Expressway service area electric power dynamic capacity expansion method considering electric vehicle charging load

Abstract

The invention provides a highway service area electric power dynamic capacity expansion method considering an electric vehicle charging load. The method is divided into a long-term scheduling optimization stage and a short-term scheduling optimization stage. In the long-term scheduling optimization stage, on the basis of long-term prediction results of wind-light power generation, daily load and electric vehicle charging load power in a micro-grid in a service area, a target function with minimum grid-connected fluctuation is added on the target basis of economical efficiency and low-carbon performance, and a long-term scheduling optimization plan is preliminarily formulated; and obtaining a power change condition between the service area and the power grid. In the short-term scheduling optimization stage, a short-term scheduling optimization plan is carried out on the basis of a long-term result grid-connected power change and a short-term prediction result of each unit in the system by taking optimal operation economy and minimum grid-connected stability as targets.

Description

Technical Field

[0001] The invention relates to the technical field of electric vehicle charging load prediction, in particular to a method for dynamically expanding the electric power capacity of a highway service area taking into account the electric vehicle charging load. Background Art

[0002] At present, most highway service areas are located in remote areas with small and scattered loads, and there is great potential for the development of wind and solar renewable resources. When a high proportion of high power density electric vehicle charging loads are connected, it will affect the stable operation of the service area power grid system. Measures such as long-distance power grid construction or transformer expansion will increase the cost of highway load power supply, make operation and maintenance difficult, and have low cost performance. Using the remaining idle land resources such as building roofs and parking sheds to access renewable energy such as wind and light, combined with energy storage systems and V2G technology for efficient energy management, is conducive to the local consumption of new energy, reducing carbon emissions while improving energy utilization; it is conducive to the dynamic expansion of the service area power grid, reducing the impact of high-power charging piles on the service area regional power grid, and accelerating the electrification of transportation.

[0003] Existing technologies and problems:

[0004] 1. Most highway service areas are located in remote areas. When large-scale electric vehicles are connected to the service areas, the original designed regional power distribution capacity will be exceeded. Make full use of other remaining idle land resources such as building roofs, parking lots or charging pile carports to build distributed wind, photovoltaic power generation and other distributed renewable energy facilities, give priority to local consumption of new energy, store and utilize surplus energy, and use the power grid as a backup energy source to solve the problem of insufficient power distribution capacity in service areas.

[0005] 2. The existing use of historical data and statistical models to infer future charging load demand ignores the charging demand and feature analysis in different scenarios. The prediction results are only of reference value. In actual application, electric vehicle users have different preferences for distance, time and cost in charging demand, which is uncertain and requires the cooperation of precise energy scheduling optimization strategies. In the highway scenario, the time-sharing charging price of electric vehicles is optimized to guide the charging behavior of vehicles, while achieving the goals of reducing carbon emissions during the operation of electric vehicles and maximizing the energy utilization efficiency of users. This type of method for guiding the charging behavior of electric vehicles in highway service areas still has the randomness of users' subjective will.

[0006] 3. At present, the energy dispatch optimization method of the service area microgrid fails to take into account the high proportion and high power density of electric vehicle charging loads, and only optimizes the dispatch based on objective functions such as economy and low-carbon performance indicators. This type of dispatch method cannot adapt to the existing large-scale electric vehicle access service area load, resulting in an increase in the total load of the service area and greatly enhanced randomness. Using other energy storage equipment such as battery energy storage in the service area to dynamically expand the service area power grid will help improve the charging capacity and grid-connection friendliness of the high-speed service area.

[0007] 4. When the NSGA type multi-objective optimization algorithm is used to solve the multi-objective energy scheduling optimization model of the grid-connected microgrid containing solar-storage-hybrid-charging, there is a problem that it is difficult to find a feasible solution due to conflicts between objectives and complex decision variables. Summary of the invention

[0008] In view of this, the purpose of the present invention is to provide a method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles, so as to solve the problem that the original service area exceeds the rated power transformation and distribution working capacity due to the access of the charging load of electric vehicles.

[0009] To achieve the above object, the present invention adopts the following technical solution: a method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles, comprising the following steps:

[0010] Step 1: Identify the service areas where power capacity needs to be dynamically expanded due to the planned installation of additional electric vehicle charging facilities;

[0011] Step 2: Design a wind-solar-hybrid storage-charging integrated microgrid based on the existing remaining idle land resources in the service area, load, planned charging piles, and substation scale; determine the scale of wind and solar distributed renewable energy, capacity allocation of medium and high power density and energy density of hybrid energy storage, and total energy;

[0012] Step 3: Establish a multi-objective optimization model for dual-time-scale energy dispatch taking into account electric vehicle charging load service areas and friendly grid connection;

[0013] Step 4: Determine a multi-objective optimization algorithm to solve the multi-objective and multi-constraint microgrid energy dispatch optimization model of wind-solar-hybrid storage-charging and determine the optimization solution algorithm;

[0014] Step 5: Obtain long-term forecast results of renewable energy output, daily load, and electric vehicle charging load in the service area;

[0015] Step 6: Determine the long-term dispatch optimization plan model through dispatch planning with the goals of operation economy, low carbon and grid-connected volatility;

[0016] Step 7: Use multi-objective optimization algorithm to solve the long-term scheduling optimization model;

[0017] Step 8: Select a suitable solution to obtain the long-term scheduling optimization plan;

[0018] Step 9: Obtain short-term forecast results of renewable energy output, daily load, and electric vehicle charging load in the service area;

[0019] Step 10: Determine the short-term dispatch optimization planning model with the objectives of economic operation, low carbon and grid-connected volatility;

[0020] Step 11: Solve the long-term scheduling optimization model using a multi-objective optimization algorithm;

[0021] Step 12: Select a suitable solution to obtain a short-term dispatch optimization plan, that is, the service area microgrid energy dispatch optimization plan to be finally executed;

[0022] Step 13: Complete the final dispatch strategy formulation to achieve dynamic expansion of power in the service area.

[0023] In a preferred embodiment, the step 3 comprises the following steps:

[0024] Step 31: Develop a long-term scheduling optimization plan;

[0025] Step 32: Formulate a short-term scheduling optimization plan;

[0026] Step 33: Establish the objective function;

[0027] Step 34: Constraints.

[0028] In a preferred embodiment, step 31 specifically includes: long-term prediction of the system's wind and solar power output, daily load and electric vehicle charging load in the future, preliminary formulation of a long-term scheduling optimization plan for the system, and obtaining an energy interaction plan between the microgrid and the AC distribution network in advance to reduce the impact of the microgrid on the large power grid.

[0029] In a preferred embodiment, the objective function in step 33 is:

[0030]

[0031] Where f OE f CL f G&MG They represent the objective function values ​​of optimal operation economy, minimum carbon emission and minimum grid connection volatility respectively; C MG_load It represents the benefits of directly utilizing distributed renewable energy sources in the microgrid, in CNY; C S C Ab Respectively represent the operating cost of distributed renewable energy and the penalty for wind and solar abandonment; C G&MG CServe They represent the electricity price transaction and service cost from the microgrid to the AC distribution network; C ES represents the operation and maintenance cost of the hybrid energy storage equipment; Represents the operation and maintenance cost of the charging pile.

[0032] In a preferred embodiment, the constraints in step 34 are:

[0033] The electric power balance constraints are as follows:

[0034]

[0035] In the formula, Indicates the output of the energy supply side in the system at time t, in kW; Represents the grid-connected power of the microgrid; Indicates the energy storage output; Indicates the output of the charging pile; Indicates load power changes;

[0036] The output power of new energy cannot exceed the upper and lower power limits:

[0037]

[0038] Where P PV_max , P PV_min , P WT_max , P WT_min Respectively represent the upper and lower output constraints of wind and photovoltaic power generation units, in kW;

[0039] The grid-connected interaction constraints are as follows:

[0040]

[0041] Where P G&MG_max and P G&MG_min Represents the upper and lower limits of the interactive power between the microgrid and the grid, in kW;

[0042] The constraints of hybrid energy storage are as follows:

[0043]

[0044] In the formula Indicates the amount of energy that can be transferred at time t; SOC ESP_max , SOC ESP_min , SOC ESE_max and SOC ESE_min Respectively represent the high power density of hybrid energy storage and SOC upper and lower limits of high energy density; P ESP_max , P ESP_min , PESE_max and P ESE_min Separate hybrid energy storage with medium and high power density and high energy density Upper and lower limits, unit: kW; u ESP_C and u ESE_C Indicates the number of times the charge and discharge state is switched, u ESP_C_max ,u ESP_C_min ,u ESE_C_max and u ESE_C_min Respectively represent the upper and lower limits of the number of times of switching the charge and discharge states;

[0045] Under the constraints of charging piles:

[0046]

[0047] In the formula represents the SOC of the electric vehicle at time t; SOC Car_max The SOC at the end of charging the electric vehicle; represents the charging power of the electric vehicle at time t; P Car_min P Car_max They respectively represent the upper and lower limits of the charging power of the electric vehicle.

[0048] In a preferred embodiment, the step 4 specifically comprises: using the TS-NSGA-II multi-objective optimization algorithm of RL, that is, using a method based on reverse learning and two-step solution to optimize and solve;

[0049] The generation of the initial population individuals of the multi-objective optimization algorithm is random within a given range, so the initial individuals are random; improving the initial population by the reverse learning method can speed up the search for feasible solutions, as shown in formula (7):

[0050]

[0051] Where P 0 P 0 ' are the two initial populations in reverse learning, P U P L are the upper and lower limits of the population within the constraint conditions, and d represents the number of decision variables. After converting the multi-objective algorithm into a single-objective with constraints, reverse learning is used to compare the fitness function values ​​of the two populations before the optimization begins, and the better population is retained as the initial population in the single-objective optimization process. Then, the differential evolution algorithm is used for fast global search to find the feasible solution space, as shown in formula (8):

[0052]

[0053] Where F is a scaling factor parameter in the DE mutation process, x i represents the i-th individual, x bestrepresents the individual with the smallest transformed objective function value in the current population, x r represents the individuals randomly generated during the mutation process, v ri and v bi They represent the two individual mutation directions of individuals towards randomness and the best individual in the current population respectively; finally, when the feasible solution area in the population exceeds k, the population under this single-objective search is retained and used as the initial population in the subsequent NSGA-II multi-objective optimization solution process, and the solution is continued to obtain the final solution.

[0054] Compared with the prior art, the present invention has the following beneficial effects: in view of the strong randomness of electric vehicle charging load and the large power fluctuation range in highway service areas, the problem of the original service area exceeding the rated power distribution capacity due to the access of electric vehicle charging load is solved through high-power and energy-density hybrid energy storage equipment and microgrid energy scheduling optimization plan, and the wind-solar-hybrid storage-charging grid-connected microgrid and reasonable objective functions and constraints are designed, and the multi-objective optimization solution algorithm of process optimization is used to formulate a microgrid energy scheduling plan with friendly grid connection and dual time scales cooperating with each other. The dual time scale optimization scheduling adopts the idea of ​​multi-level coordination and step-by-step refinement, first determines the overall operation strategy of the microgrid and the AC distribution network by formulating a long-term scheduling optimization plan, and then modifies the day-ahead plan according to the short-term scheduling optimization, and formulates an energy scheduling plan with the best economic operation of the microgrid, the lowest carbon emissions, and the lowest grid connection volatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A highway service area microgrid system topology according to a preferred embodiment of the present invention;

[0056] Figure 2 A flowchart of energy dispatch optimization of a microgrid in a service area according to a preferred embodiment of the present invention;

[0057] Figure 3 This is a flowchart of a multi-objective optimization algorithm solution for a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0059] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0060] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0061] refer to Figure 1-3 Aiming at the existing highway service areas with electric vehicle charging needs, the present invention proposes an energy dispatch optimization method for a wind-solar-hybrid storage-charging integrated grid-connected microgrid for expanding the power capacity of the service area and considering friendly grid connection.

[0062] In view of the fact that the existing electric vehicle charging load prediction methods ignore the randomness of charging demand in different scenarios, and the service area fails to consider the impact of the high randomness of electric vehicle charging load on the grid-connected stability of the microgrid, the present invention proposes a grid-friendly dual-time scale energy scheduling optimization strategy, which is divided into two stages: long-term and short-term scheduling optimization. The long-term scheduling optimization stage is based on the long-term prediction results of wind-solar power generation, daily load and electric vehicle charging load power in the microgrid of the service area. On the basis of the goals of economy and low-carbon performance, the objective function of minimizing grid-connected fluctuations is added, and a preliminary long-term scheduling optimization plan is formulated to obtain the power changes between the service area and the power grid. The short-term scheduling optimization stage is based on the long-term results of grid-connected power changes and the short-term prediction results of each unit in the system, and a short-term scheduling optimization plan is carried out with the goal of optimal operation economy and minimum grid-connected stability.

[0063] The number of electric vehicle users increases year by year, and the demand for charging of electric vehicle clusters is growing, especially in highway service areas, where most of the charging is fast charging. Fast charging demand is more random than slow charging, and the power fluctuation range is wider (the average power of highway charging piles is 90kW), and the load in the service area fluctuates widely. In view of the problem of insufficient service area power distribution capacity caused by this situation, the present invention proposes a service area microgrid system with wind-solar-hybrid storage-charging integration containing high power density and high energy density hybrid energy storage, combined with the above-mentioned grid-connected dual-time scale energy optimization scheduling strategy, and uses hybrid energy storage to dynamically expand the power of the service area, saving the expansion cost of the original power distribution.

[0064] In order to solve the problem of slow or difficult solution of NSGA type multi-objective optimization algorithm, the multi-objective model is first converted into a single-objective model with constraints; the reverse learning method and the differential evolution algorithm with global fast search characteristics are used to quickly find a better initial population and some feasible solution areas; finally, these feasible solution areas are used as the initial population of the multi-objective optimization algorithm to continue solving, shortening the solution time.

[0065] The present invention can make the service area distribution network system operate under the original power transformation and distribution design capacity, meet the charging needs of electric vehicles on highways, and also improve the economy, environmental protection, reliability and network connection of the service area microgrid, providing a reference for the transformation of new power systems in highway service areas.

[0066] The specific steps include:

[0067] Step 1: Identify the service areas where dynamic power capacity expansion is required due to the planned installation of additional electric vehicle charging facilities.

[0068] Step 2: Design a wind-solar-hybrid storage-charging integrated microgrid based on the existing remaining idle land resources in the service area, load, planned charging piles, and substation scale. Determine the scale of wind and solar distributed renewable energy, capacity allocation of medium and high power density and energy density of hybrid energy storage, and total energy.

[0069] Step 3: Establish a multi-objective optimization model for dual-time-scale energy scheduling that takes into account electric vehicle charging load service areas and friendly grid connection.

[0070] Step 31: Long-term scheduling optimization plan:

[0071] Most highway service areas are located in remote areas, and the transmission capacity and distribution capacity of the power grid are not sufficient, making it difficult to support large-scale electric vehicle loads and the wide range of random load fluctuations. At the same time, combined with the microgrid's "self-generation and self-use, surplus power to the grid" grid connection mode, it is necessary to formulate an energy interaction plan between the microgrid and the AC distribution network in advance, which is conducive to reducing the grid connection volatility of the service area microgrid during actual operation. Long-term forecasting of the system's wind and solar power output, daily load and electric vehicle charging load in the future, preliminary formulation of the system's long-term scheduling optimization plan, and advance access to the energy interaction plan between the microgrid and the AC distribution network to reduce the impact of the microgrid on the large power grid.

[0072] Step 32: Short-term scheduling optimization plan:

[0073] The results of the long-term dispatch optimization plan are affected by environmental factors, errors between environmental information and actual conditions, etc. The forecasts of wind power and photovoltaic output are not accurate enough, and the charging load of electric vehicles is highly random, resulting in deviations between the long-term power forecast and the actual situation. Therefore, the system's long-term dispatch optimization plan is not applicable to the actual situation on the day to be optimized and can only be used as a reference for the final decision. In order to further reduce the operating costs of microgrids, reduce carbon emissions, and be more friendly to the grid, a short-term dispatch optimization plan is needed.

[0074] Step 33: Objective Function:

[0075]

[0076] Where f OE f CL f G&MG They represent the objective function values ​​of optimal operation economy, minimum carbon emission and minimum grid connection volatility respectively; C MG_load It represents the benefits of directly utilizing distributed renewable energy sources in the microgrid, in CNY; C S C Ab Respectively represent the operating cost of distributed renewable energy and the penalty for wind and solar abandonment; C G&MG C Serve They represent the electricity price transaction and service cost from the microgrid to the AC distribution network; C ES represents the operation and maintenance cost of the hybrid energy storage equipment; Represents the operation and maintenance cost of the charging pile.

[0077] Step 34: Constraints:

[0078] The electric power balance constraints are as follows:

[0079]

[0080] In the formula, Indicates the output of the energy supply side in the system at time t, in kW; Represents the grid-connected power of the microgrid; Indicates the energy storage output; Indicates the output of the charging pile; Indicates the load power change.

[0081] The output power of new energy sources such as wind and solar power cannot exceed the upper and lower power limits:

[0082]

[0083] Where P PV_max , P PV_min , P WT_max , P WT_min They represent the upper and lower output constraints of wind and solar power generation units respectively, in kW.

[0084] The grid-connected interaction constraints are as follows:

[0085]

[0086] Where P G&MG_max and P G&MG_min Represents the upper and lower limits of the interactive power between the microgrid and the grid side.

[0087] kW. The hybrid energy storage constraints are as follows:

[0088]

[0089] In the formula Indicates the amount of energy that can be transferred at time t; SOC ESP_max , SOC ESP_min , SOC ESE_max and SOC ESE_min Respectively represent the high power density of hybrid energy storage and SOC upper and lower limits of high energy density; P ESP_max , P ESP_min , P ESE_max and P ESE_min Separate hybrid energy storage with medium and high power density and high energy density Upper and lower limits, unit: kW; u ESP_C and u ESE_C Indicates the number of times the charge and discharge state is switched, u ESP_C_max ,u ESP_C_min ,u ESE_C_max and u ESE_C_min They respectively represent the upper and lower limits of the number of times the charge and discharge states can be switched.

[0090] Under the constraints of charging piles:

[0091]

[0092] In the formula represents the SOC of the electric vehicle at time t; SOC Car_max The SOC at the end of charging the electric vehicle; represents the charging power of the electric vehicle at time t; P Car_min P Car_max They respectively represent the upper and lower limits of the charging power of the electric vehicle.

[0093] Step 4: Determine the multi-objective optimization algorithm to solve the multi-objective and multi-constraint microgrid energy scheduling optimization model of wind-solar-hybrid storage-charging and determine the optimization solution algorithm.

[0094] A RL multi-objective optimization algorithm TS-NSGA-II (Based On Reverse Learning Non-dominated Sorting Genetic Algorithm) is proposed, which uses a method based on reverse learning and two-step solution to optimize the solution.

[0095] The generation of the initial population individuals of the multi-objective optimization algorithm is random within a given range, so the initial individuals are random. Improving the initial population through the reverse learning method can speed up the search for feasible solutions, as shown in formula (7):

[0096]

[0097] Where P 0 P 0 ' are the two initial populations in reverse learning, P U P L are the upper and lower limits of the population within the constraint range, and d represents the number of decision variables. After converting the multi-objective algorithm into a single-objective with constraints, reverse learning is used to compare the fitness function values ​​of the two populations before the optimization begins, and the better population is retained as the initial population in the single-objective optimization process; then the differential evolution algorithm is used to quickly search globally to find a feasible solution space, as shown in formula (8):

[0098]

[0099] Where F is a scaling factor parameter in the DE mutation process, x i represents the i-th individual, x best represents the individual with the smallest transformed objective function value in the current population, x r represents the individuals randomly generated during the mutation process, v ri and v bi They represent the two individual variation directions of individuals towards randomness and the best individual in the current population. Finally, when the feasible solution area in the population exceeds k (the present invention takes 0.3 as an example), the population under this single-objective search is retained and used as the initial population in the subsequent NSGA-II multi-objective optimization solution process, and the solution is continued to obtain the final solution.

[0100] Step 5: Obtain long-term forecast results of renewable energy output, daily load, and electric vehicle charging load in the service area. The data has a resolution of 1 hour.

[0101] Step 6: Determine the long-term scheduling optimization plan model with the goals of operating economy, low carbon and grid-connected volatility. Through the scheduling plan, the wind-solar-hybrid storage-charging integrated grid-connected microgrid can achieve the best operating economy while maintaining low carbon emissions and minimum grid-connected net power curve volatility, thereby reducing the impact of the microgrid on the large power grid.

[0102] Step 7: Use multi-objective optimization algorithm to solve the long-term scheduling optimization model.

[0103] Step 8: Select a suitable solution and obtain the long-term scheduling optimization plan.

[0104] The solution obtained by the multi-objective optimization algorithm is a Pareto solution, which is an optimal solution set that meets the three objectives of economic optimization, low carbon emission, and minimum volatility. According to the needs of the dispatching center, an optimal solution is selected (the present invention takes the solution with the highest economic benefit as an example) as the final execution scheduling plan.

[0105] Step 9: Obtain short-term forecast results for renewable energy output, daily load, and electric vehicle charging load in the service area. The data is at a resolution of 15 minutes. The day-ahead forecast is based on data for every 4 hours, and the dispatch strategy is updated every 15 minutes.

[0106] Step 10: Determine the short-term dispatch optimization plan model with the goals of economic operation, low carbon and grid-connected volatility. On the basis of the long-term dispatch optimization plan, the results of grid-side dispatch optimization are used as constraints to reduce the volatility of grid connection. Similarly, the short-term dispatch optimization plan model is taken as the goal of the optimal economic operation, low carbon and minimum volatility of the microgrid.

[0107] Step 11: Use a multi-objective optimization algorithm to solve the long-term scheduling optimization model.

[0108] Step 12: Select a suitable solution to obtain the short-term scheduling optimization plan, which is the service area microgrid energy scheduling optimization plan to be finally executed.

[0109] Step 13: Complete the final dispatch strategy formulation to achieve dynamic power expansion in the service area. Upload to the wind-solar-hybrid storage-charging integrated microgrid operation and management platform of the service network, and update the dispatch plan every 15 minutes.

Claims

1. A method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles, characterized in that The following steps are involved: Step 1: Identify the service areas where power capacity needs to be dynamically expanded due to the planned installation of additional electric vehicle charging facilities; Step 2: Design a wind-solar-hybrid storage-charging integrated microgrid based on the existing remaining idle land resources in the service area, load, planned charging piles, and substation scale; determine the scale of wind and solar distributed renewable energy, capacity allocation of medium and high power density and energy density of hybrid energy storage, and total energy; Step 3: Establish a multi-objective optimization model for dual-time-scale energy dispatch taking into account electric vehicle charging load service areas and friendly grid connection; Step 4: Determine a multi-objective optimization algorithm to solve the multi-objective and multi-constraint microgrid energy dispatch optimization model of wind-solar-hybrid storage-charging and determine the optimization solution algorithm; Step 5: Obtain long-term forecast results of renewable energy output, daily load, and electric vehicle charging load in the service area; Step 6: Determine the long-term dispatch optimization plan model through the dispatch plan with the goals of economic operation, low carbon and grid-connected volatility; Step 7: Use multi-objective optimization algorithm to solve the long-term scheduling optimization model; Step 8: Select a suitable solution to obtain the long-term scheduling optimization plan; Step 9: Obtain short-term forecast results of renewable energy output, daily load, and electric vehicle charging load in the service area; Step 10: Determine the short-term dispatch optimization planning model with the objectives of economic operation, low carbon and grid-connected volatility; Step 11: Solve the long-term scheduling optimization model using a multi-objective optimization algorithm; Step 12: Select a suitable solution to obtain a short-term dispatch optimization plan, that is, the service area microgrid energy dispatch optimization plan to be finally executed; Step 13: Complete the final dispatch strategy formulation to achieve dynamic expansion of power in the service area.

2. The method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles according to claim 1, characterized in that: The step 3 comprises the following steps: Step 31: Develop a long-term scheduling optimization plan; Step 32: Formulate a short-term scheduling optimization plan; Step 33: Establish the objective function; Step 34: Constraints.

3. The method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles according to claim 2, characterized in that: The step 31 is specifically as follows: long-term prediction of the system's wind and solar output, daily load and electric vehicle charging load in the future, preliminary formulation of the system's long-term scheduling optimization plan, and obtaining the energy interaction plan between the microgrid and the AC distribution network in advance to reduce the impact of the microgrid on the large power grid.

4. The method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles according to claim 2, characterized in that: The objective function in step 33 is: Where f OE f CL f G&MG They represent the objective function values ​​of optimal operation economy, minimum carbon emission and minimum grid connection volatility respectively; C MG_load It represents the benefits of directly utilizing distributed renewable energy sources in the microgrid, in CNY; C S C Ab Respectively represent the operating cost of distributed renewable energy and the penalty for wind and solar abandonment; C G&MG C Serve They represent the electricity price transaction and service cost from the microgrid to the AC distribution network; C ES represents the operation and maintenance cost of the hybrid energy storage equipment; Represents the operation and maintenance cost of the charging pile.

5. The method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles according to claim 2, characterized in that: The constraints in step 34 are: The electric power balance constraints are as follows: In the formula, Indicates the output of the energy supply side in the system at time t, in kW; Represents the grid-connected power of the microgrid; Indicates the energy storage output; Indicates the output of the charging pile; Indicates load power changes; The output power of new energy cannot exceed the upper and lower power limits: Where P PV_max , P PV_min , P WT_max , P WT_min Respectively represent the upper and lower output constraints of wind and photovoltaic power generation units, in kW; The grid-connected interaction constraints are as follows: Where P G&MG_max and P G&MG_min Represents the upper and lower limits of the interactive power between the microgrid and the grid, in kW; The constraints of hybrid energy storage are as follows: In the formula Indicates the amount of energy that can be transferred at time t; SOC ESP_max , SOC ESP_min , SOC ESE_max and SOC ESE_min Respectively represent the high power density of hybrid energy storage and SOC upper and lower limits of high energy density; P ESP_max , P ESP_min , P ESE_max and P ESE_min Separate hybrid energy storage with medium and high power density and high energy density Upper and lower limits, unit: kW; u ESP_C and u ESE_C Indicates the number of times the charge and discharge state is switched, u ESP_C_max ,u ESP_C_min ,u ESE_C_max and u ESE_C_min Respectively represent the upper and lower limits of the number of times of switching the charge and discharge states; Under the constraints of charging piles: In the formula represents the SOC of the electric vehicle at time t; SOC Car_max The SOC at the end of charging the electric vehicle; represents the charging power of the electric vehicle at time t; P Car_min P Car_max They respectively represent the upper and lower limits of the charging power of the electric vehicle.

6. The method for dynamically expanding the power capacity of a highway service area taking into account the charging load of electric vehicles according to claim 1, characterized in that: The step 4 specifically includes: using the TS-NSGA-II multi-objective optimization algorithm of RL, that is, optimizing and solving by using a method based on reverse learning and two-step solving; The generation of the initial population individuals of the multi-objective optimization algorithm is random within a given range, so the initial individuals are random; improving the initial population by the reverse learning method can speed up the search for feasible solutions, as shown in formula (7): Where P 0 P 0 ' are the two initial populations in reverse learning, P U P L are the upper and lower limits of the population within the constraint conditions, and d represents the number of decision variables. After converting the multi-objective algorithm into a single-objective with constraints, reverse learning is used to compare the fitness function values ​​of the two populations before the optimization begins, and the better population is retained as the initial population in the single-objective optimization process. Then, the differential evolution algorithm is used for fast global search to find the feasible solution space, as shown in formula (8): Where F is a scaling factor parameter in the DE mutation process, x i represents the i-th individual, x best represents the individual with the smallest transformed objective function value in the current population, x r represents the individuals randomly generated during the mutation process, v ri and v bi They represent the two individual mutation directions of individuals towards randomness and the best individual in the current population respectively; finally, when the feasible solution area in the population exceeds k, the population under this single-objective search is retained and used as the initial population in the subsequent NSGA-II multi-objective optimization solution process, and the solution is continued to obtain the final solution.

Images