Method for quantitatively evaluating power market risk of fusion communication base transceiver station and energy storage backup

Abstract

The application relates to a power market risk quantitative evaluation method of fusion communication base station transceiving state and energy storage backup, relates to the technical field of virtual power plants and power markets, and comprises the following steps: large-scale base stations are aggregated into virtual power plants, a base station scheduling model is established by taking controllable load of transceiving equipment and charging and discharging power of backup energy storage as decision variables; a power spot market clearing model containing the base station virtual power plant is established to evaluate regional supply and demand balance and price fluctuation; base station and unit parameters are input, and power scheduling time sequence and clearing price time sequence are output; a price fluctuation risk evaluation index is established, price fluctuation and risk indexes in multiple scenes are output, and an equivalent energy storage capacity method is used to evaluate the aggregation effect of the base station virtual power plant and the price risk flattening effect. The application can accurately and intuitively quantitatively evaluate the price risk flattening effect of the base station cluster after aggregation and the equivalent replacement ability of the base station cluster to traditional energy storage units.

Description

Technical Field

[0001] This application relates to the fields of virtual power plant and electricity market technology, and in particular to a method for quantitatively assessing electricity market risks related to the transmission and reception status of converged communication base stations and energy storage reserves. Background Technology

[0002] New energy sources are gradually becoming the mainstay of the power system, but the volatility and randomness of their output significantly increase the uncertainty of system operation. While virtual power plants (VPPs) can aggregate distributed resources to alleviate grid pressure, traditional energy storage has a long construction cycle and high investment costs, making rapid deployment difficult. Communication base stations are widely distributed, and their transceiver loads are adjustable; backup energy storage has good charging and discharging performance and has the potential to be aggregated and utilized.

[0003] In related technologies, some studies have focused on the feasibility of base station energy storage participating in grid interaction. However, in actual operation, during periods of low demand for communication services, the idle power of base station transceiver equipment accounts for a relatively high proportion, and the backup energy storage is in a floating charging state for a long time, resulting in low overall utilization. Furthermore, there is a lack of virtual power plant optimization methods that simultaneously consider the coordinated scheduling of transceiver equipment and backup energy storage, making it difficult to fully tap the regulation capabilities of the base station side.

[0004] Therefore, there is an urgent need to propose an optimized scheduling method for virtual power plants of communication base stations. Based on the aggregation of base station transceiver equipment and backup energy storage resources, a reasonable scheduling strategy should be formulated, and the price fluctuation risk in the electricity market should be assessed, thereby improving the utilization rate of base station resources, reducing the investment cost of system energy storage, and supporting the safe and economical operation of the power system. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method for quantitatively assessing the power market risk by integrating the transmission and reception status of communication base stations and energy storage backup. It solves the technical problem of the lack of a virtual power plant collaborative scheduling method that simultaneously aggregates base station transmission and reception equipment and backup energy storage, and considers market price risk.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for quantitatively assessing the power market risks associated with the transceiver status of converged communication base stations and energy storage backup includes the following steps: S1. Incorporate large-scale communication base stations into the virtual power plant, establish a base station optimization scheduling cost model that considers communication service constraints, and use the controllable load of transceiver equipment and the charging and discharging power of backup energy storage as decision variables. S2. Establish a power spot market clearing model that includes virtual power plants with base stations to assess the supply and demand balance of the regional power system and electricity price fluctuations; S3. Input the base station transceiver equipment load parameters, energy storage parameters and power system unit cost parameters into the clearing model, and output the power scheduling time series data and power clearing price time series data of each unit. S4. Establish electricity price fluctuation risk assessment indicators, output electricity price fluctuation indicators and risk indicators under multiple scenarios based on clearing price time series data, and use the equivalent energy storage capacity method to evaluate the aggregation effect and electricity price risk mitigation effect of base station virtual power plants.

[0007] Furthermore, in S1, large-scale communication base stations are aggregated into clusters and incorporated into a virtual power plant. Based on the different functional areas they serve, the base stations are classified into different types and subjected to different control methods. For base station clusters in commercial areas, the control method involves reducing the load on transceiver equipment and adjusting the charging and discharging power of backup energy storage. For base station clusters in industrial areas, the control method involves reducing the load on transceiver equipment, shifting the load of transceiver equipment according to the industrial area's production plan, and adjusting the charging and discharging power of backup energy storage.

[0008] Furthermore, in S2, the electricity spot market clearing model considers resources including photovoltaic, wind power, thermal power units, and virtual power plants. For photovoltaic and wind power units, the scenario generation method is used to describe the uncertainty of their output. In addition, to ensure that new energy sources such as photovoltaic and wind power are fully utilized as much as possible, a high penalty for unused new energy sources is set to ensure that new energy output is prioritized.

[0009] Furthermore, different types of base station clusters use different load balancing methods. The load of transceiver equipment in commercial area base station clusters is divided as follows:

[0010] in, express t Time period x Total load of base station clusters in commercial areas; Indicates commercial area t Time period x Static load of a base station cluster; Indicates commercial area t Time period x Dynamically controllable load of each base station cluster; The cost level represents the controllable load.

[0011] The load of transceiver equipment in the industrial area base station cluster is divided as follows:

[0012] in, express t Time period y Total load of base station clusters in industrial areas; Indicates industrial areat Time period y Static load of a base station cluster; Indicates industrial area t Time period y Dynamically controllable load of each base station cluster; Representative industrial area t Time period y The transferable load of a base station cluster; The cost level represents the controllable load.

[0013] The operating mode of backup energy storage devices is described as follows:

[0014] in, represent t Time period z One standby energy storage state of charge; and Representing the first z The charging and discharging efficiency of a backup energy storage system; and Represent t Time period z One backup energy storage charging power and discharging power; Representing the z Total capacity of backup energy storage; Representative time period t .

[0015] Furthermore, the electricity spot market clearing model takes maximizing social welfare, i.e. minimizing the total system operating cost, as its optimization objective. Its actual objective function can be expressed as:

[0016] in, Represents the total operating cost of the system; and These represent the start and end times of the system regulation, respectively. represent t Time period g Operating costs of a single thermal power unit; , , and All represent the operating costs of resources within the base station's virtual power plant, respectively representing t Time period z Operating cost of backup energy storage t Time period y Cost of controllable load scheduling for base stations in an industrial area t Time period yThe cost of load transfer scheduling for base stations in an industrial area and t Time period x Controllable load scheduling cost of base stations in a commercial area; and Represent t Time period i The photovoltaic unit's output did not absorb the penalty costs and t Time period j The penalty cost for not absorbing the output of a single wind turbine unit; , , , , , These represent the number of thermal power units, backup energy storage for base station clusters, base station clusters in industrial areas, base station clusters in commercial areas, photovoltaic units, and wind turbine units, respectively.

[0017] Furthermore, the controllable load of the transceiver equipment regulated by the base station cluster satisfies the following constraints: Load can be reduced:

[0018]

[0019]

[0020]

[0021] Transferable load:

[0022]

[0023]

[0024]

[0025] in, and These represent the proportions of load that can be reduced and load that can be transferred in the industrial area, respectively. Representing the business area x The maximum adjustment rate of the load of each base station; Representing the industrial region y The maximum adjustment rate of the load of each base station; and These represent the loads of the base stations being transferred out and those being transferred in, respectively. and These represent the load transfer process. Time period and before load transfer Time-of-use electricity pricing for different time periods.

[0026] The backup energy storage controlled by the base station cluster satisfies the following constraints:

[0027]

[0028]

[0029]

[0030]

[0031] in, , , and These represent the maximum discharge power and maximum charging power of the backup energy storage, as well as the minimum and maximum SOC states of the backup energy storage, respectively. and Representing the first z The standby energy storage is in its SOC state at the start and end of regulation; This represents the backup time during a power outage; represent t Time period z The load of the base station where the backup energy storage is located.

[0032] Furthermore, regarding the various resources invoked by the market clearing model, taking the two-stage incentive compensation as an example, the reduction in load operating costs for transceiver equipment within a commercial area base station cluster can be described as follows:

[0033] in, and These represent the first and second reduction ratios within the commercial area base station cluster, respectively. and Represent t Time period x The cost reduction for the first and second reduction segments within a commercial area base station cluster.

[0034] Taking a two-stage incentive compensation as an example, the reduction in load operating costs for transceiver equipment within an industrial area base station cluster can be described as follows:

[0035] in, and These represent the first and second reduction ratios within the industrial area base station cluster, respectively. and Representt Time period y Cost reduction for the first and second reduction segments within an industrial area base station cluster.

[0036] Operating costs of load transferable transceiver equipment within industrial base station clusters:

[0037]

[0038] Operating costs of backup energy storage within a base station cluster:

[0039] in, and Represent t Time period z The costs of backup energy storage transactions and battery degradation within a single base station cluster.

[0040] Power generation cost of thermal power units within the power system:

[0041]

[0042] in, , and Represent t Time period g The quadratic coefficient, linear coefficient, and constant term of the cost function for a thermal power unit; represent t Time period g The actual output of each thermal power unit.

[0043] Penalty costs for non-integration of new energy units within the power system:

[0044]

[0045]

[0046] in, represent t Time period i The photovoltaic units failed to absorb the penalty costs. and Represent t Time period i The maximum output and actual dispatch output of each photovoltaic unit; represent t Time period j The penalty cost for not absorbing the wind turbine units was not absorbed. and Represent tTime period j The maximum output and actual dispatch output of each wind turbine unit.

[0047] Furthermore, in S3, the electricity price output by the market clearing model is the marginal electricity price of each node in the power system. To assess the fluctuation of electricity prices in the power system, a weighted average electricity price is calculated with node load as the weight. The calculation method is described as follows:

[0048] in, represent t Average nodal clearing price in the electricity market during the specified period; represent t Time period nodes n The clearing electricity price; represent t Time period nodes n The load; represent t Total load of all nodes in the system during the time period; This represents the total number of nodes in the system.

[0049] Furthermore, in S4, to address the uncertainty of renewable energy output, a scenario-based approach is used to simulate uncertainty. Based on the system's average nodal electricity price under each scenario, the following electricity price volatility indicators are established to quantitatively assess the system's electricity price volatility risk. These indicators include the electricity price mean, variance, standard deviation, coefficient of variation, and electricity price volatility rate, specifically described as follows:

[0050] in, Representative scenarios s The average electricity price.

[0051]

[0052] in, Representative scenarios s The variance of electricity prices.

[0053]

[0054] in, Representative scenarios s The standard deviation of electricity prices.

[0055]

[0056] in, Representative scenarios s The coefficient of variation of electricity prices.

[0057]

[0058] in, Representative scenarios s Electricity price volatility.

[0059] To evaluate the aggregation effect and electricity price risk mitigation effect of virtual power plants, a virtual power plant performance evaluation method based on equivalent energy storage capacity is established. Mahalanobis distance is used to measure the similarity between virtual power plants and energy storage units across a large number of scenarios. Various electricity price fluctuation indicators across these scenarios are used as eigenvalues ​​for the evaluation method. The Mahalanobis distance uses a covariance matrix to represent the probability distribution characteristics of these eigenvalues ​​across a large number of scenarios, eliminating dimensional differences between the eigenvalues. Specifically:

[0060] in, The Mahalanobis distance represents the feature group of the virtual power plant scenario and the feature group of the energy storage unit scenario. Characteristic values ​​representing various scenarios of energy storage units; The mean value representing the characteristics of a virtual power plant scenario; The inverse matrix represents the covariance matrix.

[0061] To assess the risks posed by extreme scenarios, Conditional Value at Risk (CvaR) is used to quantify the tail risk arising from spot market price uncertainty. Discrete probability distributions across multiple scenarios are incorporated into the calculation, specifically described as follows:

[0062]

[0063]

[0064]

[0065]

[0066] Where x represents the decision variable; y represents the random variable; For loss function Not less than The probability distribution function; Let be the probability density function of the random variable y; Represents the conditional value at risk (CVaR); This represents the set confidence level; Represents confidence level Value at Risk (VaR) is the maximum expected loss. Representing a scene s The probability of occurrence; Indicates the system in the scenario s Total operating costs below; Representative in the scene sCosts exceeding risk value The value of .

[0067] A power market price fluctuation risk assessment system applicable to the method described in any of the preceding claims, considering communication base station transceiver equipment and backup energy storage, characterized in that it comprises: The data acquisition module is used to acquire base station parameters and power system parameters; The model building module is used to establish base station scheduling models, market clearing models, and risk assessment models. The evaluation module is used to solve for base station scheduling results, clearing electricity prices, and electricity price fluctuation risks.

[0068] An electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the method as described in any of the preceding claims.

[0069] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any of the preceding claims.

[0070] In summary, this application includes at least the following methods for quantitatively assessing electricity market risks by integrating the transmission and reception status of communication base stations with energy storage reserves, and the beneficial technical effects of these methods: This invention fully explores the flexibility and adjustment potential of communication base stations. By establishing an optimized scheduling model that aggregates large-scale base stations into virtual power plants and a power spot market clearing model, it can effectively mitigate the risk of electricity price fluctuations in the power spot market and enhance the capacity for renewable energy consumption.

[0071] Furthermore, the evaluation method proposed in this invention eliminates the dimensional differences between multi-dimensional scenario feature values, enabling precise and intuitive quantification of the electricity price risk mitigation effect after base station cluster aggregation and its equivalent substitution capability for traditional energy storage units. Therefore, this invention provides a systematic electricity price risk assessment method for power market risk management and control, while also providing reliable technical support for the performance verification of virtual power plants. Attached Figure Description

[0072] Figure 1 This is a flowchart of the method mainly provided by the present invention. Detailed Implementation

[0073] To facilitate a clear understanding of the technical means, creative features, objectives, and effects of this invention, the invention will be further described below in conjunction with specific embodiments.

[0074] The following is in conjunction with the appendix Figure 1 This application will be described in further detail.

[0075] This application discloses a method for quantitatively assessing the power market risk of integrated communication base station transceiver status and energy storage backup.

[0076] Large-scale communication base stations are aggregated into clusters and incorporated into virtual power plants. Based on the different functional areas they serve, the base stations are classified into different types and different control methods are adopted. For base station clusters in commercial areas, the control method involves reducing the load on transceiver equipment and adjusting the charging and discharging power of backup energy storage. For base station clusters in industrial areas, the control method involves reducing the load on transceiver equipment, shifting the load of transceiver equipment according to the industrial area's production plan, and adjusting the charging and discharging power of backup energy storage.

[0077] Different types of base station clusters use different load balancing methods. The load of transceiver equipment in a commercial area base station cluster is divided as follows:

[0078] in, express t Time period x Total load of base station clusters in commercial areas; Indicates commercial area t Time period x Static load of a base station cluster; Indicates commercial area t Time period x Dynamically controllable load of each base station cluster; The cost level represents the controllable load.

[0079] The load of transceiver equipment in the industrial area base station cluster is divided as follows:

[0080] in, express t Time period y Total load of base station clusters in industrial areas; Indicates industrial area t Time period y Static load of a base station cluster; Indicates industrial area t Time period y Dynamically controllable load of each base station cluster; Representative industrial area t Time period y The transferable load of a base station cluster; The cost level represents the controllable load.

[0081] The operating mode of backup energy storage devices is described as follows:

[0082] in, represent t Time period z One standby energy storage state of charge; and Representing the first z The charging and discharging efficiency of a backup energy storage system; and Represent t Time period z One backup energy storage charging power and discharging power; Representing the z Total capacity of backup energy storage; Representative time period t .

[0083] The controllable load of the transceiver equipment regulated by the base station cluster satisfies the following constraints: Load can be reduced:

[0084]

[0085]

[0086]

[0087] Transferable load:

[0088]

[0089]

[0090]

[0091] in, and These represent the proportions of load that can be reduced and load that can be transferred in the industrial area, respectively. Representing the business area x The maximum adjustment rate of the load of each base station; Representing the industrial region y The maximum adjustment rate of the load of each base station; and These represent the loads of the base stations being transferred out and those being transferred in, respectively. and These represent the load transfer process. Time period and before load transfer Time-of-use electricity pricing for different time periods.

[0092] The backup energy storage controlled by the base station cluster satisfies the following constraints:

[0093]

[0094]

[0095]

[0096]

[0097] in, , , and These represent the maximum discharge power and maximum charging power of the backup energy storage, as well as the minimum and maximum SOC states of the backup energy storage, respectively. and Representing the first z The standby energy storage is in its SOC state at the start and end of regulation; This represents the backup time during a power outage; represent t Time period z The load of the base station where the backup energy storage is located.

[0098] A power spot market clearing model is established, taking into account virtual power plants created by large-scale communication base stations, to assess the supply-demand balance and market price volatility within a given power system. The model considers resources including photovoltaic (PV), wind power, thermal power units, and virtual power plants. For PV and wind power units, a scenario generation method is used to describe their output uncertainty. Furthermore, to ensure the full utilization of renewable energy sources such as PV and wind power, a high penalty for unutilized renewable energy is set to ensure priority utilization of renewable energy output.

[0099] The electricity spot market clearing model aims to maximize social welfare, i.e., minimize the total operating cost of the system. Its actual objective function can be expressed as:

[0100] in, Represents the total operating cost of the system; and These represent the start and end times of the system regulation, respectively. represent t Time period g Operating costs of a single thermal power unit; , , and All represent the operating costs of resources within the base station's virtual power plant, respectively representing t Time period z Operating cost of backup energy storaget Time period y Cost of controllable load scheduling for base stations in an industrial area t Time period y The cost of load transfer scheduling for base stations in an industrial area and t Time period x Controllable load scheduling cost of base stations in a commercial area; and Represent t Time period i The photovoltaic unit's output did not absorb the penalty costs and t Time period j The penalty cost for not absorbing the output of a single wind turbine unit; , , , , , These represent the number of thermal power units, backup energy storage for base station clusters, base station clusters in industrial areas, base station clusters in commercial areas, photovoltaic units, and wind turbine units, respectively.

[0101] Regarding the various resources utilized by the market clearing model, taking the two-stage incentive compensation as an example, the reduction in load operating costs for transceiver equipment within a commercial area base station cluster can be described as follows:

[0102] in, and These represent the first and second reduction ratios within the commercial area base station cluster, respectively. and Represent t Time period x The cost reduction for the first and second reduction segments within a commercial area base station cluster.

[0103] Taking a two-stage incentive compensation as an example, the reduction in load operating costs for transceiver equipment within an industrial area base station cluster can be described as follows:

[0104] in, and These represent the first and second reduction ratios within the industrial area base station cluster, respectively. and Represent t Time period y Cost reduction for the first and second reduction segments within an industrial area base station cluster.

[0105] Operating costs of load transferable transceiver equipment within industrial base station clusters: ;

[0106]

[0107] Operating costs of backup energy storage within a base station cluster:

[0108] in, and Represent t Time period z The costs of backup energy storage transactions and battery degradation within a single base station cluster.

[0109] Power generation cost of thermal power units within the power system:

[0110]

[0111] in, , and Represent t Time period g The quadratic coefficient, linear coefficient, and constant term of the cost function for a thermal power unit; represent t Time period g The actual output of each thermal power unit.

[0112] Penalty costs for non-integration of new energy units within the power system:

[0113]

[0114]

[0115] in, represent t Time period i The photovoltaic units failed to absorb the penalty costs. and Represent t Time period i The maximum output and actual dispatch output of each photovoltaic unit; represent t Time period j The penalty cost for not absorbing the wind turbine units was not absorbed. and Represent t Time period j The maximum output and actual dispatch output of each wind turbine unit.

[0116] The power load of the transceivers of the communication base station within the virtual power plant, the parameters of the reserve energy storage, and the generation cost parameters of each unit in the power system are input into the model. The market clearing model outputs time-series data of power dispatch for each unit, including the communication base station, and time-series data of electricity clearing prices. The electricity price output by the market clearing model represents the marginal electricity price of each node in the power system. To assess the fluctuation of electricity prices in the power system, a weighted average electricity price is calculated, weighted by node load. The calculation method is described as follows:

[0117] in, represent t Average nodal clearing price in the electricity market during the specified period; represent t Time period nodes n The clearing electricity price; represent t Time period nodes n The load; represent t Total load of all nodes in the system during the time period; This represents the total number of nodes in the system.

[0118] A risk assessment index for electricity market price fluctuations is established. Inputting time-series data of electricity clearing prices, the index outputs price fluctuation and risk indicators under multiple scenarios. The aggregation effect and price risk mitigation effect of virtual power plants based on equivalent energy storage capacity are evaluated. To address the uncertainty of renewable energy output, a scenario-based approach is used to simulate uncertainty. Based on the system's average nodal electricity price under various scenarios, the following price fluctuation indexes are established to quantitatively assess the system's price fluctuation risk. The price fluctuation indexes include the electricity price mean, variance, standard deviation, coefficient of variation, and price volatility rate, specifically described as follows:

[0119] in, Representative scenarios s The average electricity price.

[0120]

[0121] in, Representative scenarios s The variance of electricity prices.

[0122]

[0123] in, Representative scenarios s The standard deviation of electricity prices.

[0124]

[0125] in, Representative scenarios s The coefficient of variation of electricity prices.

[0126]

[0127] in, Representative scenarios s Electricity price volatility.

[0128] To evaluate the aggregation effect and electricity price risk mitigation effect of virtual power plants, a virtual power plant performance evaluation method based on equivalent energy storage capacity is established. Mahalanobis distance is used to measure the similarity between virtual power plants and energy storage units across a large number of scenarios. Various electricity price fluctuation indicators across these scenarios are used as eigenvalues ​​for the evaluation method. The Mahalanobis distance uses a covariance matrix to represent the probability distribution characteristics of these eigenvalues ​​across a large number of scenarios, eliminating dimensional differences between the eigenvalues. Specifically:

[0129] in, The Mahalanobis distance represents the feature group of the virtual power plant scenario and the feature group of the energy storage unit scenario. Characteristic values ​​representing various scenarios of energy storage units; The mean value representing the characteristics of a virtual power plant scenario; The inverse matrix represents the covariance matrix.

[0130] To assess the risks posed by extreme scenarios, Conditional Value at Risk (CvaR) is used to quantify the tail risk arising from spot market price uncertainty. Discrete probability distributions across multiple scenarios are incorporated into the calculation, specifically described as follows:

[0131]

[0132]

[0133]

[0134]

[0135] Where x represents the decision variable; y represents the random variable; For loss function Not less than The probability distribution function; Let be the probability density function of the random variable y; Represents the conditional value at risk (CVaR); This represents the set confidence level; Represents confidence level Value at Risk (VaR) is the maximum expected loss. Representing a scene s The probability of occurrence; Indicates the system in the scenario s Total operating costs below; Representative in the scene s Costs exceeding risk value The value of .

[0136] A power market price volatility risk assessment system applicable to the above method, considering communication base station transceiver equipment and backup energy storage, includes: The data acquisition module is used to acquire transceiver parameters and energy storage parameters of the communication base station, scheduling cost parameters of the virtual power plant of the communication base station, and constraint parameters and scheduling cost parameters of various resources in the power system.

[0137] The model building module is used to establish base station load and base station energy storage scheduling models that take into account communication guarantee constraints, as well as to establish electricity market clearing models and electricity price fluctuation risk assessment models.

[0138] The base station scheduling determination and electricity price fluctuation risk assessment module is used to input the data acquired by the data acquisition module into the base station load and base station energy storage scheduling model, solve for the transceiver load reduction of the base station and the charging and discharging power of the backup energy storage, input the data acquired by the data acquisition module into the electricity market clearing model and the electricity price fluctuation risk assessment model, and finally solve for the clearing price and electricity price fluctuation risk of the electricity market, so as to realize the communication base station scheduling and electricity market price risk assessment considering communication load constraints.

[0139] An electronic device includes a memory and a processor coupled to each other, wherein the memory stores program data and the processor invokes the program data to execute the method described above.

[0140] A computer-readable storage medium having program data stored thereon, wherein the program data, when executed by a processor, implements the method described above. The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for quantitatively assessing electricity market risks by integrating the transceiver status of integrated communication base stations with energy storage reserves, characterized in that... Includes the following steps: S1. Incorporate large-scale communication base stations into the virtual power plant, establish a base station optimization scheduling cost model that considers communication service constraints, and use the controllable load of transceiver equipment and the charging and discharging power of backup energy storage as decision variables. S2. Establish a power spot market clearing model that includes virtual power plants with base stations to assess the supply and demand balance of the regional power system and electricity price fluctuations; S3. Input the base station transceiver equipment load parameters, energy storage parameters and power system unit cost parameters into the clearing model, and output the power scheduling time series data and power clearing price time series data of each unit. S4. Establish electricity price fluctuation risk assessment indicators, output electricity price fluctuation indicators and risk indicators under multiple scenarios based on clearing price time series data, and use the equivalent energy storage capacity method to evaluate the aggregation effect and electricity price risk mitigation effect of base station virtual power plants.

2. The method for quantitatively assessing power market risks based on the transceiver status of converged communication base stations and energy storage reserves according to claim 1, characterized in that, In S1, large-scale communication base stations are aggregated into clusters and incorporated into a virtual power plant. Based on the different functional areas they serve, the base stations are classified into different types and subjected to different control methods. For base station clusters in commercial areas, the control methods include reducing the load on transceiver equipment and adjusting the charging and discharging power of backup energy storage. For base station clusters in industrial areas, the control methods are to reduce the load on transceiver equipment, transfer the load on transceiver equipment according to the industrial area's production plan, and adjust the charging and discharging power of backup energy storage.

3. The method for quantitatively assessing power market risks based on the transceiver status of converged communication base stations and energy storage reserves according to claim 1, characterized in that, In S2, the electricity spot market clearing model considers resources including photovoltaic, wind power, thermal power units, and virtual power plants. For photovoltaic and wind power units, the scenario generation method is used to describe the uncertainty of their output. In addition, to ensure that new energy sources such as photovoltaic and wind power are fully utilized as much as possible, a high penalty for unused new energy sources is set to ensure that new energy output is prioritized.

4. The method for quantitatively assessing electricity market risks based on the transceiver status of converged communication base stations and energy storage reserves according to claim 2, characterized in that, Different types of base station clusters use different load balancing methods. The load of transceiver equipment in a commercial area base station cluster is divided as follows: ； in, express t Time period x Total load of base station clusters in commercial areas; Indicates commercial area t Time period x Static load of a base station cluster; Indicates commercial area t Time period x Dynamically controllable load of each base station cluster; The cost hierarchy represents the controllable load; The load of transceiver equipment in the industrial area base station cluster is divided as follows: ； in, express t Time period y Total load of base station clusters in industrial areas; Indicates industrial area t Time period y Static load of a base station cluster; Indicates industrial area t Time period y Dynamically controllable load of each base station cluster; Representative industrial area t Time period y The transferable load of a base station cluster; The cost hierarchy represents the controllable load; The operating mode of backup energy storage devices is described as follows: ； in, represent t Time period z One standby energy storage state of charge; and Representing the first z The charging and discharging efficiency of a backup energy storage system; and Represent t Time period z One backup energy storage charging power and discharging power; Representing the z Total capacity of backup energy storage; Representative time period t .

5. The method for quantitatively assessing the power market risk of converged communication base station transceiver status and energy storage backup as described in claim 1, characterized in that, The electricity spot market clearing model aims to maximize social welfare, i.e., minimize the total operating cost of the system. Its actual objective function can be expressed as: ； in, Represents the total operating cost of the system; and These represent the start and end times of the system regulation, respectively. represent t Time period g Operating costs of a single thermal power unit; , , and All represent the operating costs of resources within the base station's virtual power plant, respectively representing t Time period z Operating cost of backup energy storage t Time period y Cost of controllable load scheduling for base stations in an industrial area t Time period y The cost of load transfer scheduling for base stations in an industrial area and t Time period x Controllable load scheduling cost of base stations in a commercial area; and Represent t Time period i The photovoltaic unit's output did not absorb the penalty costs and t Time period j The penalty cost for not absorbing the output of a single wind turbine unit; , , , , , These represent the number of thermal power units, backup energy storage for base station clusters, base station clusters in industrial areas, base station clusters in commercial areas, photovoltaic units, and wind turbine units, respectively.

6. The method for quantitatively assessing power market risks based on the transceiver status of converged communication base stations and energy storage reserves according to claim 1, characterized in that, The controllable load of the transceiver equipment regulated by the base station cluster satisfies the following constraints: Load can be reduced: ； ； ； ； Transferable load: ； ； ； ； in, and These represent the proportions of load that can be reduced and load that can be transferred in the industrial area, respectively. Representing the business area x The maximum adjustment rate of the load of each base station; Representing the industrial region y The maximum adjustment rate of the load of each base station; and These represent the loads of the base stations being transferred out and those being transferred in, respectively. and These represent the load transfer process. Time period and before load transfer Time-of-use electricity pricing for different time periods; The backup energy storage controlled by the base station cluster satisfies the following constraints: ； ； ； ； ； in, , , and These represent the maximum discharge power and maximum charging power of the backup energy storage, as well as the minimum and maximum SOC states of the backup energy storage, respectively. and Representing the first z The standby energy storage is in its SOC state at the start and end of regulation; This represents the backup time during a power outage; represent t Time period z The load of the base station where the backup energy storage is located.

7. The method for quantitatively assessing power market risks based on the transceiver status of converged communication base stations and energy storage reserves according to claim 1, characterized in that... ； Regarding the various resources utilized by the market clearing model, taking the two-stage incentive compensation as an example, the reduction in load operating costs for transceiver equipment within a commercial area base station cluster can be described as follows: ； in, and These represent the first and second reduction ratios within the commercial area base station cluster, respectively. and Represent t Time period x Cost reduction of the first and second reduction ratios within a commercial area base station cluster. Taking a two-stage incentive compensation as an example, the reduction in load operating costs for transceiver equipment within an industrial area base station cluster can be described as follows: ； in, and These represent the first and second reduction ratios within the industrial area base station cluster, respectively. and Represent t Time period y The cost reduction of the first and second reduction ratios within an industrial area base station cluster. Operating costs of load transferable transceiver equipment within industrial base station clusters: ； ； Operating costs of backup energy storage within a base station cluster: ； in, and Represent t Time period z The cost of backup energy storage transactions and battery degradation within a single base station cluster; Power generation cost of thermal power units within the power system: ； in, , and Represent t Time period g The quadratic coefficient, linear coefficient, and constant term of the cost function for a thermal power unit; represent t Time period g The actual output of each thermal power unit; Penalty costs for non-integration of new energy units within the power system: ； ； in, represent t Time period i The photovoltaic units failed to absorb the penalty costs. and Represent t Time period i The maximum output and actual dispatch output of each photovoltaic unit; represent t Time period j The penalty cost for not absorbing the wind turbine units was not absorbed. and Represent t Time period j The maximum output and actual dispatch output of each wind turbine unit.

8. The method for quantitatively assessing power market risks based on the transceiver status of converged communication base stations and energy storage reserves according to claim 1, characterized in that, In S3, the electricity price output by the market clearing model is the marginal electricity price of each node in the power system. To assess the fluctuation of electricity prices in the power system, a weighted average electricity price is calculated with node load as the weight. The calculation method is described as follows: ； in, represent t Average nodal clearing price in the electricity market during the specified period; represent t Time period nodes n The clearing electricity price; represent t Time period nodes n The load; represent t Total load of all nodes in the system during the time period; This represents the total number of nodes in the system.

9. The method for quantitatively assessing power market risks based on the transceiver status of converged communication base stations and energy storage reserves according to claim 1, characterized in that, In S4, to address the uncertainty of renewable energy output, a scenario-based approach is used to simulate uncertainty. Based on the system's average nodal electricity price under each scenario, the following electricity price volatility indicators are established to quantitatively assess the system's electricity price volatility risk. These indicators include the electricity price mean, variance, standard deviation, coefficient of variation, and electricity price volatility rate, specifically described as follows: ； in, Representative scenarios s The average electricity price; ； in, Representative scenarios s The variance of electricity prices; ； in, Representative scenarios s The standard deviation of electricity prices; ； in, Representative scenarios s The coefficient of variation of electricity prices; ； in, Representative scenarios s Electricity price volatility; To evaluate the aggregation effect and electricity price risk mitigation effect of virtual power plants, a virtual power plant performance evaluation method based on equivalent energy storage capacity is established. Mahalanobis distance is used to measure the similarity between virtual power plants and energy storage units across a large number of scenarios. Various electricity price fluctuation indicators across these scenarios are used as eigenvalues ​​for the evaluation method. The Mahalanobis distance uses a covariance matrix to represent the probability distribution characteristics of these eigenvalues ​​across a large number of scenarios, eliminating dimensional differences between the eigenvalues. Specifically: ； in, The Mahalanobis distance represents the feature group of the virtual power plant scenario and the feature group of the energy storage unit scenario. Characteristic values ​​representing various scenarios of energy storage units; The mean value representing the characteristics of a virtual power plant scenario; The inverse matrix representing the covariance matrix; To assess the risks posed by extreme scenarios, Conditional Value at Risk (CvaR) is used to quantify the tail risk arising from spot market price uncertainty. Discrete probability distributions across multiple scenarios are incorporated into the calculation, specifically described as follows: ； ； Where x represents the decision variable; y represents the random variable; For loss function Not less than The probability distribution function; Let be the probability density function of the random variable y; Represents the conditional value at risk (CVaR); This represents the set confidence level; Represents confidence level Value at Risk (VaR) is the maximum expected loss. Representing a scene s The probability of occurrence; Indicates the system in the scenario s Total operating costs below; Representative in the scene s Costs exceeding risk value The value of .

10. A power market price fluctuation risk assessment system applicable to the method described in any one of claims 1-9, considering communication base station transceiver equipment and backup energy storage, characterized in that, include: The data acquisition module is used to acquire base station parameters and power system parameters; The model building module is used to establish base station scheduling models, market clearing models, and risk assessment models. The evaluation module is used to solve for base station scheduling results, clearing electricity prices, and electricity price fluctuation risks.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the method as described in any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method as described in any one of claims 1-9.

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