Cross-period dynamic hydrogen storage planning method based on load demand

Abstract

The invention discloses a load demand-based cross-period dynamic hydrogen storage planning method, which relates to the technical field of energy utilization and comprises the following steps of cross-period hydrogen storage system modeling, typical day selection and feature extraction, comprehensive energy system planning model establishment, random-robust two-stage optimization method solving and objective function design. By designing the introduction and modeling of the IHS, the energy transfer function is realized, the problem that energy storage equipment in a traditional power system cannot store energy across seasons is solved, the technical challenge of unbalanced power supply and demand is overcome, the permeability of renewable energy sources is improved, wind and light abandoning phenomena are reduced, and carbon emission is reduced. And the realization of low-carbon and green energy targets is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and in particular to a cross-period dynamic hydrogen storage planning method based on load demand. Background Art

[0002] As the global energy transition continues to advance, the demand for low-carbon energy systems is increasing, especially the access to a high proportion of renewable energy has brought challenges to the stable operation of the power system. Due to the significant intermittency and volatility of renewable energy such as wind and solar energy, it is difficult for traditional power systems to directly absorb these unstable energy outputs, which in turn affects the safety and reliability of the system.

[0003] In order to solve these problems, it is particularly important to develop efficient energy storage technology (especially hydrogen storage) and dynamic scheduling methods. This application proposes a cross-period dynamic hydrogen storage planning method based on load demand, aiming to increase the penetration rate of high-proportion renewable energy, reduce carbon emissions, and ensure the stability, economy and environmental friendliness of energy supply.

[0004] 1. Patent document CN101315545B discloses a three-stage filling optimization control method for efficient hydrogenation at a hydrogen filling station. The above patent realizes the simultaneous optimization of hydrogen utilization and filling time, which can ensure high hydrogen utilization while greatly shortening the filling time; different optimized filling methods can be generated according to different filling parameters and requirements; the filling process is simple, direct and easy to operate, but the above patent cannot realize the energy transfer function.

[0005] 2. Patent document CN113642811B discloses a dynamic hydrogen freight route planning method, device, computer equipment and storage medium. The above patent realizes optimized resource allocation, and dynamically adjusts the transportation route considering the special transportation requirements, transportation efficiency and safety of hydrogen energy, thereby realizing the optimization of resource allocation and improving the efficiency and safety of hydrogen energy transportation. However, the above patent cannot identify typical days with strong representativeness and calculate their probability of occurrence.

[0006] 3. Patent document CN113783226B discloses a hierarchical predictive energy management method for an offshore wind power hydrogen production grid-connected power generation system. The above patent realizes predictive energy management of an offshore wind power hydrogen production grid-connected power generation system through hierarchical optimization, so as to absorb abandoned wind energy and maximize the economic income of the wind farm. However, the above patent cannot realize the function of dynamic energy scheduling based on real-time data.

[0007] 4. Patent document CN113689023B discloses a method for managing wind curtailment and energy consumption in a wind / storage / hydrogen grid-connected power generation system. The above patent achieves the absorption of wind curtailment and smoothing of grid-connected active power fluctuations, and effectively utilizes excess wind power. However, the above patent cannot achieve the maximization of renewable energy utilization while ensuring load demand.

[0008] In summary, the above patents cannot realize the functions of energy transfer, identifying typical days with strong representativeness and calculating their occurrence probability, dynamically dispatching energy according to real-time data, and maximizing the utilization rate of renewable energy while ensuring load demand, resulting in the inability to effectively deal with the many problems of renewable energy fluctuations, load demand uncertainty, and system security assurance;

[0009] To this end, this application proposes a cross-period dynamic hydrogen storage planning method based on load demand that can realize the energy transfer function, identify typical days with strong representativeness and calculate their probability of occurrence, dynamically dispatch energy according to real-time data, and ensure load demand while maximizing the utilization rate of renewable energy. Summary of the invention

[0010] The purpose of the present invention is to provide a cross-period dynamic hydrogen storage planning method based on load demand, so as to solve the technical problems proposed in the above background technology, such as the inability to realize the energy transfer function, identify typical days with strong representativeness and calculate their probability of occurrence, perform energy dynamic scheduling according to real-time data, and maximize the utilization rate of renewable energy while ensuring load demand, which leads to the inability to effectively cope with the fluctuation of renewable energy, the uncertainty of load demand and the technical problems of system safety assurance.

[0011] To achieve the above object, the present invention provides the following technical solution: a method for cross-period dynamic hydrogen storage planning based on load demand, the method comprising the following steps:

[0012] Modeling of inter-period hydrogen storage system: Establishing an IHS mathematical model that supports intra-day and inter-seasonal energy transfer, taking into account charging and discharging power, SOC, and energy conversion efficiency;

[0013] Typical day selection and feature extraction: Based on the annual net load data, an improved K-means clustering algorithm is used to extract typical days representing different load characteristics throughout the year and their occurrence probabilities, capturing the dynamic changes of renewable energy and load demand;

[0014] Establishment of integrated energy system planning model: Based on the IHS model, an IES distributed power supply and energy storage optimization planning model including IHS is constructed;

[0015] The solution is solved by the stochastic-robust two-stage optimization method: in the planning stage, the equipment capacity and system layout are determined through typical scenario optimization, and in the operation stage, the supply and demand balance and efficient energy utilization are achieved through dynamic scheduling;

[0016] Objective function design: Minimize the total cost of the system over its entire life cycle, including equipment installation cost, operation and maintenance cost, carbon emission cost, wind and solar power abandonment cost, and electricity and gas purchase cost.

[0017] Preferably, the inter-period hydrogen storage system realizes the following functions:

[0018] Intraday energy transfer: During the peak period of solar power generation, excess electricity is converted into hydrogen for storage through water electrolysis equipment. At night / peak load, hydrogen is converted into electricity through fuel cells or hydrogen turbines to meet load demand;

[0019] Inter-seasonal energy transfer: In seasons with abundant wind / solar power generation, hydrogen is stored for a long time through solid-state hydrogen storage / underground hydrogen storage technology, and the stored hydrogen is released in seasons with scarce power generation.

[0020] Preferably, the typical day extraction method comprises the following steps:

[0021] Using the net load curve as the classification basis, the 8760-hour load data throughout the year are divided into multiple clusters;

[0022] Based on the improved K-means algorithm, the data in each cluster are clustered and the representative typical day of each cluster is extracted;

[0023] Calculate the probability of occurrence of each typical day as the weight input in scenario optimization;

[0024] Normalize typical daily load data to optimize calculation efficiency.

[0025] Preferably, the IHS data model includes the following constraints:

[0026] Power constraint: defines the maximum charging power and discharging power of the IHS at any time;

[0027] Capacity constraints: specify upper and lower limits for IHS energy storage capacity;

[0028] SOC dynamic constraint: including the balance adjustment of SOC between the initial and final states. Between each typical scene, the final state SOC of the previous scene is used as the initial SOC of the next scene.

[0029] Energy loss and conversion efficiency constraints: Consider the energy loss in the process of hydrogen production, storage and use.

[0030] Preferably, the integrated energy system includes the following equipment:

[0031] Energy production equipment: including wind turbines, photovoltaic power generation systems and thermal power generating units;

[0032] Energy conversion equipment: including water electrolysis hydrogen production equipment, power-to-gas equipment P2G, hydrogen turbines and gas turbines;

[0033] Energy storage equipment: including batteries, compressed hydrogen tanks and underground hydrogen storage facilities;

[0034] Load equipment: including industrial power loads, commercial power loads, household loads, district heating users and gas users.

[0035] Preferably, the planning stage of the stochastic-robust two-stage optimization model includes:

[0036] Determine the installation capacity and distribution layout of IHS and conventional energy storage devices;

[0037] Optimize the selection and layout of renewable energy generation equipment;

[0038] Develop equipment configuration plans based on typical scenarios and considering safety requirements under extreme operating conditions.

[0039] Preferably, the operation phase of the stochastic-robust two-stage optimization model includes:

[0040] Prioritize the use of IHS and batteries to meet dynamic load demands during intraday dispatch;

[0041] In daytime or cross-seasonal scheduling, energy transfer across time periods is accomplished through IHS;

[0042] Dynamically allocate the output ratio of renewable energy, thermal power and energy storage to ensure the balance of energy supply and demand.

[0043] Preferably, the cost term in the objective function is defined as:

[0044] Installation cost: calculated by multiplying the unit capacity cost of the equipment type by the installed capacity;

[0045] Operation and maintenance costs: including the operation and maintenance costs of renewable energy equipment, energy storage equipment and conversion equipment;

[0046] Cost of curtailment of wind and solar power: calculated based on the amount of curtailment of wind and solar power and the penalty coefficient for unit curtailment;

[0047] Carbon emission cost: calculated based on carbon emissions and unit carbon emission price;

[0048] Cost of purchasing electricity and gas: calculated based on the amount purchased from the power grid and natural gas system.

[0049] Preferably, the constraints of the stochastic-robust two-stage optimization model include:

[0050] Energy balance constraint: ensure that the total energy supply at each moment is equal to the total load demand;

[0051] Equipment capacity constraints: specify the maximum permissible installed capacity of each type of equipment;

[0052] Renewable energy output upper and lower limits: control the actual output range of wind power and photovoltaic power generation;

[0053] Demand response constraints: limit the extent of user load reduction and load transfer.

[0054] Preferably, the method is applicable to the following scenarios:

[0055] Regions with significant seasonal load demand differences and abundant renewable energy resources;

[0056] A low-carbon / zero-carbon energy system with strict limits on total carbon emissions;

[0057] An integrated energy system is required that balances the volatility of renewable energy with the stability of long-term energy supply and demand.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention realizes the energy transfer function by introducing and modeling IHS, solves the problem that energy storage equipment in traditional power systems cannot store energy across seasons, overcomes the technical challenge of imbalance between power supply and demand, improves the penetration rate of renewable energy, reduces the phenomenon of wind and solar power abandonment, reduces carbon emissions, and promotes the realization of low-carbon and green energy goals;

[0060] 2. The present invention clusters the annual load data by designing a load optimization based on typical days and scenario analysis, thereby identifying typical days with strong representativeness and calculating their occurrence probability, optimizing the energy configuration and scheduling strategy of the system, solving the problem of ignoring the dynamic changes of load demand and failing to fully consider the impact of load fluctuations on the system in traditional methods, improving the energy system's ability to respond to load fluctuations and sudden loads, and avoiding energy shortages or over-investment caused by load forecasting errors;

[0061] 3. The present invention realizes the function of dynamic energy scheduling according to real-time data by introducing a random-robust two-stage optimization model, solves the problem that the traditional optimization model fails to fully consider the uncertainty in system operation, resulting in unrealistic optimization results, optimizes equipment configuration and scheduling strategies, reduces uncertainty and risks in system operation, improves system reliability, achieves a balance between economy and safety, and reduces the overall investment and operating costs of the system while ensuring power supply safety;

[0062] 4. The present invention achieves the goal of maximizing the utilization rate of renewable energy while ensuring load demand by designing an optimization of dynamic energy scheduling and multi-energy complementary strategies, solving the problem of imbalance between power supply and demand in traditional power systems. In particular, the system can still maintain stable operation under conditions of extreme weather and scarcity of wind and solar resources, overcoming the scheduling challenges brought about by energy fluctuations, avoiding inefficient operation modes that rely too much on thermal power, and significantly improving the system's ability to adapt to fluctuations in renewable energy and changes in load demand, thereby reducing energy waste and improving the utilization rate of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the cross-period dynamic hydrogen storage planning process of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] See also Figure 1 The present invention provides an embodiment: a method for cross-period dynamic hydrogen storage planning based on load demand, the method comprising the following steps:

[0066] Modeling of inter-period hydrogen storage system: Establishing an IHS mathematical model that supports intra-day and inter-seasonal energy transfer, taking into account charging and discharging power, SOC, and energy conversion efficiency;

[0067] Typical day selection and feature extraction: Based on the annual net load data, an improved K-means clustering algorithm is used to extract typical days representing different load characteristics throughout the year and their occurrence probabilities, capturing the dynamic changes of renewable energy and load demand;

[0068] Establishment of integrated energy system planning model: Based on the IHS model, an IES distributed power supply and energy storage optimization planning model including IHS is constructed;

[0069] The solution is solved by the stochastic-robust two-stage optimization method: in the planning stage, the equipment capacity and system layout are determined through typical scenario optimization, and in the operation stage, the supply and demand balance and efficient energy utilization are achieved through dynamic scheduling;

[0070] Objective function design: Minimize the total cost of the system over its entire life cycle, including equipment installation cost, operation and maintenance cost, carbon emission cost, wind and solar power abandonment cost, and electricity and gas purchase cost;

[0071] Furthermore, for the planning of a regional integrated energy system, the input data include: load data: electricity, heat and gas load curves for 8760 hours a year; renewable energy output data, hourly output forecast data of wind power and photovoltaic power generation in the region; energy prices: dynamic price curves of power grid purchases and natural gas purchases; carbon emission costs: carbon emission prices and carbon emission costs of various energy types; equipment technical parameters: efficiency, capacity and unit cost parameters of equipment including hydrogen production, hydrogen storage and hydrogen combustion;

[0072] Intraday energy transfer modeling: When there are abundant wind and solar resources during the day, the excess electricity is converted into hydrogen using water electrolysis hydrogen production equipment; at night or during peak loads, the stored hydrogen is released through fuel cells and converted into electricity for load use;

[0073] Formula constraints:

[0074] 0≤P charge (t), P discharge (t)≤P max

[0075] SOC(t)=SOC(t-1)+η charge P charge (t)-P discharge (t) / η discharge

[0076] Among them, η charge and η discharge is the charge and discharge efficiency, P charge and P discharge is the charge and discharge power;

[0077] Modeling of inter-seasonal energy transfer: During the spring or summer when renewable energy is in excess, hydrogen is stored in underground hydrogen storage equipment; during the autumn and winter when renewable energy is insufficient, hydrogen is released to meet load demand; Annual energy balance constraint: SOC start =SOC end

[0078] Self-discharge rate model: considers the self-discharge loss of long-term hydrogen storage;

[0079] Typical day selection and feature extraction:

[0080] The improved K-means algorithm is used to cluster the net load data of 8760 hours; typical days include peak load days, normal load days and low load days, and each type of typical day is assigned a corresponding probability of occurrence; high-load typical day: wind and solar output accounts for 20% of the load, and more fossil energy is needed to supplement; medium-load typical day: wind and solar output accounts for 50% of the load, and IHS mainly provides intraday balance; low-load typical day: wind and solar output is in excess, and water electrolysis hydrogen production equipment operates at full load;

[0081] Establishment of comprehensive energy system planning model: Model composition: including wind turbines, photovoltaic power generation equipment, water electrolysis hydrogen production equipment, fuel cells, underground hydrogen storage tanks and batteries; load side includes industrial load, commercial load and residential load; planning goal: minimize total cost;

[0082] The solution is obtained by the stochastic-robust two-stage optimization method: Planning stage: According to the probability of occurrence of typical days, the installation capacity and layout of wind power, photovoltaics, energy storage equipment and IHS are optimized; the installed capacity of wind power is 50MW, and the installed capacity of photovoltaics is 70MW; the capacity of hydrogen production equipment is 20MW, and the underground hydrogen storage capacity is 10 tons; Operation stage: IHS and batteries are used first to cope with short-term load fluctuations, and the surplus electricity is converted into hydrogen storage in the low-load season, and the hydrogen is released in the high-load season.

[0083] See also Figure 1 The present invention provides an embodiment: a cross-period dynamic hydrogen storage planning method based on load demand, wherein the cross-period hydrogen storage system realizes the following functions:

[0084] Intraday energy transfer: During the peak period of solar power generation, excess electricity is converted into hydrogen for storage through water electrolysis equipment. At night / peak load, hydrogen is converted into electricity through fuel cells or hydrogen turbines to meet load demand;

[0085] Inter-seasonal energy transfer: In seasons with abundant wind / solar power generation, hydrogen is stored for a long time through solid-state hydrogen storage / underground hydrogen storage technology, and the stored hydrogen is released in seasons with scarce power generation;

[0086] The comprehensive energy system includes the following equipment:

[0087] Energy production equipment: including wind turbines, photovoltaic power generation systems and thermal power generating units;

[0088] Energy conversion equipment: including water electrolysis hydrogen production equipment, power-to-gas equipment P2G, hydrogen turbines and gas turbines;

[0089] Energy storage equipment: including batteries, compressed hydrogen tanks and underground hydrogen storage facilities;

[0090] Load equipment: including industrial power loads, commercial power loads, household loads, district heating users and gas users;

[0091] Furthermore, during the peak power generation period of 10:00-15:00, the total output of photovoltaic power generation and wind power reaches 60MW, while the load demand is only 30MW. The water electrolysis hydrogen production equipment is in operation, using 30MW of excess electricity to produce hydrogen, and about 500kg of hydrogen can be produced per hour. The produced hydrogen is compressed and stored in hydrogen tanks; during the peak load period of 17:00-23:00 at night, photovoltaic power generation is stopped, wind power output is reduced to 10MW, and the total load demand rises to 50MW. The hydrogen in the hydrogen tank is released through fuel cells and hydrogen-fired turbines to provide 20MW of electricity. At the same time, the system uses 5MW battery output to supplement short-term load fluctuations, and the remaining 15MW is provided by thermal power generation;

[0092] In the season of abundant power generation from June to August, excess power during the day is used to produce hydrogen through water electrolysis equipment, which can produce about 10 tons of hydrogen per day. The produced hydrogen is first stored in compressed hydrogen tanks. When the capacity is exceeded, it is transferred to underground hydrogen storage facilities. Underground hydrogen storage facilities store hydrogen for a long time with low energy loss, and the total hydrogen storage capacity can reach 200 tons. In the season of scarce power generation from November to January, when the wind and solar power output is insufficient to meet the regional load demand, hydrogen is released from the underground hydrogen storage facilities, and the hydrogen-fired turbine is operated to provide the system with 10MW base load power to continuously ensure load supply. At the same time, the daily peak and valley load difference is met through dynamic adjustment of fuel cells.

[0093] Through the synergistic effect of intraday energy transfer and inter-seasonal energy transfer, the inter-period hydrogen storage system effectively solves the problems of seasonal load demand differences and renewable energy volatility, provides technical support for achieving a high proportion of renewable energy access, and significantly improves the system's economic and environmental benefits.

[0094] See also Figure 1 The present invention provides an embodiment: a cross-period dynamic hydrogen storage planning method based on load demand, wherein the typical day extraction method comprises the following steps:

[0095] Using the net load curve as the classification basis, the 8760-hour load data throughout the year are divided into multiple clusters;

[0096] Based on the improved K-means algorithm, the data in each cluster are clustered and the representative typical day of each cluster is extracted;

[0097] Calculate the probability of occurrence of each typical day as the weight input in scenario optimization;

[0098] Normalize typical daily load data to optimize calculation efficiency;

[0099] The IHS data model includes the following constraints:

[0100] Power constraint: defines the maximum charging power and discharging power of the IHS at any time;

[0101] Capacity constraints: specify upper and lower limits for IHS energy storage capacity;

[0102] SOC dynamic constraint: including the balance adjustment of SOC between the initial and final states. Between each typical scene, the final state SOC of the previous scene is used as the initial SOC of the next scene.

[0103] Energy loss and conversion efficiency constraints: Consider the energy loss in the process of hydrogen production, storage and use;

[0104] The method is applicable to the following scenarios:

[0105] Regions with significant seasonal load demand differences and abundant renewable energy resources;

[0106] A low-carbon / zero-carbon energy system with strict limits on total carbon emissions;

[0107] An integrated energy system that requires balancing the volatility of renewable energy with the stability of long-term energy supply and demand;

[0108] Furthermore, using the net load as criteria for selecting a typical day.

[0109]

[0110] For annual data The improved K-means clustering algorithm is used to cluster the data and obtain typical days and their occurrence probabilities. The IHS model is as follows:

[0111] is the IHS maximum charge and discharge power constraint. It is the maximum charge and discharge capacity constraint of IHS. is the IHS charge-discharge balance constraint, which means that the power of IHS is equal at the beginning and end of the year. Δt is the scheduling step, which is set to 1 hour. Indicates that the initial value of the state of charge (SOC) of the IHS in the first typical scenario is half of the configured capacity. The initial SOC of the (k+1)th scenario is equal to the final SOC of the kth scenario, and the final SOC at the end of all scenarios is equal to the initial SOC.

[0112]

[0113] Real-time SOC constraint for IHS. It is the SOC constraint of IHS.

[0114] In the formula, is the charge and discharge power of the IHS at time t in the kth scenario in the nth year; is the charge and discharge state of the IHS at the time t in the kth scenario in the nth year, which takes the value of 1 or 0. It is discharged when it takes 1 and charged when it takes 0. is the maximum charge and discharge power of IHS at the kth scenario in the nth year at time t; β IHS is the maximum capacity ratio of IHS; c IHS is the installed capacity of IHS; Y is the number of days in a year, δ n，k is the probability of the kth scenario occurring in the nth year; is the conversion efficiency of the IHS at the kth scenario in the nth year at time t; Δt is the scheduling step, which is set to 1 hour; is the state of charge of the IHS at the initial moment of the first scenario, is the state of charge of the IHS at the end of the kth scenario, is the state of charge of the IHS at the initial moment of the k+1th scenario, is the state of charge of the IHS at the end of the Nkth scenario; is the state of charge of the IHS at time t in the kth scenario in the nth year, is the state of charge of the IHS at time t-1 in the kth scenario in the nth year, is the self-discharge rate of IHS; SOC IHS，min , SOC IHS，max Respectively represent the minimum and maximum values ​​of the IHS state of charge;

[0115] Energy balance constraints:

[0116]

[0117] In the formula, is the gas production of P2G at time t in the kth scenario in the nth year; is the natural gas load at time t in the kth scenario in the nth year; is the natural gas sales volume at time t in the kth scenario in the nth year.

[0118]

[0119] In the formula, is the hydrogen production of the electric hydrogen production equipment at the kth scenario at time t in the nth year; is the hydrogen consumption of IHS at time t in the kth scenario in the nth year; is the hydrogen consumption of the hydrogen-fired turbine at time t in the kth scenario in the nth year.

[0120] Equipment capacity constraints:

[0121] 0≤c i ≤c i，max

[0122] In the formula, c i，maxInstall capacity caps on devices

[0123] Output constraints:

[0124]

[0125] Where: They are the upper and lower limits of renewable energy output respectively; They are the upper and lower limits of thermal power unit output respectively; are the upper and lower limits of demand response respectively.

[0126]

[0127] Where: They are the upper and lower limits of electricity sales; They are the upper and lower limits for electricity purchase; They are the upper and lower limits of gas sales respectively; They are the upper and lower limits of buying momentum respectively.

[0128] See also Figure 1 , an embodiment provided by the present invention: a method for cross-period dynamic hydrogen storage planning based on load demand, the planning stage of the stochastic-robust two-stage optimization model includes:

[0129] Determine the installation capacity and distribution layout of IHS and conventional energy storage devices;

[0130] Optimize the selection and layout of renewable energy generation equipment;

[0131] Develop equipment configuration plans based on typical scenarios and considering safety requirements under extreme operating conditions;

[0132] The operation phase of the stochastic-robust two-stage optimization model includes:

[0133] Prioritize the use of IHS and batteries to meet dynamic load demands during intraday dispatch;

[0134] In daytime or cross-seasonal scheduling, energy transfer across time periods is accomplished through IHS;

[0135] Dynamically allocate the output ratio of renewable energy, thermal power and energy storage to ensure the balance of energy supply and demand;

[0136] Furthermore, a combination of random optimization and robust optimization methods is used to consider the volatility of system load demand and renewable energy output. The capacity of IHS and conventional energy storage (such as batteries) is configured according to typical scenarios (such as typical daily and seasonal loads) to ensure that the system can cope with long-term load fluctuations and changes in renewable energy output. The total system cost is minimized through optimization algorithms (such as genetic algorithms or particle swarm optimization) while ensuring maximum load fluctuations and minimum energy supply shortages. The planning results are: IHS installation capacity: 20MW, underground hydrogen storage capacity: 100 tons; battery installation capacity: 10MWh, used to balance load fluctuations during the day; 15 wind farms and 10 photovoltaic farms are planned and designed, and the layout takes into account the optimal areas of wind and solar resources and makes full use of idle plots;

[0137] Based on the regional distribution of wind and solar energy resources, the selection, capacity and layout of wind power and photovoltaic equipment are optimized to ensure maximum energy output. Considering the volatility of wind power resources, a spatially dispersed layout is adopted when arranging wind farms to avoid drastic fluctuations in system output due to changes in wind speed in the region. When arranging photovoltaic systems, areas with sufficient sunshine and reasonable land use are selected to maximize solar energy collection;

[0138] Through typical scenario analysis methods, the system's performance under extreme operating conditions such as extreme weather, equipment failure or power grid abnormalities is simulated to ensure that the system can still maintain the stability of energy supply under these conditions. For high-load emergencies, redundant equipment and backup mechanisms are designed, and backup thermal power units (capacity 15MW) are included in the system planning as emergency response equipment;

[0139] The goal of the operation phase is to ensure the best combination of renewable energy, energy storage and thermal power generation in actual operation through dynamic scheduling methods to cope with problems such as load demand fluctuations and unstable energy supply, and to ensure stable operation of the system. In the case of large intraday load fluctuations, IHS and batteries are first used to balance short-term loads. When photovoltaic power generation and wind power output are in excess during the day, the electric energy is converted into hydrogen through water electrolysis hydrogen production equipment and stored in IHS. When the load increases in the evening and at night, the stored hydrogen is released through fuel cells to give priority to meeting the load demand and avoid starting thermal power units. Batteries participate in balancing load fluctuations, and the charging and discharging speeds are relatively fast, which is suitable for periods of rapid load fluctuations.

[0140] In seasons with abundant wind and solar power (such as summer), IHS converts excess electricity into hydrogen and stores it for a long time through underground hydrogen storage facilities. In seasons with insufficient wind and solar power (such as winter), hydrogen is released through underground hydrogen storage facilities and converted into electricity and heat energy to meet the load demand in the region. This process optimizes the utilization efficiency of long-term hydrogen storage equipment and reduces the abandonment of wind and solar power.

[0141] Through the robust optimization model, factors such as load forecast errors, fluctuations in renewable energy output, and system failures are taken into account to dynamically adjust the proportion of renewable energy generation, energy storage, and thermal power output. During the peak of renewable energy generation, wind and solar power are given priority. When renewable energy is insufficient or the load exceeds expectations, thermal power units are used as a supplement. The energy storage system (IHS and batteries) adjusts the discharge power according to the real-time load demand to ensure the power balance of the system.

[0142] See also Figure 1 , an embodiment provided by the present invention: a cross-period dynamic hydrogen storage planning method based on load demand, the cost term in the objective function is defined as:

[0143] Installation cost: calculated by multiplying the unit capacity cost of the equipment type by the installed capacity;

[0144] Operation and maintenance costs: including the operation and maintenance costs of renewable energy equipment, energy storage equipment and conversion equipment;

[0145] Cost of curtailment of wind and solar power: calculated based on the amount of curtailment of wind and solar power and the penalty coefficient for unit curtailment;

[0146] Carbon emission cost: calculated based on carbon emissions and unit carbon emission price;

[0147] Cost of electricity and gas purchase: calculated based on the amount purchased from the power grid and natural gas system;

[0148] The constraints of the stochastic-robust two-stage optimization model include:

[0149] Energy balance constraint: ensure that the total energy supply at each moment is equal to the total load demand;

[0150] Equipment capacity constraints: specify the maximum permissible installed capacity of each type of equipment;

[0151] Renewable energy output upper and lower limits: control the actual output range of wind power and photovoltaic power generation;

[0152] Demand response constraints: limit the extent of user load reduction and load transfer;

[0153] Furthermore, the model aims to minimize the total cost C during the system investment period, which includes the costs of economy, greenness and reliability.

[0154] min C=min{C In +C Of

[0155] In the formula, C In is the system installation cost, C Of It is the system operating cost.

[0156] System installation cost:

[0157]

[0158] In the formula, The installation cost per unit capacity of each device, c i The installed capacity of each device, there are M types of devices.

[0159] System operating costs:

[0160]

[0161] In the formula, is the system operation and maintenance cost of the kth scenario in the nth year; is the carbon emission cost of the system in the kth scenario in the nth year; is the reliability cost of the system in the kth scenario in the nth year; is the cost of wind and solar curtailment in the kth scenario of the system in the nth year; It is the cost of purchasing electricity and gas for the system in the kth scenario in the nth year; there are K scenarios in total.

[0162]

[0163] In the formula, is the unit electricity operation and maintenance cost of the i-th renewable energy (including wind turbines and photovoltaics) in the k-th scenario at time t in the n-th year; is the unit electricity subsidy cost of the i-th renewable energy (including wind turbines and photovoltaics) in the k-th scenario at time t in the n-th year; is the power generation of the i-th renewable energy (including wind turbines and photovoltaics) at the k-th scenario in the n-th year at time t; is the unit electricity operation and maintenance cost of the i-th energy storage (including conventional energy storage and IHS) at the k-th scenario in the n-th year at time t; is the charge and discharge amount of the i-th energy storage (including conventional energy storage and IHS) at the k-th scenario at time t in the n-th year; is the unit electricity incentive fee for load demand response in the kth scenario at time t in the nth year; is the penalty fee per unit of electricity for load demand response in the kth scenario at time t in the nth year; is the load demand response power at the kth scenario in the nth year at time t; It is the unit electricity operation and maintenance cost of the i-th conversion equipment (including gas turbines, electric boilers, water electrolysis hydrogen production equipment and power-to-gas equipment P2G) in the k-th scenario at time t in the n-th year; is the electricity consumption of the i-th conversion equipment (including gas turbines, electric boilers, water electrolysis hydrogen production equipment and power-to-gas equipment P2G) at the k-th scenario in the n-th year at time t; is the unit electricity and gas purchase price of the system at time t in the kth scenario of the nth year; It is the amount of electricity and gas purchased by the system at time t in the kth scenario of the nth year.

[0164]

[0165] In the formula, is the unit carbon price of the system at time t in the kth scenario in the nth year; is the carbon emission of the system at time t in the kth scenario in the nth year; is the allowable carbon emission of the system at time t in the kth scenario of the nth year.

[0166]

[0167] In the formula, is the system's electricity carbon emission factor at time t in the kth scenario in the nth year; is the natural gas carbon emission factor of the system at time t in the kth scenario in the nth year; is the gas purchase volume of the system at time t in the kth scenario in the nth year; is the thermal power carbon emission factor of the system at time t in the kth scenario in the nth year; It is the thermal power output of the system at time t in the kth scenario of the nth year.

[0168]

[0169] Where, I is the power shortage of the system; I max is the maximum allowable power shortage of the system; ρ is the system power shortage penalty coefficient.

[0170]

[0171] In the formula, μ is the penalty coefficient for wind and solar power abandonment, which is 1000 yuan / kW; is the amount of wind and solar power curtailment of the system in the kth scenario in the nth year.

[0172]

[0173] In the formula, is the power consumption of the hydrogen turbine at the kth scenario at time t in the nth year; is the amount of electricity sold by the system in the k-th scenario at time t in the n-th year; is the power consumption of the hydrogen production equipment at the kth scenario in the nth year at the time t; is the power consumption of P2G at time t in the kth scenario in the nth year; The load power after demand response at time t in the kth scenario in the nth year.

[0174] Working principle: During the peak period of renewable energy generation during the day, IHS converts excess electricity into hydrogen and stores it; at night or when the load is peak, hydrogen is converted into electricity through fuel cells or hydrogen-fired turbines to balance electricity demand;

[0175] In seasons with abundant wind and solar resources, IHS stores hydrogen through underground hydrogen storage facilities or solid-state hydrogen storage systems, and releases the stored hydrogen in seasons with insufficient wind and solar resources to address the long-term imbalance between energy supply and demand.

[0176] By introducing a stochastic-robust two-stage optimization model, the system dynamically dispatches hydrogen storage, energy storage equipment and thermal power generating units according to the fluctuations of renewable energy and changes in load demand to ensure an all-weather balance of energy supply and demand.

[0177] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A cross-period dynamic hydrogen storage planning method based on load demand, characterized by: The method comprises the following steps: Modeling of inter-period hydrogen storage system: Establishing an IHS mathematical model that supports intra-day and inter-seasonal energy transfer, taking into account charging and discharging power, SOC, and energy conversion efficiency; Typical day selection and feature extraction: Based on the annual net load data, an improved K-means clustering algorithm is used to extract typical days representing different load characteristics throughout the year and their occurrence probabilities, capturing the dynamic changes of renewable energy and load demand; Establishment of integrated energy system planning model: Based on the IHS model, an IES distributed power supply and energy storage optimization planning model including IHS is constructed; The solution is solved by the stochastic-robust two-stage optimization method: in the planning stage, the equipment capacity and system layout are determined through typical scenario optimization, and in the operation stage, the supply and demand balance and efficient energy utilization are achieved through dynamic scheduling; Objective function design: Minimize the total cost of the system over its entire life cycle, including equipment installation cost, operation and maintenance cost, carbon emission cost, wind and solar power abandonment cost, and electricity and gas purchase cost.

2. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The inter-period hydrogen storage system realizes the following functions: Intraday energy transfer: During the peak period of solar power generation, excess electricity is converted into hydrogen for storage through water electrolysis equipment. At night / peak load, hydrogen is converted into electricity through fuel cells or hydrogen turbines to meet load demand; Inter-seasonal energy transfer: In seasons with abundant wind / solar power generation, hydrogen is stored for a long time through solid-state hydrogen storage / underground hydrogen storage technology, and the stored hydrogen is released in seasons with scarce power generation.

3. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1 is characterized in that: The typical day extraction method comprises the following steps: Using the net load curve as the classification basis, the 8760-hour load data throughout the year are divided into multiple clusters; Based on the improved K-means algorithm, the data in each cluster are clustered and the representative typical day of each cluster is extracted; Calculate the probability of occurrence of each typical day as the weight input in scenario optimization; Normalize typical daily load data to optimize calculation efficiency.

4. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The IHS data model includes the following constraints: Power constraint: defines the maximum charging power and discharging power of the IHS at any time; Capacity constraints: specify upper and lower limits for IHS energy storage capacity; SOC dynamic constraint: including the balance adjustment of SOC between the initial and final states. Between each typical scene, the final state SOC of the previous scene is used as the initial SOC of the next scene. Energy loss and conversion efficiency constraints: Consider the energy loss in the process of hydrogen production, storage and use.

5. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The comprehensive energy system includes the following equipment: Energy production equipment: including wind turbines, photovoltaic power generation systems and thermal power generating units; Energy conversion equipment: including water electrolysis hydrogen production equipment, power-to-gas equipment P2G, hydrogen turbines and gas turbines; Energy storage equipment: including batteries, compressed hydrogen tanks and underground hydrogen storage facilities; Load equipment: including industrial power loads, commercial power loads, household loads, district heating users and gas users.

6. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The planning phase of the stochastic-robust two-stage optimization model includes: Determine the installation capacity and distribution layout of IHS and conventional energy storage devices; Optimize the selection and layout of renewable energy generation equipment; Develop equipment configuration plans based on typical scenarios and considering safety requirements under extreme operating conditions.

7. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The operation phase of the stochastic-robust two-stage optimization model includes: Prioritize the use of IHS and batteries to meet dynamic load demands during intraday dispatch; In daytime or cross-seasonal scheduling, energy transfer across time periods is accomplished through IHS; Dynamically allocate the output ratio of renewable energy, thermal power and energy storage to ensure the balance of energy supply and demand.

8. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The cost term in the objective function is defined as: Installation cost: calculated by multiplying the unit capacity cost of the equipment type by the installed capacity; Operation and maintenance costs: including the operation and maintenance costs of renewable energy equipment, energy storage equipment and conversion equipment; Cost of curtailment of wind and solar power: calculated based on the amount of curtailment of wind and solar power and the penalty coefficient for unit curtailment; Carbon emission cost: calculated based on carbon emissions and unit carbon emission price; Cost of purchasing electricity and gas: calculated based on the amount purchased from the power grid and natural gas system.

9. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The constraints of the stochastic-robust two-stage optimization model include: Energy balance constraint: ensure that the total energy supply at each moment is equal to the total load demand; Equipment capacity constraints: specify the maximum permissible installed capacity of each type of equipment; Renewable energy output upper and lower limits: control the actual output range of wind power and photovoltaic power generation; Demand response constraints: limit the extent of user load reduction and load transfer.

10. The method for cross-period dynamic hydrogen storage planning based on load demand according to claim 1, characterized in that: The method is applicable to the following scenarios: Regions with significant seasonal load demand differences and abundant renewable energy resources; A low-carbon / zero-carbon energy system with strict limits on total carbon emissions; An integrated energy system is required that balances the volatility of renewable energy with the stability of long-term energy supply and demand.

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