Electro-hydrogen conversion new energy base capacity planning configuration optimization method

Abstract

The invention relates to an electro-hydrogen conversion new energy base capacity planning configuration optimization method, belongs to the technical field of new energy, and solves the technical problems of seasonal imbalance, excessive conservative traditional robust optimization, imperfect evaluation system and insufficient multi-energy flow coupling optimization in existing new energy base capacity configuration. Comprising the steps of obtaining historical data in a new energy base scheduling period; generating a typical scene set based on the historical operation data, constructing a confidence interval of the wind and light output uncertainty of the new energy base, and obtaining a confidence robustness gamma value; constructing an electricity-hydrogen collaborative capacity optimization model with the goal of maximizing the comprehensive efficiency of the new energy base power system; converting the electricity-hydrogen collaborative capacity optimization model into a robust optimization model based on the confidence interval and the confidence robustness gamma value of the wind-solar output uncertainty; and solving the robust optimization model by using a CPLEX solver to obtain a new energy base capacity planning configuration scheme. And reliable low-carbon operation of the new energy base is realized.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to a method for optimizing the capacity planning and configuration of new energy bases that integrates electro-hydrogen conversion. Background Technology

[0002] In recent years, efforts have been accelerated to construct large-scale new energy bases in deserts, Gobi, and wastelands, and to build clean energy transmission systems to support carbon neutrality goals. Against this backdrop, as the core of energy supply, these new energy bases face inherent challenges due to the strong volatility and intermittency of their wind and solar resources, creating a cross-timescale demand for regulatory resources. The current regulation model, dominated by fossil fuels, not only perpetuates high carbon emissions but also, due to the rate of regulation, leads to both wind and solar curtailment and power supply shortages, severely restricting the bases' transmission capacity and efficiency.

[0003] Electro-hydrogen conversion technology, with its flexibility in energy transfer across time and space and the unique properties of clean fuel production, provides a new paradigm for a systematic solution to the large-scale consumption and reliable supply of highly volatile renewable energy. It demonstrates key technological path value in optimizing the capacity configuration structure of new energy bases and improving the efficiency of multi-energy synergistic operation, possessing strategic potential to break through current predicaments. However, the introduction of electro-hydrogen conversion technology involves issues such as multi-energy flow coupling and complex operation scheduling. How to achieve synergistic optimization of electro-hydrogen units with wind and solar turbines and energy storage facilities in the capacity configuration of bases still requires in-depth research.

[0004] Existing research has several key areas requiring further investigation: First, while current studies focus on the synergy of traditional elements such as wind, solar, hydro, thermal, and energy storage, they lack sufficient exploration of the coupling characteristics of emerging multi-energy flow technologies like electro-hydrogen conversion. There is a lack of spatiotemporal optimization models for the electro-hydrogen-electric conversion chain and wind and solar resources, and hydrogen energy is missing from the framework of synergistic mechanisms for new energy base configuration elements. Second, configuration models are not adequately adapted to the electro-hydrogen coupling characteristics. Mainstream two-layer optimization models suffer from two major limitations: failure to characterize the spatiotemporal coupling characteristics of hydrogen energy storage across seasons, and microgrid-level electro-hydrogen coupling models are not adapted to the ultra-high-voltage transmission scenarios of new energy bases. Third, there is a lack of quantitative analysis of hydrogen energy system characteristics in decision-making methods. In terms of uncertainty handling, stochastic optimization relies on probability distribution assumptions, while robust optimization is overly conservative. At the comprehensive evaluation level, existing systems only focus on carbon emissions, lacking quantitative standards for the unique flexibility of hydrogen energy, such as its seasonal peak-shaving elasticity. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a new energy base capacity planning and configuration optimization method that integrates electro-hydrogen conversion, in order to solve the technical problems existing in the capacity configuration of existing new energy bases, such as seasonal imbalance, overly conservative traditional robust optimization, imperfect evaluation system, and insufficient multi-energy flow coupling optimization.

[0006] The objective of this invention is mainly achieved through the following technical solutions:

[0007] This invention provides a method for optimizing the capacity planning and configuration of new energy bases that integrate electro-hydrogen conversion, comprising the following steps:

[0008] Obtain historical operating data of each device in the new energy base system during the scheduling cycle;

[0009] Based on the historical operating data, a set of typical scenarios is generated. Based on the set of typical scenarios, a confidence interval for the uncertainty of wind and solar power output in the new energy base is constructed, and the confidence robustness Γ value is obtained. An electric-hydrogen coordinated capacity optimization model is constructed with the goal of maximizing the comprehensive efficiency of the power system in the new energy base.

[0010] Based on the confidence interval and confidence robustness Γ value of the uncertainty of wind and solar power output, the electro-hydrogen co-capacity optimization model is converted into a robust optimization model.

[0011] The robust optimization model is solved using the CPLEX solver to obtain the capacity planning and configuration scheme for the new energy base.

[0012] Furthermore, the step of generating a typical scenario set based on the historical operating data, constructing a confidence interval for the uncertainty of wind and solar power output in the new energy base based on the typical scenario set, and obtaining the confidence robustness Γ value includes:

[0013] Clustering algorithms are used to perform cluster analysis on the historical data, and the scheduling cycle is divided into typical scenarios of different seasons to obtain a set of typical scenarios; the weight coefficients of each typical scenario in the set of typical scenarios are fitted based on the probability distribution.

[0014] Based on the aforementioned typical scenarios, a wind and solar power output uncertainty model is constructed using the multi-scenario confidence gap theory.

[0015] Define the confidence interval for wind and solar power output uncertainty, generate a typical scenario set containing the confidence interval for wind and solar power output uncertainty, solve the wind and solar power output uncertainty model based on the typical scenario set, and obtain the confidence robustness Γ value.

[0016] Furthermore, the electro-hydrogen synergistic capacity optimization model includes an objective function and a set of constraints aimed at maximizing the comprehensive efficiency of the power system in the new energy base;

[0017] The objective function is as follows:

[0018] maxΨ=max(I c +U o +L r +R s )

[0019] Among them, Ψ represents the comprehensive efficiency of the power system in the new energy base; Ic U o L r R s These are the installed capacity utilization rate of the power system equipment in the new energy base, the effective utilization rate of the equipment, the utilization rate of renewable energy, and the load guarantee level.

[0020] Furthermore, the set of constraints includes constraints on hydrogen storage capacity variation, constraints on the operation of the electro-hydrogen conversion device, constraints on the operation of thermal power, constraints on the operation of hydropower, constraints on the state of charge of energy storage, constraints on the utilization rate of renewable energy, constraints on load guarantee, and constraints on robustness.

[0021] Furthermore, the electro-hydrogen synergistic capacity optimization model is converted into a robust optimization model, as follows:

[0022]

[0023] Where x is the decision variable for the power system configuration of the new energy base, and ω is the power of each unit in the new energy base; c is the typical scenario index; C is the set of typical scenarios; ω c α is the scene weighting coefficient; α is the risk probability threshold; α c α is the confidence interval adjustment factor; 1-α is the confidence level of the uncertain variable; X is the decision variable matrix; F0 represents the baseline target value under the uncertainty scenario, which is the optimal value of the objective function in the electro-hydrogen synergistic capacity optimization model; β is the significance level; 1+β is the scaling factor for the upper tolerance limit of the objective function; F a This represents the total system cost during the planning period; These represent the actual wind power and solar power output at time t in scenario c, respectively; F c This represents the operating cost in typical scenario c. This represents the operating costs, including uncertainties related to wind and light, in typical scenario c. These represent the equality constraints and inequality constraints in the set of constraints under the influence of the actual output values ​​of wind power and photovoltaic power, respectively. and These are the predicted wind power and solar power outputs at time t in scenario c, respectively; U c It is a set of uncertainties; This represents the set of allowable fluctuation periods for actual wind power output, centered on the predicted value and with the fluctuation radius dynamically controlled by the confidence interval adjustment factor. This represents the set of allowable fluctuation periods for actual photovoltaic output, centered on the predicted value and with the fluctuation radius dynamically controlled by the confidence interval adjustment factor.

[0024] Furthermore, the capacity planning and configuration scheme for the new energy base includes the optimal capacity configuration scheme for the system equipment of the new energy base, the cross-seasonal scheduling path for hydrogen energy storage, robust confidence, and the actual values ​​of curtailment rate and energy shortage rate for each typical scenario.

[0025] The optimal capacity configuration scheme for the new energy base system equipment includes wind power capacity. Photovoltaic capacity Electrolytic cell capacity fuel cell capacity Energy storage capacity

[0026] Furthermore, the constraints on the variation of the hydrogen storage capacity are as follows:

[0027]

[0028] in, The amount of hydrogen stored in hydrogen energy at time t on a typical day; These represent the hydrogen storage and hydrogen release power of hydrogen energy storage at time t on a typical day (s); These represent the hydrogen storage and hydrogen release efficiencies of hydrogen energy storage, respectively; Δt is the unit time interval; S is the total number of typical days; and T is the number of hours in a typical day.

[0029] Furthermore, the ramp-up constraints of the electro-hydrogen conversion device are as follows:

[0030]

[0031] in, These represent the changes in power of the electrolyzer and the hydrogen fuel cell at time t, respectively. These are the upper limits of ramp rate for electrolyzers and hydrogen fuel cells, respectively.

[0032] The operating constraints of the electro-hydrogen conversion unit are as follows:

[0033]

[0034] Among them, U E-H These are the operating state variables of the electrolyzer and the hydrogen fuel cell, and are 0-1 variables; These represent the power outputs of the electrolyzer and the hydrogen fuel cell at time t, respectively. These are the power limits for the electrolyzer and the hydrogen fuel cell, respectively.

[0035] Furthermore, the utilization rate of the renewable energy is as follows:

[0036]

[0037] Among them, L r 1. Renewable energy utilization rate; μ3 is the normalization coefficient of energy curtailment rate; To maximize the theoretical output of renewable energy; γ R P represents the actual energy curtailment rate; π(c) represents the probability of scenario c occurring;curt (c,t) represents the power of wind and solar curtailment at time t in scenario c.

[0038] Furthermore, the load guarantee level is as follows:

[0039]

[0040] Among them, R s The load guarantee level is represented by μ4, which is the energy shortage factor normalization coefficient. Historical peak load; γ U This represents the actual energy deficit rate; P short (c,t) represents the load supply deficit at time t in a typical scenario (c).

[0041] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0042] 1. This invention, by constructing confidence intervals for the uncertainty of wind and solar power output and calculating the confidence robustness Γ value, can more accurately assess and quantify the operational risks of new energy bases in the face of uncertainty. This helps optimize the capacity configuration of new energy bases, enabling them to maintain stable operation under extreme weather conditions, thereby improving the reliability and robustness of new energy bases.

[0043] 2. This invention, by integrating electro-hydrogen conversion technology, enables the synergistic optimization of electro-hydrogen units with wind and solar turbines and energy storage facilities in new energy bases. This not only solves the problem of insufficient exploration of the coupling characteristics of emerging multi-energy flow technologies such as electro-hydrogen conversion in existing research, but also enables more effective utilization of renewable energy, reduces wind and solar curtailment, and improves energy efficiency.

[0044] 3. This invention constructs a hydrogen-electricity synergistic capacity optimization model aimed at maximizing the comprehensive efficiency of the power system in a new energy base. This model considers not only the installed capacity utilization rate, equipment effective utilization rate, renewable energy utilization rate, and load guarantee level, but also the unique flexibility of the hydrogen energy system, such as its seasonal peak-shaving resilience. This contributes to achieving low-carbon and reliable operation of the new energy base throughout its entire lifecycle and significantly improves the resource utilization rate of large-scale new energy bases.

[0045] 4. The Multi-Scenario Confidence Gap Theory (MCGDT) is used to construct asymmetric confidence intervals. The conservatism of the optimization objective is dynamically adjusted by the confidence robustness Γ. This avoids the over-conservatism of traditional robust optimization and overcomes the limitations of stochastic optimization that depends on the probability distribution assumption.

[0046] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0047] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0048] Figure 1 This is a flowchart of a new energy base capacity planning and configuration optimization method integrating electro-hydrogen conversion in an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of a new energy base in an embodiment of the present invention. Detailed Implementation

[0050] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0051] To address the aforementioned technical problems, this invention conducts research on capacity optimization configuration that integrates multi-energy synergy characteristics, which is a key path to resolve the structural contradictions in new energy bases.

[0052] This invention proposes a capacity planning and configuration optimization method for new energy bases that integrates electro-hydrogen conversion. It establishes a source-storage collaborative decision-making mechanism based on multi-scenario confidence gap decision theory (MCGDT), and constructs an electro-hydrogen bidirectional coupling optimization scheduling model that takes into account operational robustness, energy reliability, and low carbon emissions. This solves the core bottleneck problem that traditional capacity optimization methods struggle to coordinate and balance the robustness, cross-seasonal adjustment capability, and technical applicability of configuration schemes under high-proportion renewable energy access.

[0053] like Figure 2 As shown, the new energy base system includes photovoltaic units, wind turbines, thermal power units, hydropower units, energy storage units, hydrogen fuel cells, hydrogen energy storage, and electrolyzers. Electricity is transmitted to the loads via the power distribution network and ultra-high voltage direct current lines.

[0054] (1) Photovoltaic units: Solar photovoltaic panels convert solar energy into electrical energy, providing clean energy for the power system of the new energy base;

[0055] (2) Wind turbine: Wind turbines convert wind energy into electrical energy, which is another source of renewable energy;

[0056] (3) Thermal power units: Thermal power plants typically use fossil fuels (such as coal, natural gas, etc.) to generate electricity as a supplementary energy source for the system;

[0057] (4) Hydropower units: Hydropower stations use the kinetic energy of water to generate electricity, which is a renewable energy source;

[0058] (5) Energy storage units (EES, PS): including energy storage systems (EES) and pumped storage systems (PS), used to store excess electrical energy for release when needed;

[0059] (6) Hydrogen fuel cell: It is a highly efficient energy conversion device that uses hydrogen and oxygen to generate electricity through a chemical reaction.

[0060] (7) Hydrogen storage / hydrogen storage tank: used to store hydrogen for use when needed;

[0061] (8) Electrolyzer: It produces hydrogen by electrolyzing water and is a key piece of equipment for hydrogen production;

[0062] (9) Distribution network: Transmits electrical energy from generating equipment to loads, and also connects to ultra-high voltage direct current lines;

[0063] (10) Ultra-high voltage direct current lines: used for long-distance transmission of electrical energy to reduce energy loss during transmission;

[0064] (11) Load: refers to the end users of electricity, such as households, industrial enterprises, etc.

[0065] Figure 2 The red arrows in the diagram represent electrical energy flow, and the blue arrows represent hydrogen energy flow. The entire power system of the new energy base achieves comprehensive utilization and management of electrical and hydrogen energy through the conversion and transmission of these energy flows, improving the energy efficiency and reliability of the new energy base. This multi-energy flow coupling system can better adapt to the volatility of renewable energy and achieve cross-seasonal energy balance through hydrogen storage and conversion.

[0066] A specific embodiment of the present invention discloses a method for optimizing the capacity planning and configuration of a new energy base integrating electro-hydrogen conversion, such as... Figure 1 As shown, it includes the following steps:

[0067] Step S1: Obtain historical operating data of each device in the new energy base system within the scheduling cycle;

[0068] Step S2: Generate a typical scenario set based on the historical operation data; construct a confidence interval for the uncertainty of wind and solar power output of the new energy base based on the typical scenario set; obtain the confidence robustness Γ value; and construct an electric-hydrogen coordinated capacity optimization model with the goal of maximizing the comprehensive efficiency of the power system of the new energy base.

[0069] Step S3: Based on the confidence interval and confidence robustness Γ value of the uncertainty of wind and solar power output, the electro-hydrogen co-capacity optimization model is converted into a robust optimization model;

[0070] Step S4: Solve the robust optimization model using the CPLEX solver to obtain the capacity planning and configuration scheme for the new energy base.

[0071] Step S1 includes steps S11-S12.

[0072] Step S11: Obtain historical data within the scheduling cycle of the new energy base.

[0073] For example, the scheduling cycle is one year.

[0074] The operation records of each device within the scheduling cycle (e.g., one year) are used to obtain historical data of the new energy base at each sampling point at time t, as follows:

[0075] (1) Photovoltaic units: solar radiation data, actual output of photovoltaic units, start-up and shutdown status (i.e., operating status), maximum installed capacity, maximum continuous output power, equipment effective utilization rate, total operating time, total number of units, instantaneous power, upper and lower limits of output, maximum theoretical output of renewable energy, actual value of curtailment rate, and curtailed solar power.

[0076] (2) Wind turbine: wind speed data, actual output of wind turbine, start-up and shutdown status, maximum installed capacity, maximum continuous output power, equipment effective utilization rate, total operating time, total number of turbine units, instantaneous output power, upper and lower limits of output, maximum theoretical output of renewable energy, actual value of curtailment rate, and wind curtailment power.

[0077] (3) Thermal power units: actual output power of thermal power units, start-stop status, maximum installed capacity, maximum continuous output power, equipment effective utilization rate, total operating time, total number of units, instantaneous output power, upper and lower limits of output, maximum theoretical output of renewable energy, ramp rate, and upper limit of ramp rate.

[0078] (4) Hydropower units: actual output power of hydropower units, start-stop status, maximum installed capacity, maximum continuous output power, equipment effective utilization rate, total operating time, total number of units, instantaneous output power, upper and lower limits of output, climbing rate, and upper limit of climbing rate.

[0079] (5) Energy Storage Units (EES, PS): The energy storage capacity of the EES and the pumped storage of the PS in the energy storage units, start-up and shutdown status, maximum installed capacity, maximum continuous output power, equipment utilization rate, total operating time, total number of units, instantaneous output power, charging and discharging efficiency, charging and discharging power, state of charge (SOC, which represents the percentage of current remaining electricity to total capacity) of the electrical storage and the state of charge of the pumped storage; this section is the energy storage system;

[0080] (6)-(8) represent hydrogen energy systems, and historical data are obtained as follows:

[0081] (6) Hydrogen fuel cells: hydrogen production, start-up and shutdown status, maximum installed capacity, maximum continuous output power, equipment utilization rate (i.e. efficiency), total operating time, total number of units, instantaneous output power, ramp rate limit, and power limit.

[0082] (7) Hydrogen energy storage / hydrogen storage tank: capacity, actual storage volume, start-up and shutdown status, maximum installed capacity, maximum continuous output power, equipment effective utilization rate, total operating time, total number of equipment, instantaneous output power, hydrogen storage and release power, hydrogen storage and release efficiency, and initial hydrogen storage status across quarters (e.g., the first quarter depends on the accumulation of the fourth quarter).

[0083] (8) Electrolyzer: Hydrogen production, efficiency, start-up and shutdown status, maximum installed capacity, maximum continuous output power, equipment utilization rate, total operating time, total number of units, instantaneous output power, ramp rate limit, power limit;

[0084] (9) Load: Load demand, historical peak load, actual energy shortage rate, and load supply deficit;

[0085] Step S12: Preprocess the acquired historical data.

[0086] After acquiring historical data from the new energy base within the predetermined scheduling cycle, data preprocessing is performed, including data cleaning, missing value handling, and outlier detection, to ensure the quality and reliability of the historical data. This historical data will serve as the basis for building the model and solving the robust optimization model.

[0087] The purpose of step S1 is to acquire and preprocess the historical data of each device in the power system of the new energy base within the predetermined scheduling period, so as to provide basic data support for solving the robust optimization model in subsequent steps.

[0088] Step S2 includes steps S21-S22.

[0089] Step S21: Generate a typical scenario set based on the historical operation data, construct a confidence interval for the uncertainty of wind and solar power output of the new energy base based on the typical scenario set, and obtain the confidence robustness Γ value.

[0090] The process of generating a typical scenario set based on the historical operating data, constructing a confidence interval for the uncertainty of wind and solar power output in the new energy base based on the typical scenario set, and obtaining the confidence robustness Γ value includes:

[0091] Obtain multiple typical scenarios of new energy bases; fit the weight coefficients of each typical scenario based on probability distribution;

[0092] Based on the aforementioned typical scenarios, a wind and solar power output uncertainty model is constructed using the multi-scenario confidence gap theory.

[0093] Define the confidence interval for wind and solar power output uncertainty, generate a typical scenario set containing the confidence interval for wind and solar power output uncertainty, solve the wind and solar power output uncertainty model based on the typical scenario set, and obtain the confidence robustness Γ value.

[0094] Generate a set of typical scenarios, as follows:

[0095] For example, clustering algorithms (such as K-means, hierarchical clustering, etc.) are used to perform cluster analysis on historical operating data, dividing the annual data into typical scenarios for different seasons. Each typical scenario represents typical operating conditions within a specific time period;

[0096] Determine the number of typical scenarios: Based on actual needs and data characteristics, determine the number of typical scenarios. Typically, select scenarios that can cover the main operational characteristics throughout the year.

[0097] Generate typical scenarios: Extract data from each cluster center to generate a set of typical scenarios. Each typical scenario includes typical values ​​for wind and solar power output and load demand.

[0098] Given the limitations of theories such as stochastic optimization, robust optimization, and IGDT (Information Gap Decision Theory), this invention proposes a multi-scenario confidence gap method, MCGDT, based on IGDT information gap decision theory and considering multiple scenarios and confidence levels.

[0099] The MCGDT method uses confidence intervals instead of the symmetric fluctuation intervals in the IGDT information gap decision theory, thus overcoming the problem that the IGDT information gap decision theory's use of symmetric fluctuation intervals is too coarse in describing uncertainty. Based on the data-model fusion-driven output inversion method, it generates a typical scenario set containing confidence intervals for wind and solar uncertainties. Extreme weather events can be characterized by adjusting the confidence level, which better aligns with the practical needs of new energy base planning. Traditional IGDT theory aims to find the maximum fluctuation of uncertain parameters while still ensuring that the optimization result is not worse than the predetermined result. This invention improves upon traditional IGDT information gap decision theory by using the MCGDT multi-scenario confidence gap method to construct a wind and solar output uncertainty model, as follows:

[0100]

[0101] Where x is the decision variable for the power system configuration of the new energy base, and Γ is the power of each unit in the new energy base; c is the typical scenario index; C is the typical scenario set, representing the typical wind and solar power fluctuation scenario set; ω c α is the scenario weighting coefficient; α is the risk probability threshold, which is the maximum probability of system failure that is allowed. c ξ is the confidence interval adjustment factor; 1-α is the confidence level of the uncertain variable; X is the decision variable matrix, representing the power of all equipment in the new energy base at different sampling time points, which is an A-row, B-column decision variable matrix, where A is the number of decision variables for each unit in the new energy base, and B is the number of sampling points within the scheduling cycle; ξ is the uncertainty variable matrix, representing the uncertain wind and solar power output matrix of the new energy base; c f represents the true value of wind and solar power output in typical scenario c; f is the uncertainty energy function (referred to as the loss function in this invention) in the wind and solar power output uncertainty model; f(X,ξ) c ) represents the impact of the deviation between actual wind and solar power output and predicted values ​​on the power system of the new energy base; f a β represents the worst-case target value that the power system of the new energy base can tolerate after introducing uncertain variables; β is the significance level, equivalent to α, which measures the probability threshold of decision risk; 1+β is the scaling factor of the upper tolerance limit of the loss function, the tolerance coefficient for the degree of deterioration of the loss function; d c (X,ξ c ) represents an equality constraint, that is, an equation that must be strictly satisfied (such as the real-time balance of electricity and hydrogen power); g c (X,ξ c ) represents a set of safety constraints, referring to specific constraints such as equipment operating limits (e.g., ramp rate, upper and lower output limits, energy storage SOC range, etc.); U c The set of uncertainties describes all possible fluctuation ranges in wind and solar power output; Indicates the confidence uncertainty interval; These represent the maximum and minimum allowable fluctuations in wind and solar power output, respectively. The baseline value for predicting wind and solar power output in typical scenario c is the expected value of wind and solar power output in typical scenario derived from historical statistics or prediction models. These are the lower and upper bounds of the confidence interval, respectively.

[0102] For example, the risk probability threshold α is typically set to 0.05–0.1 (corresponding to 95%–90% confidence level), and is adjusted according to the system's risk tolerance; the confidence interval adjustment factor α c The setting is based on historical fluctuations (e.g., a value of 0.2 indicates a confidence interval width of ±20% of the predicted value); in practical applications, it can be adjusted according to specific needs.

[0103] Uncertain variables refer to those variables that may be affected by a variety of factors and cannot be accurately predicted during the planning and operation of new energy bases, such as wind and solar power output and load demand.

[0104] The confidence level of an uncertain variable refers to the probability that it will take a value within a given confidence interval. For example, if we say that the confidence level of an uncertain variable is 95%, it means that we have 95% confidence that the true value of the variable will fall within our defined confidence interval.

[0105] Formula (1), the physical meaning of each formula is explained as follows:

[0106] This means optimizing the confidence robustness 1-α with decision variable x as the optimization object;

[0107] To balance the weighted robustness deviation for multiple typical scenarios, the weighted average robustness deviation for all typical scenarios must be ≤ α (to avoid certain high-risk scenarios from lowering the overall reliability of the new energy base).

[0108] d c (X,ξ c ) = 0 indicates that in typical scenario c, the decision configuration scheme and the actual output of the scene's wind and solar power must satisfy the two-way balance relationship between electricity and hydrogen energy;

[0109] g c (X,ξ c ≥0 indicates a system operation safety constraint, which refers to the strict requirement that all equipment in the new energy base must not be overloaded;

[0110] This indicates that, under typical scenario c, the actual power output of wind and solar power is within the confidence interval. Within this range, the confidence interval is defined by the reference value. and confidence interval adjustment factor α c Co-generated; the probability that the true value of the uncertain output of wind and solar power falls within the confidence interval is 1-α;

[0111] 0≤α c ≤1 indicates the engineering validity range of the confidence adjustment factor;

[0112] 0≤β≤1 means that the scene weight coefficients must satisfy probability normalization, that is, the sum of all scene weight coefficients equals 1.

[0113] The purpose of step S21 is to constrain the fluctuation range of wind and solar power output through multi-scenario confidence intervals to maximize confidence robustness and generate an equipment capacity configuration scheme for electric-hydrogen synergy, so that it can still strictly meet the electric-hydrogen energy balance constraint under extreme weather conditions, thereby solving the seasonal power imbalance problem of new energy bases.

[0114] Step S22: Construct an electric-hydrogen synergistic capacity optimization model with the goal of maximizing the comprehensive efficiency of the power system in the new energy base.

[0115] This invention quantifies key elements such as environmental protection, new energy absorption efficiency, power generation resource utilization rate, and system reliability into technical evaluation indicators, which are then integrated into the objective function. With maximizing the overall system efficiency as the optimization objective, and coupling constraints such as power balance, equipment capacity, and grid security, a power-hydrogen synergistic capacity optimization model is constructed.

[0116] The electro-hydrogen synergistic capacity optimization model includes an objective function and a set of constraints aimed at maximizing the comprehensive efficiency of the power system in the new energy base.

[0117] The objective function is as follows:

[0118] maxΨ=max(I c +U o +L r +R s ) Formula (2)

[0119] Among them, Ψ represents the comprehensive efficiency of the power system in the new energy base; I c U o L r R s These are the installed capacity utilization rate of the power system equipment in the new energy base, the effective utilization rate of the equipment, the utilization rate of renewable energy, and the load guarantee level.

[0120] (1) Utilization rate of installed capacity of power system in new energy base.

[0121] Equipment installed capacity utilization rate is used to measure how close the installed capacity of the power system in a new energy base is to its maximum allowable capacity, as follows:

[0122]

[0123] Among them, I c δ represents the installed capacity utilization rate; μ1 is the normalization coefficient of capacity intensity; I For equipment capacity strength; The maximum allowed installed capacity of the power system in the new energy base; i is the equipment index; φ is the equipment set; P i rated This is the maximum continuous output power of the device.

[0124] (2) Effective utilization rate of power system equipment in new energy base.

[0125] Equipment utilization rate, used to characterize the overall utilization level of equipment, is as follows:

[0126]

[0127] Among them, U o For equipment utilization rate; μ2 is the normalization coefficient of operating intensity; T R N represents the total running time. eq δ represents the total number of devices; M For operational intensity factor; P i (t) represents the instantaneous output power.

[0128] For example,

[0129] For photovoltaic / wind turbine generators: μ2∈[0.8,1.2], δ M ∈[0.6,0.9];

[0130] For hydrogen energy storage systems: μ2∈[0.5,0.8], δ M ∈[0.4,0.7].

[0131] In specific applications, adjustments are made according to specific needs.

[0132] (3) Renewable energy utilization rate of the power system in the new energy base.

[0133] Renewable energy utilization rate is used to characterize the efficiency loss of renewable energy absorption.

[0134] The renewable energy utilization rate is as follows:

[0135]

[0136] Among them, L r μ3 represents the clean energy utilization rate; μ3 is the normalization coefficient for the energy curtailment rate. To maximize the theoretical output of renewable energy; γ RP represents the actual energy curtailment rate; π(c) represents the probability of scenario c occurring; curt (c,t) represents the power of wind and solar curtailment at time t in scenario c.

[0137] (4) Load guarantee level of the power system of the new energy base.

[0138] The load guarantee level is as follows:

[0139]

[0140] Among them, R s The load guarantee level is represented by μ4, which is the energy shortage factor normalization coefficient. Historical peak load; γ U This represents the actual energy deficit rate; P short (c,t) represents the load supply deficit at time t in a typical scenario (c).

[0141] The set of constraints includes constraints on hydrogen storage capacity variation, constraints on the operation of the electro-hydrogen conversion device, constraints on the operation of thermal power plants, constraints on the operation of hydropower plants, constraints on the state of charge of energy storage, constraints on the utilization rate of renewable energy, constraints on load guarantee, and constraints on robustness.

[0142] (1) Constraints on the variation of hydrogen energy storage capacity. The output of wind, solar and hydropower in new energy bases is uneven throughout the season. In order to balance the insufficient energy utilization caused by natural factors, this invention uses HS (Hydrogen Storage System) to realize the long-term transfer of electrical energy, converting the excess electricity in the previous quarter into hydrogen energy and storing it in HS, and converting it into electrical energy when the power supply capacity of the base is insufficient in the current quarter.

[0143] The constraints on the variation of hydrogen energy storage capacity are as follows:

[0144]

[0145] in, The amount of hydrogen stored in hydrogen energy at time t on a typical day; These represent the hydrogen storage and hydrogen release power of hydrogen energy storage at time t on a typical day (s); These represent the hydrogen storage and hydrogen release efficiencies of hydrogen energy storage, respectively; Δt is the unit time interval; S is the total number of typical days; and T is the number of hours in a typical day.

[0146] A typical day is obtained through cluster analysis of historical data throughout the year, and its equivalent number of hours is the number of days represented by that typical day multiplied by 24 hours.

[0147] A typical day is a 24-hour equivalent operating cycle extracted from historical data throughout the year through cluster analysis. It represents a certain type of operating characteristic (such as season or weather pattern). The equivalent number of hours is the number of days it represents multiplied by 24 hours.

[0148] In addition, the initial hydrogen storage state for each typical day needs to be set. Specifically, the initial hydrogen storage state for the first typical day is the cumulative hydrogen charge / discharge from the fourth typical day, and the initial hydrogen storage state for the other three typical days is the cumulative hydrogen charge / discharge from the previous typical day, as follows:

[0149]

[0150] in, This represents the initial hydrogen storage status for a typical day in the first quarter. Accumulated hydrogen charging and discharging for a typical day in the fourth quarter of hydrogen storage; This represents the initial hydrogen storage state for the second, third, or fourth quarter of hydrogen energy storage. for The corresponding cumulative hydrogen charge and discharge on a typical day in the previous quarter.

[0151] (2) Operational constraints of the electro-hydrogen conversion device.

[0152] The ramp-up constraints of the electro-hydrogen conversion device are as follows:

[0153]

[0154] in, These represent the changes in power of the electrolyzer and the hydrogen fuel cell at time t, respectively. These are the upper limits of ramp rate for electrolyzers and hydrogen fuel cells, respectively.

[0155] The operating constraints of the electro-hydrogen conversion unit are as follows:

[0156]

[0157] Among them, U E-H The operating status of the electrolyzer and hydrogen fuel cell is represented by 0-1 variables. These represent the power outputs of the electrolyzer and the hydrogen fuel cell at time t, respectively. These are the power limits for the electrolyzer and the hydrogen fuel cell, respectively.

[0158] Since the electrolyzer (EL) and the fuel cell (HFC) are hydrogen production and consumption devices respectively, there will not be both excess electricity for hydrogen production and insufficient electricity for hydrogen consumption for electricity production at the same time. Therefore, it is necessary to set operational constraints for the electro-hydrogen conversion device.

[0159] (3) Thermal power plant operation constraints. Thermal power units need to consider ramp rate constraints, the specific formulas of which are as follows:

[0160]

[0161] in, Let be the change in power of thermal power at time t; This represents the upper limit of the ramp rate for thermal power plants.

[0162] (4) Hydropower Operation Constraints. Hydropower units have rapid peak-shaving capabilities. At the hourly optimization scale, they are generally considered to be able to change from minimum / maximum power to maximum / minimum power; therefore, ramp-up constraints are not considered. Furthermore, hydropower operation needs to meet the upper and lower output limits of the units, as follows:

[0163]

[0164] in, These are the minimum and maximum output of the hydropower station, respectively. Let be the power output of the hydropower station at time t.

[0165] (5) Energy storage state of charge constraints. Both electrical energy storage and pumped hydro storage need to consider the coupling relationship between capacity and charge / discharge power, and construct a dynamic capacity evolution equation as follows:

[0166]

[0167] in, The states of charge of electrical storage at times t and t-1 on a typical day s, respectively; λ EES , λ PS These are the charging and discharging efficiencies of electric energy storage and pumped storage, respectively. Δt represents the charging and discharging power of the energy storage at time t on a typical day; Δt is the time interval. The states of charge of pumped storage at times t and t-1 on a typical day are respectively; These represent the pumped storage charging and discharging power at time t on a typical day (s).

[0168] (6) Constraints on the utilization rate of renewable energy.

[0169] L r ≤1-ε curt Formula (14)

[0170] Where, ε curt This represents the maximum permissible energy curtailment rate.

[0171] (7) Load guarantee constraints

[0172] Function: To ensure that the power supply reliability meets the standards (such as the power shortage rate ≤1%) and to prevent the risk of power outages due to insufficient capacity configuration.

[0173] R s ≤1-ε short Formula (15)

[0174] Where, ε short This represents the maximum permissible energy deficit rate.

[0175] (8) Robustness constraints

[0176] Physical significance: To avoid overly aggressive optimization results (such as sacrificing robustness for cost reduction) and to ensure the safe operation of the system under extreme weather conditions.

[0177] Γ≥Γ min Formula (16)

[0178] Where Γ is the confidence robustness (from formula (1)); Γ min This is the minimum robustness threshold.

[0179] The purpose of step S2 is to achieve robust configuration of the new energy base under extreme weather conditions by constructing a confidence interval for the uncertainty of wind and solar power output and a capacity optimization model for the coordinated power and hydrogen production, while maximizing the comprehensive efficiency of the power system of the new energy base (taking into account capacity utilization, equipment efficiency, renewable energy consumption and load guarantee).

[0180] Step S3, specifically.

[0181] As shown in step S1, wind and solar power output has significant uncertainties, and power balance constraints are closely coupled with wind and solar power output. This means that the wind and solar power output uncertainty model constructed in step S21 can only handle deterministic wind and solar scenarios and will fail when faced with actual fluctuations. Under unpredictable wind and solar power output, it may not be able to meet power balance or other constraints, leading to system operational risks.

[0182] To address the shortcomings of the aforementioned wind and solar power output uncertainty model under uncertainty, this invention applies the Moment Uncertainty Set Generalized Decision Theory (MCGDT) proposed in step S21 to modify the foundation of the wind and solar power output uncertainty model.

[0183] MCGDT provides a systematic framework that can transform objective functions and constraints into a solvable robust optimization form oriented towards uncertain sets. Based on MCGDT theory, step S3 takes the MCGDT theory proposed in step S21 as its core foundation. By introducing the confidence interval boundary and confidence robustness of the wind and solar power output generated in step S21, it transforms the electro-hydrogen synergistic capacity optimization model (objective function and constraints) constructed in step S22 into a robust optimization model that adapts to wind and solar power fluctuations.

[0184] The aforementioned electro-hydrogen synergistic capacity optimization model is converted into a robust optimization model, as follows:

[0185]

[0186] Where x is the decision variable for the power system configuration of the new energy base, and ω is the power of each unit in the new energy base; c is the typical scenario index; C is the set of typical scenarios; ωc α is the scene weighting coefficient; α is the risk probability threshold; α c α is the confidence interval adjustment factor; 1-α is the confidence level of the uncertain variable; X is the decision variable matrix; F0 represents the baseline target value under the uncertainty scenario, which is the optimal value of the objective function in the electro-hydrogen synergistic capacity optimization model; β is the significance level; 1+β is the scaling factor for the upper tolerance limit of the objective function; F a This represents the total system cost during the planning period; These represent the actual wind power and solar power output at time t in scenario c, respectively; F c Operating costs in typical scenario c; This represents the operating costs, including uncertainties related to wind and light, in typical scenario c. These represent the equality constraints and inequality constraints in the constraint sets under the influence of the actual output values ​​of wind power and photovoltaic power, respectively. and These are the predicted wind power and solar power outputs at time t in scenario c, respectively; U c It is a set of uncertainties; This represents the set of allowable fluctuation periods for actual wind power output, centered on the predicted value and with the fluctuation radius dynamically controlled by a confidence adjustment factor. This represents the set of allowable fluctuation periods for actual photovoltaic output, centered on the predicted value and with the fluctuation radius dynamically controlled by a confidence adjustment factor.

[0187] Historical data is used to generate typical scenario sets, driving robust optimization models to solve for the optimal device capacity configuration scheme.

[0188] Based on the confidence interval and confidence robustness Γ obtained in step S21, the electro-hydrogen co-capacity optimization model in step S22 is transformed into an MCGDT robust optimization model, outputting a capacity configuration scheme and robust operation strategy with resistance to extreme fluctuations. Its function is to provide an operational guarantee mechanism for the power system of new energy bases under uncertain wind and solar conditions.

[0189] The purpose of step S3 is to ensure that the new energy base can still operate safely and stably under extreme weather conditions by upgrading the deterministic optimization model to a robust optimization model that takes into account the fluctuations in wind and solar power output.

[0190] Step S4, specifically.

[0191] Based on historical data within the scheduling cycle of the new energy base, the robust optimization model is solved using the CPLEX solver to obtain the capacity planning and configuration scheme for the new energy base.

[0192] The capacity planning and configuration scheme for the new energy base includes the optimal capacity configuration scheme for the system equipment of the new energy base, the cross-seasonal dispatch path for hydrogen energy storage, robust confidence, and the actual values ​​of curtailment rate and energy shortage rate for each typical scenario.

[0193] The optimal capacity configuration scheme for the new energy base system equipment includes wind power capacity. Photovoltaic capacity Electrolytic cell capacity fuel cell capacity Energy storage capacity

[0194] Based on the present invention and the preceding steps, the correspondence, function, and physical meaning of the output items are summarized as follows:

[0195] (1) The optimal capacity configuration scheme for the equipment, corresponding to the indicators in the robust optimization model, is as follows:

[0196] Wind power capacity The predicted wind power output corresponding to the robust optimization model;

[0197] Photovoltaic capacity The photovoltaic power output prediction value corresponding to the robust optimization model;

[0198] Electrolytic cell capacity The upper limit of electrolyzer power in the operating constraints of the corresponding electro-hydrogen conversion device;

[0199] fuel cell capacity The upper limit of fuel cell power in the corresponding operating constraints of the electro-hydrogen conversion device;

[0200] Hydrogen storage / hydrogen storage tank capacity The upper limit of the hydrogen storage capacity variable in the corresponding hydrogen storage capacity change constraint conditions.

[0201] Function and physical significance:

[0202] The long-term asset scale of a new energy base is directly determined and is the main body for optimizing the overall system efficiency in the objective function. By balancing key indicators such as installed capacity utilization rate, equipment effective utilization rate, renewable energy utilization rate, and load guarantee level, the coordinated planning of multi-energy flow capacity of the new energy base can be achieved.

[0203] (2) Hydrogen storage cross-seasonal dispatch path

[0204] In the corresponding constraints of hydrogen energy storage capacity variation The hydrogen storage power state variable is generated by solving a robust optimization model. Energy transfer across seasons is achieved through time-series coupling constraints. Input data include a set of typical historical scenarios of wind and solar power / load throughout the year and data such as the charging / discharging efficiency of hydrogen energy storage equipment. The robust optimization model calculates and outputs the charging / discharging power curves of hydrogen energy storage on typical days in each season and the SOC threshold of key nodes.

[0205] Specifically,

[0206] Historical data within the scheduling cycle is divided into typical scenario sets for different seasons, with each scenario in the typical scenario set representing a typical day.

[0207] Based on the charging / discharging efficiency of hydrogen energy storage devices.

[0208] Robust optimization model: By introducing confidence intervals and confidence robustness, the electro-hydrogen co-capacity optimization model is transformed into a robust optimization model.

[0209] The model is solved using CPLEX or other optimization solvers to obtain the optimal hydrogen energy storage charging and discharging strategy.

[0210] Output hydrogen storage charge / discharge power curves: Generate hydrogen storage charge / discharge power curves for typical days in each season, showing the charge / discharge power at different time periods;

[0211] The State of Charge (SOC) threshold at critical nodes refers to the upper and lower limits of the state of charge curve of hydrogen energy storage at important time points (such as seasonal transitions). The SOC threshold ensures that the hydrogen energy storage system can maintain normal operation when switching between different seasons.

[0212] (3) Robust confidence

[0213] The confidence robustness Γ corresponds to step S21.

[0214] Function and physical significance:

[0215] The quantitative value indicates that the power system of the new energy base can withstand extreme fluctuations in wind and solar power, meaning it can withstand 90% of severe fluctuation scenarios. The higher the value, the more reliable the power supply configuration can be when wind and solar power output drops to the lower limit of the confidence interval, thus solving the problem of overly conservative traditional robust optimization.

[0216] (4) Actual values ​​of energy curtailment rate / energy shortage rate in each scenario

[0217] Energy curtailment rate: the utilization rate of renewable energy in the objective function L r Actual value of energy abandonment rate γ R It is subject to the maximum allowable energy curtailment rate limit in the clean energy utilization rate constraint;

[0218] Energy shortage rate: the load guarantee level R in the objective function s Actual value of energy deficit rate γ U It is subject to the maximum allowable energy deficit rate limit under robustness constraints.

[0219] Function and physical significance:

[0220] Curtailment rate: Reflects the proportion of actual wind and solar power curtailment, used to evaluate the improvement effect of the electric-hydrogen coupling system on renewable energy consumption (the lower the curtailment rate, the stronger the hydrogen energy storage regulation capability).

[0221] Energy deficit rate: Characterizes the proportion of load supply shortfall and is used to verify power supply reliability (the lower the energy deficit rate, the better the hydrogen energy backup power supply capability). Both are the basis for calculating energy evaluation indicators (EAI, EUR).

[0222] Step S4 is to use the CPLEX solver to solve the robust optimization model based on historical data within the scheduling cycle of the new energy base, so as to obtain the capacity planning and configuration scheme of the new energy base, including the optimal capacity configuration scheme of equipment, the cross-seasonal scheduling path of hydrogen energy storage, the robust confidence degree, and the actual values ​​of curtailment rate and energy shortage rate in various typical scenarios, so as to achieve reliable, efficient and low-carbon power system operation.

[0223] Solve for the capacity planning and configuration scheme of the new energy base output by the robust optimization model, and provide the infrastructure scale by the equipment capacity configuration; verify the cross-seasonal adjustment capability of the hydrogen dispatch path; quantify the anti-fluctuation performance by robust confidence metric; evaluate the operational efficiency by curtailment / outage rate; and complete the technical solution for common trench cost.

[0224] The power system of the new energy base was evaluated, and its technological adaptability was verified. The Energy Abandonment Index (EAI) and the Energy Undersupply Rate (EUR) were selected as evaluation indicators for the energy dimension.

[0225] EAI refers to the proportion of abandoned energy in the operation of new energy bases to the total power generation, and EUR refers to the proportion of load shedding in the operation of new energy bases to the total load demand.

[0226] The EAI and EUR indices can effectively reflect whether the regulation capacity of EHC (Electro-Hydrogen Coupling, referring to the system in a new energy base that achieves bidirectional conversion and coordinated operation of electrical energy and hydrogen energy through electrolyzers (EL), fuel cells (HFC), and hydrogen storage (HS)) meets the overall needs of the new energy base, and whether the scale of the base's generator sets is too small or too large.

[0227] (1) The Energy Curtailment Index (EAI) is as follows:

[0228]

[0229] This represents the total amount of wind and solar power curtailed during the scheduling cycle (energy that wind and solar power could not be utilized);

[0230] This represents the total power generation from wind, solar, and traditional thermal power within the dispatch cycle.

[0231] Function of the indicator: To quantify the improvement effect of the EHC system on wind and solar energy integration (the lower the EAI, the stronger the hydrogen energy storage regulation capability).

[0232] (2) Energy deficit rate EUR, as follows:

[0233]

[0234] in, This represents the load deficit at time t on a typical day (the difference between actual power supply and demand). The lower the EUR, the stronger the hydrogen energy backup power supply capacity.

[0235] (3) Flexibility Evaluation Index. Confidence robustness Γ is selected as the evaluation index for the flexibility of the new energy base. The larger Γ is, the better the new energy base can withstand adverse conditions in wind and solar power output. When the wind and solar power output is at the lower limit of the confidence interval, the system as a whole can basically meet the external transmission demand, and the scheduling cost does not exceed 1+β of the deterministic power output scenario.

[0236] (4) Environmental Assessment Indicators. Gas treatment cost (GTC) was selected as the environmental assessment indicator. Gases include greenhouse gases such as CO2, as well as SO2 and NO2 emitted by the new energy base during operation. x Harmful gases, etc. GTC (Gas Tolerance Control) reflects the environmental hazard level of the site, as shown below:

[0237]

[0238] Among them, Ω σ This refers to the collection of gases emitted during the scheduling cycle, including CO2, SO2, and NO. x Q σ σ represents the unit emission rate of gas. Let σ be the unit treatment cost of gas.

[0239] (5) Comprehensive Evaluation Method. This invention uses the G1-entropy weight method to calculate the evaluation weights. The G1 method, also known as the order relation analysis method, is a weighting method improved from the analytic hierarchy process (AHP). The entropy weight method is a data-driven objective weighting method, whose core idea is to determine objective weights based on the magnitude of indicator variability. The combination of subjective and objective methods enhances the scientific nature of weight allocation and the credibility of the evaluation results.

[0240] Based on the capacity planning and configuration scheme of new energy bases, a comprehensive evaluation system is used to quantify multi-dimensional performance: Energy dimension calculates the energy curtailment index (EAI) and energy deficit rate (EUR); engineering efficiency dimension calculates hydrogen cycle efficiency and power regulation response, confidence robustness Γ; and environmental dimension assesses gas treatment costs. The G1-entropy weight method is used to integrate subjective and objective weights to output a comprehensive evaluation score. Its purpose is to establish a quantitative standard for the unique flexibility of hydrogen energy systems.

[0241] The comprehensive evaluation first determines the weights using the G1 method: domain experts rank the evaluation indicators (such as power supply reliability, cost, carbon emissions, etc.) in descending order of importance, and assign relative importance ratios to adjacent indicators in turn, recursively calculating the subjective weights of each indicator based on the ratios. Simultaneously, the entropy weight method is used to determine the objective weights: actual indicator data for all evaluated schemes are collected, standardized, and information entropy is calculated based on the data dispersion (indicators with smaller data fluctuations have higher entropy values), thus inferring the objective weights (higher entropy values ​​result in lower weights). Finally, a combined subjective and objective weighting is performed: the subjective weights from the G1 method are multiplied by the objective weights from the entropy weight method, normalized, and then the combined weights are obtained. Each scheme is ultimately ranked according to its weighted total score, achieving a scientific decision-making process that reflects both expert experience and data objectivity.

[0242] Inputting actual wind and solar resource data and equipment physical parameters from a new energy base, the system solves a robust optimization model and outputs the optimal capacity configuration scheme for the new energy base system equipment, the cross-seasonal scheduling path for hydrogen energy storage, and technical evaluation index scores. By analyzing the correlation between system robustness and hydrogen energy regulation capability under different Γ values, the system reveals the synergistic mechanism of the electro-hydrogen system in terms of fluctuation resistance and cross-seasonal regulation. Its purpose is to verify the effectiveness of this invention in solving the problem of seasonal power imbalance.

[0243] In summary, the capacity planning and configuration optimization method for a new energy base integrating electro-hydrogen conversion according to an embodiment of the present invention has the following beneficial effects:

[0244] 1. This invention, by constructing confidence intervals for the uncertainty of wind and solar power output and calculating the confidence robustness Γ value, can more accurately assess and quantify the operational risks of new energy bases in the face of uncertainty. This helps optimize the capacity configuration of new energy bases, enabling them to maintain stable operation under extreme weather conditions, thereby improving the reliability and robustness of new energy bases.

[0245] 2. This invention, by integrating electro-hydrogen conversion technology, enables the synergistic optimization of electro-hydrogen units with wind and solar turbines and energy storage facilities in new energy bases. This not only solves the problem of insufficient exploration of the coupling characteristics of emerging multi-energy flow technologies such as electro-hydrogen conversion in existing research, but also enables more effective utilization of renewable energy, reduces wind and solar curtailment, and improves energy efficiency.

[0246] 3. This invention constructs a hydrogen-electricity synergistic capacity optimization model aimed at maximizing the comprehensive efficiency of the power system in a new energy base. This model considers not only the installed capacity utilization rate, equipment effective utilization rate, renewable energy utilization rate, and load guarantee level, but also the unique flexibility of the hydrogen energy system, such as its seasonal peak-shaving resilience. This contributes to achieving low-carbon and reliable operation of the new energy base throughout its entire lifecycle and significantly improves the resource utilization rate of large-scale new energy bases.

[0247] 4. The Multi-Scenario Confidence Gap Theory (MCGDT) is used to construct asymmetric confidence intervals. The conservatism of the optimization objective is dynamically adjusted by the confidence robustness Γ. This avoids the over-conservatism of traditional robust optimization and overcomes the limitations of stochastic optimization that depends on the probability distribution assumption.

[0248] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0249] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the capacity planning and configuration of a new energy base integrating electro-hydrogen conversion, characterized in that, include: Obtain historical operating data of each device in the new energy base system during the scheduling cycle; Based on the historical operating data, a set of typical scenarios is generated. Based on the set of typical scenarios, a confidence interval for the uncertainty of wind and solar power output in the new energy base is constructed, and the confidence robustness Γ value is obtained. An electric-hydrogen coordinated capacity optimization model is constructed with the goal of maximizing the comprehensive efficiency of the power system in the new energy base. Based on the confidence interval and confidence robustness Γ value of the uncertainty of wind and solar power output, the electro-hydrogen co-capacity optimization model is converted into a robust optimization model. The robust optimization model is solved using the CPLEX solver to obtain the capacity planning and configuration scheme for the new energy base.

2. The method according to claim 1, characterized in that, The process of generating a typical scenario set based on the historical operating data, constructing a confidence interval for the uncertainty of wind and solar power output in the new energy base based on the typical scenario set, and obtaining the confidence robustness Γ value includes: Clustering algorithms are used to perform cluster analysis on the historical data, and the scheduling cycle is divided into typical scenarios of different seasons to obtain a set of typical scenarios; the weight coefficients of each typical scenario in the set of typical scenarios are fitted based on the probability distribution. Based on the aforementioned typical scenarios, a wind and solar power output uncertainty model is constructed using the multi-scenario confidence gap theory. Define the confidence interval for wind and solar power output uncertainty, generate a typical scenario set containing the confidence interval for wind and solar power output uncertainty, solve the wind and solar power output uncertainty model based on the typical scenario set, and obtain the confidence robustness Γ value.

3. The method according to claim 2, characterized in that, The electro-hydrogen synergistic capacity optimization model includes an objective function and a set of constraints aimed at maximizing the comprehensive efficiency of the power system in the new energy base. The objective function is as follows: maxΨ=max(I c +U o +L r +R s ) Among them, Ψ represents the comprehensive efficiency of the power system in the new energy base; I c U o L r R s These are the installed capacity utilization rate of the power system equipment in the new energy base, the effective utilization rate of the equipment, the utilization rate of renewable energy, and the load guarantee level.

4. The method according to claim 3, characterized in that, The set of constraints includes constraints on hydrogen storage capacity variation, constraints on the operation of the electro-hydrogen conversion device, constraints on the operation of thermal power plants, constraints on the operation of hydropower plants, constraints on the state of charge of energy storage, constraints on the utilization rate of renewable energy, constraints on load guarantee, and constraints on robustness.

5. The method according to any one of claims 1-4, characterized in that, The aforementioned electro-hydrogen synergistic capacity optimization model is converted into a robust optimization model, as follows: Where x is the decision variable for the power system configuration of the new energy base, and ω is the power of each unit in the new energy base; c is the typical scenario index; C is the set of typical scenarios; ω c α is the scene weighting coefficient; α is the risk probability threshold; α c α is the confidence interval adjustment factor; 1-α is the confidence level of the uncertain variable; X is the decision variable matrix; F0 represents the baseline target value under the uncertainty scenario, which is the optimal value of the objective function in the electro-hydrogen synergistic capacity optimization model; β is the significance level; 1+β is the scaling factor for the upper tolerance limit of the objective function; F a This represents the total system cost during the planning period; These represent the actual wind power and solar power output at time t in scenario c, respectively; F c This represents the operating cost in typical scenario c. This represents the operating costs, including uncertainties related to wind and light, in typical scenario c. These represent the equality constraints and inequality constraints in the set of constraints under the influence of the actual output values ​​of wind power and photovoltaic power, respectively. and These are the predicted wind power and solar power outputs at time t in scenario c, respectively; U c It is a set of uncertainties; This represents the set of allowable fluctuation periods for actual wind power output, centered on the predicted value and with the fluctuation radius dynamically controlled by the confidence interval adjustment factor. This represents the set of allowable fluctuation periods for actual photovoltaic output, centered on the predicted value and with the fluctuation radius dynamically controlled by the confidence interval adjustment factor.

6. The method according to claim 5, characterized in that, The capacity planning and configuration scheme for the new energy base includes the optimal capacity configuration scheme for the system equipment of the new energy base, the cross-seasonal dispatch path for hydrogen energy storage, robust confidence, and the actual values ​​of curtailment rate and energy shortage rate for each typical scenario. The optimal capacity configuration scheme for the new energy base system equipment includes wind power capacity. Photovoltaic capacity Electrolytic cell capacity fuel cell capacity Energy storage capacity 7. The method according to claim 6, characterized in that, The constraints on the variation of hydrogen energy storage capacity are as follows: in, The amount of hydrogen stored in hydrogen energy at time t on a typical day; These represent the hydrogen storage and hydrogen release power of hydrogen energy storage at time t on a typical day (s); These represent the hydrogen storage and hydrogen release efficiencies of hydrogen energy storage, respectively; Δt is the unit time interval; S is the total number of typical days; and T is the number of hours in a typical day.

8. The method according to claim 7, characterized in that, The ramp-up constraints of the electro-hydrogen conversion device are as follows: in, These represent the changes in power of the electrolyzer and the hydrogen fuel cell at time t, respectively. These are the upper limits of ramp rate for electrolyzers and hydrogen fuel cells, respectively. The operating constraints of the electro-hydrogen conversion unit are as follows: Among them, U E-H These are the operating state variables of the electrolyzer and the hydrogen fuel cell, and are 0-1 variables; These represent the power outputs of the electrolyzer and the hydrogen fuel cell at time t, respectively. These are the power limits for the electrolyzer and the hydrogen fuel cell, respectively.

9. The method according to claim 8, characterized in that, The renewable energy utilization rate is as follows: Among them, L r 1. Renewable energy utilization rate; μ3 is the normalization coefficient of energy curtailment rate; To maximize the theoretical output of renewable energy; γ R P represents the actual energy curtailment rate; π(c) represents the probability of scenario c occurring; curt (c,t) represents the power of wind and solar curtailment at time t in scenario c.

10. The method according to claim 9, characterized in that, The load guarantee level is as follows: Among them, R s The load guarantee level is represented by μ4, which is the energy shortage factor normalization coefficient. Historical peak load; γ U This represents the actual energy deficit rate; P short (c,t) represents the load supply deficit at time t in a typical scenario (c).

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