Optimal configuration method of microgrid with photovoltaic, energy storage and charging considering system energy efficiency and carbon emission

Abstract

This invention proposes an optimized configuration method for photovoltaic-storage-charging microgrids that considers system energy efficiency and carbon emissions. The method includes: constructing load models for photovoltaic, energy storage, and electric vehicle charging within and outside the industrial park; selecting five optimization objectives: economy, carbon emissions, reliability, energy efficiency, and grid integration rate; considering grid-connected operation, and employing a load tracking management strategy to ensure stable power and charging supply to users; iteratively operating the photovoltaic-storage-charging system based on a multi-objective genetic algorithm to obtain the optimal capacity configuration result for the system's overall performance, and plotting the power curves for typical days. The purpose of optimizing the configuration of integrated photovoltaic-storage-charging industrial parks is to formulate a configuration scheme for photovoltaic equipment, charging pile equipment, and energy storage equipment based on a reasonable analysis of photovoltaic resource distribution and charging pile load demand, according to the set planning objectives and system constraints.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic-storage-DC-flexible simulation technology, specifically to an optimized configuration method for a photovoltaic-storage-charging microgrid that considers system energy efficiency and carbon emissions. Background Technology

[0002] With the advancement of the global energy transition, distributed renewable energy, represented by photovoltaics, has developed rapidly. To address the intermittency and volatility of photovoltaic power generation and improve its local absorption rate and power supply reliability, photovoltaic-storage-DC-flexible technology has emerged. Simultaneously, the number of electric vehicles in my country is also growing rapidly, and electric vehicle loads are gradually becoming an important load in the power grid. To promote the utilization of renewable energy and meet the charging needs of electric vehicles, "photovoltaic-storage-charging" microgrids, integrating photovoltaics, energy storage, charging, and industrial loads, are becoming a practical solution. Essentially, they are microgrids containing a high proportion of power electronic equipment and highly volatile source loads.

[0003] The "photovoltaic-storage-charging" system is a key technological route for achieving flexible and low-carbon energy operation in buildings and industrial parks. The capacity configuration of core equipment such as photovoltaic modules, energy storage batteries, and AC / DC converters directly determines the system's life-cycle cost and environmental performance. Currently, existing technologies have explored some aspects of capacity configuration for photovoltaic energy storage systems. However, most of these methods optimize solely based on economic efficiency or carbon emissions, failing to provide decision-makers with a Pareto-optimal solution set that covers different preferences, thus limiting the universality and optimality of the configuration scheme. Summary of the Invention

[0004] To address the problems in existing technologies, this invention proposes an optimized configuration method for photovoltaic-storage-charging microgrids that considers system energy efficiency and carbon emissions.

[0005] To address the technical problems in the background section of this application, the present invention provides the following technical solutions: A method for optimizing the configuration of a photovoltaic-storage-charging microgrid that considers system energy efficiency and carbon emissions, characterized by the following steps: Step 1: Construct a photovoltaic power generation system model and an energy storage system model, and establish a charging pile load model that includes AC slow charging and DC fast charging based on traffic flow data inside and outside the park. The charging pile load model is based on the Monte Carlo method to simulate the charging behavior of electric vehicles. Step 2: Using the annualized system cost, renewable energy generation loss rate, energy surplus rate, load deficit rate, and system exchange loss as evaluation indicators, the evaluation indicators are transformed into optimization objectives that include economic efficiency, carbon emissions, reliability, photovoltaic absorption rate, and system energy efficiency, and a minimization multi-objective optimization model is established. Step 3: Determine the system constraints and decision variable ranges. The system constraints include power balance constraints, energy storage system capacity constraints, energy storage system charging and discharging power constraints, charging pile power constraints, photovoltaic capacity constraints, and DC bus power balance constraints. The decision variable ranges include the maximum executable ranges of the number of charging piles, photovoltaic capacity, energy storage capacity, and converter capacity. Step 4: Input data and set simulation conditions: Input photovoltaic output power, electrical load and charging pile load data, and set the initial and termination conditions of the simulation operation so that the state of charge of the energy storage system is equal at the initial time and the termination time, and the charging amount of the charging pile is equal. Step 5: Design a load tracking management strategy. Divide the grid electricity price period into peak period, normal period and off-peak period. Based on the comparison between photovoltaic power and load power, the state of charge of the energy storage system and the power interaction limit of the grid, dynamically select multiple working modes to dispatch power, prioritize the consumption of photovoltaic power and optimize the charging and discharging of energy storage. Step 6: Standardize the heterogeneous optimization objectives established in Step 2, and then, according to the load tracking management strategy in Step 5, use a multi-objective genetic algorithm to iteratively solve the minimization multi-objective optimization model. Each solution corresponds to a configuration scheme.

[0006] Furthermore, the photovoltaic power generation system model is as follows: ; in, This refers to the actual power generation of the photovoltaic power generation system. It is the rated power. These are the standard and maximum irradiance, These are the actual and standard temperatures, respectively, and k is the power temperature coefficient. The energy storage system model is as follows: ; in, , These are the states of charge of the energy storage system at time t and t+1. This is the current power level of the energy storage system. It is the rated capacity of the energy storage system. and These are the charge and discharge efficiencies, and These are the charging and discharging power, It is the total energy of the energy storage device; The charging pile load model distinguishes between AC slow charging within the park and DC fast charging outside the park. The total load curve is as follows: ; in, , These refer to the conversion efficiency of DC fast charging and AC slow charging, respectively. , These represent the DC fast charging and AC slow charging power, respectively. The power was obtained by performing multiple Monte Carlo simulations on the behavior of a large number of electric vehicles and accumulating the charging power of multiple electric vehicles. The formula is as follows: ; ; in, It is the number of Monte Carlo simulations. It is the first The number of electric vehicles in this simulation. It is an index of the number of simulations. It is an index for a single vehicle. , For the first In the simulation, the first The AC or DC charging power of the vehicle at time t.

[0007] Furthermore, the optimization objective for step 2 is calculated using the following formula: Economic efficiency: ; ; reliability: ; Carbon emissions: ; Photovoltaic grid integration rate: ; System energy efficiency: ; in, This is the total investment and operating cost of a photovoltaic-storage-charging microgrid, calculated over one year. It is the annual value equipment investment cost discount rate. It is the discount rate. It is the project operation phase. This refers to the project's operational phase after equipment replacement. This refers to the initial investment cost of the equipment. This is the cost of equipment replacement. This refers to the annual maintenance and operation costs of a photovoltaic, energy storage, and charging system. These are the fees incurred from transactions with the power grid. It is the system power shortage rate. It refers to photovoltaic power generation capacity. It refers to the power of solar power curtailment. It refers to the power of the energy storage battery. This is the system's required power. It is a carbon emission factor. It refers to the power purchased by the power grid. This is the actual power output of the photovoltaic system. It is the power loss of the converter. It is the line loss power. It is a unit time interval.

[0008] Furthermore, the system constraints are as follows: System power balance constraints: Off-grid operating conditions are met: ; Grid connection conditions meet: ; in, This is the actual output power of the photovoltaic power generation system. It is the discharge power of the energy storage system. This refers to the power of all other conventional electrical loads within the park, excluding electric vehicle charging loads. It is the charging power of the energy storage system. It refers to the charging power of electric vehicles. It refers to the curtailment power of photovoltaic systems; Energy storage system capacity constraints refer to the requirement that the state of charge of the energy storage system must be maintained between the minimum and maximum allowable values. The charging and discharging power constraints of energy storage systems mean that the charging power and discharging power of the energy storage system must both be greater than or equal to zero, and must not exceed its maximum allowable charging power and maximum allowable discharging power, respectively; in addition, the energy storage system cannot charge and discharge simultaneously, that is, the product of the charging power and the discharging power must be zero. The power constraint of a charging pile refers to the requirement that the charging power of a DC charging pile must be greater than or equal to zero and not exceed the maximum rated power of the DC charging pile. Photovoltaic capacity constraints refer to the requirement that the power output of a photovoltaic power generation system must be greater than or equal to zero and not exceed [a certain limit]. The product of the light intensity normalization factor; The DC bus power balance constraint means that the transmission power of the DC bus must be greater than or equal to zero and not exceed the maximum allowable transmission power of the DC bus. The decision variable range constraint means that the decision variables in system optimization must be greater than or equal to their technically permissible minimum value and less than or equal to their technically permissible maximum value. The decision variables include photovoltaic capacity, energy storage capacity, and converter capacity.

[0009] Furthermore, the heterodimensional optimization objectives include economic objectives and carbon emission objectives.

[0010] Furthermore, the method for determining the range of decision variables in step 3 is as follows: The maximum number of electric vehicles that the park can receive is determined based on the power constraints of the charging piles, and the number of charging piles is determined based on the maximum number of electric vehicles. Based on photovoltaic capacity constraints and site area, determine the maximum photovoltaic capacity that can be installed in the park; The converter capacity is determined based on load demand and maximum photovoltaic capacity. Determine energy storage capacity based on actual needs.

[0011] Furthermore, the initial and termination conditions for the simulation are as follows: At the initial time t=0 and the final time t=T, the state of charge of the energy storage system and the charging amount of the charging pile satisfy the following: ; ; The initial state of charge of the energy storage system at t=0 has a certain margin for the charging amount of the charging pile.

[0012] Furthermore, the load tracking management strategy specifically includes: The load tracking management strategy is specifically as follows: (5.1) Compare photovoltaic power generation with load power in real time and classify the system operation status into photovoltaic surplus mode or photovoltaic deficit mode; (5.2) Divide the power grid electricity price period into peak period, normal period and off-peak period, and combine the state of charge (SOC) of the energy storage system and the power grid interaction limit to dynamically select the corresponding power allocation strategy; In the surplus photovoltaic mode: During peak periods, the control objective is to maximize self-consumption of photovoltaic power and peak shaving through energy storage discharge. If the grid interaction power does not exceed the limit, the strategy of selling all surplus power is adopted. If the photovoltaic surplus exceeds the grid's maximum receiving power and the energy storage is not fully utilized, the strategy of prioritizing the sale of surplus power and absorbing excess energy through energy storage is adopted. During normal periods, the control objective is to maintain the energy storage SOC within a healthy range, prioritizing the energy storage system to handle and balance the power difference. During off-peak periods, the control objective is to utilize low-priced electricity to increase the energy storage SOC to its maximum value, prioritizing the use of photovoltaic power and the grid to charge the energy storage. Under the photovoltaic deficit mode: During peak periods, the control objective is to maximize photovoltaic self-consumption and utilize energy storage discharge for peak shaving. If the energy storage capacity is sufficient, the strategy of having all deficit power supplemented by energy storage is selected. If the deficit still cannot be met after energy storage discharge, the strategy of having the deficit supplemented by both energy storage and the grid is selected. During normal periods, the control objective is to maintain the energy storage SOC within a healthy range, prioritizing the energy storage system to handle and balance the power difference. During off-peak periods, the control objective is to utilize low-priced electricity to increase the energy storage SOC to its maximum value, with the grid supplying power to supplement the deficit, and utilizing the grid's remaining capacity to charge the energy storage.

[0013] Furthermore, the load tracking management strategy also includes differentiated scheduling of AC / DC charging piles: DC charging piles connected to the DC bus are given priority to be powered by the DC side power supply, and AC charging piles connected to the AC bus are given priority to be powered by the AC side power supply. When there is a power surplus on one side and a load deficit on the other side, the surplus power is converted by the converter and supplied to the side with the deficit.

[0014] The beneficial effects of this invention are: The technical solution provided by this invention includes a method for optimizing the configuration of a photovoltaic-storage-charging microgrid that considers system energy efficiency and carbon emissions. This method determines the power allocation strategy for each part of the photovoltaic-storage-charging microgrid, thereby configuring a recommended number of charging piles, photovoltaic capacity, energy storage capacity, and converter capacity. This improves the accuracy of energy storage capacity configuration and simultaneously enhances renewable energy utilization and system efficiency. Furthermore, the capacity configuration method of this invention makes the differences in energy storage capacity configuration under the optimization objective more intuitive, which is beneficial for users to guide the selection of optimization objectives based on the energy storage capacity configuration results, thus addressing the technical deficiencies in existing photovoltaic-storage-DC-flexible system capacity configuration methods. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of the photovoltaic-storage-charging microgrid optimization configuration method provided in an embodiment of the present invention; Figure 2 The flowchart of the multi-objective genetic algorithm provided in the embodiments of the present invention is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0018] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms unless the context clearly indicates otherwise.

[0020] This invention provides a method for optimizing the configuration of a photovoltaic-storage-charging microgrid that considers system energy efficiency and carbon emissions, such as... Figure 1 As shown, this optimized configuration method specifically includes the following steps: Step 1: System Model Establishment This step mainly involves establishing mathematical models for the photovoltaic power generation system and energy storage system, and establishing a charging pile load model based on traffic flow data inside and outside the park using a probability model.

[0021] Photovoltaic power generation system model: The actual output power of a photovoltaic system is significantly affected by solar irradiance and ambient temperature. It is usually corrected based on the rated output power under standard test conditions. The photovoltaic power generation system model is represented as follows: in, This represents the actual power generation of the photovoltaic power generation system. This refers to the rated power of the photovoltaic power generation system. These are the standard and maximum irradiance, This refers to the actual operating temperature of the photovoltaic power generation system. This refers to the standard operating temperature under STC. This is the power temperature coefficient.

[0022] Energy storage system model: The state of charge (SOC) of the energy storage battery is used as the state variable for modeling, satisfying: in, , These are the states of charge of the energy storage system at time t and t+1. This is the current power level of the energy storage system. It is the rated capacity of the energy storage system. and These are the charge and discharge efficiencies, and These are the charging and discharging power, It is the total energy of the energy storage device.

[0023] Charging pile load model: Based on traffic flow data inside and outside the park, a charging pile load model is constructed that includes AC slow charging within the park and DC fast charging outside the park. The connection method for hybrid AC / DC charging is as follows: AC charging piles: directly connected to the AC bus, their charging power is limited by the on-board charger, and their characteristics are relatively small power and usually remain constant for most of the time (constant power charging).

[0024] DC charging piles: They are directly connected to the DC bus, and the electrical energy only needs to go through one-stage DC / DC conversion (voltage regulation / step-down) to be input into the electric vehicle battery. They have the characteristics of short "source-load" path and high conversion efficiency, which is in line with the low loss concept of "DC-flexible" system.

[0025] Specifically, the charging load power model of the AC charging piles is constructed based on the vehicle data in the park using the Monte Carlo method. The specific steps are as follows: Probability distribution of daily driving mileage of electric vehicles: Daily driving mileage of electric vehicles (EVs) The probability density function follows a log-normal distribution: in, It is the mean of the logarithms of the variables. It is the standard deviation of the logarithm of the variable.

[0026] Initial SOC distribution: Initial SOC of EV Follows a uniform distribution: in, , These are the upper and lower limits of SOC, respectively.

[0027] SOC calculation before charging: The SOC value upon arrival at the charging station after driving is: in, For driving mileage, Energy consumption per 100 kilometers This refers to the total battery capacity of the electric vehicle.

[0028] Arrival time distribution: the time the EV spends passing through the photovoltaic, energy storage, and charging park within a day. Follows a normal distribution: in, It is the mean of the logarithms of the variables. It is the standard deviation of the logarithm of the variable.

[0029] Charging time calculation: Charging time of EV via AC charging station for: in, For charging power, For charging efficiency.

[0030] Therefore, the hourly charging power of a single EV is: By conducting multiple Monte Carlo simulations on the behavior of a large number of EVs (travel time, SOC, and power demand), and accumulating the charging power of multiple EVs, the AC or DC charging pile load curve for a typical day is obtained: in, For the number of Monte Carlo simulations, For the first Number of EVs in this simulation Index of simulation number (from 1st to 1st) Second-rate), Index of a single vehicle (from vehicle 1 to ...) (vehicles) , For the first In the simulation, the first The AC or DC charging power of the vehicle at time t.

[0031] Specifically, DC fast charging directly supplies power to the battery through an off-board charger. DC fast charging can be divided into two stages: constant current charging and constant voltage charging.

[0032] Charging power function: DC charging power Battery terminal voltage and charging current The product of: The specific stage formulas are as follows: Phase 1, constant current charging: When the battery voltage is lower than the cutoff voltage, the current remains at its maximum. in, Open circuit voltage, Internal resistance; Phase 2, constant voltage charging: Once the battery voltage reaches the cutoff voltage, the voltage remains constant, and the current decreases exponentially. in, This is the cutoff voltage. This is the attenuation coefficient.

[0033] The charging pile load model distinguishes between AC slow charging within the park and DC fast charging outside the park. The total load curve is as follows: in, , These are the DC fast charging and AC slow charging powers, respectively. , These refer to the conversion efficiency of DC fast charging and AC slow charging, respectively.

[0034] Step 2: Establishing a multi-objective optimization model The evaluation indicators selected by the system are the annualized cost of the system, the renewable energy generation loss rate, the energy surplus rate, the load shortage rate, and the system conversion loss. The above five evaluation indicators include optimization objectives for economy, carbon emissions, reliability, photovoltaic absorption rate and system energy consumption. A multi-objective optimization model is established using the minimization criterion, that is, the smaller the value of the optimization objective, the better the corresponding system performance.

[0035] Economic indicator C measures the total lifecycle cost of the system and has two scenarios: off-grid (OG) and grid-connected (GC). It must satisfy the following conditions: Off-grid (OG) Grid-connected operating condition (GC): in, The total investment and operating cost of a photovoltaic-storage-direct-flex system converted to a one-year operating cost. The discount rate is the equivalent annual value of the equipment investment cost. The discount rate is... During the project operation period, For the project's operational period after equipment (converters, charging piles, etc.) replacement, For initial investment costs of equipment, For equipment replacement costs, Annual maintenance and operation costs for photovoltaic-storage-charging systems Costs incurred for transactions with the power grid.

[0036] The calculation formula is: The calculation formula is: in, The unit price for replacing the energy storage system. This means that the lifespan of the energy storage system is considered to be approximately 8 years.

[0037] The calculation formula is: in, , , These are the unit prices for operation and maintenance of photovoltaic systems, energy storage systems, and DC charging piles, respectively.

[0038] The reliability index R satisfies: in, Indicates the power load of the park. Indicates the power supplied by the power grid. This indicates the amount of solar power curtailed by the photovoltaic system.

[0039] Carbon emission index T, satisfying: in, This indicates the total carbon emissions generated by electricity purchases during the park's annual operation. The carbon emission coefficient per unit of electricity generated by the power grid. Let t be the electrical power purchased from the grid in hour t.

[0040] Photovoltaic grid integration rate index ,satisfy: in, This represents the amount of photovoltaic power that can be effectively utilized at time t. This represents the theoretical photovoltaic power generation at time t.

[0041] System energy efficiency indicators ,satisfy: in, The conversion loss of AC / DC charging piles includes DC charging pile losses, AC charging pile losses, and AC / DC inverter or rectification losses. Specifically, when the AC and DC photovoltaic power generation can respectively meet the charging load requirements of the AC and DC charging piles, the AC grid supplying the AC charging pile load will generate AC charging pile losses. When a DC power grid supplies power to a DC charging pile, losses will occur at the DC charging pile. When the power grid on one side cannot meet the load of the charging piles on that side, the remaining photovoltaic power on the other side can be rectified or inverted to supply the unmet charging load, thereby improving the photovoltaic absorption rate. This will generate AC / DC inversion or rectification losses. .

[0042] Step 3: Determine the system constraints This mainly includes system power balance constraints, energy storage system capacity constraints, energy storage system charge / discharge power constraints, photovoltaic capacity constraints, and DC bus power balance constraints. Specifically, System power balance constraint: The output power of the photovoltaic power generation system, as well as the charging and discharging power of charging piles and energy storage, should be balanced with the power demand of other electrical loads. That is, the following equations should be satisfied under off-grid (OG) and grid-connected (GC) conditions respectively: in, This is the actual output power of the photovoltaic power generation system. It is the discharge power of the energy storage system. This refers to the power of all other conventional electrical loads within the park, excluding electric vehicle charging loads. It is the charging power of the energy storage system. It is the total charging power demand of all charging stations. It refers to the curtailment power of photovoltaic systems.

[0043] Energy storage system capacity constraints: in, It refers to the state of charge of the energy storage system. , These are the minimum and maximum allowable states of charge of the energy system.

[0044] Energy storage systems are subject to power constraints during charging and discharging to prevent overcharging or over-discharging, and it is impossible for an energy storage system to be charging and discharging simultaneously. in, , These are the charging power and discharging power of the energy storage system, respectively. , These are the maximum allowable charging power and the maximum allowable discharging power of the energy storage system, respectively.

[0045] Photovoltaic capacity constraint: ensuring that the photovoltaic power generation does not exceed the system's maximum rated power, whereby... It is the normalization coefficient of light intensity: in, This is the actual output power of the photovoltaic power generation system. It is the rated installed capacity of the photovoltaic system.

[0046] Charging station maximum power limit: This limit ensures that the charging power will not exceed the charging station's maximum rated power. in, This is the actual output power of the DC charging station. This is the rated installed capacity of the DC charging pile.

[0047] DC bus power balance constraint: This constraint ensures that the charging power will not exceed the maximum rated power of the charging pile. in, It refers to the efficiency of the AC / DC converter. The power purchased from the power grid. It is the power sold from the power grid. It is the energy storage charging power. It is the energy storage discharge power. It is the efficiency of the bidirectional AC / DC converter. It refers to the number of photovoltaic panels. It refers to the area of ​​a single photovoltaic panel. This is the predicted value of photovoltaic power output per unit. It is the power of abandoned light. It refers to the efficiency of the photovoltaic converter. It is the total load on the DC bus. It is a set of scheduling intervals.

[0048] Step 4: Calculation of the objective function value based on the multi-objective genetic algorithm First, to eliminate the influence of dimensions, it is necessary to standardize the optimization objectives with different dimensions.

[0049] Due to economic objectives Since units exist, the following standardization process is performed: in, and They are respectively The maximum and minimum values ​​that exist.

[0050] carbon emission targets The following standardization process shall be performed: in, and They are respectively The maximum and minimum values ​​that exist.

[0051] After performing the above standardization process on the two optimization objectives, , The smaller the value, the better the configuration result.

[0052] Step 5: Data input.

[0053] By inputting photovoltaic output power, electrical load, and charging pile load data, the maximum feasible range of each component of the system is determined. This constraint ensures that each capacity decision variable is within its reasonably permissible range. In this embodiment, the maximum number of electric vehicles that the park can receive is determined based on the power constraints of the charging piles, and the number of charging piles is determined based on the maximum number of electric vehicles. Based on photovoltaic capacity constraints and site area, determine the maximum photovoltaic capacity that can be installed in the park; The converter capacity is determined based on load demand and maximum photovoltaic capacity. Determine the energy storage capacity based on actual needs.

[0054] It should be noted that a converter is essentially an energy transmission tool that can convert DC to AC. Here, it mainly performs the function of current conversion and calculates power loss. Generally, the efficiency of a converter is marked as 95%, which means that 1 kWh of electricity reaches the device after passing through the converter.

[0055] Step 6, conditional assumptions.

[0056] To ensure that the energy storage system and charging pile can accurately simulate a typical daily operation, the state of charge of the energy storage system should be equal at the initial time t=0 and the final time t=T. Similarly, at the initial time t=0 and the final time t=T, the charging amount of the charging pile should be equal, which can be expressed as equal instantaneous charging power, i.e. .

[0057] In addition, in order to ensure that the system has bidirectional power throughput capability in the early stage of operation and to enhance the robustness of algorithm convergence, the initial state of charge and charge amount need to have a certain margin.

[0058] Step 7: Load tracking management strategy.

[0059] Considering the need for the system to operate under grid-connected conditions, a load tracking management strategy is adopted to ensure a stable supply of electricity to users. This load tracking management strategy is further divided into multiple operating modes based on electricity price periods (peak, normal, and off-peak periods) and power deficit conditions, as fully defined below, to achieve refined energy dispatching.

[0060] In this embodiment, the energy management system classifies the system operation status into a photovoltaic surplus mode and a photovoltaic deficit mode based on the real-time difference between photovoltaic power generation and user load power. For different power differences and system states (such as grid constraints and energy storage SOC constraints), the controller configures various power allocation strategies, the specific control logic of which is as follows: 1. Operational strategy under surplus photovoltaic power generation mode: When photovoltaic power generation exceeds the load power, the system generates surplus electricity. Regarding the consumption and distribution of this surplus electricity, the system can choose to implement the following strategies based on objectives such as optimal economic efficiency or optimal self-sufficiency: Energy storage priority consumption strategy: At this time, the surplus electricity is given priority to charge the energy storage system; if the energy storage reaches the maximum charging power or is fully charged, the remaining electricity is then transmitted to the grid; if the grid also reaches the upper limit of power receiving, the excess photovoltaic power is curtailed.

[0061] Grid priority consumption strategy: In this case, surplus electricity is sold to the grid first; if the grid's power transfer reaches its limit, the remaining electricity is absorbed by the energy storage system; if the energy storage system can no longer absorb the electricity, then curtailment of the solar power will occur.

[0062] Joint regulation strategy: In specific scenarios (such as during periods of low electricity prices or to meet specific dispatch instructions), the system can execute more complex power flow control. For example, while photovoltaic power meets the load and charges energy storage, the grid can also be controlled to charge the energy storage at maximum power until the power limit is reached; or, while controlling the surplus photovoltaic power to be sold to the grid, the energy storage system can be dispatched to discharge to the grid to maximize the power sold to the grid.

[0063] 2. Operational strategies under the photovoltaic deficit mode When photovoltaic power generation is less than the load power, a power deficit exists in the system. To supplement this deficit, the system also selects and executes the following strategies based on the set target: Energy storage priority replenishment strategy: the power gap is first replenished by the discharge of the energy storage system; if the energy storage discharge power reaches the upper limit or the power is exhausted, the remaining gap is replenished by the power supply of the grid; if the power supply of the grid cannot meet the demand (such as reaching the power limit), some loads are cut off (load shedding) to maintain system stability.

[0064] The grid priority replenishment strategy is as follows: the power gap is first replenished by the grid through power purchase; if the grid power supply reaches its limit, the remaining gap is covered by the discharge of the energy storage system; similarly, if neither of these can be met, load shedding is performed.

[0065] Joint supplementation and mandatory strategies: In specific scenarios (such as to protect battery life or take advantage of low electricity prices), the system can perform joint control. For example, the grid can fully supplement the power shortage and use the grid's surplus capacity to charge the energy storage system; or, the energy storage and photovoltaic systems can be controlled to jointly sell electricity to the grid until the grid connection point power limit is reached.

[0066] In one specific embodiment of the present invention, the power supply is further divided into multiple working modes, A1 to A10 and B1 to B10, based on the electricity price period (peak period, normal period, and off-peak period) and the power shortage situation. The complete definition is shown in Table 1 below, so as to realize refined energy scheduling.

[0067] Table 1 The specific operational logic of the load tracking management strategy is as follows: 1. Peak period During periods of peak electricity prices, the control objective is to maximize self-consumption of photovoltaic power generation and peak shaving through energy storage discharge, while reducing the purchase of electricity from the grid.

[0068] Determine whether the photovoltaic power generation capacity of the charging station can meet the current charging demand of electric vehicles: when When photovoltaic power meets the load but still has a surplus, if the grid's power transfer limit is not exceeded, the system enters A2 operating mode (all surplus power is sold); if the photovoltaic surplus exceeds the grid's maximum receiving power ( And the energy storage is not full ( Charging power not reached the limit ( If the photovoltaic surplus exceeds the grid's maximum receiving power, and the energy storage is full or the charging power is limited (exceeding the grid's maximum receiving power), the system enters the A4 working mode (curtailment of photovoltaic power).

[0069] when At this time, the system has a power deficit. If the energy storage capacity is sufficient ( If the discharge power can meet the deficit, the system enters B1 operating mode (the deficit is entirely supplemented by energy storage); if the energy storage cannot fully meet the deficit after discharging at maximum power, the system enters B3 operating mode (the deficit is jointly supplemented by energy storage and the grid); if the energy storage is exhausted ( The system enters B2 operating mode (the deficit is entirely supplemented by the grid); if the energy storage cannot discharge and the grid's power purchase capacity reaches its limit ( The system enters B5 working mode (load reduction).

[0070] 2. Normal period With a moderate grid electricity price, the control objective is to maintain the energy storage SOC within a healthy range. This serves as a buffer for peak discharge periods or off-peak charging periods.

[0071] First, determine whether the energy storage is within a healthy range.

[0072] when At this time, the energy storage is in a healthy range. The surplus or deficit power of photovoltaic power is preferentially handled by the energy storage system. As long as the energy storage power is sufficient, the system enters the A1 / B1 working mode. If the energy storage power is insufficient to completely balance the difference between photovoltaic power and load, the remaining part is borne by the grid, and the system enters the A3 / B3 working mode. If the grid cannot meet the power difference (such as exceeding the limit), the peak load reduction strategy is triggered, and the system enters the A6 / B6 working mode.

[0073] when At this time, energy storage deviates from the healthy range. Utilizing photovoltaic surplus or deficit in conjunction with the grid's maximum power for charging / discharging prioritizes bringing the State of Charge (SOC) back to the healthy range. If... The system enters A8 / B8 working mode; The system enters A9 / B9 working mode.

[0074] 3. Low point With the lowest grid electricity price, the control objective is to utilize the low-priced electricity for forced charging, increasing the energy storage SOC to the target value. At the same time, we will try our best to absorb photovoltaic power.

[0075] First, determine whether the energy storage has reached the target value. .

[0076] when At times, such as photovoltaic surplus The photovoltaic system prioritizes charging the energy storage system. If the maximum charging power of the energy storage system is not reached, the grid provides supplementary charging, and the system enters the A8 operating mode. If the photovoltaic power exceeds the absorption capacity of the energy storage system and the grid, the photovoltaic power is curtailed, and the system enters the A3 operating mode. When the photovoltaic power is insufficient, the system enters the B8 operating mode.

[0077] when When power is insufficient, the energy storage stops charging, and surplus photovoltaic power is prioritized for grid connection. If the grid is constrained, the photovoltaic power is curtailed, and the system enters the A2 / A5 operating mode. When photovoltaic power is insufficient, power is supplied by the grid. If the grid is insufficient, the energy storage discharges to supplement the power, and the system enters the B2 operating mode.

[0078] To reduce system energy consumption, the strategy employs differentiated scheduling for AC and DC charging stations: Basic principle: When the system follows the above load tracking strategy, for AC and DC charging loads, DC charging piles should prioritize meeting the DC electric vehicle load, and AC charging piles should prioritize meeting the AC electric vehicle load.

[0079] Cross-side support: When there is a surplus of photovoltaic power generation on one side and the other side cannot meet the load of electric vehicles, the surplus power can be rectified or changed to supply the surplus power on the AC or DC side to the side with the deficit, thereby improving the photovoltaic absorption rate and reducing energy consumption.

[0080] Step 8: Iteratively solve the photovoltaic-storage-charging microgrid based on a multi-objective genetic algorithm.

[0081] This step uses a multi-objective genetic algorithm to iteratively optimize the photovoltaic-storage-charging system to obtain the capacity configuration with the best overall performance, and plots the power curves for a typical day of the system. The algorithm parameters are set as follows: the population size is set to 400, the maximum number of generations is set to 400, the acceleration constant c1 is set to 1.49445, the acceleration constant c2 is set to 1.49445, and the inertia weight w is linearly reduced from 0.9 to 0.4.

[0082] like Figure 2 As shown, the main execution steps of the Pareto-dominated multi-objective genetic algorithm are as follows: Step 8.1, Initialize the population An initial population of size N is randomly generated, with each individual representing a candidate capacity configuration scheme x. The initial population covers different regions of the solution space, providing diverse starting points for subsequent evolution.

[0083] Step 8.2: Calculate the objective function value For each individual in the population, based on the multi-objective optimization model established in step 2, its value in the optimization objective function such as economy, carbon emissions, reliability, photovoltaic absorption rate and system energy consumption is calculated to comprehensively evaluate the merits of candidate solutions and support non-dominated ranking and fitness assignment.

[0084] Step 8.3: Non-dominated sorting and crowding calculation The population is sorted by Pareto non-dominated order. Based on the Pareto dominance relation, the population individuals are divided into different levels of frontier layers, where the first frontier layer is the current optimal non-dominated solution set.

[0085] The crowding distance is calculated for individuals within the same front layer to measure the distribution density of solutions in the target space. Individuals within the same layer are sorted to maintain diversity, ensuring that the Pareto front solution set is evenly distributed.

[0086] Step 8.4: Select Operation Based on the individual's non-dominance level and crowding distance, strategies such as tournament selection are used to select superior individuals from the current population as parents to generate offspring.

[0087] Step 8.5: Crossover and Mutation Operations Perform crossover operations (such as simulated binary crossover SBX) on selected parent pairs, and mutate the newly generated offspring with a certain probability (such as polynomial mutation) to generate new candidate solutions and introduce population diversity.

[0088] Step 8.6: Generate a new generation of population The parent and offspring populations are merged, and the non-dominated ordination and crowding calculation are re-performed on the merged population. The top N individuals are selected based on the ordination results to form a new generation population, ensuring that the elite solution is not lost and the Pareto front is preserved.

[0089] Step 8.7: Iterative Evolution Repeat steps 8.2 to 8.6 until the preset termination condition is met (such as the maximum number of iterations or the Pareto front convergence index). The population is updated in each iteration, gradually approaching the global Pareto optimal front.

[0090] Step 8.8: Output the Pareto optimal solution set After the algorithm iterations are completed, the final Pareto optimal solution set is output. This solution set contains multiple solutions that are non-dominated to each objective function. Each solution represents a capacity configuration scheme that achieves different trade-offs among multiple objectives, reflecting the balance and trade-offs between objectives. This allows decision-makers to select the most suitable scheme based on actual needs and preferences, and to plot the typical daily power curve of the system.

[0091] This invention provides an optimized configuration method for photovoltaic-storage-charging microgrids. Based on five optimization objectives—economic efficiency, carbon emissions, reliability, grid integration rate, and energy consumption—it determines the power allocation strategy for each component of the photovoltaic-storage-charging microgrid. By constructing photovoltaic, energy storage, and electric vehicle charging load models within and outside the park, it configures the optimal number of charging piles, photovoltaic capacity, converter capacity, and energy storage capacity, thereby improving the utilization rate of renewable energy and system efficiency. Furthermore, through the capacity configuration method of this invention, it rationally analyzes the distribution of photovoltaic resources and the load demand of charging piles in the integrated photovoltaic-storage-charging park, and formulates a configuration scheme for photovoltaic equipment, charging pile equipment, and energy storage equipment in the integrated photovoltaic-storage-charging park according to the set planning objectives and system constraints.

[0092] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. Those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions, characterized in that, Includes the following steps: Step 1: Construct a photovoltaic power generation system model and an energy storage system model, and establish a charging pile load model that includes AC slow charging and DC fast charging based on traffic flow data inside and outside the park. The charging pile load model is based on the Monte Carlo method to simulate the charging behavior of electric vehicles. Step 2: Using the annualized system cost, renewable energy generation loss rate, energy surplus rate, load deficit rate, and system exchange loss as evaluation indicators, the evaluation indicators are transformed into optimization objectives that include economic efficiency, carbon emissions, reliability, photovoltaic absorption rate, and system energy efficiency, and a minimization multi-objective optimization model is established. Step 3: Determine the system constraints and decision variable ranges. The system constraints include power balance constraints, energy storage system capacity constraints, energy storage system charging and discharging power constraints, charging pile power constraints, photovoltaic capacity constraints, and DC bus power balance constraints. The decision variable ranges include the maximum executable ranges of the number of charging piles, photovoltaic capacity, energy storage capacity, and converter capacity. Step 4: Input data and set simulation conditions: Input photovoltaic output power, electrical load and charging pile load data, and set the initial and termination conditions of the simulation operation so that the state of charge of the energy storage system is equal at the initial time and the termination time, and the charging amount of the charging pile is equal. Step 5: Design a load tracking management strategy. Divide the grid electricity price period into peak period, normal period and off-peak period. Based on the comparison between photovoltaic power and load power, the state of charge of the energy storage system and the power interaction limit of the grid, dynamically select multiple working modes to dispatch power, prioritize the consumption of photovoltaic power and optimize the charging and discharging of energy storage. Step 6: Standardize the heterogeneous optimization objectives established in Step 2, and then, according to the load tracking management strategy in Step 5, use a multi-objective genetic algorithm to iteratively solve the minimization multi-objective optimization model. Each solution corresponds to a configuration scheme.

2. The method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions according to claim 1, characterized in that, The photovoltaic power generation system model is as follows: ； in, This refers to the actual power generation of the photovoltaic power generation system. It is the rated power. These are the standard and maximum irradiance, These are the actual and standard temperatures, respectively, and k is the power temperature coefficient. The energy storage system model is as follows: ； in, , These are the states of charge of the energy storage system at time t and t+1. This is the current power level of the energy storage system. It is the rated capacity of the energy storage system. and These are the charge and discharge efficiencies, and These are the charging and discharging power, It is the total energy of the energy storage device; The charging pile load model distinguishes between AC slow charging within the park and DC fast charging outside the park. The total load curve is as follows: ； in, , These refer to the conversion efficiency of DC fast charging and AC slow charging, respectively. , These represent the DC fast charging and AC slow charging power, respectively. The power was obtained by performing multiple Monte Carlo simulations on the behavior of a large number of electric vehicles and accumulating the charging power of multiple electric vehicles. The formula is as follows: ； ； in, It is the number of Monte Carlo simulations. It is the first The number of electric vehicles in this simulation. It is an index of the number of simulations. It is an index for a single vehicle. , For the first In the simulation, the first The AC or DC charging power of the vehicle at time t.

3. The method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions according to claim 1, characterized in that, The optimization objective for step 2 is calculated using the following formula: Economic efficiency: ； ； reliability: ； Carbon emissions: ； Photovoltaic grid integration rate: ； System energy efficiency: ； in, This is the total investment and operating cost of a photovoltaic-storage-charging microgrid, calculated over one year. It is the annual value equipment investment cost discount rate. It is the discount rate. It is the project operation phase. This refers to the project's operational phase after equipment replacement. This refers to the initial investment cost of the equipment. This is the cost of equipment replacement. This refers to the annual maintenance and operation costs of a photovoltaic, energy storage, and charging system. These are the fees incurred from transactions with the power grid. It is the system power shortage rate. It refers to photovoltaic power generation capacity. It refers to the power of solar power curtailment. It refers to the power of the energy storage battery. This is the system's required power. It is a carbon emission factor. It refers to the power purchased by the power grid. This is the actual power output of the photovoltaic system. It is the power loss of the converter. It is the line loss power. It is a unit time interval.

4. The method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions according to claim 1, characterized in that, The system constraints are as follows: System power balance constraints: Off-grid operating conditions are met: ； Grid connection conditions meet: ； in, This refers to the actual output power of the photovoltaic power generation system. It is the discharge power of the energy storage system. This refers to the power of all other conventional electrical loads within the park, excluding electric vehicle charging loads. It is the charging power of the energy storage system. It refers to the charging power of electric vehicles. It refers to the curtailment power of photovoltaic systems; Energy storage system capacity constraints refer to the requirement that the state of charge of the energy storage system must be maintained between the minimum and maximum allowable values. The charging and discharging power constraints of energy storage systems mean that the charging power and discharging power of the energy storage system must both be greater than or equal to zero, and must not exceed its maximum allowable charging power and maximum allowable discharging power, respectively; in addition, the energy storage system cannot charge and discharge simultaneously, that is, the product of the charging power and the discharging power must be zero. The power constraint of a charging pile refers to the requirement that the charging power of a DC charging pile must be greater than or equal to zero and not exceed the maximum rated power of the DC charging pile. Photovoltaic capacity constraints refer to the requirement that the power output of a photovoltaic power generation system must be greater than or equal to zero and not exceed [a certain limit]. The product of the light intensity normalization factor; DC bus power balance constraint means that the transmission power of the DC bus must be greater than or equal to zero and not exceed the maximum allowable transmission power of the DC bus; The decision variable range constraint means that the decision variables in system optimization must be greater than or equal to their technically permissible minimum value and less than or equal to their technically permissible maximum value. The decision variables include photovoltaic capacity, energy storage capacity, and converter capacity.

5. The method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions according to claim 1, characterized in that, The heterodimensional optimization objectives include economic objectives and carbon emission objectives.

6. The method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions according to claim 1, characterized in that, The method for determining the range of decision variables in step 3 is as follows: The maximum number of electric vehicles that the park can receive is determined based on the power constraints of the charging piles, and the number of charging piles is determined based on the maximum number of electric vehicles. Based on photovoltaic capacity constraints and site area, determine the maximum photovoltaic capacity that can be installed in the park; The converter capacity is determined based on load demand and maximum photovoltaic capacity. Determine energy storage capacity based on actual needs.

7. The method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions according to claim 1, characterized in that, The initial and termination conditions for the simulation are as follows: At the initial time t=0 and the final time t=T, the state of charge of the energy storage system and the charging amount of the charging pile satisfy the following: ； ； The initial state of charge of the energy storage system at t=0 has a certain margin for the charging amount of the charging pile.

8. The method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions according to claim 1, characterized in that, The load tracking management strategy is specifically as follows: (5.1) Compare photovoltaic power generation with load power in real time and classify the system operation status into photovoltaic surplus mode or photovoltaic deficit mode; (5.2) Divide the power grid electricity price period into peak period, normal period and off-peak period, and combine the state of charge (SOC) of the energy storage system and the power grid interaction limit to dynamically select the corresponding power allocation strategy; In the surplus photovoltaic mode: During peak periods, the control objective is to maximize self-consumption of photovoltaic power and peak shaving through energy storage discharge. If the grid interaction power does not exceed the limit, the strategy of selling all surplus power is adopted. If the photovoltaic surplus exceeds the grid's maximum receiving power and the energy storage is not fully utilized, the strategy of prioritizing the sale of surplus power and absorbing excess energy through energy storage is adopted. During normal periods, the control objective is to maintain the energy storage SOC within a healthy range, prioritizing the energy storage system to handle and balance the power difference. During off-peak periods, the control objective is to utilize low-priced electricity to increase the energy storage SOC to its maximum value, prioritizing the use of photovoltaic power and the grid to charge the energy storage. Under the photovoltaic deficit mode: During peak periods, the control objective is to maximize photovoltaic self-consumption and utilize energy storage discharge for peak shaving. If the energy storage capacity is sufficient, the strategy of having all deficit power supplemented by energy storage is selected. If the deficit still cannot be met after energy storage discharge, the strategy of having the deficit supplemented by both energy storage and the grid is selected. During normal periods, the control objective is to maintain the energy storage SOC within a healthy range, prioritizing the energy storage system to handle and balance the power difference. During off-peak periods, the control objective is to utilize low-priced electricity to increase the energy storage SOC to its maximum value, with the grid supplying power to supplement the deficit, and utilizing the grid's remaining capacity to charge the energy storage.

9. A method for optimizing the configuration of a photovoltaic-storage-charging microgrid considering system energy efficiency and carbon emissions, as described in claim 8, is characterized in that... The load tracking management strategy also includes differentiated scheduling of AC and DC charging piles: DC charging piles connected to the DC bus are given priority to be powered by the DC side power supply, and AC charging piles connected to the AC bus are given priority to be powered by the AC side power supply. When there is a power surplus on one side and a load deficit on the other side, the surplus power is converted by the converter and supplied to the side with the deficit.

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