operating status and historical data of generators, substations, user loads, and energy storage equipment in the system.

[0032] Generator data includes power output, fuel consumption rate, operating status and upper and lower power limits. The data acquisition and monitoring system of power dispatch is used to obtain the generator status and power output in real time. The power meter, temperature sensor and vibration monitor installed inside the generator are used to collect operating parameters. By accessing the power plant's management database, the power generation curve and fuel consumption history data are extracted, and the real-time status is obtained from the power plant dispatch center through the communication protocol.

[0033] Substation data includes transformer status, switchgear status, line capacity and fault alarms. Intelligent devices (such as smart circuit breakers and smart transformers) are used to collect status information, and fault and alarm data are collected through relay protection devices. Edge computing terminals are deployed in substations to process local data and report it to the central control room. Data is transmitted through fiber optic networks and wireless communications.

[0034] User load data includes real-time load, load type, power consumption pattern and peak-valley characteristics. Real-time power consumption data of users is collected through smart meters. Load monitoring devices are installed on the user side to record load current and voltage. Smart meter data is collected centrally to conduct large-scale load statistics and analysis. Historical power consumption behavior is analyzed in combination with users' electricity bills and usage patterns.

[0035] Energy storage equipment data includes energy storage power, energy storage status, power upper and lower limits, energy upper and lower limits, and charging and discharging efficiency. The charging and discharging efficiency collects the SOC, voltage, current and other data of the energy storage equipment in real time through the energy storage management system. Sensors installed on the energy storage equipment are used to monitor temperature, battery aging status, etc., and charge and discharge plans and historical data are obtained from the centralized control center of the energy storage cluster.

[0036] Scalable data architecture module 22: building a scalable data architecture based on basic data; The scalable data architecture includes the data layer, processing layer and application layer. The data layer is used to collect real-time data of 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 the optimization results in real time and provide feedback control. Specifically, the formula is used: Represents the data architecture, where D represents the data architecture, G represents the data set representing the generator set, L represents the user load data, S represents the energy storage device status, and T is the timestamp sequence to ensure the real-time nature of the data.

[0037] Optimization goals and constraints module 23: Setting dynamic optimization goals and constraints for scalable data architecture; The goal of dynamic optimization is to meet the system load requirements at the lowest cost and maximize energy efficiency. The method of setting dynamic optimization goals and constraints for scalable data architecture specifically includes the following sub-steps: Objective function module: defines the objective function of the scalable data architecture; The core idea of ​​setting the objective function is to balance the economy and operating efficiency of the power system, meet the load demand of the system with the lowest operating cost, and ensure the safety of the equipment and maximize energy utilization. The objective function usually includes the cost of power generation, the charging and discharging cost of the energy storage equipment, and considers the overall operating benefits 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 equipment, the total cost algorithm is constructed. The objective function needs to be combined with constraints such as power balance and equipment capacity limitations to ensure the practical feasibility and stability of the optimization results. Specifically, the formula is used: represents the objective function, where represents the cost function of the ith generator, represents the generator, i is 1-n, represents the charging and discharging cost of the jth energy storage device, Represents energy storage equipment, and the value of j is 1-P.

[0038] Constraint module: set constraints; The design of constraints is based on the physical characteristics and operating rules of the power system to ensure that the optimization plan is technically feasible and meets actual operating needs. It mainly includes power balance constraints: ensuring that the power of power generation and energy storage matches the load demand; equipment capacity constraints: limiting the power output of generators and energy storage equipment within a safe range; energy storage state constraints: controlling the energy level of energy storage equipment within a reasonable range; line capacity constraints: preventing transmission line overload.

[0039] Specifically, the formula is used: represents the power balance constraint, where represents the output power of the i-th generator, Represents the charging and discharging power of the jth energy storage device. Positive values ​​are discharge power, and negative values ​​are charging power. represents the real-time load demand of the kth user, N, P, and M are the number of generators, the number of energy storage devices, and the number of users, respectively. represents the equipment capacity constraint, where represents the minimum output power of the i-th generator, Represents the maximum output power of the i-th generator. The formula used is: represents the energy storage state constraint, where represents the energy storage capacity of the jth energy storage device, , represents the minimum and maximum energy storage capacity of the j-th energy storage device, represents the remaining energy of the jth energy storage device at time t, represents the charging and discharging efficiency of the jth energy storage device, Represents a time interval. Using the formula: represents the line capacity constraint, where Indicates the maximum allowable transmission power of the transmission line.

[0040] Dynamic optimization module 24: establishing a dynamic optimization model according to dynamic optimization objectives and constraints; According to the target algorithm and constraints established in the above steps, a dynamic optimization model is established. The constraints can be transformed into an unconstrained optimization problem by introducing Lagrange multipliers. Dynamic programming decomposes the overall optimization problem into a series of staged problems and solves them step by step using a recursive method. Specifically, it includes the following sub-steps: Phase target module: determine the optimization phase and construction phase targets; 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. The state variable of each stage is the remaining 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 goal is to minimize the operating cost at each time point, including the power generation cost and the charging and discharging cost of the energy storage device, while satisfying the power balance constraint.

[0041] Constraint introduction module: introduce constraints through Lagrange multiplier method; The constraints are added to the objective function through the Lagrange multiplier method and transformed into an unconstrained problem. Specifically, the formula is used: Introduce constraints, where represents the Lagrangian function, represents the objective function, represents a set of constraints, represents the Lagrange multiplier.

[0042] Dynamic programming module: perform dynamic programming; Dynamic programming is to decompose multi-stage decision-making problems into a series of recursively solved sub-problems. In power system optimization, the design of recursive formulas is centered on the value function, which represents the minimum cost at the current energy storage state. By optimizing the decision variables of the current stage (such as power generation power and energy storage power), the operating cost of the current stage and the optimal value of the next stage are calculated, and the overall optimal solution is gradually constructed. The recursive formula iterates forward from the last stage as the starting point, and combines the state transfer formula to ensure that the decisions at each stage meet the system operation constraints and physical laws, and ultimately achieve global optimization. Specifically, the formula is used: represents a recursive formula, where Represents the value function, indicating that the energy storage state at time t is The minimum cost when Represents the optimal decision value of the current stage, including the cost of the current stage and the optimal value of the next stage. represents the Lagrangian function, , Represents the generator and energy storage device at time t. The recursive solution starts from the last stage t=T and gradually traces back to the initial stage t=1.

[0043] Optimal solution module: obtain the global optimal solution based on the dynamic programming results; When the optimization of all time periods is completed, the global optimal solution is obtained The optimal solution means that in the power system, by optimizing the power output of the generator and the charging and discharging strategy of the energy storage equipment, the system's operating objectives (minimum total cost or maximum benefit) can be achieved under the premise of satisfying the constraints of supply and demand balance, equipment capacity limitation, line transmission capacity, etc. The optimal solution can not only ensure the economy and efficiency of the system in dynamic changes, but also ensure the reasonable scheduling of energy storage equipment, the efficient operation of power generation equipment, and the real-time satisfaction of load demand, thereby achieving the global optimum within the technically feasible range.

[0044] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method 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 in the scope of protection of the present invention.

Claims

1. A scalable data architecture for a power system and a dynamic optimization method thereof, characterized in that: include: S110, collect basic data of the power system; S120. Build an extensible data architecture based on basic data; S130, setting dynamic optimization goals and constraints of the scalable data architecture; S140. Establish a dynamic optimization model according to the dynamic optimization objectives and constraints.

2. The scalable data architecture of a power system and a dynamic optimization method thereof according to claim 1, characterized in that: The basic data of the power system includes generator data, substation data, user load data and energy storage equipment data.

3. The scalable data architecture of a power system and a dynamic optimization method thereof according to 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 alarm; user load data includes real-time load, load type, power consumption mode and peak and valley characteristics; energy storage device data includes energy storage power, energy storage status, power upper and lower limits, energy upper and lower limits and charging and discharging efficiency.

4. The scalable data architecture of a power system and a dynamic optimization method thereof according to claim 1, characterized in that: The method for setting dynamic optimization goals and constraints for a scalable data architecture specifically includes the following sub-steps: Define the objective function of the scalable data architecture; Set constraints.

5. The scalable data architecture of a power system and a dynamic optimization method thereof according to claim 1, characterized in that: The method of establishing a dynamic optimization model according to dynamic optimization objectives and constraints specifically includes the following sub-steps: Determine optimization and construction phase goals; Introducing constraints through the Lagrange multiplier method; Perform dynamic planning.

6. A scalable data architecture for a power system and a dynamic optimization system thereof, characterized in that: include: Data collection module (21): collects basic data of the power system; Scalable data architecture module (22): Build scalable data architecture based on basic data; Optimization Objectives and Constraints Module (23): Setting dynamic optimization objectives and constraints for scalable data architecture; Dynamic optimization module (24): establishes a dynamic optimization model according to dynamic optimization objectives and constraints.

7. The scalable data architecture and dynamic optimization system of a power system according to claim 6, characterized in that: The basic data of the power system includes generator data, substation data, user load data and energy storage equipment data.

8. The scalable data architecture and dynamic optimization system of a power system according to claim 7, 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 alarm; user load data includes real-time load, load type, power consumption mode and peak and valley characteristics; energy storage device data includes energy storage power, energy storage status, power upper and lower limits, energy upper and lower limits and charging and discharging efficiency.

9. The scalable data architecture and dynamic optimization system of a power system according to claim 6, characterized in that: The optimization objective and constraint condition module (23) is specifically used to: define the objective function of the scalable data architecture; and set the constraint conditions.

10. The scalable data architecture of a power system and its dynamic optimization system as claimed in claim 6, characterized in that: The dynamic optimization module (24) is specifically used to determine the optimization phase and construction phase objectives; introduce constraints through the Lagrange multiplier method; and perform dynamic programming.

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