Configuration method of self-consistent energy system for traffic scene

Abstract

The invention relates to a configuration method of a self-consistent energy system for a traffic scene, wind power and photovoltaic power are used as new energy, energy storage is carried out in cooperation with an energy storage unit, and a diesel generator is arranged in a non-network scene and a weak network scene; according to the method, four typical traffic scenes are comprehensively considered and respectively correspond to four scenes of no network, weak network, strong network but no power grid absorption capability and strong network but power grid absorption capability, four operation mode management and control modes corresponding to the four scenes are given, and through modeling of an energy unit, an economic target and an environmental protection target are taken as evaluation targets, so that the energy unit can be evaluated. The self-consistent rate and the power abandoning rate are used as constraint targets, corresponding constraint conditions in the four operation modes are given, adaptive functions in the four modes are constructed, the adaptive functions in the four operation modes are solved by adopting a genetic algorithm of non-dominated sorting with an elitist strategy improved by a population-guided crossover method, and the optimal power abandoning rate is obtained. And the capacity of the energy unit is reasonably configured by integrating the evaluation target and the constraint target.

Description

Technical Field:

[0001] The present invention relates to the technical field of new energy power supply, and particularly relates to a configuration method for a self-consistent energy system in a traffic scenario. Background Art:

[0002] The energy supply system in the traditional traffic field still highly depends on fossil energy. The single energy structure leads to high carbon emission intensity and low energy efficiency conversion rate, making it difficult to meet the needs of the green traffic transformation. With the increasing maturity of new energy power supply technology, clean energies such as photovoltaic and wind power have been introduced in traffic scenarios such as railways and highways. However, due to the lack of new energy power supply technology standards and the lack of coordination of new energy power supply systems, the utilization rate of new energy is generally lower than expected. In order to give full play to the role of new energy in traffic scenarios, it is necessary to fully explore the space resources of highways and the potential of renewable energy, realize energy time-shifting with the help of energy storage systems, and through the energyization of traffic assets, constructing an energy self-consistent system adapted to typical traffic scenarios is an important measure to achieve the green and flexible development of the traffic system. Summary of the Invention:

[0003] The present invention designs four typical scenarios for the self-consistent energy system applied to traffic scenarios, respectively gives the evaluation objective function and the constraint objective function under different scenarios, considers the influence of the constraint objective on the evaluation objective under different scenarios, constructs the corresponding adaptation function under different scenarios, and solves the adaptation function through intelligent algorithms to optimize the energy unit capacity configuration of the self-consistent energy system and maximize the self-sufficiency of traffic energy.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] A configuration method for a self-consistent energy system in a traffic scenario, comprising the following steps:

[0006] Step 1): Construct a self-consistent energy system, the self-consistent energy system includes an energy unit and a load, and the energy unit includes a wind power generation unit, a photovoltaic power generation unit, and an energy storage unit;

[0007] Step 2): The self-consistent energy system is set with four operating modes, namely operating mode A, operating mode B, operating mode C, and operating mode D. Operating mode A is used for the self-consistent energy system in the off-grid state, operating mode B is used for the self-consistent energy system connected to a weak power grid, and both operating mode C and operating mode D are used for the self-consistent energy system connected to a strong power grid. Among them, the power grid in operating mode C only supplies power unidirectionally, the power grid in operating mode D can both supply power and absorb the abandoned electricity of new energy, and the self-consistent energy systems in operating mode A and operating mode B both include a diesel power generation unit;

[0008] Step 3): Conduct target modeling for the capacity configurations of the wind power generation unit, photovoltaic power generation unit, and energy storage unit respectively;

[0009] The modeling expression of the wind power generation unit is as follows:

[0010]

[0011] In the above formula, P w,t and P wr are the real-time output power and rated power of the wind turbine respectively; v is the real-time wind speed; v ci , v r , v co are the cut-in, rated, and cut-out wind speeds of the wind turbine respectively;

[0012] The modeling expression of the photovoltaic power generation unit is as follows:

[0013]

[0014] In the above formula, P pv,t and P STC are the real-time output power of the photovoltaic array and the rated output power under standard conditions respectively; f pv is the power degradation coefficient; G c,t is the real-time solar irradiance at the operating point; G STC is the solar irradiance under standard conditions; k is the power temperature coefficient; T c,t is the temperature at the operating point at time t; T STC is the temperature under standard conditions;

[0015] The modeling expression of the energy storage unit is as follows:

[0016]

[0017] In the above formula, SOC t and SOC t-1 are the state of charge of the energy storage at times t and t-1 respectively; P es,t is the real-time state of the energy storage. When it is less than 0, it means the energy storage unit is charging. When it is greater than 0, it means the energy storage unit is discharging; η c and η d are the real-time charging and discharging powers of the energy storage system respectively; δ is the self-discharge rate of the energy storage; T is the working duration of the charging or discharging state;

[0018] Step 4): Optimize the target modeling and give the constraint conditions;

[0019] Under the four operating modes, the economic target and environmental protection target are used as evaluation targets. The self-consistency rate is used as the constraint target for both operating mode A and operating mode C, and the curtailment rate is used as the constraint target for both mode B and mode D;

[0020] Step 5): Construct the fitness functions F(x) corresponding to the four operating modes respectively. The formulas of the fitness functions are as follows:

[0021] G(x) = min[F * (x), F e (x)]

[0022]

[0023] In the above formula, F * is the economic objective function, where * is one of the operating modes A, B, C or D; x is the optimization variable, and x represents the configuration capacity of the energy units in the self-consistent energy system; F e is the environmental protection objective function; η ea is the curtailment rate, η ea_max is the maximum curtailment rate, η sc is the self-consistency rate, η sc_max is the maximum self-consistency rate;

[0024] Step 6): Solve the fitness functions under the four operating modes by using a genetic algorithm with elitist strategy and non-dominated sorting improved by a population-guided crossover method to obtain the optimized results of the capacity configuration of the energy units under the corresponding operating modes.

[0025] Furthermore, the control methods of the self-consistent energy system are divided into the following working conditions:

[0026] Working condition 1: The power grid has the power supply capacity, the new energy output is greater than the load energy demand, and the energy storage unit has the consumption capacity. The electric energy generated by the new energy is consumed by the load and the energy storage unit together;

[0027] Working condition 2: The power grid has the power supply capacity and the consumption capacity, and the new energy output is greater than the load energy demand. The energy storage unit does not have the consumption capacity. The electric energy generated by the new energy is consumed by the power grid and the load together;

[0028] Working condition 3: The power grid has the power supply capacity but does not have the consumption capacity, the new energy output is greater than the load energy demand, the energy storage unit does not have the consumption capacity, the load consumes the electric energy generated by the new energy, and the redundant energy is curtailed;

[0029] Working condition 4: The power grid has the power supply capacity, the new energy output is less than the load energy demand, and the energy storage unit has the power supply capacity. The load energy demand is jointly guaranteed by the new energy and the energy storage unit;

[0030] Working condition 5: The power grid has the power supply capacity, the new energy output is less than the load energy demand, and the energy storage unit does not have the power supply capacity. The load energy demand is jointly guaranteed by the power grid and the new energy;

[0031] Operating condition 6: The power grid does not have the power supply capacity, the new energy output is less than the load energy demand, and the energy storage unit has the power supply capacity. The new energy and the energy storage unit cooperate to ensure the load energy consumption;

[0032] Operating condition 7: The power grid does not have the power supply capacity, the new energy output is less than the load energy demand, and the energy storage unit does not have the power supply capacity. The new energy and the diesel power generation unit cooperate to ensure the load energy consumption;

[0033] Operating condition 8: The power grid does not have the power supply capacity, the new energy output is greater than the load energy demand, and the energy storage unit has the consumption capacity. The load and the energy storage unit cooperate to consume the electric energy generated by the new energy;

[0034] Operating condition 9: The power grid does not have the power supply capacity, the new energy output is greater than the load energy demand, the energy storage unit does not have the consumption capacity, the load consumes the new energy electric energy, and the redundant electric energy is abandoned;

[0035] Operating mode A includes operating condition 6, operating condition 7, operating condition 8 and operating condition 9;

[0036] Operating mode B includes operating condition 1, operating condition 3, operating condition 4, operating condition 5 and operating condition 7;

[0037] Operating mode C includes operating condition 1, operating condition 3, operating condition 4 and operating condition 5;

[0038] Operating mode D includes operating condition 1, operating condition 2, operating condition 3, operating condition 4 and operating condition 5.

[0039] Furthermore, in step 4), the economic objective function expression is as follows:

[0040]

[0041] In the above formula: F1, F2, F3, F4, F5, F6 and F7 are the investment cost, operation and maintenance cost, fuel cost, power purchase cost, power sale cost, replacement cost and residual value cost of the self-consistent energy system respectively; r is the discount rate; I, I wpv and I es are the operation years, new energy replacement years and energy storage unit replacement years of the self-consistent energy system respectively; P w , P pv , E es are the configuration capacities of wind power, photovoltaic and energy storage units respectively; C inv_w , C inv_pv , C inv_es are the unit capacity purchase cost coefficients of wind power, photovoltaic and energy storage units respectively; P dg,t is the diesel power generation power at the t-th moment; α, β, γ are the diesel consumption related coefficients, taking 0.00011, 0.1801 and 6 respectively; P buy,t and P sell,tare the electricity purchase and sale powers at the t-th moment; C buy,t and C sell,t are the electricity purchase and sale prices at the t-th moment; α w 、α pv 、α es are the annual average operation and maintenance cost coefficients of wind power, photovoltaic power, energy storage, and diesel generators respectively; β w 、β pv 、β es are the proportion coefficients of the remaining salvage value cost after the system reaches its operation life to the acquisition cost of wind power, photovoltaic power, and energy storage units respectively;

[0042] The economic objective relationships corresponding to the four operation modes are as follows:

[0043]

[0044] In the above formula, F A 、F B 、F C 、F D correspond to the economic objective functions of operation mode A, operation mode B, operation mode C, and operation mode D respectively;

[0045] The expression of the environmental protection objective function is as follows:

[0046]

[0047] In the above formula, F e is the environmental protection objective function, specifically referring to the annual average emissions of greenhouse and polluting gases of the self-consistent energy system; are the emissions of greenhouse and polluting gases per unit of electricity of the power grid and diesel generators respectively; are the emissions of carbon oxides, nitrogen oxides, and sulfur compounds per unit of electricity of the power grid respectively; are the emissions of carbon oxides, nitrogen oxides, and sulfur compounds per unit of electricity of the diesel generator respectively;

[0048] The expression of the curtailment rate is as follows:

[0049]

[0050] In the above formula, Q n_e is the power generation of the wind and solar new energy units consumed in the electricity consumption of the self-consistent energy system load; Q n_eall is the total power generation of the wind and solar new energy units of the self-consistent energy system;

[0051] The expression of the self-consistency rate is as follows:

[0052]

[0053] In the above formula, Q loadis the total annual power consumption of the energy-consuming unit.

[0054] Furthermore, in the said step 4), the constraint conditions are as follows:

[0055] (1) The real-time power balance constraint expression is as follows:

[0056] P w,t +P pv,t +P es_ch,t +P es_dis,t +P dg,t +P buy,t -P sell,t =P load,t

[0057] In the above formula, P load,t is the load power at the t-th moment, P es_dis,t and P es_ch,t are the discharge power and charge power of the energy storage unit at the t-th moment respectively;

[0058] (2) The new energy power generation constraint expression is as follows:

[0059]

[0060] In the above formula, P w_min and P pv_min are the lower threshold values of the output powers of the wind turbine and the photovoltaic array respectively; P w_max and P pv_max are the upper threshold values of the output powers of the wind turbine and the photovoltaic array respectively;

[0061] (3) The energy storage unit power constraint expression is as follows:

[0062]

[0063] In the above formula, SOC max and SOC min represent the upper and lower limit constraint thresholds of the state of charge of the energy storage unit respectively; P es_max and P es_min are the upper and lower limit thresholds of the charge and discharge powers of the energy storage unit;

[0064] (4) The curtailment rate constraint expression is as follows:

[0065] η ea ≤η ea_max

[0066] In the above formula, η ea_max is the maximum acceptable curtailment rate;

[0067] (5) The self-consistency rate constraint expression is as follows:

[0068] ηsc_min ≤ η sc

[0069] In the above formula, η sc_min is the minimum acceptable self - consistency rate.

[0070] Furthermore, in the said step 6), the specific steps of solving the fitness function under four operation modes by using the genetic algorithm with elitist strategy and non - dominated sorting improved by population - guided crossover method are as follows:

[0071] 1) System initialization: Input the real - time data of hourly sunlight, temperature, wind speed, and load of the scene, and set the basic parameters, configuration elements, and constraint conditions of the upper and lower limits of the capacity of the algorithm;

[0072] 2) Initialize the population: Randomly generate an initial population for the optimization target space and start the iteration count;

[0073] 3) Perform non - dominated sorting on the population according to the constraint conditions, and calculate the objective function values of each individual in the population through guided crossover and mutation;

[0074] 4) Judge whether the population rank classification and crowding degree calculation are completed;

[0075] 5) If the calculation in the above step 4 is not completed, continue to carry out fast non - dominated sorting, stratify the individuals in the population, and by comparing the domination and non - domination relationships between individuals, mark the non - dominated individual population as the first - level non - dominated layer, ignore the marked individuals, and perform the domination and non - domination relationship sorting between individuals again until the population is stratified;

[0076] 6) Calculate the crowding density of individuals within the same rank;

[0077] 7) Determine the individual with the optimal objective value by the tournament selection method;

[0078] 8) Generate the offspring population by using guided crossover and mutation operations. The offspring population is affected by the optimal population of the parent generation, and the evolution direction approaches the group - optimal direction;

[0079] 9) Merge the initial population and the offspring population and calculate the objective function value;

[0080] 10) Perform fast non - dominated sorting on the newly merged population by using the method in the above step 5;

[0081] 11) Select individuals to generate a new generation population;

[0082] 12) Judge whether the number of iterations meets the termination condition. If it meets, end the operation and output the solution set; if not, increase the number of iterations and jump back to the above step 3 again.

[0083] Furthermore, the guided crossover method is as follows:

[0084] a) If the parent population P1 is superior to P2, their generated populations are c1 and c2 respectively.

[0085] The gene difference of the parent population is set as follows:

[0086] cp = P1 - P2

[0087] The same genes of the parent population are set as follows:

[0088]

[0089] b) The evolutionary directions of the offspring populations are set as follows:

[0090]

[0091] In the above formula, K, H1, H2, R1, R2, R3, and R4 are set parameters, where K takes the value of 0.8, H1 takes the value of 1.5, H2 takes the value of 0.5, and R1, R2, R3, and R4 are random numbers between 0 and 1;

[0092] c) The offspring populations are generated as follows:

[0093]

[0094] The beneficial effects of the configuration method given by the present invention are as follows:

[0095] 1. It has the value of technology promotion and good practicability: The configuration method realizes the self - sufficiency and efficient utilization of energy in the transportation system by constructing a self - consistent energy system adapted to different traffic scenarios. This innovation not only solves the problem that traditional transportation energy supply highly depends on fossil energy, but also improves energy utilization efficiency and reduces carbon emissions; It has a wide range of applications. For various traffic scenarios such as no - network, weak - network, and strong - network, corresponding operation modes and optimal configuration methods are proposed. This enables the method to be widely applied to different types of transportation places, such as highways, stations, wharves, etc., with high generality and flexibility; It has a high degree of intelligence. During the solution process, by coordinating the energy demands and supply situations of each unit of "source - network - load - storage", the intelligent and automated operation of the system is realized, improving the stability and reliability of the optimal configuration method.

[0096] 2. Have economic promotion value: effectively save system costs. By optimizing the capacity allocation of new energy and energy storage units, this method can maximize the utilization of renewable energy and reduce dependence on traditional energy. This helps to reduce the energy costs of the transportation system and improve economic efficiency; have good long-term benefits. Combined with long-term operation, as the utilization rate of renewable energy increases and energy costs decrease, this method not only brings significant long-term benefits but also directly reduces the power grid power consumption cost. In addition, in strong power grid scenarios such as selling surplus electricity back to the grid, it can also significantly increase the additional income of the system.

[0097] 3. Have environmental friendly value: directly reduce carbon emissions. By using renewable energy and reducing dependence on traditional energy, this method can significantly reduce the carbon emissions of the transportation system and alleviate environmental problems such as climate warming; promote sustainable development. This method adheres to the concept of sustainable development. By achieving self-sufficiency and efficient utilization of energy in the transportation system, it promotes resource conservation and environmental protection, which helps to drive the green transformation and sustainable development of the transportation industry.

[0098] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Description of the drawings:

[0099] Figure 1 It is the topological architecture diagram of the self-consistent energy system in the embodiment;

[0100] Figure 2 is Figure 1 the schematic diagram of the energy flow of the self-consistent energy system in

[0101] Figure 3 is Figure 1 the schematic diagram of the management and control architecture of the self-consistent energy system in Specific embodiments:

[0102] This embodiment proposes a self-consistent energy system for typical transportation scenarios, aiming at areas without power grids, breaking the current situation of scarce traditional energy supply, and completely realizing green power supply; for strong and weak power grid areas, when the natural endowment of renewable energy in the transportation scenario is fully utilized, the transportation scenario can be transformed from an energy consumption end to an energy production end and an external service end in the region.

[0103] First, construct as Figure 1The topological architecture shown clarifies the main components and interrelationships of the system. To cover the feasibility of the self-consistent energy system in all traffic scenarios, in three typical scenarios of strong network, weak network, and no network in the traffic system, the physical terminal forms of the self-consistent energy system are defined as four key elements: "source-network-load-storage". Among them, the "source" includes new energy power sources and backup power sources. The new energy power source is the energy conversion of traffic assets, and photovoltaic, wind power and other power generation units are constructed according to local conditions, which is the main electric energy source for the integrated development of energy and transportation; the backup power source is the necessary diesel power generation unit for traffic scenarios. When the system loses power, "diesel" provides power guarantee for important loads such as first and second levels. The "network" includes the interconnected microgrid of the self-consistent system and the power grid. The interconnected microgrid of the self-consistent system uses primary energy and secondary communication networks to connect the four key elements within the system; in strong and weak network scenarios, the power grid can not only supply energy for the self-consistent system, but also assist the self-consistent system to achieve green power consumption in specific scenarios. The "load" includes conventional loads and traffic volume loads. The conventional load is the general energy demand load for traffic scenarios, such as monitoring, communication, lighting, etc.; the traffic volume load is the unique periodic and regular load in highway scenarios, such as electric, battery-swapping vehicles, locomotives, ships and other vehicle loads. The "storage" is an electrochemical energy storage unit, which can not only achieve green power consumption, but also reduce the dependence on external energy through energy time-shifting in the case of no external power supply guarantee.

[0104] Secondly, combined with the designed typical traffic scenarios, determine the corresponding operation modes. According to the typical scenarios of the self-consistent system and the characteristics of the power grid form and architecture in the region, combined with the power supply stability characteristics and interactive characteristics of the "network", in this embodiment, the self-consistent energy system is summarized into the following four operation modes:

[0105] Operation mode A: This mode operates in an off-grid scenario. The main sources of electric energy in the scenario are new energy and energy storage units. However, when the power supply is insufficient, the diesel power generation unit provides power guarantee for important loads. The energy flow diagram of this mode is as shown in Figure 2 (a) in the figure, which is suitable for traffic places such as traction stations, tunnels, management centers, etc. in remote off-grid areas.

[0106] Operation mode B: This mode operates in a weak-grid scenario. The power grid can only provide unidirectional power supply. Due to the weak grid framework, the power supply stability is poor, and there may be situations such as time-limited power supply, power rationing or temporary power outage. When the power supply is insufficient, it is necessary to be guaranteed by the energy storage system or diesel power generation. The energy flow diagram of this mode is as shown in Figure 2 (b) in the figure, which is suitable for traffic places such as docks, toll stations, service areas, management centers, etc. in remote areas in the northwest and areas with frequent natural disasters in the east and south.

[0107] Operation mode C: This mode operates in a strong-grid scenario. The power grid can only provide unidirectional power supply, but the power supply stability is good. When the power supply of new energy and energy storage units is insufficient, the power grid can provide real-time guarantee. The energy flow diagram of this mode is as shown inFigure 2 As shown in Figure (c), it can be adapted to most transportation sites in China, such as docks, traction stations, stations, service areas, toll stations, management centers, etc.

[0108] Operation Mode D: This mode operates in a strong network scenario. New energy generation guarantees the energy consumption of loads and energy storage in sequence. The power grid can not only supply power but also absorb additional green electricity. The energy flow diagram of this mode is as Figure 2 shown in Figure (d), and it can be adapted to transportation sites such as docks, traction stations, stations, service areas, toll stations, etc. in developed provinces in the east and central regions.

[0109] Again, according to the operation modes in the above different scenarios, the control methods in different operation modes are given respectively, specifically as Figure 3 shown. The design of the control method takes into account the coordinated cooperation of each unit of the "source-grid-load-storage" in the adapted scenario, takes into account the reasonable utilization of green electricity, minimizes the use of non-new energy generation to reduce carbon and pollutant emissions, and ensures energy balance. Operation Mode A includes Working Conditions 6, 7, 8, and 9; Operation Mode B includes Working Conditions 1, 3, 4, 5, and 7; Operation Mode C includes Working Conditions 1, 3, 4, and 5; Operation Mode D includes Working Conditions 1, 2, 3, 4, and 5.

[0110] Finally, by constructing energy units and optimizing the target model, different scenarios use different fitness function solving methods to solve the configuration capacity of the energy units in the self-consistent system.

[0111] The optimization configuration of the above-mentioned constructed self-consistent energy system is configured and optimized according to the following content:

[0112] 1. Conduct energy unit modeling, specifically as follows:

[0113] Modeling of wind power generation unit. The output power is related to the wind speed, and its real-time output power expression is as follows:

[0114]

[0115] In the above formula, P w,t and P wr are the real-time output power and rated power of the fan respectively; v is the real-time wind speed; v ci , v r , v co are the cut-in, rated, and cut-out wind speeds of the fan respectively.

[0116] Modeling of photovoltaic power generation unit. The output power is related to the light intensity and regional temperature of the use environment. The real-time output power expression of the photovoltaic is as follows:

[0117]

[0118] In the above formula, P pv,t and P STC are the real-time output power of the photovoltaic array and the rated output power under standard conditions, respectively; f pv is the power degradation coefficient; G c,t is the real-time solar irradiance at the operating point; G STC is the solar irradiance under standard conditions; k is the power temperature coefficient; T c,t is the temperature at the operating point at time t; T STC is the temperature under standard conditions.

[0119] The modeling of the energy storage unit, and its real-time state of charge and time expression are as follows:

[0120]

[0121] In the above formula, SOC t and SOC t-1 are the state of charge of the energy storage at times t and t-1, respectively; P es,t is the real-time state of the energy storage. When it is less than 0, it means the energy storage unit is charging. When it is greater than 0, it means the energy storage unit is discharging; η c and η d are the real-time charging and discharging powers of the energy storage system, respectively; δ is the self-discharge rate of the energy storage; T is the working duration in the charging or discharging state.

[0122] 2. Optimization objective modeling:

[0123] For the self-consistent system in different scenarios, comprehensive evaluation objectives and constraint objectives are used to reasonably plan and configure the capacity. In this embodiment, economic and environmental protection objectives are used as evaluation objectives, and system objectives are used as constraint objectives. The constraint objective is used as a penalty function to jointly form a fitness function with the evaluation objective function to optimize the energy unit. Different constraint objectives are adopted according to different scenario characteristics, and the whole scenario is analyzed in combination with the evaluation objective.

[0124] Regarding the economic objective. The economic objective of the self-consistent system is to minimize the annualized cost over the entire life cycle. The whole scenario includes investment and construction, operation and maintenance, fuel purchase, electricity purchase, electricity sale, replacement, and residual value costs. The discount rate method is used to convert future payments into present value payments to overall the total system cost. The specific expression is as follows:

[0125]

[0126] In the above formula: F1, F2, F3, F4, F5, F6, and F7 are the investment cost, operation and maintenance cost, fuel cost, electricity purchase cost, electricity sale cost, replacement cost, and residual value cost of the self-consistent energy system, respectively; r is the discount rate; I, I wpv and I es are the operating years, new energy replacement years, and energy storage unit replacement years of the self-consistent energy system, respectively; Pw 、P pv 、E es are the configured capacities of the wind power, photovoltaic, and energy storage units respectively; C inv_w 、C inv_pv 、C inv_es are the purchase cost coefficients per unit capacity of the wind power, photovoltaic, and energy storage units respectively; P dg,t is the diesel power generation at the t-th moment; α, β, and γ are the diesel consumption related coefficients, taking 0.00011, 0.1801, and 6 respectively; P buy,t and P sell,t are the purchased and sold electric powers at the t-th moment respectively; C buy,t and C sell,t are the purchase and sale electricity prices at the t-th moment respectively; α w 、α pv 、α es are the annual average operation and maintenance cost coefficients of the wind power, photovoltaic, energy storage, and diesel generator respectively; β w 、β pv 、β es are the proportion coefficients of the remaining salvage value cost to the purchase cost of the wind power, photovoltaic, and energy storage units after the system reaches the operation life respectively.

[0127] In addition to the construction, operation and maintenance, replacement, and salvage value costs, the operation modes A and B also include the fuel purchase cost; the operation modes B, C, and D also include the electricity purchase cost; the operation mode D also includes the electricity sale cost. The economic objective functions of the four operation modes are expressed as F A 、F B 、F C and F D , and the specific expressions are as follows:

[0128]

[0129] Regarding the environmental protection objective. The environmental protection objective is to minimize greenhouse and pollutant gas emissions. When the diesel generator or the power grid is required to supply energy in a coordinated manner, greenhouse and pollutant gas emissions such as carbon oxides, nitrogen oxides, and sulfides will be directly or indirectly generated. In this embodiment, the expression of the environmental protection objective function is as follows:

[0130]

[0131] In the above formula, F e is the environmental protection objective function, specifically referring to the annual average emissions of greenhouse and pollutant gases of the self-consistent energy system; are the emissions of greenhouse and pollutant gases per unit of electricity of the power grid and the diesel generator respectively; are the emissions of carbon oxides, nitrogen oxides, and sulfides per unit of electricity of the power grid respectively; They are the carbon oxide, nitrogen oxide, and sulfide emissions per kilowatt-hour of the diesel generator respectively.

[0132] Regarding the system objectives. The system objectives include the curtailment rate and the self-consistency rate objectives. When the green power output is greater than the load energy consumption and there is no consumption space, the self-consistent system needs to curtail power. The expression for the curtailment rate is as follows:

[0133]

[0134] In the above formula, Q n_e is the power generation of the wind and solar new energy units consumed in the load power consumption of the self-consistent energy system; Q n_eall is the total power generation of the wind and solar new energy units in the self-consistent energy system.

[0135] By comparing the consumption of green power energy and the total energy consumption in the load power consumption of the self-consistent system, it can reflect the ability of the energy in the scenario not to rely on external power supply, that is, the self-consistency ability of the system. When the self-consistency rate is 1, the system can not only fully achieve energy self-sufficiency but also may have the ability to supply power externally. The expression for the self-consistency rate is as follows:

[0136]

[0137] In the above formula, Q load is the total annual power consumption of the energy-consuming unit.

[0138] 3. Set the constraint conditions:

[0139] Under all scenarios, the energy of each unit of the system source-network-load-storage is subject to the following real-time power balance constraint:

[0140] P w,t +P pv,t +P es_ch,t +P es_dis,t +P dg,t +P buy,t -P sell,t =P load,t

[0141] In the above formula, P load,t is the load power at the t-th moment, and P es_dis,t , P es_ch,t are the discharge power and charge power of the energy storage unit at the t-th moment respectively.

[0142] Energy unit constraint. The real-time power generation of new energy sources such as wind and solar is related to the deployed capacity and is subject to upper limits on output due to factors such as site and weather. The constraint conditions for the power generation of wind and solar are as follows:

[0143]

[0144] In the above formula, P w_min and Ppv_min are the lower threshold values of the output powers of the fan and the photovoltaic array respectively; P w_max and P pv_max are the upper threshold values of the output powers of the fan and the photovoltaic array respectively.

[0145] The charging and discharging powers of the energy storage unit are related to the installed capacity. Considering the safety and availability of the charging and discharging processes of the energy storage unit and avoiding overcharging and over-discharging from affecting the battery life, the real-time state of charge and the charging and discharging powers of the energy storage unit should satisfy the following constraint conditions:

[0146]

[0147] In the above formula, SOC max and SOC min represent the upper and lower limit constraint thresholds of the state of charge of the energy storage unit respectively; P es_max and P es_min are the upper and lower threshold values of the charging and discharging powers of the energy storage unit.

[0148] System objective constraint. The curtailment rate should satisfy the following constraint conditions:

[0149] η ea ≤η ea_max

[0150] In the above formula, η ea_max is the maximum acceptable curtailment rate.

[0151] The self-consistency rate should satisfy the following constraint conditions:

[0152] η sc_min ≤η sc

[0153] In the above formula, η sc_min is the minimum acceptable self-consistency rate.

[0154] 4. Specific optimization configuration:

[0155] (1) Design of typical scenarios:

[0156] During the solution process, considering the influence of the constraint objectives on the evaluation objectives under different scenarios, a fitness function F(x) is constructed to facilitate the solution of the energy units of the self-consistent energy system. The specific form of the fitness function is as follows:

[0157] G(x) = min[F * (x), F e (x)]

[0158]

[0159] In the above formula, F *is the economic objective function, where * is one of operating modes A, B, C, or D; x is the optimization variable, and x represents the configured capacity of energy units within the self - consistent energy system; F e is the environmental protection objective function; η ea is the curtailment rate, η ea_max is the maximum curtailment rate, η sc is the self - consistency rate, η sc_max is the maximum self - consistency rate.

[0160] Grid - free scenario (operating mode A). In this scenario, the main objective of operating mode A is to achieve the self - consistency of green electricity in the system. This mode configures a large number of wind power generation, photovoltaic power generation, and energy storage units to ensure energy supply. Therefore, the self - consistency rate is used as a constraint objective, and the evaluation objective values corresponding to self - consistency rates of 100% and below are solved respectively to determine the configured capacity.

[0161] Weak grid scenario (operating mode B). In this scenario, considering the intermittent power supply unit of grid equipment in extreme cases. The power supply capacity of the grid randomly decays irregularly. Comparing modes A and C, it is advisable to use the curtailment rate as a constraint objective, and the evaluation objective values corresponding to curtailment rates of zero and above are solved respectively to determine the configured capacity.

[0162] Strong grid scenario (operating mode C). In this scenario, operating mode C also aims to achieve the self - consistency of green electricity. However, in this scenario, due to the strong support capacity of the grid, compared with operating mode A, the energy supply of the system is more stable. Similarly, it is advisable to use the self - consistency rate as a constraint objective, and the evaluation objective values corresponding to self - consistency rates of 100% and below are solved respectively to determine the configured capacity.

[0163] Strong grid scenario (operating mode D). In this scenario, the system can increase the system revenue by selling the redundant green electricity, that is, the curtailed electricity, to the grid. In this way, the system can maximize the utilization of the output of wind and solar power generation units, thereby significantly reducing the annualized cost. Especially when the curtailment rate (which is the grid - connection rate at this time) reaches 100%, the system obtains a large amount of revenue through selling surplus electricity to the grid. Therefore, it is advisable to use the curtailment rate as a constraint objective, and the evaluation objective values corresponding to curtailment rates of 100% and below are solved respectively to determine the configured capacity.

[0164] (2) Solving the configured capacity:

[0165] By using an intelligent algorithm to solve the fitness function, the present invention improves the genetic algorithm with elitist strategy non - dominated sorting by using the population - guided crossover method to optimize the configured capacity of energy units in the self - consistent energy system. The specific steps are as follows:

[0166] 1) System initialization: Input the real-time data of hourly illumination, temperature, wind speed, and load in the scene, and set the basic parameters, configuration elements, and constraint conditions of the upper and lower limits of the capacity.

[0167] 2) Initialize the population: Randomly generate the initial population for the optimization target space and start the iteration count.

[0168] 3) Perform non-dominated sorting on the population according to the constraint conditions, and calculate the objective function values of each individual in the population through guided crossover and mutation.

[0169] 4) Determine whether the population rank classification and crowding degree calculation are completed.

[0170] 5) If the calculation in step 4 above is not completed, continue to perform fast non-dominated sorting, stratify the individuals in the population, compare the dominance and non-dominance relationships between individuals, mark the non-dominated individual population as the first non-dominated layer, ignore the marked individuals, and perform the dominance and non-dominance relationship sorting between individuals again until the population is stratified.

[0171] 6) Calculate the crowding density of individuals within the same rank.

[0172] 7) Determine the individual with the optimal objective value by the tournament selection method.

[0173] 8) Generate the offspring population by using guided crossover and mutation operations. The offspring population is affected by the optimal population of the parent generation, and the evolution direction approaches the group optimal direction.

[0174] 9) Merge the initial population and the offspring population, and calculate the objective function value.

[0175] 10) Perform fast non-dominated sorting on the newly merged population by using the method in step 5 above.

[0176] 11) Select individuals to generate a new generation of population.

[0177] 12) Determine whether the number of iterations meets the termination condition. If it meets, end the operation and output the solution set; if it does not meet, increase the number of iterations and jump back to step 3 above again.

[0178] The specific guided crossover method mentioned above is as follows:

[0179] a) If the parent population P1 is better than P2, the populations it generates are c1 and c2 respectively.

[0180] The gene differences of the parent population are set as follows:

[0181] cp = P1 - P2

[0182] The genes of the parent population being the same are set as follows:

[0183]

[0184] b) The evolutionary directions of the offspring populations are set as follows:

[0185]

[0186] In the above formula, K, H1, H2, R1, R2, R3, and R4 are set parameters, where K takes a value of 0.8, H1 takes a value of 1.5, H2 takes a value of 0.5, and R1, R2, R3, and R4 are random numbers between 0 and 1;

[0187] c) The offspring populations are generated as follows:

[0188]

[0189] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Other modifications or equivalent replacements made by those of ordinary skill in the art to the technical solutions of the present invention shall be covered by the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.

Claims

1. A configuration method for a self-consistent energy system in a traffic scenario, characterized in that, It includes the following steps: Step 1): Construct a self-consistent energy system, which includes an energy unit and a load. The energy unit includes a wind power generation unit, a photovoltaic power generation unit, and an energy storage unit; Step 2): The self-consistent energy system is set with four operating modes, namely operating mode A, operating mode B, operating mode C, and operating mode D. Operating mode A is used for the self-consistent energy system in the off-grid state. Operating mode B is used for the self-consistent energy system connected to a weak power grid. Both operating mode C and operating mode D are used for the self-consistent energy system connected to a strong power grid. Among them, the power grid in operating mode C only supplies power unidirectionally, and the power grid in operating mode D can both supply power and absorb the abandoned electricity of new energy. The self-consistent energy systems in operating mode A and operating mode B both include a diesel power generation unit; Step 3): Respectively conduct target modeling on the capacity configurations of the wind power generation unit, the photovoltaic power generation unit, and the energy storage unit; The modeling expression of the wind power generation unit is as follows: In the above formula, P w,t and P wr are the real-time output power and rated power of the fan respectively; v is the real-time wind speed; v ci , v r , v co are the cut-in, rated, and cut-out wind speeds of the fan respectively; The modeling expression of the photovoltaic power generation unit is as follows: In the above formula, P pv,t and P STC are the real-time output power of the photovoltaic array and the rated output power under standard conditions, respectively; f pv is the power degradation coefficient; G c,t is the real-time solar irradiance at the operating point; G STC is the solar irradiance under standard conditions; k is the power temperature coefficient; T c,t is the temperature at the operating point at time t; T STC is the temperature under standard conditions. The modeling expression of the energy storage unit is as follows: In the above formula, SOC t and SOC t-1 are the state of charge of the energy storage at times t and t-1 respectively; P es,t is the real-time state of the energy storage. When it is less than 0, it means the energy storage unit is charging. When it is greater than 0, it means the energy storage unit is discharging; η c and η d are the real-time charging and discharging powers of the energy storage system respectively; δ is the self-discharge rate of the energy storage; T is the working duration in the charging or discharging state; Step 4): Optimize the target modeling and give the constraint conditions; In all four operating modes, economic goals and environmental protection goals are used as evaluation goals. Self-consistency rate is used as a constraint goal in both operating mode A and operating mode C, and abandoned electricity rate is used as a constraint goal in both mode B and mode D; Step 5): Respectively construct the adaptation function F(x) corresponding to the four operating modes. The formula of the adaptation function is as follows: G(x) = min[F * (x), F e (x)] In the above formula, F * is the economic objective function, where * is one of operating modes A, B, C, or D; x is the optimization variable, and x represents the configuration capacity of energy units within the self-consistent energy system; F e is the environmental protection objective function; η ea is the curtailment rate, η ea_max is the maximum curtailment rate, η sc is the self-consistency rate, η sc_max is the maximum self-consistency rate; Step 6): Use the genetic algorithm with elitist strategy and non-dominated sorting improved by the population-guided crossover method to solve the adaptation functions in the four operating modes, and obtain the optimized results of the capacity configurations of the energy units in the corresponding operating modes.

2. The configuration method of a self-consistent energy system for traffic scenarios according to claim 1, wherein: The control methods of the self-consistent energy system are divided into the following working conditions: Working condition 1: The power grid has the power supply capacity, the output of new energy is greater than the energy demand of the load, and the energy storage unit has the absorption capacity. The electric energy generated by new energy is absorbed by the load and the energy storage unit together; Working condition 2: The power grid has the power supply capacity and absorption capacity, and the output of new energy is greater than the energy demand of the load. The energy storage unit does not have the absorption capacity. The electric energy generated by new energy is absorbed by the power grid and the load together; Working condition 3: The power grid has the power supply capacity but does not have the absorption capacity, the output of new energy is greater than the energy demand of the load, the energy storage unit does not have the absorption capacity, the load absorbs the electric energy generated by new energy, and the redundant energy is abandoned; Working condition 4: The power grid has the power supply capacity, the output of new energy is less than the energy demand of the load, and the energy storage unit has the power supply capacity. The energy of the load is guaranteed by new energy and the energy storage unit in cooperation; Working condition 5: The power grid has the power supply capacity, the output of new energy is less than the energy demand of the load, and the energy storage unit does not have the power supply capacity. The energy of the load is guaranteed by the power grid and new energy in cooperation; Working condition 6: The power grid does not have the power supply capacity, the output of new energy is less than the energy demand of the load, and the energy storage unit has the power supply capacity. The energy of the load is guaranteed by new energy and the energy storage unit in cooperation; Working condition 7: The power grid does not have the power supply capacity, the output of new energy is less than the energy demand of the load, and the energy storage unit does not have the power supply capacity. The energy of the load is guaranteed by new energy and the diesel power generation unit in cooperation; Operating condition 8: The power grid does not have the power supply capacity, the new energy output is greater than the load energy demand, the energy storage unit has the consumption capacity, and the load and the energy storage unit jointly consume the electric energy generated by the new energy; Operating condition 9: The power grid does not have the power supply capacity, the new energy output is greater than the load energy demand, the energy storage unit does not have the consumption capacity, the load consumes the new energy electric energy, and the redundant electric energy is abandoned; Operating mode A includes operating conditions 6, 7, 8, and 9; Operating mode B includes operating conditions 1, 3, 4, 5, and 7; Operating mode C includes operating conditions 1, 3, 4, and 5; Operating mode D includes operating conditions 1, 2, 3, 4, and 5.

3. The configuration method of a self-consistent energy system for traffic scenarios according to claim 1, wherein: In the step 4), the expression of the economic objective function is as follows: In the above formula: F1, F2, F3, F4, F5, F6, and F7 are the investment cost, operation and maintenance cost, fuel cost, electricity purchase cost, electricity sale cost, replacement cost, and residual value cost of the self-consistent energy system respectively; r is the discount rate; I, I wpv and I es are the operation years, new energy replacement years, and energy storage unit replacement years of the self-consistent energy system respectively; P w 、P pv 、E es are the configured capacities of wind power, photovoltaic, and energy storage units respectively; C inv_w 、C inv_pv 、C inv_es are the unit capacity purchase cost coefficients of wind power, photovoltaic, and energy storage units respectively; P dg,t is the diesel power generation power at the t-th moment; α, β, and γ are the diesel consumption related coefficients, taking 0.00011, 0.1801, and 6 respectively; P buy,t and P sell,t are the electricity purchase and sale powers at the t-th moment respectively; C buy,t and C sell,t are the electricity purchase and sale prices at the t-th moment respectively; α w 、α pv 、α es are the annual operation and maintenance cost coefficients of wind power, photovoltaic, energy storage, and diesel generator respectively; β w , β pv , β es are the proportion coefficient of the remaining salvage value cost to the acquisition cost of the wind power, photovoltaic and energy storage units respectively after the system reaches the operation life The economic objective relations corresponding to the four operating modes are as follows: In the above formula, F A , F B , F C , F D correspond to the economic objective functions of operation modes A, B, C, and D respectively; The expression of the environmental protection objective function is as follows: In the above formula, F e is the environmental protection objective function, specifically referring to the average annual emissions of greenhouse and polluting gases in the self-consistent energy system; are the emissions of greenhouse and polluting gases per unit electricity of the power grid and diesel generator respectively; are the emissions of carbon oxides, nitrogen oxides, and sulfur oxides per unit electricity of the power grid respectively; are the emissions of carbon oxides, nitrogen oxides, and sulfur oxides per unit electricity of the diesel generator respectively; The expression of the power abandonment rate is as follows: In the above formula, Q n_e is the generated electricity of the wind and solar new energy units consumed in the electricity consumption of the self-consistent energy system load; Q n_eall is the total generated electricity of the wind and solar new energy units of the self-consistent energy system; The expression of the self-consistency rate is as follows: In the above formula, Q load is the total annual power consumption of the energy-using unit.

4. The configuration method of a self-consistent energy system for traffic scenarios according to claim 3, characterized in that: In the step 4), the constraint conditions are as follows: (1) The expression of the real-time power balance constraint is as follows: P w,t +P pv,t +P es_ch,t +P es_dis,t +P dg,t +P buy,t -P sell,t =P load,t In the above formula, P load,t is the load power at the t-th moment, and P es_dis,t , P es_ch,t are the discharge power and the charge power of the energy storage unit at the t-th moment, respectively; (2) The expression of the new energy power generation constraint is as follows: In the above formula, P w_min and P pv_min are respectively the lower threshold values of the output powers of the wind turbine and the photovoltaic array; P w_max and P pv_max are respectively the upper threshold values of the output powers of the wind turbine and the photovoltaic array; (3) The expression of the energy storage unit power constraint is as follows: In the above formula, SOC max and SOC min respectively represent the upper and lower bound constraint thresholds of the state of charge of the energy storage unit; P es_max and P es_min are the upper and lower threshold values of the charging and discharging power of the energy storage unit; (4) The expression of the power abandonment rate constraint is as follows: η ea ≤ η ea_max In the above formula, η ea_max is the maximum acceptable curtailment rate; (5) The expression of the self-consistency rate constraint is as follows: η sc_min ≤ η sc In the above formula, η sc_min is the minimum acceptable self-consistency rate.

5. The configuration method of a self-consistent energy system for traffic scenarios according to claim 1, characterized in that: In the step 6), the specific steps for solving the fitness function under the four operating modes by using the genetic algorithm with elitist strategy and non-dominated sorting improved by the population-guided crossover method are as follows: 1) System initialization: Input the hourly sunlight, temperature, wind speed, and real-time load electricity consumption data of the scenario, and set the basic parameters, configuration elements, and upper and lower limit constraint conditions of the algorithm; 2) Initialize the population: Randomly generate the initial population for the optimization target space and start the iteration count; 3) Perform non-dominated sorting on the population according to the constraint conditions, and calculate the objective function values of each individual in the population through guided crossover and mutation; 4) Judge whether the population rank classification and crowding degree calculation are completed; 5) If the above step 4 calculation is not completed, continue to perform fast non-dominated sorting, stratify the individuals in the population, and mark the non-dominated individual population as the first non-dominated layer by comparing the dominance and non-dominance relationships between individuals. Ignore the marked individuals and perform the dominance and non-dominance relationship sorting between individuals again until the population is stratified; 6) Calculate the crowding density of individuals within the same rank; 7) Determine the individual with the optimal target value by the tournament selection method; 8) Generate the offspring population by using guided crossover and mutation operations. The offspring population is affected by the optimal population of the parent generation, and the evolution direction approaches the group optimal direction; 9) Merge the initial population and the offspring population and calculate the objective function value; 10) Perform fast non-dominated sorting on the newly merged population by using the method in the above step 5; 11) Select individuals to generate a new generation of population; 12) Judge whether the number of iterations meets the termination condition. If it meets, end the operation and output the solution set; if not, increase the number of iterations and jump back to the above step 3 again.

6. The configuration method of a self-consistent energy system for traffic scenarios according to claim 5, characterized in that: The guided crossover method is as follows: a) If the parent population P1 is better than P2, the generated populations are c1 and c2 respectively. The gene difference setting of the parent population is as follows: cp = P1 - P2 The gene identity setting of the parent population is as follows: b) The evolutionary directions of the offspring populations are set as follows: In the above formula, K, H1, H2, R1, R2, R3, and R4 are set parameters, where K takes a value of 0.8, H1 takes a value of 1.5, H2 takes a value of 0.5, and R1, R2, R3, and R4 are random numbers between 0 and 1; c) The offspring populations are generated as follows:

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