Equipment capacity planning method for integrated energy system

Abstract

An equipment capacity planning method for an integrated energy system comprises the following steps: acquiring historical renewable energy and load data for many years based on an integrated energy system park, simulating the uncertainty by adopting a Monte Carlo method, and reasonably configuring the capacities of different equipment according to an uncertainty simulation result, renewable energy sources in the integrated energy system can be consumed to the maximum extent, energy waste is reduced, overall zero carbon emission in the operation period of the integrated energy system is achieved, and carbon emission of all links of integrated energy system equipment is comprehensively considered. Full-life-cycle zero-carbon emission in the equipment production, operation and recovery processes is used as an environmental constraint in the equipment capacity planning and energy optimization scheduling processes of the comprehensive energy system, so that the environmental target of full-life-cycle zero-carbon emission of the comprehensive energy system in the whole period of configuration operation and recovery can be realized; environmental benefits and economic benefits are comprehensively considered, and the environmental benefits and the economic benefits are considered.

Description

Technical Field

[0001] The present invention relates to the field of power system and energy management, and in particular to an equipment capacity planning method for an integrated energy system. Background Art

[0002] The integrated energy system usually includes a variety of energy supply methods, energy conversion equipment and energy storage equipment. For example, the energy supply side includes clean energy stations, natural gas sources, solar collectors and main grids, the energy conversion equipment includes gas turbines, electric boilers, electric chillers, absorption chillers and heat pumps, and the energy storage equipment includes electric energy storage, thermal energy storage and hydrogen energy storage; the demand side includes electric heat and cold loads. To ensure the reliability of the integrated energy system, the energy management center optimizes the collected information and transmits the scheduling results to each device. Among them, the wind turbines and photovoltaic power generation in the clean energy station are calculated by the energy management system, and part of the energy is supplied to the power-to-gas equipment, and the rest is used for the power scheduling of the integrated energy system. The power-to-gas equipment is used to absorb the carbon emissions generated by the integrated energy system throughout its life cycle and convert them into methane for use by gas turbines. During normal operation, the electric heat and cold loads of the integrated energy system are powered by equipment outside the main grid. When renewable energy fluctuates and exceeds the energy storage balance range, the integrated energy system purchases or sells electricity through the main grid to achieve energy balance.

[0003] The integrated energy system, with its unique advantages such as multi-energy complementarity, intelligent scheduling and efficient utilization, not only optimizes the energy structure and improves energy efficiency, but also provides a solid guarantee for the stability of energy supply. However, the existing low-carbon transformation technology of the integrated energy system still has the following problems: 1. Volatility problem: Existing low-carbon energy optimization methods for integrated energy systems do not fully take into account the volatility of renewable energy. Since renewable energy sources such as wind and solar energy are naturally intermittent and unstable, existing methods often cannot effectively cope with these fluctuations when optimizing energy, resulting in the reliability and stability of energy supply being affected.

[0004] 2. Heterogeneous load coupling characteristics: The loads in the integrated energy system are diverse and have heterogeneous load coupling characteristics, but the existing optimization methods fail to fully consider this. Different types of loads have different demand characteristics in time and space. The existing methods fail to achieve reasonable allocation and management of loads during the optimization process, resulting in reduced resource utilization efficiency.

[0005] 3. Capacity allocation problem: The existing methods lack specificity in capacity allocation, fail to reasonably allocate the capacity of different equipment, and ignore the maximum absorption of renewable energy in the integrated energy system and its low-carbon advantages. This deficiency leads to waste of resources and reduced overall system efficiency.

[0006] 4. Single factor optimization: Current low-carbon transformation technologies mostly focus on optimizing a single cost or environmental factor, lacking a comprehensive approach to environmental and economic benefits. In the context of global climate governance, single factor optimization cannot fully leverage the advantages of an integrated energy system and cannot achieve both environmental and economic benefits.

[0007] 5. Insufficient full life cycle carbon emission assessment: Most studies on low-carbon issues in integrated energy systems only consider carbon emissions during the use of equipment, without considering the full life cycle carbon emission assessment of production, use and recycling. This limitation leads to an underestimation of the true environmental impact of the equipment, which is not conducive to the full realization of the zero-carbon goal.

[0008] Therefore, it is of great significance to carry out reasonable capacity planning for the integrated energy system, deeply explore the low-carbon and zero-carbon characteristics of the integrated energy system, fully evaluate the renewable energy and load volatility of the integrated energy system, and conduct a full life cycle carbon emission assessment. Solving these problems will help improve the overall performance of the integrated energy system and achieve true zero-carbon and efficient energy utilization. Summary of the invention

[0009] The purpose of the present invention is to provide an equipment capacity planning method for an integrated energy system, which can reasonably configure the capacity of different equipment according to the load characteristics of the integrated energy system, reduce energy waste and achieve overall zero carbon emissions within the operation cycle of the integrated energy system.

[0010] The technical solution adopted by the present invention to solve the above technical problems is: a method for equipment capacity planning of an integrated energy system, comprising the following steps: Step 1: Collect wind power generation information, photovoltaic power generation information, and electric heating and cooling load information at the geographical location of the integrated energy system to obtain the wind power generation WT in the period t Wind power data 、Photovoltaic power generation PV in the period t Photovoltaic power generation data and heating and cooling load in the period t Electric heating and cooling load data ; Step 2: Uncertainty simulation of wind power generation, photovoltaic power generation and electric heating and cooling loads 2.1. Define the probability density function of the prediction error as: Formula (1); In formula (1), and The uncertain variables v The expected value and standard deviation of the forecast error; 2.2. Use the inverse function of the cumulative distribution to obtain the fluctuation range of the prediction error: Formula (2); In formula (2), Indicates uncertain variables v In the period t The prediction error, α represents the confidence level, and The uncertain variables v The lower and upper bounds of the prediction error; 2.3. Modeling the prediction error: Formula (3); In formula (3), and The uncertain variables v In the period t and scenes s The prediction error and standard deviation of 2.4. Based on the prediction error model, the Monte Carlo method is used to generate random scenarios for simulating prediction data errors: Formula (4); In formula (4), For uncertain variables v In the period t and scenes s The power, Display wind power generation data , Photovoltaic power generation data and heating and cooling load data ; 2.5. Use the Monte Carlo method to reduce the scenarios and obtain typical scenario data that simulates the uncertainty of wind power generation, photovoltaic power generation, and electric heating and cooling loads ; Step 3: Mathematical modeling of equipment in the integrated energy system 3.1. The mathematical model of the gas turbine is: Formula (5); Formula (6); In formula (5) and (6), and The gas turbine in the time period t The electrical and thermal power, and are the power generation efficiency and heating efficiency of the gas turbine, is the calorific value of gas, For gas turbines in the period t The gas consumption, Scheduling time for units; 3.2. The mathematical model of the electric boiler is: Formula (7); In formula (7), For electric boilers during the period t The thermal power, is the heating efficiency of the electric boiler, For electric boilers during the period t The power consumption; 3.3 The mathematical model of the electric chiller is: Formula (8); In formula (8), For electric refrigeration machine in the period t The cooling power, is the cooling efficiency of the electric chiller, For electric refrigeration machine in the period t The power consumption; 3.4. The mathematical model of the absorption refrigerator is: Formula (9); In formula (9), For absorption chillers in the period t The cooling power, is the cooling efficiency of the absorption chiller, For absorption chillers in the period t The heat power used; 3.5. The mathematical model of the heat pump is: Formula (10); Formula (11); In formula (10) and (11), and The heat pump is in the time period t The cooling and heating power, is the efficiency of the heat pump, For heat pumps during the period t The power consumption; 3.6. The mathematical model of P2G power-to-gas equipment is: Formula (12); In formula (12), For P2G power-to-gas equipment during the period t The amount of natural gas produced, The efficiency of the P2G power-to-gas equipment, For P2G power-to-gas equipment during the period t The power consumption; 3.7. The mathematical model of the electric energy storage device is: Formula (13); In formula (13), For electric energy storage equipment in the period t The state of charge, and The energy storage equipment is in the time period t The charging power and discharging power of and are the charging efficiency and discharging efficiency of the energy storage device, is the capacity of the electrical energy storage device; 3.8. The mathematical model of thermal energy storage equipment is: Formula (14); In formula (14), For thermal energy storage equipment during the period t The heat state of and The thermal energy storage equipment is t The charging power and releasing power of and are the charging efficiency and releasing efficiency of the thermal energy storage device, is the capacity of the thermal energy storage device; 3.9. The mathematical model of hydrogen energy storage equipment is: Formula (15); Formula (16); Formula (17); Formula (18); Formula (19); In formulas (15) to (19), For hydrogen energy storage equipment in the period t The hydrogen charge state, and are the hydrogen storage efficiency and hydrogen release efficiency of hydrogen energy storage equipment, is the capacity of the hydrogen energy storage device, , and The electrolytic water tank is in the time period t Hydrogen production, power consumption and recovered heat, is the hydrogen production efficiency, is the electricity-to-hydrogen conversion coefficient, , and The fuel cell in the time periodt Hydrogen consumption, power generation and heat recovery, is the power generation efficiency of the fuel cell, is the highest calorific value of hydrogen, is the hydrogen utilization rate of the fuel cell; Step 4: The typical scene data of uncertainty in step 2 Substitute it into the equipment mathematical model established in step 3 to obtain the electrical energy balance equation, thermal energy balance equation and cold energy balance equation; The electric energy balance equation is: Formula (20); In formula (20), For the period t Typical scenario data of power load, and The time periods t The purchase and sale of electricity, and The time periods t Typical scenario data of wind power generation and typical scenario data of photovoltaic power generation; The heat balance equation is: Formula (21); In formula (21), For the period t Typical scenario data of thermal load, For solar collectors in the period t Thermal power; The cold energy balance equation is: Formula (22); In formula (22), For the period t Typical scenario data of cooling load; Step 5: Perform energy balance calculations on the electric energy balance equation, the thermal energy balance equation, and the cold energy balance equation using power constraints. The power constraints are: Formula (23); In formula (23), is the minimum power capacity of the equipment, Configure values ​​for device power capacity; The power capacity configuration values ​​of all equipment that satisfy equation (23) obtained by energy balance calculation are The set is represented as , obtain the capacity configuration result set A that meets the power constraint condition; Step 6: Substitute the capacity configuration result set A that meets the power constraint into the electric energy balance equation, thermal energy balance equation, and cooling energy balance equation, and perform energy balance calculations using the carbon emission constraint. The carbon emission constraint is: Formula (24); Formula (25); Formula (26); Formula (27); In formulas (24) to (27), is the scheduling period of zero carbon emission of the system, For equipment i life cycle, is the average annual carbon emissions produced by the system, is the average annual carbon emissions of the system during the recycling process, Producing carbon emissions during system operation, For equipment i Configure the carbon emission coefficient of the production process corresponding to the unit capacity, Unit capacity device i The corresponding carbon emission coefficient in the recycling process, For equipment j The carbon emission coefficient corresponding to unit power during operation; The power capacity configuration values ​​of all equipment that satisfy equation (24) obtained by energy balance calculation are The set is represented as ,and , and obtain the capacity configuration result set B that meets the carbon emission constraint conditions; Step 7: Substitute the capacity configuration result set B that meets the carbon emission constraint into equations (28) and (29) to calculate and obtain the investment cost F corresponding to each capacity configuration result; Formula (28); Formula (29); In formula (28) and (29), is the average annual investment cost of the system, For equipment i The investment cost corresponding to the configuration unit capacity; Select the lowest investment cost F from the investment costs F corresponding to all capacity configuration results min , the minimum investment cost F min The corresponding equipment power capacity configuration value Equipment capacity planning configuration as an integrated energy system.

[0011] Preferably, in step 1, wind power generation data of the integrated energy system within five years is collected. , Photovoltaic power generation data and heating and cooling load data .

[0012] According to the above technical solution, the beneficial effects of the present invention are: The present invention is based on many years of historical renewable energy and load data collected by the integrated energy system park, and adopts the Monte Carlo method to simulate its uncertainty. According to the uncertainty simulation results, the capacity of different equipment is reasonably configured, which can maximize the consumption of renewable energy in the integrated energy system, reduce energy waste and achieve overall zero carbon emissions during the operation cycle of the integrated energy system. The carbon emissions of all links of the integrated energy system equipment are comprehensively considered, and the zero carbon emissions throughout the life cycle of the equipment production, operation and recycling process are used as environmental constraints in the process of equipment capacity planning and energy optimization scheduling of the integrated energy system. The environmental goal of zero carbon emissions throughout the life cycle of the integrated energy system in the entire cycle of configuration, operation and recycling can be achieved, and the environmental and economic benefits are comprehensively considered to achieve a balance between environmental and economic benefits. DETAILED DESCRIPTION

[0013] This embodiment provides a method for equipment capacity planning of an integrated energy system, wherein the integrated energy system includes a variety of energy supply modes, energy conversion equipment, and energy storage equipment. The energy supply side includes clean energy sites (electricity-to-gas equipment, fans, and photovoltaics), natural gas sources, solar collectors, and the main grid. The energy conversion equipment includes gas turbines, electric boilers, electric chillers, absorption chillers, and heat pumps. The energy storage equipment includes electric energy storage, thermal energy storage, and hydrogen energy storage. The demand side also includes electric heating and cooling loads. The method of this embodiment can plan the capacity of the above-mentioned equipment, and includes the following steps.

[0014] Step 1: Collect wind power generation information, photovoltaic power generation information, and electric heating and cooling load information in the geographical location of the integrated energy system within five years, and obtain the wind power generation WT in the period t Wind power data 、Photovoltaic power generation PV in the period t Photovoltaic power generation data and heating and cooling load in the period t Electric heating and cooling load data .

[0015] Step 2: Uncertainty simulation of wind power generation, photovoltaic power generation and electric heating and cooling loads 2.1. Define the probability density function of the prediction error as: Formula (1); In formula (1), and The uncertain variables v The expected value and standard deviation of the forecast error.

[0016] 2.2. Use the inverse function of the cumulative distribution to obtain the fluctuation range of the prediction error: Formula (2); In formula (2), Indicates uncertain variables v In the period t The prediction error, α represents the confidence level, and The uncertain variables v The lower and upper bounds of the prediction error.

[0017] 2.3. Modeling the prediction error: Formula (3); In formula (3), and The uncertain variables v In the period t and scenes s The prediction error and standard deviation.

[0018] 2.4. Based on the prediction error model, the Monte Carlo method is used to generate random scenarios for simulating prediction data errors: Formula (4); In formula (4), For uncertain variables v In the period t and scenes s The power, Display wind power generation data , Photovoltaic power generation data and heating and cooling load data .

[0019] 2.5. Use the Monte Carlo method to reduce the scenarios and obtain typical scenario data that simulates the uncertainty of wind power generation, photovoltaic power generation, and electric heating and cooling loads .

[0020] Step 3: Mathematical modeling of equipment in the integrated energy system 3.1. The mathematical model of the gas turbine is: Formula (5); Formula (6); In formula (5) and (6), and The gas turbine in the time period t The electrical and thermal power, and are the power generation efficiency and heating efficiency of the gas turbine, is the calorific value of gas, For gas turbines in the period t The gas consumption, Schedule time for the unit.

[0021] 3.2. The mathematical model of the electric boiler is: Formula (7); In formula (7), For electric boilers during the period t The thermal power, is the heating efficiency of the electric boiler, For electric boilers during the period t of electrical power.

[0022] 3.3 The mathematical model of the electric chiller is: Formula (8); In formula (8), For electric refrigeration machine in the period t The cooling power, is the cooling efficiency of the electric chiller, For electric refrigeration machine in the period t of electrical power.

[0023] 3.4. The mathematical model of the absorption refrigerator is: Formula (9); In formula (9), For absorption chillers in the period t The cooling power, is the cooling efficiency of the absorption chiller, For absorption chillers in the period t The heating power.

[0024] 3.5. The mathematical model of the heat pump is: Formula (10); Formula (11); In formula (10) and (11), and The heat pump is in the time period t The cooling and heating power, is the efficiency of the heat pump, For heat pumps during the period tof electrical power.

[0025] 3.6. The mathematical model of P2G power-to-gas equipment is: Formula (12); In formula (12), For P2G power-to-gas equipment during the period t The amount of natural gas produced, The efficiency of the P2G power-to-gas equipment, For P2G power-to-gas equipment during the period t of electrical power.

[0026] 3.7. The mathematical model of the electric energy storage device is: Formula (13); In formula (13), For electric energy storage equipment in the period t The state of charge, and The energy storage equipment is in the time period t The charging power and discharging power of and are the charging efficiency and discharging efficiency of the energy storage device, is the capacity of the electrical energy storage device.

[0027] 3.8. The mathematical model of thermal energy storage equipment is: Formula (14); In formula (14), For thermal energy storage equipment during the period t The heat state of and The thermal energy storage equipment is t The charging power and releasing power of and are the charging efficiency and releasing efficiency of the thermal energy storage device, is the capacity of the thermal energy storage device.

[0028] 3.9. The mathematical model of hydrogen energy storage equipment is: Formula (15); Formula (16); Formula (17); Formula (18); Formula (19); In formulas (15) to (19), For hydrogen energy storage equipment in the period t The hydrogen charge state, and are the hydrogen storage efficiency and hydrogen release efficiency of hydrogen energy storage equipment, is the capacity of the hydrogen energy storage device, , and The electrolytic water tank is in the time period t Hydrogen production, power consumption and recovered heat, is the hydrogen production efficiency, is the electricity-to-hydrogen conversion coefficient, , and The fuel cell in the time period t Hydrogen consumption, power generation and heat recovery, is the power generation efficiency of the fuel cell, is the highest calorific value of hydrogen, is the hydrogen utilization rate of the fuel cell.

[0029] Step 4: The typical scene data of uncertainty in step 2 Substitute the equipment mathematical model established in step three to obtain the electrical energy balance equation, thermal energy balance equation and cold energy balance equation.

[0030] The electric energy balance equation is: Formula (20); In formula (20), For the period t Typical scenario data of power load, and The time periods t The purchase and sale of electricity, and The time periods t Typical scenario data of wind power generation and typical scenario data of photovoltaic power generation.

[0031] The heat balance equation is: Formula (21); In formula (21), For the period t Typical scenario data of thermal load, For solar collectors in the period t of thermal power.

[0032] The cold energy balance equation is: Formula (22); In formula (22), For the period tTypical scenario data of cooling load.

[0033] Step 5: Perform energy balance calculations on the electric energy balance equation, the thermal energy balance equation, and the cold energy balance equation using power constraints. The power constraints are: Formula (23); In formula (23), is the minimum power capacity of the equipment, Configure a value for the device power capacity.

[0034] The power capacity configuration values ​​of all equipment that satisfy equation (23) obtained by energy balance calculation are The set is represented as , and obtain the capacity configuration result set A that meets the power constraint.

[0035] Step 6: Substitute the capacity configuration result set A that meets the power constraint conditions into the electric energy balance equation, the thermal energy balance equation, and the cooling energy balance equation, and perform energy balance calculations through the carbon emission constraint conditions.

[0036] The carbon emission constraints are: Formula (24); Formula (25); Formula (26); Formula (27); In formulas (24) to (27), is the scheduling period of zero carbon emission of the system, For equipment i life cycle, is the average annual carbon emissions produced by the system, is the average annual carbon emissions of the system during the recycling process, Producing carbon emissions during system operation, For equipment i Configure the carbon emission coefficient of the production process corresponding to the unit capacity, Unit capacity device i The corresponding carbon emission coefficient in the recycling process, For equipment j The carbon emission coefficient corresponding to unit power during operation.

[0037] The power capacity configuration values ​​of all equipment that satisfy equation (24) obtained by energy balance calculation are The set is represented as ,and , and obtain the capacity configuration result set B that meets the carbon emission constraints.

[0038] Step 7: Substitute the capacity configuration result set B that meets the carbon emission constraint into equations (28) and (29) to calculate and obtain the investment cost F corresponding to each capacity configuration result; Formula (28); Formula (29); In formula (28) and (29), is the average annual investment cost of the system, For equipment i Configure the investment cost per unit capacity.

[0039] Select the lowest investment cost F from the investment costs F corresponding to all capacity configuration results min , the minimum investment cost F min The corresponding equipment power capacity configuration value Equipment capacity planning configuration as an integrated energy system.

[0040] This embodiment makes reasonable capacity configuration for different equipment in the integrated energy system, maximizes the consumption of renewable energy in the integrated energy system, and gives full play to its low-carbon advantages. Through reasonable capacity configuration, the present invention achieves zero carbon emissions of equipment in the integrated energy system during the operation cycle, and can maximize the utilization of equipment and minimize investment costs.

Claims

1. A method for equipment capacity planning of an integrated energy system, characterized in that: The following steps are involved: Step 1: Collect wind power generation information, photovoltaic power generation information, and electric heating and cooling load information at the geographical location of the integrated energy system to obtain the wind power generation WT in the period t Wind power data 、Photovoltaic power generation PV in the period t Photovoltaic power generation data and heating and cooling load in the period t Electric heating and cooling load data ; Step 2: Uncertainty simulation of wind power generation, photovoltaic power generation and electric heating and cooling loads 2.

1. Define the probability density function of the prediction error as: Formula (1); In formula (1), and The uncertain variables v The expected value and standard deviation of the forecast error; 2.

2. Use the inverse function of the cumulative distribution to obtain the fluctuation range of the prediction error: Formula (2); In formula (2), Indicates uncertain variables v In the period t The prediction error, α represents the confidence level, and The uncertain variables v The lower and upper bounds of the prediction error; 2.

3. Modeling the prediction error: Formula (3); In formula (3), and The uncertain variables v In the period t and scenes s The prediction error and standard deviation of 2.

4. Based on the prediction error model, the Monte Carlo method is used to generate random scenarios for simulating prediction data errors: Formula (4); In formula (4), For uncertain variables v In the period t and scenes s The power, Display wind power generation data , Photovoltaic power generation data and heating and cooling load data ; 2.

5. Use the Monte Carlo method to reduce the scenarios and obtain typical scenario data that simulates the uncertainty of wind power generation, photovoltaic power generation, and electric heating and cooling loads ; Step 3: Mathematical modeling of equipment in the integrated energy system 3.

1. The mathematical model of the gas turbine is: Formula (5); Formula (6); In formula (5) and (6), and The gas turbine in the time period t The electrical and thermal power, and are the power generation efficiency and heating efficiency of the gas turbine, is the calorific value of gas, For gas turbines in the period t The gas consumption, Scheduling time for units; 3.

2. The mathematical model of the electric boiler is: Formula (7); In formula (7), For electric boilers during the period t The thermal power, is the heating efficiency of the electric boiler, For electric boilers during the period t The power consumption; 3.3 The mathematical model of the electric chiller is: Formula (8); In formula (8), For electric refrigeration machine in the period t The cooling power, is the cooling efficiency of the electric chiller, For electric refrigeration machine in the period t The power consumption; 3.

4. The mathematical model of the absorption refrigerator is: Formula (9); In formula (9), For absorption chillers in the period t The cooling power, is the cooling efficiency of the absorption chiller, For absorption chillers in the period t The heat power used; 3.

5. The mathematical model of the heat pump is: Formula (10); Formula (11); In formula (10) and (11), and The heat pump is in the time period t The cooling and heating power, is the efficiency of the heat pump, For heat pumps during the period t The power consumption; 3.

6. The mathematical model of P2G power-to-gas equipment is: Formula (12); In formula (12), For P2G power-to-gas equipment during the period t The amount of natural gas produced, The efficiency of the P2G power-to-gas equipment, For P2G power-to-gas equipment during the period t The power consumption; 3.

7. The mathematical model of the electric energy storage device is: Formula (13); In formula (13), For electric energy storage equipment in the period t The state of charge, and The energy storage devices are t The charging power and discharging power of and are the charging efficiency and discharging efficiency of the energy storage device, is the capacity of the electrical energy storage device; 3.

8. The mathematical model of thermal energy storage equipment is: Formula (14); In formula (14), For thermal energy storage equipment during the period t The heat state, and The thermal energy storage equipment is t The charging power and releasing power of and are the charging efficiency and dissipating efficiency of the thermal energy storage device, is the capacity of the thermal energy storage device; 3.

9. The mathematical model of hydrogen energy storage equipment is: Formula (15); Formula (16); Formula (17); Formula (18); Formula (19); In formulas (15) to (19), For hydrogen energy storage equipment in the period t The hydrogen charge state, and are the hydrogen storage efficiency and hydrogen release efficiency of hydrogen energy storage equipment, is the capacity of the hydrogen energy storage device, , and The electrolytic water tank is in the time period t Hydrogen production, power consumption and recovered heat, is the hydrogen production efficiency, is the electricity-to-hydrogen conversion coefficient, , and The fuel cell in the time period t Hydrogen consumption, power generation and heat recovery, is the power generation efficiency of the fuel cell, is the highest calorific value of hydrogen, is the hydrogen utilization rate of the fuel cell; Step 4: The typical scene data of uncertainty in step 2 Substitute it into the equipment mathematical model established in step 3 to obtain the electrical energy balance equation, thermal energy balance equation and cold energy balance equation; The electrical energy balance equation is: Formula (20); In formula (20), For the period t Typical scenario data of power load, and The time periods t The purchase and sale of electricity, and The time periods t Typical scenario data of wind power generation and typical scenario data of photovoltaic power generation; The heat balance equation is: Formula (21); In formula (21), For the period t Typical scenario data of thermal load, For solar collectors in the period t Thermal power; The cold energy balance equation is: Formula (22); In formula (22), For the period t Typical scenario data of cooling load; Step 5: Perform energy balance calculations on the electric energy balance equation, the thermal energy balance equation, and the cold energy balance equation using power constraints. The power constraints are: Formula (23); In formula (23), is the minimum power capacity of the equipment, Configure values ​​for device power capacity; The power capacity configuration values ​​of all equipment that satisfy equation (23) obtained by energy balance calculation are The set is represented as , obtain the capacity configuration result set A that meets the power constraint condition; Step 6: Substitute the capacity configuration result set A that meets the power constraint into the electric energy balance equation, thermal energy balance equation, and cooling energy balance equation, and perform energy balance calculations using the carbon emission constraint. The carbon emission constraint is: Formula (24); Formula (25); Formula (26); Formula (27); In formulas (24) to (27), is the scheduling period of zero carbon emission of the system, For equipment i life cycle, is the average annual carbon emissions produced by the system, is the average annual carbon emissions of the system during the recycling process, Producing carbon emissions during system operation, For equipment i Configure the carbon emission coefficient of the production process corresponding to the unit capacity, Unit capacity device i The corresponding carbon emission coefficient in the recycling process, For equipment j The carbon emission coefficient corresponding to unit power during operation; The power capacity configuration values ​​of all equipment that satisfy equation (24) obtained by energy balance calculation are The set is represented as ,and , and obtain the capacity configuration result set B that meets the carbon emission constraint conditions; Step 7: Substitute the capacity configuration result set B that meets the carbon emission constraint into equations (28) and (29) to calculate and obtain the investment cost F corresponding to each capacity configuration result; Formula (28); Formula (29); In formula (28) and (29), is the average annual investment cost of the system, For equipment i The investment cost corresponding to the configuration unit capacity; Select the lowest investment cost F from the investment costs F corresponding to all capacity configuration results min , the minimum investment cost F min The corresponding equipment power capacity configuration value Equipment capacity planning configuration as an integrated energy system.

2. The method for equipment capacity planning of an integrated energy system according to claim 1, characterized in that: In step 1, wind power generation data for the integrated energy system is collected over a five-year period. , Photovoltaic power generation data and heating and cooling load data .

Images