A new energy storage regulation system adaptive to cross-period power spot market

Abstract

The application relates to the technical field of energy storage regulation, in particular to a new energy energy storage regulation system suitable for a cross-period electric power spot market, which comprises the following: a data acquisition module, which is used for acquiring the electricity price of the electric power spot market in each regulation period, and the state of charge and the charging and discharging power of the energy storage system, the new energy power generation power and the total load power of the demand side at different time points in each regulation period; a data analysis module, which is used for acquiring the response coefficient of each regulation period, specifically: calculating the electricity price disturbance degree, the switching frequency, the coupling influence degree and the response coefficient of each regulation period; and an energy storage regulation module, which is used for adjusting the predicted time domain of the next regulation period according to the response coefficient and executing the rolling optimization scheduling of the charging and discharging power of the energy storage system by using a model predictive control algorithm. The application effectively improves the regulation accuracy and operation benefit of the energy storage system in the cross-period electric power spot market.

Description

Technical Field

[0001] This application relates to the field of energy storage regulation technology, specifically to a new energy storage regulation system adapted to the cross-time period electricity spot market. Background Technology

[0002] With the deepening of power market reforms, the electricity spot market has gradually become the core platform for power resource allocation. In the inter-time period electricity spot market, electricity prices are determined in real time by supply and demand, exhibiting significant time-varying and volatile characteristics. Energy storage systems, as a key buffer link connecting renewable energy generation with load demand, can not only realize arbitrage profits by charging during low-price periods and discharging during high-price periods, but also effectively mitigate the volatility of renewable energy generation, supporting the supply-demand balance of the power grid.

[0003] Due to the volatility of renewable energy output, the power regulation of energy storage systems becomes significantly more difficult. Existing technologies typically employ model predictive control algorithms for energy storage dispatch. These algorithms often use a fixed prediction time domain and do not fully consider the dynamic and drastic changes in market electricity prices and grid supply and demand matching that energy storage systems face with varying operating conditions. This leads to constantly changing regulation requirements for energy storage systems, and consequently, the fixed prediction time domain is prone to control response lag or frequent power command oscillations in complex regulation scenarios. This not only reduces the overall arbitrage profits of energy storage systems in the electricity spot market but also poses risks of overcharging or over-discharging due to untimely regulation, affecting the stable operation of the power grid. Summary of the Invention

[0004] To address the aforementioned technical issues, a new energy storage and control system adapted to the cross-time period electricity spot market is provided to solve existing problems.

[0005] The solution to the technical problem presented in this application is to provide a new energy storage and regulation system adapted to the inter-time-of-use electricity spot market, the system comprising:

[0006] The data acquisition module is used to obtain the electricity price in the spot market during each regulation period, as well as the battery state of charge and charging / discharging power of the energy storage system, the power generation of new energy sources, and the total load power on the demand side at different times during each regulation period.

[0007] The data analysis module is used to obtain the response coefficients for each control period, specifically:

[0008] To assess the impact of external market electricity price fluctuations on energy storage regulation, we analyze the characteristics of electricity price fluctuations and the stability of electricity price change trends during different regulation periods, and calculate the degree of electricity price disturbance during each regulation period.

[0009] The frequency of switching between charging and discharging states of the energy storage system is assessed based on the charging and discharging power. Combined with the degree of electricity price disturbance, the coupling influence of each regulation period is determined, which is used to characterize the synergistic effect between external market fluctuations and internal state switching.

[0010] The response coefficient for each control period is determined by the supply and demand balance between new energy power generation and total load power during each control period, combined with the coupling influence and the regulation capability reflected by the remaining state of charge of the battery at the end of the control period.

[0011] The energy storage regulation module is used to adjust the predicted time domain for the next regulation period based on the response coefficient. Based on the adjusted predicted time domain, the module uses a model predictive control algorithm to perform rolling optimization scheduling of the energy storage system's charging and discharging power.

[0012] Preferably, the calculation of electricity price fluctuations during each regulation period includes:

[0013] Each control period and the multiple control periods preceding it are combined into a local window; the dispersion of electricity prices for all control periods within the local window is calculated.

[0014] Calculate the electricity price difference between two adjacent control periods within a local window; analyze the pattern of sign change in the electricity price difference and calculate the trend instability;

[0015] The electricity price disturbance is the result of a positive fusion of dispersion and trend instability.

[0016] Preferably, the calculation of trend instability includes: using a sign function to extract the sign identifiers of all electricity price differences within a local window, forming a sign sequence, performing a runs test on it, and counting the number of runs as the trend instability.

[0017] Preferably, the specific process of the positive fusion is as follows: the product of the degree of dispersion and the degree of trend instability is used as the electricity price disturbance degree.

[0018] Preferably, the method for obtaining the switching frequency is as follows: multiple control periods before each control period are defined as adjacent periods; the charging and discharging power of each control period and all adjacent periods at all times are combined into a charging and discharging sequence, and the number of times the symbols of adjacent elements in the charging and discharging sequence change is counted as the switching frequency.

[0019] Preferably, the degree of coupling influence is positively correlated with the degree of electricity price disturbance and the frequency of switching.

[0020] Preferably, the method for obtaining the response coefficient is as follows:

[0021] The difference between the average value of renewable energy power generation at all times during each regulation period and the average value of total load power at all times is calculated as the supply-demand difference.

[0022] Extract the battery state of charge at the last moment within each control period, and define it as the last charge level;

[0023] If the supply-demand difference is greater than or equal to 0, the response coefficient of each regulation period is positively correlated with the degree of coupling influence and the last load power; conversely, the response coefficient is positively correlated with the degree of coupling influence and negatively correlated with the last load power.

[0024] Preferably, the specific calculation process of the response coefficient is as follows: if the supply and demand difference is greater than or equal to 0, then the response coefficient of each control period is the product of the coupling influence degree and the last load power; otherwise, the response coefficient is the ratio of the coupling influence degree to the last load power.

[0025] Preferred, the first Each regulation period corresponds to the adjusted prediction time domain. The calculation formula is: ,in, This is the preset initial prediction time domain; For the first The response coefficient for each control period. The preset adjustment range; It is an exponential function with the natural constant as its base; This represents the floor function.

[0026] Preferably, the step of using model predictive control algorithm to perform rolling optimization scheduling of energy storage system charging and discharging power includes: obtaining the estimated electricity price in the adjusted prediction time domain, as well as the estimated new energy power generation and estimated total load power at each corresponding moment; taking the maximization of the cumulative revenue of the energy storage system in the prediction time domain as the objective function; and taking the battery state of charge safety limit, charging and discharging power limit and supply and demand balance of the energy storage system as constraints; solving for the optimal charging and discharging power at each moment in the prediction time domain; and controlling the energy storage system to perform charging and discharging operations in real time.

[0027] This application has at least the following beneficial effects:

[0028] This application calculates the electricity price disturbance degree during each regulation period. Its beneficial effects include: by quantifying the volatility and instability of electricity prices, it accurately identifies the characteristics of price changes from an external market perspective, enabling timely perception of drastic changes in the market environment and assessing the dynamic adjustment pressure on the energy storage system caused by market electricity price fluctuations and directional instability; it calculates the switching frequency, which provides a quantitative basis for assessing the drastic changes in the external environment faced by the energy storage system by characterizing the frequency of charging and discharging state switching in the recent period; it determines the coupling influence degree, which integrates the electricity price disturbance degree, representing the characteristics of external market electricity price fluctuations, with the frequency of internal state switching of the energy storage system to comprehensively assess the complexity of the regulation environment faced by the energy storage system, revealing the regulation response sensitivity required for the energy storage system to adapt to market changes; and it determines the response coefficient for each regulation period, which, based on the assessment of external pressure, further introduces the supply-demand balance relationship and the real-time state of charge of the energy storage system, enabling a comprehensive assessment of the energy storage system's response to market electricity price pressure and its own state. This design considers the overall response requirements under state constraints and supply-demand changes, implementing an adaptive control logic of "the more full, the more urgent; the more empty, the more urgent," providing a quantitative basis for the urgency of control when dynamically adjusting the prediction time domain. The prediction time domain for the next control period is adjusted, and based on the adjusted prediction time domain, a model predictive control algorithm is used to perform rolling optimization scheduling of the energy storage system's charging and discharging power. Its beneficial effect lies in the fact that by dynamically adjusting the prediction time domain, it can dynamically change according to the real-time control demands faced by the energy storage system. In complex scenarios with drastic price fluctuations, frequent state switching, and limited adjustment space (i.e., a large response coefficient), a shortened prediction time domain allows the MPC algorithm to focus more on recent information, improving the energy storage system's response speed to market changes and effectively avoiding control lag. When the response coefficient is small, a longer prediction time domain is restored, ensuring that the energy storage system optimizes its charging and discharging strategy over a longer time dimension to pursue global benefits. Through this adaptive mechanism, a dynamic balance between control sensitivity and operational stability is achieved, effectively improving the control accuracy and operational efficiency of the energy storage system in the cross-time period electricity spot market. Attached Figure Description

[0029] The following description, in conjunction with the accompanying drawings, provides a more detailed explanation of a new energy storage and control system adapted to the inter-time period electricity spot market.

[0030] Figure 1 A block diagram of a new energy storage control system adapted to the inter-time period electricity spot market is provided as an embodiment of this application;

[0031] Figure 2 This is a block diagram illustrating the implementation of a data analysis module according to one embodiment of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description, in conjunction with the accompanying drawings and implementation examples, provides a new energy storage control system adapted to the inter-time-slot electricity spot market. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0034] Please see Figure 1 The diagram illustrates a block diagram of a new energy storage control system adapted to the cross-time period electricity spot market, provided by an embodiment of this application. The system includes: a data acquisition module, a data analysis module, and an energy storage control module.

[0035] The data acquisition module is used to obtain the electricity price in the spot market during each regulation period, as well as the battery state of charge and charging / discharging power of the energy storage system, the power generation of new energy sources, and the total load power on the demand side at different times during each regulation period.

[0036] With the expansion of renewable energy grid connection, the electricity spot market directly reflects the real-time supply and demand relationship of the power grid through cross-period dynamic pricing. Energy storage systems, as a flexible regulatory resource, are regulated to charge and absorb surplus electricity during periods of low electricity prices and discharge to fill load gaps during periods of high electricity prices. This approach can both smooth out fluctuations in renewable energy to maintain grid stability and realize energy storage revenue through price differences. Due to frequent fluctuations in spot market electricity prices, to achieve precise matching between energy storage charging and discharging strategies and market rhythms, and to avoid regulatory lag, it is necessary to obtain real-time information on the status of energy storage devices, grid supply and demand, and market prices. Specifically:

[0037] The battery management system of the energy storage system can be used to obtain the state of charge (SOC) of the battery in real time; and the power conversion system can be used to obtain the charging and discharging power of the energy storage system in real time.

[0038] It should be noted that the SOC value is converted from a percentage format to a numerical format; at the same time, since the energy storage system has charging, discharging and standby states, the collected charging and discharging power data is discretized and mapped according to the state: when the energy storage system is in the charging state, its charging and discharging power is a positive value; when the energy storage system is in the discharging state, its charging and discharging power is a negative value; and when the energy storage system is in the standby state, its charging and discharging power is 0.

[0039] A power analyzer is installed at the new energy power generation end connected to the energy storage system to collect the new energy power generation power in real time.

[0040] The total load power on the demand side is collected in real time by using the metering devices on the distribution network side connected to the energy storage system;

[0041] In this embodiment, the data acquisition time interval is 5 seconds. As for other implementation methods, the implementer can set it according to the actual situation.

[0042] Multiple moments are defined as a control period, and the battery state of charge, charging and discharging power, new energy power generation and total load power are obtained at each moment within each control period.

[0043] In this embodiment, since transactions in the electricity spot market are usually settled every 15 minutes, the duration of the regulation period is 15 minutes. As for other implementation methods, the implementer can set it according to the actual situation.

[0044] The electricity price for each control period is obtained from the data interface terminal in the electricity spot market; it should be noted that the electricity price is constant within a control period.

[0045] Thus, we obtain the electricity price for each regulation period, as well as the battery state of charge and charging / discharging power of the energy storage system, the power generation of new energy sources, and the total load power on the demand side at different times.

[0046] The data analysis module is used to obtain the response coefficients for each control period.

[0047] Furthermore, the implementation block diagram of the data analysis module provided in this application is as follows: Figure 2 As shown.

[0048] Step 1: Analyze the characteristics of electricity price fluctuations and the stability of electricity price change trends during different control periods to assess the impact of external market electricity price fluctuations on energy storage control, and calculate the degree of electricity price disturbance during each control period.

[0049] In the inter-time-of-use electricity spot market, electricity price is key information guiding the charging and discharging behavior of energy storage systems. Electricity prices at different control periods reflect the magnitude of changes in electricity supply and demand over time. The core control objective of energy storage systems is to achieve arbitrage and supply-demand balance through low-storage-high-discharge. However, its actual control effect is highly susceptible to constraints from the rate of price change and the efficiency of energy storage system state switching. When market electricity prices fluctuate drastically and frequently, it indicates a significant change in the grid supply-demand relationship within a short period. In this situation, to capitalize on price advantages or avoid adverse operations, energy storage systems typically need to rapidly adjust their charging and discharging states within a short timeframe. If the energy storage system's control sensitivity is insufficient, resulting in a failure to complete the state transition in a timely manner, its control actions will lag significantly behind the market price rhythm, directly leading to a reduction in arbitrage opportunities and the failure of control commands.

[0050] Currently, the charging and discharging scheduling of energy storage systems typically employs Model Predictive Control (MPC) algorithms. These algorithms rely on a fixed prediction time domain to continuously optimize the charging and discharging control trajectory. However, in cross-time spot markets, a fixed prediction time domain is prone to becoming out of sync with dynamic market rhythms. Specifically, when electricity prices exhibit a single trend of continuous rise or fall, it indicates that the market supply and demand relationship has a strong unidirectional change characteristic. In this case, the energy storage system only needs to maintain its current charging and discharging state to meet the adjustment requirements, and the control trajectory under a fixed prediction time domain has good continuity. However, when electricity prices fluctuate frequently, the energy storage system faces high-frequency state switching requirements. If a fixed, longer prediction time domain is still used, the algorithm will overemphasize long-term planning and smooth out short-term price jumps, resulting in a large adjustment inertia in the control trajectory of the energy storage system. Consequently, the energy storage system cannot keep up with the current market rhythm in a timely manner.

[0051] Based on the above analysis, the degree of electricity price disturbance is calculated by examining the trends and fluctuation characteristics of electricity prices during different control periods. Specifically:

[0052] Each control period and the multiple control periods preceding it are combined to form a local window;

[0053] In this embodiment, each control period and the 20 control periods preceding it are combined into a local window. As for other implementation methods, the implementer can set it according to the actual situation.

[0054] Calculate the dispersion of electricity prices for all control periods within a local window;

[0055] In this embodiment, the degree of dispersion is measured by calculating the coefficient of variation of electricity prices for all control periods within the local window; the calculation of the coefficient of variation is a well-known technique and will not be described in detail here; it should be noted that, in order to avoid the denominator being 0 when calculating the coefficient of variation, a parameter adjustment factor is added to the denominator. In this embodiment, the parameter adjustment factor is set to 0.001. As for other implementation methods, the implementer can set it according to the actual situation.

[0056] It should be noted that the greater the degree of dispersion, the more drastic the recent electricity price fluctuations, and the higher the market price uncertainty faced by the energy storage system. In this case, a rapid response to market price changes is more necessary. Conversely, the smaller the degree of dispersion, the more stable the recent electricity price changes, and the more continuous the regulation process of the energy storage system is, without the need for drastic regulation.

[0057] Calculate the electricity price difference between two adjacent control periods within a local window;

[0058] In this embodiment, the difference between the electricity price of each control period within the local window and the electricity price of the previous control period is calculated as the electricity price difference;

[0059] The sign function is used to extract the sign identifiers of all electricity price differences within a local window, forming a sign sequence. The run test is then performed on the sequence, and the number of runs is counted as the trend instability.

[0060] It should be noted that the sign function Specific representation: When When greater than 0, ;when When less than 0, ;when When equal to 0, Therefore, when the price difference is greater than 0, its sign is 1; when the price difference is less than 0, its sign is -1; and when the price difference is equal to 0, its sign is 0. Secondly, the runs test is a well-known technique and will not be elaborated upon here. A run is a subsequence consisting of consecutive identical signs within a symbol sequence. For ease of understanding, let's assume the symbol sequence is... Two consecutive 1s constitute a run. If a sequence of symbols forms a run, and three consecutive -1s form another run, then the number of runs for this symbol sequence is 3.

[0061] It should be noted that the greater the instability of the trend, the more frequent the alternation between rising and falling electricity prices and the more volatile the market, the more frequently the energy storage system needs to adjust its charging and discharging state, the greater the regulatory pressure it faces, and the more it needs to shorten the prediction time domain to improve response sensitivity. Conversely, the more concentrated the direction of electricity price changes, the stronger the unidirectional trend of the market, and the less pressure the energy storage system faces in switching states.

[0062] By positively fusing the degree of dispersion and the degree of trend instability, the degree of electricity price disturbance in each regulation period is obtained;

[0063] It should be noted that positive fusion specifically refers to multiplicative relationships, additive relationships, etc. In this embodiment, the product of the degree of dispersion and the degree of trend instability is used as the degree of electricity price disturbance in each control period.

[0064] It should be noted that the electricity price fluctuation reflects the dynamic adjustment pressure faced by the energy storage system under the current market fluctuation. The larger the value, the more drastic the fluctuation of the current electricity price and the poorer the stability of the direction of electricity price change. The energy storage system needs to frequently adjust the charging and discharging power in a short period of time to continuously adapt to changes in market supply and demand, and thus faces greater regulatory pressure.

[0065] Thus, the electricity price fluctuations for each regulation period are obtained.

[0066] Step 2: Assess the frequency of switching between charging and discharging states of the energy storage system based on the charging and discharging power, and determine the coupling influence degree of each control period in combination with the electricity price disturbance degree. This is used to characterize the synergistic effect of external market fluctuations and internal state switching. By considering the supply and demand balance between new energy power generation and total load power in each control period, combined with the coupling influence degree and the regulation capacity reflected by the remaining SOC value at the end of the control period, determine the response coefficient of each control period.

[0067] Furthermore, the regulation of energy storage systems is not only affected by external electricity price fluctuations, but also closely related to the battery state of charge of the energy storage system itself and the supply-demand matching relationship of new energy sources. In actual operation, new energy power generation has significant randomness and intermittent characteristics. The electricity generated by new energy sources can be used to supply power to demand-side loads or to charge energy storage systems. When there is a surplus of new energy generation, energy storage needs to be charged to absorb the excess power; when there is a shortage of new energy generation, energy storage needs to be discharged to compensate for the power gap in order to maintain supply-demand balance. It can be seen that the charging and discharging behavior of energy storage systems not only serves the goal of electricity price arbitrage, but also undertakes the important function of supporting the absorption of new energy sources and the balance of supply and demand.

[0068] Furthermore, the state of charge (SOC) of the battery, which characterizes the remaining regulatory capacity of the energy storage system, directly determines its safe margin for continued charging or discharging. When there is a surplus of renewable energy generation and the energy storage system needs to be charged, the battery is already close to full charge, so there is limited room for further charging and a risk of overcharging. Conversely, when there is a shortage of renewable energy generation and the energy storage system needs to be discharged, the battery is already close to depletion, so there is severely limited room for further discharging. Both of these situations require improved regulation sensitivity to avoid overcharging or over-discharging. At the same time, the frequency of the energy storage system's charge and discharge state switching in the recent period can also directly reflect the degree of dynamic change in the supply and demand relationship of renewable energy. The more frequent the state switching, the more drastic the changes in the external environment faced by the energy storage system.

[0069] Based on the above analysis, the charging and discharging power is used to evaluate the charging and discharging state switching of the energy storage system, and the coupling effect is calculated in conjunction with the electricity price disturbance degree, specifically:

[0070] Multiple control periods preceding each control period are defined as adjacent periods;

[0071] In this embodiment, the three control periods preceding each control period are defined as adjacent periods; in other implementation methods, implementers can set them according to actual conditions.

[0072] The charging and discharging power of each control period and all adjacent periods are used to form a charging and discharging sequence. The number of times the symbols of adjacent elements in the charging and discharging sequence change is counted as the switching frequency.

[0073] It should be noted that the greater the frequency of switching, the more drastic the dynamic changes in the supply and demand relationship between new energy power generation and load demand. This indicates that the energy storage system switches its charging and discharging directions more frequently in the near term, faces more drastic changes in the external environment, and needs to shorten the prediction time domain to improve the sensitivity of the control response. The smaller the value, the more stable the charging and discharging direction of the energy storage system is in the near term, without frequent state switching. The changes in the external environment are relatively mild, and the energy storage system can meet the regulation requirements by maintaining the current charging and discharging state. The regulation process has good continuity and does not require large-scale adjustments.

[0074] The coupling effect of each regulation period is positively correlated with the degree of electricity price disturbance and the frequency of switching.

[0075] It should be noted that a positive correlation means that the dependent variable increases as the independent variable increases and decreases as the independent variable decreases, while a negative correlation means that the dependent variable decreases as the independent variable increases and increases as the independent variable decreases.

[0076] In this embodiment, the frequency of switching is positively mapped, and its product with the electricity price disturbance is used as the coupling influence degree. The positive mapping process is as follows: the sum of the frequency of switching and the value 1 is used as the result of the positive mapping. Through the positive mapping process, the frequency of switching is prevented from being equal to 0.

[0077] It should be noted that the coupling influence degree reflects the coupling pressure exerted on the energy storage system by external market electricity price fluctuations and internal charging and discharging state switching. The larger the value, the more severe the electricity price fluctuations and the more frequent the charging and discharging state switching, the more complex the regulation environment faced by the energy storage system, and the more necessary it is to dynamically adjust the prediction time domain to improve response sensitivity.

[0078] Furthermore, the supply-demand difference between new energy power generation and total load power on the demand side is analyzed, specifically as follows:

[0079] The difference between the average value of renewable energy power generation at all times during each regulation period and the average value of total load power at all times is calculated as the supply-demand difference.

[0080] In this embodiment, the difference between the average value of new energy power generation at all times during each control period and the average value of total load power at all times is taken as the supply-demand difference.

[0081] It should be noted that if the supply-demand difference is greater than or equal to 0, it means that the power generation of new energy sources can meet the load demand, and the energy storage system does not need to discharge. The energy storage system should be in a charging or standby state to absorb the surplus power or maintain the current state. Conversely, if the supply-demand difference is less than 0, it means that the power generation of new energy sources is insufficient to meet the load demand, and the energy storage system needs to discharge to compensate for the supply-demand gap.

[0082] Secondly, the state of charge (SOC) of the energy storage system's batteries is analyzed to assess its remaining charging or discharging capacity, in order to determine whether the energy storage system has a safe margin to continue executing charging and discharging commands. Specifically:

[0083] Extract the battery state of charge at the last moment within each control period, and define it as the last charge level;

[0084] It should be noted that the larger the last charge, the more abundant the remaining power of the energy storage system, and the greater the adjustment margin for discharge. However, the smaller the space for continued charging, and there is a risk of overcharging in scenarios where charging is required. Conversely, the smaller the last charge, the lower the remaining power of the energy storage system, and the more limited the subsequent discharge capacity. If the supply and demand situation requires continued discharge, there is a risk of over-discharge. In this case, it is necessary to improve the control sensitivity to avoid over-discharge.

[0085] Based on the above analysis, in scenarios where renewable energy generation can meet load demand, energy storage systems are suitable for charging. However, if the energy storage system's batteries are close to full charge (i.e., the end-of-charge capacity is large), the space for further charging is limited, posing a risk of power waste and cell aging due to overcharging. In this case, the prediction time domain needs to be reduced to improve the energy storage system's sensitivity to charging power regulation, allowing it to slow down or stop charging in time near its capacity limit. Conversely, if the energy storage system's batteries are close to a partially charged state (i.e., the end-of-charge capacity is small), it indicates that the energy storage system has ample charging capacity and can continue to absorb surplus power; therefore, excessive adjustment of the prediction time domain is unnecessary. The time domain is adjusted to improve response speed, and normal charging is sufficient. In scenarios where renewable energy generation cannot meet load demand, the energy storage system needs to discharge to compensate for the supply-demand gap. However, if the last load is large, it means that the energy storage system still has ample discharge space. In this case, there is no need to adjust the prediction time domain to provide response speed, and subsequent discharge can maintain the supply-demand balance. On the other hand, if the last load is small, the energy storage system is close to being depleted, and the subsequent discharge space is severely limited. In this case, it is necessary to reduce the prediction time domain to improve the control sensitivity of the energy storage system and avoid the energy storage system being unable to stop discharging or switch to charging in time due to control lag.

[0086] Therefore, the response coefficients for each control period are calculated by considering the coupling effect, supply-demand differences, and last-load capacity, as follows:

[0087] If the supply-demand difference is greater than or equal to 0, the response coefficient for each control period is the product of the coupling influence degree and the last load power; otherwise, the response coefficient is the ratio of the coupling influence degree to the last load power.

[0088] It should be noted that, in order to avoid the denominator being 0 when calculating the ratio, a parameter adjustment factor is added to the denominator. In this embodiment, the parameter adjustment factor is set to 0.01. As for other implementation methods, the implementer can set it according to the actual situation.

[0089] It should be noted that when the supply-demand difference is greater than or equal to 0, through product logic, the higher the end-of-life charge, the greater the risk of overcharging due to continued charging, resulting in a higher response coefficient. The more complex the regulation scenario faced by the energy storage system, the more necessary it is to quickly reduce the prediction time domain to make the energy storage system extremely sensitive. When the supply-demand difference is less than 0, through ratio logic, the lower the end-of-life charge, the greater the risk of over-discharge due to continued discharging, causing the response coefficient to spike rapidly when the battery is almost depleted. Therefore, it is necessary to quickly reduce the prediction time domain to improve regulation sensitivity.

[0090] Thus, the response coefficients for each control period are obtained.

[0091] The energy storage regulation module is used to adjust the predicted time domain for the next regulation period based on the response coefficient. Based on the adjusted predicted time domain, the module uses a model predictive control algorithm to perform rolling optimization scheduling of the energy storage system's charging and discharging power.

[0092] Furthermore, based on the response coefficient, the prediction time domain of the model predictive control algorithm for the next control period is adjusted, specifically as follows:

[0093] in, For the first Each control period corresponds to the adjusted forecast time domain. This is the preset initial prediction time domain; For the first The response coefficient for each control period. The preset adjustment range; It is an exponential function with the natural constant as its base; Represents the floor function;

[0094] In this embodiment, the preset initial prediction time domain is set to 8, and the preset adjustment range is used to control the maximum single-step adjustment amount of the prediction time domain to avoid the control trajectory from oscillating violently due to excessive adjustment range. The value range is 2 to 4. In this embodiment, the preset adjustment range is set to 2. As other implementation methods, the implementer can set it according to the actual situation.

[0095] It should be noted that the larger the response coefficient, the shorter the prediction time domain, the higher the control sensitivity of the energy storage system, and the faster it can respond to the switching needs of charging and discharging states.

[0096] Based on the adjusted prediction time domain, the rolling optimization scheduling of the energy storage system's charging and discharging power is performed using the Model Predictive Control (MPC) algorithm. The specific execution process of the MPC algorithm is as follows:

[0097] The system obtains the estimated electricity price for multiple future control periods within the adjusted prediction time domain, as well as the estimated renewable energy generation power and estimated total load power at each moment within these multiple control periods. Taking the maximization of the energy storage system's revenue within this prediction time domain as the objective function, and using the energy storage system's battery state-of-charge safety limit, charge / discharge power limit, and supply-demand balance as constraints, the system solves for the optimal charge / discharge power at each moment within this prediction time domain. The system then extracts the optimal charge / discharge power at each moment within the first control period within the adjusted prediction time domain, thereby controlling the energy storage system to perform charge / discharge operations.

[0098] It should be noted that, regarding the first The first control period in the adjusted forecast time domain refers to the [number] control period. The first control period, therefore, through the first The optimal charging and discharging power within each control period is used to control the energy storage system to perform charging and discharging operations.

[0099] It should be noted that the model predictive control algorithm is a well-known technology and will not be described in detail here. In this embodiment, the energy storage system earns the price difference by charging when the electricity price is low and discharging when the electricity price is high. Therefore, the objective function is set as follows:

[0100] in, The objective function is... For the future The estimated electricity price for the control period to which each moment belongs. For the future The charging and discharging power at any given moment; This represents the function that takes the maximum value. A value greater than 0 indicates charging. A value less than 0 indicates discharge.

[0101] The constraints are: ; ; ;

[0102] in, For the future The state of charge of the battery at a given moment. For the future The state of charge of the battery at a given moment. For the future The charging and discharging power at any given moment. The duration of the interval between two adjacent moments. The rated capacity of the energy storage system, In this embodiment, the charging and discharging efficiency of the energy storage system is set to 0.92. In other implementation methods, the implementer can set the efficiency according to the actual situation.

[0103] This represents the minimum state of charge of the battery in the energy storage system. This represents the maximum battery state of charge of the energy storage system; in this embodiment, The value is 20%. If the value is 100%, then the discharge will stop when the battery energy of the energy storage system is less than 20%. The rated capacity is 900. As other implementation methods, the implementer can set it according to the actual situation.

[0104] Secondly For maximum discharge power, Maximum charging power, For the future The estimated total load power at each time point For the future The estimated power generation capacity of new energy sources at a given moment.

[0105] Solve the above objective function to obtain the optimal charging and discharging power at each moment in the prediction time domain. Repeat the above steps to achieve rolling optimization scheduling of the charging and discharging power of the energy storage system.

[0106] By adjusting the prediction time domain of the model predictive control algorithm, the power regulation sensitivity of the energy storage system is improved when facing complex control scenarios such as drastic fluctuations in market electricity prices and frequent switching between charging and discharging states. This effectively avoids control lag caused by excessively long prediction time domains. After solving for the optimal charging and discharging power, the power conversion system (PCS) is used to perform charging or discharging operations on the energy storage system in the next control period. This allows the power scheduling trajectory of the energy storage system to continuously adapt to changes in market electricity prices and the operating status of the energy storage itself, thereby achieving adaptive and refined control of the energy storage system in cross-time period electricity spot market scenarios.

[0107] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0108] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A new energy storage and control system adapted to the cross-time period electricity spot market, characterized in that, The system includes: The data acquisition module is used to obtain the electricity price in the spot market during each regulation period, as well as the battery state of charge and charging / discharging power of the energy storage system, the power generation of new energy sources, and the total load power on the demand side at different times during each regulation period. The data analysis module is used to obtain the response coefficients for each control period, specifically: To assess the impact of external market electricity price fluctuations on energy storage regulation, we analyze the characteristics of electricity price fluctuations and the stability of electricity price change trends during different regulation periods, and calculate the degree of electricity price disturbance during each regulation period. The frequency of switching between charging and discharging states of the energy storage system is assessed based on the charging and discharging power. Combined with the degree of electricity price disturbance, the coupling influence of each regulation period is determined, which is used to characterize the synergistic effect between external market fluctuations and internal state switching. The response coefficient for each control period is determined by the supply and demand balance between new energy power generation and total load power during each control period, combined with the coupling influence and the regulation capability reflected by the remaining state of charge of the battery at the end of the control period. The energy storage regulation module is used to adjust the predicted time domain for the next regulation period based on the response coefficient. Based on the adjusted predicted time domain, the module uses a model predictive control algorithm to perform rolling optimization scheduling of the energy storage system's charging and discharging power.

2. The new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 1, characterized in that, The calculation of electricity price fluctuations during each regulation period includes: Each control period and the multiple control periods preceding it are combined into a local window; the dispersion of electricity prices for all control periods within the local window is calculated. Calculate the electricity price difference between two adjacent control periods within a local window; analyze the pattern of sign change in the electricity price difference and calculate the trend instability; The electricity price disturbance is the result of a positive fusion of dispersion and trend instability.

3. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 2, characterized in that, The calculation of trend instability includes: using a sign function to extract the sign identifiers of all electricity price differences within a local window, forming a sign sequence, performing a runs test on it, and counting the number of runs as the trend instability.

4. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 2, characterized in that, The specific process of the positive fusion is as follows: the product of the degree of dispersion and the degree of trend instability is used as the electricity price disturbance degree.

5. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 1, characterized in that, The method for obtaining the frequency of switching is as follows: multiple control periods preceding each control period are defined as adjacent periods; The charging and discharging power of each control period and all adjacent periods are used to form a charging and discharging sequence. The number of times the symbols of adjacent elements in the charging and discharging sequence change is counted as the switching frequency.

6. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 1, characterized in that, The degree of coupling influence is positively correlated with the degree of electricity price disturbance and the frequency of switching.

7. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 1, characterized in that, The method for obtaining the response coefficient is as follows: The difference between the average value of renewable energy power generation at all times during each regulation period and the average value of total load power at all times is calculated as the supply-demand difference. Extract the battery state of charge at the last moment within each control period, and define it as the last charge level; If the supply-demand difference is greater than or equal to 0, the response coefficient of each regulation period is positively correlated with the coupling influence degree and the last load power. Conversely, the response coefficient is positively correlated with the degree of coupling influence, but negatively correlated with the last charge.

8. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 7, characterized in that, The specific calculation process of the response coefficient is as follows: if the supply and demand difference is greater than or equal to 0, the response coefficient of each control period is the product of the coupling influence degree and the last load power; otherwise, the response coefficient is the ratio of the coupling influence degree to the last load power.

9. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 1, characterized in that, No. Each regulation period corresponds to the adjusted prediction time domain. The calculation formula is: ,in, This is the preset initial prediction time domain; For the first The response coefficient for each control period. The preset adjustment range; It is an exponential function with the natural constant as its base; This represents the floor function.

10. A new energy storage and control system adapted to the cross-time period electricity spot market as described in claim 1, characterized in that, The method of using model predictive control algorithm to perform rolling optimization scheduling of energy storage system charging and discharging power includes: obtaining the estimated electricity price in the adjusted prediction time domain, as well as the estimated new energy power generation and estimated total load power at each corresponding time; taking the maximization of the cumulative revenue of the energy storage system in the prediction time domain as the objective function; and taking the battery state of charge safety limit, charging and discharging power limit and supply and demand balance of the energy storage system as constraints; solving for the optimal charging and discharging power at each time in the prediction time domain; and controlling the energy storage system to perform charging and discharging operations in real time.

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