Multi-time scale risk quantification high-proportion new energy power system planning method

Abstract

The invention discloses a high-proportion new energy power system planning method for multi-time scale risk quantification, and the method comprises the steps: constructing a 1 * 8760 + N * 12 mixed granularity scene library based on historical weather and load data; under the hour-level time scale, a short-time-scale load shedding risk assessment model is established, and short-time power imbalance risk constraints are formed; under the year-month time scale, applying an inter-year fluctuation coefficient to the new energy monthly generating capacity and the load monthly electric quantity, generating N scene years, establishing a long-time scale monthly electric quantity imbalance risk assessment model, and forming a long-term electric quantity imbalance risk constraint; the short-time CVaR risk constraint and the long-term CVaR risk constraint are embedded into the same mixed integer linear programming framework, a double-layer optimization framework is reconstructed according to nested analysis to solve the model, an investment variable, an operation variable and a risk auxiliary variable are optimized in a unified mode, and power-electric quantity multi-time scale risk cooperative control is achieved; and outputting a multi-time scale joint planning result.

Description

Technical Field

[0001] This invention relates to the field of power system analysis technology, and in particular to a planning method for a high proportion of new energy power systems with multi-timescale risk quantification. Background Technology

[0002] With the advancement of the "dual-carbon" strategy, the proportion of renewable energy sources such as wind and solar power in the power system has rapidly increased. As of April 2025, China's cumulative installed capacity of wind and solar power had exceeded 1.53 billion kilowatts. This high proportion of renewable energy brings significant spatiotemporal uncertainties: on the one hand, hourly fluctuations in wind and solar power output and drastic load changes can easily lead to power imbalances and trigger load shedding events; on the other hand, annual and monthly scales are affected by seasonal climate changes and interannual variations, resulting in misalignments between "high wind and solar power years" and "low wind and solar power years," causing seasonal power shortages. Traditional planning methods typically use a typical day or an annual 8760-hour curve as the boundary, focusing only on short-term power balance. This makes it difficult to quantify and control the risk of long-term power shortages in advance, leading to mismatches in decisions regarding energy storage, thermal power capacity, and transmission scale. The system still faces the dual pressures of ensuring supply and absorbing power over the long term.

[0003] To characterize uncertainties across multiple time scales, existing research primarily employs two approaches: statistical risk assessment, which involves first generating planning schemes, then extracting massive scenarios for operational simulations, and post-hoclyitizing risks using indicators such as EENS and LOLP. This approach is computationally burdensome and the risk results cannot drive optimization in reverse. Embedded risk measurement, on the other hand, incorporates Value at Risk (VaR) or Conditional Value at Risk (CVaR) into the optimization model to achieve pre-hoc risk control. However, current work often focuses on a single time scale, using only annual-monthly electricity consumption scenarios to measure long-term deficits. It lacks a method to unify the modeling of tail risks across both power and electricity consumption time scales and embed them into the same optimization framework, making it difficult to simultaneously address short-term load shedding and seasonal energy shortages during the planning phase. Furthermore, directly using an "N×8760" all-time scenario results in an explosion of variable dimensionality, making the solution infeasible. Using typical daily clustering can easily miss extreme tail events, leading to a systematic underestimation of risk. Summary of the Invention

[0004] The main objective of this invention is to provide a planning method for high-proportion new energy power systems with multi-timescale risk quantification.

[0005] Another objective of this invention is to propose a high-proportion new energy power system planning device with multi-timescale risk quantification.

[0006] The third objective of this invention is to provide an electronic device.

[0007] To achieve the above objectives, a first aspect of the present invention proposes a high-proportion renewable energy power system planning method with multi-timescale risk quantification, comprising: S1, based on historical meteorological and load data, constructs a "1×8760 + N×12" mixed granularity scenario library; uses a typical 8760-hour curve for hourly power balance simulation, and uses N sets of annual-monthly power consumption scenarios for seasonal power imbalance risk analysis. S2, at the hourly time scale, establish a short-term load shedding risk assessment model that includes power balance of power network nodes, thermal power unit combination, long-term and short-term energy storage operation constraints, renewable energy operation constraints, line power flow and node load shedding variables, and quantify the tail risk of load shedding through Conditional Value at Risk (CVaR) to form short-term power imbalance risk constraints. S3, at the year-month time scale, applies an interannual fluctuation coefficient to the monthly power generation and load of new energy, generates N scenario years, establishes a long-term monthly power imbalance risk assessment model that includes annual energy balance constraints, node monthly power balance constraints, line monthly transmission power constraints, renewable energy power generation constraints, renewable energy penetration constraints, and seasonal energy storage operation constraints, and quantifies the tail risk of monthly power deficit through CVaR to form long-term power imbalance risk constraints; S4 embeds short-term CVaR risk constraints and long-term CVaR risk constraints into the same mixed integer linear programming framework. The model is solved by reconstructing a two-layer optimization framework based on nested analysis, and the investment variables, operation variables and risk auxiliary variables are optimized in a unified manner to achieve coordinated risk control of power and electricity across multiple time scales. S5 outputs multi-timescale joint planning results including thermal power unit capacity, wind and solar installed capacity, short-term energy storage capacity, seasonal energy storage capacity, and transmission line expansion schemes.

[0008] In one embodiment of the invention, the short-timescale CVaR constraint captures the real-time equilibrium tail loss by measuring the load shearing, including: Based on the CVaR method, the risk of load shedding in short time scales is quantitatively assessed. By introducing hourly operation simulation and node-level load shedding variables, the tail events of power shortage under extreme weather or sudden drop in the output of new energy sources are captured, so as to achieve unified measurement and collaborative optimization of power balance and risk cost within the same optimization framework. Construct short-timescale load shedding risk constraints for the power system, defining the system's load shedding risk as the amount of load shedding at the node level and hourly level within a specified confidence level. Conditional expected loss Among them, the load shedding risk is caused by the load shedding losses at each node in the hourly operation simulation. Exceeding the risk threshold The tail expectation deviation is obtained by summing linearized auxiliary variables; the expression for constructing the short-timescale load shedding risk constraint of the power system is:

[0009]

[0010]

[0011] in, This represents the auxiliary variable at time t.

[0012] In one embodiment of the present invention, the long-timescale CVaR constraint measures the seasonal energy shift tail loss by calculating the additional output of thermal power plants, including: The monthly power imbalance risk of the system is defined as the risk of power supply imbalance caused by annual monthly power fluctuations in massive scenarios. The formula for constructing the long-timescale CVaR constraint is as follows:

[0013]

[0014]

[0015]

[0016] in, The annual electricity deficit refers to the extra power generation forced upon thermal power units to compensate for insufficient output from renewable energy sources throughout the year. Total compensation power; This represents the long-term CVaR risk value. Confidence level Risk threshold below The expected value of the excess loss. This is a tail risk amplification factor. It is an auxiliary variable.

[0017] In one embodiment of the present invention, on a short time scale, a power balance model considering short-term load shedding is established, and the Conditional Value at Risk (CVaR) is introduced to quantify the risk of power shortage. This model, through hourly operating constraints, uses a typical 8760-hour curve to characterize intraday peak shaving, ramp-up, and network characteristics, ensuring that unit configuration, energy storage charging and discharging, line power flow, and load shedding meet instantaneous power balance. Specifically, this includes: The node power balance constraints are constructed, and their constraint formulas are as follows:

[0018]

[0019]

[0020] Where I refers to a collection of different types of generator sets. This includes thermal power (TG), hydropower (HY), wind power (WG), and photovoltaic (PV); energy storage systems. Including short-term energy storage (SS) and seasonal energy storage (LS), For the output of generator set i in hour t, , These represent the charging and discharging power of energy storage s in t hours, respectively. Let l be the transmission power of line l. Let n be the load power. Let n be the load shedding capacity of node n. Let n be the set of generator sets connected to node n. For the energy storage collection connected to node n, , Let n represent the sets of transmission lines that start at node n and end at node n, respectively. Construct constraints for the combination of rapid thermal power units and linearize the start-up and shutdown operation of conventional thermal power plants. The constraint formula is as follows:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] in, To aggregate the power output of each unit within the thermal power unit cluster The sum of For the online operating capacity of thermal power unit TG, This refers to the output power of the thermal power unit TG. This represents the minimum output rate of the thermal power unit TG. , Adjust the ramp rate of the thermal power unit TG for both upward and downward ramps; , This indicates the start-up and shutdown time constraints of the thermal power unit during time period t; , This indicates the minimum start-up and shutdown time of the thermal power unit TG. This indicates the installed capacity of the thermal power unit (TG). Establish power flow constraints for transmission lines, with the constraint formula as follows:

[0027]

[0028] in, The existing capacity of line l, For the expansion capacity of line l; Establish a constraint on renewable energy output, the constraint formula of which is:

[0029]

[0030]

[0031]

[0032] in, For hourly power output from wind / solar power, For wind / solar power curtailment, This represents the hourly wind and light fluctuation curve. This indicates the theoretical maximum output of wind / solar power. Indicates the installed capacity of hydropower. This indicates the hourly output of hydropower. Establish constraints for energy storage operation; the constraint formula is as follows:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] in, This represents the state of charge of the stored energy at time t. Indicates charging. Indicates discharge loss. Indicates the rated power capacity of energy storage. The number of hours that the energy storage s continues to discharge.

[0039] In one embodiment of the present invention, on a long-term scale, N annual-monthly energy scenarios are generated based on Monte Carlo simulations. The impact of years with strong and weak winds and years with high and low water levels on the monthly energy balance is explicitly modeled, and the risk of tail-end energy shortage is quantified using CVaR. Specifically, this includes: Based on the CVaR risk analysis method, by introducing massive scenario annual data... Annual uncertainty in modeling landscape sequences; Starting with the original 8760-hour time-series curve, two sets of data are constructed: power balance and energy balance. On the power balance side, unperturbed hourly wind, solar, and hydropower output and nodal load curves are used to form a deterministic scenario with a probability of 1, driving the instantaneous power balance calculation for the 8760 hours, which includes unit output, energy storage charging and discharging, line power flow, and load shedding. On the energy balance side, hourly data is aggregated into a total energy consumption for 12 months. A coefficient of variation is applied to the monthly energy consumption of renewable energy and the monthly energy consumption of nodal loads to generate Y scenario years. Each scenario year includes the monthly output curve of renewable energy. Monthly load demand Used for annual-monthly power balance constraints and subsequent CVaR risk assessment; Construct a monthly power balance model for the power system, which includes the following constraints: The annual capacity balance constraint for the entire system is given by the following formula:

[0040]

[0041] in, For wind power, photovoltaic and hydropower installed capacity; The monthly power generation curves of wind power, photovoltaic, and hydropower unit i in the m-th month of a typical scenario year y are shown. Indicates generator set i Investment capacity and scenarios per year y The m Monthly renewable energy units The product of the monthly power generation curves, Indicating a typical scenario year The The planned monthly power generation of the Yuehuo power unit TG. , The variable to be optimized; , The curve is known. The monthly electricity balance constraint for node n is given by the following formula:

[0042] in, This indicates that, in order to mitigate the risk of monthly power imbalance, the TG of thermal power units will be based on the originally planned power generation. The additional power generation required on top of the existing capacity; Indicates thermal power output; This represents the power generation of wind power, solar power, and hydropower in the m-th month of year y in a typical scenario; This represents the electricity transported by transmission line l in the m-th month of year y in a typical scenario. , This indicates the monthly charging and discharging capacity of seasonal energy storage. The monthly power transmission limit constraint for power transmission lines is given by the following formula:

[0043] in, , These represent the lower and upper limits of the monthly power transmission capacity for line l, respectively. The number of hours within a day; The constraint on renewable energy generation is given by the following formula:

[0044] in, This represents the monthly abandoned power of new energy generator unit i in the m-th month of year y in a typical scenario; The constraint on the power generation penetration rate of new energy sources is given by the following formula:

[0045] in, The renewable energy power penetration rate of the entire system; Seasonal energy storage operation constraints, the constraint formula is:

[0046]

[0047]

[0048]

[0049]

[0050] in, This represents the seasonal energy storage capacity in the m-th month of year y in the scenario. , These are the charging and discharging efficiencies for seasonal energy storage, respectively. Installed capacity representing seasonal energy storage The number of continuous discharge hours representing seasonal energy storage. Let m be the month number, and M represent the number of months in a year.

[0051] In one embodiment of the present invention, the mixed-integer linear programming aims to minimize the total system cost, and its specific objective function includes: Optimize total system cost The formula for calculation is:

[0052] in, This represents minimizing the annualized investment cost. This represents the annual operating cost for power balance. This represents the average annual electricity consumption balance operating cost across a large number of scenarios. The weighting factors for power balance operation cost and electricity balance operation cost in the total operating cost; System annualized investment cost The formula for calculation is:

[0053] Where r is the discount rate and p is the payback period. , , These are the unit investment costs for generating units, transmission lines, and energy storage, respectively. , , These are the planned capacities for generating units, transmission lines, and energy storage, respectively. System operating costs for achieving power balance within the benchmark year The formula for calculation is:

[0054] in, This represents the unit power generation cost of a thermal power polymerization unit u. This represents the unit start-up cost of a thermal power polymerization unit (u). Indicates the mth month. The total number of actual calendar days corresponding to a typical day. This represents the unit CVaR load shedding cost; System operating costs for handling massive scenarios and balancing electricity consumption within the year The formula for calculation is:

[0055] in, For the total number of scenarios per year, This represents the unit power generation cost (TG) of a thermal power unit. This represents the unit power generation cost of the extra output of thermal power units in order to mitigate seasonal imbalances.

[0056] In one embodiment of the present invention, the nested parsing and reconstructing two-layer optimization framework specifically includes: The data layer is used to generate Y scenario years based on historical meteorological and load data through Monte Carlo simulation, forming a risk quantification sample library; The risk quantification layer is used to quantify the tail risk of CVaR for the 8760-hour load shedding and the annual power shortage in Y scenarios, and transforms CVaR into a linear constraint that can be directly embedded into the optimization problem by linearizing auxiliary variables. The optimization decision layer is used to incorporate all capacity variables, operation variables and CVaR auxiliary variables into the same mixed integer linear programming, with the goal of minimizing the sum of annualized investment and weighted risk cost, satisfying the linear constraints including 8760 hours of power balance and Y scenarios of monthly and annual power balance, and realizing joint global optimization of capacity and operation. The solution layer is used to write the objective and constraints into the Pyomo modeling language at once to generate a standard MILP. It uses the branch-and-cut algorithm of CPLEX 12.10 to complete the global optimal search in a unified variable space and outputs the optimal capacity configuration and corresponding risk value.

[0057] In one embodiment of the present invention, the capacity-risk decoupling strategy specifically includes: Using the capacity of generating units, energy storage, and transmission lines as the primary decision variables, a capacity space to be optimized is constructed. Power balance simulation was completed based on a single baseline 8760-hour curve. The node load shedding CVaR was transformed into an analytically expressible linear constraint by linearizing auxiliary variables. Monthly power balance calculations are performed for N sets of year-month power consumption scenarios, and the monthly power shortage CVaR is linearized using auxiliary variables; The two types of CVaR constraints, along with constraints including operation, investment costs, and carbon emissions, are incorporated into the same mixed-integer linear programming problem. A branch-cut algorithm is used to simultaneously update capacity decision variables and risk variables in a single solution until the objective function reaches the global optimum.

[0058] To achieve the above objectives, a second aspect of this application proposes a high-proportion renewable energy power system planning device with multi-timescale risk quantification, comprising: The scenario generation module constructs a "1×8760 + N×12" mixed granularity scenario library based on historical meteorological and load data; a typical 8760-hour curve is used for hourly power balance simulation, and N sets of annual-monthly power scenarios are used for seasonal power imbalance risk analysis. The hourly-level risk quantification module establishes a short-term load shedding risk assessment model at the hourly time scale, which includes power balance of power network nodes, thermal power unit combination, long-term and short-term energy storage operation constraints, renewable energy operation constraints, line power flow and node load shedding variables. It also quantifies the tail risk of load shedding through Conditional Value at Risk (CVaR) to form short-term power imbalance risk constraints. The monthly electricity risk quantification module applies an interannual fluctuation coefficient to the monthly power generation and load of new energy sources on an annual-month time scale, generates N scenario years, and establishes a long-term monthly electricity imbalance risk assessment model that includes annual energy balance constraints, node monthly electricity balance constraints, line monthly transmission electricity constraints, renewable energy power generation constraints, renewable energy penetration constraints, and seasonal energy storage operation constraints. It also quantifies the tail risk of monthly electricity deficit through CVaR to form long-term electricity imbalance risk constraints. The joint optimization solution module embeds short-term CVaR risk constraints and long-term CVaR risk constraints into the same mixed integer linear programming framework. It solves the model by reconstructing the two-layer optimization framework based on nested analysis, and unifies the optimization of investment variables, operation variables and risk auxiliary variables to achieve coordinated risk control of power and electricity across multiple time scales. The results output module outputs multi-timescale joint planning results including thermal power unit capacity, wind and solar installed capacity, short-term energy storage capacity, seasonal energy storage capacity, and transmission line expansion schemes.

[0059] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing the high-proportion new energy power system planning method with multi-timescale risk quantification as described in the first aspect embodiment.

[0060] The embodiments of the present invention have the following beneficial effects: This invention, by determining the boundary conditions of the target power system, can more accurately reflect the actual operating environment and constraints of the target power system. Based on the generated initial operating data, relatively precise scheduling optimization can be performed, helping operators better understand the operating status and uncertainties of the target power system, such as the volatility of renewable energy processing and changes in load demand. Performing multidimensional data analysis and scenario-based dimensionality reduction on the initial operating data can effectively reduce data complexity, remove redundant information, and retain key features, thereby improving data processing efficiency. This allows the obtained operating mode distribution data to more intuitively display the operating modes and changing patterns of the target power system, thus efficiently addressing the uncertainties of the target power system and improving its overall performance. Attached Figure Description

[0061] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of a high-proportion renewable energy power system planning method with multi-timescale risk quantification provided in an embodiment of the present invention; Figure 2 This is a flowchart of the solution process for nested parsing reconstruction two-layer optimization provided in an embodiment of the present invention; Figure 3 A structural diagram of a high-proportion new energy power system planning device with multi-timescale risk quantification provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0062] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0063] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0064] The following description, with reference to the accompanying drawings, describes a high-proportion new energy power system planning method and apparatus based on multi-timescale risk quantification according to an embodiment of the present invention.

[0065] Example 1 This embodiment provides a planning method for a high-proportion renewable energy power system with multi-timescale risk quantification. For example... Figure 1 As shown, the method includes the following steps: S1, based on historical meteorological and load data, constructs a mixed granularity scenario library of "1×8760 + N×12"; uses a typical 8760-hour curve for hourly power balance simulation, and uses N sets of annual-monthly power consumption scenarios for seasonal power imbalance risk analysis.

[0066] Specifically, using a typical 8760-hour curve for hourly power balance simulation is to ensure accurate characterization of the system's hourly operating characteristics, while using N sets of annual-monthly power consumption scenarios for seasonal power imbalance risk analysis is to reflect the statistical characteristics of long-term power fluctuations.

[0067] S2 establishes a short-term load shedding risk assessment model at the hourly time scale, which includes power balance of power network nodes, thermal power unit combination, long-term and short-term energy storage operation constraints, renewable energy operation constraints, line power flow and node load shedding variables, and quantifies the tail risk of load shedding through Conditional Value at Risk (CVaR) to form short-term power imbalance risk constraints.

[0068] Furthermore, the short-timescale CVaR constraint captures the real-time equilibrium tail loss by measuring the shear load, including: The CVaR method is used to quantitatively assess the risk of load shedding on a short time scale. By introducing hourly operation simulation and node-level load shedding variables, the tail events of power shortage under extreme weather or sudden drop in renewable energy output are captured, so as to achieve unified measurement and collaborative optimization of power balance and risk cost within the same optimization framework.

[0069] Construct short-timescale load shedding risk constraints for the power system, defining the system's load shedding risk as the amount of load shedding at the node level and hourly level within a specified confidence level. Conditional expected loss Among them, the load shedding risk is caused by the load shedding losses at each node in the hourly operation simulation. Exceeding the risk threshold The tail expectation deviation is obtained by summing linearized auxiliary variables; the expression for constructing the short-timescale load shedding risk constraint of the power system is:

[0070]

[0071]

[0072] in, This represents the auxiliary variable at time t.

[0073] Furthermore, on a short-term timescale, a power balance model considering short-term load shedding is established, and the Conditional Value at Risk (CVaR) is introduced to quantify the risk of power shortage. This model, through hourly operational constraints, uses a typical 8760-hour curve to characterize intraday peak shaving, ramp-up, and network characteristics, ensuring that unit configuration, energy storage charging and discharging, line power flow, and load shedding meet instantaneous power balance requirements. Specifically, this includes: The node power balance constraints are constructed, and their constraint formulas are as follows:

[0074]

[0075]

[0076] Where I refers to a collection of different types of generator sets. This includes thermal power (TG), hydropower (HY), wind power (WG), and photovoltaic (PV); energy storage systems. Including short-term energy storage (SS) and seasonal energy storage (LS), For the output of generator set i in hour t, , These represent the charging and discharging power of energy storage s in t hours, respectively. Let l be the transmission power of line l. Let n be the load power. Let n be the load shedding capacity of node n. Let n be the set of generator sets connected to node n. For the energy storage collection connected to node n, , Let n represent the sets of power transmission lines that start at node n and end at node n, respectively.

[0077] Construct constraints for the combination of rapid thermal power units and linearize the start-up and shutdown operation of conventional thermal power plants. The constraint formula is as follows:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] in, To aggregate the power output of each unit within the thermal power unit cluster The sum of For the online operating capacity of thermal power unit TG, This refers to the output power of the thermal power unit TG. This represents the minimum output rate of the thermal power unit TG. , Adjust the ramp rate of the thermal power unit TG for both upward and downward ramps; , This indicates the start-up and shutdown time constraints of the thermal power unit during time period t; , This indicates the minimum start-up and shutdown time of the thermal power unit TG. This indicates the installed capacity of thermal power units (TG).

[0084] Specifically, the formula To constrain the upper and lower limits of thermal power output, the formula is... For the ramping constraint of thermal power units, the formula is... To model the change in the online operating capacity of thermal power units between adjacent time periods, the formula... and This represents the minimum start / stop time constraint for thermal power units.

[0085] Establish power flow constraints for transmission lines, with the constraint formula as follows:

[0086]

[0087] in, The existing capacity of line l, This refers to the expansion capacity of line l.

[0088] Specifically, the formula is based on the grid flow model to construct the power flow model of existing / to-be-built lines. The grid flow model assumes that the power flow of transmission lines can be freely dispatched within capacity constraints, and the expansion plan of transmission lines is only reflected in the investment expansion of transmission capacity.

[0089] Establish a constraint on renewable energy output, the constraint formula of which is:

[0090]

[0091]

[0092]

[0093] in, For hourly power output from wind / solar power, For wind / solar power curtailment, This represents the hourly wind and light fluctuation curve. This indicates the theoretical maximum output of wind / solar power. Indicates the installed capacity of hydropower. This indicates the hourly output of hydropower.

[0094] Specifically, the formula It is the upper and lower limit constraint of new energy output, formula It is the new energy consumption balance equation, formula The upper and lower limits of hourly output for hydropower.

[0095] Establish constraints for energy storage operation; the constraint formula is as follows:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] in, This represents the state of charge of the stored energy at time t. Indicates charging. Indicates discharge loss. Indicates the rated power capacity of energy storage. The number of hours that the energy storage s continues to discharge.

[0102] Specifically, the formula The formula represents the change in stored energy between adjacent time periods. , The formula represents the energy storage capacity constraint. The formula represents the intraday energy balance of short-term energy storage. This indicates the annual energy balance of seasonal energy storage.

[0103] S3 applies an interannual fluctuation coefficient to the monthly power generation and load of new energy sources on a year-month time scale, generating N scenario years. It then establishes a long-term monthly power imbalance risk assessment model that includes annual energy balance constraints, node monthly power balance constraints, line monthly transmission power constraints, renewable energy power generation constraints, renewable energy penetration constraints, and seasonal energy storage operation constraints. Finally, it quantifies the tail risk of monthly power deficit through CVaR to form long-term power imbalance risk constraints.

[0104] Furthermore, the long-term CVaR constraint measures the seasonal energy shift tail loss by calculating the additional output of thermal power, including: The monthly power imbalance risk of the system is defined as the risk of power supply imbalance caused by annual monthly power fluctuations in massive scenarios. The formula for constructing the long-timescale CVaR constraint is as follows:

[0105]

[0106]

[0107]

[0108] in, The annual electricity deficit refers to the extra power generation forced upon thermal power units to compensate for insufficient output from renewable energy sources throughout the year. Total compensation power; This represents the long-term CVaR risk value. Confidence level Risk threshold below The expected value of the excess loss. This is a tail risk amplification factor. It is an auxiliary variable.

[0109] Specifically, the formula The formula for calculating the annual electricity deficit represents the additional power generation required by thermal power units to compensate for insufficient output from renewable energy sources (such as during dry seasons). Total compensation power. Formula This is the formula for calculating the long-term CVaR risk value. As an auxiliary variable constraint, the CVaR risk analysis method can effectively consider the tail risk of variable distribution, enabling quantitative modeling and evaluation of the seasonal fluctuation characteristics of power system electricity.

[0110] Furthermore, on a long-term scale, based on Monte Carlo simulations, N year-month energy scenarios are generated. The impact of years with strong and weak winds and years with high and low water levels on the monthly energy balance is explicitly modeled, and the risk of tail-end energy shortage is quantified using CVaR, specifically including: Based on the CVaR risk analysis method, by introducing massive scenario annual data... Modeling the annual uncertainty of landscape sequences.

[0111] In the scenario generation phase, starting with the original 8760-hour time-series curve, two sets of data are constructed: power balance and energy balance. On the power balance side, unperturbed hourly wind, solar, and hydropower output and nodal load curves are used to form a deterministic scenario with a probability of 1, which drives the instantaneous power balance calculation of 8760 hours, including unit output, energy storage charging and discharging, line power flow, and load shedding. On the energy balance side, the hourly data is aggregated into a total energy consumption for 12 months, and a coefficient of variation is applied to the monthly energy consumption of new energy sources and the monthly energy consumption of nodal loads to generate Y scenario years. Each scenario year includes the monthly output curve of renewable energy sources. Monthly load demand It is used for annual-monthly power balance constraints and subsequent CVaR risk assessment.

[0112] Construct a monthly power balance model for the power system, which includes the following constraints: The annual capacity balance constraint of the entire system, ensuring that the system's energy supply remains balanced throughout the entire timescale, is defined by the following formula:

[0113]

[0114] in, For wind power, photovoltaic and hydropower installed capacity; The monthly power generation curves of wind power, photovoltaic, and hydropower unit i in the m-th month of a typical scenario year y are shown. Indicates generator seti Investment capacity and scenarios per year y The m Monthly renewable energy units The product of the monthly power generation curves, Indicating a typical scenario year The The planned monthly power generation of the Yuehuo power unit TG. , The variable to be optimized; , The curve is known.

[0115] The monthly electricity balance constraint for node n is given by the following formula:

[0116] in, This indicates that, in order to mitigate the risk of monthly power imbalance, the TG of thermal power units will be based on the originally planned power generation. The additional power generation required on top of the existing capacity; Indicates thermal power output; This represents the power generation of wind power, solar power, and hydropower in the m-th month of year y in a typical scenario; This represents the electricity transported by transmission line l in the m-th month of year y in a typical scenario. , This indicates the monthly charging and discharging volume of seasonal energy storage.

[0117] The monthly power transmission limit constraint for power transmission lines is given by the following formula:

[0118] in, , These represent the lower and upper limits of the monthly power transmission capacity for line l, respectively. This refers to the number of hours within a day.

[0119] The constraint on renewable energy generation is given by the following formula:

[0120] in, This represents the monthly abandoned power of new energy generator unit i in the m-th month of year y in a typical scenario.

[0121] The constraint on the power generation penetration rate of new energy sources is given by the following formula:

[0122] in, This refers to the renewable energy power penetration rate of the entire system.

[0123] Seasonal energy storage operation constraints, the constraint formula is:

[0124]

[0125]

[0126]

[0127]

[0128] in, This represents the seasonal energy storage capacity in the m-th month of year y in the scenario. , These are the charging and discharging efficiencies for seasonal energy storage, respectively. Installed capacity representing seasonal energy storage The number of continuous discharge hours representing seasonal energy storage. Let m be the month number, and M represent the number of months in a year.

[0129] Specifically, seasonal energy storage enables the storage of energy across seasons, serving as an effective technical means to improve the adequacy of power system energy supply and manage the risk of energy imbalance. (Formula) It is the lunar energy transfer equation, formula It is a constraint on the charge and discharge capacity, formula It is an energy storage capacity constraint, the formula It is the annual cycle balance constraint of seasonal energy storage.

[0130] S4 embeds short-term CVaR risk constraints and long-term CVaR risk constraints into the same mixed-integer linear programming framework. The model is solved by reconstructing a two-layer optimization framework based on nested analysis, and investment variables, operation variables and risk auxiliary variables are optimized in a unified manner to achieve coordinated risk control of power and electricity across multiple time scales.

[0131] Furthermore, a mixed-integer linear programming objective function is constructed. The constructed optimization model considers minimizing the total cost of the power system, including annualized investment cost, power balance cost, and energy balance operating cost. The power balance operating cost includes CVaR load shedding cost, and the energy balance operating cost includes CVaR monthly energy imbalance cost. The specific objective function includes: Optimize total system cost The formula for calculation is:

[0132] in, This represents minimizing the annualized investment cost. This represents the annual operating cost for power balance. This represents the average annual electricity consumption balance operating cost across a large number of scenarios. This indicates the weighting factors of power balance operation cost and electricity balance operation cost in the total operating cost.

[0133] It should be noted that, This represents the weighting factor of power balance operation cost and electricity balance operation cost in the total operating cost, reflecting the proportion of the risk cost of load shedding in the short time scale and the risk cost of monthly electricity imbalance in the long time scale in the optimization problem.

[0134] System annualized investment cost The formula for calculation is:

[0135] Where r is the discount rate and p is the payback period. , , These are the unit investment costs for generating units, transmission lines, and energy storage, respectively. , , These are the planned capacities for generating units, transmission lines, and energy storage, respectively.

[0136] System operating costs for achieving power balance within the benchmark year The formula for calculation is:

[0137] in, This represents the unit power generation cost of a thermal power polymerization unit u. This represents the unit start-up cost of a thermal power polymerization unit (u). Indicates the mth month. The total number of actual calendar days corresponding to a typical day. This represents the unit CVAR load shedding cost.

[0138] Specifically, the system operating costs for power balance within the base year include fuel costs, start-up costs, and CVaR load shedding costs for thermal power units.

[0139] System operating costs for handling massive scenarios and balancing electricity consumption within the year The formula for calculation is:

[0140] in, For the total number of scenarios per year, This represents the unit power generation cost (TG) of a thermal power unit. This represents the unit power generation cost of the extra output of thermal power units in order to mitigate seasonal imbalances.

[0141] Specifically, the system operating costs for addressing the annual power balance across massive scenarios include the annual power generation costs of generator sets and the monthly power imbalance risk costs.

[0142] In embodiments of the present invention, such as Figure 2 As shown, the nested parsing and refactoring of the two-layer optimized NARBO framework specifically includes: The data layer is used to generate Y scenario years based on historical meteorological and load data through Monte Carlo simulation, forming a risk quantification sample library; The risk quantification layer is used to quantify the tail risk of CVaR for the 8760-hour load shedding and the annual power shortage in Y scenarios, and transforms CVaR into a linear constraint that can be directly embedded into the optimization problem by linearizing auxiliary variables. The optimization decision layer is used to incorporate all capacity variables, operation variables and CVaR auxiliary variables into the same mixed integer linear programming, with the goal of minimizing the sum of annualized investment and weighted risk cost, satisfying the linear constraints including 8760 hours of power balance and Y scenarios of monthly and annual power balance, and realizing joint global optimization of capacity and operation. The solution layer is used to write the objective and constraints into the Pyomo modeling language at once to generate a standard MILP. It uses the branch-and-cut algorithm of CPLEX 12.10 to complete the global optimal search in a unified variable space and outputs the optimal capacity configuration and corresponding risk value.

[0143] Furthermore, the present invention employs a capacity-risk decoupling strategy, specifically including: Using the capacity of generating units, energy storage, and transmission lines as the primary decision variables, a capacity space to be optimized is constructed. Power balance simulation was completed based on a single baseline 8760-hour curve. The node load shedding CVaR was transformed into an analytically expressible linear constraint by linearizing auxiliary variables. Monthly power balance calculations are performed for N sets of year-month power consumption scenarios, and the monthly power shortage CVaR is linearized using auxiliary variables; The two types of CVaR constraints, along with constraints including operation, investment costs, and carbon emissions, are incorporated into the same mixed-integer linear programming problem. A branch-cut algorithm is used to simultaneously update capacity decision variables and risk variables in a single solution until the objective function reaches the global optimum.

[0144] Therefore, the NARBO framework compactly reconstructs the real-time safety constraints and the tail risk of monthly power imbalance into a single-layer MILP, achieving a one-time global optimal solution without explicit master-sub iteration.

[0145] S5 outputs multi-timescale joint planning results including thermal power unit capacity, wind and solar installed capacity, short-term energy storage capacity, seasonal energy storage capacity, and transmission line expansion schemes.

[0146] Specifically, the multi-timescale joint planning results include complete decision-making data: in terms of equipment configuration, it clarifies the capacity and deployment parameters of thermal power units, wind and solar power installations, and short / seasonal energy storage, as well as the expansion path and capacity increase scale of transmission lines; in terms of decision support, it simultaneously outputs hourly and annual-monthly CVaR risk values, and assesses the annualized investment, operation, and risk costs of the system, providing direct support for project initiation, equipment selection, and scheduling operation.

[0147] The core of this invention lies in constructing a "1×8760 + N×12" hybrid granularity scenario library, establishing risk assessment models for hourly and year-monthly time scales respectively, quantifying the tail risks of load shedding and power deficit through CVaR, embedding the two types of risk constraints into the same hybrid integer linear programming framework, and achieving joint global optimization of capacity-operation-risk with the help of the NARBO framework, ultimately outputting the optimal planning scheme for generating units, energy storage, and lines, thus solving the technical challenge of coordinated control of power and power risks across multiple time scales in the power system under high-proportion renewable energy access.

[0148] Example 2 This application also proposes a high-proportion renewable energy power system planning device with multi-timescale risk quantification. For example... Figure 3 As shown, the device includes: The scenario generation module 100 constructs a "1×8760 + N×12" mixed granularity scenario library based on historical meteorological and load data; it uses a typical 8760-hour curve for hourly power balance simulation and N sets of annual-monthly power consumption scenarios for seasonal power imbalance risk analysis. The hourly risk quantification module 200 establishes a short-term load shedding risk assessment model at the hourly time scale, which includes power balance of power network nodes, thermal power unit combination, long-term and short-term energy storage operation constraints, renewable energy operation constraints, line power flow and node load shedding variables. It also quantifies the tail risk of load shedding through conditional value of risk (CVaR) to form short-term power imbalance risk constraints. The Monthly Electricity Risk Quantification Module 300 applies an interannual fluctuation coefficient to the monthly power generation and load of new energy sources on an annual-month time scale, generating N scenario years. It establishes a long-term monthly electricity imbalance risk assessment model that includes annual energy balance constraints, node monthly electricity balance constraints, line monthly transmission electricity constraints, renewable energy power generation constraints, renewable energy penetration constraints, and seasonal energy storage operation constraints. It also quantifies the tail risk of monthly electricity deficit through CVaR to form long-term electricity imbalance risk constraints. The joint optimization solution module 400 embeds short-term CVaR risk constraints and long-term CVaR risk constraints into the same mixed integer linear programming framework. It solves the model by reconstructing the two-layer optimization framework based on nested analysis, and unifies the optimization of investment variables, operation variables and risk auxiliary variables to achieve coordinated risk control of power and electricity across multiple time scales. The output module 500 outputs multi-timescale joint planning results including thermal power unit capacity, wind and solar installed capacity, short-term energy storage capacity, seasonal energy storage capacity, and transmission line expansion schemes.

[0149] Example 3 To implement the methods of the above embodiments, the present invention also provides an electronic device. For example... Figure 4 As shown, the electronic device 600 includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory 602 or a program loaded from a memory 606 into a random access memory 603. The RAM 603 also stores various programs and data required for the operation of the electronic device 600. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output interface 605 is also connected to the bus 604.

[0150] The following components are connected to I / O interface 605: memory 606 including hard disk; and communication section 607 including network interface card such as LAN (Local Area Network) card, modem, etc., which performs communication processing via a network such as the Internet; and driver 608 is also connected to I / O interface 605 as needed.

[0151] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0152] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0153] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A planning method for a high-proportion renewable energy power system with multi-timescale risk quantification, characterized in that, Includes the following steps: S1, based on historical meteorological and load data, constructs a "1×8760 + N×12" mixed granularity scenario library; uses a typical 8760-hour curve for hourly power balance simulation, and uses N sets of annual-monthly power consumption scenarios for seasonal power imbalance risk analysis. S2, at the hourly time scale, establish a short-term load shedding risk assessment model that includes power balance of power network nodes, thermal power unit combination, long-term and short-term energy storage operation constraints, renewable energy operation constraints, line power flow and node load shedding variables, and quantify the tail risk of load shedding through conditional value of risk (CVaR) to form short-term power imbalance risk constraints. S3, at the year-month time scale, applies an interannual fluctuation coefficient to the monthly power generation and load of new energy, generates N scenario years, establishes a long-term monthly power imbalance risk assessment model that includes annual energy balance constraints, node monthly power balance constraints, line monthly transmission power constraints, renewable energy power generation constraints, renewable energy penetration constraints, and seasonal energy storage operation constraints, and quantifies the tail risk of monthly power deficit through CVaR to form long-term power imbalance risk constraints; S4 embeds the short-term CVaR risk constraints and the long-term CVaR risk constraints into the same mixed integer linear programming framework. The model is solved by reconstructing the two-layer optimization framework based on nested analysis, and the investment variables, operation variables and risk auxiliary variables are optimized in a unified manner to achieve multi-timescale risk coordination control of power and electricity. S5 outputs multi-timescale joint planning results including thermal power unit capacity, wind and solar installed capacity, short-term energy storage capacity, seasonal energy storage capacity, and transmission line expansion schemes.

2. The method according to claim 1, characterized in that, Short-timescale CVaR constraints capture real-time equilibrium tail losses by measuring shear loads, including: Based on the CVaR method, the risk of load shedding in short time scales is quantitatively assessed. By introducing hourly operation simulation and node-level load shedding variables, the tail events of power shortage under extreme weather or sudden drop in the output of new energy sources are captured, so as to achieve unified measurement and collaborative optimization of power balance and risk cost within the same optimization framework. Construct short-timescale load shedding risk constraints for the power system, defining the system's load shedding risk as the amount of load shedding at the node level and hourly level within a specified confidence level. Conditional expected loss Among them, the load shedding risk is caused by the load shedding losses at each node in the hourly operation simulation. Exceeding the risk threshold The tail expectation deviation is obtained by summing linearized auxiliary variables; the expression for constructing the short-timescale load shedding risk constraint of the power system is: in, This represents the auxiliary variable at time t.

3. The method according to claim 1, characterized in that, Long-term CVaR constraints measure the seasonal energy shift tail loss by calculating the additional output of thermal power plants, including: The monthly power imbalance risk of the system is defined as the risk of power supply imbalance caused by annual monthly power fluctuations in massive scenarios. The formula for constructing the long-timescale CVaR constraint is as follows: in, The annual electricity deficit refers to the extra power generation forced upon thermal power units to compensate for insufficient output from renewable energy sources throughout the year. Total compensation power; This represents the long-term CVaR risk value. Confidence level Risk threshold below The expected value of the excess loss. This is a tail risk amplification factor. It is an auxiliary variable.

4. The method according to claim 1, characterized in that, In the short-term timescale, a power balance model considering short-term load shedding is established, and the Conditional Value at Risk (CVaR) is introduced to quantify the power shortage risk. This model, through hourly operating constraints, uses a typical 8760-hour curve to characterize intraday peak shaving, ramp-up, and network characteristics, ensuring that unit combination, energy storage charging and discharging, line power flow, and load shedding meet instantaneous power balance requirements. Specifically, this includes: The node power balance constraints are constructed, and their constraint formulas are as follows: Where I refers to a collection of different types of generator sets. This includes thermal power (TG), hydropower (HY), wind power (WG), and photovoltaic (PV); energy storage systems. Including short-term energy storage (SS) and seasonal energy storage (LS), For the output of generator set i in hour t, , These represent the charging and discharging power of energy storage s in t hours, respectively. Let l be the transmission power of line l. Let n be the load power. Let n be the load shedding capacity of node n. Let n be the set of generator sets connected to node n. For the energy storage collection connected to node n, , Let n represent the sets of transmission lines that start at node n and end at node n, respectively. Construct constraints for the combination of rapid thermal power units and linearize the start-up and shutdown operation of conventional thermal power plants. The constraint formula is as follows: in, To aggregate the power output of each unit within the thermal power unit cluster the sum of For the online operating capacity of thermal power unit TG, This refers to the output power of the thermal power unit TG. This represents the minimum output rate of the thermal power unit TG. , Adjust the ramp rate of the thermal power unit TG for both upward and downward ramps; , This indicates the start-up and shutdown time constraints of the thermal power unit during time period t; , This indicates the minimum start-up and shutdown time of the thermal power unit TG. This indicates the installed capacity of the thermal power unit (TG). Establish power flow constraints for transmission lines, with the constraint formula as follows: in, This represents the existing capacity of line l. For the expansion capacity of line l; Establish a constraint on renewable energy output, the constraint formula of which is: in, For hourly power output from wind / solar power, For wind / solar power curtailment, This represents the hourly wind and light fluctuation curve. This indicates the theoretical maximum output of wind / solar power. Indicates the installed capacity of hydropower. This indicates the hourly output of hydropower. Establish constraints for energy storage operation; the constraint formula is as follows: in, This represents the state of charge of the stored energy at time t. Indicates charging. Indicates discharge loss. Indicates the rated power capacity of energy storage. The number of hours that the energy storage s continues to discharge.

5. The method according to claim 1, characterized in that, On a long-term scale, N year-month energy scenarios are generated based on Monte Carlo simulations. The impact of years with strong and weak winds and years with high and low water levels on the monthly energy balance is explicitly modeled, and the risk of tail-end energy shortage is quantified using CVaR, specifically including: Based on the CVaR risk analysis method, by introducing massive scenario annual data... Annual uncertainty in modeling landscape sequences; Starting with the original 8760-hour time-series curve, two sets of data are constructed: power balance and energy balance. On the power balance side, unperturbed hourly wind, solar, and hydropower output and nodal load curves are used to form a deterministic scenario with a probability of 1, driving the instantaneous power balance calculation for the 8760 hours, which includes unit output, energy storage charging and discharging, line power flow, and load shedding. On the energy balance side, hourly data is aggregated into a total energy consumption for 12 months. A coefficient of variation is applied to the monthly energy consumption of renewable energy and the monthly energy consumption of nodal loads to generate Y scenario years. Each scenario year includes the monthly output curve of renewable energy. Monthly load demand Used for annual-monthly power balance constraints and subsequent CVaR risk assessment; Construct a monthly power balance model for the power system, which includes the following constraints: The annual capacity balance constraint for the entire system is given by the following formula: in, For wind power, photovoltaic and hydropower installed capacity; The monthly power generation curves of wind power, photovoltaic, and hydropower unit i in the m-th month of a typical scenario year y are shown. Indicates generator set i Investment capacity and scenarios per year y The m Monthly renewable energy units The product of the monthly power generation curves, Indicating a typical scenario year The The planned monthly power generation of the Yuehuo power unit TG. , The variable to be optimized; , The curve is known. The monthly electricity balance constraint for node n is given by the following formula: in, This indicates that, in order to mitigate the risk of monthly power imbalance, the TG of thermal power units will be based on the originally planned power generation. The additional power generation required on top of the existing capacity; Indicates thermal power output; This represents the power generation of wind power, solar power, and hydropower in the m-th month of year y in a typical scenario; This represents the electricity transported by transmission line l in the m-th month of year y in a typical scenario. , This indicates the monthly charging and discharging capacity of seasonal energy storage. The monthly power transmission limit constraint for power transmission lines is given by the following formula: in, , These represent the lower and upper limits of the monthly power transmission capacity for line l, respectively. The number of hours within a day; The constraint on renewable energy generation is given by the following formula: in, This represents the monthly abandoned power of new energy generator unit i in the m-th month of year y in a typical scenario; The constraint on the power generation penetration rate of new energy sources is given by the following formula: in, The renewable energy power penetration rate of the entire system; Seasonal energy storage operation constraints, the constraint formula is: in, This represents the seasonal energy storage capacity in the m-th month of year y in the scenario. , These are the charging and discharging efficiencies for seasonal energy storage, respectively. Installed capacity representing seasonal energy storage The number of continuous discharge hours representing seasonal energy storage. Let m be the month number, and M represent the number of months in a year.

6. The method according to claim 1, characterized in that, Mixed-integer linear programming aims to minimize the total system cost. Its specific objective functions include: Optimize total system cost The formula for calculation is: in, This represents minimizing the annualized investment cost. This represents the annual operating cost for power balance. This represents the average annual electricity consumption balance operating cost across a large number of scenarios. The weighting factors for power balance operation cost and electricity balance operation cost in the total operating cost; System annualized investment cost The formula for calculation is: Where r is the discount rate and p is the payback period. , , These are the unit investment costs for generating units, transmission lines, and energy storage, respectively. , , These are the planned capacities for generating units, transmission lines, and energy storage, respectively. System operating costs for achieving power balance within the benchmark year The formula for calculation is: in, This represents the unit power generation cost of a thermal power polymerization unit u. This represents the unit start-up cost of a thermal power polymerization unit (u). Indicates the mth month. The total number of actual calendar days corresponding to a typical day. This represents the unit CVaR load shedding cost; System operating costs for handling massive scenarios and balancing electricity consumption within the year The formula for calculation is: in, For the total number of scenarios per year, This represents the unit power generation cost (TG) of a thermal power unit. This represents the unit power generation cost of the extra output of thermal power units in order to mitigate seasonal imbalances.

7. The method according to claim 1, characterized in that, The nested parsing and reconstruction two-layer optimization framework specifically includes: The data layer is used to generate Y scenario years based on historical meteorological and load data through Monte Carlo simulation, forming a risk quantification sample library; The risk quantification layer is used to quantify the tail risk of CVaR for the 8760-hour load shedding and the annual power shortage in Y scenarios, and transforms CVaR into a linear constraint that can be directly embedded into the optimization problem by linearizing auxiliary variables. The optimization decision layer is used to incorporate all capacity variables, operation variables and CVaR auxiliary variables into the same mixed integer linear programming, with the goal of minimizing the sum of annualized investment and weighted risk cost, satisfying the linear constraints including 8760 hours of power balance and Y scenarios of monthly and annual power balance, and realizing joint global optimization of capacity and operation. The solution layer is used to write the objective and constraints into the Pyomo modeling language at once to generate a standard MILP. It uses the branch-and-cut algorithm of CPLEX 12.10 to complete the global optimal search in a unified variable space and outputs the optimal capacity configuration and corresponding risk value.

8. The method according to claim 1, characterized in that, The capacity-risk decoupling strategy specifically includes: Using the capacity of generating units, energy storage, and transmission lines as the primary decision variables, a capacity space to be optimized is constructed. Power balance simulation was completed based on a single baseline 8760-hour curve. The node load shedding CVaR was transformed into an analytically expressible linear constraint by linearizing auxiliary variables. Monthly power balance calculations are performed for N sets of year-month power consumption scenarios, and the monthly power shortage CVaR is linearized using auxiliary variables; The two types of CVaR constraints, along with constraints including operation, investment costs, and carbon emissions, are incorporated into the same mixed-integer linear programming problem. A branch-cut algorithm is used to simultaneously update capacity decision variables and risk variables in a single solution until the objective function reaches the global optimum.

9. A planning device for a high-proportion new energy power system with multi-timescale risk quantification, characterized in that, include: The scene generation module constructs a "1×8760 + N×12" mixed granularity scene library based on historical meteorological and load data; A typical 8760-hour curve is used for hourly power balance simulation, and N sets of annual-monthly power consumption scenarios are used for seasonal power imbalance risk analysis. The hourly-level risk quantification module establishes a short-term load shedding risk assessment model at the hourly time scale, which includes power balance of power network nodes, thermal power unit combination, long-term and short-term energy storage operation constraints, renewable energy operation constraints, line power flow and node load shedding variables. It also quantifies the tail risk of load shedding through Conditional Value at Risk (CVaR) to form short-term power imbalance risk constraints. The monthly electricity risk quantification module applies an interannual fluctuation coefficient to the monthly power generation and load of new energy sources on an annual-month time scale, generates N scenario years, and establishes a long-term monthly electricity imbalance risk assessment model that includes annual energy balance constraints, node monthly electricity balance constraints, line monthly transmission electricity constraints, renewable energy power generation constraints, renewable energy penetration constraints, and seasonal energy storage operation constraints. It also quantifies the tail risk of monthly electricity deficit through CVaR to form long-term electricity imbalance risk constraints. The joint optimization solution module embeds short-term CVaR risk constraints and long-term CVaR risk constraints into the same mixed integer linear programming framework. It solves the model by reconstructing the two-layer optimization framework based on nested analysis, and unifies the optimization of investment variables, operation variables and risk auxiliary variables to achieve coordinated risk control of power and electricity across multiple time scales. The results output module outputs multi-timescale joint planning results including thermal power unit capacity, wind and solar installed capacity, short-term energy storage capacity, seasonal energy storage capacity, and transmission line expansion schemes.

10. An electronic device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement a high-proportion new energy power system planning method with multi-timescale risk quantification as described in any one of claims 1-8.

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