A park light storage and charging system configuration and dynamic scheduling method fusing intelligent power distribution

Abstract

This invention discloses a method for configuring and dynamically scheduling a photovoltaic-storage-charging system in an industrial park that integrates smart power distribution, belonging to the fields of new energy microgrids and smart power distribution technology. The method includes: constructing an integrated photovoltaic-storage-charging system capable of both grid-connected and islanded operation; selecting key equipment for photovoltaics, energy storage, and charging piles and modeling their full lifecycle costs; constructing a collaborative optimization configuration model with economic and operational constraints, aiming at optimizing the total system cost, and solving it using a mixed integer programming algorithm; establishing a comprehensive power quality evaluation system from the dimensions of flexibility, power supply quality, new energy absorption rate, and carbon emissions, and using a combined weighting method to achieve quantitative grading of system performance; and developing an interactive visualization design platform based on MATLAB / GUIDE, supporting multi-source data import, parameter adjustment, and multi-scheme comparison. This invention achieves synergistic optimization of the economy and energy efficiency of the photovoltaic-storage-charging system, lowers the planning threshold, and is applicable to the low-carbon transformation of industrial parks and smart microgrid planning.

Description

Technical Field

[0001] This invention relates to new energy microgrids and smart power distribution technology, and in particular to a method for configuring and dynamically scheduling a park photovoltaic-storage-charging system that integrates smart power distribution. Background Technology

[0002] With the large-scale application of new energy units and the rapid popularization of electric vehicles, industrial park energy systems face new challenges. The inherent intermittency and volatility of renewable energy (such as photovoltaic) power generation lead to a mismatch between power supply and demand in industrial parks. This results in load shedding and power curtailment, which not only reduces system stability but also limits the improvement of renewable energy absorption capacity, causing resource waste and hindering the achievement of carbon reduction goals. Traditional independent planning methods struggle to coordinate the capacity configuration of photovoltaic, energy storage, and charging piles, and lack a comprehensive assessment of the system's life-cycle economics and power quality. Furthermore, existing planning tools have high professional barriers, making it difficult for non-professionals to quickly obtain optimal system configuration solutions, thus restricting the promotion of integrated photovoltaic-energy storage-charging technology in small and medium-sized industrial parks. Summary of the Invention

[0003] Purpose of the invention: The purpose of this invention is to provide a method for configuring and dynamically scheduling a park photovoltaic, energy storage and charging system that integrates intelligent power distribution. It aims to solve the problems of poor coordination in the capacity configuration of photovoltaic, energy storage and charging piles, insufficient economic assessment of the entire life cycle of the system, and the high professional threshold of existing planning tools, making it difficult for non-professional users to quickly obtain the optimal configuration scheme.

[0004] Technical solution: A method for configuring and dynamically scheduling a park-based photovoltaic, energy storage, and charging system that integrates intelligent power distribution, comprising: S1. Construct a typical integrated photovoltaic-storage-charging system: Adopting a common bus topology, the system integrates distributed photovoltaic power generation units, electrochemical energy storage devices, and composite loads within the park through the bus, forming a multi-energy coupling architecture capable of operating in both grid-connected and islanded modes. In grid-connected mode, peak shaving and valley filling, as well as price arbitrage, are implemented based on time-of-use pricing policies. In islanded mode, a three-tiered power supply strategy of "photovoltaic priority, energy storage supplement, and diesel generator emergency response" is adopted.

[0005] S2. Key Equipment Selection and Modeling: S21. Photovoltaic Equipment Selection and Modeling: Monocrystalline silicon photovoltaic modules are selected. Based on the solar radiation calculation model of the tilted surface and the photoelectric conversion model, a full life cycle cost model considering the degradation coefficient of photovoltaic modules is established.

[0006] S22. Energy storage equipment selection and modeling: Lithium iron phosphate batteries are selected, and a capacity decay model considering the aging rate and cycle life of the energy storage system is constructed. With the goal of maximizing the absorption of new energy and reducing system costs, an energy storage capacity optimization configuration model is established.

[0007] S23. Charging Pile System Selection and Modeling: Adopting a modular design that covers both AC slow charging and DC fast charging modes, a cost mathematical model is constructed that includes configuration costs, installation costs, operation and maintenance costs, and electricity sales revenue.

[0008] S3. Construct a collaborative optimization configuration model for photovoltaic-storage-charging systems: S31. With the goal of optimizing the total life cycle cost of the photovoltaic-storage-charging integrated system, establish an objective function that includes the cost of the photovoltaic system, the cost of the energy storage system, the cost of the charging pile system, the cost of diesel generator generation, the cost of load shedding, and the cost of curtailment penalty.

[0009] S32. Set the constraints for system operation, including energy balance constraints, distributed photovoltaic operation constraints, energy storage system operation constraints, and diesel generator set operation constraints.

[0010] S33. For the two typical operating conditions of the photovoltaic-storage-charging system, namely grid connection and islanding, a mixed integer programming algorithm is used to solve for the optimal equipment configuration parameters.

[0011] S4. Construct a comprehensive power quality assessment system: S41. Select multiple power quality assessment indicators, including flexibility adequacy, power supply quality, renewable energy absorption rate, and carbon emissions per kilowatt-hour.

[0012] S42. The combined weighting method combining the Analytic Hierarchy Process (AHP) and the entropy weighting method is used to calculate the combined weight of each evaluation indicator.

[0013] S43. Based on the evaluation indicators and combined weights, a comprehensive power quality evaluation model is constructed, and the system power quality is divided into multiple levels such as "excellent, good, medium, and needing improvement".

[0014] S5. Develop an interactive visualization design platform: Based on MATLAB / GUIDE, integrate multi-source data interfaces and parameter analysis modules to develop an interactive planning platform. The platform allows users to drag and drop to adjust variables such as illumination parameters and electricity pricing strategies, generate corresponding economic and power quality assessment results with one click, and support rapid comparison of multiple schemes.

[0015] Beneficial Effects: This invention breaks through the limitations of traditional single-economy optimization and innovatively proposes a photovoltaic-storage-charging system planning method based on optimal full life-cycle cost. It incorporates dynamic parameters such as photovoltaic module degradation rate and battery cycle life loss into the capacity configuration model, thereby reducing the average annual cost of the system. This invention constructs a comprehensive power quality evaluation system covering multiple dimensions such as flexibility, power supply quality, renewable energy consumption, and carbon emissions. Combined with a combined weighting method, it achieves a comprehensive quantitative evaluation of system performance. This invention develops an interactive planning platform based on MATLAB / GUIDE. Through drag-and-drop scene construction and one-click multi-scheme comparison functions, even non-professionals can complete the optimized configuration of an integrated system in a short time, significantly lowering the technical threshold and improving planning efficiency. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the topology of an integrated photovoltaic, energy storage, and charging system. Figure 3 This is a schematic diagram of the spatiotemporal energy transfer strategy of an energy storage system under source-load imbalance scenarios; Figure 4 This is a flowchart of the comprehensive power quality assessment process; Figure 5 These are screenshots of the software's interface. Detailed Implementation

[0017] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Example 1

[0018] like Figure 1 ~as Figure 5 As shown in this embodiment, the method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution specifically includes: Step 1: Construct a typical integrated photovoltaic, energy storage, and charging system The integrated photovoltaic-storage-charging system designed using this method is a smart microgrid system based on renewable energy. It adopts a common bus topology, integrating distributed photovoltaic power generation units, long-life electrochemical energy storage devices, and composite loads within the industrial park via the bus. The system has dual-mode operation capability: grid-connected and islanded. In grid-connected mode, peak shaving and valley filling, along with price arbitrage based on the time-of-use pricing policy, significantly reduce electricity purchase costs. In islanded mode, the photovoltaic units provide independent power as the main power source, supplemented by energy storage and emergency diesel generators to ensure continuous power supply to critical loads. Through a three-tiered power supply strategy of "photovoltaic priority, energy storage supplementation, and diesel generator emergency response," the system achieves efficient renewable energy consumption and maximizes operational revenue.

[0019] Step 2: Key Equipment Selection and Modeling 2.1 Photovoltaic Equipment Selection and Modeling In the design process of a photovoltaic power generation system, the first step is to accurately model the system. The amount of solar radiation on the tilted surface is calculated using the following formula: Photovoltaic panels convert captured solar radiation into electrical energy output, a process that can be represented by the following formula: Based on the above model, and combined with technical parameters such as the degradation coefficient of photovoltaic modules, a full life-cycle cost model including initial investment and operation and maintenance costs is constructed: Based on a comparative analysis of the advantages and disadvantages of different types of equipment, this method selects the monocrystalline silicon technology route, which has advantages such as high conversion efficiency, low attenuation rate, long service life, and excellent high-temperature performance.

[0020] 2.2 Energy Storage Equipment Selection and Modeling Energy storage systems aim to maximize renewable energy absorption while minimizing the system's total lifecycle cost. This method constructs a capacity decay model considering the aging rate and cycle life of the energy storage system. Combining the park load forecast curve and the probability distribution characteristics of photovoltaic output, it establishes an energy storage capacity optimization configuration model aimed at improving renewable energy absorption and maintaining stable system operation. The total cost of the energy storage system can be expressed by the following formula: Based on the superior overall performance of lithium iron phosphate batteries in terms of safety, cycle life, and high-temperature environments, lithium iron phosphate batteries were ultimately selected for this system.

[0021] 2.3 Charging Pile System Selection and Modeling The charging pile system adopts a modular design, divided into DC charging piles and AC charging piles. The core functions of the system include bidirectional energy interaction and precise monitoring of charging demand distribution within the park. It employs high-efficiency energy conversion devices and dynamically optimizes the output power of each charging pile by coordinating photovoltaic power generation, energy storage status, and charging demand in real time. Its cost mathematical model can be expressed as: in, This represents the total cost of the charging pile system; This represents the total number of charging stations planned and designed. , , These represent the unit configuration cost, installation cost, and operation and maintenance cost of the charging pile, respectively. This represents the revenue generated by the charging station through electricity sales.

[0022] Step 3: Construct a collaborative optimization configuration model for the photovoltaic-storage-charging system 3.1 Objective Function During the operation of an integrated photovoltaic, energy storage, and charging system, the goal is to ensure its own economic optimization, that is, to minimize the total system cost: in, This represents the total cost of an integrated photovoltaic, energy storage, and charging system. The unit power generation cost of the diesel generator set; Cost of loss of load; Cost of per unit of abandoned light penalty; For the unit operating cost of energy storage, For load in The load shedding power at any given time For diesel generator sets in Power generation at any given moment for The amount of curtailed solar power at any given time. for Charging power for continuous energy storage for The discharge power that stores energy at all times. This refers to the number of diesel generator sets. The number of load nodes, This represents the total runtime.

[0023] 3.2 Constraints 1) Energy balance constraint: The system operation is controlled by equality constraints, ensuring that the total power generation is always equal to the system load demand, as shown in the following formula: in, This indicates the actual power of the ESS charging and discharging; a positive value indicates discharging, and a negative value indicates charging. The power output of the photovoltaic unit; For microgrid system load power; This represents the charging power of the charging station; This represents the load power removed.

[0024] 2) Operational Constraints of Distributed Photovoltaics: In the actual operation of distributed photovoltaic systems, they are directly affected by meteorological conditions such as temperature and sunlight, resulting in significant fluctuations and intermittency in their power output. Their power output constraints can generally be described as follows: in, This represents the real-time output of the photovoltaic unit. The specific values ​​of the parameters representing the output characteristics of photovoltaic power generation can also be obtained through relevant meteorological condition functions. This represents the total installed capacity of distributed photovoltaic power in the system.

[0025] 3) Energy Storage System Operation Constraints: In order to maximize the utilization of the energy storage system's capacity and prevent damage from overcharging, a reasonable range needs to be set for the energy storage system's charge / discharge capacity. Simultaneously, the system must meet constraints on its charging and discharging power and satisfy the law of conservation of energy. Specifically, this can be expressed as: in, , These represent the minimum and maximum values ​​of the SOC (State of Charge) of the energy storage system, respectively. , These represent the energy storage system in time, The state at any given moment; This represents the rated capacity of the energy storage system. , These represent the energy storage system in The charging and discharging power at any given time; This represents the charging and discharging efficiency of the energy storage system.

[0026] 4) Diesel generator set operating constraints: The operating characteristics of a diesel generator set during production can typically be described by energy conservation, output range, ramp rate, and start-stop time, as follows: in, Representing diesel generator sets Real-time output at all times; , Represents respectively in The output increases and decreases constantly; This represents the minimum output percentage allowed during the day of operation. This represents the maximum capacity that can be used within the day; , These represent the maximum upward and downward gradient rates, respectively. Represents total capacity; , These are the minimum continuous running time and the minimum downtime, respectively.

[0027] Step 4: Construct a comprehensive power quality assessment system 4.1 Selection of Evaluation Indicators 1) Flexibility Adequacy: Flexibility adequacy is the comprehensive adjustment capability of a power generation system to cope with fluctuations in renewable energy and load changes, based on adjustable units, energy storage, and other flexible resources, while meeting basic power supply needs. It is defined as the ratio of the system's potential adjustment capacity to load demand, and can be expressed as: in, Represents the level of flexibility; This represents the total potential regulatory capacity of the system, that is, the total regulatory capacity provided by the system by adjusting various flexible resources during this period. This represents the system load.

[0028] 2) Power Supply Quality: Power supply quality is the comprehensive level of continuity, reliability, and compliance of power parameters with standards when a power system supplies power to users. It is defined as the ratio of the total time the system meets power demand to the total system operating time, and can be expressed as: in, Represents the power supply quality of the system; This represents the period during which the power supply and demand in the system are matched, meaning that there is no load shedding or curtailment of solar power during this period. This represents the total runtime of the system.

[0029] Renewable energy absorption rate: The renewable energy absorption rate is an indicator that measures the actual utilization of renewable energy in the power system. It is defined as the proportion of electricity generated that is effectively absorbed, and expressed as: in, Represents the rate of new energy consumption; Representative system photovoltaic units in Electricity generation at any given moment; Then it means The amount of light discarded at any given moment.

[0030] Carbon emissions per kilowatt-hour: Carbon emissions per kilowatt-hour refer to the amount of carbon dioxide equivalent generated during the entire process of producing, transmitting, and consuming each kilowatt-hour of electricity. It is expressed as: in, This represents the carbon emissions per kilowatt-hour of the system. represent Carbon dioxide emissions at any given time. represent Total power generation of the time system.

[0031] 4.2 Indicator Weight Allocation A single indicator cannot fully reflect power quality; therefore, multiple indicators must be combined and weighted for comprehensive evaluation as needed. This project uses a combined weighting method, combining the Analytic Hierarchy Process (AHP) and the entropy weighting method, to calculate the combined weights of the evaluation indicators. Given n evaluation indicators, their subjective weights can be denoted as... Objective weights can be denoted as Combining these two weights yields the combined weights for each evaluation indicator. .

[0032] 4.3 Evaluation Model Construction This model is based on mathematical theory and uses a combined weighting method that combines the analytic hierarchy process (AHP) and the entropy weighting method to calculate the values ​​of each index and conduct a comprehensive evaluation of power quality, thus obtaining the evaluation results of the quality level.

[0033] This paper designs the power quality levels of the photovoltaic-storage microgrid as "Ⅰ, Ⅱ, Ⅲ, Ⅳ", which correspond to the degree of excellence, good, medium and need improvement, respectively.

[0034] Step 5: Develop an interactive visual design platform This method utilizes an interactive visualization design platform developed based on MATLAB / GUIDE. The platform employs a structured interface design in its data processing module, independently categorizing and inputting key economic parameters such as time-of-use pricing, load shedding costs, and wind curtailment costs. The results processing module presents simulation results from multiple scenarios to users through hierarchical lists, allowing for quick switching and comparison of different configuration schemes. The platform incorporates a standardized data interface, supporting seamless import of various formats such as Excel and CSV, and features version control for comparing differences between multiple branch schemes. These measures significantly shorten the iterative cycle of "parameter adjustment-simulation verification" in traditional photovoltaic-storage-charging system design, making it particularly suitable for non-professionals lacking power system expertise to quickly verify the impact of different equipment selections and operating strategies on the overall energy efficiency of the system.

[0035] The embodiments described above 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 present invention. It should be noted that 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. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for configuring and dynamically scheduling a park light storage and charging system with integrated intelligent power distribution, characterized in that, include: S1. Constructing a typical integrated photovoltaic-storage-charging system: Adopting a common bus topology, the system integrates distributed photovoltaic power generation units, electrochemical energy storage devices, and composite loads within the industrial park via the bus, forming a multi-energy coupling architecture capable of operating in both grid-connected and islanded modes. In grid-connected mode, peak shaving and valley filling, along with price arbitrage, are implemented based on time-of-use pricing policies. In islanded mode, a three-tiered power supply strategy is adopted: "PV priority, energy storage supplement, and diesel generator emergency response." S2. Key Equipment Selection and Modeling: S21. Photovoltaic equipment selection and modeling: Select monocrystalline silicon photovoltaic modules, and establish a full life cycle cost model that considers the degradation coefficient of photovoltaic modules based on the calculation model of solar radiation on tilted surfaces and the photoelectric conversion model. S22. Energy storage equipment selection and modeling: Lithium iron phosphate batteries are selected, and a capacity decay model considering the aging rate and cycle life of the energy storage system is constructed. With the goal of maximizing the absorption of new energy and reducing system costs, an energy storage capacity optimization configuration model is established. S23. Charging pile system selection and modeling: Adopt a modular design that covers both AC slow charging and DC fast charging modes, and construct a cost mathematical model that includes configuration cost, installation cost, operation and maintenance cost and electricity sales revenue; S3. Construct a collaborative optimization configuration model for photovoltaic-storage-charging systems: S31. With the goal of optimizing the total life cycle cost of the photovoltaic-storage-charging integrated system, establish an objective function, including the cost of the photovoltaic system, the cost of the energy storage system, the cost of the charging pile system, the cost of diesel generator generation, the cost of load shedding, and the cost of curtailment penalty. S32. Set the constraints for system operation, including energy balance constraints, distributed photovoltaic operation constraints, energy storage system operation constraints, and diesel generator set operation constraints; S33. For the two typical operating conditions of the photovoltaic-storage-charging system, grid connection and islanding, a mixed integer programming algorithm is used to solve for the optimal equipment configuration parameters; S4. Construct a comprehensive power quality assessment system: S41. Select multiple power quality assessment indicators, including flexibility adequacy, power supply quality, renewable energy absorption rate, and carbon emissions per kilowatt-hour. S42. Calculate the combined weights of each evaluation indicator using a combination of the analytic hierarchy process and the entropy weighting method. S43. Based on the evaluation indicators and combined weights, construct a comprehensive power quality evaluation model to divide the system power quality into multiple levels of excellence and inferiority. S5. Develop an interactive visual design platform: Based on MATLAB / GUIDE, integrate multi-source data interfaces and parameter analysis modules to develop an interactive planning platform that supports users in adjusting parameters and generating economic and power quality assessment results in real time.

2. The method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S1, peak shaving and valley filling and electricity price arbitrage are implemented based on the time-of-use pricing policy under the grid-connected mode; under the islanded mode, a three-level power supply strategy of "photovoltaic priority, energy storage supplement, and diesel generator emergency" is adopted.

3. The method for configuring and dynamically scheduling a park-based photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S21, the amount of solar radiation on the inclined surface is calculated using the following formula: in, This represents the direct solar irradiance received by the surface of a photovoltaic panel. This represents the amount of reflected solar irradiance received by the surface of a photovoltaic panel. Represents direct normal irradiance. Represents global horizontal irradiance; The zenith angle represents the angle between the line of sight from the observation point to the sun and the direction of the zenith. , These represent the ground reflectivity and the tilt angle of the photovoltaic panel, respectively.

4. The method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S21, the output power of the photovoltaic power generation system is calculated using the following formula: in, , These represent the open-circuit voltage and short-circuit current of the photovoltaic panel, respectively. , These represent the photovoltaic fill factor and derating factor, respectively. This represents the inverter efficiency.

5. The method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S21, the life-cycle cost model of the photovoltaic system is expressed as follows: in, This represents the unit capacity cost of a photovoltaic system; This represents the system operation and maintenance cost after taking into account factors such as photovoltaic degradation; Represents construction capacity; This represents the total number of generating units.

6. The method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S22, the total cost model of the energy storage system is expressed as follows: in, , , , These represent the total cost of the energy storage system, investment and construction cost, operation and maintenance cost, and cycle life depreciation cost, respectively.

7. The method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S23, the cost mathematical model of the charging pile system is expressed as follows: in, This represents the total cost of the charging pile system; This represents the total number of charging stations planned and designed. , , These represent the unit configuration cost, installation cost, and operation and maintenance cost of the charging pile, respectively. This represents the revenue generated by the charging station through electricity sales.

8. The method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S31, the objective function for optimizing the total lifecycle cost of the integrated photovoltaic-storage-charging system is expressed as: in, This represents the total cost of an integrated photovoltaic, energy storage, and charging system. The unit power generation cost of the diesel generator set; Cost of loss of load; Cost of per unit of abandoned light penalty; For the unit operating cost of energy storage, For load in The load shedding power at any given time For diesel generator sets in Power generation at any given moment for The amount of curtailed solar power at any given time. for Charging power for continuous energy storage for The discharge power that stores energy at all times. This refers to the number of diesel generator sets. The number of load nodes, This represents the total runtime.

9. The method for configuring and dynamically scheduling a park photovoltaic-storage-charging system integrating intelligent power distribution according to claim 1, characterized in that, In step S5, the interactive visual design platform has the following functions: it supports importing data in Excel and CSV formats; it supports drag-and-drop parameter adjustment and comparison of multiple schemes; it has a built-in data diagnostic function, which actively prompts the source of abnormal data and recommends feasible parameter adjustment ranges when logical conflicts are detected in input parameters; it has a version management function, which automatically records the historical versions of each parameter modification and supports the comparison of differences between multiple branch schemes.

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