Adjustment capability evaluation and improvement method, system and equipment based on new energy and energy storage cooperation, and medium

Abstract

The invention discloses an adjustment capability evaluation and improvement method, system and device based on new energy and energy storage cooperation and a medium. The adjustment capability evaluation and improvement method comprises the following steps: constructing a probability density function and a confidence interval of adjustment capacity based on a new energy output probability prediction model; a probability density function of capacity adjustment is remodeled through an energy storage cooperation strategy, and a confidence interval is narrowed; constructing a joint probability model of the adjustment instruction and the adjustment capacity; and deriving the probability distribution of the adjustment precision and the shortest high-density confidence interval according to the joint probability model. According to the method, the probability distribution of the new energy adjustment capacity is actively remodeled and optimized by using the energy storage system, the confidence interval width of the adjustment capacity is effectively narrowed under the same confidence level, and the improvement effect of energy storage on the new energy adjustment precision is accurately quantified.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of new energy and energy storage coordination, and particularly relates to a method, system, device and medium for evaluating and improving the regulation capacity based on new energy and energy storage coordination. BACKGROUND

[0002] When large-scale new energy is required to participate in the secondary frequency modulation task with high accuracy requirements, a core technical problem emerges: the regulation capacity of the new energy station presents significant uncertainty. The regulation capacity of the new energy station mainly reflects two dimensions: regulation capacity and regulation accuracy. The regulation capacity refers to the range of upward or downward adjustment of the output power under the current operating state, and the upward regulation capacity depends on the difference between the current actual output and the theoretical maximum output (i.e. the maximum power that can be generated under the current weather conditions). Due to the unpredictability of weather conditions, the theoretical maximum output is a random variable, resulting in a significant probability distribution characteristic of the regulation capacity boundary. The regulation accuracy refers to the accuracy of responding to and executing the dispatching instructions. The randomness of the capacity boundary directly leads to a decrease in the regulation accuracy: when the dispatching instruction exceeds the regulation capacity boundary that the station can actually achieve, the instruction cannot be accurately executed, resulting in regulation deviation. For the grid dispatching mechanism, the uncertainty of the regulation capacity (including capacity and accuracy) seriously weakens the value of new energy as a reliable frequency modulation resource and brings great decision-making confusion. If the regulation capacity is optimistically estimated, the frequency safety may be threatened due to the inability to execute the instruction; if it is conservatively estimated, the use of clean energy will be limited, increasing the overall regulation cost of the system.

[0003] To address this challenge, the prior art has explored two paths. One is to use probabilistic prediction technology to obtain the probability density function of new energy output or adjustment capacity by learning from massive historical data. This method quantitatively describes the uncertainty in mathematics, which is an important step in recognizing uncertainty. However, it is essentially a passive description and does not actively improve the adjustment capacity itself. In actual scheduling, to ensure high reliability, the scheduling agency needs to make decisions at a high confidence level, which inevitably corresponds to a wide confidence interval of adjustment capacity, resulting in a significant reduction in the credibility and practicality of scheduling decisions. The second is to introduce an electrochemical energy storage system to cooperate with the new energy station. The excellent dynamic characteristics of the energy storage system are considered to be an ideal tool for improving the performance of new energy grid connection. In existing applications, energy storage is mostly used to smooth output or as a backup, which improves the overall controllability of the station to some extent. However, existing new energy-storage coordination control strategies are mostly based on deterministic control objectives and fail to systematically design from the two core dimensions of adjustment capacity (i.e., the probability characteristics of capacity and the execution precision of adjustment). They fail to propose a systematic method to actively reshape the probability distribution of the coordination system's adjustment capacity and lack a scientific mathematical framework to accurately quantify the improvement effect on adjustment precision. Current effect evaluation relies on macro statistical indicators, making it difficult to reveal the internal logic and specific contribution of energy storage to uncertainty improvement from a mechanistic perspective. SUMMARY

[0004] To address the problems of the prior art described above, the present application provides a new energy and energy storage collaborative adjustment capacity evaluation and improvement method, system, device and medium, which actively reshapes and optimizes the probability distribution of new energy adjustment capacity using the energy storage system. Under the same confidence level, it effectively narrows the confidence interval width of the adjustment capacity to accurately quantify the improvement effect of energy storage on new energy adjustment precision, providing key technical support for frequency stability control and resource optimization configuration of high-proportion new energy power systems.

[0005] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows.

[0006] In a first aspect, the present application provides a new energy and energy storage collaborative adjustment capacity evaluation and improvement method, which comprises:

[0007] Based on the new energy output probability prediction model, the probability density function and confidence interval of the adjustment capacity are constructed;

[0008] The probability density function of the adjustment capacity is reshaped by the energy storage coordination strategy to narrow the confidence interval;

[0009] A joint probability model of the adjustment instruction and the adjustment capacity is constructed;

[0010] The probability distribution of the regulation accuracy and the shortest high-density confidence interval are derived according to the joint probability model.

[0011] Further, the construction process of the probability density function of the regulation capacity is as follows:

[0012] Suppose that the new energy output P obeys a normal distribution where μ P is the mean value of the output, σ P is the standard deviation, and the probability density function f P (p) of the new energy output P is:

[0013]

[0014] The relationship between the regulation capacity X and the new energy output P is:

[0015] X = η HTG · P

[0016] In the formula, P ∈ [p min , p max ], p min represents the minimum value of the new energy output P, p max represents the maximum value of the new energy output P, and η HTG represents the derating coefficient.

[0017] According to the Jacobian transformation of the random variable, the probability density function of the regulation capacity X is:

[0018]

[0019] where X ∈ [x min , x max ], the minimum value of the regulation capacity x min = η HTG · p min , and the maximum value of the regulation capacity x max = η HTG · p max .

[0020] Further, the construction process of the confidence interval of the regulation capacity is as follows:

[0021] In order to quantify the uncertainty of the regulation capacity, the equal-tail probability confidence interval is calculated; at a confidence level of 1-α, α is the significance level, and the confidence interval [A, B] satisfies:

[0022]

[0023] F(x) is the cumulative distribution function of the regulation capacity, and x is A or B:

[0024]

[0025] The confidence interval width of the regulation capacity is:

[0026] ΔP α =B-A.

[0027] The present application uses the energy storage system to determine the original probability distribution of the new energy regulation capacity for the deterministic mathematical transformation to actively optimize its statistical characteristics.

[0028] Further, the energy storage coordination strategy is: minimum capacity full compensation, maximum capacity no compensation.

[0029] Further, the specific process of remodeling the probability density function of the regulation capacity through the energy storage coordination strategy is as follows:

[0030] Let the rated capacity of the energy storage account for the proportion of the installed capacity of the new energy, and the installed capacity of the new energy is S N , then the regulation capacity interval after coordination is:

[0031] x′ min =x min +βS N

[0032] x′ max =x max

[0033] In the formula, x′ min represents the minimum value of the regulation capacity after coordination, and x′ max represents the maximum value of the regulation capacity after coordination.

[0034] Define the interval narrowing ratio λ, which is the ratio of the length of the regulation capacity interval after coordination to the length of the regulation capacity interval before coordination:

[0035]

[0036] λ<1 indicates that the energy storage effectively narrows the fluctuation range of the regulation capacity;

[0037] Through scaling and translation transformation, the probability density function f(x) of the original regulation capacity is remodeled into the probability density function h(x) after coordination;

[0038]

[0039] The cumulative distribution function of the regulation capacity after coordination is:

[0040]

[0041] Under the confidence level 1-α, the confidence interval width after coordination satisfies:

[0042]

[0043] The above formula directly reflects that the smaller the interval narrowing ratio λ is, the narrower the confidence interval is, and the lower the uncertainty is.

[0044] The specific cooperative control strategy adopted by the present application is that by performing linear scaling and translation transformation on the probability density function of the original regulation capacity, the effective narrowing of the regulation capacity confidence interval is realized at any given confidence level.

[0045] Further, the construction process of the joint probability model of the regulation instruction and the regulation capacity is as follows:

[0046] Assuming that the regulation capacity X probability density function is f1(x), the probability distribution function is F1(x), and the confidence interval is [c, d] at a certain confidence level, the regulation capacity X probability density function f1(x) is re-normalized on [c, d] to obtain:

[0047]

[0048] The regulation instruction Y is the active demand issued by the power system according to the frequency deviation, and it is assumed that it is uniformly distributed in the regulation capacity interval, and the probability density function of the regulation instruction Y ~ U(c, d) is:

[0049]

[0050] Further, the probability distribution of the regulation accuracy is derived as follows:

[0051] The regulation accuracy Z is defined as:

[0052]

[0053] Value range analysis: when X≥Y, Z=1; when X<Y, Y∈[x, d], so Therefore

[0054] The distribution of the cooperative regulation accuracy Z is composed of point mass Z=1 and continuous interval ,

[0055] When Z=1:

[0056]

[0057] In the formula,

[0058] When Z∈[z min ,1), it is obtained that:

[0059] y=x(2-z)

[0060] ​From the conditional density transformation formula, we have

[0061]

[0062] From we have:

[0063]

[0064] Since x≥c, we have:

[0065]

[0066] The confidence interval of the adjustment accuracy needs to reflect the region SHDI with the highest probability density and the shortest length. Since Z=1 is a point mass, the form of SHDI is [e,1]. At the confidence level 1-δ, SHDI needs to satisfy:

[0067] P(e≤Z≤1)=F z (1)-F z (e)=1-δ

[0068] In the formula,

[0069] The joint probability model of the application is the mathematical basis for accurately deriving the probability distribution of the adjustment accuracy. Based on the joint probability model, a systematic mathematical method for deriving the complete probability distribution of the adjustment accuracy random variable is derived.

[0070] In the second aspect, the application provides a regulation capacity evaluation and improvement system based on new energy and energy storage cooperation, which is used to realize the regulation capacity evaluation and improvement method described above, and includes:

[0071] A confidence interval construction unit is configured to construct a probability density function of the regulation capacity and a confidence interval based on a new energy output probability prediction model.

[0072] A confidence interval narrowing unit is configured to narrow the confidence interval by remodeling the probability density function of the regulation capacity through an energy storage cooperation strategy.

[0073] A joint probability model construction unit is configured to construct a joint probability model of the regulation instruction and the regulation capacity.

[0074] A shortest high-density confidence interval derivation unit is configured to derive the probability distribution of the adjustment accuracy and the shortest high-density confidence interval according to the joint probability model.

[0075] In the third aspect, the application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the regulation capacity evaluation and improvement method described above are realized.

[0076] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for evaluating and improving adjustment capabilities.

[0077] Compared with existing technologies, this invention has the following advantages and beneficial effects: This invention achieves effective narrowing of the confidence interval of the regulation capacity at any given confidence level by linearly scaling and translating the probability density function of the original regulation capacity, thus accurately quantifying the improvement effect of energy storage on the regulation accuracy of new energy sources; This invention defines the "regulation accuracy" of the new energy-energy storage collaborative system as a random variable for the first time and uses a probabilistic modeling evaluation method, breaking through the limitations of traditional macro-statistical indicators and achieving a refined and mechanistic characterization of regulation accuracy; This invention derives a systematic mathematical method for the complete probability distribution of the random variable of regulation accuracy based on a joint probability model; This invention uses the shortest high-density confidence interval (SHDI) to determine and quantify the confidence interval of regulation accuracy, which, compared with the traditional equal-tailed confidence interval, can more scientifically and compactly reflect the core distribution range of regulation accuracy; This invention integrates a closed-loop technical solution of "active improvement of regulation capability" and "quantitative evaluation of regulation effect," which not only improves the frequency regulation performance of new energy sources but also scientifically and transparently measures the improvement effect, forming a complete technical closed loop. Attached Figure Description

[0078] Figure 1 This is a flowchart illustrating a method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage according to the present invention.

[0079] Figure 2 This is a comparison chart of the adjustment capacity before and after the synergy of new energy and energy storage under a 90% confidence level in the simulation analysis of Embodiment 1 of the present invention;

[0080] Figure 3 This is a comparison chart of the adjustment accuracy before and after the new energy-energy storage synergy under a 90% confidence level in the simulation analysis of Embodiment 1 of the present invention;

[0081] Figure 4 This is a diagram illustrating the composition of a regulation capability assessment and enhancement system based on the synergy of new energy and energy storage according to the present invention.

[0082] Figure 5 This is a schematic diagram of the logical structure of a computer device provided in Embodiment 4 of the present invention. Detailed Implementation

[0083] The present invention will now be described in further detail with reference to the embodiments and accompanying drawings.

[0084] Explanation of relevant technical terms

[0085] 1. Grid Frequency: This refers to the frequency of alternating current (AC) in a power system. It is the most important and intuitive indicator of whether the power generation and consumption of the entire power grid are balanced. When power generation exceeds consumption, the frequency rises; conversely, it falls. Maintaining frequency stability is the primary task for the safe operation of the power system.

[0086] 2. Secondary Frequency Regulation / Automatic Generation Control (AGC): The second line of defense for power system frequency control (the first being the regulation of the generator's own prime mover, i.e., primary frequency regulation). It is a centralized closed-loop control method where the computer system of the power grid dispatch center automatically calculates regulation commands based on the frequency deviation of the entire network and issues them to designated generation units (frequency regulation resources) to restore the system frequency to the standard value and correct time errors. This invention mainly addresses the problems encountered when new energy sources participate in secondary frequency regulation.

[0087] 3. Frequency Regulation Resource: This refers to a power generation unit or load unit capable of receiving and responding to grid AGC commands and adjusting its own output power to help stabilize the grid frequency. Traditionally, this is undertaken by thermal power and hydropower units. This invention aims to develop a new energy-energy storage collaborative system as a high-quality frequency regulation resource.

[0088] 4. Renewable Energy Output: This refers to the actual power transmitted to the power grid by new energy units such as wind turbines or photovoltaic power plants at a given moment. Its core characteristic is that it is affected by natural weather conditions (wind speed, sunlight), exhibiting strong randomness, volatility, and uncertainty.

[0089] 5. Regulation Capability: This comprehensively describes the performance of a frequency modulation resource in participating in frequency regulation. In this invention, it mainly includes two core dimensions: regulation capacity and regulation accuracy.

[0090] 6. Regulation Capacity: This refers to the maximum range within which the frequency regulation resource can adjust its output power upwards or downwards under the current operating conditions. For example, a wind farm with an actual output of 60 MW and a theoretical maximum output of 100 MW has a "regulation capacity" of 40 MW. Since the theoretical maximum output is random, its regulation capacity also has uncertainty.

[0091] 7. Regulation Accuracy: This refers to the accuracy with which frequency regulation resources execute AGC commands issued by the power grid. In this invention, it is quantified as the ratio of the "actual power change in response" to the "power change required by the command." Ideally, this value is 1 (i.e., 100% accurate execution). Due to the randomness of regulation capacity, the regulation accuracy will be lower than 1 when the command exceeds the actual capacity boundary.

[0092] 8. Probability Density Function (PDF): A function that describes the likelihood of a continuous random variable (such as renewable energy output) taking a value near a specific point. The area under the function curve represents the probability of the event occurring. This invention manages the uncertainty of regulation capacity by constructing and reshaping the PDF.

[0093] 9. Cumulative Distribution Function (CDF): Describes the probability that a random variable takes a value less than or equal to a certain value. It is the integral of the probability density function from negative infinity to that point. The CDF is used when calculating confidence intervals.

[0094] 10. Confidence Level: A probability value (usually expressed as a percentage, such as 90% or 95%) used to measure the degree of confidence in a conclusion or range of estimates. For example, a 95% confidence level means that there is a 95% certainty that the true value will fall within a specific range.

[0095] 11. Confidence Interval: A range of estimates for an unknown parameter (such as adjustment capacity) at a given confidence level. The width of the interval reflects the accuracy or uncertainty of the estimate. The narrower the interval, the lower the uncertainty and the more accurate the estimate.

[0096] 12. Jacobian Transformation: A mathematical method used to calculate the probability density function of a new random variable obtained by transforming one or more random variables through a certain function. In this invention, it is used to derive the PDF of "regulation capacity" from the known PDF of "new energy output".

[0097] 13. Shortest Highest Density Interval (SHDI): A special type of confidence interval. Unlike traditional "equal-tailed" confidence intervals, SHDI seeks the shortest interval at a given confidence level that includes all points with high probability density. For asymmetric or special probability distributions (such as the adjustment precision distribution in this invention), SHDI can more scientifically and compactly describe the core distribution range.

[0098] 14. Point Mass: In probability theory, this refers to the situation where the probability is concentrated at a single point. In this invention, when the adjustment command is perfectly executed (adjustment precision Z = 1), its probability is represented as a point mass, rather than a continuous interval.

[0099] 15. Derating Factor: To ensure the safe operation of the system, renewable energy power plants are required to maintain a portion of reserve capacity and not operate at full capacity. This factor is the proportional control parameter between the actual output and the theoretical maximum output. By actively reducing the derating capacity, a certain amount of upward adjustment capacity can be reserved.

[0100] Example 1

[0101] This embodiment presents a method for assessing and improving the regulation capacity based on the synergy of new energy sources and energy storage. Figure 1 As shown, it includes the following steps: S1, constructing the probability density function and confidence interval of the regulating capacity based on the probability prediction model of new energy output; S2, reshaping the probability density function of the regulating capacity through the energy storage coordination strategy to narrow the confidence interval; S3, constructing a joint probability model of regulating command and regulating capacity; S4, deriving the probability distribution of regulating accuracy and the shortest high-density confidence interval based on the joint probability model.

[0102] In step S1, the process of constructing the probability density function of the adjustment capacity is as follows:

[0103] Assume that the power output P of the new energy source follows a normal distribution. Where μ P For the average output, σ P Let f be the standard deviation of the probability density function f of the new energy power output P. P (p) is:

[0104]

[0105] The relationship between regulation capacity X and renewable energy output P is as follows:

[0106] X = η HTG ·P

[0107] In the formula, P∈[pmin ,p max ], p min p represents the minimum value of the new energy output P. max η represents the maximum value of the new energy output P; HTG This represents the reduction factor, which is 0.2.

[0108] Based on the Jacobian transformation of the random variable, the probability density function of the adjustment capacity X is:

[0109]

[0110] Where, X∈[x min ,x max Adjust the minimum capacity value x min =η HTG ·p min Adjust the maximum capacity x max =η HTG ·p max .

[0111] In step S1, the process of constructing the confidence interval of the adjustment capacity is as follows:

[0112] To quantify the uncertainty of the adjustment capacity, calculate the equal-tailed probability confidence interval; at a confidence level of 1-α, where α is the significance level, the confidence interval [A, B] satisfies:

[0113]

[0114] F(x) is the cumulative distribution function of the adjustment capacity, where x is A or B:

[0115]

[0116] The confidence interval width for the adjusted capacity is:

[0117] ΔP α =BA.

[0118] This invention utilizes an energy storage system to perform a deterministic mathematical transformation on the original probability distribution of new energy regulation capacity in order to actively optimize its statistical characteristics.

[0119] In step S2, the energy storage coordination strategy is: full replenishment for minimum capacity, and no replenishment for maximum capacity.

[0120] The specific process of reshaping the probability density function of the regulation capacity through the energy storage collaborative strategy is as follows:

[0121] Let β be the ratio of the rated capacity of energy storage to the installed capacity of new energy, and let S be the installed capacity of new energy. N Then the adjustment capacity range after coordination is:

[0122] x′ min =x min +βS N

[0123] x′ max =x max

[0124] In the formula, P∈[p min ,p max ];

[0125] x′ min x′ represents the minimum regulation capacity after coordination. max This represents the maximum regulation capacity after coordination;

[0126] Define the interval narrowing ratio λ, which is the ratio of the length of the regulation capacity interval after coordination to the length of the regulation capacity interval before coordination:

[0127]

[0128] λ<1 indicates that energy storage effectively narrows the fluctuation range of regulation capacity;

[0129] By scaling and translation transformation, the probability density function f(x) of the original adjustment capacity is reshaped into the probability density function h(x) after coordination.

[0130]

[0131] The cumulative distribution function of the regulation capacity after coordination is:

[0132]

[0133] At confidence level 1-α, the width of the confidence interval after collaboration satisfies:

[0134]

[0135] The above formula intuitively reflects that the smaller the energy storage narrowing ratio λ, the narrower the confidence interval and the lower the uncertainty.

[0136] The specific collaborative control strategy adopted in this invention is to effectively narrow the confidence interval of the regulation capacity by performing linear scaling and translation transformation on the probability density function of the original regulation capacity, thereby achieving effective narrowing of the regulation capacity confidence interval at any given confidence level.

[0137] In step S3, the construction process of the joint probabilistic model of the adjustment command and the adjustment capacity is as follows:

[0138] Suppose the probability density function of the regulation capacity \(X\) is \(f_1(x)\) and the probability distribution function is \(F_1(x)\). At a certain confidence level, the confidence interval is \([c, d]\). The probability density function \(f_1(x)\) of the regulation capacity \(X\) is renormalized on \([c, d]\) to obtain:

[0139]

[0140] The regulation command \(Y\) is the active power demand issued by the power system according to the frequency deviation. Suppose it is uniformly distributed within the regulation capacity interval. The probability density function of the regulation command \(Y\sim U(c, d)\) is:

[0141]

[0142] In step S4, the probability distribution of the regulation accuracy is derived as follows:

[0143] Define the regulation accuracy \(Z\) as:

[0144]

[0145] Analysis of the value range: When \(X\geq Y\), \(Z = 1\); when \(X\lt Y\), \(Y\in[x, d]\), so Therefore

[0146] The distribution of the coordinated regulation accuracy \(Z\) consists of a point mass \(Z = 1\) and a continuous interval composed of

[0147] When \(Z = 1\):

[0148]

[0149] In the formula,

[0150] When \(Z\in[z min , 1)\), from we get:

[0151] \(y = x(2 - z)\)

[0152] From the conditional density transformation formula, we get:

[0153]

[0154] From we get:

[0155]

[0156] Since \(x\geq c\), we get:

[0157]

[0158] The confidence interval for adjusting precision needs to reflect the region of highest probability density and shortest length (SHDI). Since the mass at Z=1 is a point mass, the SHDI has the form [e,1]. At a confidence level of 1-δ, the SHDI must satisfy:

[0159] P(e≤Z≤1)=F z (1)-F z (e)=1-δ

[0160] In the formula,

[0161] The joint probability model of this invention is the mathematical foundation for accurately deriving the probability distribution of adjustment precision. Based on the joint probability model, a systematic mathematical method is used to derive the complete probability distribution of the random variable of adjustment precision.

[0162] Simulation Analysis

[0163] To verify the effectiveness of the aforementioned method for assessing and improving regulation capacity based on the synergy of new energy sources and energy storage, this simulation uses data from a wind farm and photovoltaic power station with a total installed capacity of 250MW and is programmed using MATLAB. An energy storage station with a rated capacity of 5% of the total installed capacity of the new energy power station (wind and photovoltaic power station) is selected to provide backup adjustable capacity.

[0164] At a 90% confidence level, the confidence interval for the increased capacity of the new energy-storage synergistic system and the new energy power station is as follows: Figure 2 As shown in the figure, the dark gray area represents the confidence interval for the upward adjustment capacity of the renewable energy-storage synergistic system, while the light gray area represents the original confidence interval for the upward adjustment capacity of wind and solar power plants without energy storage. Under the same load shedding rate, compared to renewable energy plants without energy storage, the confidence interval for the upward adjustment capacity of the renewable energy-storage synergistic system is narrower, and both the upper and lower limits of the adjustment capacity range are significantly improved. This indicates that equipping renewable energy plants with energy storage not only improves the confidence level of their adjustment capacity but also increases the adjustment capacity itself, enhancing their ability to participate in secondary frequency regulation.

[0165] At a 90% confidence level, the confidence interval for the adjustment accuracy of the new energy-storage synergistic system and the new energy power station is as follows: Figure 3 As shown in the figure, the dark gray area represents the confidence interval of the regulation accuracy of the new energy-energy storage synergistic system, while the light gray area represents the original confidence interval of the regulation accuracy of wind and solar power plants without energy storage. Under the same load shedding rate, compared to new energy power plants without energy storage, the new energy-energy storage synergistic system exhibits higher regulation accuracy and a narrower confidence interval. This indicates that equipping new energy power plants with energy storage not only improves their regulation accuracy but also reduces the width of the confidence interval.

[0166] Existing technologies either focus on static planning, are applied to unrelated scenarios such as peak shaving, or are limited to passive output smoothing. Their common problem lies in failing to provide a closed-loop technical solution that combines "active enhancement" and "scientific evaluation" to address the dynamic, real-time, and high-precision requirements of secondary frequency regulation (AGC). The advantage of this invention lies precisely in constructing this closed loop, transforming the uncertain regulation capacity of new energy sources into a quantifiable and reliable high-quality frequency regulation resource. Its specific advantages can be clearly demonstrated through the following reasoning process.

[0167] First, this invention fundamentally solves the problem that existing technologies fail to actively manage and optimize the probabilistic characteristics of regulatory capabilities through a "regulatory capability enhancement method," thereby achieving active suppression of regulatory capability uncertainty.

[0168] Existing technologies have not proposed an online-executable control strategy designed to optimize the probability distribution of regulation capability. This technological gap makes the broad confidence range of regulation capability of renewable energy power plants an insurmountable obstacle for grid dispatching agencies when participating in secondary frequency regulation.

[0169] This invention differs from others in that one of its core innovations lies in proposing a collaborative control strategy that actively reshapes the probability density function (PDF) of regulation capability. This invention does not simply treat energy storage as a backup power source; rather, based on a deep understanding of the probability distribution mechanism of renewable energy regulation capability, it utilizes the rapid and precise power throughput capability of the energy storage system to mathematically transform the original probability distribution. The direct technical effect of this is that, at the same confidence level, the confidence interval (i.e., uncertainty range) of the regulation capacity of the renewable energy-energy storage system, after being coordinated using the method of this invention, is significantly narrowed compared to the original renewable energy power station. As shown in the simulation results… Figure 2 As shown, the confidence interval after coordination is much smaller than that before coordination.

[0170] This direct technological effect further leads to a more "certain" and "reliable" regulation capability for the renewable energy-storage synergistic system for power grid dispatching agencies. The narrowing of the confidence interval means that, in most cases (determined by the confidence level), the actual available regulation capacity of the power system will stably fall within a smaller, more precise range. This increased "certainty" allows dispatching agencies to have greater confidence in the responsiveness of the synergistic system when issuing AGC commands, just as they would with conventional thermal power units, thus enabling them to issue deeper and more precise regulation commands.

[0171] Ultimately, this shift from uncertainty to relative certainty brings multiple profound technological benefits to the power system. First, it significantly enhances the availability and value of renewable energy resources participating in secondary frequency regulation, transforming large-scale wind and solar resources from mere "random power providers" into "reliable frequency supporters," which is crucial for maintaining frequency stability in a high-proportion renewable energy power system. Second, due to the enhanced reliability of renewable energy regulation capabilities, the system can reduce its reliance on traditional fossil fuel frequency regulation units and decrease the spinning reserve capacity reserved to cope with uncertainty, thereby bringing significant energy-saving, emission-reduction, and economic benefits. Third, it provides dispatching agencies with a new and flexible control method, enabling them to dynamically adjust coordination strategies based on the actual needs of the grid, optimize the probability distribution of regulation capabilities, and achieve more refined control of the grid frequency.

[0172] Secondly, this invention creatively solves the problem that existing technologies completely lack a quantitative evaluation framework for regulatory accuracy through the "regulatory capability assessment method," thereby achieving reliable quantification and scientific verification of regulatory capability.

[0173] Existing technological solutions do not address the core indicator of "regulation accuracy." They fail to answer a crucial question: how much does the accuracy of AGC commands executed by new energy power plants actually improve after energy storage is installed? This lack of an evaluation system makes it impossible to accurately measure the value of energy storage, and related market mechanisms and ancillary service pricing lack a scientific basis.

[0174] This invention addresses this technological gap by proposing a quantitative evaluation method for regulation accuracy based on a joint probability model. Its core causal logic lies in the fact that regulation accuracy is essentially the result of the interaction between "the actual regulation capacity that the system can provide" and "the regulation commands issued by the power grid." Therefore, this invention first constructs a joint probability density function for these two independent random variables (regulation capacity X and regulation command Y). The direct technical effect of this step is that it provides a solid mathematical foundation for accurately deriving the probability distribution of regulation accuracy itself. Through this model, this invention can fully characterize the distribution of regulation accuracy Z, including its point quality probability at Z=1 and its continuous distribution in the interval [0,1).

[0175] Based on the complete probability distribution of adjustment accuracy, this invention further introduces the concept of the shortest high-density confidence interval (SHDI). This leads to the invention being able to derive a confidence interval with clear probabilistic significance that best represents the core distribution region of adjustment accuracy. Figure 3As shown, this invention can clearly calculate and compare that the confidence interval (dark gray area) of the regulation accuracy after synergistic regulation not only shifts upward overall, but also has a significantly narrower interval width than before synergistic regulation. This is no longer a vague, qualitative description, but a precise, repeatable, and verifiable quantitative result.

[0176] Ultimately, the technological advantages brought by this scientific and rigorous evaluation system are revolutionary. For the first time, it provides a powerful mathematical proof and quantitative tool for the industry consensus that "energy storage improves the frequency regulation performance of new energy sources." For grid operators, this method allows them to accurately assess the marginal benefits of different energy storage configurations in improving regulation accuracy, thereby making optimal investment decisions and avoiding underinvestment or resource waste. For electricity market designers, this method makes it possible to establish a performance-based ancillary service market mechanism, where payment is no longer solely for "capacity," but for scientifically evaluated, high-confidence "regulation accuracy," which will greatly incentivize technological innovation and optimal resource allocation. It transforms the new energy-storage system from a "black box" of unknown performance into a "white box" with completely transparent, quantifiable, and assessable performance indicators, laying a crucial technological foundation for the safe, economical, and efficient operation of future high-proportion renewable energy power systems.

[0177] In summary, this invention forms a complete technological closed loop through the two major technological pillars of "enhancement" and "evaluation." It not only provides a "scalpel" for proactively reducing uncertainty but also a "CT scanner" for verifying surgical results, perfectly solving the core deficiencies of existing technologies in the two dimensions of dynamic control and quantitative evaluation, and possessing significant inventive and practical value.

[0178] Example 2

[0179] This embodiment provides a regulation capacity assessment and improvement system based on the synergy of new energy and energy storage, used to implement the regulation capacity assessment and improvement method described in Embodiment 1, such as... Figure 4 As shown, it consists of a confidence interval construction unit, a confidence interval narrowing unit, a joint probability model construction unit, and a shortest high-density confidence interval derivation unit.

[0180] The confidence interval construction unit is used to construct the probability density function and confidence interval of the regulating capacity based on the probability prediction model of new energy output.

[0181] The confidence interval narrowing unit is used to reshape the probability density function of the regulation capacity through energy storage coordination strategies, thereby narrowing the confidence interval.

[0182] The joint probability model construction unit is used to construct a joint probability model of the adjustment command and the adjustment capacity.

[0183] The aforementioned shortest high-density confidence interval derivation unit is used to derive the probability distribution of adjustment accuracy and the shortest high-density confidence interval based on the joint probability model.

[0184] It should be noted that each unit in the aforementioned regulation capacity assessment and improvement system based on the synergy of new energy and energy storage can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each unit. For specific limitations regarding the regulation capacity assessment and improvement system based on the synergy of new energy and energy storage, please refer to the limitations of the regulation capacity assessment and improvement method based on the synergy of new energy and energy storage (i.e., Example 1) above; both have the same function and role, and will not be repeated here.

[0185] Example 3

[0186] This embodiment provides a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method according to Embodiment 1 of the present invention.

[0187] Example 4

[0188] This embodiment provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the method according to Embodiment 1 of the present invention.

[0189] refer to Figure 5 The present invention will now be described in the form of a structural block diagram of an electronic device 400 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0190] like Figure 5As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0191] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, output unit 407, storage unit 408, and communication unit 409. Input unit 406 can be any type of device capable of inputting information to electronic device 400. Input unit 406 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 407 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 408 may include, but is not limited to, disks and optical discs. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0192] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the aforementioned method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. In some embodiments, the computing unit 401 can be configured to perform the aforementioned method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage by any other suitable means (e.g., by means of firmware).

[0193] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0194] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0195] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0196] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0197] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0198] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0199] The above description of the embodiments is provided to enable those skilled in the art to understand and apply the present invention. It will be apparent to those skilled in the art that various modifications can be made to the above embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made to the present invention by those skilled in the art based on the disclosure thereof should be within the scope of protection of the present invention.

Claims

1. A method for assessing and improving the regulation capacity based on the synergy of new energy sources and energy storage, characterized in that, include: Based on the probability prediction model of new energy output, the probability density function and confidence interval of the regulating capacity are constructed. By reshaping the probability density function of the regulation capacity through energy storage synergy strategies, the confidence interval is narrowed. Construct a joint probabilistic model of regulation instructions and regulation capacity; The probability distribution of adjustment accuracy and the shortest high-density confidence interval are derived based on the joint probability model.

2. The method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage as described in claim 1, characterized in that, The process of constructing the probability density function of the adjustment capacity is as follows: Assume that the power output P of the new energy source follows a normal distribution. Where μ P For the average output, σ P Let f be the standard deviation of the probability density function f of the new energy power output P. P (p) is: The relationship between regulation capacity X and renewable energy output P is as follows: X=η HTG ·P In the formula, P∈[p min ,p max ], p min p represents the minimum value of the new energy output P. max η represents the maximum value of the new energy output P; HTG Indicates the reduction coefficient; Based on the Jacobian transformation of the random variable, the probability density function of the adjustment capacity X is: Where, X∈[x min ,x max Adjust the minimum capacity value x min =η HTG ·p min Adjust the maximum capacity x max =η HTG ·p max .

3. The method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage according to claim 2, characterized in that, The process of constructing the confidence interval for the adjustment capacity is as follows: To quantify the uncertainty of the adjustment capacity, calculate the equal-tailed probability confidence interval; at a confidence level of 1-α, where α is the significance level, the confidence interval [A, B] satisfies: F(x) is the cumulative distribution function of the adjustment capacity, where x is A or B: The confidence interval width for the adjusted capacity is: ΔP α =B-A。 4. The method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage as described in claim 3, characterized in that, The energy storage coordination strategy is as follows: full replenishment for minimum capacity, no replenishment for maximum capacity.

5. The method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage as described in claim 4, characterized in that, The specific process of reshaping the probability density function of the regulation capacity through the energy storage collaborative strategy is as follows: Let β be the ratio of the rated capacity of energy storage to the installed capacity of new energy, and let S be the installed capacity of new energy. N Then the adjustment capacity range after coordination is: x′ min =x min +βS N x′ max =x max In the formula, x′ min x′ represents the minimum regulation capacity after coordination. max This represents the maximum regulation capacity after coordination; Define the interval narrowing ratio λ, which is the ratio of the length of the regulation capacity interval after coordination to the length of the regulation capacity interval before coordination: λ<1 indicates that energy storage effectively narrows the fluctuation range of regulation capacity; By scaling and translation transformation, the probability density function f(x) of the original adjustment capacity is reshaped into the probability density function h(x) after coordination. The cumulative distribution function of the regulation capacity after coordination is: At confidence level 1-α, the width of the confidence interval after collaboration satisfies: The above formula intuitively reflects that the smaller the interval narrowing ratio λ, the narrower the confidence interval and the lower the uncertainty.

6. The method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage according to claim 2, characterized in that, The process of constructing the joint probabilistic model of adjustment command and adjustment capacity is as follows: Assuming the probability density function of the adjustment capacity X is f1(x) and the probability distribution function is F1(x), and the confidence interval is [c, d] at a certain confidence level, the following can be obtained by renormalizing the probability density function f1(x) of the adjustment capacity X on [c, d]: The regulation command Y is the active power demand issued by the power system based on the frequency deviation. Assuming it is uniformly distributed within the regulation capacity range, the probability density function of the regulation command Y~U(c,d) is:

7. The method for assessing and improving the regulation capacity based on the synergy of new energy and energy storage as described in claim 6, characterized in that, The probability distribution of adjustment accuracy is derived as follows: Define the adjustment accuracy Z as: Range analysis of values: When X ≥ Y, Z = 1; when X < Y, Y ∈ [x, d], so Therefore The distribution of the post-coordinated adjustment accuracy Z is determined by the point mass Z=1 and the continuous interval. composition, When Z = 1: In the formula, When Z∈[z min When ,1), by have to: y = x(2-z) From the conditional density transformation formula, we get: Depend on have to: Since x≥c, we have: The confidence interval for adjusting precision needs to reflect the region of highest probability density and shortest length (SHDI). Since the mass at Z=1 is a point mass, the SHDI has the form [e,1]. At a confidence level of 1-δ, the SHDI must satisfy: P(e≤Z≤1)=F z (1)-F z (e)=1-δ In the formula, 8. A system for assessing and improving regulation capacity based on the synergy of new energy sources and energy storage, used to implement the regulation capacity assessment and improvement method according to any one of claims 1-7, characterized in that, include: Confidence interval construction unit: used to construct the probability density function and confidence interval of regulating capacity based on the probability prediction model of new energy output; Confidence interval narrowing unit: used to reshape the probability density function of the regulation capacity through energy storage synergy strategy, and narrow the confidence interval; Joint Probability Model Building Unit: Used to build a joint probability model of the regulation command and the regulation capacity; Shortest high-density confidence interval derivation unit: used to derive the probability distribution of adjustment accuracy and the shortest high-density confidence interval based on the joint probability model.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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