Wind-solar-thermal storage integrated system capacity configuration optimization method considering climbing event

Abstract

The invention discloses a wind-solar-thermal storage integrated system capacity configuration optimization method and system considering a climbing event, and the method comprises the steps: constructing an extreme climbing demand model, generating an integrated scene set containing an extreme climbing event scene set, and enabling the integrated scene set to serve as the input of a planning and scheduling optimization model in a power system; introducing the probability that the power system cannot meet the climbing demand in the whole scheduling period, and establishing an LOLR constraint condition through opportunity constraint or conditional value-at-risk; establishing a shared climbing standby pool, taking the shared climbing standby pool as a basic constraint condition of the planning and scheduling optimization model, and determining other constraint conditions of the planning and scheduling optimization model including an LOLR condition and a carbon emission constraint; determining a planning and scheduling optimization model according to a joint optimization target including minimizing total cost, maximizing reliability, a basic constraint condition and other constraint conditions; and solving the planning and scheduling optimization model to obtain an optimal capacity configuration scheme.

Description

A method for optimizing the capacity configuration of a wind-solar-thermal-storage integrated system considering ramp-up events Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method for optimizing the capacity configuration of a wind-solar-thermal-storage integrated system considering ramp-up events. Background Technology

[0002] With a high proportion of renewable energy integrated into the power system, the output of new energy sources such as wind power and photovoltaics exhibits significant intermittency and volatility, leading to frequent and substantial power ramp-up events during system dispatch and operation. The existing power system primarily relies on thermal power units for ramp-up backup, but limitations in unit start-up and shutdown speeds and insufficient adjustment flexibility make it difficult to effectively cope with the rapidly changing demands of new energy output. Meanwhile, while energy storage devices can alleviate ramp-up pressure to some extent, their limited capacity, lifespan constraints, and high operating costs make it difficult for a single resource to support the system's reliability under extreme operating conditions.

[0003] In existing research, ramp-up reserve requirements are usually assessed using static deterministic methods, which fail to fully consider new energy prediction errors, energy storage state constraints, and load uncertainties. At the same time, traditional optimization methods often rely on scenario enumeration or robust optimization, which have large computational scale and low solution efficiency, making them difficult to apply to the planning and real-time scheduling of large-scale systems.

[0004] Therefore, existing technologies have the following problems that urgently need to be solved: ramp-up backup configurations rely too much on a single power source type, resulting in insufficient resource utilization; insufficient consideration of multi-dimensional constraints such as wind and solar forecasting errors, energy storage lifespan, and carbon emissions, making it difficult to guarantee reliability and sustainability; and the optimization model has a large solution scale, resulting in low computational efficiency and making it difficult to meet the needs of practical applications.

[0005] To address the aforementioned issues, this invention proposes a capacity configuration optimization method for integrated wind-solar-thermal-storage systems that considers ramp-up events. This method constructs a shared ramp-up backup pool, comprehensively utilizing various resources such as the ramp-up margin of thermal power units, the charge / discharge margin of energy storage equipment, energy state constraints, and upper and lower limits of wind and solar forecast errors. It also introduces opportunity constraints or conditional value at risk (CVaR) to control the ramp-up reliability of the system. Simultaneously, it employs an algorithm combining distributed bar optimization (DRO) and column constraint generation (C&CG), effectively reducing the computational scale and improving solution efficiency, thereby achieving a comprehensive balance between economy, reliability, and sustainability. Summary of the Invention

[0006] According to the present invention, a method, system, storage medium and electronic device for optimizing the capacity configuration of a wind-solar-thermal-storage integrated system considering ramp-up events are provided to solve the technical problems of ramp-up backup configuration relying too much on a single power source type, resulting in insufficient resource utilization; and insufficient consideration of multi-dimensional constraints such as wind and solar forecasting errors, energy storage life and carbon emissions, making it difficult to guarantee reliability and sustainability.

[0007] According to a first aspect of the present invention, a method for optimizing the capacity configuration of a wind-solar-thermal-storage integrated system considering ramp-up events is provided, comprising:

[0008] Historical configuration data under weather forecast disturbances are obtained, and extreme value theory is combined to construct an extreme ramp demand model. A comprehensive scenario set containing extreme ramp event scenarios is generated, and the comprehensive scenario set is used as the input to the planning and scheduling optimization model in the power system.

[0009] Introduce the probability that the power system fails to meet the ramping demand throughout the entire dispatch cycle, and establish reliability LOLR constraints through opportunity constraints or conditional value of risk.

[0010] Establish a shared ramp-up reserve pool with unified constraints for wind, solar, thermal and energy storage. Use the shared ramp-up reserve pool as the basic constraint condition for the planning and scheduling optimization model, and determine the remaining constraints of the planning and scheduling optimization model, including LOLR conditions and carbon emission constraints.

[0011] The planning and scheduling optimization model is determined by a joint optimization objective that includes minimizing total cost, maximizing reliability, basic constraints, and other constraints.

[0012] The planning and scheduling optimization model is solved by using the bibliometric optimization and column constraint generation algorithm. The worst-case scenario is gradually introduced to obtain the optimal capacity configuration scheme.

[0013] Optionally, the process of acquiring historical configuration data under weather forecast disturbances, combining it with extreme value theory, constructing an extreme ramp demand model, and generating a comprehensive scenario set containing extreme ramp event scenarios, with the comprehensive scenario set serving as input to the planning and scheduling optimization model in the power system, including:

[0014] Historical configuration data under weather forecast disturbances includes acquiring numerical sequences D over continuous time periods. t , where D t This represents the load demand or renewable energy output at time t under weather forecast disturbances, where t∈{1,2,...,T} and T is the total time length;

[0015] Define the gradient between adjacent time periods:

[0016] R t =D t -Dt-1 ,t=2,…,T

[0017] In the formula, R t This represents the magnitude of the change at time t relative to time t-1;

[0018] To identify extreme climbing samples, a preset interval or threshold is set: the sequence is divided into intervals using the block maximum value method, and the maximum climbing amount Mn = max(R1,...,R) is taken for each interval. n Alternatively, an over-threshold method can be used, setting a threshold u and retaining only those satisfying |R t |>u's sample;

[0019] Statistical modeling is performed on the extracted extreme samples to fit their tail distribution:

[0020] Under the block maximum method, the extreme ramp rate follows a generalized extreme value distribution:

[0021]

[0022] Where μ is the position parameter, σ>0 is the scale parameter, and ξ is the shape parameter;

[0023] Under the over-threshold method, the extreme gradient amounts follow a generalized Pareto distribution:

[0024]

[0025] Where β is the scale parameter and ξ is the shape parameter;

[0026] The above distribution parameters μ, σ, ξ, β are obtained through maximum likelihood estimation or probability weighted moment estimation.

[0027] Extreme gradient amounts are generated based on sampling from the fitted distribution:

[0028] R extreme ~GEV(μ,σ,ξ)orR extreme ~GPD(β,ξ)

[0029] By superimposing the extreme gradients onto the baseline sequence, we obtain the extreme scenario:

[0030] D t extreme =D t-1 +R extreme

[0031] Among them, D t extreme This indicates the load or output value including extreme uphill sections;

[0032] The extreme climbing scenarios are combined with the set of regular scenarios to form a comprehensive scenario set:

[0033] Ω={Ω normal ,Ω extreme}

[0034] Among them, Ω normal Ω represents a set of common scenarios. extreme This represents the set of extreme scenarios generated by extreme climbing events, and the comprehensive scenario set serves as the input to the planning and scheduling optimization model.

[0035] Optionally, the probability that the power system fails to meet the ramp-up demand throughout the entire dispatch cycle is introduced, and LOLR reliability constraints are established through opportunity constraints or conditional value-at-risk (VAT) conditions, including:

[0036] During the scheduling period, let the system load demand at time t be D. t The total power supplied at time t is Ptot,t, where Ptot,t represents the total power supplied by dispatchable power sources in the system at time t, including thermal power, renewable energy, and energy storage.

[0037] Define the system's ramp requirement at time t as:

[0038] ΔD t =D t -D t-1

[0039] Where, ΔD t >0 indicates an upward climbing demand, ΔD t <0 indicates a downward ramp demand;

[0040] Define the backup climbing capacity that the system can provide at time t as: These represent the system's available upside reserve capacity and downside reserve capacity, respectively.

[0041] Define the LOLR constraint as the probability that the system fails to meet the ramp requirement within the entire scheduling cycle:

[0042]

[0043] Here, 1(.) is an indicator function that takes the value 1 when the condition inside the parentheses is true, and 0 otherwise;

[0044] LOLR constraint modeling based on chance constraints: Set the reliability level parameter β∈(0,1) to represent the maximum allowable probability of the LOLR constraint;

[0045] The LOLR constraint conditions are modeled using a chance constraint form.

[0046]

[0047] Wherein, the constraint indicates that at least at the probability level of 1-β, the system's ramping capability can meet the load ramping requirements;

[0048] LOLR constraint modeling based on conditional value at risk: defining a random variable Z t Indicates the amount of hill-climbing default at time t:

[0049]

[0050] Among them, Z t >0 indicates that the system's climbing ability is insufficient at time t;

[0051] Define the risk level parameter α∈(0,1) to represent the confidence level of the conditional value of risk. Then the expression for the conditional value of risk under the LOLR constraint is:

[0052]

[0053] Where η is an auxiliary decision variable; (x) + =max(x,0); E represents the expectation operator;

[0054] Apply constraints to the model:

[0055] CVaR α (Z)≤ζ

[0056] Where ζ represents the maximum permissible expected climbing loss level;

[0057] By employing opportunity constraint modeling or conditional value-at-risk (VAT) modeling, the LOLR constraints can be embedded into the planning and scheduling optimization models of the power system to ensure system reliability under extreme ramping events. When using opportunity constraints, the focus is on controlling the probability of ramping failure; when using conditional VAT, the focus is on controlling the expected severity of ramping failure.

[0058] Optionally, the basic constraints include:

[0059] Available ramping margin for thermal power units: Let the output of the thermal power unit at time t be P. t th The installed capacity is C th If the maximum allowable climbing rate is RR, then the available climbing margin of the thermal power unit at time t is:

[0060]

[0061] in, This indicates the maximum reserve capacity that a thermal power unit can provide at time t;

[0062] Energy storage device ramp-up capability: Let the capacity of the energy storage device be C. st The charging power at time t is P t ch The discharge power is P t dis The energy storage's state of charge is SOC. t The charge and discharge efficiencies are η ch ,η dis .

[0063] The upper limit of the charging power of the energy storage device at time t is:

[0064] P t ch ≤P ch,max =γ·C st

[0065] The upper limit of the discharge power of the energy storage device at time t is:

[0066] P t dis ≤P dis,max =γ·C st

[0067] Wherein, γ is the rated power capacity coefficient;

[0068] The dynamic energy constraint of the energy storage device at time t is:

[0069]

[0070] And satisfy the energy boundary constraints:

[0071] 0≤SOC t ≤C st

[0072] Define the power margin of the energy storage device for hill climbing at time t as:

[0073]

[0074] Upper and lower limits of wind and solar forecast error:

[0075] Assume the predicted wind power output is Photovoltaic power output forecast value The upper and lower deviation factors of the wind and solar uncertainty are δ w ,δ s :

[0076] The actual wind power output then satisfies:

[0077]

[0078] The actual output of photovoltaic power meets the following requirements:

[0079]

[0080] The reserve margin introduced by the wind and solar power output error is defined as follows:

[0081]

[0082] Overall definition of a shared backup pool: At time t, the capacity of the shared ramp-up backup pool is:

[0083]

[0084] The backup pool serves as a joint resource pool for the system to meet the ramp-up rate requirements, and its constraints are as follows:

[0085]

[0086] Optionally, the remaining constraints include:

[0087] In addition to the basic constraints of the shared ramp backup pool, the remaining constraints are as follows:

[0088] Load reduction constraint: Let the load reduction amount be L. t Its upper limit is L max Then we have:

[0089] 0≤L t ≤L max

[0090] Energy storage lifetime constraint: During the entire cycle, the charge and discharge cycles shall not exceed the upper limit of the cycle life N. cycle ·C st :

[0091] Σ t (P t ch +P t dis )≤N cycle ·C st

[0092] Carbon emission constraints: Let the emission factor for thermal power be EF, and the carbon emission ceiling be C. cap Then we have:

[0093] ∑t EF·P t th ≤C cap

[0094] Investment budget constraints: Let the unit investment costs of wind power, photovoltaic, thermal power, and energy storage be IC, respectively. w IC s IC th IC st The upper limit of the investment budget is B. cap Then we have:

[0095] IC w C w +IC s C s +IC th C th +IC st C st ≤B cap

[0096] Reliability metric constraints: including opportunity-based LOLR control or risk mitigation mechanisms based on conditional value at risk (CVaR).

[0097] Optionally, the determination of the planning and scheduling optimization model based on a joint optimization objective including minimizing total cost, maximizing reliability, basic constraints, and other constraints includes:

[0098] The objective function for minimizing total cost is defined as including electricity supply cost, energy storage charging and discharging cost, and emissions cost.

[0099] The cost of electricity supply is:

[0100] C supply =Σ t P tot,t ·c tot,t

[0101] Among them, P tot,t Let c be the total power supply at time t. tot,t This represents the unit electricity supply cost at time t.

[0102] The cost of energy storage charging and discharging is:

[0103] C storage =Σ t∈T (P t ch ·c ch +P t dis ·c dis )

[0104] Among them, P tch ,P t dis Let c be the charging and discharging power of the stored energy at time t. ch ,c dis This refers to the charging and discharging cost coefficient.

[0105] The emission cost is:

[0106] C emission =Σ t∈T P t th ·c emission

[0107] Among them, P t th Let c be the power output of the thermal power unit at time t. emission Unit emission cost of thermal power units;

[0108] The total cost function is:

[0109] C total =C supply +C storage +C emissions

[0110] Definition of the objective function for maximizing reliability: In the planning and scheduling optimization model, a reliability objective is introduced. One part of the objective function is set to maximize the system's ramp reliability, with the objective being to minimize the LOLR constraint.

[0111]

[0112] Where, ΔD t Let t be the hill-climbing requirement. Let be the capacity of the shared ramp-up backup pool at time t, and 1(.) be the indicator function;

[0113] In summary, the planning and scheduling optimization model is as follows:

[0114] minC total =∑ t∈T (P tot,t ·c tot,t +P t ch ·c ch +P t dis ·c dis +P t th ·c emission ).

[0115] Optionally, the method of using a split-bar optimization and column constraint generation algorithm to solve the joint optimization objective function to obtain the optimal capacity configuration scheme includes:

[0116] Distributed robust optimization modeling: Let the uncertainty set U represent the possible distribution set of random variables such as wind power, photovoltaic output, and load demand. The distributed robust optimization objective is:

[0117] min x∈X max P∈U E P [f(x,ξ)]

[0118] Where x represents the set of variables for capacity configuration and scheduling decisions; ξ represents the vector of uncertain parameters, including wind and solar power output, load demand, etc.; f(x,ξ) represents the system operating cost or risk indicator; U represents the set of uncertain distributions, i.e., the set of all possible probability distributions; E P The expectation operator is represented under distribution P; the uncertainty set is characterized by moment constraints or Wasserstein distance to ensure robustness to deviations from the probability distribution.

[0119] The column constraint generation algorithm solution framework decomposes the robustness optimization problem into a main problem and sub-problems: the main problem is used to determine the capacity configuration decision variables and includes a finite number of uncertain scenarios; the sub-problems, given the conditions, search for the most unfavorable scenario or distribution to update the robustness constraints.

[0120] Initialize scene set S 0 Solve the initial principal problem:

[0121]

[0122] Among them, c T x represents the capacity investment cost; g (x,s) represents the system operation constraints in scenario s; S 0 Represents the initial set of scenes;

[0123] After solving the main problem, a new scenario S is generated through subproblems. * And determine its impact on the feasibility and robustness of the current solution. If the conditions are not met, then S will be removed. * Add the new scenario to the scenario set and return to the main problem iteration; stop the iteration when the new scenario no longer improves the objective value, or when the robustness constraint satisfies the convergence accuracy ∈ , and output the final optimal capacity configuration solution x. * .

[0124] According to another aspect of the present invention, a capacity configuration optimization system for a wind-solar-thermal-storage integrated system that takes into account ramping events is also provided, comprising:

[0125] The module for generating a comprehensive scenario set is used to acquire historical configuration data under weather forecast disturbances, combine extreme value theory to construct an extreme ramp demand model, and generate a comprehensive scenario set containing a set of extreme ramp event scenarios. The comprehensive scenario set serves as the input to the planning and scheduling optimization model in the power system.

[0126] Establish a LOLR constraint module to introduce the probability that the power system fails to meet the ramping demand during the entire dispatch cycle, and establish reliability LOLR constraints through opportunity constraints or conditional risk value.

[0127] Establish a basic and other constraint module to establish a shared ramp-up reserve pool with unified constraints for wind, solar, thermal and energy storage. Use the shared ramp-up reserve pool as the basic constraint for the planning and scheduling optimization model, and determine the other constraints for the planning and scheduling optimization model, including LOLR conditions and carbon emission constraints.

[0128] The optimization model determination module is used to determine the planning and scheduling optimization model with a joint optimization objective that includes minimizing total cost, maximizing reliability, basic constraints, and other constraints.

[0129] The capacity configuration scheme module is used to solve the planning and scheduling optimization model by employing the bibliometric optimization and column constraint generation algorithm, gradually introducing the worst-case scenario to obtain the optimal capacity configuration scheme.

[0130] According to another aspect of the invention, a storage medium is also provided that stores a computer program thereon, which, when executed by a processor, implements the steps of any of the methods described herein.

[0131] According to another aspect of the present invention, an electronic device is also provided, characterized in that it comprises: the aforementioned computer-readable storage medium; and

[0132] One or more processors for executing a program in the computer-readable storage medium.

[0133] Therefore, by constructing a shared ramp-up backup pool, this approach comprehensively utilizes various resources such as the ramp-up margin of thermal power units, the charge / discharge margin of energy storage equipment and energy state constraints, and the upper and lower limits of wind and solar forecast errors. Furthermore, it introduces opportunity constraints or conditional value at risk (CVaR) to control the ramp-up reliability of the system. Simultaneously, by employing an algorithm combining distributed bar optimization (DRO) and column constraint generation (C&CG), the computational scale is effectively reduced and the solution efficiency is improved, thus achieving a comprehensive balance between economy, reliability, and sustainability.

[0134] Thus, by constructing a comprehensive scenario set covering extreme scenarios, establishing LOLR constraints for quantified reliability, creating a unified and coordinated shared ramp-up backup pool for multiple types of resources, and employing efficient solution algorithms, a comprehensive optimization of the capacity configuration of the integrated wind-solar-thermal-storage system in response to ramp-up events was achieved. Specifically, by combining weather forecast disturbances with extreme value theory to generate a comprehensive scenario set, the ability to predict rare but high-risk ramp-up events was significantly enhanced; by establishing LOLR reliability indices through chance constraints or CVaR, fuzzy reliability requirements were transformed into precise mathematical constraints; by constructing a shared ramp-up backup pool to uniformly constrain wind, solar, thermal, and storage resources, the traditional "each power source fights its own battle" model was broken, greatly improving resource coordination efficiency and overall system flexibility; by constructing a multi-objective optimization model considering total cost, reliability, and carbon emissions, a comprehensive balance between economy, reliability, and sustainability was achieved; finally, by employing a bibliometric optimization and column constraint generation algorithm, the solution efficiency of large-scale optimization problems was ensured while fully considering uncertainties. This method effectively solves key technical problems such as over-reliance on a single power source in ramp-up backup configuration, insufficient consideration of multi-dimensional constraints, and low computational efficiency, providing a reliable capacity configuration scheme for the safe and stable operation of power systems with a high proportion of renewable energy access. Attached Figure Description

[0135] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0136] Figure 1 is a flowchart illustrating a method for optimizing the capacity configuration of a wind-solar-thermal-storage integrated system considering ramp-up events, as described in this embodiment.

[0137] Figure 2 is a schematic diagram of the specific process of the capacity configuration optimization method of a wind-solar-thermal-storage integrated system considering ramping events as described in this embodiment.

[0138] Figure 3 is a schematic diagram comparing the convergence process of the algorithm described in this embodiment;

[0139] Figure 4 is a schematic diagram of a capacity configuration optimization system for a wind-solar-thermal-storage integrated system that takes into account ramping events, as described in this embodiment. Detailed Implementation

[0140] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0141] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0142] According to a first aspect of the present invention, a method 100 for optimizing the capacity configuration of a wind-solar-thermal-storage integrated system considering ramp-up events is provided. Referring to FIG1, the method 100 includes:

[0143] S101: Obtain historical configuration data under weather forecast disturbances, combine extreme value theory to construct an extreme ramp demand model, and generate a comprehensive scenario set containing extreme ramp event scenarios. The comprehensive scenario set serves as the input to the planning and scheduling optimization model in the power system.

[0144] S102: Introduce the probability that the power system fails to meet the ramping demand during the entire dispatch cycle, and establish the reliability LOLR constraint condition through opportunity constraint or conditional value of risk.

[0145] S103: Establish a shared ramp-up reserve pool with unified constraints for wind, solar, thermal and energy storage, use the shared ramp-up reserve pool as the basic constraint condition for the planning and scheduling optimization model, and determine the remaining constraints of the planning and scheduling optimization model including LOLR conditions and carbon emission constraints.

[0146] S104: Determine the planning and scheduling optimization model with a joint optimization objective that includes minimizing total cost, maximizing reliability, basic constraints, and other constraints;

[0147] S105: The planning and scheduling optimization model is solved by using the sub-Blu-bar optimization and column constraint generation algorithm, and the worst-case scenario is gradually introduced to obtain the optimal capacity configuration scheme.

[0148] Specifically, this embodiment considers the capacity configuration optimization problem of a wind-solar-thermal-storage integrated system under different load levels. The system includes thermal power units, wind power units, photovoltaic units, energy storage systems, and their corresponding scheduling strategies. Changes in load demand, uncertainties in wind and solar power output, and the capacity and charge / discharge characteristics of energy storage devices complicate the system optimization problem. By applying an algorithm combining Distributed Bar Optimization (DRO) and Column Constraint Generation (C&CG), this embodiment effectively improves the solution efficiency and ensures system reliability and economy under various operating conditions. The algorithm flowchart is shown in Figure 2.

[0149] Power System Overview: The parameters for a specific 24-hour power system, including wind, solar, thermal, load, and energy storage, are as follows:

[0150] Thermal power units: maximum single unit capacity 600MW, minimum output 200MW, regulation rate 50MW / 5min;

[0151] Wind turbine units: wind power capacities are set at 300MW, 400MW, and 500MW respectively, with wind power output fluctuating between ±50MW;

[0152] Photovoltaic units: Maximum 400MW during the day, 0MW at night, with a fluctuation of ±30MW;

[0153] Energy storage system: capacities Cst are 100MWh and 200MWh respectively, upper limits of charge and discharge power are 50MW and 100MW respectively, and charge and discharge efficiency is 90%;

[0154] Load demand D t Three different load levels: Low load: maximum 600MW, daily average 500MW; Medium load: maximum 900MW, daily average 750MW; High load: maximum 1200MW, daily average 1000MW.

[0155] The system cost parameters are as follows: thermal power fuel and emission cost: 300 yuan / MWh; energy storage charging and discharging cost: 50 yuan / MWh; load loss penalty cost: 2000 yuan / MWh.

[0156] This embodiment uses Distributed Bar Optimization (DRO) to handle the randomness of wind and solar power output and load demand, constructs an uncertainty set using Wasserstein distance, and combines it with Column Constraint Generation (C&CG) algorithm to reduce computational scale.

[0157] By iteratively solving the main problem and its subproblems, and continuously updating the worst-case scenario, the optimal solution is eventually approximated.

[0158] This example demonstrates the optimization results of different energy storage configurations under three load levels. The detailed values ​​and performance comparisons for each configuration are shown in Table 1.

[0159] Table 1 Optimization results of energy storage configuration under different load levels

[0160]

[0161] Under low load conditions, the total operating cost is approximately RMB 1.85 × 10^6 per day; the utilization rates of wind power and photovoltaic power are 75% and 85% respectively; the utilization rate of energy storage equipment is 0.8 times per day; the maximum ramp-up requirement is 100MW, which is fully met; and no load loss occurred.

[0162] For medium load conditions, the total operating cost is approximately RMB 2.15 × 10^6 per day; the utilization rates of wind and solar power are 80% for wind power and 78% for solar power; the utilization rate of energy storage equipment is 1.2 times per day; the maximum ramp-up requirement is 150MW, which is fully met; and no load loss occurred.

[0163] For high load conditions, the total operating cost is approximately RMB 2.55 × 10^6 per day; wind and solar power utilization rates are 75% for wind power and 70% for solar power; energy storage equipment utilization rate is 1.5 times per day; the maximum ramp-up requirement is 250MW, which is fully met; no load loss occurred. The convergence changes of the algorithm for the three load conditions are shown in Figure 3.

[0164] This embodiment demonstrates that the optimization method combining Distributed Bar Optimization (DRO) and Column Constraint Generation (C&CG) can significantly reduce system operating costs and ensure system reliability during ramp-up events under different load levels and energy storage configurations. Furthermore, by optimizing energy storage configuration, this method can effectively improve system flexibility and stability, adapting to different operating conditions and demand fluctuations.

[0165] Optionally, the acquisition of historical configuration data under weather forecast disturbances, combined with extreme value theory, constructs an extreme ramp demand model, and generates a comprehensive scenario set containing extreme ramp event scenarios. This comprehensive scenario set serves as input to the planning and scheduling optimization model in the power system, including:

[0166] Historical configuration data under weather forecast disturbances includes acquiring numerical sequences D over continuous time periods. t , where D t This represents the load demand or renewable energy output at time t under weather forecast disturbance, where t∈{1,2,…,T} and T is the total time length;

[0167] Define the gradient between adjacent time periods:

[0168] R t =D t -D t-1 ,t=2,…,T

[0169] In the formula, R t This represents the magnitude of the change at time t relative to time t-1;

[0170] Set a preset interval or threshold to identify extreme climbing samples: Use the block maximum value method to divide the sequence into intervals, and take the maximum climbing amount Mn = max(R1,…,R) for each interval. n Alternatively, an over-threshold method can be used, setting a threshold u and retaining only those satisfying |R t |>u's sample;

[0171] Statistical modeling is performed on the extracted extreme samples to fit their tail distribution:

[0172] Under the block maximum method, the extreme ramp rate follows a generalized extreme value distribution:

[0173]

[0174] Where μ is the position parameter, σ>0 is the scale parameter, and ξ is the shape parameter;

[0175] Under the over-threshold method, the extreme gradient amounts follow a generalized Pareto distribution:

[0176]

[0177] Where β is the scale parameter and ξ is the shape parameter;

[0178] The above distribution parameters μ, σ, ξ, β are obtained through maximum likelihood estimation or probability weighted moment estimation.

[0179] Extreme gradient amounts are generated based on sampling from the fitted distribution:

[0180] R extreme ~GEV(μ,σ,ξ)orR extreme ~GPD(β,ξ)

[0181] By superimposing the extreme gradients onto the baseline sequence, we obtain the extreme scenario:

[0182] D t extreme =D t-1 +R extreme

[0183] Among them, D t extreme This indicates the load or output value including extreme uphill sections;

[0184] The extreme climbing scenarios are combined with the set of regular scenarios to form a comprehensive scenario set:

[0185] Ω={Ω normal ,Ω extreme}

[0186] Among them, Ω normal Ω represents a set of common scenarios. extreme This represents the set of extreme scenarios generated by extreme climbing events, and the comprehensive scenario set serves as the input to the planning and scheduling optimization model.

[0187] Optionally, the probability that the power system fails to meet the ramp-up demand throughout the entire dispatch cycle is introduced, and LOLR reliability constraints are established through opportunity constraints or conditional value-at-risk (VAT) conditions, including:

[0188] During the scheduling period, let the system load demand at time t be D. t The total power supplied at time t is Ptot,t, where Ptot,t represents the total power supplied by dispatchable power sources in the system at time t, including thermal power, renewable energy, and energy storage.

[0189] Define the system's ramp requirement at time t as:

[0190] ΔD t =D t -D t-1

[0191] Where, ΔD t >0 indicates an upward climbing demand, ΔD t <0 indicates a downward ramp demand;

[0192] Define the backup climbing capacity that the system can provide at time t as: These represent the system's available upside reserve capacity and downside reserve capacity, respectively.

[0193] Define the LOLR constraint as the probability that the system fails to meet the ramp requirement within the entire scheduling cycle:

[0194]

[0195] Here, 1(.) is an indicator function that takes the value 1 when the condition inside the parentheses is true, and 0 otherwise;

[0196] LOLR constraint modeling based on chance constraints: Set the reliability level parameter β∈(0,1) to represent the maximum allowable probability of the LOLR constraint;

[0197] The LOLR constraint conditions are modeled using a chance constraint form.

[0198]

[0199] Wherein, the constraint indicates that at least at the probability level of 1-β, the system's ramping capability can meet the load ramping requirements;

[0200] LOLR constraint modeling based on conditional value at risk: defining a random variable Z t Indicates the amount of hill-climbing default at time t:

[0201]

[0202] Among them, Z t >0 indicates that the system's climbing ability is insufficient at time t;

[0203] Define the risk level parameter α∈(0,1) to represent the confidence level of the conditional value of risk. Then the expression for the conditional value of risk under the LOLR constraint is:

[0204]

[0205] Where η is an auxiliary decision variable; (x) + =max(x,0); E represents the expectation operator;

[0206] Apply constraints to the model:

[0207] CVaR α (Z)≤ζ

[0208] Where ζ represents the maximum permissible expected climbing loss level;

[0209] By employing opportunity constraint modeling or conditional value-at-risk (VAT) modeling, the LOLR constraints can be embedded into the planning and scheduling optimization models of the power system to ensure system reliability under extreme ramping events. When using opportunity constraints, the focus is on controlling the probability of ramping failure; when using conditional VAT, the focus is on controlling the expected severity of ramping failure.

[0210] Optionally, the basic constraints include:

[0211] Available ramping margin for thermal power units: Let the output of the thermal power unit at time t be P. t th The installed capacity is C th If the maximum allowable climbing rate is RR, then the available climbing margin of the thermal power unit at time t is:

[0212]

[0213] in, This indicates the maximum reserve capacity that a thermal power unit can provide at time t;

[0214] Energy storage device ramp-up capability: Let the capacity of the energy storage device be C. st The charging power at time t is P t ch The discharge power is P t dis The energy storage's state of charge is SOC. t The charge and discharge efficiencies are η ch ,ηdis .

[0215] The upper limit of the charging power of the energy storage device at time t is:

[0216] P t ch ≤P ch,max =γ·C st

[0217] The upper limit of the discharge power of the energy storage device at time t is:

[0218] P t dis ≤P dis,max =γ·C st

[0219] Wherein, γ is the rated power capacity coefficient;

[0220] The dynamic energy constraint of the energy storage device at time t is:

[0221]

[0222] And satisfy the energy boundary constraints:

[0223] 0≤SOC t ≤C st

[0224] Define the power margin of the energy storage device for hill climbing at time t as:

[0225]

[0226] Upper and lower limits of wind and solar forecast error:

[0227] Assume the predicted wind power output is Photovoltaic power output forecast value The upper and lower deviation factors of the wind and solar uncertainty are δ w ,δ s :

[0228] The actual wind power output then satisfies:

[0229]

[0230] The actual output of photovoltaic power meets the following requirements:

[0231]

[0232] The reserve margin introduced by the wind and solar power output error is defined as follows:

[0233]

[0234] Overall definition of a shared backup pool: At time t, the capacity of the shared ramp-up backup pool is:

[0235]

[0236] The backup pool serves as a joint resource pool for the system to meet the ramp-up rate requirements, and its constraints are as follows:

[0237]

[0238] Optionally, the remaining constraints include:

[0239] In addition to the basic constraints of the shared ramp backup pool, the remaining constraints are as follows:

[0240] Load reduction constraint: Let the load reduction amount be L. t Its upper limit is L max Then we have:

[0241] 0≤L t ≤L max

[0242] Energy storage lifetime constraint: During the entire cycle, the charge and discharge cycles shall not exceed the upper limit of the cycle life N. cycle ·C st :

[0243] ∑ t (P t ch +P t dis )≤N cycle ·C st

[0244] Carbon emission constraints: Let the emission factor for thermal power be EF, and the carbon emission ceiling be C. cap Then we have:

[0245] ∑ t EF·P t th ≤C cap

[0246] Investment budget constraints: Let the unit investment costs of wind power, photovoltaic, thermal power, and energy storage be IC, respectively. w IC s IC th IC st The upper limit of the investment budget is B. cap Then we have:

[0247] ICw C w +IC s C s +IC th C th +IC st C st ≤B cap

[0248] Reliability metric constraints: including opportunity-based LOLR control or risk mitigation mechanisms based on conditional value at risk (CVaR).

[0249] Optionally, the determination of the planning and scheduling optimization model based on a joint optimization objective including minimizing total cost, maximizing reliability, basic constraints, and other constraints includes:

[0250] The objective function for minimizing total cost is defined as including electricity supply cost, energy storage charging and discharging cost, and emissions cost.

[0251] The cost of electricity supply is:

[0252] C supply =∑ t P tot,t ·c tot,t

[0253] Among them, P tot,t Let c be the total power supply at time t. tot,t This represents the unit electricity supply cost at time t.

[0254] The cost of energy storage charging and discharging is:

[0255] C storage =∑ t∈T (P t ch ·c ch +P t dis ·c dis )

[0256] Among them, P t ch ,P t dis Let c be the charging and discharging power of the stored energy at time t. ch ,c dis This refers to the charging and discharging cost coefficient.

[0257] The emission cost is:

[0258] C emission =∑ t∈T P t th ·c emission

[0259] Among them, P t th Let c be the power output of the thermal power unit at time t. emission Unit emission cost of thermal power units;

[0260] The total cost function is:

[0261] C total =C supply +C storage +C emissions

[0262] Definition of the objective function for maximizing reliability: In the planning and scheduling optimization model, a reliability objective is introduced. One part of the objective function is set to maximize the system's ramp reliability, with the objective being to minimize the LOLR constraint.

[0263]

[0264] Where, ΔD t Let t be the hill-climbing requirement. Let be the capacity of the shared ramp-up backup pool at time t, and 1(.) be the indicator function;

[0265] In summary, the planning and scheduling optimization model is as follows:

[0266] minC total =∑ t∈T (P tot,t ·c tot,t +P t ch ·c ch +P t dis ·c dis +P t th ·c emission ).

[0267] Optionally, the method of using a split-bar optimization and column constraint generation algorithm to solve the joint optimization objective function to obtain the optimal capacity configuration scheme includes:

[0268] Distributed robust optimization modeling: Let the uncertainty set U represent the possible distribution set of random variables such as wind power, photovoltaic output, and load demand. The distributed robust optimization objective is:

[0269] min x∈X max P∈U E P [f(x,ξ)]

[0270] Where x represents the set of variables for capacity configuration and scheduling decisions; ξ represents the vector of uncertain parameters, including wind and solar power output, load demand, etc.; f(x,ξ) represents the system operating cost or risk indicator; U represents the set of uncertain distributions, i.e., the set of all possible probability distributions; E P The expectation operator is represented under distribution P; the uncertainty set is characterized by moment constraints or Wasserstein distance to ensure robustness to deviations from the probability distribution.

[0271] The column constraint generation algorithm solution framework decomposes the robustness optimization problem into a main problem and sub-problems: the main problem is used to determine the capacity configuration decision variables and includes a finite number of uncertain scenarios; the sub-problems, given the conditions, search for the most unfavorable scenario or distribution to update the robustness constraints.

[0272] Initialize scene set S 0 Solve the initial principal problem:

[0273]

[0274] Among them, c T x represents the capacity investment cost; g (x,s) represents the system operation constraints in scenario s; S 0 Represents the initial set of scenes;

[0275] After solving the main problem, a new scenario S is generated through subproblems. * And determine its impact on the feasibility and robustness of the current solution. If the conditions are not met, then S will be removed. * Add the new scenario to the scenario set and return to the main problem iteration; stop the iteration when the new scenario no longer improves the objective value, or when the robustness constraint satisfies the convergence accuracy ∈ , and output the final optimal capacity configuration solution x. * .

[0276] Thus, by constructing a comprehensive scenario set covering extreme scenarios, establishing LOLR constraints for quantified reliability, creating a unified and coordinated shared ramp-up backup pool for multiple types of resources, and employing efficient solution algorithms, a comprehensive optimization of the capacity configuration of the integrated wind-solar-thermal-storage system in response to ramp-up events was achieved. Specifically, by combining weather forecast disturbances with extreme value theory to generate a comprehensive scenario set, the ability to predict rare but high-risk ramp-up events was significantly enhanced; by establishing LOLR reliability indices through chance constraints or CVaR, fuzzy reliability requirements were transformed into precise mathematical constraints; by constructing a shared ramp-up backup pool to uniformly constrain wind, solar, thermal, and storage resources, the traditional "each power source fights its own battle" model was broken, greatly improving resource coordination efficiency and overall system flexibility; by constructing a multi-objective optimization model considering total cost, reliability, and carbon emissions, a comprehensive balance between economy, reliability, and sustainability was achieved; finally, by employing a bibliometric optimization and column constraint generation algorithm, the solution efficiency of large-scale optimization problems was ensured while fully considering uncertainties. This method effectively solves key technical problems such as over-reliance on a single power source in ramp-up backup configuration, insufficient consideration of multi-dimensional constraints, and low computational efficiency, providing a reliable capacity configuration scheme for the safe and stable operation of power systems with a high proportion of renewable energy access.

[0277] According to another aspect of the present invention, a capacity configuration optimization system 400 for a wind-solar-thermal-storage integrated system that takes into account ramping events is also provided. Referring to FIG4, the system 400 includes:

[0278] The module 410 for generating a comprehensive scenario set is used to acquire historical configuration data under weather forecast disturbances, combine extreme value theory to construct an extreme ramp demand model, and generate a comprehensive scenario set containing a set of extreme ramp event scenarios. The comprehensive scenario set serves as the input to the planning and scheduling optimization model in the power system.

[0279] Establish LOLR constraint module 420 to introduce the probability that the power system fails to meet the ramping demand during the entire dispatch cycle, and establish reliability LOLR constraints through opportunity constraints or conditional risk value.

[0280] Establish a basic and remaining constraint module 430 to establish a shared ramp-up reserve pool with unified constraints for wind, solar, thermal and energy storage. Use the shared ramp-up reserve pool as the basic constraint for the planning and scheduling optimization model, and determine the remaining constraints of the planning and scheduling optimization model including LOLR conditions and carbon emission constraints.

[0281] The optimization model module 440 is used to determine the planning and scheduling optimization model with a joint optimization objective that includes minimizing total cost, maximizing reliability, basic constraints and other constraints.

[0282] The capacity configuration scheme module 450 is obtained, which is used to solve the planning and scheduling optimization model by using the sub-Bruker optimization and column constraint generation algorithm, and gradually introduces the worst scenario to obtain the optimal capacity configuration scheme.

[0283] The wind-solar-thermal-storage integrated system capacity configuration optimization system 400 considering ramp events in one embodiment of the present invention corresponds to the wind-solar-thermal-storage integrated system capacity configuration optimization method 100 considering ramp events in another embodiment of the present invention, and will not be described again here.

[0284] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0285] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0286] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0287] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0288] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

Claims

1. A method for optimizing the capacity configuration of a wind-solar-thermal-storage integrated system considering ramp-up events, characterized in that, include: Historical configuration data under weather forecast disturbances is acquired, and an extreme ramp demand model is constructed using extreme value theory. This generates a comprehensive scenario set containing extreme ramp event scenarios, which serves as the input to the power system planning and dispatch optimization model. The probability that the power system fails to meet ramp demand throughout the entire dispatch cycle is introduced, and reliability LOLR constraints are established through chance constraints or conditional value of risk. A unified shared ramp reserve pool for wind, solar, thermal, and energy storage is established, serving as the basic constraint for the planning and dispatch optimization model. Other constraints for the planning and dispatch optimization model, including LOLR and carbon emission constraints, are determined. The planning and dispatch optimization model is determined using a joint optimization objective that includes minimizing total cost, maximizing reliability, the basic constraints, and the remaining constraints. The planning and dispatch optimization model is solved using a split-Brow bar optimization and column constraint generation algorithm, gradually introducing the most unfavorable scenario to obtain the optimal capacity configuration scheme.

2. The method according to claim 1, characterized in that, The process involves acquiring historical configuration data under weather forecast disturbances, combining it with extreme value theory to construct an extreme ramp demand model, and generating a comprehensive scenario set containing extreme ramp event scenarios. This comprehensive scenario set serves as the input to the planning and scheduling optimization model in the power system. The model includes historical configuration data under weather forecast disturbances, comprising numerical sequences D over continuous time periods. t , where D t This represents the load demand or renewable energy output at time t under weather forecast disturbances. t∈ {1,2,...,T}, where T is the total time duration; the gradient between adjacent time periods is defined as: R t =D t -D t-1 In the formula t=2,…,T, R t This represents the change in magnitude from time t to time t-1; a preset interval or threshold is set to identify extreme climbing samples: the block maximum value method is used to divide the sequence into intervals, and the maximum climbing amount Mn = max(R1,…,R) is taken for each interval. n Alternatively, an over-threshold method can be used, setting a threshold u and retaining only those satisfying |R t |>u’s sample; statistical modeling is performed on the extracted extreme samples to fit their tail distribution: under the block maximum method, the extreme ramp amount follows the generalized extreme value distribution: Where μ is the location parameter, σ > 0 is the scale parameter, and ξ is the shape parameter; under the over-threshold method, the extreme gradient follows a generalized Pareto distribution: Where β is the scale parameter and ξ is the shape parameter; the above distribution parameters μ, σ, ξ, β are obtained through maximum likelihood estimation or probability weighted moment estimation; extreme gradient amounts are generated based on the fitted distribution sampling: R extreme ~GEV(μ,σ,ξ)orR extreme ~GPD(β,ξ) superimposes the extreme gradients onto the baseline sequence to obtain the extreme scenario: D t extreme =D t-1 +R extreme Among them, D t extreme This represents the load or output value involving extreme climbing; the extreme climbing scenario is merged with the set of regular scenarios to form a comprehensive scenario set: Ω = {Ω normal ,Ω extreme } where Ω normal Ω represents a set of common scenarios. extreme This represents the set of extreme scenarios generated by extreme climbing events, and the comprehensive scenario set serves as the input to the planning and scheduling optimization model.

3. The method according to claim 1, characterized in that, The probability that the power system fails to meet ramp-up demand throughout the entire dispatch cycle is introduced, and LOLR reliability constraints are established through opportunity constraints or conditional value of risk. This includes: assuming the system load demand at time t is D within the dispatch cycle. t Let the total power supply at time t be Ptot,t, where Ptot,t represents the total power supply of dispatchable power sources in the system at time t, including thermal power, renewable energy, and energy storage; and define the system's ramp demand at time t as ΔD. t =D t -D t-1 Where, ΔD t >0 indicates an upward climbing demand, ΔD t <0 indicates a descent / climbing demand; the system's available backup climbing capacity at time t is defined as... These represent the system's available ramp-up and ramp-down reserve capacities, respectively; the LOLR constraint is defined as the probability that the system fails to meet ramp-up requirements throughout the entire scheduling cycle: Where 1(.) is an indicator function, which takes the value of 1 when the condition in parentheses is true, and 0 otherwise; LOLR constraint modeling based on chance constraints: set the reliability level parameter β∈(0,1) to represent the maximum permissible probability of the LOLR constraint; model the LOLR constraint in the form of chance constraints; The constraint indicates that, at least at a probability level of 1-β, the system's ramping capability can meet the load ramping requirements; LOLR constraint modeling based on conditional value of risk: defining a random variable Z. t Indicates the amount of hill-climbing default at time t: Among them, Z t >0 indicates that the system's climbing ability is insufficient at time t; defining the risk level parameter α∈(0,1), representing the confidence level of the conditional risk value, the expression for the conditional risk value under the LOLR constraint is: Where η is an auxiliary decision variable; (x) + =max(x,0); E represents the expectation operator; impose constraints in the model: CVaR α (Z)≤ζ, where ζ represents the maximum permissible expected ramp loss level. Through chance constraint modeling or conditional value-at-risk (VAT) modeling, the LOLR constraint can be embedded into the planning and scheduling optimization model of the power system to ensure system reliability under extreme ramp events. When using chance constraints, the focus is on controlling the probability of ramp failure; when using conditional VAT, the focus is on controlling the expected severity of ramp failure.

4. The method according to claim 1, characterized in that, The basic constraints include: available ramping margin for thermal power units: Let the output of the thermal power unit at time t be P. t th The installed capacity is C th If the maximum allowable climbing rate is RR, then the available climbing margin of the thermal power unit at time t is: in, This represents the maximum reserve capacity that a thermal power unit can provide at time t; the ramp-up capability of the energy storage device: Let the capacity of the energy storage device be C. st The charging power at time t is P t ch The discharge power is P t dis The energy storage's state of charge is SOC. t The charge and discharge efficiencies are η ch ,η dis The upper limit of the charging power of the energy storage device at time t is: P t ch ≤P ch,max =γ·C st The upper limit of the discharge power of the energy storage device at time t is: P t dis ≤P dis,max =γ·C st Where γ is the rated power capacity coefficient; the energy dynamic constraint of the energy storage device at time t is: And it satisfies the energy boundary constraint: 0 ≤ SOC t ≤C st Define the power margin of the energy storage device for hill climbing at time t as: Upper and lower limits of wind and solar forecasting error: Let the predicted wind power output be... Photovoltaic power output forecast value The upper and lower deviation factors of the wind and light uncertainty are δ w ,δ s Then the actual wind power output satisfies: The actual output of photovoltaic power meets the following requirements: The reserve margin introduced by the wind and solar power output error is defined as follows: Overall definition of a shared backup pool: At time t, the capacity of the shared ramp-up backup pool is: The backup pool serves as a joint resource pool for the system to meet the ramp-up rate requirements, and its constraints are as follows:

5. The method according to claim 4, characterized in that, The remaining constraints include, in addition to the basic constraints of the shared ramp-up backup pool, the following constraints: load reduction constraint: let the load reduction amount be L. t Its upper limit is L max Then we have: 0≤L t ≤L max Energy storage lifetime constraint: During the entire cycle, the charge and discharge cycles shall not exceed the upper limit of the cycle life N. cycle ·C st :∑ t (P t ch +P t dis )≤N cycle ·C st Carbon emission constraints: Let the emission factor for thermal power be EF, and the carbon emission ceiling be C. cap Then we have: ∑ t EF·P t th ≤C cap Investment budget constraints: Let the unit investment costs of wind power, photovoltaic, thermal power, and energy storage be IC, respectively. w IC s IC th IC st The upper limit of the investment budget is B. cap Then we have: IC w C w +IC s C s +IC th C th +IC st C st ≤B cap Reliability metric constraints: including opportunity-based LOLR control or risk mitigation mechanisms based on conditional value at risk (CVaR).

6. The method according to claim 1, characterized in that, The aforementioned joint optimization objective, encompassing minimizing total cost, maximizing reliability, basic constraints, and other constraints, determines the planning and scheduling optimization model. This includes: defining the objective function to minimize total cost, which includes electricity supply cost, energy storage charging / discharging cost, and emission cost; the electricity supply cost is: C supply =∑ t P tot,t ·c tot,t Among them, P tot,t Let c be the total power supply at time t. tot,t Let C be the unit electricity supply cost at time t; the energy storage charging and discharging cost is: C storage =∑ t∈T (P t ch ·c ch +P t dis ·c dis Among them, P t ch ,P t dis Let c be the charging and discharging power of the stored energy at time t. ch ,c dis The charging and discharging cost coefficient is given by: C. The emission cost is: emission =∑ t∈T P t th ·c emission Among them, P t th Let c be the power output of the thermal power unit at time t. emission The unit emission cost of the thermal power unit is C; the total cost function is: C total =C supply +C storage +C emissions Definition of the objective function for maximizing reliability: In the planning and scheduling optimization model, a reliability objective is introduced. One part of the objective function is set to maximize the system's ramp reliability, with the objective being to minimize the LOLR constraint. Where, ΔD t Let t be the hill-climbing requirement at time t. Let C be the capacity of the shared ramp-up backup pool at time t, and 1(.) be the indicator function; in summary, the planning and scheduling optimization model is: minC total =∑ t∈T (P tot,t ·c tot,t +P t ch ·c ch +P t dis ·c dis +P t th ·c emission ).

7. The method according to claim 6, characterized in that, The method described above employs a sub-Bruker optimization and column constraint generation algorithm to solve the joint optimization objective function and obtain the optimal capacity configuration scheme. This includes: distributed robust optimization modeling: Let the uncertainty set U represent the possible distribution set of random variables such as wind power, photovoltaic output, and load demand. The sub-Bruker optimization objective is: min x∈X max P∈U E P [f(x,ξ)] where x represents the set of capacity configuration and scheduling decision variables; ξ represents the vector of uncertain parameters, including wind and solar power output, load demand, etc.; f(x,ξ) represents the system operating cost or risk indicator; U represents the set of uncertain distributions, i.e., the set of all possible probability distributions; E P The expectation operator is represented under distribution P; the uncertainty set is characterized using moment constraints or Wasserstein distance to ensure robustness to deviations from the probability distribution; the column constraint generation algorithm solution framework decomposes the scalar robust optimization problem into a main problem and subproblems: the main problem is used to determine the capacity configuration decision variables and includes a finite number of uncertain scenarios; the subproblems, given conditions, search for the most unfavorable scenario or distribution to update the robustness constraints; the scenario set S is initialized. 0 Solve the initial principal problem: Among them, c T x represents the capacity investment cost; g (x,s) represents the system operation constraints in scenario s; S 0 This represents the initial set of scenarios; after solving the main problem, a new scenario S is generated through subproblems. * And determine its impact on the feasibility and robustness of the current solution. If the conditions are not met, then S will be removed. * Add the scenario to the scenario set and return to the main problem iteration; stop the iteration when the newly added scenario no longer improves the objective value, or when the robustness constraint satisfies the convergence accuracy ∈ , and output the final optimal capacity configuration solution x. * .

8. A capacity configuration optimization system for a wind-solar-thermal-storage integrated system considering ramp-up events, characterized in that, include: The module for generating a comprehensive scenario set is used to acquire historical configuration data under weather forecast disturbances. Combining extreme value theory, it constructs an extreme ramping demand model and generates a comprehensive scenario set containing extreme ramping event scenarios. This comprehensive scenario set serves as the input to the planning and dispatch optimization model in the power system. The module for establishing LOLR constraints is used to introduce the probability that the power system will fail to meet ramping demand throughout the entire dispatch cycle. Reliability LOLR constraints are established through chance constraints or conditional value of risk. The module for establishing basic and remaining constraints is used to establish a shared ramping reserve pool with unified constraints for wind, solar, thermal, and energy storage. This shared ramping reserve pool serves as the basic constraint for the planning and dispatch optimization model, and the remaining constraints for the planning and dispatch optimization model, including LOLR conditions and carbon emission constraints, are determined. The optimization model determination module is used to determine the planning and scheduling optimization model with a joint optimization objective that includes minimizing total cost, maximizing reliability, basic constraints, and other constraints. The capacity configuration scheme module is used to solve the planning and scheduling optimization model by employing the bibliometric optimization and column constraint generation algorithm, gradually introducing the worst-case scenario to obtain the optimal capacity configuration scheme.

9. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 9; and one or more processors for executing the program in the computer-readable storage medium.

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