A method for quantifying the credible capacity of new energy combined with energy storage by fusing electricity price signals

Abstract

The application discloses a kind of new energy joint energy storage's credible capacity quantitative calculation method of fusion electricity price signal, comprising the following steps: generating daily multi-period simulation electricity price sequence of whole year;Establish energy storage output timing model;Get energy storage whole year output timing curve;Form boundary condition of different timing node;Simulate random outage scene of conventional unit, and under the random outage scene of conventional unit, calculate the reliability of conventional unit;Determine the energy storage output to be introduced in current period based on energy storage whole year output timing curve;With the minimum difference between system reliability after introducing new energy and energy storage and conventional unit reliability as the target, determine the credible capacity Y of new energy joint energy storage.The application forms the complete technical system of "electricity price input-energy storage output generation-scenario simulation-reliability evaluation-credible capacity determination", realizes the integration of credible capacity quantitative.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation, specifically a reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals. Background Technology

[0002] The proportion of new energy sources such as wind power and photovoltaic power in the power system continues to increase. However, the volatility, intermittency, and randomness of new energy output mean that their actual reliable capacity is far lower than their installed capacity, posing a challenge to the safe and stable operation of the power system. As a key piece of equipment for smoothing out the fluctuations of new energy sources and improving energy utilization efficiency, energy storage systems have become the mainstream mode for large-scale grid connection of new energy sources through joint operation with them.

[0003] Reliable capacity is a core indicator for measuring the contribution of integrated renewable energy systems to the reliable power supply of the power system. Its calculation results directly affect power source planning, grid dispatching, and the design of electricity market trading mechanisms. Currently, many scholars have conducted research on the calculation of reliable capacity.

[0004] However, current research on reliable capacity calculation methods for combined renewable energy storage often assumes that energy storage operates according to a fixed strategy, neglecting the temporal variations in energy storage output driven by electricity prices. It also fails to adequately consider the impact of electricity price signals on energy storage operation strategies and ignores the profit-seeking nature of energy storage providers. This leads to significant discrepancies between simulated and actual operating states of the combined system, resulting in overestimated ELCC values ​​for combined renewable energy storage. Furthermore, some methods focus only on a single reliability indicator, failing to simultaneously consider both power capacity and probability-based reliability indicators. This fails to comprehensively characterize the supporting value of reliable capacity to the power system, and the indicator system is not sufficiently adapted to actual needs. Summary of the Invention

[0005] The purpose of this invention is to provide a reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals, comprising the following steps:

[0006] Step 1) Use time series analysis to process historical electricity market data and generate simulated electricity price series for multiple time periods throughout the year;

[0007] Step 2) Establish a time-series model for energy storage output;

[0008] Step 3) Input the simulated electricity price series for each day of the year into the energy storage output time series model in sequence to obtain the annual energy storage output time series curve;

[0009] Step 4) Obtain the time-series data of new energy output and system load, and perform time-series matching with the annual output time-series curve of energy storage to form boundary conditions for different time-series nodes;

[0010] Step 5) Simulate a random outage scenario for conventional generating units, and calculate the reliability of conventional generating units under this scenario. , For load, To provide power to thermal power units; For probability;

[0011] Step 6) Determine the energy storage output to be introduced in the current period based on the annual energy storage output time-series curve. ;

[0012] Step 7) Based on the boundary conditions at different time nodes, assess the system reliability after introducing new energy sources and energy storage. Compared with the reliability of conventional units With the goal of minimizing the difference, the reliable capacity Y of the new energy combined energy storage is determined; Contribute to new energy sources It contributes to energy storage.

[0013] Furthermore, historical operating data of the electricity market includes historical electricity price data and load forecasting results.

[0014] Furthermore, the objective function of the energy storage output time-series model is as follows:

[0015] (1)

[0016] in, For discharge power, This refers to the charging power. This is the energy storage aging cost coefficient; For a period of time; For electricity prices.

[0017] Furthermore, the constraints of the energy storage output time series model include the energy storage charge and discharge state constraints, charge and discharge power constraints, and state of charge constraints.

[0018] Furthermore, the charge / discharge state constraints for energy storage are as follows:

[0019] (2)

[0020] in, and These represent the charging and discharging states of energy storage, with 1 indicating charging or discharging and 0 indicating no operation.

[0021] The charging and discharging power constraints are as follows:

[0022] (3)

[0023] (4)

[0024] in, and These are the maximum charging and discharging power of the energy storage; , The charging and discharging power of the energy storage at time t;

[0025] The state of charge constraints are as follows:

[0026] (5)

[0027] (6)

[0028] (7)

[0029] in, The state of charge of energy storage. and These represent the charging and discharging efficiency of energy storage. and These represent the minimum and maximum states of charge of energy storage, respectively.

[0030] Furthermore, the energy storage output time series model is solved using the Gurobi solver.

[0031] Furthermore, in step 5), the random shutdown scenario of conventional units is simulated using the Monte Carlo method;

[0032] The available capacity of conventional generating units under different random outage scenarios is as follows:

[0033] (8)

[0034] In the formula, This represents the available capacity of conventional generating units; This is the rated capacity of a conventional unit; This represents the forced outage rate for conventional generating units.

[0035] Furthermore, in step 6), the reliable capacity Y of the new energy combined energy storage is determined by the dichotomy method.

[0036] Furthermore, in step 6), the step of determining the reliable capacity Y of the new energy combined energy storage includes:

[0037] Step 6.1) Define the range of load increment Y for equivalent substitution of new energy and energy storage, denoted as [ =0, ]; This represents the total installed capacity of new energy sources and energy storage. The lower limit;

[0038] Step 6.2) Let Y be equal to the midpoint of the current range interval, denoted as And calculate the current system reliability;

[0039] Step 6.3) Determine the system reliability after introducing new energy sources and energy storage. Compared with the reliability of conventional units If the difference is less than the preset value, output the reliable capacity of the new energy combined energy storage and end the iteration; otherwise, proceed to step 6.4.

[0040] Step 6.4) Determine the system reliability after introducing new energy sources and energy storage. Is the reliability greater than that of conventional units? If so, then update the range to [ = , If the condition is not met, return to step 6.2); otherwise, update the range interval to [ =0, = ], and return to step 6.2).

[0041] Furthermore, after determining the reliable capacity Y of the combined new energy and energy storage, the reliability of the capacity after introducing new energy and energy storage is also calculated, namely:

[0042] (9)

[0043] In the formula, For capacity reliability.

[0044] The technical effectiveness of this invention is undeniable. This invention proposes a reliable capacity calculation method for new energy combined with energy storage based on electricity prices. It generates a profit-seeking output curve for energy storage by simulating electricity prices, integrates new energy and energy storage output as boundary conditions, and combines an equivalent outage rate model for generator units with Monte Carlo multi-scenario simulation to construct a price taker model from electricity price data to energy storage output. Based on this model, scenario simulation and reliability assessment are performed, including two major indicators covering power loss and power loss probability. Finally, a complete technical system is formed, encompassing "electricity price input - energy storage output generation - scenario simulation - reliability assessment - reliable capacity determination," achieving integrated quantification of reliable capacity and overcoming the shortcomings of existing technologies. Attached Figure Description

[0045] Figure 1 This is a trend curve showing the change in capacity reliability with installed capacity in scenarios without energy storage.

[0046] Figure 2 The trend of energy storage capacity reliability with changes in installed capacity;

[0047] Figure 3 This is to show the capacity reliability of wind turbine units as energy storage capacity changes across multiple installed capacity levels.

[0048] Figure 4 This is to show the capacity reliability of photovoltaic units as energy storage capacity changes across multiple installed capacity levels. Detailed Implementation

[0049] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0050] Example 1:

[0051] See Figures 1 to 4 A reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals includes the following steps:

[0052] Step 1) Use time series analysis to process historical electricity market data and generate simulated electricity price series for multiple time periods throughout the year;

[0053] Step 2) Establish a time-series model for energy storage output;

[0054] Step 3) Input the simulated electricity price series for each day of the year into the energy storage output time series model in sequence to obtain the annual energy storage output time series curve;

[0055] Step 4) Obtain the time-series data of new energy output and system load, and perform time-series matching with the annual output time-series curve of energy storage to form boundary conditions for different time-series nodes;

[0056] Step 5) Simulate a random outage scenario for conventional generating units, and calculate the reliability of conventional generating units under this scenario. , For load, To provide power to thermal power units; The probability is used to characterize system reliability through the probability of system failure.

[0057] Step 6) Determine the energy storage output to be introduced in the current period based on the annual energy storage output time-series curve. ;

[0058] Step 7) Based on the boundary conditions at different time nodes, assess the system reliability after introducing new energy sources and energy storage. Compared with the reliability of conventional units With the goal of minimizing the difference, the reliable capacity Y of the new energy combined energy storage is determined; Contribute to new energy sources It contributes to energy storage. The new energy output X is set based on the typical output matrix throughout the year.

[0059] Example 2:

[0060] A reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals is provided. The technical content is the same as in Example 1. Furthermore, the historical operation data of the electricity market includes historical electricity price data and load forecast results.

[0061] Example 3:

[0062] A reliable capacity quantification calculation method for new energy combined energy storage integrating electricity price signals, with the same technical content as any one of Embodiments 1-2, further wherein the objective function of the energy storage output time series model is as follows:

[0063] (1)

[0064] in, For discharge power, This refers to the charging power. This is the energy storage aging cost coefficient; For a period of time; For electricity prices.

[0065] Example 4:

[0066] A reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals, with the same technical content as any one of Examples 1-3. Furthermore, the constraints of the energy storage output time series model include energy storage charge and discharge state constraints, charge and discharge power constraints, and state of charge constraints.

[0067] Example 5:

[0068] A reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals, with the same technical content as any one of Examples 1-4, further wherein the charge and discharge state constraints of energy storage are as follows:

[0069] (2)

[0070] in, and These represent the charging and discharging states of energy storage, with 1 indicating charging or discharging and 0 indicating no operation.

[0071] The charging and discharging power constraints are as follows:

[0072] (3)

[0073] (4)

[0074] in, and These are the maximum charging and discharging power of the energy storage; , The charging and discharging power of the energy storage at time t;

[0075] The state of charge constraints are as follows:

[0076] (5)

[0077] (6)

[0078] (7)

[0079] in, The state of charge of energy storage. and These represent the charging and discharging efficiency of energy storage. and These represent the minimum and maximum states of charge of energy storage, respectively.

[0080] Example 6:

[0081] A reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals, with the same technical content as any one of Examples 1-5. Furthermore, the energy storage output time series model is solved using the Gurobi solver.

[0082] Example 7:

[0083] A reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals, the technical content of which is the same as any one of embodiments 1-6, further wherein, in step 5), the random shutdown scenario of conventional units is simulated by the Monte Carlo method;

[0084] The available capacity of conventional generating units under different random outage scenarios is as follows:

[0085] (8)

[0086] In the formula, This represents the available capacity of conventional generating units; This is the rated capacity of a conventional unit; This represents the forced outage rate for conventional generating units.

[0087] Example 8:

[0088] A reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals, with the same technical content as any one of embodiments 1-7, further wherein, in step 6), the reliable capacity Y of new energy combined energy storage is determined by the bisection method.

[0089] Example 9:

[0090] A method for quantifying the reliable capacity of new energy combined energy storage by integrating electricity price signals, with technical content identical to any one of embodiments 1-8, further comprising, in step 6), determining the reliable capacity Y of new energy combined energy storage, including:

[0091] Step 6.1) Define the range of load increment Y for equivalent substitution of new energy and energy storage, denoted as [ =0, ]; This represents the total installed capacity of new energy sources and energy storage. The lower limit;

[0092] Step 6.2) Let Y be equal to the midpoint of the current range interval, denoted as And calculate the current system reliability;

[0093] Step 6.3) Determine the system reliability after introducing new energy sources and energy storage. Compared with the reliability of conventional units If the difference is less than the preset value, output the reliable capacity of the new energy combined energy storage and end the iteration; otherwise, proceed to step 6.4.

[0094] Step 6.4) Determine the system reliability after introducing new energy sources and energy storage. Is the reliability greater than that of conventional units? If so, then update the range to [ = , If the condition is not met, return to step 6.2); otherwise, update the range interval to [ =0, = ], and return to step 6.2).

[0095] Example 10:

[0096] A reliable capacity quantification calculation method for new energy combined with energy storage that integrates electricity price signals, with technical content the same as any one of embodiments 1-9, further comprising, after determining the reliable capacity Y of new energy combined with energy storage, also calculating the reliability of the capacity after introducing new energy and energy storage, that is:

[0097] (9)

[0098] In the formula, For capacity reliability.

[0099] Example 11:

[0100] A reliable capacity quantification calculation method for new energy combined energy storage integrating electricity price signals, comprising the following steps:

[0101] 1. Construction of Energy Storage Output Time Series Curve Driven by Electricity Price

[0102] First, based on historical electricity price data, load forecasting results, and the characteristics of renewable energy output, a multi-period simulated electricity price series is generated using time series analysis. The electricity price is then input as a boundary condition into the price-taking model for energy storage. Second, a profit-seeking optimization model for energy storage is established, aiming to maximize the total lifecycle revenue of energy storage, generating a time-series curve of energy storage output. This model uses maximizing the net daily revenue of energy storage as a sub-objective (the total annual revenue is the sum of the revenues of 365 individual days), and the objective function is in the form of…

[0103] (1)

[0104] in, For discharge power, For charging power variables, For charged state variables, , These are binary variables representing the charging and discharging states. The first term represents the revenue from the electricity price difference during energy storage charging and discharging. Taking 1 hour, the simplified value is the sum of the product of the daily electricity price and the difference between charging and discharging power. The second term is the total daily operating cost of energy storage, with an energy storage aging cost coefficient of 10.

[0105] The constraints are as follows:

[0106] (2)

[0107] (3)

[0108] (4)

[0109] (5)

[0110] (6)

[0111] (7)

[0112] in, and These represent the charging and discharging states of energy storage, with 1 indicating charging or discharging and 0 indicating no operation. and These represent the maximum charging and discharging power of the energy storage. The state of charge of energy storage. and These represent the charging and discharging efficiency of energy storage. and These represent the minimum and maximum states of charge (SOC) of the energy storage, respectively. To ensure the energy sufficiency of the energy storage in the next scheduling cycle, a constraint requiring equal SOC at the beginning and end needs to be introduced. and These represent the energy storage SOC at the end of the scheduling period and the beginning of the scheduling period, respectively.

[0113] Iterate through all 365 days of the year, inputting the simulated electricity price sequence for each day. The Gurobi solver is then used to solve the mixed integer programming problem. The optimization results for each time period over 365 days are spliced ​​together to obtain the time-series curves of energy storage charging power and discharging power for 8760 hours throughout the year. The net output of energy storage is then calculated to finally form the time-series curve of energy storage profit-seeking output driven by electricity prices.

[0114] 2. Multi-scenario simulation based on Monte Carlo method

[0115] First, time-series matching and standardization are performed on the power output of new energy sources, the power output of energy storage, and the system load data to form the boundary condition inputs required for subsequent calculations. Second, to characterize the availability of conventional generator units during operation, this step establishes an equivalent outage rate model for the generator units, taking into account their random outage characteristics. Specifically, the unit's state is divided into two categories: "operating" and "out of service," and a two-state reliability model is used to describe its state characteristics.

[0116] Based on the aforementioned equivalent forced outage rate, the available capacity of the unit is determined. This available capacity follows a two-point distribution: when the unit is in operation, the corresponding probability is... The available capacity is the rated capacity of the unit. When the unit is out of service, the corresponding probability is: The available capacity is 0, that is:

[0117] (8)

[0118] 2. Reliability assessment based on Monte Carlo simulation

[0119] The capacity reliability of new energy generating units refers to the ratio of equivalent reliable capacity to their installed capacity (EFC). Equivalent reliable capacity refers to the capacity of a new energy generating unit that, under the premise of equal reliability, can replace a fully reliable conventional generating unit. Let the capacity of the conventional (thermal power) unit be... The installed capacity of new energy units connected to the grid is Timing output is . This indicates the unit capacity with 100% reliability, equivalent to the system reliability after the new energy unit is connected. This indicates that, within the timescale of the study, the system has a capacity of The load that the assembly of the machine group should be able to handle is Reliability.

[0120] Considering the configuration of energy storage for either wind power or photovoltaic power, the calculation criteria are as follows: under the condition of equal reliability, the output of the new energy power source can replace 100% of the capacity of the conventional unit.

[0121] (9)

[0122] The above equation is used to perform a one-dimensional search for equivalent load-bearing capacity using a bisection method, and this equation is taken as the core criterion for reliability matching. In this criterion, Representing new energy, Both represent the output curves of energy storage, and both are output data at the source end of the power system, belonging to known boundary conditions; while The only unknown in the equation is , which represents the equivalent load increment that the resource can replace in the system, and is also the core characteristic quantity of the capacity reliability we need to solve for. The left side of the equation represents the system reliability index without the resource, which is a predetermined constant; the right side of the equation represents the system reliability index after the resource is introduced. and energy storage resources Then, increase the system load. The system reliability at that time, this reliability will change with The value increases and then monotonically decreases. This occurs when the equation holds true. Add resources to the system The equivalent load-carrying capacity. At this point, the reliability of the capacity obtained after combining new energy with energy storage is:

[0123] (10)

[0124] Based on the monotonicity of the search process described above, we can efficiently locate the equivalent load-carrying capacity that satisfies the equation using a bisection method. Value. First, we need to determine... Reasonable search interval: lower bound of the interval Set it to 0, then introduce resources. The reliability of the subsequent system will be higher than that of the baseline scenario; the upper bound of the interval is The total installed capacity of new energy and energy storage is taken, ensuring that the system reliability at this time is lower than that of the baseline scenario, thereby guaranteeing the target. It must fall within that range.

[0125] During the iteration process, we continuously take the midpoint of the current interval. Substitute this into the reliability calculation formula on the right to obtain the corresponding reliability index. Repeat the process of updating the interval until... If the difference between the obtained reliability index and the benchmark is less than the preset convergence accuracy, then... This is the equivalent load increment we are looking for. .

[0126] Note: This method selects As a core reliability indicator of the system, this indicator characterizes the power supply reliability of the power system from the perspective of power outage duration. Its physical meaning is the expected average power outage duration of the system, which reflects the average cumulative power outage duration during the long-term operation of the power system due to insufficient power supply capacity, resulting in the inability to meet the load. The unit is usually hours / year.

[0127] By modifying energy storage configurations and renewable energy installations, we obtained system reliability levels under different boundary conditions. Under a unified reliability index standard, we performed a one-dimensional search for the equivalent load-carrying capacity. This allows us to derive the reliable capacity and reliability of wind-storage (or solar-storage) configurations for different energy storage setups. Simultaneously, from the perspective of new energy manufacturers, we can obtain the reliable capacity gain for energy storage configurations, providing guidance for energy storage planning.

[0128] Example 12:

[0129] Verification of a reliable capacity quantification calculation method for new energy combined energy storage that integrates electricity price signals, including:

[0130] This embodiment, based on the Hrp-38 standard testing system and combined with actual domestic wind and solar power output data and electricity market price characteristics, verifies the effectiveness of the electricity price-based reliable capacity calculation method for new energy combined energy storage described in this invention. The implementation process strictly follows the technical path of "electricity price modeling - energy storage output generation - scenario simulation - reliability assessment - reliable capacity determination" to analyze the reliable capacity of wind and solar power systems.

[0131] The generated simulated electricity price sequence is input into the energy storage price taker model. With the objective of "maximizing daily net revenue" as described in the invention, charging and discharging power constraints, SOC dynamic constraints, and charging and discharging mutual exclusion constraints are introduced. The mixed integer programming problem is solved using solvers such as Gurobi or COPT to obtain the time-series output curve of the energy storage price taker model on an annual time scale. The basic power curve shows the following characteristics: the energy storage charges at maximum power during valley hours and discharges at maximum power during peak hours, which fully conforms to the profit-seeking operation characteristics and verifies the rationality of the price taker model.

[0132] (1) In the scenario without energy storage, when the installed capacity of wind power and photovoltaic power gradually increases from 2000MW to 22000MW, the absolute load carrying capacity of both shows a monotonically increasing trend: the load carrying capacity of wind power increases from 492.1875MW to 3185.546875MW, while the equivalent load carrying capacity of photovoltaic units increases from 246.094MW to 840.820MW. However, from the perspective of the effective load carrying capacity (ELCC) indicator, the marginal benefits of both types of power sources show a continuous decline: the reliability of wind power capacity decreases from 24.61% to 14.48%, and the ELCC of photovoltaic power decreases from 12.30% to 3.82%. This phenomenon reveals the inherent bottleneck of high-proportion renewable energy grid connection: the intermittency and volatility of wind power and photovoltaic output cause the effective load support capacity provided by the unit installed capacity increment to continuously weaken after the scale expands.

[0133] (2) In the scenario where the system is only equipped with energy storage, when the installed capacity of energy storage increases from 500MW to 3000MW, the absolute load carrying capacity of the energy storage system gradually increases from 198.24MW to 765.63MW, also showing a positive growth trend. However, from the perspective of the capacity reliability index of energy storage, its value drops sharply from 39.65% to 25.52%, indicating that the marginal load carrying benefit of energy storage also has a significant diminishing effect, that is, the value of energy storage decreases marginally as the installed capacity increases.

[0134] This pattern indicates that energy storage systems face a scale effect bottleneck in enhancing the load-carrying capacity of power systems. Excessively increasing the installed capacity of energy storage will lead to a decrease in the load-supporting capacity per unit of energy storage capacity. Therefore, it is necessary to rationally plan the scale of energy storage configuration in accordance with system requirements.

[0135] (3) Change the installed capacity of new energy and the configuration of energy storage, use the equivalent outage rate model to calculate the system reliability, and then calculate the reliable capacity and capacity reliability.

[0136] Under scenarios with fixed wind power installed capacities of 2000MW, 7000MW, 12000MW, 17000MW, and 22000MW, the load-bearing capacity of the combined wind power and energy storage system was analyzed as the energy storage installed capacity increased from 500MW to 3000MW. The results are as follows: Figure 3 As shown.

[0137] By comparing the capacity reliability across multiple scenarios in the figure, and examining the initial ELCC values ​​for a storage capacity of 500MW in different scenario groups, it can be observed that the overall ELCC of the wind-storage integrated system shows a continuous downward trend as the installed wind power capacity increases. This indicates that the compensating effect of energy storage on load support capacity exhibits diminishing marginal returns as the installed wind power capacity increases.

[0138] Regarding the capacity reliability variation pattern in stationary wind power installation scenarios, the system's capacity reliability exhibits a non-linear characteristic of first increasing and then decreasing, or monotonically increasing, indicating the existence of an optimal energy storage capacity that allows the ELCC to reach its peak. Calculations show that: when the wind power installed capacity is 2000MW, an energy storage capacity of 1000MW corresponds to 29.62% of the peak capacity reliability for this scenario; when the wind power capacity is 7000MW, an energy storage capacity of 2000MW corresponds to 24.61% of the peak capacity reliability for this scenario.

[0139] Similarly, in the comparative experiments with multiple fixed photovoltaic installed capacities, five capacity levels were still covered: 2000MW, 7000MW, 12000MW, 17000MW, and 22000MW. By dynamically adjusting the energy storage installed capacity, the evolution law of the load-bearing capacity of the photovoltaic-energy storage combined system was explored. The results are as follows: Figure 4 As shown.

[0140] The reliability of the capacity of the two new energy sources paired with energy storage and the changes in the installed capacity of energy storage further verify the law of diminishing marginal returns of new energy paired with energy storage: under a certain level of new energy installed capacity, excessively increasing the energy storage capacity cannot continuously improve the relative load support efficiency. The optimal energy storage capacity must be matched to maximize the load carrying efficiency of the new energy paired with energy storage system.

Claims

1. A method for quantifying the credible capacity of new energy combined with energy storage based on the fusion of electricity price signals, characterized in that, Includes the following steps: Step 1) Use time series analysis to process historical electricity market data and generate simulated electricity price series for multiple time periods throughout the year; Step 2) Establish a time-series model for energy storage output; Step 3) Input the simulated electricity price series for each day of the year into the energy storage output time series model in sequence to obtain the annual energy storage output time series curve; Step 4) Obtain the time-series data of new energy output and system load, and perform time-series matching with the annual output time-series curve of energy storage to form boundary conditions for different time-series nodes; Step 5) Simulate a random outage scenario for conventional generating units, and calculate the reliability of conventional generating units under this scenario. , For load, To provide power to thermal power units; For probability; Step 6) Determine the energy storage output to be introduced in the current period based on the annual energy storage output time-series curve. ; Step 7) Based on the boundary conditions at different time nodes, assess the system reliability after introducing new energy sources and energy storage. Compared with the reliability of conventional units With the goal of minimizing the difference, the reliable capacity Y of the new energy combined energy storage is determined; Contribute to new energy sources It contributes to energy storage.

2. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 1, characterized in that, Historical operating data of the electricity market includes historical electricity price data and load forecast results.

3. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 1, characterized in that, The objective function of the energy storage output time-series model is shown below: （1） in, For discharge power, This refers to the charging power. This is the energy storage aging cost coefficient; For a period of time; For electricity prices.

4. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 1, characterized in that, The constraints of the energy storage output time series model include the energy storage charge and discharge state constraints, charge and discharge power constraints, and state of charge constraints.

5. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 4, characterized in that, The charge / discharge state constraints for energy storage are as follows: （2） in, and These represent the charging and discharging states of energy storage, with 1 indicating charging or discharging and 0 indicating no operation. The charging and discharging power constraints are as follows: （3） （4） in, and These are the maximum charging and discharging power of the energy storage; , The charging and discharging power of the energy storage at time t; The state of charge constraints are as follows: （5） （6） （7） in, , For the state of charge of energy storage at time t and time t-1, and These represent the charging and discharging efficiency of energy storage. and These represent the minimum and maximum states of charge of the energy storage, respectively. For a period of time; and This refers to the energy storage state of charge at the end of the scheduling period and the beginning of the scheduling period.

6. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 1, characterized in that, The energy storage output time series model was solved using the Gurobi solver.

7. The reliable capacity quantification calculation method for new energy joint energy storage based on integrated electricity price signals according to claim 1, characterized in that, In step 5), the random shutdown scenario of conventional units is simulated using the Monte Carlo method; The available capacity of conventional generating units under different random outage scenarios is as follows: （8） In the formula, This represents the available capacity of conventional generating units; This is the rated capacity of a conventional unit; This represents the forced outage rate for conventional generating units.

8. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 1, characterized in that, In step 6), the reliable capacity Y of the new energy combined energy storage is determined by the dichotomy method.

9. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 1, characterized in that, Step 6) involves determining the reliable capacity Y of the new energy combined energy storage system, including: Step 6.1) Define the range of load increment Y for equivalent substitution of new energy and energy storage, denoted as [ =0, ]; This represents the total installed capacity of new energy sources and energy storage. The lower limit; Step 6.2) Let Y be equal to the midpoint of the current range interval, denoted as And calculate the current system reliability; Step 6.3) Determine the system reliability after introducing new energy sources and energy storage. Compared with the reliability of conventional units If the difference is less than the preset value, output the reliable capacity of the new energy combined energy storage and end the iteration; otherwise, proceed to step 6.

4. Step 6.4) Determine the system reliability after introducing new energy sources and energy storage. Is the reliability greater than that of conventional units? If so, then update the range interval to [ = , If the condition is not met, return to step 6.2); otherwise, update the range interval to [ =0, = ], and return to step 6.2).

10. The reliable capacity quantification calculation method for new energy combined energy storage based on integrated electricity price signals according to claim 1, characterized in that, After determining the reliable capacity Y of the new energy combined with energy storage, the reliability of the capacity after introducing new energy and energy storage is also calculated, that is: （9） In the formula, For capacity reliability.

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