Comprehensive evaluation method for operation effect of power grid side energy storage power station

Abstract

The invention provides a comprehensive evaluation method for the operation effect of a power grid side energy storage power station, and relates to the technical field of energy storage power station evaluation.The comprehensive evaluation method comprises the steps that a historical evaluation data set is obtained, discrete processing is conducted on the evaluation data set, then the time length t is selected as the evaluation time length, and the normal distribution of voltage in the power grid side evaluation time length is calculated; obtaining an adjusted voltage difference curve according to the voltage of the power grid side and the point voltage of the user side, calculating the adjustment coefficient of the energy storage power station, calculating the daily average loss and the economic coefficient by calculating the theoretical electricity price and the actual electricity price of the energy storage power station according to the historical maintenance cost, carrying out regional division on each energy storage power station, and clustering the regions through a clustering method. And calculating a coordination coefficient of the energy storage power station according to a clustering result, dynamically adjusting the weights of the adjustment coefficient and the coordination coefficient through a weighted adaptive algorithm, and calculating a comprehensive evaluation index according to the adjusted weights.

Description

Technical Field

[0001] The present invention relates to the technical field of evaluation of side energy storage power stations, and particularly to a comprehensive evaluation method for the operation effect of a grid-side energy storage power station. Background Technique

[0002] In the past few decades, with the development of the power system towards the intelligent direction, the grid-side energy storage technology has played an increasingly important role in the power system. Traditional evaluation methods for energy storage power stations often only consider the economy and environmental protection of the energy storage power stations, and cannot fully consider the impacts of grid voltage, load fluctuations, power consumption fluctuations, etc. on the regulation and economic operation of the energy storage system. However, the operation effect of the energy storage power station is affected by various factors, including power demand fluctuations on the grid side, charge and discharge capabilities of the energy storage power station, battery health status, grid voltage fluctuations, etc. Changes in these factors will affect the regulation performance and economic benefits of the energy storage power station. Therefore, how to scientifically and comprehensively evaluate the operation effect of the energy storage power station has become an urgent technical problem to be solved in the optimal operation of the current power system.

[0003] In the prior art, the publication number CN 109829604 A discloses a comprehensive evaluation method for the operation effect of a grid-side energy storage power station. According to the actual operation data of each power station, the evaluation indexes of all the schemes in the to-be-evaluated scheme set of each power station are calculated and extracted to form an initial decision matrix, and after standardized processing, a normalized decision matrix is obtained; the analytic hierarchy process is used to obtain the subjective weight vector of the evaluation indexes, the entropy weight method is used to determine the objective weight vector of the evaluation indexes, and the comprehensive weight vector of the evaluation indexes is obtained through the game theory combined weighting method; the operation effects of each power station are comprehensively evaluated. However, this scheme fails to fully consider the coordination among the energy storage power stations, and at the same time lacks an effective dynamic weighting algorithm and cannot flexibly adjust the weights of the evaluation indexes according to different operation conditions, thus affecting the accuracy and real-time performance of the comprehensive evaluation.

[0004] The above information disclosed in the background art section is only used to strengthen the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a comprehensive evaluation method for the operation effect of a grid-side energy storage power station to solve the problems raised in the above background technique.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A comprehensive evaluation method for the operation effect of a grid-side energy storage power station, the specific steps include:

[0008] Step 1: Obtain historical grid - side data, user - side data, and energy storage power station data to form an evaluation data set, align them one - to - one by stamping time stamps, and after discretizing the evaluation data set, select the time length t as the evaluation duration;

[0009] Step 2: Calculate the normal distribution of the voltage within the evaluation duration on the grid side, judge the power regulation ability of the energy storage power station according to the mean value and probability density of the voltage, obtain the differential pressure curve for regulation by the voltage on the grid side and the point voltage on the user side, calculate the regulation rate, and calculate the regulation coefficient of the energy storage power station according to the power regulation ability and the regulation rate;

[0010] Step 3: Calculate the theoretical electricity price according to the peak - valley electricity price on the grid side and the maximum storage capacity of the energy storage power station within the evaluation duration, calculate the actual electricity price according to the electricity output and input of the energy storage power station and the real - time electricity price, calculate the daily average loss according to the historical maintenance cost, and calculate the economic coefficient according to the theoretical electricity price, actual electricity price, and daily average loss;

[0011] Step 4: Conduct regional division according to the power supply area of the grid branch to which each energy storage power station is connected. Taking the average electricity quantity on the grid side and the average energy storage ratio of the energy storage station within the regional evaluation duration as features, cluster the regions by the clustering method, and calculate the coordination coefficient of the energy storage power station according to the number of clustering groups, the number of regions in each group, the average electricity quantity, and the real - time storage capacity;

[0012] Step 5: Evaluate the energy storage power station by assigning weights to the regulation coefficient, economic coefficient, and coordination coefficient within the evaluation duration, count the failure rate and power consumption volatility of the energy storage power station within the evaluation duration, dynamically adjust the weights of the regulation coefficient and coordination coefficient by the weighted adaptive algorithm, and calculate the comprehensive evaluation index according to the adjusted weights.

[0013] Furthermore, the grid - side data includes the voltage, current, and electricity quantity on the grid side;

[0014] The user - side data includes the voltage, current, and electricity quantity on the user side;

[0015] The energy storage power station data includes the voltage, current, maximum storage capacity, real - time storage capacity, usage duration, maintenance times, cost per maintenance, and failure rate of the energy storage power station;

[0016] The method of the discretization process is as follows:

[0017] Observe the maximum frequency fv of the continuous voltage signal max , determine the sampling time interval ω according to the maximum frequency, and sample the continuous voltage signal through the sampling time interval ω to form discrete voltage;

[0018] The calculation formula for the sampling time interval is:

[0019]

[0020] where ω is the sampling time interval, and fv max is the maximum frequency of the voltage signal.

[0021] Furthermore, the calculation steps for the normal distribution of the voltage within the grid-side evaluation duration are as follows: calculate the mean and standard deviation of the voltage, and the calculation formulas are:

[0022]

[0023] where Vlp is the mean of the grid-side voltage, σ is the standard deviation of the grid-side voltage, V ia is the historical discrete voltage of the ia-th grid-side data sample, na is the number of samples of the historical discrete voltage of the grid-side data, and na is a positive integer;

[0024] Establish a normal distribution model for the grid-side voltage, and the calculation formula is:

[0025]

[0026] where f(Vl) is the probability density function of the discrete voltage value Vl in the grid-side data, and Vl is the voltage value that appears in the historical discrete voltage of the grid-side data;

[0027] Based on the normal distribution model of the grid-side voltage, analyze the normal distribution of the historical discrete voltage on the grid side, and calculate the probability density of the safety floating threshold through the grid-side voltage safety floating threshold:

[0028]

[0029] where Vd1 and Vd1 are respectively the upper threshold and the lower threshold of the grid-side voltage safety floating threshold, and αv1 and αv2 are respectively the probability densities of the upper threshold and the lower threshold of the grid-side voltage safety floating threshold;

[0030] The calculation formula for judging the power regulation ability of the energy storage power station according to the mean value and probability density of the voltage is:

[0031]

[0032] where Qtj is the power regulation ability.

[0033] Furthermore, the calculation method of the differential pressure curve is:

[0034] Fyc t = Vyd t ―Vfd t

[0035] Among them, Fyc t is the regulating pressure difference curve at time t, Vfd t is the voltage on the historical power grid side at time t, Vyd t is the voltage on the historical user side at time t;

[0036] The regulating rate is calculated by the fluctuation coefficient of the pressure difference curve. The specific method is as follows:

[0037] Mark time points for the continuous pressure difference curve at the same time interval ω. Calculate the volatility of the pressure difference curve at each time point, and calculate the regulating rate through the volatility:

[0038] Calculate the volatility of the pressure difference curve at each time point:

[0039] Qbd icω =(Fyc icω ) ′

[0040]

[0041] Among them, Qts is the regulating rate, Qbd icω is the volatility of the pressure difference curve at the icω-th time interval, nc is the number of time points marked for the pressure difference curve, and nc is a positive integer.

[0042] Furthermore, the calculation formula for the regulation coefficient of the energy storage power station according to the power regulation capacity and the regulation rate is:

[0043]

[0044] Among them, Qtx is the regulation coefficient of the energy storage power station, Qtj is the power regulation capacity, and Qts is the regulation rate.

[0045] Furthermore, the calculation method for the daily average loss is:

[0046]

[0047] Among them, Mrj is the daily average loss, Vwx id is the maintenance cost for the id-th time, nd is the total number of maintenance times, and Tn is the usage duration of the energy storage power station;

[0048] The calculation formula for the economic coefficient is:

[0049]

[0050] Among them, Qjj is the economic coefficient, Nll is the theoretical electricity price, and Msj is the actual electricity price.

[0051] Further, the calculation methods for the average power of the grid side and the average energy storage ratio of the energy storage station within the regional evaluation duration are as follows:

[0052]

[0053] Among them, Pjz is the average power of the grid side, Pcn is the average energy storage ratio of the energy storage station, V ia is the historical discrete voltage of the grid side at the ia-th sampling, I ia is the historical discrete current of the grid side at the ia-th sampling, na is the number of samplings of the historical discrete voltage of the grid side, na is a positive integer, Ccn is the maximum energy storage capacity of the energy storage power station, CSS ia is the storage capacity of the energy storage power station at the ia-th sampling;

[0054] Regarding the average power of the grid side connected to the energy storage power station in each region and the average energy storage ratio of the energy storage power station as a data node, randomly select K data nodes as the initial centroids, where K represents the number of categories after clustering, and K is a positive integer. The i-th initial centroid is expressed as: C i [Pjz(i), Pcn(i)], where Pjz(i) and Pcn(i) respectively represent the average power of the grid side and the average energy storage ratio of the energy storage station in the region of the i-th data node serving as the initial centroid. i is a positive integer, and i = 1, 2,..., K;

[0055] Taking the average power of the grid side and the average energy storage ratio of the energy storage station as the node feature vectors, for the feature vectors in each region, calculate the distance from it to each initial centroid, and assign it to the cluster represented by the nearest centroid. The formula for calculating the distance to the initial centroid is as follows:

[0056]

[0057] Among them, d(i, j) represents the distance between the i-th initial centroid and the j-th data node, and Pjz(j) and Pcn(j) respectively represent the average power of the grid side and the average energy storage ratio of the energy storage station in the region corresponding to the j-th data node;

[0058] For each cluster, after each clustering is completed, recalculate the average of all points within the cluster, and use this average as the characteristic data of the new centroid. The update formula for the i-th centroid characteristic data is;

[0059]

[0060] Among them, m represents the number of data nodes assigned to the i-th centroid, Pjz(i) old 、Pcn(i) oldDenote the average power consumption on the grid side and the average energy storage ratio of the energy storage station corresponding to the data nodes assigned to the \(i\)th centroid as \(P_{jz}(i)\). new and \(P_{cn}(i)\). new Denote the average power consumption on the grid side and the average energy storage ratio of the energy storage station corresponding to the updated centroid.

[0061] Based on the eigenvector of the updated centroid, re - cluster until the change in the position of all centroids is less than the threshold, then consider the clustering stable and end the clustering.

[0062] Furthermore, the calculation formula for the coordination coefficient of the energy storage power station according to the number of clustering families, the number of regions in each family, the average power consumption, and the real - time storage capacity is as follows:

[0063]

[0064] \(Q_{xx}=Q_{dy} + Q_{yu}\)

[0065] where \(Q_{xx}\) is the coordination coefficient of the energy storage power station, \(Q_{dy}\) is the power - consumption coordination coefficient of the energy storage power station, \(Q_{yu}\) is the voltage - coordination coefficient of the energy storage power station, \(C_{sq}\) ig is the real - time storage capacity of the \(ig\)th energy storage power station in the \(ir\)th family, \(P_{jz}\) ig is the average power consumption of the \(ig\)th region in the \(ir\)th family, \(n_g\) is the number of regions in the \(ir\)th family, and \(n_g\) is a positive integer.

[0066] Furthermore, the calculation formula for the power - consumption volatility is as follows:

[0067]

[0068] where \(P_{jz}\) ih is the average power consumption in the \(ih\)th region, \(V_{bv}\) is the power - consumption volatility, \(n_h\) is the number of energy storage power stations, and \(n_h\) is a positive integer;

[0069] Dynamically adjust the weights of the regulation coefficient and the coordination coefficient through the weighted adaptive algorithm, and calculate the comprehensive evaluation index according to the adjusted weights. The specific calculation formula is as follows:

[0070]

[0071] where \(E_{qb}\) is the comprehensive evaluation index, \(A(t)\), \(B(t)\), and \(C(t)\) are the weights of the regulation coefficient, economic coefficient, and coordination coefficient respectively within the current evaluation time period, \(\alpha\), \(\beta\), and \(\gamma\) are the adjustment factors of the regulation coefficient, economic coefficient, and coordination coefficient respectively, \(q_{gz}(t)\) is the failure rate of the energy storage power station within the current evaluation time period, \(A(t)+B(t)+C(t)=1\), \(0\lt A(t)\lt1\), \(0\lt B(t)\lt1\), \(0\lt C(t)\lt1\).

[0072] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0073] In the present invention, by obtaining a historical evaluation data set, after discretizing the evaluation data set, the time length t is selected as the evaluation duration. By calculating the normal distribution of the voltage within the evaluation duration on the grid side, the differential pressure curve for regulation is obtained from the voltage on the grid side and the point voltage on the user side. The regulation coefficient of the energy storage power station is calculated. By calculating the theoretical electricity price and the actual electricity price of the energy storage power station, and calculating the daily average loss based on the historical maintenance cost, the economic coefficient is calculated. Each energy storage power station is divided into regions, the regions are clustered by the clustering method, and the coordination coefficient of the energy storage power station is calculated according to the clustering result. The weights of the regulation coefficient and the coordination coefficient are dynamically adjusted by the weighted adaptive algorithm, and the comprehensive evaluation index is calculated according to the adjusted weights.

[0074] The present invention comprehensively evaluates according to the real-time data obtained from the grid side, the user side, and the energy storage power station. First, through the normal distribution analysis of the grid voltage and the calculation of the differential pressure curve for regulation, the power regulation ability of the energy storage power station is accurately evaluated. Secondly, based on the peak-valley electricity price of the grid and the capacity of the energy storage power station, the theoretical electricity price and the actual electricity price are calculated, comprehensively reflecting the economy of the energy storage power station. At the same time, the grid branches connected to the energy storage power station are divided into regions by the clustering method, and combined with the calculation of the coordination coefficient, the coordination ability of the energy storage power station in different regions can be effectively evaluated. Most importantly, the weighted adaptive algorithm is used to dynamically adjust the weights of various coefficients, ensuring that when the grid load fluctuates greatly, the regulation ability and coordination ability of the energy storage power station can be fully demonstrated, thereby improving the accuracy and rationality of the evaluation. This method comprehensively considers multi-dimensional data and influencing factors, while ensuring the accuracy of the evaluation index, provides a dynamic and flexible evaluation method, can effectively improve the operation efficiency and economic benefits of the energy storage power station, and provides strong support for the intelligent operation and optimal dispatching of grid-side energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0076] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to specific embodiments.

[0077] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0078] Embodiment:

[0079] Please refer to Figure 1 , the present invention provides a technical solution:

[0080] A comprehensive evaluation method for the operation effect of a grid-side energy storage power station, the specific steps include:

[0081] Step 1: Obtain historical grid-side data, user-side data and energy storage power station data to form an evaluation data set, align them one by one by stamping time stamps, and after discretizing the evaluation data set, select the time length t as the evaluation duration.

[0082] By obtaining historical real-time data and using time stamp alignment, the high precision and timeliness of the data can be guaranteed. Time alignment makes the data between different systems consistent and reduces the errors caused by time differences. Combining the data of the grid side, user side and energy storage power station constitutes a multi-dimensional evaluation data set. Such a multi-dimensional data set can provide a more comprehensive system operation situation and help with more accurate analysis and decision-making.

[0083] In this embodiment, the grid-side data includes the voltage, current and power consumption of the grid side;

[0084] The user-side data includes the voltage, current and power consumption of the user side;

[0085] The energy storage power station data includes the voltage, current, maximum storage capacity, real-time storage capacity, usage duration, maintenance times, cost per maintenance and failure rate of the energy storage power station;

[0086] The method of the discretization process is:

[0087] By observing the maximum frequency fv of the continuous voltage signal max, determine the sampling time interval ω according to the maximum frequency, and sample the continuous voltage signal through the sampling time interval ω to form a discrete voltage;

[0088] The calculation formula for the sampling time interval is:

[0089]

[0090] where ω is the sampling time interval, and fv max is the maximum frequency of the voltage signal.

[0091] Determining the sampling time interval ω according to the maximum frequency can achieve accurate sampling of the voltage signal. This is an optimization based on the Nyquist sampling theorem, ensuring that important high-frequency information of the voltage signal will not be lost. In the existing technology, due to inappropriate sampling intervals, voltage signals with high-frequency changes may not be captured. It provides sufficiently dense and accurate voltage data, thereby reducing sampling errors and enhancing the reliability of the evaluation results. By selecting an appropriate sampling interval, data redundancy caused by oversampling is avoided, saving storage space and computing resources, while ensuring data accuracy. This is particularly important for the real-time evaluation of large-scale power grid systems. Especially when the voltage signal changes frequently, an appropriate sampling strategy can reduce unnecessary data volume and improve the processing efficiency of the system.

[0092] Step 2: Calculate the normal distribution of the voltage within the evaluation duration on the grid side, judge the power regulation ability of the energy storage power station according to the mean value and probability density of the voltage, obtain the regulation pressure difference curve through the voltage on the grid side and the point voltage on the user side, calculate the regulation rate, and calculate the regulation coefficient of the energy storage power station according to the power regulation ability and the regulation rate.

[0093] Voltage fluctuations in the power grid are usually affected by various factors, such as load changes, imbalance between power production and consumption, weather conditions, etc. The voltage fluctuations caused by these factors often show statistical regularity. Especially in large-scale power systems, voltage fluctuations can be modeled by a normal distribution (or close to a normal distribution). According to the central limit theorem, when voltage changes are caused by the superposition of multiple independent small factors such as load fluctuations and power source changes, voltage fluctuations tend to conform to a normal distribution. Therefore, voltage fluctuations on the grid side can be effectively described by a normal distribution model.

[0094] The power regulation ability of the energy storage power station is often designed and adjusted based on the intensity and amplitude of voltage fluctuations on the grid side. The greater the voltage fluctuations on the grid side, the more power the energy storage power station needs to regulate, and the higher the possible requirements for the regulation speed and frequency. Through the normal distribution of the voltage, the risk of voltage fluctuations can be quantified, and then the regulation ability required by the energy storage power station under specific voltage fluctuations can be determined.

[0095] In a normal distribution, the mean value of the voltage can reflect the normal operating state of the power grid, while the standard deviation reflects the degree of voltage fluctuation of the power grid. The consistency between the mean value and the standard voltage indicates that the power grid operates relatively stably, which means that the energy storage power station effectively balances the power grid load and reduces voltage fluctuations. A smaller standard deviation means that the voltage fluctuation of the power grid is smaller, indicating the response speed of the energy storage power station to adjust the power. The energy storage power station not only needs to quickly respond to the voltage fluctuation of the power grid in the short term, but also should have a long-term stable regulation effect. Through the normal distribution, the change trend of the voltage fluctuation of the power grid can be quantified, so as to judge the regulation effect of the energy storage power station on different time scales.

[0096] In this embodiment, the calculation steps for calculating the normal distribution of the voltage within the evaluation duration on the power grid side are as follows: calculate the mean value and the standard deviation of the voltage, and the calculation formulas are:

[0097]

[0098] where, Vlp is the mean value of the voltage on the power grid side, σ is the standard deviation of the voltage on the power grid side, V ia is the historical discrete voltage of the ia-th power grid side data sampled, na is the number of samples of the historical discrete voltage of the power grid side data, and na is a positive integer;

[0099] Establish a normal distribution model for the voltage on the power grid side, and the calculation formula is:

[0100]

[0101] where, f(Vl) is the probability density function of the discrete voltage value Vl in the power grid side data, and Vl is the voltage value that appears in the historical discrete voltage of the power grid side data;

[0102] According to the normal distribution model of the voltage on the power grid side, analyze the normal distribution of the historical discrete voltage on the power grid side, and calculate the probability density of the safety floating threshold through the voltage safety floating threshold on the power grid side:

[0103]

[0104] where, Vd1 and Vd1 are respectively the upper threshold and the lower threshold of the voltage safety floating threshold on the power grid side, and αv1 and αv2 are respectively the probability densities of the upper threshold and the lower threshold of the voltage safety floating threshold on the power grid side;

[0105] The calculation formula for judging the power regulation ability of the energy storage power station according to the mean value and the probability density of the voltage is:

[0106]

[0107] where, Qtj is the power regulation ability.

[0108] In this embodiment, the calculation method of the differential pressure curve is as follows:

[0109] Fyc t = Vyd t − Vfd t

[0110] where Fyc t is the adjusted differential pressure curve at time t, Vfd t is the voltage on the historical grid side at time t, and Vyd t is the voltage on the historical user side at time t;

[0111] The adjustment rate is calculated through the fluctuation coefficient of the differential pressure curve. The specific method is as follows:

[0112] Mark time points for the continuous differential pressure curve at the same time interval ω, calculate the volatility of the differential pressure curve at each time point, and calculate the adjustment rate through the volatility:

[0113] Calculate the volatility of the differential pressure curve at each time point:

[0114] Qbd icω =(Fyc icω ) ′

[0115]

[0116] where Qst is the adjustment rate, and Qbd icω is the volatility of the differential pressure curve at the icω-th time interval, and nc is the number of time points marked for the differential pressure curve. nc is a positive integer.

[0117] The voltage differential pressure is an important indicator of the regulation ability of the energy storage power station. By combining the grid-side voltage and the user-side voltage, the adjusted differential pressure curve is obtained, and then the adjustment rate is calculated. This method can capture the voltage difference and its impact on the charge and discharge rate of the energy storage power station at a fine granularity, and evaluate the ability of the energy storage power station's regulation response to match the actual grid demand.

[0118] The main role of the energy storage power station is to regulate the electric energy in the power grid through charge and discharge to balance the grid load and ensure the stability of the grid voltage. When the energy storage power station intervenes in the power grid, it adjusts its output power to fill the voltage difference caused by the grid load fluctuation. When the grid-side voltage fluctuates, the adjustment rate of the energy storage power station determines to what extent it can quickly respond to this fluctuation. If the energy storage power station can quickly adjust its charge and discharge state and shorten the time for the grid voltage to return to normal, then the adjustment rate is higher, indicating that the regulation ability of the energy storage power station is stronger. Through the differential pressure curve, the effectiveness of the energy storage power station's regulation can be quantified.

[0119] In this embodiment, the calculation formula for adjusting the adjustment coefficient of the energy storage power station according to the power adjustment ability and adjustment rate is as follows:

[0120]

[0121] Among them, Qtx is the adjustment coefficient of the energy storage power station, Qtj is the power adjustment ability, and Qts is the adjustment rate.

[0122] The power adjustment ability refers to the ability of the energy storage power station to adjust its charge and discharge power within a certain period of time according to the change of the grid load demand, so as to maintain the stability of the grid voltage. It reflects the adjustment ability of the energy storage power station to the power fluctuation of the grid. A higher power adjustment ability means that the energy storage power station can adjust the grid power within a larger range to balance the load fluctuation and ensure the stable operation of the grid.

[0123] The adjustment rate refers to the response speed of the energy storage power station to adjust the power when the grid voltage or load fluctuates. It reflects the sensitivity and response time of the energy storage power station to the change of the grid demand. If the adjustment rate of the energy storage power station is high, it means that it can quickly charge and discharge according to the change of the grid voltage to meet the change of the grid demand.

[0124] The adjustment coefficient is a parameter that comprehensively reflects the power adjustment ability and adjustment rate. It reflects the overall adjustment effect of the energy storage power station under different grid load fluctuations. Usually, the adjustment coefficient is a function of the power adjustment ability and adjustment rate. It comprehensively reflects the charge and discharge ability and response speed of the energy storage power station, and finally measures the contribution of the energy storage power station to the grid stability.

[0125] Step 3: Calculate the theoretical electricity price according to the peak-valley electricity price on the grid side and the maximum storage capacity of the energy storage power station during the evaluation period, calculate the actual electricity price according to the electricity output and input of the energy storage power station and the real-time electricity price, calculate the daily average loss according to the historical maintenance cost, and calculate the economic coefficient according to the theoretical electricity price, actual electricity price, and daily average loss.

[0126] The theoretical electricity price is calculated according to the peak-valley electricity price on the grid side and the maximum storage capacity of the energy storage power station. This reflects the maximum benefit that the energy storage power station can achieve theoretically through the charging and discharging process, that is, the economic benefit of the energy storage power station under ideal conditions.

[0127] The actual electricity price is calculated based on the actual electricity output and input of the energy storage power station, as well as the real-time electricity price. The actual electricity price takes into account the charge and discharge behavior of the energy storage power station during the actual operation process, as well as the real-time price fluctuation of the power market, and reflects the adjustment ability according to the actual electricity price.

[0128] Calculate the energy loss and economic loss of the energy storage power station during daily operation based on historical maintenance costs. This calculation helps to evaluate the energy efficiency and operating costs of the energy storage power station during long-term operation.

[0129] Existing technologies often focus on the performance evaluation of grid-side energy storage power stations, while ignoring the details of economy, such as factors like actual electricity price fluctuations, losses, and maintenance costs. Through the calculation of the economic coefficient introduced in this step, the theoretical income can be compared with the actual income, and at the same time, the losses during operation are comprehensively considered to provide a more accurate economic evaluation.

[0130] Calculate by comprehensively considering the theoretical electricity price, actual electricity price, and daily average loss to measure the economic performance of the energy storage power station during actual operation. The higher the value of the economic coefficient, the better the economic benefits of the energy storage power station, and it can effectively charge and discharge in the power market to maximize the income.

[0131] The core task of the energy storage power station is to obtain income by charging at a low electricity price and discharging at a high electricity price. The actual electricity price can also directly reflect the interaction between the energy storage power station and electricity price fluctuations during actual operation. If the energy storage power station can discharge in time (provide electricity) and obtain a high actual electricity price income when the electricity price is high, it indicates that the operation effect of the energy storage power station is good. And if the energy storage power station charges when the electricity price is low, it means that it effectively stores energy at a low electricity price to prepare for future discharging, which is also part of the excellent operation effect of the energy storage power station.

[0132] In this embodiment, the calculation method of the daily average loss is as follows:

[0133]

[0134] Among them, Mrj is the daily average loss, Vwx id is the maintenance cost for the id-th time, nd is the total number of maintenance times, and Tn is the usage duration of the energy storage power station;

[0135] The calculation formula of the economic coefficient is:

[0136]

[0137] Among them, Qjj is the economic coefficient, Mll is the theoretical electricity price, and Msj is the actual electricity price.

[0138] The economic coefficient is the core index reflecting the economic operation effect of the energy storage power station. It comprehensively considers the theoretical electricity price, actual electricity price, and daily average loss of the energy storage power station. Through the change of the numerical value, it can clearly reflect the operation effect of the energy storage power station during a specific period. The higher the economic coefficient, the more efficient and economic the operation of the energy storage power station; the lower the economic coefficient, the less ideal the operation effect of the energy storage power station, and further optimization is needed.

[0139] Step 4: Divide the regions according to the power supply areas of the grid branches connected to each energy storage power station. Using the average power on the grid side and the average energy storage ratio of the energy storage station during the regional evaluation period as features, cluster the regions through the clustering method, and calculate the coordination coefficient of the energy storage power station according to the number of clustering groups, the number of regions in each group, the average power, and the real-time storage capacity.

[0140] The synergistic effect of energy storage power stations determines the quality of the grid regulation effect. The power regulation effect of a single energy storage power station may be limited by factors such as its own energy storage capacity and charge-discharge speed. However, when multiple energy storage power stations work together, they can achieve the balance and optimal scheduling of the grid load through collaborative discharging, distributed charging, etc.

[0141] The synergistic effect of energy storage power stations can achieve the balance of the grid load, avoid the situation of power surplus or shortage in some regions, reduce the difference in power consumption between different regions of the grid, and play an important role in improving the resource utilization efficiency of the overall system.

[0142] In the traditional evaluation of energy storage power stations, usually only the economy and operation effect of a single energy storage power station are concerned, while the coordination between multiple energy storage power stations connected to the grid is ignored. By dividing according to the power supply area of the grid branch, the regional division can reveal the characteristics of grid load, energy storage demand, and electricity price fluctuations in different regions, thus making the evaluation more refined. The clustering method can automatically discover groups of regions in the grid that are similar or highly correlated according to features such as the average energy storage ratio and power of the energy storage station. By analyzing the distribution differences between energy storage power stations in the region, the coordination and complementarity between different energy storage power stations can be evaluated. For example, some energy storage power stations may show high charge-discharge efficiency at certain times, while other power stations may have large differences in storage capacity. Through the coordinated scheduling between energy storage power stations, the grid's over-reliance on a single energy storage power station can be avoided, thus achieving the goal of optimizing the overall grid efficiency.

[0143] There will be significant differences in the grid load demands, charge-discharge capabilities, and power regulation demands of energy storage power stations in different regions. Through cluster analysis, regions in the grid with similar power demands and load fluctuation characteristics can be grouped together to clarify the regulation demands of each region and the regulation capabilities of energy storage power stations. This method helps to more accurately understand and utilize the characteristics of energy storage power stations during the scheduling process.

[0144] Concentrating regions with similar demand fluctuations together helps to determine the synergy between energy storage power stations in each region. For example, in regions with small power demand fluctuations, the energy storage power stations may be in a relatively stable charging state, while regions with large load fluctuations may require more regulation capabilities. In this case, multiple energy storage power stations can provide support to regions with large power demand fluctuations under coordination.

[0145] In this embodiment, the calculation methods for the average power of the grid side and the average energy storage ratio of the energy storage station within the regional evaluation duration are as follows:

[0146]

[0147] Among them, Pjz is the average power of the grid side, Pcn is the average energy storage ratio of the energy storage station, and V ia is the historical discrete voltage of the grid side at the ia-th sampling, and I ia is the historical discrete current of the grid side at the ia-th sampling. na is the number of samplings of the historical discrete voltage of the grid side, and na is a positive integer. Ccn is the maximum energy storage capacity of the energy storage power station, and CSS ia is the storage capacity of the energy storage power station at the ia-th sampling;

[0148] Taking the average power of the grid side and the average energy storage ratio of the energy storage station as a data node, randomly select K data nodes as the initial centroids. K represents the number of categories after clustering, and K is a positive integer. The i-th initial centroid is represented as: C i [Pjz(i), Pcn(i)], where Pjz(i) and Pcn(i) respectively represent the average power of the grid side and the average energy storage ratio of the energy storage station in the area of the i-th data node used as the initial centroid. i is a positive integer, and i = 1, 2,..., K;

[0149] Using the average power of the grid side and the average energy storage ratio of the energy storage station as the node feature vectors, for the feature vectors in each area, calculate its distance to each initial centroid, and assign it to the cluster represented by the nearest centroid. The formula for calculating the distance to the initial centroid is:

[0150]

[0151] Among them, d(i, j) represents the distance between the i-th initial centroid and the j-th data node, and Pjz(j) and Pcn(j) respectively represent the average power of the grid side and the average energy storage ratio of the energy storage station in the area corresponding to the j-th data node;

[0152] For each cluster, after each clustering is completed, recalculate the average of all points within the cluster, and use this average as the characteristic data of the new centroid. The update formula for the i-th centroid characteristic data is;

[0153]

[0154] Among them, m represents the number of data nodes assigned to the i-th centroid, and Pjz(i) old 、Pcn(i)old Denote the average power consumption on the grid side and the average energy storage ratio of the energy storage station corresponding to the data nodes assigned to the \(i\)th centroid as \(P_{jz}(i)\). new and \(P_{cn}(i)\). new Denote the average power consumption on the grid side and the average energy storage ratio of the updated centroid.

[0155] Re - cluster according to the eigenvector of the updated centroid until the change in the position of all centroids is less than the threshold, then consider the clustering stable and end the clustering.

[0156] In this embodiment, the calculation formula for calculating the coordination coefficient of the energy storage power station according to the number of clustering clusters, the number of regions in each cluster, the average power consumption, and the real - time storage capacity is as follows:

[0157]

[0158] \(Q_{xx}=Q_{dy} + Q_{yu}\)

[0159] where \(Q_{xx}\) is the coordination coefficient of the energy storage power station, \(Q_{dy}\) is the power coordination coefficient of the energy storage power station, \(Q_{yu}\) is the voltage coordination coefficient of the energy storage power station, \(C_{sq}\) ig is the real - time storage capacity of the \(ig\)th energy storage power station in the \(ir\)th cluster, \(P_{jz}\) ig is the average power consumption of the \(ig\)th region in the \(ir\)th cluster, \(n_g\) is the number of regions in the \(ir\)th cluster, and \(n_g\) is a positive integer.

[0160] The calculation of the coordination coefficient comprehensively considers factors such as the number of regions in each cluster, the average power consumption, and the real - time storage capacity. The basic principle of the coordination coefficient is to evaluate how energy storage power stations interact with each other in each region and whether they can complement and regulate each other when the demand changes, so as to achieve the purpose of optimizing the power distribution of the power grid.

[0161] The stronger the coordination of the energy storage power station, the higher the value of its coordination coefficient, indicating that these energy storage power stations can work efficiently in cooperation and share the grid load. A low value of the coordination coefficient indicates poor coordination between energy storage power stations, which may lead to resource waste or insufficient regulation, thus affecting the stability of the power grid.

[0162] Step 5: Evaluate the energy storage power stations by assigning weights to the regulation coefficient, economic coefficient, and coordination coefficient during the evaluation period, count the failure rate and power consumption volatility of the energy storage power stations during the evaluation period, dynamically adjust the weights of the regulation coefficient and coordination coefficient through a weighted adaptive algorithm, and calculate the comprehensive evaluation index according to the adjusted weights.

[0163] Traditional energy storage power station evaluation methods often adopt a fixed weight allocation method. However, changes in different grid regions, different time periods, and power loads may cause the influence degrees of various coefficients to change. By introducing a weighted adaptive algorithm, it is possible to dynamically adjust the weights of the regulation coefficient, coordination coefficient, and economic coefficient according to real-time grid operating conditions, the performance of the energy storage power station, as well as factors such as failure rate and power consumption volatility. This method can flexibly adjust the focus of evaluation according to the actual situation, improving the real-time and accuracy of evaluation.

[0164] In the evaluation process, two factors, namely the failure rate and power consumption volatility, are introduced, which can effectively consider the possible fault problems and power demand volatility that the energy storage power station may encounter during actual operation. This method can better reflect the reliability and stability of the energy storage power station, especially for the grid-side energy storage system in long-term operation.

[0165] The weighted adaptive algorithm is an algorithm that automatically adjusts weight allocation according to changes in input data. Specifically, in the comprehensive evaluation of the operating effect of the grid-side energy storage power station, the weighted adaptive algorithm continuously monitors the operating status of the energy storage power station during the evaluation period (such as the regulation coefficient, economic coefficient, coordination coefficient, etc.) and related performance indicators (such as failure rate, power consumption volatility, etc.), and then autonomously adjusts the weights of various evaluation indicators according to changes in real-time or historical data. The weighted adaptive algorithm can automatically adjust the weights of different evaluation coefficients according to the operating performance of the energy storage power station during the evaluation period. This means that the system can gradually optimize its evaluation strategy over time, automatically identifying which coefficients are most critical to the operating effect of the energy storage power station and which coefficients have higher influence under certain specific conditions.

[0166] According to changes in the regulation difficulty and coordination difficulty, the weights will be dynamically adjusted. For example, when the load volatility of the grid is large, the coordination difficulty between energy storage power stations increases, and a higher weight needs to be assigned to the coordination coefficient of the energy storage power station in order to better evaluate the energy storage power station.

[0167] In this embodiment, the calculation formula for the power consumption volatility is as follows:

[0168]

[0169] Among them, Pjz ih is the average power in the ih-th region, Vbv is the power consumption volatility, nh is the number of energy storage power stations, and nh is a positive integer;

[0170] The weights of the regulation coefficient and coordination coefficient are dynamically adjusted through the weighted adaptive algorithm, and the comprehensive evaluation index is calculated according to the adjusted weights. The specific calculation formula is as follows:

[0171]

[0172] Among them, Eqb is the comprehensive evaluation index. A(t), B(t), and C(t) are the weights of the adjustment coefficient, economic coefficient, and coordination coefficient respectively within the current evaluation duration. α, β, and γ are the adjustment factors of the adjustment coefficient, economic coefficient, and coordination coefficient respectively. qgz(t) is the failure rate of the energy storage power station within the current evaluation duration. A(t) + B(t) + C(t) = 1, 0 < A(t) < 1, 0 < B(t) < 1, 0 < C(t) < 1.

[0173] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0174] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by the combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0175] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application.

Claims

1. A method for comprehensively evaluating the operation effect of a grid-side energy storage power station, characterized in that: The specific steps include: Step 1: Obtain historical grid-side data, user-side data, and energy storage power station data to form an evaluation data set, align them one by one by printing timestamps, and after discretizing the evaluation data set, select the time length t as the evaluation duration; Step 2: Calculate the normal distribution of the voltage during the evaluation period on the grid side, determine the power regulation capability of the energy storage power station based on the mean value and probability density of the voltage, obtain the regulated voltage difference curve through the voltage on the grid side and the point voltage on the user side, calculate the regulation rate, and calculate the regulation coefficient of the energy storage power station based on the power regulation capability and regulation rate; Step 3: Calculate the theoretical electricity price based on the peak and valley electricity prices on the grid side during the evaluation period and the maximum storage capacity of the energy storage power station. Calculate the actual electricity price based on the output and input electricity of the energy storage power station and the real-time electricity price. Calculate the average daily loss based on the historical maintenance cost. Calculate the economic coefficient based on the theoretical electricity price, actual electricity price, and average daily loss. Step 4: Divide the regions according to the power supply areas of the grid branches to which each energy storage power station is connected. Cluster the regions using the clustering method based on the average power consumption on the grid side and the average energy storage ratio of the energy storage station during the regional evaluation period. Calculate the coordination coefficient of the energy storage power station based on the number of cluster groups, the number of regions in each group, the average power consumption, and the real-time storage capacity. Step 5: Evaluate the energy storage power station by assigning weights to the adjustment coefficient, economic coefficient, and coordination coefficient within the evaluation period, count the failure rate and power consumption volatility within the evaluation period of the energy storage power station, dynamically adjust the weights of the adjustment coefficient and coordination coefficient through a weighted adaptive algorithm, and calculate the comprehensive evaluation index based on the adjusted weights.

2. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 1, characterized in that: The grid-side data includes voltage, current and power quantity on the grid side; User-side data includes voltage, current, and power at the user side; Energy storage power station data includes the voltage, current, maximum storage capacity, real-time storage capacity, usage time, maintenance times, maintenance cost per time and failure rate of the energy storage power station; The discrete processing method is: By observing the maximum frequency fv of the continuous voltage signal max , determine the sampling time interval ω according to the maximum frequency, sample the continuous voltage signal through the sampling time interval ω to form a discrete voltage; The calculation formula for the sampling time interval is: Where ω is the sampling time interval, fv max is the maximum frequency of the voltage signal.

3. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 1, characterized in that: The calculation steps for calculating the normal distribution of the voltage during the grid-side evaluation period are to calculate the mean and standard deviation of the voltage, and the calculation formula is: Where Vlp is the mean value of the voltage on the grid side, σ is the standard deviation of the voltage on the grid side, V ia is the historical discrete voltage of the grid-side data sampled for the iath time, na is the sampling times of the historical discrete voltage of the grid-side data, and na is a positive integer; A normal distribution model of the voltage on the grid side is established, and the calculation formula is: Wherein, f(Vl) is the probability density function of the discrete voltage value Vl in the grid side data, and Vl is the voltage value appearing in the historical discrete voltage of the grid side data; According to the normal distribution model of the voltage on the grid side, the normal distribution of the historical discrete voltage on the grid side is analyzed, and the probability density of the safe floating threshold is calculated through the voltage safe floating threshold on the grid side: Among them, Vd1 and Vd1 are the upper and lower thresholds of the voltage safety floating threshold on the grid side, respectively; αv1 and αv2 are the probability densities of the upper and lower thresholds of the voltage safety floating threshold on the grid side, respectively; The calculation formula for judging the power regulation capability of the energy storage power station based on the mean value and probability density of the voltage is: Among them, Qtj is the power regulation capability.

4. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 3, characterized in that: The calculation method of the pressure difference curve is: Fyck t =Vyd t ―Vfd t Among them, Fyc t is the regulation pressure difference curve at time t, Vfd t is the historical grid voltage at time t, Vyd t is the historical user-side voltage at time t; The regulation rate is calculated by the fluctuation coefficient of the pressure difference curve. The specific method is: The continuous pressure difference curves are marked at the same time interval ω, and the volatility of the pressure difference curve is calculated at each time point. The regulation rate is calculated by the volatility: Calculate the volatility of the pressure difference curve at each time point: What? icω =(Fyc icω ) ′ Among them, Qts is the regulation rate, Qbd icω is the volatility of the pressure difference curve at the icωth time interval, nc is the number of time points marked by the pressure difference curve, and nc is a positive integer.

5. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 4, characterized in that: The calculation formula for calculating the regulation coefficient of the energy storage power station according to the power regulation capability and the regulation rate is: Among them, Qtx is the regulation coefficient of the energy storage power station, Qtj is the power regulation capability, and Qts is the regulation rate.

6. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 1, characterized in that: The calculation method of the average daily loss is: Among them, Mrj is the average daily loss, Vwx id is the idth maintenance cost, nd is the total number of maintenance times, and Tn is the usage time of the energy storage power station; The calculation formula of the economic coefficient is: Among them, Qjj is the economic coefficient, Mll is the theoretical electricity price, and Msj is the actual electricity price.

7. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 1, characterized in that: The calculation method of the average power consumption on the grid side and the average energy storage ratio of the energy storage station within the regional evaluation period is: Among them, Pjz is the average power on the grid side, Pcn is the average energy storage ratio of the energy storage station, V ia is the historical discrete voltage of the grid side sampled at the iath time, I ia is the sampling history discrete current of the grid side for the iath time, na is the sampling number of the history discrete voltage on the grid side, na is a positive integer, Ccn is the maximum energy storage capacity of the energy storage power station, CSS ia is the storage capacity of the iath energy storage power station sampled; The average power consumption of the grid side connected to the energy storage power station in each area and the average energy storage ratio of the energy storage power station are taken as a data node, and K data nodes are randomly selected as the initial centroids. K represents the number of categories after clustering, and K is a positive integer. The i-th initial centroid is expressed as: C i [Pjz(i), Pcn(i)], Pjz(i), Pcn(i) represent the average power of the grid side and the average energy storage ratio of the energy storage station in the area of ​​the i-th data node as the initial centroid, i is a positive integer, and i=1, 2, ..., K; The average power consumption on the grid side and the average energy storage ratio of the energy storage station are used as node feature vectors. For the feature vectors in each area, the distance from it to each initial centroid is calculated and assigned to the cluster represented by the nearest centroid. The formula for calculating the distance to the initial centroid is: Wherein, d(i, j) represents the distance between the i-th initial centroid and the j-th data node, Pjz(j) and Pcn(j) represent the average power of the grid side and the average energy storage ratio of the energy storage station in the area corresponding to the j-th data node, respectively; For each cluster, after each clustering is completed, the mean of all points in the cluster is recalculated, and this mean is used as the feature data of the new centroid. The update formula of the i-th centroid feature data is: Where m represents the number of data nodes assigned to the i-th centroid, Pjz(i) old 、Pcn(i) old represents the average power consumption on the grid side and the average energy storage ratio of the energy storage station in the area corresponding to the data node assigned to the i-centroid, Pjz(i) new 、Pcn(i) new The average power consumption on the grid side and the average energy storage ratio of the energy storage station represent the updated centroid; Based on the updated feature vector of the centroid, clustering is performed again until the change of all centroid positions is less than the threshold, then the clustering is considered stable and the clustering is terminated.

8. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 1, characterized in that: The calculation formula for calculating the coordination coefficient of the energy storage power station according to the number of clusters, the number of regions in each cluster, the average power, and the real-time storage capacity is: Among them, Qxx is the coordination coefficient of the energy storage power station, Qdy is the power coordination coefficient of the energy storage power station, Qyu is the voltage coordination coefficient of the energy storage power station, Csq ig is the real-time storage capacity of the igth energy storage power station in the irth group, Pjz ig is the average charge of the igth region in the irth group, ng is the number of regions in the irth group, and ng is a positive integer.

9. A method for comprehensive evaluation of the operation effect of a grid-side energy storage power station according to claim 1, characterized in that: The calculation formula for the power consumption volatility is: Among them, Pjz ih is the average electricity in the ihth region, Vbv is the volatility of electricity consumption, nh is the number of energy storage power stations, and nh is a positive integer; The weights of the adjustment coefficient and the coordination coefficient are dynamically adjusted by the weighted adaptive algorithm, and the comprehensive evaluation index is calculated according to the adjusted weights. The specific calculation formula is: Among them, Eqb is a comprehensive evaluation index, A(t), B(t), and C(t) are the weights of the adjustment coefficient, economic coefficient, and coordination coefficient within the current evaluation period, α, β, and γ are the adjustment factors of the adjustment coefficient, economic coefficient, and coordination coefficient, qgz(t) is the failure rate of the energy storage power station within the current evaluation period, A(t)+B(t)+C(t)=1,0 <A(t)<1,0<B(t)<1,0<C(t)<1。

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