Wind-solar-thermal-hydrogen integrated energy system configuration and dispatch optimization method

Abstract

The application discloses a wind-solar-thermal-hydrogen comprehensive energy system configuration scheduling optimization method, and steps are as follows: step 1, constructing a comprehensive energy system; step 2, constructing a multi-energy flow mathematical model of the comprehensive energy system; step 3, constructing a four-dimensional multi-objective evaluation index system; and step 4, according to the four-dimensional multi-objective evaluation index system, obtaining a system capacity configuration scheme and a corresponding time sequence scheduling scheme thereof. The method solves the problem that the existing scheduling optimization method focuses on a single index and capacity configuration bias exists.

Description

Technical Field

[0001] This invention belongs to the technical field of integrated energy system optimization scheduling methods, specifically relating to an optimization method for the configuration scheduling of integrated wind, solar, thermal, and hydrogen energy systems. Background Technology

[0002] Integrated Energy Systems (IES), as a key carrier for achieving multi-energy flow synergy, can break down the barriers of separate planning and operation among traditional energy networks, and greatly improve the local absorption capacity of renewable energy.

[0003] However, with the integration of a high proportion and multiple dimensions of intermittent renewable energy sources (such as solar and wind power) into distributed energy networks, the supply side exhibits extremely strong volatility, and dynamic mismatches exist among diverse loads such as cooling, heating, electricity, and hydrogen on the user side, posing significant challenges to the stable and efficient operation of the system. To address this, the above problems are generally mitigated by optimizing the capacity configuration and operation scheduling of integrated energy systems. However, traditional integrated energy systems, when optimizing capacity planning and scheduling, often focus on a single economic indicator (such as static payback period or lowest total life-cycle cost) or a single environmental indicator, lacking a holistic balance, leading to capacity configuration bias in system operation. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the configuration and scheduling of integrated wind, solar, thermal, and hydrogen energy systems, thereby addressing the problem that existing scheduling optimization methods focus on a single indicator and suffer from capacity configuration bias.

[0005] The technical solution adopted in this invention is a method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system, the steps of which are as follows: Step 1: Construct an integrated energy system; Step 2: Construct a multi-energy flow mathematical model for the integrated energy system; Step 3: Construct a four-dimensional multi-objective evaluation index system; Step 4: Based on the four-dimensional multi-objective evaluation index system, obtain the system capacity configuration scheme and its corresponding time-series scheduling scheme.

[0006] The invention is further characterized by: In step 1, the integrated energy system includes a wind-solar hybrid integrated energy system and an external power grid; A wind-solar hybrid integrated energy system includes a renewable energy supply unit, an energy conversion unit, and a time-shift regulation unit. Renewable energy supply units include photovoltaic power generation devices and wind turbine generators; Energy conversion units include heat pumps, electrolyzers, and solid oxide fuel cells; The time-shift regulation unit includes energy storage equipment, thermal storage equipment, and hydrogen storage equipment.

[0007] In step 2, the multi-energy flow mathematical model includes an energy balance constraint model, an output constraint model, and an energy storage state constraint model.

[0008] Balance constraint models include electrical energy flow balance constraint models, thermal energy flow balance constraint models, and hydrogen energy flow balance constraint models. The expression for the power flow balance constraint model is: (1) In equation (1), The amount of electricity output by a photovoltaic power generation device; The amount of electricity output by the wind turbine generator set; This refers to the electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid; when purchasing electricity from the grid, the value is positive. This refers to the electrical output of a solid oxide fuel cell; The amount of electricity consumed by the electrolytic cell; The amount of electricity consumed by the heat pump; For the pure electric load demand on the user side; and These are the discharge capacity and stored capacity of the energy storage device, respectively. The expression for the heat flow balance constraint model is: (2) In equation (2), The heat supplied to the heat pump; This refers to the heat generated during the power generation process of a solid oxide fuel cell; and These refer to the heat storage and heat release of the thermal storage equipment, respectively. For the user's heat load requirements; The expression for the hydrogen energy flow balance constraint model is: (3) In equation (3), The hydrogen production rate for hydrogen production via electrolysis in an electrolytic cell; The rate at which hydrogen is consumed during the operation of a solid oxide fuel cell; and These refer to the hydrogen release and hydrogen storage capacity of the hydrogen storage device, respectively. This is to meet the hydrogen load demand on the user side.

[0009] The output constraint models include photovoltaic power generation device output constraint models, heat pump output constraint models, wind turbine generator output constraint models, solid oxide fuel cell output constraint models, and electrolyzer output constraint models. The expression for the output constraint model of a photovoltaic power generation device is: (4) In equation (4), Indicates the conversion efficiency of a photovoltaic power generation device; For inverter efficiency; R The intensity of solar incident radiation; The installation area of ​​the photovoltaic power generation device; The time interval is expressed in hours (h). The expression for the heat pump output constraint model is: (5) In equation (5), The heat supplied to the heat pump; For performance index; The active power consumed by the heat pump compressor and system, in kW; The expression for the output constraint model of a wind turbine generator is as follows: (6) In equation (6), The amount of electricity output by the wind turbine generator set; This refers to the rated power of the wind turbine generator set. The real-time wind speed of the wind turbine generator; , and These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively. The expression for the output constraint model of a solid oxide fuel cell is: (7) (8) (9) In equations (7) to (9), and These are the thermal efficiency and electrical efficiency of a solid oxide fuel cell, respectively. This refers to the capacity of a solid oxide fuel cell; This refers to the calorific value of hydrogen, expressed in kWh·Nm³. -3 ; The expression for the electrolytic cell output constraint model is: (10) (11) In equations (10) to (11), Indicates the energy conversion efficiency of the electrolysis process; This indicates the capacity of the electrolytic cell.

[0010] Energy storage state constraint models include constraint models for electrical storage devices, thermal storage devices, and hydrogen storage devices. The expression for the constraint model of energy storage devices is: (12) In equation (12), Let t be the power supply of the energy storage device at time t; , To improve the charging and discharging efficiency of energy storage devices; The rated discharge voltage of the energy storage device; The rated discharge current of the energy storage device; For the capacity of energy storage devices; This represents the state of charge of the energy storage device during the k-th scheduling period. This represents the state of charge of the energy storage device during the (k-1)th scheduling period. The total energy generated by the wind-solar hybrid integrated energy system during the simulation period is expressed in kWh. The expression for the constraint model of the thermal storage device is: (13) In equation (13), The capacity of the thermal storage equipment; , These are the heat storage efficiency and heat release efficiency of the thermal storage equipment, respectively. , These represent the heat storage and heat release of the thermal storage equipment per unit time interval, respectively. The thermal storage status of the thermal storage equipment during the k-th scheduling period; The thermal storage status of the thermal storage equipment during the (k-1)th scheduling period; The expression for the constraint model of hydrogen storage equipment is: (14) In equation (14), The gas state in the hydrogen storage device during the k-th scheduling period; The gas state in the hydrogen storage device during the (k-1)th scheduling period; The capacity of the hydrogen storage device; , These represent the hydrogen storage capacity and hydrogen release capacity of the hydrogen storage device per unit time, respectively. , These are hydrogen charging efficiency and hydrogen discharging efficiency, respectively.

[0011] In step 3, the four-dimensional multi-objective evaluation index system includes energy cost of energy (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR). The expression for the Cost of Energy (COE) is: (18) In equations (16) to (18), r is the annual interest rate on capital; n is the life cycle of the wind-solar hybrid integrated energy system; M is the total number of time intervals; N is the total number of devices participating in the cost calculation in the wind-solar hybrid integrated energy system; and k is a certain scheduling period. This represents the configuration capacity of the i-th device in a wind-solar hybrid integrated energy system. This represents the amount of electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th dispatch period. The purchase price for the k-th scheduling period; Investment costs for auxiliary equipment in a wind-solar hybrid integrated energy system; Maintenance costs for wind-solar hybrid integrated energy systems; These represent the energy changes of energy storage devices, thermal storage devices, and hydrogen storage devices, respectively. The unit capacity investment cost of the i-th device; This represents the cumulative processing capacity over the physical lifespan of the equipment. This refers to the cumulative processing volume throughout the entire equipment lifecycle. The energy supply for all equipment in the wind-solar hybrid integrated energy system within a unit time interval; The expression for the proportion of renewable energy (RF) is: (19) In equation (19), This represents the power generation of the photovoltaic power generation device during the k-th scheduling period. This represents the power generation of the wind turbine generator during the k-th scheduling period. These represent the electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th scheduling period; The expression for the energy efficiency ratio (ER) is: (20) In the formula, This represents the power generation of the photovoltaic power generation device during the k-th scheduling period. This represents the power generation of the wind turbine generator during the k-th scheduling period. d represents the amount of electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th dispatch period; k The effective energy output provided by the wind-solar hybrid integrated energy system to the load side during the kth dispatch period; The expression for System Self-Sufficiency Rate (SSR) is: (twenty one) In equation (21), This represents the electricity consumed by the heat pump during the k-th scheduling period. Let be the power generation of the solid oxide fuel cell during the k-th scheduling period; The value is the equivalent electricity value for the hydrogen production of the electrolyzer during the k-th scheduling period.

[0012] The specific process of step 4 is as follows: Step 4.1: Within the preset range of capacity configuration values ​​for each device, a candidate system capacity configuration scheme is generated using a uniform random sampling method. Candidate system capacity configuration scheme Including the installed area of ​​photovoltaic power generation devices Rated power of wind turbine generator set Active power consumed by the heat pump compressor and system Electrolytic cell capacity The capacity of solid oxide fuel cells Energy storage capacity Thermal storage equipment capacity Hydrogen storage equipment capacity ; Step 4.2: Fix the candidate system capacity configuration scheme generated in Step 4.1. x b With the minimum energy cost (COE) as the time-series optimization objective, and using equations (1) to (14) as constraints, a sequential quadratic programming algorithm is employed to solve the problem and obtain candidate system capacity configuration schemes. The corresponding optimal time-series scheduling scheme and the optimal energy cost of equity (COE); Step 4.3, Calculate the optimal time-series scheduling scheme The corresponding renewable energy share (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are used to determine the candidate system configuration schemes. Candidate system configuration scheme The corresponding optimal time-series scheduling scheme and optimal timing scheduling scheme The four evaluation indicators corresponding to the energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are stored in the candidate solution performance database. Step 4.4: When the number of candidate solutions in the candidate solution performance database reaches a preset value, the four evaluation indicators corresponding to each candidate solution are dimensionless according to equation (22), and the objective weights of the dimensionless four evaluation indicators are calculated using the CRITIC method. Subjective weights are calculated using the Analytic Hierarchy Process (AHP). The comprehensive weight of each evaluation indicator is calculated using equation (23). ; Equation (22) is: (twenty two) In equation (22), For the first b The first system capacity configuration scheme corresponds to the first j Evaluation index values, j =1,2,3,4 correspond to energy cost of energy (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR), respectively. The evaluation index value after dimensionless transformation; and These are the first of the total system capacity configuration schemes. j The maximum and minimum values ​​of each evaluation indicator; Equation (23) is: (twenty three) In equation (23), l Indicates the number of evaluation indicators; Step 4.5, based on the comprehensive weight of each evaluation indicator. Construct comprehensive evaluation indicators; The expression is: (twenty four) In equation (24), This represents the comprehensive evaluation value corresponding to the b-th system capacity configuration scheme. Step 4.6: Calculate the comprehensive evaluation value of each candidate system capacity configuration scheme in the candidate solution performance database using equation (24), and sort the candidate system capacity configuration schemes in descending order of comprehensive evaluation value. Step 4.7: Select the candidate system capacity configuration schemes that rank first in the comprehensive evaluation value according to the set proportion as the excellent scheme set. Take the maximum and minimum values ​​of the capacity configuration of each device in the excellent scheme set as the value range of the updated capacity configuration of each device. Repeat steps 4.1 to 4.7 until the preset maximum number of iterations is reached or the preset convergence condition is met. Finally, output the system capacity configuration scheme with the largest comprehensive evaluation value and its corresponding optimal timing scheduling scheme.

[0013] In step 4.4, the objective weights are calculated using the CRITIC method. When the evaluation index matrix is ​​used, the behavior of the system capacity configuration scheme is listed as the dimensionless energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) corresponding to the system capacity configuration scheme.

[0014] The beneficial effects of this invention are: (1) The method of the present invention integrates wind energy, solar energy, electric energy storage, thermal energy storage and hydrogen energy storage into the same comprehensive energy system. Through the coupling of multiple energy flows of electricity, heat and hydrogen, it improves the energy absorption capacity and system operation flexibility under the condition of wind and solar fluctuations. (2) The method of the present invention uses four indicators, namely COE, RF, ER and SSR, to form a multi-objective evaluation system, which overcomes the capacity configuration bias problem caused by single indicator optimization and can better reflect the comprehensive performance of the system. (3) The method of the present invention avoids the subjectivity of traditional Pareto solution set screening by combining SQP with AHP and CRITIC weighting, and realizes global collaborative optimization of system economy, energy efficiency, emission reduction capability and power supply security. (4) The method of the present invention can effectively alleviate the dynamic mismatch between the power output fluctuation of renewable energy and the multi-loads such as electricity, heat and hydrogen on the user side by wind-solar complementary power supply, coordinated regulation of electric energy storage-thermal energy storage-hydrogen energy storage and electric-thermal-hydrogen multi-energy flow coupling optimization, improve the system power supply stability and overall operating efficiency, and realize the stable and efficient operation of the integrated energy system. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the energy transfer principle of the integrated energy system in the method of the present invention; Figure 2 This is a flowchart illustrating the scheduling principles in the method of this invention. Figure 3 This is a performance index diagram under different optimization scenarios in Embodiment 6 of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0017] Example 1 The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system of the present invention comprises the following steps: Step 1: Construct an integrated energy system; Step 2: Construct a multi-energy flow mathematical model for the integrated energy system; Step 3: Construct a four-dimensional multi-objective evaluation index system. By constructing a four-dimensional multi-objective evaluation system, the problem of focusing only on a single index in the existing technology is avoided. Step 4: Based on the four-dimensional multi-objective evaluation index system, obtain the system capacity configuration scheme and its corresponding time-series scheduling scheme.

[0018] Example 2 Based on Example 1, in step 1, the integrated energy system includes a wind-solar hybrid integrated energy system and an external power grid; The external power grid provides supplemental power to the user-side electrical load; A wind-solar hybrid integrated energy system includes a renewable energy supply unit, an energy conversion unit, and a time-shift regulation unit. Renewable energy supply units include photovoltaic power generation devices and wind turbine generators; photovoltaic power generation devices and wind turbine generators prioritize supplying power to user-side electrical loads; The energy conversion unit includes a heat pump, an electrolyzer, and a solid oxide fuel cell; the heat pump is used to convert electrical energy into heat energy to meet heat load requirements; the electrolyzer is used to produce hydrogen and store the hydrogen in a hydrogen storage device when the photovoltaic power generation device and wind turbine generator have surplus power; the solid oxide fuel cell is used to convert hydrogen into electrical energy and heat energy when the output of the photovoltaic power generation device and wind turbine generator is insufficient or when the load is at its peak. The time-shift regulation unit includes an energy storage device, a thermal storage device, and a hydrogen storage device; the energy storage device is used to smooth out power fluctuations; the thermal storage device is used for heat time shifting; and the hydrogen storage device is used for hydrogen energy time shifting.

[0019] Example 3 Based on Example 2, in step 2, the multi-energy flow mathematical model includes an energy balance constraint model, an output constraint model, and an energy storage state constraint model.

[0020] Among them, the balance constraint models include the electrical energy flow balance constraint model, the thermal energy flow balance constraint model, and the hydrogen energy flow balance constraint model; The expression for the power flow balance constraint model is: (1) In equation (1), The amount of electricity output by a photovoltaic power generation device; The amount of electricity output by the wind turbine generator set; This refers to the electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid; when purchasing electricity from the grid, the value is positive. This refers to the electrical output of a solid oxide fuel cell; The amount of electricity consumed by the electrolytic cell; The amount of electricity consumed by the heat pump; For the pure electric load demand on the user side; and These are the discharge capacity and stored capacity of the energy storage device, respectively. The expression for the heat flow balance constraint model is: (2) In equation (2), The heat supplied to the heat pump; This refers to the heat generated during the power generation process of a solid oxide fuel cell; and These refer to the heat storage and heat release of the thermal storage equipment, respectively. For the user's heat load requirements; The expression for the hydrogen energy flow balance constraint model is: (3) In equation (3), The hydrogen production rate for hydrogen production via electrolysis in an electrolytic cell; The rate at which hydrogen is consumed during the operation of a solid oxide fuel cell; and These refer to the hydrogen release and hydrogen storage capacity of the hydrogen storage device, respectively. This is to meet the hydrogen load demand on the user side.

[0021] The output constraint models include photovoltaic power generation device output constraint models, heat pump output constraint models, wind turbine generator output constraint models, solid oxide fuel cell output constraint models, and electrolyzer output constraint models. The expression for the output constraint model of a photovoltaic power generation device is: (4) In equation (4), Indicates the conversion efficiency of a photovoltaic power generation device; For inverter efficiency; R The intensity of solar incident radiation; The installation area of ​​the photovoltaic power generation device; The time interval is expressed in hours (h). The expression for the heat pump output constraint model is: (5) In equation (5), The heat supplied to the heat pump; For performance index; The active power consumed by the heat pump compressor and system, in kW; The expression for the output constraint model of a wind turbine generator is as follows: (6) In equation (6), The amount of electricity output by the wind turbine generator set; This refers to the rated power of the wind turbine generator set. The real-time wind speed of the wind turbine generator; , and These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively. The expression for the output constraint model of a solid oxide fuel cell is: (7) (8) (9) In equations (7) to (9), and These are the thermal efficiency and electrical efficiency of a solid oxide fuel cell, respectively. This refers to the capacity of a solid oxide fuel cell; This refers to the calorific value of hydrogen, expressed in kWh·Nm³. -3 ; The expression for the electrolytic cell output constraint model is: (10) (11) In equations (10) to (11), Indicates the energy conversion efficiency of the electrolysis process; This indicates the capacity of the electrolytic cell.

[0022] Energy storage state constraint models include constraint models for electrical storage devices, thermal storage devices, and hydrogen storage devices. The expression for the constraint model of energy storage devices is: (12) In equation (12), Let t be the power supply of the energy storage device at time t; , To improve the charging and discharging efficiency of energy storage devices; The rated discharge voltage of the energy storage device; The rated discharge current of the energy storage device; For the capacity of energy storage devices; This represents the state of charge of the energy storage device during the k-th scheduling period. This represents the state of charge of the energy storage device during the (k-1)th scheduling period. The total energy generated by the wind-solar hybrid integrated energy system during the simulation period is expressed in kWh. The expression for the constraint model of the thermal storage device is: (13) In equation (13), The capacity of the thermal storage equipment; , These are the heat storage efficiency and heat release efficiency of the thermal storage equipment, respectively. , These represent the heat storage and heat release of the thermal storage equipment during the k-th scheduling period, respectively. The thermal storage status of the thermal storage equipment during the k-th scheduling period; The thermal storage status of the thermal storage equipment during the (k-1)th scheduling period; The expression for the constraint model of hydrogen storage equipment is: (14) In equation (14), The gas state in the hydrogen storage device during the k-th scheduling period; The gas state in the hydrogen storage device during the (k-1)th scheduling period; The capacity of the hydrogen storage device; , These represent the hydrogen storage capacity and hydrogen release capacity of the hydrogen storage device during the k-th scheduling period, respectively. , These are hydrogen charging efficiency and hydrogen discharging efficiency, respectively.

[0023] The wind-solar hybrid integrated energy system in this invention adopts the scheduling principle of "renewable energy priority utilization - energy storage regulation - grid supplementation" to determine the operating status of each device. That is, the functions are performed in the order of renewable energy supply unit supply, reuse energy conversion unit supply, and time shift regulation unit supply. When there is a surplus of renewable energy supply, the surplus energy is stored or converted through energy storage equipment, thermal storage equipment and electrolysis cell hydrogen production, and hydrogen storage equipment hydrogen storage. When the renewable energy supply is insufficient, energy is supplemented by energy storage equipment, thermal storage equipment, hydrogen storage equipment and solid oxide fuel cells. When the internal energy of the system still cannot meet the load demand, the external power grid supplements the surplus power demand.

[0024] The specific scheduling principles are as follows: like Figure 2 As shown, the system obtains the output power of the photovoltaic power generation device. The power output of wind turbine generators Electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid The amount of electricity output by a solid oxide fuel cell Pure electric load demand on the user side Heat pump provides heat User-side heat load demand Hydrogen production rate of hydrogen produced by electrolysis in an electrolytic cell Hydrogen load demand on the user side ; when At the same time, hydrogen storage devices store hydrogen energy or increase the output of solid oxide fuel cells (i.e., increase the amount of electricity output by solid oxide fuel cells). Otherwise, the hydrogen storage device releases hydrogen energy or adjusts the electrolyzer to bring the system to equilibrium; when When the thermal energy storage device stores heat energy, it releases heat energy; otherwise, the system reaches equilibrium when the heat pump and solid oxide fuel cell are adjusted. At that time, among them , The electricity consumed by the electrolyzer, solid oxide fuel cell, and heat pump is stored in energy storage devices, thermal energy storage devices store thermal energy, and hydrogen storage devices store hydrogen energy. Otherwise, the energy storage devices discharge or regulate the power grid to bring the system into balance.

[0025] Example 4 Based on Example 3, in step 3, the four-dimensional multi-objective evaluation index system includes energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR). The objective function for the Capital Recovery Factor (CRF) is in the form of: (15) The expression for the Cost of Energy (COE) is: (16) (17) (18) In equations (16) to (18), r is the annual interest rate on capital; n is the life cycle of the wind-solar hybrid integrated energy system; Let be the depreciation cost of the i-th device; M be the total number of time intervals; N be the total number of devices participating in the cost calculation in the wind-solar hybrid integrated energy system; and k be a certain scheduling period. This represents the configuration capacity of the i-th device in a wind-solar hybrid integrated energy system. The amount of electricity purchased from the power grid during the k-th scheduling period; The purchase price for the k-th scheduling period; Depreciation costs for auxiliary equipment in a wind-solar hybrid integrated energy system; Investment cost for auxiliary equipment (BoP) of a wind-solar hybrid integrated energy system; Maintenance costs for wind-solar hybrid integrated energy systems; The effective energy output provided by the wind-solar hybrid integrated energy system to the load side during the kth dispatch period; These represent the energy changes of energy storage devices, thermal storage devices, and hydrogen storage devices, respectively. The unit capacity investment cost of the i-th device; This represents the cumulative processing capacity over the physical lifespan of the equipment. This refers to the cumulative processing volume throughout the entire equipment lifecycle. The energy supply for all equipment in the wind-solar hybrid integrated energy system within a unit time interval; Auxiliary equipment refers to the supporting equipment used to ensure the normal connection, operation, control and safety of the integrated energy system, excluding the key equipment used in photovoltaic power generation devices, wind turbine generators, heat pumps, electrolyzers, solid oxide fuel cells and energy storage equipment, thermal storage equipment and hydrogen storage equipment. It mainly includes DC / DC converters, transformers, power distribution and protection equipment, circulating pumps, fans, heat exchangers, hydrogen compression, purification, drying and pressure regulating equipment, pipeline valves, safety pressure relief devices, as well as measurement, control and communication equipment, etc. It typically depends on the initial capital investment in key equipment of the wind-solar hybrid energy system, and is unrelated to the operational strategy. The initial investment cost of key equipment is set at 30%; capacity is mainly determined by load characteristics and operating parameters of key equipment; further consideration is given to the physical lifespan of each piece of equipment. The annualized cost is used to obtain the equipment depreciation cost.

[0026] The expression for the proportion of renewable energy (RF) is: (19) In equation (19), This represents the power generation of the photovoltaic power generation device during the k-th scheduling period. This represents the power generation of the wind turbine generator during the k-th scheduling period. These represent the electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th scheduling period; The expression for the energy efficiency ratio (ER) is: (20) In the formula, This represents the power generation of the photovoltaic power generation device during the k-th scheduling period. This represents the power generation of the wind turbine generator during the k-th scheduling period. d represents the amount of electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th dispatch period; k The effective energy output provided by the wind-solar hybrid integrated energy system to the load side during the kth dispatch period; The expression for System Self-Sufficiency Rate (SSR) is: (twenty one) In equation (21), This represents the electricity consumed by the heat pump during the k-th scheduling period. Let be the power generation of the solid oxide fuel cell during the k-th scheduling period; The value is the equivalent electricity value for the hydrogen production of the electrolyzer during the k-th scheduling period.

[0027] Example 5 Based on Example 4, the specific process of step 4 is as follows: Step 4.1: Within the preset range of capacity configuration values ​​for each device, a candidate system capacity configuration scheme is generated using a uniform random sampling method. Candidate system capacity configuration scheme Including the installed area of ​​photovoltaic power generation devices Rated power of wind turbine generator set Active power consumed by the heat pump compressor and system Electrolytic cell capacity The capacity of solid oxide fuel cells Energy storage capacity Thermal storage equipment capacity Hydrogen storage equipment capacity ; Step 4.2: Fix the candidate system capacity configuration scheme generated in Step 4.1. x b (Upcoming candidate system capacity configuration scheme) x b Assigning values ​​to the corresponding models), with the minimum energy cost (COE) as the time-series scheduling optimization objective, and using equations (1) to (14) as constraints, the sequential quadratic programming algorithm (SQP) is used to solve the problem and obtain candidate system capacity configuration schemes. The corresponding optimal time-series scheduling scheme and the optimal energy cost of equity (COE); Among them, the time-series scheduling scheme Including the output power of photovoltaic power generation devices Wind turbine generator output power Electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid Active power consumed by the heat pump compressor and system The amount of electricity consumed by the electrolytic cell The amount of electricity output by a solid oxide fuel cell Discharge of energy storage devices Energy storage capacity of energy storage devices The heat storage capacity of thermal storage equipment Heat release of thermal storage equipment Hydrogen release capacity of hydrogen storage equipment Hydrogen storage capacity of hydrogen storage equipment ; Step 4.3, Calculate the optimal time-series scheduling scheme The corresponding renewable energy share (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are used to determine the candidate system configuration schemes. Candidate system configuration scheme The corresponding optimal time-series scheduling scheme and optimal timing scheduling scheme The four evaluation indicators corresponding to the energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are stored in the candidate solution performance database. Step 4.4: When the number of candidate solutions in the candidate solution performance database reaches a preset value, the four evaluation indicators corresponding to each candidate solution are dimensionless according to equation (22), and the objective weights of the dimensionless four evaluation indicators are calculated using the CRITIC method. Subjective weights are calculated using the Analytic Hierarchy Process (AHP). The comprehensive weight of each evaluation indicator is calculated using equation (23). ; Equation (22) is: (twenty two) In equation (22), For the first b The first system capacity configuration scheme corresponds to the first j Evaluation index values, j =1,2,3,4 correspond to energy cost of energy (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR), respectively. The evaluation index value after dimensionless transformation; and These are the first of the total system capacity configuration schemes. j The maximum and minimum values ​​of each evaluation indicator; When processing dimensionless parameters, the energy cost (COE) is a cost-type indicator, and the smaller the better, so it is processed in reverse. The renewable energy share (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are benefit-type indicators, and the larger the better, so they are processed in forward. Equation (23) is: (twenty three) In equation (23), l Indicates the number of evaluation indicators; Calculating objective weights using the CRITIC method At that time, the dimensionless energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are combined into an evaluation index matrix. The behavior of the evaluation index matrix is ​​for each system capacity configuration scheme, and the dimensionless energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) corresponding to each system capacity configuration scheme are listed. Step 4.5, based on the comprehensive weight of each evaluation indicator. Construct comprehensive evaluation indicators; The expression is: (twenty four) In equation (24), This represents the comprehensive evaluation value corresponding to the b-th system capacity configuration scheme. Step 4.6: Calculate the comprehensive evaluation value of each candidate system capacity configuration scheme in the candidate solution performance database using equation (24), and sort the candidate system capacity configuration schemes in descending order of comprehensive evaluation value. Step 4.7: Select the candidate system capacity configuration schemes that rank first in the comprehensive evaluation value according to the set proportion as the excellent scheme set. Take the maximum and minimum values ​​of the capacity configuration of each device in the excellent scheme set as the value range of the updated capacity configuration of each device. Repeat steps 4.1 to 4.7 until the preset maximum number of iterations is reached or the preset convergence condition is met. Finally, output the system capacity configuration scheme with the largest comprehensive evaluation value and its corresponding optimal timing scheduling scheme.

[0028] Example 6 Numerical Example Verification and Scenario Comparison Analysis: To demonstrate the superior performance of the method of this invention in improving overall system energy efficiency and reducing investment costs, four conventional single-objective scenarios were established for numerical example simulation comparison with the method of this invention under a unified evaluation benchmark. The optimal capacity design results for each scenario are shown in Table 1, and the final comparison of the overall system performance is as follows: Figure 3 As shown, the cost per kilowatt-hour is lowest under the COE optimization scenario, at $0.0229; the energy efficiency ratio is highest under the ER optimization scenario, at 0.9522; the renewable energy penetration rate reaches 1 under the RF optimization scenario; and the energy self-sufficiency rate is 0.9969 under the SSR optimization scenario. The single-objective optimization results show that there is a clear trade-off between the performance indicators, that is, the optimization of a single indicator often comes at the cost of the performance degradation of other indicators.

[0029] Table 1

[0030] The capacity and performance data show that: When performing single-objective ER optimization with the goal of maximizing the overall system efficiency (Scenario 3), the photovoltaic installation area is severely compressed to 29.38m² because the system tries to avoid additional losses caused by the multi-stage energy transmission and conversion of large-capacity photovoltaic and wind turbine units. As a result, a huge share of 98.16% of the photovoltaic installation area relies on external grid input. Although this scenario achieves an optimal energy efficiency ratio of 0.9522, its renewable energy penetration rate (RF) almost degrades to zero, and the overall life cycle energy cost (COE) of the system increases sharply. In addition, because the efficiency function is not established with direct constraints on equipment capacity, the capacity of SOFC and PEM components is excessively amplified under this single objective.

[0031] When planning with a single objective scenario of low carbon penetration rate RF (Scenario 2) or power supply reliability self-sufficiency rate SSR (Scenario 4), in order to fully extract clean energy or ensure high isolation and independence, the system significantly increases the scale of photovoltaic (up to 2205.5 m² in the RF scenario) and wind turbine deployment, and multiplies the capacity of battery energy storage BT. Although this large-area redundant equipment stacking achieves RF of 100% or SSR as high as 0.9969, its initial capital expenditure and full-cycle levelized cost also significantly exceed the standard.

[0032] In contrast, when using the method of this invention, the capacities of various core equipment and thermal and hydrogen storage tanks remain at a highly reasonable compromise level under the constraints of a mixture of subjective and objective factors. This method not only avoids severely deteriorating any single performance objective, but also establishes a more perfect global optimal balance among the four conflicting dimensions of energy cost of energy (COE), renewable energy share (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR), achieving the best overall operational performance benefit.

Claims

1. A method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system, characterized in that, The steps are as follows: Step 1: Construct an integrated energy system; Step 2: Construct a multi-energy flow mathematical model for the integrated energy system; Step 3: Construct a four-dimensional multi-objective evaluation index system; Step 4: Based on the four-dimensional multi-objective evaluation index system, obtain the system capacity configuration scheme and its corresponding time-series scheduling scheme.

2. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 1, characterized in that, In step 1, the integrated energy system includes a wind-solar hybrid integrated energy system and an external power grid; A wind-solar hybrid integrated energy system includes a renewable energy supply unit, an energy conversion unit, and a time-shift regulation unit. Renewable energy supply units include photovoltaic power generation devices and wind turbine generators; Energy conversion units include heat pumps, electrolyzers, and solid oxide fuel cells; The time-shift regulation unit includes energy storage equipment, thermal storage equipment, and hydrogen storage equipment.

3. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 2, characterized in that, In step 2, the multi-energy flow mathematical model includes an energy balance constraint model, an output constraint model, and an energy storage state constraint model.

4. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 3, characterized in that, The balance constraint models include the electrical energy flow balance constraint model, the thermal energy flow balance constraint model, and the hydrogen energy flow balance constraint model. The expression for the power flow balance constraint model is: （1） In equation (1), The amount of electricity output by a photovoltaic power generation device; The amount of electricity output by the wind turbine generator; This refers to the electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid; when purchasing electricity from the grid, the value is positive. This refers to the electrical output of a solid oxide fuel cell; The amount of electricity consumed by the electrolytic cell; The amount of electricity consumed by the heat pump; For the pure electric load demand on the user side; and These are the discharge capacity and stored capacity of the energy storage device, respectively. The expression for the heat flow balance constraint model is: （2） In equation (2), The heat supplied to the heat pump; This refers to the heat generated during the power generation process of a solid oxide fuel cell; and These refer to the heat storage and heat release of the thermal storage equipment, respectively. For the user's heat load requirements; The expression for the hydrogen energy flow balance constraint model is: （3） In equation (3), The hydrogen production rate for hydrogen production via electrolysis in an electrolytic cell; The rate at which hydrogen is consumed during the operation of a solid oxide fuel cell; and These refer to the hydrogen release and hydrogen storage capacity of the hydrogen storage device, respectively. This is to meet the hydrogen load demand on the user side.

5. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 4, characterized in that, The output constraint models include photovoltaic power generation device output constraint models, heat pump output constraint models, wind turbine generator output constraint models, solid oxide fuel cell output constraint models, and electrolyzer output constraint models. The expression for the output constraint model of a photovoltaic power generation device is: （4） In equation (4), Indicates the conversion efficiency of a photovoltaic power generation device; For inverter efficiency; R The intensity of solar incident radiation; The installation area of ​​the photovoltaic power generation device; The time interval is expressed in hours (h). The expression for the heat pump output constraint model is: （5） In equation (5), The heat supplied to the heat pump; For performance index; The active power consumed by the heat pump compressor and system, in kW; The expression for the output constraint model of a wind turbine generator is as follows: （6） In equation (6), The amount of electricity output by the wind turbine generator; This refers to the rated power of the wind turbine generator set. This refers to the real-time wind speed transmitted through the wind turbine generator. , and These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively. The expression for the output constraint model of a solid oxide fuel cell is: （7） （8） （9） In equations (7) to (9), and These are the thermal efficiency and electrical efficiency of a solid oxide fuel cell, respectively. This refers to the capacity of a solid oxide fuel cell; This refers to the calorific value of hydrogen, expressed in kWh·Nm³. -3 ; The expression for the electrolytic cell output constraint model is: （10） （11） In equations (10) to (11), Indicates the energy conversion efficiency of the electrolysis process; This indicates the capacity of the electrolytic cell.

6. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 5, characterized in that, Energy storage state constraint models include constraint models for electrical storage devices, thermal storage devices, and hydrogen storage devices. The expression for the constraint model of energy storage devices is: （12） In equation (12), Let t be the power supply of the energy storage device at time t; , To improve the charging and discharging efficiency of energy storage devices; The rated discharge voltage of the energy storage device; The rated discharge current of the energy storage device; For the capacity of energy storage devices; This represents the state of charge of the energy storage device during the k-th scheduling period. This represents the state of charge of the energy storage device during the (k-1)th scheduling period. The total energy generated by the wind-solar hybrid integrated energy system during the simulation period is expressed in kWh. The expression for the constraint model of the thermal storage device is: （13） In equation (13), The capacity of the thermal storage equipment; , These are the heat storage efficiency and heat release efficiency of the thermal storage equipment, respectively. , These represent the heat storage and heat release of the thermal storage equipment during the k-th scheduling period, respectively. The thermal storage status of the thermal storage equipment during the k-th scheduling period; The thermal storage status of the thermal storage equipment during the (k-1)th scheduling period; The expression for the constraint model of hydrogen storage equipment is: （14） In equation (14), The gas state in the hydrogen storage device during the k-th scheduling period; The gas state in the hydrogen storage device during the (k-1)th scheduling period; The capacity of the hydrogen storage device; , These represent the hydrogen storage capacity and hydrogen release capacity of the hydrogen storage device during the k-th scheduling period, respectively. , These are hydrogen charging efficiency and hydrogen discharging efficiency, respectively.

7. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 6, characterized in that, In step 3, the four-dimensional multi-objective evaluation index system includes energy cost of energy (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR). The expression for the Cost of Energy (COE) is: （18） In equations (16) to (18), r is the annual interest rate on capital; n is the life cycle of the wind-solar hybrid integrated energy system; M is the total number of time intervals; N is the total number of devices participating in the cost calculation in the wind-solar hybrid integrated energy system; and k is a certain scheduling period. This represents the configuration capacity of the i-th device in a wind-solar hybrid integrated energy system. This represents the amount of electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th dispatch period. The purchase price for the k-th scheduling period; Investment costs for auxiliary equipment in a wind-solar hybrid integrated energy system; Maintenance costs for wind-solar hybrid integrated energy systems; These represent the energy changes of energy storage devices, thermal storage devices, and hydrogen storage devices, respectively. The unit capacity investment cost of the i-th device; This represents the cumulative processing capacity over the physical lifespan of the equipment. This refers to the cumulative processing volume throughout the entire equipment lifecycle. The energy supply for all equipment in the wind-solar hybrid integrated energy system within a unit time interval; The expression for the proportion of renewable energy (RF) is: （19） In equation (19), This represents the power generation of the photovoltaic power generation device during the k-th scheduling period. This represents the power generation of the wind turbine generator during the k-th scheduling period. These represent the electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th scheduling period; The expression for the energy efficiency ratio (ER) is: （20） In the formula, This represents the power generation of the photovoltaic power generation device during the k-th scheduling period. This represents the power generation of the wind turbine generator during the k-th scheduling period. d represents the amount of electricity exchanged between the wind-solar hybrid integrated energy system and the external power grid during the k-th dispatch period; k The effective energy output provided by the wind-solar hybrid integrated energy system to the load side during the kth dispatch period; The expression for System Self-Sufficiency Rate (SSR) is: （21） In equation (21), This represents the electricity consumed by the heat pump during the k-th scheduling period. Let be the power generation of the solid oxide fuel cell during the k-th scheduling period; The value is the equivalent electricity value for the hydrogen production of the electrolyzer during the k-th scheduling period.

8. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 7, characterized in that, The specific process of step 4 is as follows: Step 4.1: Within the preset range of capacity configuration values ​​for each device, a candidate system capacity configuration scheme is generated using a uniform random sampling method. Candidate system capacity configuration scheme Including the installed area of ​​photovoltaic power generation devices Rated power of wind turbine generator set Active power consumed by the heat pump compressor and system The capacity of the electrolytic cell The capacity of solid oxide fuel cells Energy storage capacity Thermal storage equipment capacity Hydrogen storage equipment capacity ; Step 4.2: Fix the candidate system capacity configuration scheme generated in Step 4.

1. x b With the minimum energy cost (COE) as the time-series optimization objective, and using equations (1) to (14) as constraints, a sequential quadratic programming algorithm is employed to solve the problem and obtain candidate system capacity configuration schemes. The corresponding optimal time-series scheduling scheme and the optimal energy cost of equity (COE); Step 4.3, Calculate the optimal time-series scheduling scheme The corresponding renewable energy share (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are used to determine the candidate system configuration schemes. Candidate system configuration scheme The corresponding optimal time-series scheduling scheme and optimal timing scheduling scheme The four evaluation indicators corresponding to the energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) are stored in the candidate solution performance database. Step 4.4: When the number of candidate solutions in the candidate solution performance database reaches a preset value, the four evaluation indicators corresponding to each candidate solution are dimensionless according to equation (22), and the objective weights of the dimensionless four evaluation indicators are calculated using the CRITIC method. Subjective weights are calculated using the Analytic Hierarchy Process (AHP). The comprehensive weight of each evaluation indicator is calculated using equation (23). ; Equation (22) is: （22） In equation (22), For the first b The first system capacity configuration scheme corresponds to the first j Evaluation index values, j =1,2,3,4 correspond to energy cost of energy (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR), respectively. The evaluation index value after dimensionless transformation; and These are the first of the total system capacity configuration schemes. j The maximum and minimum values ​​of each evaluation indicator; Equation (23) is: （23） In equation (23), l Indicates the number of evaluation indicators; Among them, the CRITIC method is used to calculate the objective weights. When the evaluation index matrix is ​​used, the behavior system capacity configuration scheme is listed as the dimensionless energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) corresponding to the system capacity configuration scheme. Step 4.5, based on the comprehensive weight of each evaluation indicator. Construct comprehensive evaluation indicators; The expression is: （24） In equation (24), This represents the comprehensive evaluation value corresponding to the b-th system capacity configuration scheme. Step 4.6: Calculate the comprehensive evaluation value of each candidate system capacity configuration scheme in the candidate solution performance database using equation (24), and sort the candidate system capacity configuration schemes in descending order of comprehensive evaluation value. Step 4.7: Select the candidate system capacity configuration schemes that rank first in the comprehensive evaluation value according to the set proportion as the excellent scheme set. Take the maximum and minimum values ​​of the capacity configuration of each device in the excellent scheme set as the value range of the updated capacity configuration of each device. Repeat steps 4.1 to 4.7 until the preset maximum number of iterations is reached or the preset convergence condition is met. Finally, output the system capacity configuration scheme with the largest comprehensive evaluation value and its corresponding optimal timing scheduling scheme.

9. The method for optimizing the configuration and scheduling of a wind-solar-thermal-hydrogen integrated energy system according to claim 8, characterized in that, In step 4.4, the objective weights are calculated using the CRITIC method. When the evaluation index matrix is ​​used, the behavior of the system capacity configuration scheme is listed as the dimensionless energy cost (COE), renewable energy ratio (RF), energy efficiency ratio (ER), and system self-sufficiency rate (SSR) corresponding to the system capacity configuration scheme.

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