A day-ahead power optimization distribution method for a light-storage-charging integrated system considering energy storage loss

Abstract

The present application relates to the technical field of energy storage optimization control, and particularly relates to a day-ahead power optimization distribution method of a photovoltaic energy storage and charging integrated system considering energy storage loss, comprising: obtaining day-ahead operation data of the photovoltaic energy storage and charging integrated system; establishing an energy storage loss model based on the day-ahead operation data; taking energy storage charging and discharging power and grid interactive power as decision variables, establishing a day-ahead power optimization model based on the energy storage loss model, the day-ahead power optimization model comprising an economic benefit target, a carbon emission target and an energy storage loss target; based on the day-ahead power optimization model and preset constraint conditions, optimizing and solving the decision variables to obtain day-ahead power distribution results of each scheduling period in a future preset scheduling period; and outputting the day-ahead power distribution results. The present application is used to solve the problem that in the day-ahead power distribution process of the photovoltaic energy storage and charging integrated system, only the energy storage charging and discharging efficiency is considered, the energy storage body loss and power conversion loss are not uniformly modeled, and the distribution results deviate from the actual operation state.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization and control technology, specifically to a day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system that takes into account energy storage losses. Background Technology

[0002] In integrated photovoltaic-storage-charging systems, the output power of photovoltaic power generation units fluctuates, and the electric vehicle charging load exhibits random variations. The energy storage unit is used to coordinate the energy balance between photovoltaic power generation, electric vehicle charging load, and the power grid. Day-ahead power optimization allocation is typically based on historical photovoltaic output data and historical load data to schedule and plan the photovoltaic power consumption, energy storage charging and discharging power, and grid interaction power within a preset future time period.

[0003] Existing day-ahead scheduling methods for integrated photovoltaic, energy storage, and charging systems typically use charging service revenue, electricity purchase cost, carbon emissions, or photovoltaic absorption rate as optimization objectives, and combine genetic algorithms, particle swarm optimization, and non-dominated sorting genetic algorithms to solve for energy storage charging and discharging power and grid interaction power.

[0004] However, existing technologies typically treat energy storage systems as ideal charging and discharging devices, considering only charging and discharging efficiency or state of charge constraints, without providing a unified model for the inherent losses and auxiliary equipment losses during energy storage operation. Taking vanadium redox flow batteries as an example, their operation involves not only stack internal resistance heating losses and parasitic branch losses, but also electrolyte circulation pump power consumption and energy storage converter losses. When existing day-ahead scheduling models do not fully consider these loss factors, it can easily lead to large fluctuations in energy storage charging and discharging power and frequent charging and discharging, resulting in a deviation between the day-ahead power allocation results and the actual operating state. Summary of the Invention

[0005] The purpose of this invention is to provide a day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system that takes into account energy storage losses, in order to solve the above-mentioned technical problems. This method aims to address the issue that existing integrated photovoltaic-storage-charging systems only consider energy storage charging and discharging efficiency or state of charge constraints in the day-ahead power optimization allocation process, without uniformly modeling energy storage losses and power conversion losses, resulting in a deviation between the day-ahead power allocation results and the actual operating state of energy storage.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for optimizing day-ahead power allocation in a photovoltaic-storage-charging integrated system that takes into account energy storage losses includes: S1. Obtain the day-ahead operating data of the integrated photovoltaic, energy storage and charging system; S2. Establish an energy storage loss model based on day-to-day operational data; S3. Using energy storage charging and discharging power and grid interaction power as decision variables, establish a day-ahead power optimization model based on the energy storage loss model. The day-ahead power optimization model includes economic benefit targets, carbon emission targets, and energy storage loss targets. S4. Based on the day-ahead power optimization model and preset constraints, optimize the decision variables to obtain the day-ahead power allocation results for each scheduling period within the future preset scheduling cycle. S5, Output the day-ahead power allocation results.

[0007] Furthermore, step S1 includes: unifying the photovoltaic power output data, charging load data, and electricity price data to the same time scale, and removing and correcting missing values, outliers, and unreasonable data in the data sequence.

[0008] Furthermore, the energy storage loss model includes an energy storage body loss model and a power conversion loss model. The energy storage body loss model is established based on the equivalent loss circuit of a full vanadium redox flow battery.

[0009] Furthermore, the energy storage body loss model includes the controlled voltage source, parasitic branches, electrode polarization capacitance, reaction internal resistance, ohmic internal resistance, and pump equivalent branches. The energy storage body loss includes stack internal resistance loss, parasitic branch loss, and pump power consumption; the stack internal resistance loss is expressed as: in, This is the current in the fuel cell circuit. For ohmic internal resistance, For parasitic branch current, Internal resistance to the reaction; Parasitic branch loss Represented as: in, Port voltage, For parasitic branch resistance; Pump power consumption Represented as: in, The power consumption coefficient of the pump set. This is the current in the fuel cell circuit. This represents the current state of charge. Energy storage body loss Represented as: .

[0010] Furthermore, the power conversion loss model includes the on-state voltage drop loss, switching loss, and static power consumption of the energy storage converter, and determines the power conversion loss based on the power conversion efficiency of the energy storage converter, as well as the comprehensive loss on the energy storage side. Represented as: in, This is the electricity price coefficient. For a single resource allocation time window, This refers to power conversion losses.

[0011] Furthermore, the economic benefit target is constructed based on charging service revenue, photovoltaic surplus electricity grid connection revenue, grid power purchase cost, energy storage replacement cost, and system operation and maintenance cost; the carbon emission target is constructed based on grid power purchase and carbon emission coefficient; and the energy storage loss target is constructed based on the energy storage loss model. The comprehensive objective function of the current power optimization model is expressed as: in, As an indicator for evaluating economic benefits. As a carbon emission assessment indicator, As an indicator for evaluating energy storage losses, , as well as Let be the weighting coefficient, and satisfy: Economic benefit evaluation indicators Represented as: in, Revenue from charging electric vehicles For the revenue from surplus photovoltaic power sold to the grid, For the cost of replacing the energy storage system, For the cost of purchasing electricity from the power grid, To reduce the operation and maintenance costs of the photovoltaic-storage-charging system; Electric vehicle charging revenue Represented as: in, The price is for charging service. The charging price at the charging station at time t; Revenue from surplus photovoltaic power fed into the grid Represented as: in, The unit price of electricity sold to the grid. for The power of the photovoltaic surplus that can be fed into the grid at any given time; Energy storage system replacement cost Represented as: in, For the number of energy storage batteries, The unit price of energy storage batteries, For the operation and maintenance costs of energy storage batteries; The percentage of energy storage batteries that are phased out each year; , , which represent the discount rate and service life of the energy storage battery, respectively; Electricity purchase cost Represented as: in, For a moment The power that the charging station purchases from the power grid; Operation and maintenance costs of photovoltaic energy storage and charging systems Represented as: in, , These are the annual operation and maintenance coefficients for the photovoltaic unit and the energy storage unit, respectively. The unit price per photovoltaic power. For total photovoltaic installed capacity, The rated total capacity of the energy storage system The price of power conversion equipment for energy storage systems, Rated power of the energy storage unit; Carbon emission assessment indicators Represented as: in, This refers to the net input electrical energy drawn from the external main grid by the charging station. Defined as the equivalent carbon dioxide emissions per kilowatt-hour of electricity consumed; Net input electrical energy Represented as: in, To obtain photovoltaic power generation, Charge the electric vehicle. This refers to the amount of electricity generated from energy storage.

[0012] Furthermore, the preset constraints include power balance constraints, energy storage charging and discharging power constraints, grid power purchase and sale constraints, energy storage state of charge constraints, and photovoltaic power generation constraints. The power balance constraints are constructed based on charging load, photovoltaic output, energy storage charging and discharging power, and grid interaction power. The power balance constraints are expressed as: in, For charging load power, Photovoltaic power generation capacity, For energy storage charging and discharging power, Power exchange between the power grid; The energy storage charging and discharging power constraint is expressed as: in, Maximum charging power, For maximum discharge power, For charge and discharge efficiency; The power grid interaction constraint is expressed as: in, The rated power of the bidirectional AC-DC rectifier unit. This refers to the rated capacity of the distribution transformer. The energy storage state of charge constraint is expressed as: in, This represents the lower limit of the battery's remaining energy. S represents the upper limit of the battery's remaining energy, and S represents the amount of charge stored in the battery. Photovoltaic power generation constraints are expressed as follows: in, This represents the lower limit of photovoltaic power generation. This represents the upper limit of photovoltaic power generation.

[0013] Furthermore, after step S1, the K-means++ clustering method is used to cluster the historical photovoltaic power output data and historical charging load data to obtain typical daily data, and the analytic hierarchy process is used to determine the weight coefficients corresponding to the economic benefit target, carbon emission target and energy storage loss target.

[0014] Furthermore, step S4 includes: Encode the energy storage charging and discharging power and the grid interaction power, and initialize the population; Generate a first-generation population of candidate scheduling schemes; Based on the fitness and crossover / mutation probability of the current generation population, perform crossover and mutation operations on the current generation population; Merge the populations after crossover and mutation operations; Perform non-dominated sorting and crowding distance calculation on the merged population; Population selection is performed based on non-dominated ordination results and crowding distance calculation results to generate a new generation of population; Determine if the current iteration count has reached the maximum iteration count. If the current iteration count has not reached the maximum iteration count, continue to execute the next generation of population optimization. If the current iteration count has reached the maximum iteration count, output the day-ahead power allocation result.

[0015] Furthermore, the population selection adopts an elite retention strategy; the crossover and mutation operations adopt an adaptive crossover and mutation strategy; the adaptive crossover and mutation strategy dynamically adjusts the crossover and mutation probabilities based on the fitness of the current generation population.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention establishes an energy storage loss model that includes energy storage body loss and power conversion loss, and incorporates the energy storage loss target into the day-ahead power optimization model, so that the day-ahead power allocation process takes into account the energy reduction factor in the energy storage operation process.

[0017] 2. This invention simultaneously constructs economic benefit targets, carbon emission targets, and energy storage loss targets in the day-ahead power optimization model, so that the energy storage charging and discharging scheduling process simultaneously considers charging service revenue, grid electricity purchase carbon emissions, and energy storage operation losses.

[0018] 3. This invention enables energy storage units to perform power scheduling according to preset operating boundaries by establishing energy storage charging and discharging power constraints, energy storage state of charge constraints, and grid interaction power constraints.

[0019] 4. This invention employs an improved NSGA-II algorithm, including non-dominated sorting, distribution entropy crowding calculation, elite retention operation, and adaptive crossover mutation operation, to iteratively solve the day-ahead power optimization model. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system that takes into account energy storage losses, according to the present invention. Figure 2 A schematic diagram of the equivalent loss model of a full vanadium redox flow battery; Figure 3 A schematic diagram of the improved NSGA-II optimization process; Figure 4 Energy flow characteristics diagram for day-ahead power allocation taking losses into account; Figure 5Energy flow characteristics diagram for day-ahead power allocation without considering losses. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0022] like Figures 1 to 5 The method for optimizing day-ahead power allocation in a photovoltaic-storage-charging integrated system, taking into account energy storage losses, includes: S1. Obtain the day-ahead operating data of the integrated photovoltaic-storage-charging system. This data includes photovoltaic output data, charging load data, electricity price data, carbon emission coefficient, energy storage capacity, initial state of charge (SBC), upper and lower limits of SBC, maximum charging power, maximum discharging power, grid-connected power limit, and energy storage converter efficiency. During data processing, photovoltaic output, charging load, and electricity price data are unified to the same time scale, preferably using 15-minute intervals to form a data sequence of 96 scheduling points for the next 24 hours. Missing values, outliers, and obviously unreasonable data are removed or corrected to ensure that the subsequent optimization model can calculate photovoltaic power, load power, energy storage power, and grid interaction power on a unified time axis. After step S1, K-means++ clustering is used to cluster historical photovoltaic output data and historical charging load data to obtain typical daily data. The analytic hierarchy process (AHP) is then used to determine the weighting coefficients corresponding to economic benefit targets, carbon emission targets, and energy storage loss targets.

[0023] S2. Establish an energy storage loss model based on day-ahead operating data. The energy storage loss model includes an energy storage body loss model and a power conversion loss model. The energy storage body loss model is established based on the equivalent loss circuit of a full vanadium redox flow battery. Step S2 incorporates the energy storage body loss and power conversion loss during energy storage operation into the day-ahead power optimization process by establishing the energy storage loss model, allowing the energy reduction factor during energy storage operation to participate in the day-ahead power allocation calculation. Figure 2The equivalent circuit of the vanadium redox flow battery equivalent loss model is shown below. The physical quantities in the equivalent circuit structure are as follows: The electrical characteristics of the vanadium redox flow battery are represented by the port voltage and the total charging / discharging current. The open-circuit voltage of the stack is closely related to the state of charge (SOC), so in the equivalent representation, the stack appears as a controlled voltage source with a stack loop current; the mechanical power consumption generated by the circulating pump is also equivalently replaced by a certain current source. In addition, the dynamic processes and energy losses included in the model mainly consist of three parts: first, the self-discharge branch containing parasitic impedance and corresponding leakage current; second, the electrode polarization capacitance representing the instantaneous response; and third, the reaction internal resistance caused by electrochemical activation polarization and the ohmic internal resistance brought by physical components such as the ion exchange membrane. Theoretically, the equivalent internal resistance of the stack will vary depending on the number of cascaded systems, operating temperature, and real-time charge level.

[0024] The energy loss of the energy storage unit is caused by the losses of the VRFB itself and the losses of the supporting equipment during operation. Based on equivalent circuit losses, the power loss of the VRFB can be decomposed into electrolyte circulation pump consumption, parasitic branch losses, and heat generation from equivalent internal resistance. When the performance of each individual stack is consistent, the internal resistance loss... Due to internal reaction resistance and Ohmic internal resistance These factors, according to statistics, contribute 60% and 40% respectively to the total internal losses. Furthermore, the model also considers port voltage. The generation of parasitic resistance loss And converted into pump loss current Specifically, the energy storage body loss model includes the controlled voltage source, parasitic branches, electrode polarization capacitance, reaction internal resistance, ohmic internal resistance, and pump equivalent branches. The energy storage body loss includes stack internal resistance loss, parasitic branch loss, and pump power consumption. The stack internal resistance loss is expressed as: in, This is the current in the fuel cell circuit. For ohmic internal resistance, For parasitic branch current, Internal resistance to the reaction; Parasitic branch loss Represented as: in, Port voltage, For parasitic branch resistance; Pump power consumption Represented as: in, The power consumption coefficient of the pump set. Take a fixed value of 42.5. This is the current in the fuel cell circuit. The current state of charge; Based on the above calculation, the energy storage body loss during system operation can be expressed as: .

[0025] Energy storage loss is a component of the DC / DC power electronic conversion process. Energy loss on the energy storage side includes three parts: on-state voltage drop loss of semiconductor devices, high-frequency switching loss, and static power consumption for circuit operation. Based on typical converter operating parameters, the overall energy conversion efficiency is defined as 95%, and the corresponding power loss is calculated at 5% of the rated power. The power conversion loss is determined based on the energy storage converter's power conversion efficiency, resulting in the comprehensive energy storage side loss. Represented as: in, This is the electricity price coefficient. For a single resource allocation time window, This refers to power conversion losses.

[0026] S3. Using the energy storage charging and discharging power and grid interaction power during each scheduling period within 24 hours as decision variables, a day-ahead power optimization model is established based on the energy storage loss model. The day-ahead power optimization model includes economic benefit targets, carbon emission targets, and energy storage loss targets. Step S3 establishes a day-ahead power optimization model based on the energy storage loss model, and uses the economic benefit targets, carbon emission targets, and energy storage loss targets together as day-ahead power optimization targets, enabling coordinated scheduling of energy storage charging and discharging power and grid interaction power under a unified optimization framework. Specifically, the economic benefit target is constructed based on charging service revenue, photovoltaic surplus power grid connection revenue, grid power purchase cost, energy storage replacement cost, and system operation and maintenance cost; the carbon emission target is constructed based on grid power purchase and carbon emission coefficients; and the energy storage loss target is constructed based on the energy storage loss model. To facilitate the solution, this embodiment integrates multiple mutually exclusive sub-criteria into a unified comprehensive benefit function by performing a weighted mapping transformation on the multi-dimensional objective space. This achieves a single-objective dimensionality reduction solution for the multi-objective problem. The comprehensive objective function of the current power optimization model is expressed as follows: in, As an indicator for evaluating economic benefits. As a carbon emission assessment indicator, As an indicator for evaluating energy storage losses, , as well as Let be the weighting coefficients, and satisfy: Before the weighted operation, the objective function was preprocessed and normalized to ensure that the optimization algorithm performs a global search on different criteria at the same order of magnitude, so that its numerical range is uniformly mapped to the [0,1] interval, thus ensuring that each sub-objective is comparable in the optimization process; An energy dispatch model is constructed with the goal of maximizing the net daily revenue of charging stations. Total revenue includes charging service revenue and revenue from grid connection of surplus photovoltaic power, while total costs include operation and maintenance costs, energy storage system replacement costs, and grid power purchase expenditures. Economic benefit evaluation indicators are used... Represented as: in, Revenue from charging electric vehicles For the revenue from surplus photovoltaic power sold to the grid, For the cost of replacing the energy storage system, For the cost of purchasing electricity from the power grid, To reduce the operation and maintenance costs of the photovoltaic-storage-charging system; Electric vehicle charging revenue Represented as: in, The price is for charging service. The charging price at the charging station at time t; Revenue from surplus photovoltaic power fed into the grid Represented as: in, The unit price of electricity sold to the power grid for The power of the photovoltaic surplus that can be fed into the grid at any given time; Energy storage system replacement cost Represented as: in, For the number of energy storage batteries, The unit price of energy storage batteries, For the operation and maintenance costs of energy storage batteries; This represents the percentage of energy storage batteries that are phased out each year. , , which represent the discount rate and service life of the energy storage battery, respectively; Electricity purchase cost Represented as: in, For a moment The power that the charging station purchases from the power grid; Operation and maintenance costs of photovoltaic energy storage and charging systems Represented as: in, , These are the annual operation and maintenance coefficients for the photovoltaic unit and the energy storage unit, respectively. The unit price per photovoltaic power. For total photovoltaic installed capacity, The rated total capacity of the energy storage system The price of power conversion equipment for energy storage systems, Rated power of the energy storage unit; Carbon emission assessment indicators Represented as: in, This refers to the net input electrical energy that the charging station draws from the external main grid. Defined as the equivalent carbon dioxide emissions per kilowatt-hour of electricity consumed; Net input electrical energy Represented as: in, For photovoltaic power generation, Charge the electric vehicle. This refers to the amount of electricity generated from energy storage.

[0027] S4. Based on the day-ahead power optimization model and preset constraints, the specific constraints are as follows: The preset constraints include power balance constraints, energy storage charging and discharging power constraints, grid power purchase and sale constraints, energy storage state of charge constraints, and photovoltaic power generation constraints. The power balance constraints are constructed based on charging load, photovoltaic output, energy storage charging and discharging power, and grid interaction power. The power balance constraints are expressed as follows: in, For charging load power, Photovoltaic power generation capacity, For energy storage charging and discharging power, Power exchange between the power grid; The energy storage charging and discharging power constraint is expressed as: in, Maximum charging power, For maximum discharge power, For charge and discharge efficiency; The power grid interaction constraint is expressed as: in, The rated power of the bidirectional AC-DC rectifier unit. This refers to the rated capacity of the distribution transformer. The energy storage state of charge constraint is expressed as: in, This represents the lower limit of the battery's remaining energy. S represents the upper limit of the battery's remaining energy, and S represents the amount of charge stored in the battery. Photovoltaic power generation constraints are expressed as follows: in, This represents the lower limit of photovoltaic power generation. This represents the upper limit of photovoltaic power generation.

[0028] The decision variables are optimized to obtain the day-ahead power allocation results for each scheduling period within the future preset scheduling cycle; specifically: In step S4, the improved NSGA-II algorithm is used to solve the day-ahead power optimization model. Specifically, firstly, the energy storage charging and discharging power and the grid interaction power are encoded as real numbers. The energy storage charging power, energy storage discharging power, and grid interaction power corresponding to each scheduling period are used as chromosome gene variables to construct an initial candidate scheduling scheme population. Multiple candidate scheduling schemes are randomly generated according to the preset population size, and each candidate scheduling scheme is used as an individual in the first generation population.

[0029] Subsequently, the current generation of individuals is substituted into the day-ahead power optimization model to calculate the economic benefit evaluation index, carbon emission evaluation index, and energy storage loss evaluation index for each individual. The fitness value of each individual is then calculated based on the comprehensive objective function. After obtaining the fitness values, crossover and mutation operations are performed on the current generation of individuals according to preset crossover and mutation probabilities to generate the offspring population. The crossover operation is used to recombine genetic variables in different candidate scheduling schemes, while the mutation operation is used to randomly perturb some genetic variables in the candidate scheduling schemes.

[0030] In this embodiment, the crossover and mutation operations employ an adaptive crossover-mutation strategy. Specifically, the crossover and mutation probabilities are dynamically adjusted based on the fitness values ​​of the current generation's individuals. When the fitness differences among individuals in the population are significant, the crossover probability is increased and the mutation probability is decreased; when the fitness of individuals in the population gradually converges, the crossover probability is decreased and the mutation probability is increased to enhance the population's search capability.

[0031] After completing the crossover and mutation operations, the parent and offspring populations are merged to form a joint population, and a non-dominated sorting is performed on the joint population. Specifically, based on the Pareto dominance relationships between the candidate scheduling schemes, the joint population is divided into multiple non-dominated levels. Simultaneously, crowding distance calculations are performed on candidate scheduling schemes within the same non-dominated level to characterize their distribution in the target space.

[0032] In this embodiment, the congestion distance calculation adopts a congestion measurement method based on distribution entropy. By statistically analyzing the local distribution of candidate scheduling schemes within the target space, the distribution entropy value corresponding to each candidate scheduling scheme is obtained, and the congestion distance of the candidate scheduling scheme is determined based on the distribution entropy value.

[0033] Subsequently, based on the non-dominated ranking results and crowding distance calculation results, the joint population was screened. During the screening process, candidate scheduling schemes with higher non-dominated levels were prioritized for retention; for candidate scheduling schemes within the same non-dominated level, those with larger crowding distances were prioritized for retention, in order to form a new generation of population. The population screening employed an elite retention strategy, directly retaining superior individuals from the current generation to the next generation.

[0034] Next, it is determined whether the current iteration count has reached the maximum iteration count. If the current iteration count has not reached the maximum iteration count, the next generation population is used as the current generation population to continue the next generation population optimization; if the current iteration count has reached the maximum iteration count, the day-ahead power allocation result that meets the preset optimization objective is output. The day-ahead power allocation result includes the energy storage charging and discharging power and grid interaction power corresponding to each scheduling period.

[0035] Step S4 optimizes the energy storage charging and discharging power and grid interaction power based on the day-ahead power optimization model and preset constraints, so that the obtained day-ahead power allocation results satisfy the power balance constraints, energy storage operation boundary constraints and grid operation constraints.

[0036] S5. Output the day-ahead power allocation results and generate the energy storage power scheduling scheme and grid interaction power scheduling scheme for the integrated photovoltaic-storage-charging system. Based on the solution results of the improved NSGA-II, output the energy storage charging power, energy storage discharging power, grid power purchase power, grid power sale power, and photovoltaic power consumption power for each scheduling period in the next 24 hours, forming a day-ahead power plan that can be sent to the photovoltaic-storage-charging energy management system.

[0037] Figure 4 The diagram shows the day-ahead power allocation results considering energy storage losses. Figure 5The first two figures show the day-ahead power allocation results without considering energy storage losses. Both figures reflect the changes in photovoltaic output, charging load, energy storage charging and discharging power, and grid interaction power during different dispatch periods, and are used to compare and analyze the impact of the energy storage loss model on the day-ahead power dispatch results. Figure 4 It can be seen that, after taking into account energy storage losses, the charging and discharging power changes of the energy storage unit in each scheduling period are relatively continuous, and the peak charging and discharging power and power reversal amplitude are relatively small. The energy storage power scheduling process considers both the energy storage unit's own losses and power conversion losses. Correspondingly, the grid interaction power is adjusted in coordination with the energy storage's operating status.

[0038] Depend on Figure 5 It can be seen that, without considering energy storage losses, energy storage units experience significant variations in charging and discharging power during certain scheduling periods, resulting in numerous energy storage charge / discharge switching operations and large fluctuations in energy storage power. Because the losses during energy storage operation are not modeled, the day-ahead power optimization process tends to adjust power by frequently calling energy storage units. Through comparison... Figure 4 and Figure 5 It can be seen that the day-ahead power allocation results obtained after taking into account energy storage losses can simultaneously consider the relationship between energy storage operation losses, grid interaction power and load demand during energy storage dispatch, so that the day-ahead power optimization results correspond to the actual operating status of energy storage.

[0039] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0040] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0041] The present invention has been further described above with reference to specific embodiments. However, it should be understood that the specific description herein should not be construed as limiting the nature and scope of the present invention. Various modifications made to the above embodiments by those skilled in the art after reading this specification are all within the scope of protection of the present invention.

Claims

1. A method for optimizing day-ahead power allocation in a photovoltaic-storage-charging integrated system considering energy storage losses, characterized in that, include: S1. Obtain the day-ahead operating data of the integrated photovoltaic, energy storage and charging system; S2. Establish an energy storage loss model based on the aforementioned daytime operating data; S3. Using energy storage charging and discharging power and grid interaction power as decision variables, establish a day-ahead power optimization model based on the energy storage loss model. The day-ahead power optimization model includes economic benefit targets, carbon emission targets, and energy storage loss targets. S4. Based on the day-ahead power optimization model and preset constraints, the decision variables are optimized and solved using the improved non-dominated sorting genetic algorithm NSGA-II to obtain the day-ahead power allocation results for each scheduling period within the future preset scheduling cycle. S5. Output the daytime power allocation results.

2. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 1, characterized in that, The day-ahead operating data includes photovoltaic power output data, charging load data, electricity price data, carbon emission coefficient, energy storage capacity, initial state of charge of energy storage, upper and lower limits of state of charge of energy storage, maximum charging power of energy storage, maximum discharging power of energy storage, upper limit of grid-connected power, and energy storage converter efficiency; step S1 includes: unifying the photovoltaic power output data, the charging load data, and the electricity price data to the same time scale, and removing and correcting missing values, outliers, and unreasonable data in the data sequence.

3. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 1, characterized in that, The energy storage loss model includes an energy storage body loss model and a power conversion loss model. The energy storage body loss model is established based on the equivalent loss circuit of a full vanadium redox flow battery.

4. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 3, characterized in that, The energy storage body loss model includes a controlled voltage source, parasitic branches, electrode polarization capacitance, reaction internal resistance, ohmic internal resistance, and pump equivalent branches. The energy storage body loss includes stack internal resistance loss, parasitic branch loss, and pump power consumption. The stack internal resistance loss is expressed as: in, This is the current in the fuel cell circuit. For ohmic internal resistance, For parasitic branch current, Internal resistance to the reaction; The parasitic branch loss Represented as: in, Port voltage, For parasitic branch resistance; The power consumption of the pump set Represented as: in, The power consumption coefficient of the pump set. This is the current in the fuel cell circuit. The current state of charge; The energy storage body loss Represented as: 。 5. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 3, characterized in that, The power conversion loss model includes the on-state voltage drop loss, switching loss, and static power consumption of the energy storage converter, and determines the power conversion loss based on the power conversion efficiency of the energy storage converter. The comprehensive loss on the energy storage side... Represented as: in, This is the electricity price coefficient. For a single resource allocation time window, This refers to power conversion losses.

6. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses as described in claim 1, characterized in that, The economic benefit target is constructed based on charging service revenue, photovoltaic surplus electricity grid connection revenue, grid power purchase cost, energy storage replacement cost, and system operation and maintenance cost; the carbon emission target is constructed based on grid power purchase and carbon emission coefficient; and the energy storage loss target is constructed based on the energy storage loss model. The comprehensive objective function of the daytime power optimization model is expressed as: in, As an indicator for evaluating economic benefits. As a carbon emission assessment indicator, As an indicator for evaluating energy storage losses, , as well as Let be the weighting coefficients, and satisfy: The economic benefit evaluation indicators Represented as: in, Revenue from charging electric vehicles For the revenue from surplus photovoltaic power sold to the grid, For the cost of replacing the energy storage system, For the cost of purchasing electricity from the power grid, To reduce the operation and maintenance costs of the photovoltaic-storage-charging system; Electric vehicle charging revenue Represented as: in, The price is for charging service. The charging price at the charging station at time t; The revenue from surplus photovoltaic power being fed into the grid Represented as: in, The unit price of electricity sold to the power grid for The power of the photovoltaic surplus that can be fed into the grid at any given time; The replacement cost of the energy storage system Represented as: in, For the number of energy storage batteries, The unit price of energy storage batteries, For the operation and maintenance costs of energy storage batteries; This represents the percentage of energy storage batteries that are phased out each year. , , which represent the discount rate and service life of the energy storage battery, respectively; The electricity purchase cost of the power grid Represented as: in, For a moment The power that the charging station purchases from the power grid; The operation and maintenance cost of the photovoltaic energy storage and charging system Represented as: in, , These are the annual operation and maintenance coefficients for the photovoltaic unit and the energy storage unit, respectively. The unit price per photovoltaic power. For total photovoltaic installed capacity, The rated total capacity of the energy storage system The price of power conversion equipment for energy storage systems, Rated power of the energy storage unit; The carbon emission assessment indicators Represented as: in, This refers to the net input electrical energy drawn from the external main grid by the charging station. Defined as the equivalent carbon dioxide emissions per kilowatt-hour of electricity consumed; Net input electrical energy Represented as: in, To obtain photovoltaic power generation, Charge the electric vehicle. This refers to the amount of electricity generated from energy storage.

7. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 1, characterized in that, The preset constraints include power balance constraints, energy storage charging and discharging power constraints, grid power purchase and sale constraints, energy storage state of charge constraints, and photovoltaic power generation constraints; the power balance constraints are constructed based on charging load, photovoltaic output, energy storage charging and discharging power, and grid interaction power; the power balance constraints are expressed as follows: in, For charging load power, Photovoltaic power generation capacity, For energy storage charging and discharging power, Power exchange between the power grid; The energy storage charging and discharging power constraint is expressed as follows: in, Maximum charging power, For maximum discharge power, For charge and discharge efficiency; The power grid interaction constraint is expressed as: in, The rated power of the bidirectional AC-DC rectifier unit. This refers to the rated capacity of the distribution transformer. The energy storage state of charge constraint is expressed as follows: in, This represents the lower limit of the battery's remaining energy. S represents the upper limit of the battery's remaining energy, and S represents the amount of charge stored in the battery. The photovoltaic power generation constraint is expressed as follows: in, This represents the lower limit of photovoltaic power generation. This represents the upper limit of photovoltaic power generation.

8. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 1, characterized in that, After step S1, the historical photovoltaic power output data and historical charging load data are clustered using the K-means++ clustering method to obtain typical daily data, and the weight coefficients corresponding to the economic benefit target, the carbon emission target and the energy storage loss target are determined using the analytic hierarchy process.

9. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 1, characterized in that, Step S4 includes: The energy storage charging and discharging power and the grid interaction power are encoded to initialize the population; Generate a first-generation population of candidate scheduling schemes; Based on the fitness and crossover / mutation probability of the current generation population, crossover and mutation operations are performed on the current generation population. Merge the populations after crossover and mutation operations; Perform non-dominated sorting and crowding distance calculation on the merged population; Population selection is performed based on non-dominated ordination results and crowding distance calculation results to generate a new generation of population; Determine if the current iteration count has reached the maximum iteration count. If the current iteration count has not reached the maximum iteration count, continue to execute the next generation of population optimization. If the current iteration count has reached the maximum iteration count, output the day-ahead power allocation result.

10. The day-ahead power optimization allocation method for an integrated photovoltaic-storage-charging system considering energy storage losses according to claim 9, characterized in that, The population selection adopts an elite retention strategy; the crossover and mutation operations adopt an adaptive crossover and mutation strategy; the adaptive crossover and mutation strategy dynamically adjusts the crossover and mutation probabilities based on the fitness of the current generation population.

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