A method for configuring capacity of a self-consistent energy system of a highway considering multi-energy flow

Abstract

The application discloses a kind of highway self-consistent energy system capacity configuration method considering multi-energy flow belonging to the technical field of integrated energy system capacity configuration.The method is as follows: the system historical data is clustered using K-means clustering method, the irradiance intensity, wind speed, multi-energy load data set of highway self-consistent energy system typical day is established, and the typical day frequency is obtained;A multi-objective optimization model for highway self-consistent energy system capacity configuration is established;The multi-objective optimization model is solved using gurobi solver on MATLAB platform, and the capacity configuration scheme of the equipment in the highway self-consistent energy system is obtained.The application realizes the triple guarantee of economy, environment and self-consistency, effectively improves the energy consumption of highway in weak / no network area, and promotes the local consumption of renewable energy.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system capacity configuration technology, and in particular to a method for configuring the capacity of a self-consistent energy system for highways that takes into account multiple energy flows. Background Technology

[0002] Under the dual-carbon goals, coordinating the continuously growing energy demand of highways with reducing carbon dioxide emissions has become a pressing challenge for green transportation. Integrated energy systems can improve energy efficiency and support the stable operation of renewable energy power supply systems through the combined application of multiple energy technologies. However, current highway energy systems in areas with weak or no power grids often face severe energy shortages, resulting in the ineffective utilization of abundant natural resources and low local absorption rates of renewable energy. Therefore, to ensure the future green, efficient, and sustainable development of hydrogen-containing integrated energy systems on highways, it is crucial to comprehensively consider economic efficiency, environmental impact, and self-consistency, and to establish a capacity configuration method for self-consistent energy systems on highways that takes into account multiple energy flows. Summary of the Invention

[0003] The purpose of this invention is to propose a capacity configuration method for a self-consistent energy system for highways that takes into account multiple energy flows, comprising the following steps:

[0004] Step A: Use K-means clustering to cluster the historical data of the system, establish a dataset of typical days of the highway self-consistent energy system, including irradiance, wind speed, and multi-energy load, and obtain the frequency of typical days;

[0005] Step B: Establish a multi-objective optimization model for capacity configuration of self-consistent energy systems for highways; wherein, the multi-objective optimization model includes a multi-objective function for minimizing deviation and constraints, the multi-objective function for minimizing deviation optimizes the total annual economic cost, the total life-cycle carbon dioxide emissions, and the total annual self-consistency rate by assigning weights; the constraints include power balance constraints, renewable energy equipment output constraints, energy storage equipment output constraints, energy conversion equipment constraints, and grid transmission capacity constraints.

[0006] Step C: Based on steps A and B, the multi-objective optimization model is solved using the gurobi solver on the MATLAB platform to obtain the capacity configuration scheme of photovoltaic equipment, wind power equipment, electric refrigeration equipment, electric heating equipment, electric hydrogen production equipment, absorption chiller, energy storage equipment, cold storage equipment, heat storage equipment, and hydrogen storage equipment in the self-consistent energy system of the highway.

[0007] The single objective function, which aims to minimize the total annual economic cost, is as follows:

[0008] minF1=F inv +FOM +F buy +F tax

[0009]

[0010] In the formula, F1 is the total annual economic cost, F inv F represents the total annual investment cost. OM For the total annual operating and maintenance cost, F buy F represents the total annual electricity purchase cost. tax Γ represents the annual carbon tax cost, and Γ represents the set of equipment types. Let i be the unit investment cost of equipment i. Let a be the rated capacity of device i, a be the annual interest rate, and n be the rated capacity of device i. i For the service life of device i. f represents the unit maintenance cost of device i. buy,t Let f be the unit electricity purchase cost at time t. sell,t Let P be the revenue per unit of electricity sold at time t. buy,t Purchase electricity at time t, P sell,t Let f be the electricity sold at time t. tax This refers to the unit price of carbon tax.

[0011] The single objective function, taking the minimum CO2 emissions over the entire life cycle, is as follows:

[0012]

[0013] In the formula: F2 is the annual value of CO2 emissions, α i The carbon emission coefficient for the entire life cycle of device i;

[0014] The single objective function is to maximize the annual total self-consistency rate, or to minimize the reciprocal of the annual total self-consistency rate, as follows:

[0015]

[0016] In the formula: F3 is the reciprocal of the annual total self-consistency rate, P t PV Let P be the photovoltaic power generation at time t. t WT Let P be the wind power generation at time t. t LOAD Let t be the load power consumption at time t.

[0017] The multi-objective function for minimizing the bias is:

[0018]

[0019] In the formula, Δd is the deviation, ω1 is the economic weight, and F 1,2F is the total annual economic cost obtained through multi-objective optimization with minimum deviation. 1,1 The total annual economic cost is obtained through single-objective optimization, ω2 is the carbon emission weight, and F 2,2 F represents the annual CO2 emissions over the entire life cycle obtained through multi-objective optimization with minimum bias. 2,1 The annual CO2 emissions over the entire life cycle are obtained through single-objective optimization, ω3 is the annual total self-consistency rate weight, and F 3,2 F is the reciprocal of the annual total self-consistency rate obtained by multi-objective optimization with minimum bias. 3,1 It is the reciprocal of the annual total self-consistency rate obtained from single-objective optimization.

[0020] The power balance constraint is:

[0021]

[0022] In the formula: P t ST,out P t ST,in P represents the discharge and storage capacity of the energy storage device at time t, respectively. t eload Let P be the electrical load consumption at time t. t c P t h , These represent the electricity consumption for electric cooling, electric heating, and electric hydrogen production at time t, respectively. These represent the hydrogen energy, cold energy, and heat energy generated by the electric hydrogen production, cooling, and heating equipment at time t, respectively. These represent the hydrogen load, cold load, and heat load at time t, respectively. These represent the amount of hydrogen released and the amount of hydrogen stored by the hydrogen storage device at time t, respectively. These represent the cold release capacity and cold storage capacity of the cold storage equipment at time t, respectively. P represents the heat released and heat stored by the thermal storage device at time t, respectively. AR E AR These represent the heat energy consumed and the cold energy generated by the absorption chiller at time t, respectively.

[0023] The power grid transmission capacity constraint is:

[0024]

[0025] In the formula, This represents the upper limit of the power grid transmission capacity.

[0026] The output constraints for renewable energy equipment are:

[0027]

[0028] In the formula: These represent the maximum configured capacity of photovoltaic and wind power, respectively, ηPV Let A be the photovoltaic panel efficiency, k be the standard rated power per unit area of ​​the photovoltaic panel, and A be the rated power per unit area of ​​the panel. t Let v be the irradiance at time t. t Let v be the wind speed at time t. in v r v out These are the cut-in, rated, and cut-out wind velocities, respectively.

[0029] Energy storage equipment output constraints include electricity storage, thermal storage, hydrogen storage, and cold storage equipment, specifically:

[0030]

[0031] In the formula: SOC is the energy stored in energy storage device i at time t and time t-1. i,min SOC i,max These represent the minimum and maximum energy storage states, respectively. For the maximum configured capacity of energy storage, SOC i,0 SOC i,24 These represent the initial and final energy states of the stored energy within a day. Let P be the charge / discharge state of energy storage device i at time t, a 0-1 variable. i ST,in_max P i ST ,out_max To maximize the charging and discharging energy of the energy storage device;

[0032] Energy conversion equipment constraints include electric refrigeration, electric hydrogen production, electric heating, and absorption chillers, specifically:

[0033]

[0034] In the formula, These represent the energy consumed and produced by energy conversion device i at time t, respectively, in μ. i P is the conversion factor. i 1 ,min P i 1,max These are the lower and upper limits of energy consumption for energy conversion equipment, respectively. For the maximum configured capacity of energy conversion equipment i, ΔP min ΔP max The lower and upper limits of ramp rate for energy conversion equipment.

[0035] The beneficial effects of this invention are as follows:

[0036] This invention establishes and solves an optimization model from three aspects: economic cost, carbon dioxide emissions throughout the life cycle, and energy self-sufficiency rate. It achieves triple protection of economic efficiency, environmental friendliness, and self-sufficiency rate, effectively improves the energy consumption of highways in areas with weak or no grid, promotes the local consumption of renewable energy, and can also perform sensitivity analysis on the configuration results by changing the weight assignment of economic performance, environmental performance, and self-sufficiency rate performance. Attached Figure Description

[0037] Figure 1 This is a flowchart of the capacity configuration method for a self-consistent energy system for highways that takes into account multiple energy flows, as described in this invention.

[0038] Figure 2 Diagram of a self-consistent energy system structure for a highway;

[0039] Figure 3 Optimize the power balance diagram for multiple objectives. Detailed Implementation

[0040] This invention proposes a capacity configuration method for a self-consistent energy system for highways that takes into account multiple energy flows. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0041] Figure 1 This is a flowchart of the capacity configuration method for a self-consistent energy system for highways that takes into account multiple energy flows, according to the present invention. The method includes the following steps:

[0042] Step A. Based on the K-means clustering method, establish a typical day dataset of irradiance intensity, wind speed, and multiple energy loads for the self-consistent energy system of the highway, and obtain the frequency of typical days.

[0043] Step B. Establish a multi-objective function and constraints for minimizing the deviation in the capacity configuration of the self-consistent energy system for highways. The multi-objective function for minimizing the deviation optimizes the total annual economic cost, the total life cycle carbon dioxide emissions, and the total annual self-consistency rate by assigning weights. The constraints include power balance constraints, renewable energy equipment output constraints, energy storage equipment output constraints, energy conversion equipment constraints, and grid transmission capacity constraints.

[0044] The single objective function, which aims to minimize the total annual economic cost, is as follows:

[0045] minF1=F inv +F OM +F buy +F tax

[0046]

[0047] In the formula, F1 is the total annual economic cost, F inv F represents the total annual investment cost.OM For the total annual operating and maintenance cost, F buy F represents the total annual electricity purchase cost. tax Γ represents the annual carbon tax cost, and Γ represents the set of equipment types. Let i be the unit investment cost of equipment i. Let a be the rated capacity of device i, a be the annual interest rate, and n be the rated capacity of device i. i For the service life of device i. f represents the unit maintenance cost of device i. buy,t Let f be the unit electricity purchase cost at time t. sell,t Let P be the revenue per unit of electricity sold at time t. buy,t Purchase electricity at time t, P sell,t Let f be the electricity sold at time t. tax This is the unit price for carbon tax.

[0048] The single objective function, taking the minimum CO2 emissions over the entire life cycle, is as follows:

[0049]

[0050] In the formula: F2 is the annual value of CO2 emissions, α i Let be the carbon emission coefficient of device i throughout its entire life cycle.

[0051] The single objective function is to maximize the annual total self-consistency rate, or to minimize the reciprocal of the annual total self-consistency rate, as follows:

[0052]

[0053] In the formula: F3 is the reciprocal of the annual total self-consistency rate, P t PV Let P be the photovoltaic power generation at time t. t WT Let P be the wind power generation at time t. t LOAD Let t be the load power consumption at time t.

[0054] The multi-objective optimization function with minimum deviation is established as follows:

[0055]

[0056] In the formula, Δd is the deviation, ω1 is the economic weight, and F 1,2 F is the total annual economic cost obtained through multi-objective optimization with minimum deviation. 1,1 The total annual economic cost is obtained through single-objective optimization, ω2 is the carbon emission weight, and F 2,2 F represents the annual CO2 emissions over the entire life cycle obtained through multi-objective optimization with minimum bias. 2,1 The annual CO2 emissions over the entire life cycle are obtained through single-objective optimization, ω3 is the annual total self-consistency rate weight, and F3,2 F is the reciprocal of the annual total self-consistency rate obtained by multi-objective optimization with minimum bias. 3,1 It is the reciprocal of the annual total self-consistency rate obtained from single-objective optimization.

[0057] The specific constraints are as follows:

[0058] 1) Power balance constraints

[0059]

[0060] In the formula: P t ST,out P t ST,in P represents the discharge and storage capacity of the energy storage device at time t, respectively. t eload Let P be the electrical load consumption at time t. t c P t h , These represent the electricity consumption for electric cooling, electric heating, and electric hydrogen production at time t, respectively. These represent the hydrogen energy, cold energy, and heat energy generated by the electric hydrogen production, cooling, and heating equipment at time t, respectively. These represent the hydrogen load, cold load, and heat load at time t, respectively. These represent the amount of hydrogen released and the amount of hydrogen stored by the hydrogen storage device at time t, respectively. These represent the cold release capacity and cold storage capacity of the cold storage equipment at time t, respectively. P represents the heat released and heat stored by the thermal storage device at time t, respectively. AR E AR These represent the heat energy consumed and the cold energy generated by the absorption chiller at time t, respectively.

[0061] 2) Power grid transmission capacity constraints

[0062]

[0063] In the formula, This represents the upper limit of the power grid transmission capacity.

[0064] 3) Renewable energy output constraints

[0065]

[0066] In the formula: These represent the maximum configured capacity of photovoltaic and wind power, respectively, η PV Let A be the photovoltaic panel efficiency, k be the standard rated power per unit area of ​​the photovoltaic panel, and A be the rated power per unit area of ​​the panel. t Let v be the irradiance at time t. t Let v be the wind speed at time t. in v r vout These are the cut-in, rated, and cut-out wind speeds, respectively.

[0067] 4) Energy storage constraints, including electricity storage, thermal storage, hydrogen storage, and cold storage devices.

[0068]

[0069] In the formula: SOC is the energy stored in energy storage device i at time t and time t-1. i,min SOC i,max These represent the minimum and maximum energy storage states, respectively. For the maximum configured capacity of energy storage, SOC i,0 SOC i,24 These represent the initial and final energy states of the stored energy within a day. Let P be the charge / discharge state of energy storage device i at time t, a 0-1 variable. i ST,in_max P i ST ,out_max To provide the maximum charging and discharging energy for the energy storage device.

[0070] 5) Constraints on energy conversion equipment, including electric refrigeration, electric hydrogen production, electric heating, and absorption chillers.

[0071]

[0072] In the formula, These represent the energy consumed and produced by energy conversion device i at time t, respectively, in μ. i P is the conversion factor. i 1 ,min P i 1,max These are the lower and upper limits of energy consumption for energy conversion equipment, respectively. For the maximum configured capacity of energy conversion equipment i, ΔP min ΔP max The lower and upper limits of ramp rate for energy conversion equipment.

[0073] Step C. Based on Steps A and B, the multi-objective optimization model is solved using the gurobi solver on the MATLAB platform to obtain the capacity configuration scheme of photovoltaic equipment, wind power equipment, electric refrigeration equipment, electric heating equipment, electric hydrogen production equipment, absorption chiller, energy storage equipment, cold storage equipment, heat storage equipment, and hydrogen storage equipment in the self-consistent energy system of the highway.

[0074] Figure 2To construct a self-consistent energy system structure diagram for a highway, wind speed, irradiance, and multi-energy load data from 8784 hours of a leap year were clustered into 6 classes using K-means clustering, and the average values ​​were taken to obtain data for 6 typical days. The frequencies were 48, 69, 61, 41, 85, and 62. After multi-objective optimization with minimal bias, the power balance for 144 hours on the 6 typical days was obtained as follows: Figure 3 As shown in Table 1, which compares the economic costs, life-cycle CO2 emissions, and annual total self-consistency rate of the configuration schemes obtained under single-objective and multi-objective conditions.

[0075] Table 1 Comparison of Configuration Schemes under Different Objectives

[0076]

[0077] In a traditional power grid (thermal power) supply scenario, where there is no renewable energy generation and the annual total self-sufficiency rate is always 0, if the single objective is to minimize economic cost, the annual total economic cost of the configuration scheme is 14,899,670 yuan, and the annual CO2 emissions over the entire life cycle are 956.369 tons. If the single objective is to minimize carbon emissions, the annual total economic cost of the configuration scheme is 16,817,460 yuan, and the annual CO2 emissions over the entire life cycle are 944.098 tons. As can be seen from Table 1, the multi-objective optimization model with minimum deviation proposed in this invention is effective in reducing economic cost, reducing CO2 emissions, and improving energy self-sufficiency rate.

[0078] The proposed capacity configuration method for highway self-consistent energy systems, which takes into account multiple energy flows, establishes and solves optimization models from three aspects: economic cost, carbon dioxide emissions throughout the life cycle, and energy self-consistency rate. This achieves triple protection of economic efficiency, environmental friendliness, and self-consistency rate, effectively improves the energy consumption of highways in areas with weak or no grid, promotes the local consumption of renewable energy, and can also perform sensitivity analysis on the configuration results by changing the weight assignment of economic performance, environmental performance, and self-consistency rate performance.

Claims

1. A capacity configuration method for a self-consistent energy system for highways considering multiple energy flows, characterized in that, Includes the following steps: Step A: Use K-means clustering to cluster the historical data of the system, establish a dataset of typical days of the highway self-consistent energy system, including irradiance, wind speed, and multi-energy load, and obtain the frequency of typical days; Step B: Establish a multi-objective optimization model for capacity configuration of self-consistent energy systems for highways; wherein, the multi-objective optimization model includes a multi-objective function for minimizing deviation and constraints, the multi-objective function for minimizing deviation optimizes the total annual economic cost, the total life-cycle carbon dioxide emissions, and the total annual self-consistency rate by assigning weights; the constraints include power balance constraints, renewable energy equipment output constraints, energy storage equipment output constraints, energy conversion equipment constraints, and grid transmission capacity constraints. The single objective function, which aims to minimize the total annual economic cost, is as follows: ； In the formula, The total annual economic cost, The total annual investment cost is The total annual maintenance cost, The total annual cost of electricity purchase, Annual carbon tax cost A collection of equipment types. For equipment The unit investment cost For equipment The rated capacity of the configuration The annual interest rate is For equipment Service life, For equipment The unit operation and maintenance cost Let t be the unit electricity purchase cost. Let t be the revenue per unit of electricity sold. Purchase electricity at time t. The electricity sold at time t. This refers to the unit price of carbon tax. The single objective function, taking the minimum CO2 emissions over the entire life cycle, is as follows: ； In the formula: This represents the annual CO2 emissions. For equipment The life-cycle carbon emission coefficient; The single objective function is to maximize the annual total self-consistency rate, or to minimize the reciprocal of the annual total self-consistency rate, as follows: ； In the formula: It is the reciprocal of the annual total self-consistency rate. Let be the photovoltaic power generation at time t. Let be the wind power generation at time t. Let be the load power consumption at time t; The multi-objective function for minimizing the bias is: ； In the formula, For deviation, As an economic weight, The total annual economic cost obtained through multi-objective optimization with minimum bias is... The total annual economic cost obtained by single-objective optimization. As carbon emission weights, The annual values ​​of carbon dioxide emissions over the entire life cycle are obtained through multi-objective optimization with minimum bias. The annual CO2 emissions over the entire life cycle obtained through single-objective optimization. As the weight of the annual overall self-consistency rate, The reciprocal of the annual total self-consistency rate obtained by multi-objective optimization with minimum bias. The inverse of the annual total self-consistency rate obtained from single-objective optimization; Step C: Based on steps A and B, the multi-objective optimization model is solved using the gurobi solver on the MATLAB platform to obtain the capacity configuration scheme of photovoltaic equipment, wind power equipment, electric refrigeration equipment, electric heating equipment, electric hydrogen production equipment, absorption chiller, energy storage equipment, cold storage equipment, heat storage equipment, and hydrogen storage equipment in the self-consistent energy system of the highway.

2. The capacity configuration method for a self-consistent energy system for highways considering multiple energy flows according to claim 1, characterized in that, The power balance constraint is: ； In the formula: , Let be the discharge amount and the stored amount of the energy storage device at time t, respectively. Let t be the electrical load consumption at time t. , , These represent the electricity consumption for electric cooling, electric heating, and electric hydrogen production at time t, respectively. , , These represent the hydrogen energy, cold energy, and heat energy generated by the electric hydrogen production, cooling, and heating equipment at time t, respectively. , , These represent the hydrogen load, cold load, and heat load at time t, respectively. , These represent the amount of hydrogen released and the amount of hydrogen stored by the hydrogen storage device at time t, respectively. , These represent the cold release capacity and cold storage capacity of the cold storage equipment at time t, respectively. , These represent the heat released and heat stored by the thermal storage device at time t, respectively. , These represent the heat energy consumed and the cold energy generated by the absorption chiller at time t, respectively. The power grid transmission capacity constraint is: ； In the formula, This represents the upper limit of the power grid transmission capacity. The output constraints for renewable energy equipment are: ； In the formula: , These represent the maximum configuration capacity for photovoltaic and wind power, respectively. Here, k represents the photovoltaic panel efficiency, and k represents the standard rated power of the photovoltaic panel per unit area. Let be the irradiance at time t. Let t be the wind speed. , , These are the cut-in, rated, and cut-out wind velocities, respectively. Energy storage equipment output constraints include electricity storage, thermal storage, hydrogen storage, and cold storage equipment, specifically: ； In the formula: , For energy storage devices The energy present at time t and time t-1 , These represent the minimum and maximum energy storage states, respectively. For the maximum configured capacity of energy storage, , These represent the initial and final energy states of the stored energy within a day. For energy storage devices The energy state at time t is a 0-1 variable. , For energy storage devices Maximum charge and discharge energy; Energy conversion equipment constraints include electric refrigeration, electric hydrogen production, electric heating, and absorption chillers, specifically: ； In the formula, , Energy conversion equipment The energy consumed and produced at time t For conversion factors, , Energy conversion equipment Lower and upper limits of energy consumption Energy conversion equipment Maximum configuration capacity, , Energy conversion equipment Lower and upper limits of the slope.

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