A configuration method of an electric-thermal bidirectional conversion device for a high-proportion new energy power grid

Abstract

This invention discloses a configuration method for bidirectional electrothermal conversion devices in power grids with high proportions of renewable energy, belonging to the field of power system planning and integrated energy optimization. The method first collects and normalizes wind and solar power output and electrothermal load data, extracts typical scenarios through clustering, and constructs models for renewable energy growth, load growth, and conventional unit retirement. Next, it establishes an electrothermal coupled power grid operation optimization model to determine equipment strategies with minimum operating costs. Then, it constructs a device capacity configuration model, co-optimizes it with the operation model, and solves for optimal power and thermal storage capacity with minimum life-cycle costs. Finally, it merges these to form a co-optimization model, which is solved using mixed-integer linear programming, with nonlinear terms linearized. This method can improve the renewable energy absorption rate and system regulation flexibility, adapt to the power grid transformation needs of the gradual withdrawal of thermal power units, and ensure the safe and economical operation of the system.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and integrated energy system optimization technology, specifically relating to a configuration method for a bidirectional electrothermal conversion device for a high-proportion renewable energy power grid. Background Technology

[0002] With the continuous expansion of new energy power generation, the proportion of new energy sources such as wind power and photovoltaic power in the power system continues to increase. Their output is volatile and random, posing significant challenges to the grid's frequency regulation, voltage regulation, and power and heat load balance. Traditional coal-fired power units and combined heat and power (CHP) units have long served the functions of system peak shaving and ancillary services. However, under the "dual carbon" target, some units are gradually facing retirement or output limitations, resulting in insufficient flexible adjustment resources in the power system. Existing bidirectional electrothermal conversion devices, such as magnesium brick-type bidirectional electrothermal conversion devices, are equipped with bidirectional power converters and control systems, possessing bidirectional energy conversion capabilities for converting electrical energy to heat energy, storing heat energy, and converting heat energy back to electrical energy. However, they lack system-level configuration capabilities. For example, capacity configuration and layout methods in high-proportion new energy grids are lacking. With the continuous growth of new energy installed capacity and the gradual retirement of conventional units, without a reasonable configuration method, it is difficult to fully leverage the role of bidirectional electrothermal conversion devices in new energy consumption and system regulation. Therefore, there is a need for a configuration method for bidirectional electrothermal conversion devices for grids with a high proportion of renewable energy, which is of great significance for improving the renewable energy absorption capacity and the system's operational flexibility. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a configuration method for a bidirectional electrothermal conversion device for high-proportion renewable energy power grids. This invention allows for reasonable capacity configuration of the bidirectional electrothermal conversion device based on the characteristics of renewable energy output and the changing patterns of power and heating loads. When renewable energy is abundant, the device absorbs excess electrical energy and converts it into heat energy for storage. When the system requires power, it outputs electrical energy to the grid through thermoelectric conversion and participates in system regulation, thereby improving the renewable energy absorption capacity and enhancing the flexibility of power system regulation. This solves the problems of insufficient power system regulation capacity under the background of high-proportion wind and solar power integration, as well as the reduction of flexible regulation resources caused by the gradual retirement of traditional coal-fired units and cogeneration units.

[0004] The technical solution adopted in this invention is as follows: A method for configuring a high-proportion new energy grid electrothermal bidirectional conversion device, comprising the following steps:

[0005] Step 1: Obtain the hourly output data of wind power and photovoltaic power plants in the planning area throughout the year, as well as the hourly demand data of electricity load and heat load in the area. Normalize the wind power output, photovoltaic power output, electricity load and heat load data, and extract typical operating scenarios through scenario clustering method. Construct annual new energy installed capacity growth and output models, electricity and heat load growth models, and traditional coal-fired units and cogeneration units gradual retirement models respectively.

[0006] Step 2: Construct an electrothermal coupled power grid operation optimization model. Under the conditions of satisfying the constraints of electric power balance, thermal power balance, system frequency regulation, equipment operation, and thermal storage dynamics, determine the operation strategies of various equipment with the goal of minimizing system operating costs.

[0007] Step 3: Construct a capacity configuration model for the bidirectional electrothermal conversion device, and perform collaborative optimization with the electrothermal coupled power grid operation optimization model constructed in Step 2. Determine the optimal power capacity and thermal storage capacity configuration scheme for the bidirectional electrothermal conversion device with the goal of minimizing the overall cost throughout the system's life cycle.

[0008] Step 4: Integrate the electrothermal coupled power grid operation optimization model from Step 2 with the capacity configuration model of the electrothermal bidirectional conversion device from Step 3 to form a capacity planning and operation control collaborative optimization model. Use mixed integer linear programming for unified solution, approximate the nonlinear cost function using piecewise linearization, and transform the product term using McCormick envelope linearization.

[0009] Furthermore, in step one, the normalization process employs a max-min linear mapping method to uniformly map the disparate magnitudes of wind power, photovoltaic output, and electrothermal load data to the 0-1 numerical range. The calculation formula is as follows:

[0010]

[0011] In the formula: , The first The annual maximum and minimum values ​​of the collected data; , The first Class of collected data in the first sky The original actual data value and the normalized value after per-unit conversion at that time. Indexes are provided for data types, including wind power output, photovoltaic power output, electricity load, and heat load;

[0012] The formula for the annual growth of installed new energy capacity and power output model is as follows:

[0013]

[0014] In the formula: , The first Annual wind power installed capacity and base year wind power installed capacity; , The first Annual photovoltaic installed capacity and base year photovoltaic installed capacity; , These are the annual growth rates of wind power and photovoltaic installed capacity, respectively. , These are the planning year and the base year, respectively. For the first Year Available wind power output during certain periods; For the first Year Available photovoltaic power output during certain periods; This is a normalized curve of wind power output for a typical day in a typical operating scenario; This is a normalized curve of photovoltaic power output for a typical day in a typical operating scenario;

[0015] The expressions for the constructed electricity and heat load growth models are as follows:

[0016]

[0017] In the formula: , For the first Year Electricity and heat load demand during specific time periods; , For the base year Electricity and heat load during specific time periods; , The annual growth rate of electricity and heat load;

[0018] The formula for the gradual decommissioning model of traditional coal-fired power units and combined heat and power units is as follows:

[0019]

[0020] In the formula: , The first Year and the Annual available installed capacity of conventional generating units; For the first Annual conventional units The capacity for decommissioning; , The first Year and the Available installed capacity of cogeneration units per year; For the first Annual capacity of decommissioned combined heat and power units.

[0021] Furthermore, in step two, the objective function of the high-proportion renewable energy power grid operation optimization model is constructed using the operating costs of system power purchase, conventional unit operation, wind and solar curtailment penalties, frequency regulation insufficiency penalties, and power and heat load shortage penalties; the wind and solar power grid aims to minimize the annual operating cost, as detailed below:

[0022] The objective function for the operation of a wind-solar power grid is expressed as follows:

[0023]

[0024] In the formula: Annualized operating cost of wind and solar power grid system; This represents the number of typical days. For the first The probability weight of a typical day The number of hours in a typical day; For the planning year On a typical day No. Power purchased by the power grid at any given time; For the planning year On a typical day No. The power output of the power grid at any given time; Electricity purchase price per unit; Electricity price per unit; For conventional units In the planning year On a typical day No. The cost of generating electricity at any given moment; For combined heat and power units In the planning year On a typical day No. The operating cost at any given moment; For regional boilers In the planning year On a typical day No. Heating power at any given time Unit heating cost; , These are respectively wind curtailment power and solar curtailment power. This is the power curtailment penalty coefficient. , These represent adjusting the insufficient relaxation amount of frequency modulation, either upwards or downwards. This is the frequency modulation relaxation penalty coefficient. , These are the power load and heat load supply shortages, respectively. , These are the power shortage penalty coefficient and the preset non-negative penalty coefficient, respectively. The constraints of the wind-solar power grid include power balance constraints, equipment operation constraints, electrothermal conversion constraints, and energy storage characteristic constraints. Among them, the power balance constraints include hourly electrical power balance constraints and hourly thermal power balance constraints, expressed as follows:

[0025]

[0026] In the formula: , They are respectively in the planning year On a typical day No. Real-time wind power utilization and photovoltaic power utilization; In the planning year On a typical day No. Regular unit output at all times; , They are respectively in the planning year On a typical day No. The electrical and thermal power output of the cogeneration unit at all times; , They are respectively in the planning year On a typical day No. The amount of electricity purchased from the upper-level power grid and the amount of electricity sold to the upper-level power grid at all times; In the planning year On a typical day No. The charging power of the bidirectional electrothermal conversion device at any given time; It is the heat-to-electric power of the bidirectional electrothermal conversion device; In the planning year On a typical day No. The output heat power of the electrode-type electric boiler at any time; , In the planning year respectively On a typical day No. The heat release power and heat charge power of the bidirectional electrothermal conversion device at all times; , They are respectively in the planning year On a typical day No. The electrical and thermal loads are slack variables in case of power shortage; , They are respectively in the planning year On a typical day No. The power demand for electricity and heat load at any given time;

[0027] The expression for the system frequency regulation constraint is as follows:

[0028]

[0029] In the formula: , The first Typical days of the year Time period The up-frequency regulation capacity and down-frequency regulation capacity provided by conventional generating units; , The first Typical days of the year Time period The frequency regulation capacity and frequency regulation capacity provided by the combined heat and power unit; , The first Typical days of the year Time period The up-frequency modulation capacity and down-frequency modulation capacity provided by the electrothermal bidirectional conversion device;

[0030] Equipment operating constraints include those for conventional units, combined heat and power units, wind and solar power units, electric boilers, and energy storage equipment, as detailed below:

[0031] The mathematical model for a conventional generating unit is expressed as follows:

[0032]

[0033] In the formula: For the planning year , typical day ,time Reduce the output of conventional generating units; , These are the minimum and maximum active power that a conventional unit is allowed to output, respectively. , These are the maximum downhill ramp power and maximum uphill ramp power of conventional units, respectively. This is the active power output of the conventional units at the previous moment.

[0034] The mathematical model for a combined heat and power (CHP) unit is expressed as follows:

[0035]

[0036] In the formula: For the planning year , typical day ,time The electrical power output of the combined heat and power unit; For the thermal power output of the combined heat and power unit; , These are the minimum and maximum electrical power outputs allowed for a combined heat and power (CHP) unit, respectively. , These are the minimum and maximum thermal power outputs allowed for a combined heat and power unit, respectively. , These are the electrothermal coupling coefficients of the combined heat and power unit; , These are the maximum downhill ramp power and the maximum uphill ramp power of the cogeneration unit, respectively. This represents the electrical power output of the cogeneration unit at the previous moment;

[0037] The mathematical model of the wind turbine is expressed as follows:

[0038]

[0039] In the formula: , , For each planning year , typical day ,time The following includes wind power utilization, wind curtailment, and available wind power.

[0040] The mathematical model of a photovoltaic unit is expressed as follows:

[0041]

[0042] In the formula: , , For each planning year , typical day ,time The following figures represent the photovoltaic utilization power, curtailed power, and available power.

[0043] The expression for the operating constraints of an electrode-type electric boiler is as follows:

[0044]

[0045] In the formula: Input electrical power to the electrode-type electric boiler; This provides the thermal power output for electrode-type electric boilers. The electrothermal conversion efficiency of an electrode-type electric boiler; This refers to the maximum permissible input electrical power for an electrode-type electric boiler.

[0046] The equipment operation constraint model expression for the bidirectional electrothermal conversion device is as follows:

[0047]

[0048] In the formula: The power required to charge the electric-thermal bidirectional converter; , These refer to the electro-to-heat and heat-to-electricity efficiencies of the bidirectional electro-thermal conversion device. , These are the maximum allowable heat charging power and the maximum heat dissipation power, respectively. Store heat energy for the electrothermal bidirectional conversion device; , These are the minimum and maximum allowable thermal storage energy for the electrothermal bidirectional conversion device, respectively. For the time step, take ; Stored thermal energy from the previous moment; , These represent the stored thermal energy at the beginning and end of a typical day, respectively.

[0049] Furthermore, in step three, the expression for the capacity configuration model of the bidirectional electrothermal converter under the conditions of satisfying the needs of new energy consumption, the needs of electric and heat load supply, and the constraints of system operation safety is as follows:

[0050]

[0051] In the formula: , , These are the total cost of the system's entire lifecycle, the investment cost, and the annualized operating cost; The annual discount factor is used to account for the time value of money; , These are the unit heat capacity investment cost and unit power capacity investment cost of the newly added bidirectional electrothermal conversion device, respectively. , To increase the heat capacity and power capacity of the newly added bidirectional electrothermal conversion device; , , This is the power generation cost coefficient for conventional generating units; For CHP units at time Operating costs; , These are the electrical power output and thermal power output of the CHP unit, respectively. This is the operating state variable of the CHP unit, which is taken when the unit is running. Take when stopping ; This is a function for the fuel consumption and variable operation and maintenance costs of the CHP unit, reflecting the operating costs of the unit under different electrical and thermal outputs. This is a piecewise linearization function used to transform... It is approximately in linear constraint form; This represents the fixed cost coefficient for the unit's online operation.

[0052] Furthermore, in step four, the electrothermal coupled power grid operation optimization model constructed in step two and the electrothermal bidirectional conversion device capacity configuration model constructed in step three are nested and coupled to form a capacity planning and operation control collaborative optimization model. Under the conditions of satisfying the constraints of electric power balance, thermal power balance, unit operation, and dynamic characteristics of thermal storage device, the collaborative optimization model aims to minimize the comprehensive cost of the entire system life cycle, thereby achieving collaborative optimization of the capacity configuration of the electrothermal bidirectional conversion device and the power grid operation scheduling.

[0053] The collaborative optimization model includes a planning layer and an operation layer. The planning layer uses the rated energy capacity and rated power capacity of the bidirectional electrothermal conversion device as the main decision variables, and combines them with the rated heat capacity and rated power capacity of the thermal storage device to optimize and determine the device configuration scale, with the goal of minimizing the comprehensive cost throughout the system's life cycle. Under the capacity constraints determined by the planning layer, the operation layer coordinates and schedules the operating variables of wind power utilization, photovoltaic utilization, conventional unit output, combined heat and power unit electrothermal output, electric boiler conversion power, and thermal storage device charging and discharging power to meet the electricity and heat load demands and system operation safety constraints, with the goal of minimizing system operating costs.

[0054] The planning layer and the operation layer exchange information bidirectionally through capacity variables and operation variables. The planning layer transmits capacity constraints to the operation layer based on the device capacity decision. The operation layer solves the system operation variables under the premise of satisfying the capacity constraints and feeds back the annualized operation cost to the planning layer. The planning layer re-optimizes the device capacity configuration based on the feedback results, and iterates repeatedly until the convergence condition is reached, thus forming a closed-loop optimization process.

[0055] Furthermore, in step four, the collaborative optimization model is solved using a mixed-integer linear programming method. The nonlinear cost function in the model is approximated using a piecewise linearization method, and the product terms of the variables are linearized using the McCormick envelope method. By jointly optimizing the rated power capacity, rated energy capacity, and thermal storage capacity variables of the bidirectional electrothermal converter with the system operation variables, the optimal rated energy capacity and rated power capacity configuration scheme of the bidirectional electrothermal converter is obtained under the constraints of meeting the new energy consumption demand, the electrothermal load supply demand, and the system safety operation.

[0056] This invention also provides a configuration system for a bidirectional electrothermal conversion device in a high-proportion renewable energy power grid. The system optimizes the power capacity and thermal storage capacity of the bidirectional electrothermal conversion device in a high-proportion renewable energy power grid. It includes a data processing and model building module, an electrothermal coupling operation optimization module, a capacity configuration optimization module, and a collaborative optimization solution module. This system is used to implement the configuration method for a bidirectional electrothermal conversion device in a high-proportion renewable energy power grid as described above.

[0057] The data processing and model building module is used to acquire the hourly output data of wind power and photovoltaic power plants in the planning area throughout the year and the hourly demand data of regional electricity and heat load. It performs normalization processing on the data and extracts typical operating scenarios through scenario clustering. It is also used to build annual new energy installed capacity growth and output models, electricity and heat load growth models, and traditional coal-fired units and cogeneration units gradually decommissioning models.

[0058] The electrothermal coupling operation optimization module is used to construct the electrothermal coupling power grid operation optimization model. Under the constraints of power balance, thermal power balance, system frequency regulation, equipment operation and thermal storage dynamics, it solves the optimal operation strategy of various equipment with the goal of minimizing system operating cost.

[0059] The capacity configuration optimization module is used to construct a capacity configuration model for the bidirectional electrothermal converter, and to perform collaborative optimization with the electrothermal coupled power grid operation optimization model to plan the power capacity and thermal storage capacity configuration scheme of the bidirectional electrothermal converter with the goal of minimizing the overall cost of the system's entire life cycle.

[0060] The collaborative optimization solution module is used to integrate and form a collaborative optimization model for capacity planning and operation control. It uses a mixed integer linear programming method to solve the model in a unified manner, approximates the nonlinear cost function through a piecewise linearization method, and transforms the product terms in the model using the McCormick envelope linearization method. It outputs the optimal power capacity and thermal storage capacity configuration results of the bidirectional electrothermal conversion device.

[0061] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements a configuration method for a high-proportion new energy grid electrothermal bidirectional conversion device as described above.

[0062] The present invention also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement a configuration method for a high-proportion new energy grid electrothermal bidirectional conversion device as described above.

[0063] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements a configuration method for a high-proportion new energy grid electrothermal bidirectional conversion device as described above.

[0064] This invention has the following advantages and beneficial effects: Compared with the prior art, this invention introduces a phased retirement plan for traditional coal-fired power units and combined heat and power (CHP) units into the model. By constructing a collaborative optimization framework of an electrothermal coupling system operation optimization model and an electrothermal bidirectional conversion device capacity configuration model, it achieves collaborative optimization of the configuration scale of the electrothermal bidirectional conversion device and the system operation strategy, taking into account the fluctuations in renewable energy output, electrothermal load demand, and unit operation constraints. When renewable energy output is surplus, the device can absorb excess electrical energy and convert it into thermal energy for storage. When the system power is insufficient or requires regulation capacity, it outputs electrical energy to the grid through thermoelectric conversion. Thus, in the context of the retirement of some coal-fired power and CHP units, it provides new flexible regulation resources for the system, effectively improving the renewable energy absorption capacity and enhancing the flexibility and economy of grid operation. It also facilitates the smooth retirement of traditional coal-fired power units and CHP units, while ensuring the safe and stable operation of the power system. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention;

[0066] Figure 2 This is a structural diagram of the method of the present invention;

[0067] Figure 3 This is a comparison chart showing the effects of the method of the present invention. Specific implementation methods

[0068] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] Example 1

[0070] This embodiment takes a regional wind-solar power grid with a high proportion of renewable energy as the research object, with a planning period from 2025 to 2035. To characterize the system's annual operating characteristics, the typical day method is used to perform equivalent modeling of the annual operating scenario.

[0071] like Figure 1-2 As shown, a configuration method for a bidirectional electrothermal conversion device for a high-proportion renewable energy grid includes the following steps:

[0072] Step 1: Obtain hourly data on wind power output, solar power output, electricity load, and heat load for the planning area throughout the year, and preprocess and normalize the data. A max-min linear mapping method is used to uniformly map the disparate magnitudes of wind power, solar power output, and electricity / heat load to a 0-1 value range, eliminating the magnitude differences between different data types and improving the rationality of subsequent scenario selection. The calculation formula is as follows:

[0073]

[0074] In the formula, , The first The annual maximum and minimum values ​​of the collected data; , The first Class of collected data in the first sky The original actual data value and the normalized value after per-unit conversion at that time. Indexes are provided for data types, including wind power output, photovoltaic power output, electricity load, and heat load.

[0075] Subsequently, a scenario clustering method was used to reduce the dimensionality of the annual operating scenarios. For example, the K-means clustering algorithm was used to extract several typical operating days. This embodiment selects 12 typical day scenarios, each containing 24 operating segments, and assigns a corresponding occurrence probability to each typical day. System operating costs and energy statistics are weighted and converted using the typical day probabilities to obtain the equivalent operating results for the whole year.

[0076] Meanwhile, to reflect the expansion of new energy capacity and load growth trend during the planning period, annual growth coefficients are set for wind power, photovoltaic installed capacity, and electricity and heat load demand. The annual new energy installed capacity increases year by year according to the preset growth rate, while electricity and heat loads are adjusted according to the load growth coefficients.

[0077] The formula for the annual growth of installed capacity and output of new energy sources is shown below:

[0078]

[0079] In the formula: , The first Annual wind power installed capacity and base year wind power installed capacity; , The first Annual photovoltaic installed capacity and base year photovoltaic installed capacity; , These are the annual growth rates of wind power and photovoltaic installed capacity, respectively. , These are the planning year and the base year, respectively. For the first Year Available wind power output during certain periods; For the first Year Available photovoltaic power output during certain periods; This is a normalized curve of wind power output for a typical day in a typical operating scenario; This is a normalized curve of photovoltaic power output for a typical day in a typical operating scenario;

[0080] The expressions for the electricity and heat load growth model are as follows:

[0081]

[0082] In the formula: , For the first Year Electricity and heat load demand during specific time periods; , For the base year Electricity and heat load during specific time periods; , This represents the annual growth rate of electricity and heat load.

[0083] The formula for the gradual decommissioning model of traditional coal-fired power units and combined heat and power units is as follows:

[0084]

[0085] In the formula: , The first Year and the Annual available installed capacity of conventional generating units; For the first Annual conventional units The capacity for decommissioning; , The first Year and the Available installed capacity of cogeneration units per year; For the first Annual capacity of decommissioned combined heat and power units.

[0086] Step 2: For the high-proportion renewable energy power grid operation optimization model, its objective function is constructed around all dimensions of operating costs, including system power purchase, conventional unit operation, wind and solar curtailment penalties, frequency regulation insufficiency penalties, and power and heat load shortage penalties. It also incorporates the operating characteristics constraints of unit operation, thermal storage devices, and power-to-thermal conversion devices. The wind and solar power grid aims to minimize annual operating costs, with guaranteed power and heat supply as the demand, as detailed below:

[0087] The expression for the objective function of the wind-solar power grid operation is as follows:

[0088]

[0089] In the formula, Annualized operating cost of wind and solar power grid system; This represents the number of typical days. For the first The probability weight of a typical day The number of hours in a typical day; For the planning year On a typical day No. Power purchased by the power grid at any given time; For the planning year On a typical day No. The power output of the power grid at any given time; Electricity purchase price per unit; Electricity price per unit; For conventional units In the planning year On a typical day No. The cost of generating electricity at any given moment; For combined heat and power units In the planning year On a typical day No. The operating cost at any given moment; For regional boilers In the planning year On a typical day No. Heating power at any given time Unit heating cost; , These are respectively wind curtailment power and solar curtailment power. This is the power curtailment penalty coefficient. , These represent adjusting the insufficient relaxation amount of frequency modulation, either upwards or downwards. This is the frequency modulation relaxation penalty coefficient. , These are the power load and heat load supply shortages, respectively. , These are the power shortage penalty coefficient and the preset non-negative penalty coefficient, respectively. The constraints of the wind-solar power grid include power balance constraints, equipment operation constraints, electrothermal conversion constraints, and energy storage characteristic constraints. Among them, the power balance constraints include hourly electrical power balance constraints and hourly thermal power balance constraints, expressed as follows:

[0090]

[0091] In the formula, , They are respectively in the planning year On a typical day No. Real-time wind power utilization and photovoltaic power utilization; In the planning year On a typical day No. Regular unit output at all times; , They are respectively in the planning year On a typical day No. The electrical and thermal power output of the cogeneration unit at all times; , They are respectively in the planning year On a typical day No. The amount of electricity purchased from the upper-level power grid and the amount of electricity sold to the upper-level power grid at all times; In the planning year On a typical day No. The charging power of the bidirectional electrothermal conversion device at any given time; It is the heat-to-electric power of the bidirectional electrothermal conversion device; In the planning year On a typical day No. The output heat power of the electrode-type electric boiler at any time; , In the planning year respectively On a typical day No. The heat release power and heat charge power of the bidirectional electrothermal conversion device at all times; , They are respectively in the planning year On a typical day No. The electrical and thermal loads are slack variables in case of power shortage; , They are respectively in the planning year On a typical day No. The power demand for electricity and heat load at any given time.

[0092] The expression for the system frequency regulation constraint is as follows:

[0093]

[0094] In the formula: , The first Typical days of the year Time period The up-frequency regulation capacity and down-frequency regulation capacity provided by conventional generating units; , The first Typical days of the year Time period The frequency regulation capacity and frequency regulation capacity provided by the combined heat and power unit; , The first Typical days of the year Time period The up-frequency modulation capacity and down-frequency modulation capacity provided by the electrothermal bidirectional conversion device;

[0095] Equipment operating constraints include those for conventional units, combined heat and power units, wind and solar power units, electric boilers, and energy storage equipment, as detailed below:

[0096] The mathematical model for a conventional generating unit is expressed as follows:

[0097]

[0098] In the formula: For the planning year , typical day ,time Reduce the output of conventional generating units; , These are the minimum and maximum active power that a conventional unit is allowed to output, respectively. , These are the maximum downhill ramp power and maximum uphill ramp power of conventional units, respectively. This is the active power output of the conventional units at the previous moment.

[0099] The mathematical model for a combined heat and power (CHP) unit is expressed as follows:

[0100]

[0101] In the formula: For the planning year , typical day ,time The electrical power output of the combined heat and power unit; For the thermal power output of the combined heat and power unit; , These are the minimum and maximum electrical power outputs allowed for a combined heat and power (CHP) unit, respectively. , These are the minimum and maximum thermal power outputs allowed for a combined heat and power unit, respectively. , These are the electrothermal coupling coefficients of the combined heat and power unit; , These are the maximum downhill ramp power and the maximum uphill ramp power of the cogeneration unit, respectively. This represents the electrical power output of the cogeneration unit at the previous moment.

[0102] The mathematical model of the wind turbine is expressed as follows:

[0103]

[0104] In the formula: , , For each planning year , typical day ,time The following includes wind power utilization, wind curtailment, and available wind power.

[0105] The mathematical model of a photovoltaic unit is expressed as follows:

[0106]

[0107] In the formula: , , For each planning year , typical day ,time The following figures represent the photovoltaic power utilization, curtailment power, and available power.

[0108] The expression for the operating constraints of an electrode-type electric boiler is as follows:

[0109]

[0110] In the formula: Input electrical power to the electrode-type electric boiler; This provides the thermal power output for electrode-type electric boilers. The electrothermal conversion efficiency of an electrode-type electric boiler; This represents the maximum permissible input electrical power for an electrode-type electric boiler.

[0111] The equipment operation constraint model expression for the bidirectional electrothermal conversion device is as follows:

[0112]

[0113] In the formula: The power required to charge the electric-thermal bidirectional converter; , These refer to the electro-to-heat and heat-to-electricity efficiencies of the bidirectional electro-thermal conversion device. , These are the maximum allowable heat charging power and the maximum heat dissipation power, respectively. Store heat energy for the electrothermal bidirectional conversion device; , These are the minimum and maximum allowable thermal storage energy for the electrothermal bidirectional conversion device, respectively. For the time step, take ; Stored thermal energy from the previous moment; , These represent the stored thermal energy at the beginning and end of a typical day, respectively.

[0114] Step 3: Based on the system operation model, a capacity configuration model for the bidirectional electrothermal converter is further constructed. This model aims to minimize the overall cost throughout the system's lifecycle. It comprehensively considers factors such as the investment cost, operation and maintenance cost, and system operating cost of the bidirectional electrothermal converter. By optimizing the rated power capacity and thermal energy storage capacity of the converter, a reasonable configuration of the converter's scale is achieved. The expression for the capacity configuration model of the bidirectional electrothermal converter under the conditions of meeting the needs of new energy consumption, the demand for electrothermal load supply, and system operational safety constraints is as follows:

[0115]

[0116] In the formula: , , These are the total cost of the system's entire lifecycle, the investment cost, and the annualized operating cost; The annual discount factor, which takes into account the time value of money, is derived from the benchmark discount rate. With planning years Calculated; , These are the unit heat capacity investment cost and unit power capacity investment cost of the newly added bidirectional electrothermal conversion device, respectively. , To increase the heat capacity and power capacity of the newly added bidirectional electrothermal conversion device; , , This is the power generation cost coefficient for conventional generating units; For CHP units at time Operating costs; , These are the electrical power output and thermal power output of the CHP unit, respectively. This is the operating state variable of the CHP unit, which is taken when the unit is running. Take when stopping ; This is a function for the fuel consumption and variable operation and maintenance costs of the CHP unit, reflecting the operating costs of the unit under different electrical and thermal outputs. This is a piecewise linearization function used to transform... It is approximately in linear constraint form; This represents the fixed cost coefficient for the unit's online operation.

[0117] Step four: The electrothermal coupled power grid operation optimization model constructed in step two and the electrothermal bidirectional conversion device capacity configuration model constructed in step three are nested and coupled to form a capacity planning and operation control collaborative optimization model. Under the conditions of satisfying the constraints of electric power balance, thermal power balance, unit operation and dynamic characteristics of thermal storage device, the collaborative optimization model takes the minimum comprehensive cost of the entire system life cycle as the optimization objective, and realizes the collaborative optimization of the capacity configuration of electrothermal bidirectional conversion device and power grid operation scheduling.

[0118] The collaborative optimization model includes a planning layer and an operation layer. The planning layer uses the rated energy capacity and rated power capacity of the bidirectional electrothermal conversion device as the main decision variables, and combines them with the rated heat capacity and rated power capacity of the thermal storage device to optimize and determine the device configuration scale, with the goal of minimizing the comprehensive cost throughout the system's life cycle. Under the capacity constraints determined by the planning layer, the operation layer coordinates and schedules the operating variables of wind power utilization, photovoltaic utilization, conventional unit output, combined heat and power unit electrothermal output, electric boiler conversion power, and thermal storage device charging and discharging power to meet the electricity and heat load demands and system operation safety constraints, with the goal of minimizing system operating costs.

[0119] The planning layer and the operation layer exchange information bidirectionally through capacity variables and operation variables. The planning layer transmits capacity constraints to the operation layer based on the device capacity decision. The operation layer solves the system operation variables under the premise of satisfying the capacity constraints and feeds back the annualized operation cost to the planning layer. The planning layer re-optimizes the device capacity configuration based on the feedback results, and iterates repeatedly until the convergence condition is reached, thus forming a closed-loop optimization process.

[0120] The collaborative optimization model is solved using a mixed-integer linear programming method. The nonlinear cost function in the model is approximated using a piecewise linearization method, and the product terms of the variables are linearized using the McCormick envelope method. By jointly optimizing the rated power capacity, rated energy capacity, and thermal storage capacity variables of the bidirectional electrothermal converter with the system operation variables, the optimal rated energy capacity and rated power capacity configuration scheme of the bidirectional electrothermal converter is obtained under the constraints of meeting the new energy consumption demand, the electrothermal load supply demand, and the system safety operation.

[0121] Example 2

[0122] This embodiment also provides a configuration system for a bidirectional electrothermal conversion device in a high-proportion renewable energy power grid. The system is used to optimize the power capacity and thermal storage capacity of the bidirectional electrothermal conversion device in a high-proportion renewable energy power grid. It includes a data processing and model building module, an electrothermal coupling operation optimization module, a capacity configuration optimization module, and a collaborative optimization solution module. The configuration system for a bidirectional electrothermal conversion device in a high-proportion renewable energy power grid is built through the steps of Embodiment 1.

[0123] The data processing and model building module is used to acquire the hourly output data of wind power and photovoltaic power plants in the planning area throughout the year and the hourly demand data of regional electricity and heat load. It performs normalization processing on the data and extracts typical operating scenarios through scenario clustering. It is also used to build annual new energy installed capacity growth and output models, electricity and heat load growth models, and traditional coal-fired units and cogeneration units gradually decommissioning models.

[0124] The electrothermal coupling operation optimization module is used to construct the electrothermal coupling power grid operation optimization model. Under the constraints of power balance, thermal power balance, system frequency regulation, equipment operation and thermal storage dynamics, it solves the optimal operation strategy of various equipment with the goal of minimizing system operating cost.

[0125] The capacity configuration optimization module is used to construct a capacity configuration model for the bidirectional electrothermal converter, and to perform collaborative optimization with the electrothermal coupled power grid operation optimization model to plan the power capacity and thermal storage capacity configuration scheme of the bidirectional electrothermal converter with the goal of minimizing the overall cost of the system's entire life cycle.

[0126] The collaborative optimization solution module is used to integrate and form a collaborative optimization model for capacity planning and operation control. It uses a mixed integer linear programming method to solve the model in a unified manner, approximates the nonlinear cost function through a piecewise linearization method, and transforms the product terms in the model using the McCormick envelope linearization method. It outputs the optimal power capacity and thermal storage capacity configuration results of the bidirectional electrothermal conversion device.

[0127] like Figure 3 As shown, this embodiment uses system solutions to obtain the optimal equipment capacity configuration, wind and solar power renewable energy consumption, thermal energy storage and electrothermal coupled energy storage configuration scale, system curtailment rate changes, and annual operating energy statistics for various equipment from 2025 to 2035. Compared with a control group without an electrothermal bidirectional conversion device, the analysis results show that after introducing multi-stage capacity planning for the electrothermal bidirectional conversion device, the system can absorb electrical energy and convert it into thermal energy storage when renewable energy output is surplus, and release thermal energy or perform thermoelectric conversion when renewable energy output is insufficient, thereby significantly improving renewable energy consumption capacity and reducing overall system operating costs.

[0128] In summary, this invention comprehensively considers the fluctuation characteristics of renewable energy output, electrothermal load demand, operational constraints of conventional units, and the operating characteristics of electrothermal conversion devices. By constructing an operational optimization model for the electrothermal coupling system and a capacity configuration model for the bidirectional electrothermal conversion device, it achieves coordinated optimization of device capacity planning and system operation scheduling. This method enables the conversion of surplus renewable energy output into thermal energy for storage, and the output of electrical energy to the grid through thermoelectric conversion when the system needs it, participating in grid frequency and voltage regulation. This enhances the renewable energy absorption capacity and improves the system's operational flexibility and economy.

Claims

1. A method for configuring a high-proportion renewable energy grid electrothermal bidirectional conversion device, characterized in that, Includes the following steps: Step 1: Obtain the hourly output data of wind power and photovoltaic power plants in the planning area throughout the year, as well as the hourly demand data of electricity load and heat load in the area. Normalize the wind power output, photovoltaic power output, electricity load and heat load data, and extract typical operating scenarios through scenario clustering method. Construct annual new energy installed capacity growth and output models, electricity and heat load growth models, and traditional coal-fired units and cogeneration units gradual retirement models respectively. Step 2: Construct an electrothermal coupled power grid operation optimization model. Under the conditions of satisfying the constraints of electric power balance, thermal power balance, system frequency regulation, equipment operation, and thermal storage dynamics, determine the operation strategies of various equipment with the goal of minimizing system operating costs. Step 3: Construct a capacity configuration model for the bidirectional electrothermal conversion device, and perform collaborative optimization with the electrothermal coupled power grid operation optimization model constructed in Step 2. Determine the optimal power capacity and thermal storage capacity configuration scheme for the bidirectional electrothermal conversion device with the goal of minimizing the overall cost throughout the system's life cycle. Step 4: Integrate the electrothermal coupled power grid operation optimization model from Step 2 with the capacity configuration model of the electrothermal bidirectional conversion device from Step 3 to form a capacity planning and operation control collaborative optimization model. Use mixed integer linear programming for unified solution, approximate the nonlinear cost function using piecewise linearization, and transform the product term using McCormick envelope linearization.

2. The configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device according to claim 1, characterized in that: In step one, the normalization process uses the max-min linear mapping method to uniformly map the heterogeneous data of wind power, photovoltaic power output, and electric heating load to the 0-1 numerical range. The calculation formula is as follows: In the formula: , The first The annual maximum and minimum values ​​of the collected data; , The first Class of collected data in the first sky The original actual data value and the normalized value after per-unit conversion at that time. Indexes are provided for data types, including wind power output, photovoltaic power output, electricity load, and heat load. The formula for the annual growth of installed new energy capacity and power output model is as follows: In the formula: , The first Annual wind power installed capacity and base year wind power installed capacity; , The first Annual photovoltaic installed capacity and base year photovoltaic installed capacity; , These are the annual growth rates of wind power and photovoltaic installed capacity, respectively. , These are the planning year and the base year, respectively. For the first Year Available wind power output during certain periods; For the first Year Available photovoltaic power output during certain periods; This is a normalized curve of wind power output for a typical day in a typical operating scenario; This is a normalized curve of photovoltaic power output for a typical day in a typical operating scenario; The expressions for the constructed electricity and heat load growth models are as follows: In the formula: , For the first Year Electricity and heat load demand during specific time periods; , For the base year Electricity and heat load during specific time periods; , The annual growth rate of electricity and heat load; The formula for the gradual decommissioning model of traditional coal-fired power units and combined heat and power units is as follows: In the formula: , The first Year and the Annual available installed capacity of conventional generating units; For the first Annual conventional units The capacity for decommissioning; , The first Year and the Available installed capacity of cogeneration units per year; For the first Annual capacity of decommissioned combined heat and power units.

3. The configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device according to claim 1, characterized in that: In step two, for the high-proportion renewable energy power grid operation optimization model, its objective function is constructed using the operating costs of system power purchase, conventional unit operation, wind and solar curtailment penalties, frequency regulation insufficiency penalties, and power and heat load shortage penalties; the wind and solar power grid aims to minimize the annual operating cost, as detailed below: The objective function for the operation of a wind-solar power grid is expressed as follows: In the formula: Annualized operating cost of wind and solar power grid system; This represents the number of typical days. For the first The probability weight of a typical day The number of hours in a typical day; For the planning year On a typical day No. Power purchased by the power grid at any given time; For the planning year On a typical day No. The power output of the power grid at any given time; Electricity purchase price per unit; Electricity price per unit; For conventional units In the planning year On a typical day No. The cost of generating electricity at any given moment; For combined heat and power units In the planning year On a typical day No. The operating cost at any given moment; For regional boilers In the planning year On a typical day No. Heating power at any given time Unit heating cost; , These are respectively wind curtailment power and solar curtailment power. This is the power curtailment penalty coefficient. , These represent adjusting the insufficient relaxation amount of frequency modulation, either upwards or downwards. This is the frequency modulation relaxation penalty coefficient. , These are the power load and heat load supply shortages, respectively. , These are the power shortage penalty coefficient and the preset non-negative penalty coefficient, respectively. The constraints of the wind-solar power grid include power balance constraints, equipment operation constraints, electrothermal conversion constraints, and energy storage characteristic constraints. Among them, the power balance constraints include hourly electrical power balance constraints and hourly thermal power balance constraints, expressed as follows: In the formula: , They are respectively in the planning year On a typical day No. Real-time wind power utilization and photovoltaic power utilization; In the planning year On a typical day No. Regular unit output at all times; , They are respectively in the planning year On a typical day No. The electrical and thermal power output of the cogeneration unit at all times; , They are respectively in the planning year On a typical day No. The amount of electricity purchased from the upper-level power grid and the amount of electricity sold to the upper-level power grid at all times; In the planning year On a typical day No. The charging power of the bidirectional electrothermal conversion device at any given time; It is the heat-to-electric power of the bidirectional electrothermal conversion device; In the planning year On a typical day No. The output heat power of the electrode-type electric boiler at any time; , In the planning year respectively On a typical day No. The heat release power and heat charge power of the bidirectional electrothermal conversion device at all times; , They are respectively in the planning year On a typical day No. The electrical and thermal loads are slack variables in case of power shortage; , They are respectively in the planning year On a typical day No. The power demand for electricity and heat load at any given time; The expression for the system frequency regulation constraint is as follows: In the formula: , The first Typical days of the year Time period The up-frequency regulation capacity and down-frequency regulation capacity provided by conventional generating units; , The first Typical days of the year Time period The frequency regulation capacity and frequency regulation capacity provided by the combined heat and power unit; , The first Typical days of the year Time period The up-frequency modulation capacity and down-frequency modulation capacity provided by the electrothermal bidirectional conversion device; Equipment operating constraints include those for conventional units, combined heat and power units, wind and solar power units, electric boilers, and energy storage equipment, as detailed below: The mathematical model for a conventional generating unit is expressed as follows: In the formula: For the planning year , typical day ,time Reduce the output of conventional generating units; , These are the minimum and maximum active power that a conventional unit is allowed to output, respectively. , These are the maximum downhill ramp power and maximum uphill ramp power of conventional units, respectively. This is the active power output of the conventional units at the previous moment; The mathematical model for a combined heat and power (CHP) unit is expressed as follows: In the formula: For the planning year , typical day ,time The electrical power output of the combined heat and power unit; For the thermal power output of the combined heat and power unit; , These are the minimum and maximum electrical power outputs allowed for a combined heat and power (CHP) unit, respectively. , These are the minimum and maximum thermal power outputs allowed for a combined heat and power unit, respectively. , These are the electrothermal coupling coefficients of the combined heat and power unit; , These are the maximum downhill ramp power and the maximum uphill ramp power of the cogeneration unit, respectively. This represents the electrical power output of the cogeneration unit at the previous moment; The mathematical model of the wind turbine is expressed as follows: In the formula: , , For each planning year , typical day ,time The following includes wind power utilization, wind curtailment, and available wind power. The mathematical model of a photovoltaic unit is expressed as follows: In the formula: , , For each planning year , typical day ,time The following figures represent the photovoltaic utilization power, curtailed power, and available power. The expression for the operating constraints of an electrode-type electric boiler is as follows: In the formula: Input electrical power to the electrode-type electric boiler; This provides the thermal power output for electrode-type electric boilers. The electrothermal conversion efficiency of an electrode-type electric boiler; This refers to the maximum permissible input electrical power for an electrode-type electric boiler. The equipment operation constraint model expression for the bidirectional electrothermal conversion device is as follows: In the formula: The power required to charge the electric-thermal bidirectional converter; , These refer to the electro-to-heat and heat-to-electricity efficiencies of the bidirectional electro-thermal conversion device. , These are the maximum allowable heat charging power and the maximum heat dissipation power, respectively. Store heat energy for the electrothermal bidirectional conversion device; , These are the minimum and maximum allowable thermal storage energy for the electrothermal bidirectional conversion device, respectively. For the time step, take ; Stored thermal energy from the previous moment; , These represent the stored thermal energy at the beginning and end of a typical day, respectively.

4. The configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device according to claim 1, characterized in that: In step three, the expression for the capacity configuration model of the bidirectional electrothermal converter under the conditions of satisfying the needs of new energy consumption, the needs of electric and heat load supply, and the constraints of system operation safety is as follows: In the formula: , , These are the total cost of the system's entire lifecycle, the investment cost, and the annualized operating cost; The annual discount factor is used to account for the time value of money; , These are the unit heat capacity investment cost and unit power capacity investment cost of the newly added bidirectional electrothermal conversion device, respectively. , To increase the heat capacity and power capacity of the newly added bidirectional electrothermal conversion device; , , This is the power generation cost coefficient for conventional generating units; For CHP units at time Operating costs; , These are the electrical power output and thermal power output of the CHP unit, respectively. This is the operating state variable of the CHP unit, which is taken when the unit is running. Take when stopping ; This is a function for the fuel consumption and variable operation and maintenance costs of the CHP unit, reflecting the operating costs of the unit under different electrical and thermal outputs. This is a piecewise linearization function used to transform... It is approximately in linear constraint form; This represents the fixed cost coefficient for the unit's online operation.

5. The configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device according to claim 1, characterized in that: In step four, the electrothermal coupled power grid operation optimization model constructed in step two and the electrothermal bidirectional conversion device capacity configuration model constructed in step three are nested and coupled to form a capacity planning and operation control collaborative optimization model. Under the conditions of satisfying the constraints of electric power balance, thermal power balance, unit operation, and dynamic characteristics of thermal storage device, the collaborative optimization model takes the minimum comprehensive cost of the entire system life cycle as the optimization objective, and realizes the collaborative optimization of the capacity configuration of the electrothermal bidirectional conversion device and the power grid operation scheduling. The collaborative optimization model includes a planning layer and an operation layer. The planning layer uses the rated energy capacity and rated power capacity of the bidirectional electrothermal conversion device as the main decision variables, and combines them with the rated heat capacity and rated power capacity of the thermal storage device to optimize and determine the device configuration scale, with the goal of minimizing the comprehensive cost throughout the system's life cycle. Under the capacity constraints determined by the planning layer, the operation layer coordinates and schedules the operating variables of wind power utilization, photovoltaic utilization, conventional unit output, combined heat and power unit electrothermal output, electric boiler conversion power, and thermal storage device charging and discharging power to meet the electricity and heat load demands and system operation safety constraints, with the goal of minimizing system operating costs. The planning layer and the operation layer exchange information bidirectionally through capacity variables and operation variables. The planning layer transmits capacity constraints to the operation layer based on the device capacity decision. The operation layer solves the system operation variables under the premise of satisfying the capacity constraints and feeds back the annualized operation cost to the planning layer. The planning layer re-optimizes the device capacity configuration based on the feedback results, and iterates repeatedly until the convergence condition is reached, thus forming a closed-loop optimization process.

6. The configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device according to claim 5, characterized in that: In step four, the collaborative optimization model is solved using a mixed-integer linear programming method. The nonlinear cost function in the model is approximated using a piecewise linearization method, and the product terms of the variables are linearized using the McCormick envelope method. By jointly optimizing the rated power capacity, rated energy capacity, and thermal storage capacity variables of the bidirectional electrothermal converter with the system operation variables, the optimal rated energy capacity and rated power capacity configuration scheme of the bidirectional electrothermal converter is obtained under the constraints of meeting the new energy consumption demand, the electrothermal load supply demand, and the system safety operation.

7. A configuration system for a high-proportion new energy grid electrothermal bidirectional conversion device, characterized in that, The system is used to optimize the power capacity and thermal storage capacity of bidirectional electrothermal conversion devices in high-proportion renewable energy power grids. It includes a data processing and model building module, an electrothermal coupling operation optimization module, a capacity configuration optimization module, and a collaborative optimization solution module. It is used to implement the configuration method for bidirectional electrothermal conversion devices in high-proportion renewable energy power grids as described in any one of claims 1-6. The data processing and model building module is used to acquire the hourly output data of wind power and photovoltaic power plants in the planning area throughout the year and the hourly demand data of regional electricity and heat load. It performs normalization processing on the data and extracts typical operating scenarios through scenario clustering. It is also used to build annual new energy installed capacity growth and output models, electricity and heat load growth models, and traditional coal-fired units and cogeneration units gradually decommissioning models. The electrothermal coupling operation optimization module is used to construct the electrothermal coupling power grid operation optimization model. Under the constraints of power balance, thermal power balance, system frequency regulation, equipment operation and thermal storage dynamics, it solves the optimal operation strategy of various equipment with the goal of minimizing system operating cost. The capacity configuration optimization module is used to construct a capacity configuration model for the bidirectional electrothermal converter, and to perform collaborative optimization with the electrothermal coupled power grid operation optimization model to plan the power capacity and thermal storage capacity configuration scheme of the bidirectional electrothermal converter with the goal of minimizing the overall cost of the system's entire life cycle. The collaborative optimization solution module is used to integrate and form a collaborative optimization model for capacity planning and operation control. It uses a mixed integer linear programming method to solve the model in a unified manner, approximates the nonlinear cost function through a piecewise linearization method, and transforms the product terms in the model using the McCormick envelope linearization method. It outputs the optimal power capacity and thermal storage capacity configuration results of the bidirectional electrothermal conversion device.

8. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device as described in any one of claims 1-6.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the configuration method of a high-proportion new energy grid electrothermal bidirectional conversion device as described in any one of claims 1-6.

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