Coordinated scheduling method for improving novel energy storage calling efficiency

Abstract

The invention discloses a coordinated scheduling method for improving novel energy storage calling efficiency. The method comprises the following steps: S1, establishing an energy storage resource database; s2, evaluating the running state of the power grid; s3, calculating an energy storage demand; s4, energy storage resource optimization combination; and S5, performing hierarchical scheduling: (1) in a normal condition, judging whether to participate in the electricity market, and scheduling energy storage participating in the electricity market according to a market clearing result; energy storage which does not participate in the market is directly called through a scheduling instruction; (2) emergency: uniformly and directly calling the novel stored energy in all regulation and management ranges according to the safety requirements of the power grid; (3) preventive scheduling: judging whether the future power grid is about to have insufficient standby or abandoned new energy power or not, and when the future power grid is about to have insufficient standby or abandoned new energy power, issuing an energy storage charging and discharging instruction or SOC (State of Charge) requirement in advance; and S6, performing dynamic adjustment and feedback optimization.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, and in particular to a coordinated scheduling method for improving the efficiency of new energy storage deployment. Background Technology

[0002] New energy power generation is characterized by randomness, volatility, and intermittency, posing significant challenges to power system operation. 1. In terms of renewable energy consumption: New installed capacity maintains a high growth rate, and the peak-shaving period has shifted to the midday photovoltaic peak generation period, resulting in increased daily fluctuations in renewable energy. New energy storage mechanisms are urgently needed to promote consumption.

[0003] 2. Power supply guarantee: New energy sources are characterized by "large installed capacity but small output". Their power generation capacity is highly dependent on weather conditions and does not match the actual power demand. New energy storage is urgently needed to provide supply guarantee support.

[0004] The current new energy storage dispatch system has the following problems: 1. In most provinces, energy storage participates in the electricity spot market through a self-dispatch planning model. The plans formulated by the power stations themselves differ significantly from the grid demand, resulting in low energy storage utilization and an inability to better meet the regulation needs.

[0005] 2. When the error in the forecast of new energy output increases, causing a discrepancy between the spot market clearing plan and actual demand, dispatchers need to call upon energy storage resources within the grid. At this time, the optimal selection of energy storage power stations is an urgent problem to be solved. The current selection method is to select in rotation according to the capacity that each energy storage power station can provide, which cannot achieve the optimal utilization of energy storage resources.

[0006] 3. Existing energy storage dispatch methods are unable to fully leverage the performance advantages of different types of energy storage power stations in different scenarios. Currently, the energy storage power stations connected to the dispatch are basically still electrochemical energy storage, and the optimal selection of various types of energy storage in coordination and regulation has not been fully considered after energy storage participates in the electricity market. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a coordinated scheduling method for improving the efficiency of novel energy storage deployment.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: A coordinated scheduling method for improving the efficiency of new energy storage deployment includes the following steps: S1. Establish an energy storage resource database: (1) Collect the technical parameters of all new energy storage power stations within the jurisdiction; (2) Record the application scenario classification of each energy storage power station in order to establish an energy storage resource database; (3) Monitor the operating status and SOC information of each energy storage power station in real time; S2. Power Grid Operation Status Assessment: (1) Real-time monitoring of power grid operation status; (2) Evaluate the operational scenarios that the power grid may encounter in the future based on forecast data; S3, Energy Storage Demand Calculation: (1) Calculate the regulation capacity requirement that energy storage needs to provide based on the power grid operation status assessment results; (2) Determine the total power and electricity requirements for energy storage; (3) Identify the geographical distribution characteristics of demand; S4. Optimized combination of energy storage resources: (1) Based on the energy storage resource database, establish a multi-objective optimization model to determine whether the following requirements are met: 1) technical performance matching degree requirements; 2) economic indicators requirements; 3) power grid security constraints. If the requirements are met, proceed to the next step. (2) Solve the optimization model to determine the optimal combination and scheduling scheme of energy storage power stations; S5, Hierarchical and tiered scheduling execution: (1) Normal situation: Determine whether to participate in the electricity market. For energy storage that participates in the electricity market, dispatch according to the market clearing results; for energy storage that does not participate in the market, directly call it through dispatch instructions. (2) Emergency situations: Based on the power grid security requirements, all new energy storage devices within the scope of regulation and control can be directly and uniformly called upon; (3) Preventive dispatch: Determine whether the power grid will experience insufficient reserves or curtailment of new energy in the future. If insufficient reserves or curtailment of new energy is expected, issue energy storage charging and discharging instructions or SOC requirements in advance. S6. Dynamic Adjustment and Feedback Optimization: (1) Monitor the energy storage response and changes in grid operation status in real time; (2) Dynamically adjust scheduling instructions based on actual response results; (3) Accumulate historical data to optimize scheduling strategy parameters.

[0009] In step S1, the technical parameters of all new energy storage power stations within the management scope are collected, including rated power, energy storage capacity, response time, charge and discharge efficiency, cycle life, and SOC range.

[0010] In step S1, the application scenario classification includes grid side, power supply side, and user side.

[0011] In step S2, the real-time monitoring of the power grid operation status includes load level, new energy power generation output, cross-sectional power flow, and reserve capacity.

[0012] In step S2, the operating scenarios include power shortage, limited absorption of new energy sources, and frequency anomalies.

[0013] In step S3, the geographical distribution characteristics of the identified demand include network-wide demand or local section demand.

[0014] In step S4, the technical performance matching degree includes response speed and adjustment accuracy; 2) economic indicators include charging and discharging loss cost; 3) power grid safety constraints include cross-sectional limits and voltage levels.

[0015] In step S5, the emergency situation includes: (1) Power grid accidents, including: unit tripping, large-scale power grid disconnection, and tripping of important lines or main transformers; (2) A power deficit occurs in the power grid; (3) The power grid has a demand for the consumption of new energy sources; (4) Abnormal power grid frequency; (5) The power flow of the power transmission and transformation equipment exceeds the stability limit; (6) Excessive deviation in new energy forecasting or load forecasting; (7) Other emergency situations that require intervention from the power dispatching agency.

[0016] In step S5, preventative scheduling includes: (1) When the standby capacity is expected to be insufficient during the peak period of grid load, the energy storage charging instruction or SOC requirement shall be issued in advance; (2) When it is expected that new energy sources will be blocked in the whole network or section, the energy storage discharge command or SOC requirement will be issued in advance.

[0017] The beneficial effects of this invention are: 1. This invention comprehensively considers the technical performance of various types of energy storage power stations, optimizes the combined scheduling of energy storage power stations based on load demand and power generation forecasts, and maximizes the energy storage efficiency in different application scenarios.

[0018] 2. This invention improves the dispatch efficiency and utilization rate of new energy storage, giving full play to its multiple roles in the power system.

[0019] 3. This invention achieves the optimal combination of energy storage resources under different application scenarios, thereby improving the overall system regulation capability.

[0020] 4. This invention takes into account both market mechanisms and power grid security requirements through a hierarchical scheduling strategy.

[0021] 5. The preventive scheduling mechanism of this invention effectively addresses the challenges brought about by the uncertainty of new energy forecasting.

[0022] 6. This invention improves the safety, stability, and economic efficiency of high-proportion renewable energy power systems. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the principle of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the invention.

[0025] like Figure 1 As shown, a coordinated scheduling method for improving the efficiency of new energy storage deployment includes the following steps: S1. Establish an energy storage resource database: (1) Collect the technical parameters of all new energy storage power stations within the jurisdiction; (2) Record the application scenario classification of each energy storage power station in order to establish an energy storage resource database; (3) Monitor the operating status and SOC information of each energy storage power station in real time; Specifically, the system collects parameters such as rated power, charge / discharge efficiency, and SOC range of all energy storage power stations within its jurisdiction through the provincial dispatch and control cloud platform, and establishes a structured database; it classifies and labels grid-side, power supply-side, and user-side energy storage into scenarios; and it deploys edge computing nodes to upload energy storage operation status data in real time. S2. Power Grid Operation Status Assessment: (1) Real-time monitoring of power grid operation status; (2) Evaluate the operational scenarios that the power grid may encounter in the future based on forecast data; Specifically, the system integrates SCADA to acquire real-time load data (accuracy ≤ 1 second) and uses WAMS to monitor cross-sectional power flow; it uses the load forecasting system and renewable energy power forecasting system in the dispatch management system to acquire short-term and ultra-short-term forecast data of load and new energy; and it generates probability distributions for scenarios such as power deficit and frequency anomalies through Monte Carlo simulation. Among these, the power grid operation status assessment includes real-time monitoring and predictive analysis to identify possible operation scenarios that the power grid may face. S3, Energy Storage Demand Calculation: (1) Calculate the regulation capacity requirement that energy storage needs to provide based on the power grid operation status assessment results; (2) Determine the total power and electricity requirements for energy storage; (3) Identify the geographical distribution characteristics of demand; Specifically, the existing power balance assessment method is combined with the cross-sectional transmission quota constraint; fuzzy logic algorithm is used to divide the network-wide / local demand, and GIS spatial analysis is used to determine the demand hotspot areas; S4. Optimized combination of energy storage resources: (1) Based on the energy storage resource database, establish a multi-objective optimization model to determine whether the following requirements are met: 1) technical performance matching degree requirements; 2) economic indicators requirements; 3) power grid security constraints. If the requirements are met, proceed to the next step. (2) Solve the optimization model to determine the optimal combination and scheduling scheme of energy storage power stations; Specifically, the Safety-Constrained Economic Dispatch (SCED) algorithm is used for centralized optimization calculations to continuously determine the energy storage resource combination results for the next 15 minutes to 2 hours. The objective function includes: a. Technical performance objective: Maximize response speed weight in, w i For the first i When prioritizing the performance of energy storage, with response speed as the primary consideration... w i Increase; v i For the first i The response rate of an energy storage unit (MW / min). P target To achieve 90% active power at the step command time, P start To test the active power at the moment the target command is issued.

[0026] For the first i Unit charge / discharge loss cost of energy storage (RMB / MW). P discharge For charging power, P charge This represents the discharge power.

[0027] c. Power grid security objective: To meet cross-sectional power flow constraints. P flow ≤ P limit in, P flow To monitor the actual power flow at the cross-section of the control area. P limit Safety limits for power flow at the monitoring section of the control area.

[0028] Dynamic constraints: Safety interlocking rules: When the current SOC is higher than the upper limit of the prohibition, the charging prohibition logic is executed; when the current SOC is lower than the lower limit of the prohibition, the discharging prohibition logic is executed.

[0029] When the cross-sectional power flow exceeds the limit, the energy storage output is adjusted.

[0030] S5, Hierarchical and tiered scheduling execution: (1) Normal situation: Determine whether to participate in the electricity market. For energy storage that participates in the electricity market, dispatch according to the market clearing results; for energy storage that does not participate in the market, call directly through dispatch instructions. Specifically, connect to the electricity trading platform, parse the AGC instruction set of the clearing results, issue AGC instructions through the electricity market EMS system, and use the IEC 60870-5-104 protocol for communication. (2) Emergency situations: Based on the grid security requirements, all new energy storage devices within the dispatching scope are directly called up. Specifically, the IEC 61850 protocol fast control module is deployed in the energy storage coordination control system to support millisecond-level power regulation and trigger the grid security zone III defense system. The dispatcher directly controls the energy storage PCS device through the energy storage coordination control system. Among them, emergency situations include grid accidents, power shortages, new energy consumption needs, frequency anomalies, power flow exceeding limits, and excessive prediction deviations. (3) Preventive dispatch: Determine whether the power grid will experience insufficient reserves or curtailment of new energy in the future. If insufficient reserves or curtailment of new energy will occur, issue energy storage charging and discharging instructions or SOC requirements in advance. Specifically, generate preventive charging and discharging strategies based on Markov decision process, integrate numerical weather forecast data, use Transformer model to predict the probability distribution of new energy output, calculate energy storage demand in advance, and carry out human intervention dispatch. S6. Dynamic Adjustment and Feedback Optimization: (1) Monitor the energy storage response and changes in grid operation status in real time; (2) Dynamically adjust scheduling instructions based on actual response results; (3) Accumulate historical data to optimize scheduling strategy parameters; Specifically, a dual closed-loop control structure is designed, with the inner loop using PID regulation to track power commands and the outer loop using Q-learning algorithm to optimize the dead zone; the LSTM model is used to predict the SOC trajectory in the next 5 minutes and dynamically correct the scheduling plan. That is, (1) real-time monitoring of energy storage response and grid operation status changes, and collecting the actual energy storage output and response time every second through the WAMS system.

[0031] (2) Adjust the scheduling instructions dynamically based on the actual response effect, and adopt dual closed-loop control: Inner loop: PID tracking power command deviation; Outer loop: The dead zone is optimized based on the Q-learning algorithm, and the PID parameters are adjusted according to the SOC deviation.

[0032] SOC Correction Bias: △ P bias = α ×( SOC target - SOC actual ) Among them, △ P bias For SOC correction power bias, α This is the correction factor (adjustable from 0 to 1, default 0.5). SOC target Set the target SOC value (e.g., 80% for preventative scheduling). SOC actual This is the actual SOC value (real-time monitoring).

[0033] (3) Accumulate historical data to optimize scheduling strategy parameters, use LSTM model to predict the SOC trajectory in the next 5 minutes, and continuously correct the scheduling plan.

[0034] In step S1, the technical parameters of all new energy storage power stations within the management scope are collected, including rated power, energy storage capacity, response time, charge and discharge efficiency, cycle life, and SOC range.

[0035] In step S1, the application scenario classification includes grid side, power supply side, and user side.

[0036] In step S2, the real-time monitoring of the power grid operation status includes load level, new energy power generation output, cross-sectional power flow, and reserve capacity.

[0037] In step S2, the operating scenarios include power shortage, limited absorption of new energy sources, and frequency anomalies.

[0038] In step S3, the geographical distribution characteristics of the identified demand include network-wide demand or local section demand.

[0039] In step S4, the technical performance matching degree includes response speed and adjustment accuracy; 2) economic indicators include charging and discharging costs and loss costs; 3) power grid safety constraints include cross-sectional limits and voltage levels.

[0040] In step S5, the emergency situation includes: (1) Power grid accidents, including: unit tripping, large-scale power grid disconnection, and tripping of important lines or main transformers; (2) A power deficit occurs in the power grid; (3) The power grid has a demand for the consumption of new energy sources; (4) Abnormal power grid frequency; (5) The power flow of the power transmission and transformation equipment exceeds the stability limit; (6) Excessive deviation in new energy forecasting or load forecasting; (7) Other emergency situations that require intervention from the power dispatching agency.

[0041] In step S5, preventative scheduling includes: (1) When the standby capacity is expected to be insufficient during the peak period of grid load, the energy storage charging instruction or SOC requirement shall be issued in advance; (2) When it is expected that new energy sources will be blocked in the whole network or section, the energy storage discharge command or SOC requirement will be issued in advance.

[0042] Example 1: Energy storage dispatch under normal conditions 1. Based on the recent market clearing results, the power dispatching agency issues power plans to energy storage power stations participating in the market.

[0043] 2. The energy storage station can independently call upon energy storage based on the market clearing results and the instructions issued by the active power control system.

[0044] 3. For energy storage that does not participate in the market, the dispatching agency directly issues dispatching instructions based on the grid demand.

[0045] 4. Prioritize grid-side energy storage power stations; only use power source-side energy storage when insufficient.

[0046] Example 2: Energy Storage Dispatch in Emergency Situations 1. An emergency response was triggered when the power grid frequency was detected to be below 49.8 Hz.

[0047] 2. One-click discharge mode: Under the premise of ensuring that the cross-sectional power flow meets the requirements, the power dispatching agency switches the entire network energy storage control mode to discharge mode in the energy storage AGC system, with the base point power being the rated discharge power of the energy storage.

[0048] 3. Real-time calculation of SOC correction bias Δ P bias Dynamically adjust the energy storage discharge power.

[0049] 4. After the frequency returns to normal, gradually restore the original operation mode of the energy storage and start the SOC balancing strategy.

[0050] Example 3: Preventive Scheduling 1. Based on weather forecasts and load predictions, peak-shaving difficulties are expected during the peak photovoltaic power generation period at noon the following day.

[0051] 2. Issue charging instructions to the relevant regional energy storage power stations 8 hours in advance, requiring them to reach 80% SOC during peak photovoltaic power generation periods.

[0052] 3. Monitor the output and load changes of new energy sources in real time. If the actual SOC deviates from the target value by more than 10%, trigger Δ. P bias Correct the charging rate and dynamically adjust the energy storage charging power.

[0053] 4. During peak photovoltaic power generation periods, utilize energy storage and charging capacity as planned to promote the consumption of new energy sources.

[0054] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A coordinated scheduling method for improving the efficiency of new energy storage deployment, characterized in that, Includes the following steps: S1. Establish an energy storage resource database: (1) Collect the technical parameters of all new energy storage power stations within the jurisdiction; (2) Record the application scenario classification of each energy storage power station in order to establish an energy storage resource database; (3) Monitor the operating status of each energy storage power station and the state of charge (SOC) information of the energy storage power station in real time; S2. Power Grid Operation Status Assessment: (1) Real-time monitoring of power grid operation status; (2) Evaluate the operational scenarios that the power grid may encounter in the future based on forecast data; S3, Energy Storage Demand Calculation: (1) Calculate the regulation capacity requirement that energy storage needs to provide based on the power grid operation status assessment results; (2) Determine the total power and electricity requirements for energy storage; (3) Identify the geographical distribution characteristics of demand; S4. Optimized combination of energy storage resources: (1) Based on the energy storage resource database, establish a multi-objective optimization model to determine whether the following requirements are met: 1) technical performance matching degree requirements; 2) economic indicators requirements; 3) power grid security constraints. If the requirements are met, proceed to the next step. (2) Solve the optimization model to determine the optimal combination and scheduling scheme of energy storage power stations; S5, Hierarchical and tiered scheduling execution: (1) Normal situation: Determine whether to participate in the electricity market. For energy storage that participates in the electricity market, dispatch according to the market clearing results; for energy storage that does not participate in the market, directly call it through dispatch instructions. (2) Emergency situations: Based on the power grid security requirements, all new energy storage devices within the scope of regulation and control can be directly and uniformly called upon; (3) Preventive dispatch: Determine whether the power grid will experience insufficient reserves or curtailment of new energy in the future. If insufficient reserves or curtailment of new energy is expected, issue energy storage charging and discharging instructions or SOC requirements in advance. S6. Dynamic Adjustment and Feedback Optimization: (1) Monitor the energy storage response and changes in grid operation status in real time; (2) Dynamically adjust scheduling instructions based on actual response results; (3) Accumulate historical data to optimize scheduling strategy parameters.

2. The coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S1, the technical parameters of all new energy storage power stations within the regulatory range are collected, including rated power, energy storage capacity, response time, charge and discharge efficiency, cycle life, and SOC range.

3. The coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S1, the application scenario classification includes grid side, power supply side, and user side.

4. The coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S2, the grid operation status is monitored in real time, including load level, power output of new energy generation, power flow at cross sections, and reserve capacity.

5. The coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S2, the operating scenarios include power shortage, limited absorption of new energy sources, and frequency anomalies.

6. The coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S3, the geographical distribution characteristics of the identified demand include network-wide demand or local section demand.

7. The coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S4, the technical performance matching degree includes response speed and adjustment accuracy; 2) economic indicators include charging and discharging costs and loss costs; 3) power grid safety constraints include cross-sectional limits and voltage levels.

8. The coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S5, the emergency situations include: (1) Power grid accidents, including: unit tripping, large-scale power grid disconnection, and tripping of important lines or main transformers; (2) A power deficit occurs in the power grid; (3) The power grid has a demand for the consumption of new energy sources; (4) Abnormal power grid frequency; (5) The power flow of the power transmission and transformation equipment exceeds the stability limit; (6) Excessive deviation in new energy forecasting or load forecasting; (7) Other emergency situations that require intervention from the power dispatching agency.

9. A coordinated scheduling method for improving the efficiency of novel energy storage deployment according to claim 1, characterized in that, In step S5, preventative scheduling includes: (1) When the standby capacity is expected to be insufficient during the peak period of grid load, the energy storage charging instruction or SOC requirement shall be issued in advance; (2) When it is expected that new energy sources will be blocked in the whole network or section, the energy storage discharge command or SOC requirement will be issued in advance.

Images