Energy storage facility demand measuring and calculating method based on long-period electric power and electric quantity balance

Abstract

The invention relates to the technical field of energy storage facilities, in particular to an energy storage facility demand measuring and calculating method based on long-period electric power and electric quantity balance, which comprises the following steps: S1, collecting and analyzing long-period electric power and electric quantity data: collecting the long-period electric power and electric quantity data; s2, optimizing a multi-source power supply structure: optimizing the power generation capacity of various power supplies in the region; s3, layered measurement and calculation of the energy storage facility demand: layered measurement and calculation of the energy storage facility demand; s4, dynamic electricity market price prediction and energy storage optimization scheduling: performing dynamic prediction on the electricity market price; s5, performing scene clustering analysis on the demand quantity of the energy storage facilities: performing clustering analysis on the demand quantity of the energy storage facilities; and S6, economic evaluation of the energy storage facility configuration scheme: optimizing the energy storage facility configuration scheme. According to the invention, the response capability of the power grid under complex demand conditions is improved, and efficient operation and supply-demand balance stability of the power grid are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage facilities, and in particular to a method for estimating energy storage facility demand based on long-term electric power and electricity balance. Background Art

[0002] With the transformation of the global energy structure and the widespread application of renewable energy, the operating environment of the power system has become increasingly complex. Renewable energy such as wind energy and solar energy has brought new challenges to the supply and demand balance of the power system due to its volatility and intermittency. At the same time, with the popularization of electric vehicles and the rapid development of distributed energy, the distribution network is facing greater pressure and uncertainty. In order to meet these challenges, energy storage facilities, as an important part of the power system, have gradually become a key means to ensure the stability and economy of the power grid. Through reasonable configuration and optimized scheduling of energy storage facilities, the supply and demand of electricity can be effectively balanced and the flexibility and reliability of the power grid can be enhanced.

[0003] However, existing technologies still have many shortcomings in the estimation and configuration of energy storage facility demand. Traditional methods often fail to fully consider the complexity of long-term power fluctuations, resulting in unreasonable configuration of energy storage facilities and difficulty in coping with the dynamic changes in the multi-source power supply structure. In addition, existing energy storage facility scheduling strategies lack the ability to dynamically predict and adjust electricity market price fluctuations in real time, resulting in the inability to maximize the economic benefits of energy storage facilities. In a power grid environment with complex scenarios and diverse demands, the limitations of existing technologies not only affect the operating efficiency of the power grid, but also increase operating costs and weaken the market competitiveness of the system.

[0004] In view of the above problems, the present invention proposes a method for calculating the demand for energy storage facilities based on long-term power and electricity balance, which not only improves the scientificity and economy of the configuration of energy storage facilities, but also enhances the flexibility and competitiveness of energy storage facilities in a complex power market environment, ultimately achieving efficient and stable operation of the power grid and maximizing the economic benefits of energy storage facilities. Summary of the invention

[0005] The present invention provides a method for estimating energy storage facility demand based on long-term electric power and electricity balance.

[0006] A method for calculating energy storage facility demand based on long-term power and electricity balance comprises the following steps:

[0007] S1, long-term power data collection and analysis: collect long-term power data of different voltage levels in the distribution network, including power demand, power consumption, distributed renewable energy power generation and external transmission unit power supply within the power supply range of the substation, and conduct power supply and demand balance analysis based on long-term power data to build a power supply and demand balance model;

[0008] S2, Optimization of multi-source power supply structure: Optimize the power generation capacity of multiple power sources in the region to achieve a dynamic balance between power supply and demand and maximize the utilization of renewable energy;

[0009] S3, hierarchical calculation of energy storage facility demand: for distribution networks of different voltage levels, based on the power supply and demand balance model and the optimization results of the multi-source power supply structure, hierarchical calculation of energy storage facility demand is carried out, including energy storage power demand and electricity demand of each level of distribution network in different time periods;

[0010] S4, Dynamic electricity market price forecasting and energy storage optimization scheduling: Combined with the price fluctuations of the electricity market, the electricity market price is dynamically forecasted, and the charging and discharging strategies of energy storage facilities are adjusted in real time according to the forecast results, including:

[0011] S41, power market price data collection and processing: collect real-time price data from the power market and process the price data to remove noise and outliers;

[0012] S42, power market price forecasting model construction: based on the processed price data, a power market price forecasting model is constructed to predict the future power market price fluctuation trend;

[0013] S43, energy storage facility charging and discharging strategy formulation: based on the forecast results of the electricity market price, combined with the current status and remaining capacity of the energy storage facility, formulate an optimized charging and discharging strategy;

[0014] S44, real-time strategy execution and feedback adjustment: Execute the formulated charging and discharging strategy in real time, monitor the actual execution results, and dynamically adjust the charging and discharging strategy according to the latest changes in market prices and the operating status of energy storage facilities;

[0015] S5, scenario cluster analysis of energy storage facility demand: Combined with the adjusted charging and discharging strategies of energy storage facilities, cluster analysis is performed on the demand for energy storage facilities to form application scenarios for distribution network energy storage facilities. Based on the needs of different application scenarios, configuration plans for energy storage facilities are developed.

[0016] S6, Economic evaluation of energy storage facility configuration plan: Conduct an economic evaluation of the energy storage facility configuration plan, comprehensively consider investment costs, operating costs, electricity market price fluctuations and benefits, and optimize the energy storage facility configuration plan.

[0017] Optionally, the long-period power data collection and analysis in S1 includes:

[0018] S11, data collection: using smart meters, data collectors and sensor networks to collect long-term power data at different voltage levels in the distribution network;

[0019] S12, data preprocessing: cleaning, filtering and interpolation of the collected long-period power data to remove data noise and outliers;

[0020] S13, power supply and demand balance model construction: Based on the preprocessed long-term power data, a power supply and demand balance model is constructed, which is expressed as:

[0021] P balance (t) = P load (t)+P external (t)-P renewable (t)-P storage (t);

[0022] Among them, P balance (t) is the supply and demand balance at time t, P load (t) is the power demand at time t, P external (t) is the power supply of the external transmission unit, P renewable (t) is the power generation of distributed renewable energy, P storage (t) is the output power of the energy storage facility;

[0023] S14, long-term power analysis: by performing time series analysis on long-term power data, the autoregressive integrated moving average (ARIMA) model is used to predict future power demand and power generation, which is expressed as:

[0024]

[0025] Among them, P forecast (t) is the predicted power demand or generation at the future time t, p and q are the orders of autoregression and moving average, respectively, β i and γ j is the model parameter, ∈(tj) is the residual term;

[0026] S15, supply and demand balance correction: input the predicted power demand and power generation into the power supply and demand balance model, calculate the future power supply and demand balance state, correct the deviation between the model prediction value and the actual value, and form the final supply and demand balance analysis result, which is expressed as:

[0027] P balance (t) = P forecast (t)-P actual (t);

[0028] Among them, P balance (t) is the supply and demand balance correction value at time t, P forecast (t) is the predicted value at time t, P actual (t) is the actual value at time t;

[0029] Pcorrected (t) = P forecast (t)+α×P balance (t);

[0030] Among them, P corrected (t) is the supply and demand balance result after correction, and α is the correction coefficient.

[0031] Optionally, the multi-source power supply structure optimization in S2 includes:

[0032] S21, Construction of a matching model between power demand and power generation capacity: According to the power generation characteristics of different power sources (such as the volatility of wind power, the diurnal nature of photovoltaic power, and the stability of thermal power), a matching model between power demand and power generation capacity is constructed to meet the power demand P in different time periods. load (t), expressed as:

[0033]

[0034] Among them, P i (t) represents the power generated by the i-th power source at time t, P load (t) is the power demand at time t, P balance (t) indicates the supply-demand balance state;

[0035] S22, multi-source power supply optimization objective: maximize the utilization rate of renewable energy (wind power and photovoltaic power) while ensuring the balance between power supply and demand. The optimization objective function is expressed as:

[0036]

[0037] Where f(P) is the objective function, n is the total number of power types, c i is the cost coefficient of the i-th power source, r i is the utilization coefficient of renewable energy, t is the time variable;

[0038] S23, Constraints: The constraints in the optimization process specifically include:

[0039] The conditions for meeting the power demand are:

[0040] Physical limitations of power generation capacity:

[0041] in, and are the minimum and maximum power generation of the i-th power source respectively;

[0042] S24, optimization algorithm application: Use the mixed integer linear programming (MILP) algorithm to solve the objective function and obtain the optimal power distribution P for each power source.i,opt (t).

[0043] Optionally, the hierarchical calculation of the energy storage facility demand in S3 includes:

[0044] S31, grid stratification: According to the distribution network structure of different voltage levels, the grid is divided into multiple levels, including high voltage (HV), medium voltage (MV) and low voltage (LV);

[0045] S32, calculation of energy storage demand: At each level L, based on the power supply and demand balance model P balance,L (t) and the optimization results of the multi-source power supply structure, the demand for energy storage facilities is calculated and expressed as:

[0046] P storage,L (t)=max(0,P balance,L (t)-P renewable,L (t));

[0047]

[0048] Among them, P balance,L (t) is the power supply and demand balance at level L, P renewable,L (t) is the power generation of renewable energy at level L, P storage,L (t) is the energy storage power demand of level L at time t, E storage,L is the energy storage power demand in the time period [t1, t2];

[0049] S33, multi-level coordination and integration: coordinate and integrate the calculation results of energy storage demand at each level, expressed as:

[0050]

[0051] Among them, P storage,total (t) is the total energy storage power demand of the entire network at time t, P storage,L (t) is the energy storage power requirement of the Lth level, α L is the weight coefficient of level L, and N is the total number of levels in the power grid stratification.

[0052] Optionally, the electricity market price data collection and processing in S41 includes:

[0053] S411, real-time price data collection: through the real-time data interface connection with the power market, continuously collect the real-time price data of the power market market (t), including electricity spot prices, futures prices and dispatch prices;

[0054] S412, data cleaning: use the Z-score method to perform preliminary cleaning on the collected real-time price data;

[0055] S413, data filtering: filtering the noise of the cleaned real-time price data, and smoothing the time series data using the moving average method to reduce the impact of random fluctuations on the prediction;

[0056] S414, Data integrity processing: Use linear interpolation to fill in missing data.

[0057] Optionally, the power market price prediction model in S42 adopts a Bayesian time series model, and the Bayesian time series model includes:

[0058] S421, define the prior distribution: In the electricity market price forecast, the normal distribution is selected as the prior, which is expressed as:

[0059]

[0060] Among them, γ is the parameter vector of the model (such as trend, seasonal coefficient, etc.), μ0 and are the mean and variance of the prior distribution respectively;

[0061] S422, construct likelihood function: construct likelihood function using time series model of price data to capture dynamic changes in electricity market price, expressed as:

[0062]

[0063] Where D is the observed data set, P(t) is the market price at time t, is the autoregressive coefficient, σ 2 is the variance of the noise, P(t-1), P(t-2), ..., P(tp) are the market price data at time t-1, t-2, ..., tp respectively;

[0064] S423, calculate the posterior distribution: calculate the posterior distribution of the parameter by combining the prior distribution and the likelihood function through Bayes' theorem, expressed as:

[0065]

[0066] Where P(θ|D) is the posterior distribution of the parameter, P(D|θ) is the likelihood function, P(θ) is the prior distribution, and P(D) is the marginal likelihood of the observed data;

[0067] S424, Bayesian prediction distribution: Calculate the prediction distribution of future prices based on the posterior distribution, consider the volatility of the market, and construct the prediction distribution into a prediction model with dynamic adjustment capabilities, expressed as:

[0068] P(P t+ h|D t )=∫P(P t+ j|θ)P(θ|D t )dθ;

[0069] Among them, P t+j is the predicted price value for the next j steps, P(θ|D t ) is the posterior distribution based on the current data, P(P t+ j|θ) is the predicted price distribution under given parameters;

[0070] S425, dynamic update and real-time adjustment: Every time new price data arrives, the model parameters are adjusted in real time using the Bayesian update rule, expressed as:

[0071]

[0072] Among them, P(D t+1 |θ) is the likelihood function of the new data point, P(θ|D t ) is the posterior distribution at the current moment, P(θ|D t+1 ) is the updated posterior distribution after the new data arrives.

[0073] Optionally, the energy storage facility charging and discharging strategy formulation in S43 includes:

[0074] S431, power market price forecast results: The future power market price is predicted by the Bayesian time series model to obtain the predicted market price value at the future moment, which is expressed as:

[0075]

[0076] in, is the predicted value of the electricity market price at time t, D t is the price data at time t;

[0077] S432, Energy storage facility status and remaining capacity assessment: assess the current state of charge (SOC) and remaining capacity of the energy storage facility to determine the charging or discharging capability of the energy storage facility, expressed as:

[0078]

[0079] Among them, SOC(t) represents the state of charge of the energy storage facility at time t, E current (t) is the remaining energy of the energy storage facility at time t, E max is the maximum energy capacity of the energy storage facility;

[0080] S433, charging and discharging strategy optimization goal: formulate a charging and discharging strategy based on the market price forecast results and the status of the energy storage facilities, expressed as:

[0081]

[0082] Among them, Profit(t) is the economic benefit at time t, t0 is the start time of the charging and discharging strategy, T is the end time of the charging and discharging strategy, P disc h arge (t) is the discharge power at time t, P c h arge (t) is the charging power at time t;

[0083] S434, strategy constraints: When formulating the charging and discharging strategy, the physical limitations of the energy storage facilities must be met, expressed as:

[0084] 0≤P charge (t)≤P charge_max ,0≤P discharge (t)≤P discharge_max ;

[0085] SOC min ≤SOC(t)≤SOC max ;

[0086] Among them, P charge_max is the maximum charging power limit of the energy storage facility, P discharge_max is the maximum discharge power limit of the energy storage facility, SOC min SOC is the minimum state of charge allowed by the energy storage facility. max The maximum state of charge allowed for the energy storage facility;

[0087] S435, charge and discharge execution and dynamic adjustment: based on the threshold of market price prediction, dynamically adjust the charge and discharge strategy, expressed as:

[0088]

[0089] Among them, P action (t) is the charge and discharge execution power at time t, threshold discharge is the price threshold for the discharge operation, threshold charge is the price threshold for charging operations.

[0090] Optionally, the real-time strategy execution and feedback adjustment in S44 includes:

[0091] S441, real-time strategy execution: at each time t, according to the current electricity market price forecast value and the state of the energy storage facility, the formulated charging and discharging strategy is executed in real time, and according to the charging or discharging instructions, the energy storage facility is controlled to perform corresponding operations, which is expressed as:

[0092]

[0093] S442, monitor the actual execution results: During the strategy execution process, monitor the actual charging and discharging power, market price and status of the energy storage facilities in real time, and evaluate the effect of the charging and discharging strategy execution, which is expressed as:

[0094] ΔP(t)=P actual (t)-P action (t);

[0095] Among them, P actual (t) is the actual charging and discharging power of the energy storage facility at time t, ΔP(t) is the difference between the actual execution power and the planned power at time t;

[0096] S443, feedback adjustment: according to the difference between the actual data monitored and the expected strategy, the charging and discharging strategy is adjusted in real time, which is expressed as:

[0097] P new_action (t) = P action (t)+β·ΔP(t);

[0098] Among them, P new_action (t) is the new charge and discharge strategy power after feedback adjustment at time t, and β is the feedback adjustment coefficient.

[0099] Optionally, the scenario cluster analysis of energy storage facility demand in S5 includes:

[0100] S51, data collection and preprocessing: based on the adjusted charging and discharging strategy of energy storage facilities, collect the demand data of energy storage facilities in different time periods and under different power market conditions, and preprocess the demand data, including noise removal and standardization;

[0101] S52, demand cluster analysis: perform cluster analysis on the pre-processed energy storage facility demand data based on K-means clustering to identify the demand patterns of energy storage facilities in different application scenarios;

[0102] S53, forming application scenarios: forming different application scenarios according to the results of cluster analysis;

[0103] S54, Development of energy storage facility configuration plan: Based on the requirements of different application scenarios, develop a configuration plan for energy storage facilities. The configuration plan includes energy storage capacity, charging and discharging power, and operation strategy.

[0104] Optionally, the economic evaluation of the energy storage facility configuration scheme in S6 includes:

[0105] S61, cost composition analysis: Conduct a cost composition analysis on the configuration plan of the energy storage facility, including the initial investment cost, operation and maintenance cost, and operating cost caused by fluctuations in electricity market prices, expressed as:

[0106]

[0107] Among them, C total is the total cost of the energy storage facility, C investment is the initial investment cost of the energy storage facility, C operation (t,P market (t)) is the operating cost of the energy storage facility at time t, C maintenance (t) is the maintenance cost of the energy storage facility at time t, t0 is the starting time of the economic evaluation, and T is the end time of the economic evaluation;

[0108] S62, Benefit Analysis: By analyzing the benefits of energy storage facilities under different market conditions, the economic benefits that energy storage facilities can obtain under various charging and discharging strategies are calculated and expressed as:

[0109]

[0110] Among them, R total is the total revenue of the energy storage facility in the time period [t0,T], P disc h arge (t) is the discharge power of the energy storage facility at time t, P c h arge (t) is the charging power of the energy storage facility at time t, P market (t) is the market electricity price at time t;

[0111] S63, calculation of economic evaluation indicators: Calculate the key indicators of economic evaluation, including net present value (NPV), internal rate of return (IRR) and payback period, to evaluate the economic feasibility of energy storage facility configuration scheme, expressed as:

[0112]

[0113] Where NPV is the net present value, R(t) is the revenue at time t, C(t) is the cost at time t, and r is the discount rate;

[0114]

[0115] Where IRR is the internal rate of return;

[0116]

[0117] Where Payback Period is the payback period, R(i) is the revenue at time i, C(i) is the cost at time i, and C investment is the initial investment cost;

[0118] S64, configuration scheme optimization: The configuration scheme of the energy storage facility is adjusted through the optimization algorithm, which is expressed as:

[0119]

[0120] in, Indicates that in all energy storage facility configuration schemes P storage (S m ) is used to find the optimal energy storage facility configuration plan by maximizing the net present value (NPV).

[0121] Beneficial effects of the present invention:

[0122] The present invention constructs a power supply and demand balance model through comprehensive collection and analysis of long-term power and electricity data, and combines it with the optimization of multi-source power supply structure to achieve coordination and complementarity between different power types, maximize the utilization rate of renewable energy, and reduce dependence on traditional energy. It effectively improves the response capability of the power grid under complex demand conditions by calculating the demand for energy storage facilities in a layered manner and adopting dynamic power market price forecasting and optimized scheduling strategies, thereby ensuring the efficient operation of the power grid and the stability of the supply and demand balance.

[0123] The present invention, through scenario clustering analysis of energy storage facility demand, develops energy storage facility configuration plans for different application scenarios, ensuring that energy storage facilities can meet demand in each scenario and achieve optimal configuration. At the same time, through systematic economic evaluation, using net present value, internal rate of return and payback period, it helps decision makers accurately evaluate the economic feasibility of energy storage facility configuration plans, and improves the economic benefits of energy storage facilities through optimization algorithms, maximizes return on investment and reduces investment risks.

[0124] The present invention ensures that the system can quickly respond to market price fluctuations and optimize the economic benefits of charging and discharging operations by adjusting the charging and discharging strategies of energy storage facilities in real time, thereby improving the flexibility of energy storage facilities in the power market. It also enhances the competitiveness of energy storage facilities in the market by effectively balancing the supply and demand of the power grid and reducing operating costs, thereby providing a solid guarantee for the sustainable development of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0125] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0126] Figure 1 A schematic diagram of a flow chart of a calculation method according to an embodiment of the present invention;

[0127] Figure 2 A schematic diagram of dynamic electricity market price prediction and energy storage optimization scheduling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0128] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0129] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0130] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0131] like Figure 1-Figure 2 As shown, a method for calculating energy storage facility demand based on long-term power and electricity balance includes the following steps:

[0132] S1, long-term power data collection and analysis: collect long-term power data of different voltage levels in the distribution network, including power demand, power consumption, distributed renewable energy power generation and external transmission unit power supply within the power supply range of the substation, and conduct power supply and demand balance analysis based on long-term power data to build a power supply and demand balance model;

[0133] S2, Optimization of multi-source power supply structure: Optimize the power generation capacity of various power sources in the region (such as wind power, photovoltaic power, thermal power, etc.), ensure the coordination and complementarity between different types of power sources, achieve a dynamic balance between power supply and demand, maximize the utilization rate of renewable energy, and reduce dependence on traditional energy;

[0134] S3, hierarchical calculation of energy storage facility demand: for distribution networks of different voltage levels, based on the power supply and demand balance model and the optimization results of the multi-source power supply structure, hierarchical calculation of energy storage facility demand is carried out, including the energy storage power demand and electricity demand of each level of distribution network in different time periods, to ensure that the configuration of energy storage facilities can adapt to the power load fluctuations and supply characteristics under different voltage levels;

[0135] S4, Dynamic electricity market price forecasting and energy storage optimization scheduling: Combined with the price fluctuations of the electricity market, the electricity market price is dynamically forecasted, and the charging and discharging strategies of energy storage facilities are adjusted in real time according to the forecast results to optimize economic benefits, including:

[0136] S41, Power market price data collection and processing: Collect real-time price data from the power market and process the price data to remove noise and outliers to ensure the reliability of subsequent analysis;

[0137] S42, power market price forecasting model construction: based on the processed price data, a power market price forecasting model is constructed to predict the future power market price fluctuation trend;

[0138] S43, energy storage facility charging and discharging strategy formulation: Based on the forecast results of electricity market prices, combined with the current status and remaining capacity of energy storage facilities, formulate optimized charging and discharging strategies to maximize the economic benefits of energy storage facilities and reduce operating costs;

[0139] S44, real-time strategy execution and feedback adjustment: Execute the formulated charging and discharging strategy in real time and monitor the actual execution results. According to the latest changes in market prices and the operating status of energy storage facilities, dynamically adjust the charging and discharging strategy to ensure the efficient operation of the system in market fluctuations;

[0140] S5, scenario cluster analysis of energy storage facility demand: Combined with the adjusted charging and discharging strategies of energy storage facilities, cluster analysis is performed on the demand for energy storage facilities to form application scenarios for distribution network energy storage facilities. Based on the needs of different application scenarios, configuration plans for energy storage facilities are formulated to ensure that the configuration plans can adapt to the power supply and demand characteristics in different scenarios and meet the requirements of long-term power and electricity balance.

[0141] S6, Economic evaluation of energy storage facility configuration scheme: Conduct economic evaluation of energy storage facility configuration scheme, comprehensively consider investment cost, operation cost, electricity market price fluctuation and income, optimize energy storage facility configuration scheme, and ensure that energy storage facility configuration not only meets the long-term power balance demand, but also has economic feasibility;

[0142] Through the above steps, combined with multi-source power supply optimization and market dynamics forecasting, the energy storage configuration can not only adapt to the complex needs of the power grid, but also achieve economic optimization, ensuring the efficiency, reliability and cost-effectiveness of the system, and providing a scientific basis for the rational configuration of energy storage facilities.

[0143] The long-term power data collection and analysis in S1 includes:

[0144] S11, data collection: Use smart meters, data collectors and sensor networks to collect long-term power data at different voltage levels in the distribution network, including:

[0145] Power demand collection within the power supply range of the substation: The power demand within the power supply range of the substation is collected through smart meters and power monitoring systems, and load data is recorded in hours;

[0146] Electricity consumption collection: Smart meters are used to collect statistics on electricity consumption in each billing cycle;

[0147] Distributed renewable energy power generation collection: Use power generation meters of photovoltaic power generation systems and wind power generation systems to collect data;

[0148] External power transmission unit power supply collection: monitor the power supply of the external power transmission system through sensors on the transmission line;

[0149] S12, data preprocessing: cleaning, filtering and interpolation of the collected long-period power data to remove data noise and outliers to ensure data accuracy and continuity;

[0150] Cleaning uses the Z-Score method to detect and remove outliers;

[0151] Filtering uses the moving average method to smooth time series data and reduce random noise, which can be expressed as:

[0152]

[0153] Where MA(t) is the moving average at time point t, N is the window size, and X(ti) is the data value at time point ti;

[0154] Data interpolation uses linear interpolation to fill in missing data, expressed as:

[0155]

[0156] Where X(t) is the interpolated data value at time point t, X(t1) and X(t2) are known data points, and t1 and t2 are the corresponding time points;

[0157] S13, power supply and demand balance model construction: Based on the preprocessed long-term power data, a power supply and demand balance model is constructed, which is expressed as:

[0158] P balance (t) = P load (t)+P external (t)-P renewable (t)-P storage (t);

[0159] Among them, P balance (t) is the supply and demand balance at time t, P load (t) is the power demand at time t, P external (t) is the power supply of the external transmission unit, P renewable (t) is the power generation of distributed renewable energy, P storage (t) is the output power of the energy storage facility;

[0160] S14, long-term power analysis: by performing time series analysis on long-term power data, the autoregressive integrated moving average (ARIMA) model is used to predict future power demand and power generation, which is expressed as:

[0161]

[0162] Among them, P forecast (t) is the predicted power demand or generation at the future time t, p and q are the orders of autoregression and moving average, respectively, β i and γ j is the model parameter, ∈(tj) is the residual term;

[0163] S15, supply and demand balance correction: input the predicted power demand and power generation into the power supply and demand balance model, calculate the future power supply and demand balance state, correct the deviation between the model prediction value and the actual value, and form the final supply and demand balance analysis result, which is expressed as:

[0164] P balance (t) = P forecast (t)-P actual (t);

[0165] Among them, P balance (t) is the supply and demand balance correction value at time t, P forecast (t) is the predicted value at time t, P actual (t) is the actual value at time t;

[0166] P corrected (t) = P forecast (t)+α×P balance (t);

[0167] Among them, P corrected (t) is the supply and demand balance result after correction, α is the correction coefficient, which usually takes a value between 0 and 1;

[0168] Through the above content, the dynamic changes of the power system can be reflected in real time, the accuracy of prediction and the stability of supply and demand balance can be improved, and a scientific basis can be provided for the subsequent demand estimation and optimization of energy storage facilities, thereby optimizing the operating efficiency and economic benefits of the power grid.

[0169] The optimization of multi-source power supply structure in S2 includes:

[0170] S21, Construction of a matching model between power demand and power generation capacity: According to the power generation characteristics of different power sources (such as the volatility of wind power, the diurnal nature of photovoltaic power, and the stability of thermal power), a matching model between power demand and power generation capacity is constructed to meet the power demand P in different time periods. load (t), expressed as:

[0171]

[0172] Among them, P i (t) represents the power generated by the i-th power source at time t, P load (t) is the power demand at time t, P balance (t) indicates the supply-demand balance state;

[0173] S22, multi-source power supply optimization objective: maximize the utilization rate of renewable energy (wind power and photovoltaic power) while ensuring the balance between power supply and demand. The optimization objective function is expressed as:

[0174]

[0175] Where f(P) is the objective function, n is the total number of power types, c i is the cost coefficient of the i-th power source, r iis the utilization coefficient of renewable energy, t is the time variable;

[0176] S23, Constraints: The constraints in the optimization process specifically include:

[0177] The conditions for meeting the power demand are:

[0178] Physical limitations of power generation capacity:

[0179] in, and are the minimum and maximum power generation of the i-th power source respectively;

[0180] S24, optimization algorithm application: Use the mixed integer linear programming (MILP) algorithm to solve the objective function and obtain the optimal power distribution P for each power source. i,opt (t) to ensure supply-demand balance and optimal energy use;

[0181] The mixed integer linear programming (MILP) algorithm specifically includes:

[0182] Objective function: Minimize c T x;

[0183] Where c is the coefficient vector of the objective function, and x is the decision variable vector containing continuous variables and integer variables;

[0184] Linear inequality constraints: Ax≤b;

[0185] Among them, A is the constraint coefficient matrix, b is the constraint vector;

[0186] Linear equality constraints: A eq x=b eq ;

[0187] Among them, A eq is the equality constraint coefficient matrix, b eq is the equality constraint vector;

[0188] x j ∈ Z;

[0189] Among them, x j Represents the integer part of the decision variable, which is required to take an integer value;

[0190] Non-negative constraint: x k ≥0;

[0191] Among them, x k Represents the continuous part of the decision variable, which is required to be non-negative;

[0192] Through the above contents, we ensure that the utilization rate of renewable energy is maximized while meeting electricity demand and the cost of using traditional energy is minimized, which not only improves the economic benefits and environmental friendliness of the power system, but also enhances the flexibility and stability of power supply, providing a solid guarantee for the efficient operation and sustainable development of the power grid.

[0193] The hierarchical calculation of energy storage facility demand in S3 includes:

[0194] S31, grid stratification: According to the distribution network structure of different voltage levels, the grid is divided into multiple levels, including high voltage (HV), medium voltage (MV) and low voltage (LV);

[0195] S32, calculation of energy storage demand: At each level L, based on the power supply and demand balance model P balance,L (t) and the optimization results of the multi-source power supply structure, the demand for energy storage facilities is calculated and expressed as:

[0196] P storage,L (t)=max(0,P balance,L (t)-P renewable,L (t));

[0197]

[0198] Among them, P balance,L (t) is the power supply and demand balance at level L, P renewable,L (t) is the power generation of renewable energy at level L, P storage,L (t) is the energy storage power demand of level L at time t, E storage,L is the energy storage power demand in the time period [t1, t2];

[0199] S33, multi-level coordination and integration: coordinate and integrate the calculation results of energy storage demand at each level to ensure that the energy storage facilities of the overall distribution network are configured to match each other at different levels, and achieve supply and demand balance and optimization of the entire network, expressed as:

[0200]

[0201] Among them, P storage,total (t) is the total energy storage power demand of the entire network at time t, P storage,L (t) is the energy storage power requirement of the Lth level, α L is the weight coefficient of level L, indicating the contribution of different levels to the energy storage of the whole grid, and N is the total number of levels of the grid stratification;

[0202] Through the above content, it can ensure that the configuration of energy storage facilities is highly matched with actual electricity demand, improve the overall operating efficiency and stability of the power grid, optimize resource utilization and maximize economic benefits. This layered measurement method provides a scientific basis for the rational layout and efficient management of energy storage facilities, and effectively supports the sustainable development of the power grid.

[0203] The collection and processing of electricity market price data in S41 includes:

[0204] S411, real-time price data collection: through the real-time data interface connection with the power market, continuously collect the real-time price data of the power market market (t), including electricity spot prices, futures prices and dispatch prices;

[0205] S412, data cleaning: Use the Z-Score method to perform preliminary cleaning on the collected real-time price data to remove obviously erroneous data points and unreasonable extreme values, expressed as:

[0206]

[0207] Among them, μ P is the mean of real-time price data, σ P is the standard deviation. When |Z|>threshold, the data point is considered an outlier and is removed;

[0208] S413, data filtering: noise filtering is performed on the cleaned real-time price data, and the moving average method is used to smooth the time series data to reduce the impact of random fluctuations on the prediction, which is expressed as:

[0209]

[0210] Among them, P filtered (t) is the filtered price data at time t, N is the size of the moving average window, P market (ti) is the price data at the previous i time points;

[0211] S414, data integrity processing: use linear interpolation to fill in missing data, expressed as:

[0212]

[0213] Among them, t1 and t2 are adjacent time points with data, P market (t1) and P market (t2) is the price data at the corresponding time point;

[0214] Through the above content, it is possible to provide reliable market price data support for the optimal scheduling of energy storage facilities, improve the accuracy and economy of system decision-making, and ensure the efficient operation of energy storage systems in the power market.

[0215] The electricity market price prediction model in S42 adopts the Bayesian time series model, which includes:

[0216] S421, define the prior distribution: In the electricity market price forecast, the normal distribution is selected as the prior, which is expressed as:

[0217]

[0218] Among them, θ is the parameter vector of the model (such as trend, seasonal coefficient, etc.), μ0 and They are the mean and variance of the prior distribution, reflecting the knowledge of domain experts or the statistical properties of historical data;

[0219] S422, construct likelihood function: construct likelihood function using time series model of price data to capture dynamic changes in electricity market price, expressed as:

[0220]

[0221] Where D is the observed data set, P(t) is the market price at time t, is the autoregressive coefficient, σ 2 is the variance of the noise, P(t-1), P(t-2), ..., P(tp) are the market price data at time t-1, t-2, ..., tp respectively;

[0222] S423, calculate the posterior distribution: calculate the posterior distribution of the parameter by combining the prior distribution and the likelihood function through Bayes' theorem, expressed as:

[0223]

[0224] Where P(θ|D) is the posterior distribution of the parameter, P(D|θ) is the likelihood function, P(θ) is the prior distribution, and P(D) is the marginal likelihood of the observed data;

[0225] S424, Bayesian prediction distribution: Calculate the prediction distribution of future prices based on the posterior distribution, consider the volatility of the market, and construct the prediction distribution into a prediction model with dynamic adjustment capabilities, expressed as:

[0226] P(P t+ h|D t )=∫P(P t+ h|θ)P(θ|D t )dθ;

[0227] Among them, P t+h is the predicted price value for the next h steps, P(θ|D t ) is the posterior distribution based on the current data, P(P t+ h|θ) is the predicted price distribution under given parameters;

[0228] S425, dynamic update and real-time adjustment: Every time new price data arrives, the model parameters are adjusted in real time using the Bayesian update rule so that the model can dynamically adapt to market changes, expressed as:

[0229]

[0230] Among them, P(D t+1 |θ) is the likelihood function of the new data point, P(θ|D t ) is the posterior distribution at the current moment, P(θ|D t+1 ) is the updated posterior distribution after the new data arrives;

[0231] Through the above content, not only can the fluctuation trend of electricity market prices be captured dynamically, but also the uncertainty of prediction can be quantified. The real-time update mechanism enables the model to quickly adapt to market changes and maintain high prediction accuracy, thereby enhancing the reliability of electricity market price forecasts and providing strong data support for the optimal scheduling of energy storage facilities, which helps to improve the economic benefits and stability of the system and ensure the efficient operation of the power grid.

[0232] The energy storage facility charging and discharging strategy formulation in S43 includes:

[0233] S431, power market price forecast results: The future power market price is predicted by the Bayesian time series model to obtain the predicted market price value at the future moment, which is expressed as:

[0234]

[0235] in, is the predicted value of the electricity market price at time t, D t is the price data at time t;

[0236] S432, Energy storage facility status and remaining capacity assessment: Evaluate the current state of charge (SOC) and remaining capacity of the energy storage facility to determine the charging or discharging capacity of the energy storage facility and provide a basis for formulating optimization strategies, expressed as:

[0237]

[0238] Among them, SOC(t) represents the state of charge of the energy storage facility at time t, E current (t) is the remaining energy of the energy storage facility at time t, Emax is the maximum energy capacity of the energy storage facility;

[0239] S433, charging and discharging strategy optimization goal: According to the market price forecast results and the status of energy storage facilities, a charging and discharging strategy is formulated. The goal is to maximize economic benefits by discharging when the electricity price is high and charging when the electricity price is low. It is expressed as:

[0240]

[0241] Among them, Profit(t) is the economic benefit at time t, t0 is the start time of the charging and discharging strategy, T is the end time of the charging and discharging strategy, P discharge (t) is the discharge power at time t, P charge (t) is the charging power at time t;

[0242] S434, strategy constraints: When formulating the charging and discharging strategy, the physical limitations of the energy storage facility must be met to ensure that the energy storage facility is operated within a safe range, expressed as:

[0243] 0≤P c h arge (t)≤P c h arge_max ,0≤P disc h arge (t)≤P disc h arge_max ;

[0244] SOC min ≤SOC(t)≤SOC max ;

[0245] Among them, P c h arge_max is the maximum charging power limit of the energy storage facility, that is, the maximum charging power allowed by the device, P disc h arge_max is the maximum discharge power limit of the energy storage facility, that is, the maximum discharge power allowed by the device, SOC min The minimum state of charge allowed for energy storage facilities. A value lower than this may cause damage to the equipment. SOC max The maximum state of charge allowed by the energy storage facility. Exceeding this value may cause the device to be overcharged;

[0246] S435, charge and discharge execution and dynamic adjustment: Based on the threshold of market price prediction, the charge and discharge strategy is dynamically adjusted to ensure that the operation is performed at the best time, thereby optimizing economic benefits, which is expressed as:

[0247]

[0248] Among them, P action(t) is the charge and discharge execution power at time t, which dynamically determines whether to charge, discharge or not perform the operation according to the market price prediction results, threshold discharge The price threshold for the discharge operation. When the predicted electricity price exceeds this value, the discharge operation is triggered. charge The price threshold for charging operation. When the predicted electricity price is lower than this value, charging operation is triggered.

[0249] The price threshold for discharge operation discharge Based on the battery discharge efficiency, the predicted electricity market price, the discharge-related operating costs, and the target profit margin setting, it is expressed as:

[0250]

[0251] Among them, C operation_discharge is the unit operating cost of the discharge operation, including equipment maintenance, power loss, etc., C battery_degradation is the battery life loss cost caused by each discharge, η disc h arge is the discharge efficiency of the energy storage facility, P profit is the target unit profit rate, which indicates the profit expected to be obtained in the discharge operation;

[0252] The price threshold for charging operation charge Based on battery charging efficiency, future electricity market price forecasts, charging-related operating costs, and target profit margin settings, it is expressed as:

[0253]

[0254] Among them, C operation_c h arge is the unit operating cost of charging operation, including electricity purchase cost, equipment maintenance cost, etc. battery_degradation is the battery life loss cost caused by each charge, η charge is the charging efficiency of the energy storage facility, P profit is the target unit profit rate, which represents the profit expected to be obtained by discharging after charging;

[0255] Through the above content, not only can economic benefits be maximized, but also unnecessary costs and losses can be avoided. By charging and discharging at the right time, the system can effectively balance the supply and demand of the power grid, while improving the overall operating efficiency and economic returns of energy storage facilities, and enhancing the flexibility and competitiveness of electricity market participation.

[0256] Real-time policy execution and feedback adjustments in S44 include:

[0257] S441, real-time strategy execution: at each time t, according to the current electricity market price forecast value and the state of the energy storage facility, the formulated charging and discharging strategy is executed in real time, and according to the charging or discharging instructions, the energy storage facility is controlled to perform corresponding operations, which is expressed as:

[0258]

[0259] S442, monitor actual execution results: During the strategy execution process, monitor the actual charging and discharging power, market price and status of the energy storage facilities in real time, evaluate the effect of the charging and discharging strategy execution, and ensure that the actual operation is consistent with expectations, expressed as:

[0260] ΔP(t)=P actual (t)-P action (t);

[0261] Among them, P actual (t) is the actual charging and discharging power of the energy storage facility at time t, ΔP(t) is the difference between the actual execution power and the planned power at time t;

[0262] S443, feedback adjustment: according to the difference between the actual data monitored and the expected strategy, the charging and discharging strategy is adjusted in real time, which is expressed as:

[0263] P new_action (t) = P action (t)+β·ΔP(t);

[0264] Among them, P new_action (t) is the new charge and discharge strategy power after feedback adjustment at time t, β is the feedback adjustment coefficient, which is used to control the amplitude of feedback adjustment;

[0265] Through the above contents, not only can the operating efficiency of energy storage facilities be improved, but also the flexibility and stability of electricity market participation can be enhanced, providing strong guarantees for the smooth operation of the power grid.

[0266] The scenario cluster analysis of energy storage facility demand in S5 includes:

[0267] S51, data collection and preprocessing: Based on the adjusted energy storage facility charging and discharging strategy, the demand data of energy storage facilities in different time periods and under different power market conditions are collected, and the demand data are preprocessed, including noise removal and standardization, to ensure data quality, expressed as:

[0268]

[0269] Among them, D i,j represents the standardized demand in the i-th time period under the j-th electricity market condition, P demand,ij is the actual demand, μj and σ j are the mean and standard deviation of demand under the jth market condition;

[0270] S52, demand cluster analysis: Based on K-means clustering, cluster analysis is performed on the pre-processed energy storage facility demand data to identify the demand patterns of energy storage facilities in different application scenarios, which are expressed as:

[0271]

[0272] Among them, C k represents the kth cluster center, D i,j is the standardized demand data belonging to this cluster, is the mean of the cluster center, N is the total number of all data points, and k is the number of cluster categories;

[0273] S53, Formation of application scenarios: Based on the results of cluster analysis, different application scenarios are formed. These scenarios represent typical demand patterns of energy storage facilities under specific market conditions and in a specific time period, expressed as:

[0274] S m ={D i,j |D i,j ∈C m};

[0275] Among them, S m Indicates the mth application scenario;

[0276] S54, energy storage facility configuration plan formulation: According to the needs of different application scenarios, formulate energy storage facility configuration plans to ensure that energy storage facilities can meet the needs in each scenario and achieve the best configuration. The configuration plan includes energy storage capacity, charging and discharging power, and operation strategy, which is expressed as:

[0277]

[0278] Among them, P storage (S m ) is in scene S m The configuration plan of the energy storage facility is as follows: t0 is the starting time, T is the end time, P demand (t) is the power demand at time t, which represents the power demand of the energy storage facility at that time point, P profit (t) is the unit profit at time t, which represents the economic benefit that can be obtained from the energy storage operation at that time point;

[0279] Through the above content, we can ensure that energy storage facilities can operate efficiently and maximize economic benefits. Through scenario clustering analysis, we can respond to market fluctuations more flexibly and improve the overall benefits of energy storage facilities and the stability of the power grid.

[0280] The economic evaluation of the energy storage facility configuration scheme in S6 includes:

[0281] S61, cost composition analysis: Conduct a cost composition analysis on the configuration plan of the energy storage facility, including the initial investment cost, operation and maintenance cost, and operating cost caused by fluctuations in electricity market prices, expressed as:

[0282]

[0283] Among them, C total is the total cost of the energy storage facility, C investment is the initial investment cost of energy storage facilities, including the purchase of equipment, installation and other initial investments, C operation (t,P market (t)) is the operating cost of the energy storage facility at time t, including electricity purchase cost and charging loss, C maintenance (t) is the maintenance cost of the energy storage facility at time t, including daily maintenance and repair costs, t0 is the starting time of the economic evaluation, and T is the end time of the economic evaluation;

[0284] S62, Benefit Analysis: By analyzing the benefits of energy storage facilities under different market conditions, the economic benefits that energy storage facilities can obtain under various charging and discharging strategies are calculated and expressed as:

[0285]

[0286] Among them, R total is the total revenue of the energy storage facility in the time period [t0,T], P discharge (t) is the discharge power of the energy storage facility at time t, P charge (t) is the charging power of the energy storage facility at time t, P market (t) is the market electricity price at time t;

[0287] S63, calculation of economic evaluation indicators: Calculate the key indicators of economic evaluation, including net present value (NPV), internal rate of return (IRR) and payback period, to evaluate the economic feasibility of energy storage facility configuration scheme, expressed as:

[0288]

[0289] Among them, NPV is the net present value, which is used to measure the economic benefits of the energy storage facility configuration plan. NPV greater than zero indicates that the investment project is economically feasible. R(t) is the income at time t, C(t) is the cost at time t, and r is the discount rate.

[0290]

[0291] Among them, IRR is the internal rate of return, which is the discount rate that makes NPV equal to zero, reflecting the expected annual rate of return of the project;

[0292]

[0293] Where Payback Period is the payback period, R(i) is the revenue at time i, C(i) is the cost at time i, and C investment is the initial investment cost;

[0294] S64, configuration scheme optimization: The configuration scheme of energy storage facilities is adjusted through the optimization algorithm to maximize the economic benefits, which is expressed as:

[0295]

[0296] in, Indicates that in all energy storage facility configuration schemes P storage (S m ) to find the optimal energy storage facility configuration plan by maximizing the net present value (NPV);

[0297] Through the above content, using net present value, internal rate of return and payback period, decision makers can accurately evaluate the long-term profitability, return on investment and capital recovery time of the project. Through the evaluation method, decision makers can optimize the configuration of energy storage facilities to ensure maximum economic benefits, while reducing investment risks and improving the overall economy and market competitiveness of the project.

[0298] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0299] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for calculating energy storage facility demand based on long-term power and electricity balance, characterized in that: The following steps are involved: S1, long-term power data collection and analysis: collect long-term power data of different voltage levels in the distribution network, including power demand, power consumption, distributed renewable energy power generation and external transmission unit power supply within the power supply range of the substation, and conduct power supply and demand balance analysis based on long-term power data to build a power supply and demand balance model; S2, Optimization of multi-source power supply structure: Optimize the power generation capacity of multiple power sources in the region to achieve a dynamic balance between power supply and demand and maximize the utilization of renewable energy; S3, hierarchical calculation of energy storage facility demand: for distribution networks of different voltage levels, based on the power supply and demand balance model and the optimization results of the multi-source power supply structure, hierarchical calculation of energy storage facility demand is carried out, including energy storage power demand and electricity demand of each level of distribution network in different time periods; S4, Dynamic electricity market price forecasting and energy storage optimization scheduling: Combined with the price fluctuations of the electricity market, the electricity market price is dynamically forecasted, and the charging and discharging strategies of energy storage facilities are adjusted in real time according to the forecast results, including: S41, power market price data collection and processing: collect real-time price data from the power market and process the price data to remove noise and outliers; S42, power market price forecasting model construction: based on the processed price data, a power market price forecasting model is constructed to predict the future power market price fluctuation trend; S43, energy storage facility charging and discharging strategy formulation: based on the forecast results of the electricity market price, combined with the current status and remaining capacity of the energy storage facility, formulate an optimized charging and discharging strategy; S44, real-time strategy execution and feedback adjustment: Execute the formulated charging and discharging strategy in real time, monitor the actual execution results, and dynamically adjust the charging and discharging strategy according to the latest changes in market prices and the operating status of energy storage facilities; S5, scenario cluster analysis of energy storage facility demand: Combined with the adjusted charging and discharging strategies of energy storage facilities, cluster analysis is performed on the demand for energy storage facilities to form application scenarios for distribution network energy storage facilities. Based on the needs of different application scenarios, configuration plans for energy storage facilities are developed. S6, Economic evaluation of energy storage facility configuration plan: Conduct an economic evaluation of the energy storage facility configuration plan, comprehensively consider investment costs, operating costs, electricity market price fluctuations and benefits, and optimize the energy storage facility configuration plan.

2. The method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 1, characterized in that: The long-term power data collection and analysis in S1 includes: S11, data collection: using smart meters, data collectors and sensor networks to collect long-term power data at different voltage levels in the distribution network; S12, data preprocessing: cleaning, filtering and interpolation of the collected long-period power data to remove data noise and outliers; S13, power supply and demand balance model construction: Based on the preprocessed long-term power data, a power supply and demand balance model is constructed, which is expressed as: P balance (t)=P load (t)+P external (t)-P renewable (t)-P storaqe (t); Among them, P balance (t) is the supply and demand balance at time t, P load (t) is the power demand at time t, P external (t) is the power supply of the external transmission unit, P renewable (t) is the power generation of distributed renewable energy, P storaqe (t) is the output power of the energy storage facility; S14, long-term power and electricity analysis: by performing time series analysis on long-term power and electricity data, the autoregressive moving average model is used to predict future power demand and power generation, which is expressed as: Among them, P forecast (t) is the predicted power demand or generation at the future time t, p and q are the orders of autoregression and moving average, respectively, β i and γ j is the model parameter, ∈(tj) is the residual term; S15, supply and demand balance correction: input the predicted power demand and power generation into the power supply and demand balance model, calculate the future power supply and demand balance state, correct the deviation between the model prediction value and the actual value, and form the final supply and demand balance analysis result, which is expressed as: P balance (t)=P forecast (t)-P actual (t); Among them, P balance (t) is the supply and demand balance correction value at time t, P forecast (t) is the predicted value at time t, P actual (t) is the actual value at time t; P corrected (t)=P forecast (t)+α×P balance (t); Among them, P corrected (t) is the supply and demand balance result after correction, and α is the correction coefficient.

3. The method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 2 is characterized in that: The multi-source power supply structure optimization in S2 includes: S21, Construction of power demand and power generation capacity matching model: According to the power generation characteristics of different power types, a power demand and power generation capacity matching model is constructed to meet the power demand P in different time periods. load (t), expressed as: Among them, P i (t) represents the power generated by the i-th power source at time t, P load (t) is the power demand at time t, P balance (t) indicates the supply-demand balance state; S22, multi-source power supply optimization objective: maximize the utilization rate of renewable energy while ensuring the balance between power supply and demand. The optimization objective function is expressed as: Where f(P) is the objective function, n is the total number of power types, c i is the cost coefficient of the i-th power source, r i is the utilization coefficient of renewable energy, t is the time variable; S23, Constraints: The constraints in the optimization process specifically include: The conditions for meeting the power demand are: Physical limitations of power generation capacity: in, and are the minimum and maximum power generation of the i-th power source respectively; S24, optimization algorithm application: Use mixed integer linear programming algorithm to solve the objective function and obtain the optimal power distribution P for each power source. i，opt (t).

4. The method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 3 is characterized in that: The hierarchical calculation of the demand for energy storage facilities in S3 includes: S31, grid stratification: According to the distribution network structure of different voltage levels, the grid is divided into multiple levels, including high voltage layer, medium voltage layer and low voltage layer; S32, calculation of energy storage demand: At each level L, based on the power supply and demand balance model P balance，L (t) and the optimization results of the multi-source power supply structure, the demand for energy storage facilities is calculated and expressed as: P storage，L (t)=max(0,P balance，L (t)-P renewable，L (t)); Among them, P balance，L (t) is the power supply and demand balance at level L, P renewable，L (t) is the power generation of renewable energy at level L, P storage，L (t) is the energy storage power demand of level L at time t, E storage，L is the energy storage power demand in the time period [t1, t2]; S33, multi-level coordination and integration: coordinate and integrate the calculation results of energy storage demand at each level, expressed as: Among them, P storage，total (t) is the total energy storage power demand of the entire network at time t, P storage，L (t) is the energy storage power requirement of the Lth level, α L is the weight coefficient of level L, and N is the total number of levels in the power grid stratification.

5. The method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 1, characterized in that: The electricity market price data collection and processing in S41 includes: S411, real-time price data collection: through the real-time data interface connection with the power market, continuously collect the real-time price data of the power market market (t), including electricity spot prices, futures prices and dispatch prices; S412, data cleaning: using the Z-score method to perform preliminary cleaning on the collected real-time price data; S413, data filtering: filtering the noise of the cleaned real-time price data, and smoothing the time series data using the moving average method to reduce the impact of random fluctuations on the prediction; S414, Data integrity processing: Use linear interpolation to fill in missing data.

6. A method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 5, characterized in that: The power market price prediction model in S42 adopts a Bayesian time series model, and the Bayesian time series model includes: S421, define the prior distribution: In the electricity market price forecast, the normal distribution is selected as the prior, which is expressed as: where θ is the parameter vector of the model, μ0 and are the mean and variance of the prior distribution respectively; S422, construct likelihood function: construct likelihood function using time series model of price data to capture dynamic changes in electricity market price, expressed as: Where D is the observed data set, P(t) is the market price at time t, is the autoregressive coefficient, σ 2 is the variance of the noise, P(t-1), P(t-2), ..., P(tp) are the market price data at time t-1, t-2, ..., tp respectively; S423, calculate the posterior distribution: calculate the posterior distribution of the parameter by combining the prior distribution and the likelihood function through Bayes' theorem, expressed as: Where P(θ|D) is the posterior distribution of the parameter, P(D|θ) is the likelihood function, P(θ) is the prior distribution, and P(D) is the marginal likelihood of the observed data; S424, Bayesian prediction distribution: Calculate the prediction distribution of future prices based on the posterior distribution, consider the volatility of the market, and construct the prediction distribution into a prediction model with dynamic adjustment capabilities, expressed as: P(P t+h |D t )=∫P(P t+h |θ)P(θ|D t )dθ; Among them, P t+h is the predicted price value for the next h steps, P(θ|D t ) is the posterior distribution based on the current data, P(P t+h |θ) is the predicted price distribution under given parameters; S425, dynamic update and real-time adjustment: Every time new price data arrives, the model parameters are adjusted in real time using the Bayesian update rule, expressed as: Among them, P(D t+1 |θ) is the likelihood function of the new data point, P(θ|D t ) is the posterior distribution at the current moment, P(θ|D t+1 ) is the updated posterior distribution after the new data arrives.

7. A method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 6, characterized in that: The energy storage facility charging and discharging strategy formulation in S43 includes: S431, power market price forecast results: The future power market price is predicted by the Bayesian time series model to obtain the predicted market price value at the future moment, which is expressed as: in, is the predicted value of the electricity market price at time t, D t is the price data at time t; S432, energy storage facility status and remaining capacity assessment: assess the current state of charge and remaining capacity of the energy storage facility to determine the charging or discharging capability of the energy storage facility, expressed as: Among them, SOC(t) represents the state of charge of the energy storage facility at time t, E current (t) is the remaining energy of the energy storage facility at time t, E max is the maximum energy capacity of the energy storage facility; S433, charging and discharging strategy optimization goal: formulate a charging and discharging strategy based on the market price forecast results and the status of the energy storage facilities, expressed as: Among them, Profit(t) is the economic benefit at time t, t0 is the start time of the charging and discharging strategy, T is the end time of the charging and discharging strategy, P discharge (t) is the discharge power at time t, P charge (t) is the charging power at time t; S434, strategy constraints: When formulating the charging and discharging strategy, the physical limitations of the energy storage facilities must be met, expressed as: 0≤P charge (t)≤P charge_max ,0≤P discharge (t)≤P discharge_max ; SOC min ≤SOC(t)≤SOC max ; Among them, P charge_max is the maximum charging power limit of the energy storage facility, P discharge_max is the maximum discharge power limit of the energy storage facility, SOC min SOC is the minimum state of charge allowed by the energy storage facility. max The maximum state of charge allowed for the energy storage facility; S435, charge and discharge execution and dynamic adjustment: based on the threshold of market price prediction, dynamically adjust the charge and discharge strategy, expressed as: Among them, P action (t) is the charge and discharge execution power at time t, threshold discharge is the price threshold for the discharge operation, threshold charge is the price threshold for charging operations.

8. The method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 7, characterized in that: The real-time strategy execution and feedback adjustment in S44 include: S441, real-time strategy execution: at each time t, according to the current electricity market price forecast value and the state of the energy storage facility, the formulated charging and discharging strategy is executed in real time, and according to the charging or discharging instructions, the energy storage facility is controlled to perform corresponding operations, which is expressed as: S442, monitor the actual execution results: During the strategy execution process, monitor the actual charging and discharging power, market price and status of the energy storage facilities in real time, and evaluate the effect of the charging and discharging strategy execution, which is expressed as: ΔP(t)=P actual (t)-P action (t); Among them, P actual (t) is the actual charging and discharging power of the energy storage facility at time t, ΔP(t) is the difference between the actual execution power and the planned power at time t; S443, feedback adjustment: according to the difference between the actual data monitored and the expected strategy, the charging and discharging strategy is adjusted in real time, which is expressed as: P new_action (t)=P action (t)+β·ΔP(t); Among them, P new_action (t) is the new charge and discharge strategy power after feedback adjustment at time t, and β is the feedback adjustment coefficient.

9. A method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 8, characterized in that: The scenario cluster analysis of the energy storage facility demand in S5 includes: S51, data collection and preprocessing: based on the adjusted charging and discharging strategy of energy storage facilities, collect the demand data of energy storage facilities in different time periods and under different power market conditions, and preprocess the demand data, including noise removal and standardization; S52, demand cluster analysis: perform cluster analysis on the pre-processed energy storage facility demand data based on K-means clustering to identify the demand patterns of energy storage facilities in different application scenarios; S53, forming application scenarios: forming different application scenarios according to the results of cluster analysis; S54, Development of energy storage facility configuration plan: Based on the requirements of different application scenarios, develop a configuration plan for energy storage facilities. The configuration plan includes energy storage capacity, charging and discharging power, and operation strategy.

10. A method for calculating energy storage facility demand based on long-term power and electricity balance according to claim 9, characterized in that: The economic evaluation of the energy storage facility configuration scheme in S6 includes: S61, cost composition analysis: Conduct a cost composition analysis on the configuration plan of the energy storage facility, including the initial investment cost, operation and maintenance cost, and operating cost caused by fluctuations in electricity market prices, expressed as: Among them, C total is the total cost of the energy storage facility, C investment is the initial investment cost of the energy storage facility, C operation (t,P market (t)) is the operating cost of the energy storage facility at time t, C maintenance (t) is the maintenance cost of the energy storage facility at time t, t0 is the starting time of the economic evaluation, and T is the end time of the economic evaluation; S62, Benefit Analysis: By analyzing the benefits of energy storage facilities under different market conditions, the economic benefits that energy storage facilities can obtain under various charging and discharging strategies are calculated and expressed as: Among them, R total is the total revenue of the energy storage facility in the time period [t0, T], P discharge (t) is the discharge power of the energy storage facility at time t, P charge (t) is the charging power of the energy storage facility at time t, P market (t) is the market electricity price at time t; S63, calculation of economic evaluation indicators: Calculate the key indicators of economic evaluation, including net present value, internal rate of return and payback period, and evaluate the economic feasibility of energy storage facility configuration scheme, expressed as: Where NPV is the net present value, R(t) is the revenue at time t, C(t) is the cost at time t, and r is the discount rate; Where IRR is the internal rate of return; Where Payback Period is the payback period, R(i) is the revenue at time i, C(i) is the cost at time i, and C investment is the initial investment cost; S64, configuration scheme optimization: The configuration scheme of the energy storage facility is adjusted through the optimization algorithm, which is expressed as: in, Indicates that in all energy storage facility configuration schemes P storage (S m ) is used to find the optimal energy storage facility configuration plan by maximizing the net present value (NPV).

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