New energy consumption-oriented power grid-energy storage-load cooperative scheduling platform and capacity configuration method

Abstract

The invention discloses a new energy consumption-oriented power grid-energy storage-load cooperative scheduling platform and a capacity configuration method, and belongs to the technical field of new energy consumption scheduling, and the platform comprises a data layer, a multi-objective optimization scheduling layer, a control response layer and a simulation evaluation layer. According to the invention, the platform stabilizes the output fluctuation of the new energy through the energy storage system, combines the flexible response of the load demand side, remarkably reduces the wind and light abandoning rate, improves the new energy consumption capability, provides a rapid adjustment capability for the energy storage and demand response resources of the platform, deals with the new energy prediction error and load sudden change, guarantees the stability of the frequency and voltage, and improves the energy consumption capability. According to the platform, long-term planning and short-term scheduling can be combined, investment economy and operation reliability are both considered, the whole platform reduces the start-stop frequency and fuel consumption of a thermal power peak regulation unit by optimizing an energy storage charging and discharging strategy and load demand response, energy storage capacity configuration is optimized through a two-stage stochastic programming model, and the energy storage capacity configuration is optimized. And excessive investment is avoided.

Description

Technical Field

[0001] This invention belongs to the field of renewable energy consumption and dispatching technology, and particularly relates to a grid-energy storage-load coordinated dispatching platform and capacity configuration method for renewable energy consumption. Background Technology

[0002] In simple terms, renewable energy consumption is the process of absorbing and utilizing the electricity generated by power plants (especially renewable energy plants such as wind and solar power). Because electricity is readily available and not easily stored on a large scale, the electricity generated by power plants and the electricity consumed by loads need to be dynamically balanced in real time within the entire power system. This process involves renewable energy consumption, which requires an energy perspective and the application of a dispatch platform.

[0003] Chinese patent CN113381399A discloses a load dispatching method and apparatus that balances grid security and renewable energy consumption, comprising the following steps: Step 1: Read the predicted output of renewable energy and the predicted load level from the dispatching operation platform; Step 2: Based on the predicted output of renewable energy and the predicted load level, formulate the minimum positive and negative reserve capacity of the power grid and the renewable energy consumption plan; Step 3: Construct a mathematical optimization model for formulating the load dispatching plan; Step 4: Calculate the load dispatching demand based on the mathematical optimization model; Step 5: Publish the load dispatching demand, evaluate the user application results, and if the dispatchable resources meet the load dispatching demand, proceed to Step 6; otherwise, return to Step 2 and reduce the renewable energy consumption plan or the minimum positive and negative reserve capacity; Step 6: Based on the load dispatching demand obtained in Step 4 and the user application results in Step 5, execute load dispatching based on the demand response mechanism. While current dispatching platforms can perform dispatching functions, they do not mitigate the output fluctuations of new energy sources through energy storage systems, thus failing to reduce wind and solar curtailment rates and consequently hindering the absorption of new energy. These platforms cannot combine long-term planning with short-term dispatching, nor can they balance investment economics and operational reliability, resulting in poor practical application effects. To address these issues, there is an urgent need for a grid-energy storage-load collaborative dispatching platform and capacity configuration method oriented towards new energy absorption. Summary of the Invention

[0004] The purpose of this invention is to address the problems that current dispatching platforms, while capable of dispatching functions, fail to mitigate power output fluctuations from renewable energy sources through energy storage systems, thus failing to reduce wind and solar curtailment rates and consequently hinder renewable energy absorption capacity. Furthermore, these platforms cannot combine long-term planning with short-term dispatching, cannot balance investment economics and operational reliability, and therefore suffer from poor practical application results. The invention proposes a grid-energy storage-load coordinated dispatching platform and capacity configuration method for renewable energy absorption.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A grid-energy storage-load coordinated dispatching platform for renewable energy consumption includes a data layer, a multi-objective optimization dispatching layer, a control response layer, and a simulation evaluation layer. The data layer is used for sensing and predicting relevant data, the multi-objective optimization scheduling layer is used to establish optimization functions and realize optimized scheduling, the control response layer is used to control the energy storage charging and discharging strategy, and the simulation evaluation layer is used for simulation verification and evaluation under different conditions.

[0006] Through the aforementioned platform, the energy storage system smooths out the output fluctuations of new energy sources, and combined with flexible response on the load demand side, significantly reducing the curtailment rate of wind and solar power, thereby improving the capacity for new energy absorption. The platform's energy storage and demand response resources provide rapid adjustment capabilities to cope with new energy forecasting errors and load surges, ensuring frequency and voltage stability and effectively enhancing grid flexibility. The platform can combine long-term planning with short-term dispatch, taking into account both investment economy and operational reliability. The overall platform reduces the number of start-ups and shutdowns and fuel consumption of thermal power peak-shaving units by optimizing energy storage charging and discharging strategies and load demand response. It also optimizes energy storage capacity configuration through a two-stage stochastic programming model to avoid over-investment.

[0007] As a further description of the above technical solution: The data layer includes new energy output forecasting, load forecasting, and energy storage status monitoring. New energy output forecasting specifically includes forecasting short-term and ultra-short-term wind / solar output based on meteorological data (wind speed, irradiance) and machine learning models. The machine learning model is either LSTM or XGBoost. Load forecasting uses historical load data, weather, and holiday information to predict demand. Energy storage status monitoring obtains the state of charge (SOC) and charging / discharging power of the energy storage system in real time.

[0008] As a further description of the above technical solution: The multi-objective optimization scheduling layer includes establishing an objective function, generating constraints, and adopting an optimization algorithm. The objective function is to minimize operating costs, maximize the proportion of new energy consumption, or the lifespan of energy storage. The constraints are grid security (voltage, frequency), energy storage charging and discharging rate, and load supply and demand balance. The optimization algorithm adopts a mixed integer programming algorithm, a dynamic programming algorithm, and a deep reinforcement learning algorithm.

[0009] As a further description of the above technical solution: The control response layer specifically includes dynamically adjusting energy storage charging and discharging strategies and demand response, including interruptible loads, peak-valley electricity price incentives, and responding to real-time electricity price signals in conjunction with electricity market mechanisms.

[0010] As a further description of the above technical solution: The simulation evaluation layer specifically includes simulating power grid stability under different scenarios, evaluating the penetration rate of new energy sources and the economic indicators of energy storage configuration. Different scenarios include extreme weather and load surges. The final simulation evaluation is conducted, and the evaluation results are divided into three categories: excellent, good, and poor.

[0011] This invention also discloses a grid-energy storage-load capacity configuration method for renewable energy consumption, comprising the following steps: S1. Establish an optimization model based on time series simulation; S2. Perform probability analysis and scenario reduction; S3. Conduct economic evaluation indicators; S4. Establish a typical capacity configuration model.

[0012] As a further description of the above technical solution: In step S1, an optimization model based on time-series simulation is established. The inputs are historical or simulated time-series data of renewable energy output and load demand. The optimization variables are energy storage capacity, power, and load demand response threshold. The optimization method is one of multi-timescale rolling optimization and stochastic programming. In step S2, probability analysis and scenario reduction are performed. Specifically, random scenarios of renewable energy output and load are generated through Monte Carlo simulation. Clustering algorithms (such as K-means) are used to reduce the number of scenarios and reduce computational complexity. In step S3, economic evaluation indicators are performed. Specifically, the levelized cost of energy storage is calculated first, then the investment payback period is calculated, and finally sensitivity analysis is performed. The levelized cost of energy storage is the ratio of the total life cycle cost to the discharge capacity. Sensitivity analysis is used to analyze the impact of electricity price, energy storage cost, and renewable energy penetration rate on the configuration results. In step S4, a typical capacity configuration model is established. Two-stage optimization is performed first, and then a joint planning model is established. The first stage of the two-stage optimization plans the energy storage capacity, and the second stage optimizes the operation strategy. The established joint planning model can simultaneously optimize energy storage configuration and grid expansion scheme.

[0013] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: In this invention, the platform smooths out power output fluctuations of new energy sources through an energy storage system, and combined with flexible response on the load demand side, significantly reduces wind and solar curtailment rates, thereby improving the capacity for new energy absorption. The platform's energy storage and demand response resources provide rapid adjustment capabilities to cope with new energy forecasting errors and load surges, ensuring frequency and voltage stability and effectively enhancing grid flexibility. The platform can combine long-term planning with short-term scheduling, balancing investment economy and operational reliability. The overall platform reduces the number of start-ups and shutdowns and fuel consumption of thermal power peak-shaving units by optimizing energy storage charging and discharging strategies and load demand response. It also optimizes energy storage capacity configuration through a two-stage stochastic programming model to avoid over-investment. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the module structure of a grid-energy storage-load coordinated dispatching platform for renewable energy consumption. Detailed Implementation

[0015] 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. Example

[0016] Please see Figure 1 The present invention provides a technical solution: a grid-energy storage-load coordinated dispatching platform for new energy consumption, comprising a data layer, a multi-objective optimization dispatching layer, a control response layer and a simulation evaluation layer; Among them, the data layer is used for the perception and prediction of relevant data, the multi-objective optimization scheduling layer is used to establish the optimization function and realize the optimization scheduling, the control response layer is used to control the energy storage charging and discharging strategy, and the simulation evaluation layer is used for simulation verification and evaluation under different conditions. The data layer includes new energy output forecasting, load forecasting, and energy storage status monitoring. New energy output forecasting specifically includes forecasting short-term and ultra-short-term wind / solar output based on meteorological data (wind speed, irradiance) and machine learning models. The machine learning model is LSTM. Load forecasting uses historical load data, weather, and holiday information to predict demand. Energy storage status monitoring obtains the state of charge (SOC) and charging / discharging power of the energy storage system in real time. The multi-objective optimization scheduling layer includes establishing an objective function, generating constraints, and adopting optimization algorithms. The objective function is to minimize operating costs, maximize the proportion of new energy consumption, or the lifespan of energy storage. The constraints are grid security (voltage, frequency), energy storage charging and discharging rate, and load supply and demand balance. The optimization algorithms adopt mixed integer programming, dynamic programming, and deep reinforcement learning algorithms.

[0017] The control response layer specifically includes dynamically adjusting energy storage charging and discharging strategies and demand response, including interruptible loads, peak-valley electricity price incentives, and responding to real-time electricity price signals in conjunction with electricity market mechanisms; The simulation evaluation layer specifically includes simulating power grid stability under different scenarios, evaluating the penetration rate of new energy sources and the economic indicators of energy storage configuration. Different scenarios include extreme weather and load surges. The final simulation evaluation is conducted, and the evaluation results are divided into three categories: excellent, good, and poor.

[0018] In this embodiment, the platform smooths out power output fluctuations from new energy sources through an energy storage system. Combined with flexible response on the load demand side, it significantly reduces wind and solar curtailment rates, thereby improving the capacity for new energy absorption. The platform's energy storage and demand response resources provide rapid adjustment capabilities to cope with new energy forecasting errors and load surges, ensuring frequency and voltage stability and effectively enhancing grid flexibility. The platform can combine long-term planning with short-term scheduling, balancing investment economy and operational reliability. The overall platform reduces the number of start-ups and shutdowns and fuel consumption of thermal power peak-shaving units by optimizing energy storage charging and discharging strategies and load demand response. It also optimizes energy storage capacity configuration through a two-stage stochastic programming model to avoid over-investment. Example

[0019] This application also proposes a grid-storage-load capacity configuration method for renewable energy consumption, including the following steps: S1. Establish an optimization model based on time-series simulation, where the inputs are historical or simulated time-series data of new energy output and load demand, the optimization variables are energy storage capacity, power, and load demand response threshold, and the optimization method is multi-time-scale rolling optimization. S2. Perform probability analysis and scenario reduction, specifically: generate random scenarios of new energy output and load through Monte Carlo simulation, and use clustering algorithms (such as K-means) to reduce the number of scenarios and reduce computational complexity; S3. Conduct economic evaluation indicators, specifically: first calculate the levelized cost of energy storage, then calculate the investment payback period, and finally conduct sensitivity analysis. The levelized cost of energy storage is the ratio of total life cycle cost to discharge capacity, and the sensitivity analysis is to analyze the impact of electricity price, energy storage cost, and new energy penetration rate on the configuration results. S4. Establish a typical capacity configuration model, first perform two-stage optimization, and then establish a joint planning model. In the two-stage optimization, the first stage plans the energy storage capacity, and the second stage optimizes the operation strategy. The established joint planning model can simultaneously optimize energy storage configuration and grid expansion scheme.

[0020] This application also proposes an electronic device, which includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods of the embodiments of this application. Example

[0021] In addition, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of the embodiments of this application. The following is a detailed introduction to the various components of the electronic device: In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0022] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0023] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0024] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device; the embodiments of the present invention do not specifically limit this.

[0025] A transceiver is used to communicate with network devices or with terminal devices.

[0026] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0027] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0028] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method described in the above method embodiments, and will not be repeated here.

[0029] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0030] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0031] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0032] It should be understood that the term "and / or" in this article only describes the relationship between related objects in the grid-energy storage-load coordinated dispatch platform for renewable energy consumption. It indicates that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects in the grid-energy storage-load coordinated dispatch platform for renewable energy consumption. However, it may also indicate an "and / or" relationship. Please refer to the preceding and following text for a more detailed understanding.

[0033] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0034] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0035] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A grid-energy storage-load coordinated dispatch platform for renewable energy consumption, characterized in that, It includes a data layer, a multi-objective optimization scheduling layer, a control response layer, and a simulation evaluation layer; The data layer is used for sensing and predicting relevant data, the multi-objective optimization scheduling layer is used to establish optimization functions and realize optimized scheduling, the control response layer is used to control the energy storage charging and discharging strategy, and the simulation evaluation layer is used for simulation verification and evaluation under different conditions.

2. The grid-energy storage-load coordinated dispatch platform for new energy consumption according to claim 1, characterized in that, The data layer includes new energy output forecasting, load forecasting, and energy storage status monitoring. New energy output forecasting specifically includes forecasting short-term and ultra-short-term wind / solar output based on meteorological data (wind speed, irradiance) and machine learning models. The machine learning model is either LSTM or XGBoost. Load forecasting uses historical load data, weather, and holiday information to predict demand. Energy storage status monitoring obtains the state of charge (SOC) and charging / discharging power of the energy storage system in real time.

3. The grid-energy storage-load coordinated dispatch platform for new energy consumption according to claim 1, characterized in that, The multi-objective optimization scheduling layer includes establishing an objective function, generating constraints, and adopting an optimization algorithm. The objective function is to minimize operating costs, maximize the proportion of new energy consumption, or the lifespan of energy storage. The constraints are grid security (voltage, frequency), energy storage charging and discharging rate, and load supply and demand balance. The optimization algorithm adopts a mixed integer programming algorithm, a dynamic programming algorithm, and a deep reinforcement learning algorithm.

4. The grid-energy storage-load coordinated dispatch platform for new energy consumption according to claim 1, characterized in that, The control response layer specifically includes dynamically adjusting energy storage charging and discharging strategies and demand response, including interruptible loads, peak-valley electricity price incentives, and responding to real-time electricity price signals in conjunction with electricity market mechanisms.

5. The grid-energy storage-load coordinated dispatch platform for new energy consumption according to claim 1, characterized in that, The simulation evaluation layer specifically includes simulating power grid stability under different scenarios, evaluating the penetration rate of new energy sources and the economic indicators of energy storage configuration. Different scenarios include extreme weather and load surges. The final simulation evaluation is conducted, and the evaluation results are divided into three categories: excellent, good, and poor.

6. A capacity configuration method for grid-energy storage-load for new energy consumption, characterized in that, Includes the following steps: S1. Establish an optimization model based on time series simulation; S2. Perform probability analysis and scenario reduction; S3. Conduct economic evaluation indicators; S4. Establish a typical capacity configuration model.

7. The grid-energy storage-load capacity configuration method for new energy consumption according to claim 6, characterized in that, In step S1, an optimization model based on time-series simulation is established. The inputs are historical or simulated time-series data of renewable energy output and load demand. The optimization variables are energy storage capacity, power, and load demand response threshold. The optimization method is one of multi-timescale rolling optimization and stochastic programming. In step S2, probability analysis and scenario reduction are performed. Specifically, random scenarios of renewable energy output and load are generated through Monte Carlo simulation. Clustering algorithms (such as K-means) are used to reduce the number of scenarios and reduce computational complexity. In step S3, economic evaluation indicators are performed. Specifically, the levelized cost of energy storage is calculated first, then the investment payback period is calculated, and finally sensitivity analysis is performed. The levelized cost of energy storage is the ratio of the total life cycle cost to the discharge capacity. Sensitivity analysis is used to analyze the impact of electricity price, energy storage cost, and renewable energy penetration rate on the configuration results. In step S4, a typical capacity configuration model is established. Two-stage optimization is performed first, and then a joint planning model is established. The first stage of the two-stage optimization plans the energy storage capacity, and the second stage optimizes the operation strategy. The established joint planning model can simultaneously optimize energy storage configuration and grid expansion scheme.

8. An electronic device, characterized in that, The electronic device includes: processor; The memory stores computer-readable instructions, which, when executed by the processor, implement the grid-energy storage-load coordinated dispatch platform for renewable energy consumption as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code, which can be called by a processor to execute the grid-energy storage-load coordinated dispatching platform for new energy consumption as described in any one of claims 1 to 5.

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