Expressway service area light storage and charging system energy storage capacity optimal configuration method and system

Abstract

This invention discloses a method and system for optimizing the energy storage capacity of a photovoltaic-energy storage-charging system in highway service areas, belonging to the field of microgrid design technology. Addressing the problem that commercial building loads and charging pile loads in service areas belong to two independent power supply networks, making it difficult to share distributed energy resources, this invention proposes establishing a capacity optimization configuration model with dual-network power balance constraints under mutually exclusive topology switching constraints. A hierarchical optimization algorithm is used to solve the energy storage capacity configuration decision variables, obtaining a target energy storage capacity configuration scheme that satisfies the constraints. This invention also provides a corresponding photovoltaic-energy storage-charging system, including two electrically isolated power supply networks, distributed energy units, a topology switching network, and a control module. The topology switching network enables mutually exclusive switching between the photovoltaic and energy storage systems between the two networks. This invention achieves the shared utilization of distributed energy resources while maintaining independent metering and electrical isolation between the two networks, improving asset utilization and system economy.

Description

Technical Field

[0001] This invention belongs to the field of microgrid design technology, specifically relating to a method and system for optimizing the energy storage capacity of a photovoltaic-storage-charging system in a highway service area. Background Technology

[0002] With the continuous improvement of the comprehensive service capabilities of highway service areas and the rapid growth of the number of new energy vehicles, the energy consumption structure of highway service areas is becoming increasingly complex, and the load scale is constantly expanding. Existing highway service areas typically have two main types of electrical loads: one is the service area commercial building load, which mainly includes catering, retail, lighting, air conditioning, water supply and drainage, monitoring and ancillary operating equipment, etc. Its load characteristics are strong daily operation continuity, high base load proportion, and relatively stable load curve, but with obvious peaks at specific times; the other is the electric vehicle charging pile load, whose load level is greatly affected by factors such as vehicle arrival time, dwell time, holiday traffic flow and fast charging demand, and has the characteristics of strong volatility, high peak power, obvious short-term impact, and strong time randomness.

[0003] To reduce the energy consumption costs of service area operations, improve the absorption of renewable energy, and enhance the economic efficiency and flexibility of the energy supply system, highway service areas are gradually introducing distributed photovoltaic (PV) power generation and energy storage facilities, forming a typical integrated PV-storage-charging configuration. By configuring PV power generation units, on-site power generation can be achieved using service area rooftops, carports, and other areas; by configuring energy storage units, peak shaving and valley filling can be achieved to a certain extent, fluctuations can be mitigated, and the level of local energy self-consumption can be improved. Therefore, building a PV-storage-charging collaborative operation system around the service area scenario has become an important development direction in the current integration of transportation energy.

[0004] However, in actual engineering construction, due to factors such as historical construction planning, phased implementation models, asset ownership, and differences in investment and operation entities, the load of commercial buildings in service areas and the load of electric vehicle charging piles are often not included in the same power supply and distribution system. Instead, they are connected to their respective independent power supply networks, forming separate grid connection points and metering systems. In other words, the two types of loads usually correspond to two independently operating local power supply networks, each exchanging power with the public power grid, and maintaining relative independence in electrical connection relationships, energy metering boundaries, and operation and management methods.

[0005] Under the aforementioned structure, common configurations of existing photovoltaic-storage systems mainly include: fixing photovoltaic power generation facilities and energy storage facilities to the load side of commercial buildings in the service area, or fixing them to the load side of electric vehicle charging, or independently configuring photovoltaic and energy storage assets on both sides. While these methods can achieve renewable energy consumption and energy regulation within a localized area, distributed energy assets typically only serve one side of the power grid. This makes it difficult to flexibly share and uniformly utilize them at the system level based on the time-varying load characteristics of both sides. This can easily lead to situations where one side has surplus resources while the other side still needs to purchase electricity at a high price, resulting in low equipment utilization, insufficient overall economic efficiency, and even redundant investment and asset idleness.

[0006] On the other hand, if an attempt is made to achieve energy sharing between two independent power supply networks through direct electrical interconnection, multiple requirements must be met simultaneously, including independent metering boundaries, grid-connected operation safety, electrical isolation, prevention of circulating current, power reverse current constraints, and adaptation to existing distribution topologies. In engineering practice, once two independent power supply networks form an uncontrolled or non-mutually exclusive direct electrical connection, it may lead to problems such as unclear metering boundaries, complex power flow paths, difficulties in grid-connected control, increased risk of local backfeeding, and unclear operation and maintenance responsibility interfaces, thereby increasing the complexity of system design, control implementation, and operation management.

[0007] Furthermore, in application scenarios where independent power supply networks coexist and distributed energy resources are difficult to share directly, existing technologies for photovoltaic and energy storage capacity configurations are typically designed with a single power supply network as the boundary, lacking overall coordinated capacity configuration technologies for conditions constrained by multiple independent power supply networks. Especially when there are simultaneously different load characteristics, different power purchase and sale boundaries, different energy flow restrictions, and constraints on the local absorption of renewable energy, there is still a lack of effective technical means to determine the reasonable configuration scale of distributed energy equipment such as energy storage.

[0008] In summary, existing technologies have at least the following technical problems: First, photovoltaic and energy storage assets usually cannot be effectively shared between independent power supply networks, resulting in low energy utilization and asset utilization rates; Second, direct physical interconnection requires meeting complex topological constraints, control constraints, and grid-connected operation constraints, which makes engineering implementation difficult and operation risky; Third, in scenarios with the above structural constraints, there is a lack of capacity optimization configuration technologies that take into account the collaborative operation characteristics of multiple power supply networks. Summary of the Invention

[0009] The technical objective of this invention is to address the technical problems in the prior art, such as low utilization rate of photovoltaic and energy storage assets, insufficient system operation flexibility, and unreasonable capacity configuration caused by the independent power supply circuits of commercial building loads and electric vehicle charging pile loads in service areas. This invention provides a method and system for optimizing the energy storage capacity configuration of photovoltaic, energy storage, and charging systems in highway service areas.

[0010] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution.

[0011] In a first aspect, embodiments of the present invention provide a method for optimizing the energy storage capacity configuration of a photovoltaic-storage-charging system in a highway service area, comprising:

[0012] Step S1: Construct a planning scenario model: Collect basic data on the power generation potential, load time series data, and system grid connection conditions of the planning area to construct a planning scenario model;

[0013] Step S2: Establish a system architecture and capacity optimization configuration model: Based on the planning scenario model, determine the system components, decision variables, and objective function of the system to be optimized, and construct a capacity optimization configuration model; wherein, the system components include distributed energy units, topology switching networks, a first power supply network, and a second power supply network; the distributed energy units include photovoltaic power generation units and energy storage units; the first power supply network and the second power supply network correspond to different load types and are electrically isolated from each other; the capacity optimization configuration model uses the mutual exclusion topology switching constraints of the photovoltaic power generation units and the energy storage units under the topology switching network, as well as the power balance constraints of the first power supply network and the second power supply network, as boundary conditions;

[0014] Step S3: Solve for the energy storage capacity configuration scheme: Based on the planning scenario model, use a hierarchical optimization algorithm to solve the capacity optimization configuration model to obtain the target energy storage capacity configuration scheme that satisfies the topology switching constraint and the power balance constraint.

[0015] Secondly, embodiments of this application provide a photovoltaic-storage-charging system for highway service areas, comprising:

[0016] The first power supply network is used to supply power to the first local load. It is connected to the public power grid through the first grid connection point and is equipped with an independent first power metering node. The first power supply network has the ability to transmit power bidirectionally with the public power grid.

[0017] The second power supply network is used to supply power to the second local load. It is connected to the public power grid through the second grid connection point and is equipped with an independent second power metering node. The second power supply network is electrically isolated from the first power supply network and is configured to detect and limit the reverse power flowing to the public power grid through the second grid connection point.

[0018] Distributed energy units include photovoltaic power generation units and energy storage units;

[0019] A topology switching network includes at least one topology switching unit, which is connected to the first power supply network, the second power supply network, and the distributed energy unit respectively. It is used to receive topology switching control commands and, in response to the topology switching control commands, enable the distributed energy unit to connect to the first power supply network or the second power supply network while keeping the first power supply network and the second power supply network from forming a direct electrical connection.

[0020] The control module is configured to execute the energy storage capacity optimization configuration method for the highway service area photovoltaic-storage-charging system provided in any possible implementation of the first aspect, and to generate a topology switching control command to be sent to the topology switching network.

[0021] It should be understood that the summary section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description.

[0022] The beneficial technical effects achieved by this invention are: 1. While maintaining independent metering and electrical isolation between the two power supply networks, the shared utilization of distributed energy resources is realized.

[0023] In this invention, the first and second power supply networks are connected to the public power grid through independent grid connection points and are equipped with independent power metering nodes, maintaining electrical isolation between them. By setting up a topology switching network, photovoltaic power generation units and energy storage units can selectively connect to the first or second power supply network at the overall system level. This allows distributed energy resources, which were originally fixed to serve only one side of the network, to switch and support according to the time-varying demands of different load sides. Thus, without disrupting existing independent metering boundaries and network isolation relationships, the sharing and utilization efficiency of photovoltaic and energy storage assets in the overall service area are improved, reducing the probability of one side having idle resources while the other side still relies on high-cost electricity purchases.

[0024] 2. Avoid grid connection and operation risks caused by direct electrical interconnection of two independent power supply networks.

[0025] This invention employs a mutually exclusive topology switching method, establishing only a single electrical connection between the distributed energy unit and one of the power supply networks at any controlled time, thus avoiding direct electrical connections between the first and second power supply networks. Compared to direct physical interconnection schemes, this invention effectively reduces engineering risks such as unclear metering boundaries, complex power flow paths, local circulating currents, uncontrolled reverse power supply, and increased grid connection control complexity, while improving the clarity of the system topology and the feasibility of operation and control.

[0026] 3. It can adapt to the operating characteristics of significant differences between the load of service area infrastructure and the load of charging piles, and improve the system's power supply matching.

[0027] Commercial building loads in highway service areas typically exhibit strong continuity, a high proportion of base load, and relatively smooth load fluctuations, while charging pile loads are characterized by high peak power, strong volatility, significant short-term impacts, and high randomness. This invention, through a switchable distributed energy access method, allows energy storage units to output power to the corresponding power supply network based on load characteristics at different times. This provides continuous support for relatively stable infrastructure loads while simultaneously offering peak-shaving compensation for sudden high-power demands from charging piles, thus improving the matching degree between distributed energy output characteristics and different load characteristics.

[0028] 4. It can simultaneously consider topology switching constraints and dual-network power balance constraints in a unified model, thereby improving the rationality of energy storage capacity configuration results.

[0029] The energy storage capacity configuration method of this invention is designed for an overall system comprising a first power supply network, a second power supply network, and a topology switching network. It establishes a capacity optimization configuration model that simultaneously includes topology switching constraints, power balance constraints for each of the two networks, equipment operation constraints, and an objective function. Therefore, the obtained energy storage capacity configuration results not only reflect the equipment's own capacity requirements but also the switching operation relationships between different networks, their respective load timing differences, and the impact of different power purchase boundaries on operating costs. Thus, it is more consistent with actual engineering scenarios than traditional single-network optimization methods.

[0030] 5. The hierarchical optimization solution method can take into account both capacity planning and operation scheduling, improving the effectiveness of the solution and the feasibility of the solution.

[0031] This invention employs a hierarchical optimization approach, where the upper layer searches for energy storage capacity configuration variables, and the lower layer solves for system operation and scheduling results under given capacity conditions. This approach links the capacity planning problem with the operation control problem: the upper layer filters candidate capacity schemes, while the lower layer verifies whether these schemes can achieve optimal operating results under topology switching constraints, power balance constraints, and energy storage operation constraints. Therefore, the resulting target energy storage capacity configuration scheme is not only economical at the planning level but also feasible at the scheduling level. Attached Figure Description

[0032] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this application in any way. Furthermore, the shapes and scales of the components in the drawings are merely illustrative to aid in understanding this application and do not specifically limit the shapes and scales of the components. Those skilled in the art, guided by the teachings of this application, can select various possible shapes and scales to implement this application according to specific circumstances. In the drawings:

[0033] Figure 1This is a schematic diagram of a method for optimizing the energy storage capacity of a photovoltaic energy storage and charging system according to an embodiment of the present invention;

[0034] Figure 2 This is a system architecture diagram of the photovoltaic, energy storage, and charging system in a highway service area, as shown in the embodiment.

[0035] Figure 3 This is a power balance diagram of the system corresponding to architecture 5 in one embodiment of the present invention, showing the system's annual power balance.

[0036] Figure 4 This is a diagram showing the typical weekly operation details of the system corresponding to architecture 5 in summer according to one embodiment of the present invention;

[0037] Figure 5 This is a diagram showing the relationship between the summer energy storage state of charge and electricity price for architecture 5 in one embodiment of the present invention. Detailed Implementation

[0038] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0039] Example 1: A method for optimizing the energy storage capacity of a photovoltaic-storage-charging system in a highway service area, including: Step S1: Constructing a planning scenario model: Collecting basic data on the power generation potential of the planning area, load time series data, and system grid connection conditions to construct a planning scenario model;

[0040] Step S2: Establish a system architecture and capacity optimization configuration model: Based on the planning scenario model, determine the system components, decision variables, and objective function of the system to be optimized, and construct a capacity optimization configuration model; wherein, the system components include distributed energy units, topology switching networks, a first power supply network, and a second power supply network; the distributed energy units include photovoltaic power generation units and energy storage units; the first power supply network and the second power supply network correspond to different load types and are electrically isolated from each other; the capacity optimization configuration model uses the mutual exclusion topology switching constraints of the photovoltaic power generation units and the energy storage units under the topology switching network, as well as the power balance constraints of the first power supply network and the second power supply network, as boundary conditions;

[0041] Step S3: Solve for the energy storage capacity configuration scheme: Based on the planning scenario model, use a hierarchical optimization algorithm to solve the capacity optimization configuration model to obtain the target energy storage capacity configuration scheme that satisfies the topology switching constraint and the power balance constraint.

[0042] Specifically, in the embodiments, such as Figure 1 As shown, step S1 involves establishing a planning scenario model. Basic data on power generation potential includes natural resource endowment data and land space resource data for the planning area; load time series data includes infrastructure load data and mobile charging / swapping load-related characteristic data, and load time series data is formed based on the collected data; determining system grid connection conditions and system operation mode; and constructing a planning scenario model to characterize the planning scenario based on natural resource endowment data, land space resource data, load time series data, system grid connection conditions, and system operation mode; including a photovoltaic power generation model, a deployable area calculation model, a load model, and an electricity price model.

[0043] Specifically, in this embodiment, step S2, establishing a system architecture and capacity optimization configuration model, includes: based on the planning scenario model obtained in step S1, determining the system components, topology, and operating strategies of the system to be optimized, and constructing the constraints, decision variables, and objective function for capacity optimization configuration; the decision variables include at least the energy storage capacity configuration decision variables; the objective function is used to reflect the overall configuration cost and operating benefits of the system.

[0044] Specifically, in step S3, based on the system architecture and capacity optimization configuration model, load time series data of multiple discrete operating segments are used as input, and a hierarchical optimization algorithm is used to solve the capacity optimization configuration model constructed in step S2. The upper-level optimization is used to search for energy storage capacity configuration decision variables and generate multiple candidate capacity configuration schemes, and the lower-level optimization is used to solve the system operation scheduling results that satisfy topology switching constraints and power balance constraints under a given candidate capacity configuration scheme, and determine the target energy storage capacity configuration scheme according to the objective function value corresponding to each candidate capacity configuration scheme.

[0045] In this embodiment, the method is preferably executed by a processor calling program instructions, but it can also be executed by a server, energy management system, edge controller, industrial computer or other devices with computing capabilities.

[0046] In this embodiment, natural resource endowment data may include meteorological data such as solar irradiance and ambient temperature. Land space resource data may include the roof area of ​​service area buildings, the area of ​​carports, or other areas suitable for photovoltaic installation. Infrastructure load data may include typical daily power curves of commercial building loads in the service area. Mobile charging and battery swapping load-related characteristic data may include typical daily power curves of charging pile loads. System grid connection conditions may include grid connection point capacity boundaries, whether reverse power feeding is allowed, metering boundary constraints, etc. System operation modes may include different load-side electricity pricing mechanisms, energy storage operation strategies, and demand response participation methods, etc.

[0047] In this embodiment, the typical daily load data for each season can be extended to 8760 hours of load time-series data for the whole year. Specifically, based on the correspondence between months and seasons, the 24-hour typical daily curves for spring, summer, autumn, and winter can be mapped to each hour of the whole year, thereby obtaining the first power supply network side load sequence and the second power supply network side load sequence, respectively.

[0048] In one specific implementation, the load time-series data for multiple discrete runtime segments can be composed of the following data: photovoltaic power generation sequence. First power supply network load sequence Second power supply network load sequence The annual electricity purchase price sequence of the first power supply network The annual electricity purchase price sequence for the second power supply network Demand response event sequence.

[0049] In this embodiment, calculations can be performed based on load time-series data, and the calculation results can be output as an hourly operation detail table, which includes at least one or more of the following fields: photovoltaic power generation, service area load power, charging pile load power, power purchased by the first power supply network, power purchased by the second power supply network, photovoltaic grid connection power, curtailed power, energy storage charging power, energy storage discharging power, energy storage state of charge, and demand response power.

[0050] Furthermore, in the embodiments, the results can also be visualized, such as: a daily view of annual power balance, a detailed weekly operation diagram for typical summer or winter seasons, a diagram showing the relationship between energy storage state of charge and electricity purchase price, and a detailed daily operation diagram for typical demand response. The relevant graphical data and calculation results can serve as the basis for system evaluation and scheme comparison.

[0051] The embodiment utilizes load time-series data for capacity optimization, which improves the adaptability of the configuration scheme to year-round operating conditions. Preferably, time-series data covering multiple discrete time periods throughout the year are used as input to establish the power balance relationship between the first and second power supply networks for each time period, and optimization is performed by combining photovoltaic power generation models, load models, and electricity price models. Because this method can reflect the impact of seasonal changes, intraday electricity price differences, random load fluctuations, and photovoltaic output fluctuations on system operation, the resulting energy storage capacity configuration has stronger time-series adaptability and engineering reference value, avoiding configuration deviations caused by relying solely on typical daily or static empirical parameters.

[0052] In the embodiments, the established planning scenario model includes a photovoltaic power generation model, a layable area calculation model, a load model, and an electricity price model.

[0053] In this embodiment, the photovoltaic power generation model can be established as follows:

[0054] ;

[0055] ;

[0056] in, Indicates the operating temperature of the photovoltaic module. Indicates ambient temperature. Indicates time period Solar irradiance, Indicates the rated operating battery temperature. Indicates the standard test irradiance. Indicates the temperature power coefficient. Indicates the standard test temperature. Indicates the overall system efficiency. Indicates time period Photovoltaic output power, This indicates the installed capacity of photovoltaic power.

[0057] In a specific example, the following parameters can be taken: ; ; ; ; .

[0058] When calculated When it is less than zero, it can be truncated to zero.

[0059] The available area calculation model can determine the maximum installable photovoltaic capacity based on the area of ​​service area rooftops, parking sheds, and other usable areas, combined with the installation density per unit area. It can be expressed as: ;in, Indicates the area that can be laid. This indicates the installed capacity density per unit area. This indicates the maximum photovoltaic installation capacity that can be built.

[0060] In this embodiment, the load model can be used to characterize the time-varying load characteristics of the first and second power supply networks within a preset planning period. Based on typical daily load curves for each of the four seasons, an annual load time-series sequence is constructed, which can be expressed as:

[0061] ;

[0062] ;

[0063] in, Indicates the first power supply network during the time period The load power, Indicates the second power supply network during the time period The load power, and These represent the corresponding power supply networks in different seasons. and hours Typical daily load values ​​under the following conditions Indicates time period Season of origin Indicates time period The corresponding hourly index.

[0064] The electricity price model can be used to characterize the electricity purchase price of the first and second power supply networks in each discrete time period, and it can be expressed as:

[0065] ;

[0066] ;

[0067] in, Indicates the first power supply network during the time period The electricity purchase price, Indicates the second power supply network during the time period The electricity purchase price, and This represents a mapping function that determines the time-of-use electricity price based on the season type and hour index.

[0068] For the feed-in tariff of photovoltaic power, a fixed feed-in tariff can be adopted in one implementation method, namely: ;in, This represents the on-grid electricity price corresponding to the photovoltaic power generation unit feeding electricity to the public grid through the first grid connection point during time period t. This represents a pre-set fixed feed-in tariff constant. In implementations using a fixed feed-in tariff mechanism, the feed-in tariff is the same for each discrete time period, therefore... The value does not change with time; in other implementations, if the grid-connected electricity price changes with time, then It can also be set as a time-division function with respect to time period t.

[0069] In the embodiment, after establishing the planning scenario model in step S1, step S2 further establishes the system architecture and capacity optimization configuration model, including the system components, topology and operation strategy of the system to be optimized, and constructs the constraints, decision variables and objective function of capacity optimization configuration.

[0070] The decision variables should include at least the energy storage capacity configuration decision variables.

[0071] In some embodiments, the energy storage capacity configuration decision variables include at least the energy storage capacity variable, the energy storage charging and discharging power variable for each time period, the state of charge variable, the photovoltaic output allocation variable, the power supply network purchase variable, and the topology switching state variable.

[0072] For example, the following decision variables can be set: Energy storage capacity configuration variables; The first power supply network during the time period Power purchased from the public power grid; The second power supply network during the time period Power purchased from the public power grid; The power output of photovoltaic power to the primary power grid; : Power of photovoltaic power to charge energy storage; Photovoltaic power connected to the grid via the first connection point; Discarded light power; Energy storage is charged by the second power supply network; : The power of energy storage discharging to the primary power supply network; : Energy storage discharge power to the second power supply network; : The state of charge of energy storage during time period (t); Photovoltaic switching state variables; Energy storage charging and discharging state variables; : Switching state variables of the energy storage discharge target network. Among them, , and It can be either 0 or 1, and is used to characterize the mutual exclusion topology switching logic.

[0073] In this embodiment, the topology switching constraint can be expressed as:

[0074] ;

[0075] ;

[0076] ;

[0077] Its meaning is: when At that time, the photovoltaic system is connected to the first power supply network, can supply power to the first power supply network, and is allowed to connect to the grid; when At this time, the photovoltaic system is supplying power to the energy storage system and no longer supplies power to the first power supply network or to the grid through the first grid connection point.

[0078] Meanwhile, the photovoltaic power balance constraint can be expressed as:

[0079] ;

[0080] The power balance constraint of the first power supply network can be expressed as:

[0081] ;

[0082] The power balance constraint of the second power supply network can be expressed as:

[0083] ;

[0084] In this embodiment, the mutual exclusion constraint for energy storage charging and discharging can be expressed as:

[0085] ;

[0086] ;

[0087] in, The rated charge and discharge power for energy storage, This represents the total charging power of the energy storage unit during time period t. This represents the total discharge power of the energy storage unit during time period t.

[0088] ;

[0089] ;

[0090] The mutual exclusion constraint of the energy storage discharge target network can be expressed as:

[0091] ;

[0092] ;

[0093] This ensures that the energy storage discharges to only one target power supply network at any given time.

[0094] The energy storage state of charge update equation can be expressed as:

[0095] ;

[0096] in, For energy storage, the state of charge at time t+1, For energy storage charging efficiency, This refers to the energy storage and discharge efficiency.

[0097] The boundary constraints of the energy storage charge state can be expressed as:

[0098] ;

[0099] At minimum state of charge, This is the maximum state of charge.

[0100] Annual boundary conditions can be set as follows:

[0101]

[0102] in, Indicates the time at the end of the optimization cycle. This indicates the state of charge of the energy storage during time period T. This indicates the state of charge of the energy storage during time period 0.

[0103] In a specific example, the energy storage parameter can be: charging efficiency. Discharge efficiency Minimum state of charge Maximum state of charge The ratio of rated power to capacity of energy storage That is, when the energy storage capacity is At that time, its rated charging and discharging power can be taken as: .

[0104] In this embodiment, the objective function constructed in step S2 is used to reflect the overall configuration cost and operational efficiency of the system, preferably with the goal of minimizing the total cost.

[0105] As an example, the objective function can be expressed as: ;

[0106] in, Indicates one-time investment cost. This indicates the operating cost within the planning period (e.g., year).

[0107] In one implementation, the one-time investment cost can be expressed as:

[0108] ;

[0109] in, This represents the investment cost per unit of photovoltaic capacity. Indicates photovoltaic installed capacity. This indicates the investment cost per unit of energy storage capacity.

[0110] Operating costs can be expressed as: ;

[0111] in: This represents the cost of purchasing electricity for the primary power supply network; This indicates the cost of purchasing electricity for the second power supply network; This indicates the cost of photovoltaic operation and maintenance; This indicates the cost of energy storage operation and maintenance; This indicates the revenue from grid connection of photovoltaic power.

[0112] Furthermore,

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] in, For the first power supply network during the time period The electricity purchase price, For the second power supply network during the time period The electricity purchase price, The cost of operation and maintenance per unit of photovoltaic power generation. The operation and maintenance cost per unit of energy storage throughput, This refers to the feed-in tariff for photovoltaic power.

[0119] In this embodiment, the objective function can comprehensively consider at least the energy storage investment cost, system electricity purchase cost, photovoltaic operation and maintenance cost, energy storage operation and maintenance cost, and photovoltaic grid connection revenue, and further include demand response revenue when necessary. By uniformly modeling and collaboratively optimizing the operational relationships of photovoltaic, energy storage, dual power supply networks, and topology switching, this invention can obtain better energy storage capacity configuration results while satisfying operational boundaries and topology constraints, thereby reducing unnecessary capacity redundancy, lowering the risk of repeated investment, helping to reduce the overall system configuration cost, and improving the overall economic efficiency throughout the system's life cycle.

[0120] In one specific implementation, the first power supply network and the second power supply network adopt different annual time-of-use (TOU) electricity price sequences. TOU prices can be generated separately according to the season and hour. For example, for spring and autumn, corresponding prices are set for peak, flat, and valley periods; for summer and winter, corresponding prices are set for peak, peak, flat, and valley periods.

[0121] In this embodiment, seasonal time-of-use electricity prices can be mapped to discrete time periods throughout the year by generating an 8760-hour electricity price array for subsequent optimization solutions.

[0122] In this embodiment, step S3 preferably employs a hierarchical optimization method. The upper-level optimization is used to search for energy storage capacity configuration variables, while the lower-level optimization is used to solve for the system operation scheduling results under given capacity conditions.

[0123] As an example, upper-level optimization can employ heuristic search methods, such as particle swarm optimization, genetic search, or other swarm intelligence optimization methods. For each candidate capacity, the lower-level optimization model is called to solve the operating cost and calculate the total cost, thereby selecting the energy storage capacity with the minimum total cost as the target energy storage capacity configuration scheme.

[0124] In the implementation method that uses a heuristic search method for upper-level optimization, for each candidate energy storage capacity The total cost can be expressed as:

[0125] ;

[0126] By comparing the corresponding candidate capacities This yields the optimal energy storage capacity.

[0127] As an example, lower-level optimization can employ mixed-integer programming, linear programming, dynamic programming, or other mathematical programming methods. Any method that can achieve capacity and operational coordination optimization while considering topology switching constraints and dual-power supply network power balance constraints falls within the scope of this invention.

[0128] In some embodiments, demand response control logic is further introduced on the basis of the above implementation to form an extended implementation of demand response.

[0129] As an example, after receiving a demand response command from the public power grid, an energy storage discharge control command is generated during the corresponding demand response period to control the energy storage unit to discharge to the local load side within the power supply network that is currently electrically connected to it, so as to reduce the power consumption through the public power grid during the demand response period.

[0130] In this embodiment, the system baseline electricity purchase volume can be determined first based on the optimization results when not participating in demand response. Let the baseline total electricity purchase volume be... for: ;

[0131] For the first power supply network during the time period Baseline power for purchasing electricity from the public grid; For the second power supply network during the time period Baseline power purchased from the public grid.

[0132] Under actual operating conditions after participating in demand response, the total purchased electricity volume for:

[0133] ;

[0134] Then the demand response power It can be represented as:

[0135] ;

[0136] During non-demand response periods, let:

[0137] ;

[0138] Demand Response Benefits It can be represented as:

[0139] ;

[0140] in, This indicates the unit price for demand response compensation.

[0141] The objective function can be further modified as follows:

[0142] ;

[0143] By incorporating demand response benefits into the optimization objectives, the overall economic efficiency of energy storage configuration schemes in grid interaction scenarios can be further improved.

[0144] In this embodiment, the method for optimizing the energy storage capacity of the photovoltaic-storage-charging system in a highway service area can be implemented by a computer program. The program can be stored in a computer-readable storage medium and, when executed by the processor, completes data reading, model building, constraint construction, objective function calculation, candidate capacity traversal, optimization solution, result evaluation, and graphical output.

[0145] In this embodiment, the introduction of demand response control further enhances the system's responsiveness to external regulatory demands and improves economic benefits. This embodiment generates energy storage discharge control commands during the demand response period through a demand response control module. This commands control the energy storage unit to discharge to the local load side within the power supply network with which it is currently electrically connected. The response power and response benefits are determined based on the difference between the baseline purchased power and the actual purchased power. Therefore, this invention not only reduces the power drawn from the public grid during the demand response period but also incorporates demand response benefits into the overall optimization objective, thereby improving the system's comprehensive operational benefits in scenarios involving interaction with the external power grid.

[0146] Example 2: A photovoltaic-storage-charging system for highway service areas, such as... Figure 2 As shown, it includes a first power supply network, a second power supply network, a distributed energy unit, and a control module.

[0147] The first power supply network is used to supply power to the first local load. It is connected to the public power grid through the first grid connection point and is equipped with an independent first power metering node. The first power supply network has the ability to transmit power bidirectionally with the public power grid.

[0148] The second power supply network is used to supply power to the second local load. It is connected to the public power grid through the second grid connection point and is equipped with an independent second power metering node. The second power supply network is electrically isolated from the first power supply network and is configured to detect and limit the reverse power flowing to the public power grid through the second grid connection point.

[0149] Distributed energy units include photovoltaic power generation units and energy storage units.

[0150] Topology switching network (example) Figure 2The power switch cabinet includes at least one topology switching unit, which is connected to a first power supply network, a second power supply network, and a distributed energy unit, respectively. It is used to receive topology switching control commands and, in response to the topology switching control commands, enable the distributed energy unit to connect to the first power supply network or the second power supply network while keeping the first power supply network and the second power supply network from forming a direct electrical connection.

[0151] The control module is configured to execute the energy storage capacity optimization configuration method for the highway service area photovoltaic-storage-charging system provided by any feasible implementation of the above embodiments, and generate topology switching control commands to send to the topology switching network.

[0152] like Figure 2 As shown, the control module in this embodiment may include an energy management center and a cloud platform. The energy management center is a local controller deployed at the highway service area. It is configured to generate topology switching control commands and send them to the topology switching network to achieve real-time switching control of the access status of distributed energy units. The cloud platform is a remote planning and analysis platform, communicatively connected to the energy management center. It is configured to execute the energy storage capacity optimization configuration method for the highway service area photovoltaic-storage-charging system provided in any feasible implementation of the above embodiments, and to distribute the determined target energy storage capacity configuration scheme and operation scheduling strategy to the energy management center. The energy management center and the cloud platform are independent of each other, but together constitute the control module.

[0153] like Figure 2 As shown in the example, the energy management center is deployed locally in the service area, while the cloud platform is deployed on a remote server. The two interact with each other via the network. The energy management center is responsible for real-time data acquisition, local operation monitoring, and immediate execution of topology switching commands; the cloud platform is responsible for offline capacity optimization, scenario model updates, operational strategy iteration, and long-term planning analysis. Through this layered architecture, the separation of planning optimization and real-time control is achieved, ensuring both the global optimization capability of capacity configuration results and the real-time response performance of on-site operations.

[0154] In this embodiment, the highway service area photovoltaic-storage-charging system is applied to a highway service area scenario with two independent power supply boundaries. One power supply network is mainly used to supply power to the commercial building loads in the service area, while the other power supply network is mainly used to supply power to the charging piles for new energy vehicles. The two power supply networks are connected to the public power grid through their respective independent grid connection points and are equipped with independent power metering nodes, maintaining electrical isolation between them.

[0155] The first local load is the commercial building load of the highway service area. The commercial building load includes at least the operating equipment load, which includes at least one or more of the following: lighting load, air conditioning load, catering equipment load, retail equipment load, water supply and drainage load, and monitoring load, and corresponds to the first electricity purchase price mechanism.

[0156] The second local load is the electric vehicle charging pile load. The new energy vehicle charging pile load can include DC fast charging pile load, AC charging pile load, or a combination thereof. The former usually has the characteristics of a higher proportion of base load, stronger operational continuity, and relatively smooth fluctuations, while the latter usually has the characteristics of high peak power, strong fluctuations, obvious randomness, and significant short-term impacts.

[0157] The first power supply network and the second power supply network are electrically isolated from each other and do not form a fixed direct-connection bus structure.

[0158] Photovoltaic power generation units can be installed on the rooftops of service area buildings, parking sheds, vacant lots, and other areas; energy storage units can be electrochemical energy storage units, such as lithium iron phosphate energy storage units, or other energy storage devices that can achieve bidirectional charge and discharge control.

[0159] In some embodiments, the topology switching network includes a power generation switching cabinet and an energy storage switching cabinet. The power generation switching cabinet is used to control the mutually exclusive switching of the output power of the photovoltaic power generation unit between the first power supply network and the energy storage unit; the energy storage switching cabinet is used to coordinate the switching between the charging state and the discharging state of the energy storage unit, and to control the energy storage unit to mutually exclusive output to the first power supply network or the second power supply network in the discharging state.

[0160] In this embodiment, the cooperation between the power generation switching cabinet and the energy storage switching cabinet facilitates a more flexible energy storage charging and discharging path, improving energy storage turnover efficiency. The power generation switching cabinet can control the photovoltaic power generation unit to switch mutually exclusively between the first power supply network and the energy storage unit, while the energy storage switching cabinet can coordinate the switching between the energy storage unit's charging and discharging states, and perform mutually exclusive switching between the first and second power supply networks during the discharging state. Therefore, the energy storage unit can not only be charged using the power from the second power supply network, but also replenish its power using the power output from the photovoltaic power generation unit, and can discharge to the corresponding load side during subsequent high-price or high-load periods. Compared to a single-path energy storage operation mode, this invention is beneficial for increasing the frequency of charging and discharging regulation and energy turnover efficiency of energy storage within a single day, thereby enhancing peak shaving and valley filling capabilities and price difference utilization.

[0161] In this embodiment, the topology switching network is configured to establish only a single, mutually exclusive electrical connection between the distributed energy unit and either the first or second power supply network at any controlled time, thereby avoiding a direct electrical connection between the first and second power supply networks. In other words, this embodiment does not achieve energy mutual assistance by directly connecting two independent power supply networks in parallel, but rather achieves switching operation for different power supply networks through the selective access of the distributed energy unit.

[0162] In a typical operating mode, when the power generation switching cabinet is in the first connection state, the photovoltaic power generation unit is connected to the first power supply network. The photovoltaic output power is preferentially consumed by the loads within the first power supply network, and the surplus power is allowed to be fed back to the public power grid via the first grid connection point. When the power generation switching cabinet is in the second connection state, the photovoltaic power generation unit is connected to the energy storage unit for charging the energy storage unit. At this time, the photovoltaic output is not directly fed into the first power supply network. Through the above structure, photovoltaic power can be switched in a controlled and mutually exclusive manner between "direct consumption / grid connection in the service area" and "charging energy storage".

[0163] In this embodiment, the energy storage switching cabinet enables the energy storage unit to receive electrical energy from the second power supply network and / or the photovoltaic power generation unit while charging, and to perform mutually exclusive discharge switching between the first and second power supply networks while discharging. Thus, during off-peak hours, the energy storage unit can utilize the lower electricity purchase cost of the second power supply network for charging; during periods of high photovoltaic output and low load on the first power supply network, the energy storage unit can also receive supplementary power from the photovoltaic power generation unit; and during subsequent periods of high electricity prices or high load, the energy storage unit can discharge to the local load side within the target power supply network with which it has established an electrical connection. This operating mode helps to improve the daily charging and discharging frequency and energy turnover efficiency of the energy storage unit, thereby forming a flexible operating mode of multiple charging and discharging operations per day.

[0164] Furthermore, in this embodiment, when the photovoltaic power generation unit is connected to the first power supply network, the photovoltaic power flow satisfies the following relationship:

[0165] ;

[0166] Alternatively, considering the insufficient local absorption capacity of the primary power supply network, it can be written as:

[0167] ;

[0168] in, Indicates time period Photovoltaic output power, This indicates the power delivered by photovoltaics to local loads within the first power supply network. This indicates the power fed to the public power grid through the first grid connection point. This indicates the power of abandoned light.

[0169] When a photovoltaic power generation unit is connected to a second power supply network or coupled with an energy storage unit, the photovoltaic power is preferably used only for load absorption within the second power supply network and / or for charging the energy storage unit, in which case the following conditions are met:

[0170] ;

[0171] And through control constraints, the following can be achieved:

[0172] ;

[0173] in, This indicates the power supplied by the photovoltaic system to the local load side within the second power supply network. This indicates the power of the photovoltaic system charging the energy storage unit. This represents the reverse power fed back to the public grid via the second grid connection point. Through control constraints, the second power supply network can maintain its local priority consumption and limited grid connection operation boundaries.

[0174] For an energy storage unit, in the discharge state, its output power is constrained to flow only to the local load side within the power supply network to which it is currently electrically connected, thereby satisfying:

[0175] ;

[0176] The discharge power output by the energy storage unit to the first power supply network during time period t; This refers to the discharge power output by the energy storage unit to the second power supply network during time period t.

[0177] And at any time period satisfy:

[0178] ;

[0179] Alternatively, it can be represented using mutually exclusive control variables:

[0180] ;

[0181] ;

[0182] in, Switching state variables for the energy storage discharge target network. This refers to the rated charging and discharging power of the energy storage. This method can limit the return of stored electrical energy to the public grid via any grid connection point.

[0183] In some embodiments, the energy storage unit includes at least two energy storage sub-units, such as a first energy storage sub-unit and a second energy storage sub-unit. The total energy storage capacity is allocated to each energy storage sub-unit according to a preset capacity ratio. Each energy storage sub-unit is connected to a corresponding topology switching unit and can independently perform mutually exclusive topology switching between the first power supply network and the second power supply network.

[0184] Assume the total energy storage capacity is The capacity allocation ratio is ,but:

[0185] ;

[0186] ;

[0187] in, and These represent the capacities of the first and second energy storage sub-units, respectively.

[0188] In a specific parameter example, the following can be taken: This means that the total capacity is allocated between the two energy storage sub-units in a 1:1 ratio. For the first and second energy storage sub-units, independent charge / discharge variables, state-of-charge variables, and topology switching variables are established respectively. For example:

[0189] , , , ;

[0190] , , , .

[0191] Both satisfy the corresponding power constraints, SOC constraints, and discharge target switching constraints, respectively.

[0192] By setting up multiple energy storage sub-units and jointly optimizing them, different energy storage sub-units can achieve finer-grained collaborative scheduling between different power supply networks, thereby improving the flexibility of energy storage power allocation and adaptability to various load scenarios.

[0193] Employing at least two energy storage sub-units improves the flexibility of energy storage resource scheduling and the granularity of capacity allocation. The total energy storage capacity is allocated to each sub-unit according to a preset capacity ratio. Each sub-unit is connected to its corresponding topology switching unit and can independently perform mutually exclusive topology switching. Compared to a single energy storage unit, this structure allows for more flexible coordinated scheduling of different sub-units for different load sides or time periods, helping to improve the granularity of energy storage output allocation and enhance dynamic adaptability in scenarios with multiple load types coexisting.

[0194] In some embodiments, the control module is further configured to: evaluate the determined target energy storage capacity configuration scheme based on economic indicators; when the evaluation result does not meet the preset requirements, trigger the control module to recalculate the capacity optimization configuration by adjusting the parameters and / or operating strategy in the planning scenario model, until the evaluation result meets the preset requirements and then output the final energy storage capacity configuration result.

[0195] In this embodiment, after obtaining the target energy storage capacity configuration scheme, a system evaluation of the scheme is also conducted. The evaluation indicators may include at least economic indicators, and may further include indicators such as renewable energy absorption rate, curtailed solar power, energy storage utilization rate, peak power purchase, demand response contribution, and system power supply flexibility.

[0196] For example, a comprehensive evaluation can be conducted based on the following indicators: one-time investment cost; annual operating cost; annualized total cost; electricity purchase cost of the first power supply network; electricity purchase cost of the second power supply network; photovoltaic grid connection revenue; photovoltaic operation and maintenance cost; energy storage operation and maintenance cost; total annual curtailment of solar power; energy storage state of charge change curve; and typical daily or typical weekly power balance results.

[0197] When the evaluation results do not meet the preset requirements, the system components, topology, operating strategies, and / or planning scenario model parameters can be adjusted, and capacity optimization can be re-executed. For example, the energy storage capacity search range, the capacity allocation ratio of dual energy storage sub-units, demand response strategy parameters, peak-valley electricity price boundary settings, or photovoltaic access strategies can be adjusted.

[0198] In some embodiments of the present invention, in order to compare the operational performance and economic efficiency under different system architectures, a unified calculation and comparative analysis can be performed on multiple system architectures based on the same service area scenario, the same photovoltaic resource conditions, the same infrastructure load data, the same charging pile load data, and the same electricity price parameters.

[0199] In this embodiment, the relevant operating results can be calculated by a computer program based on discrete time period data of 8760 hours throughout the year. Multiple system architectures include architecture 1, architecture 2, architecture 3, architecture 4, and architecture 5 corresponding to the code. Architecture 5 can be considered as a comprehensive architecture oriented towards the complete functional boundaries of this invention, while architectures 1, 2, 3, and 4 can be considered as simplified subsets of architecture 5 under different constraints or functional conditions.

[0200] Specifically: Architecture 1 is the most basic single-sided photovoltaic (PV) power supply architecture. In this architecture, only PV power generation units are configured, and these units are fixedly connected to one side of the power supply network, preferably the first power supply network; the other side's power supply network independently purchases electricity from the public grid. This architecture does not include energy storage units, the switching relationship between PV and energy storage, or the switching of energy storage discharge targets.

[0201] Architecture 2 is an architecture where photovoltaic (PV) power generation units and energy storage units are dedicated to a second power supply network. In this architecture, PV power generation units and energy storage units jointly serve the second power supply network. PV power can be distributed among local load absorption, charging of energy storage units, and curtailment within the second power supply network, but it does not form a switching relationship to supply power to the first power supply network, nor does it include topology switching logic for the first power supply network. Under this architecture, the capacity of PV and energy storage can be jointly optimized, but it does not possess the complete characteristic of distributed energy switching between two independent power supply networks as described in the core solution of this invention.

[0202] Architecture 3 is an architecture where photovoltaic units are fixedly connected to the first power supply network, and energy storage units are fixedly connected to the second power supply network using a preset charging and discharging strategy. In this architecture, the photovoltaic power generation units are fixedly connected to the first power supply network, while the energy storage units mainly operate for peak-valley arbitrage or according to preset rules for the second power supply network. The energy storage does not participate in the optimized discharge switching between the first and second power supply networks, and therefore does not possess the mutually exclusive discharge target switching capability of the core architecture of this invention.

[0203] Architecture 4 is an architecture where photovoltaic (PV) units are permanently connected to the first power supply network, and energy storage units can perform optimized discharge switching between the two power supply networks. In this architecture, the PV power generation units are still permanently connected to the first power supply network, allowing surplus electricity to be fed into the grid; the energy storage units can choose to discharge to either the first or second power supply network based on the optimization results. However, the PV power generation units do not have the ability to perform mutually exclusive switching between the first power supply network and the energy storage units, so this architecture still does not include complete power generation switching switch logic.

[0204] Architecture 5 is the core basic architecture of this invention. In this architecture, the photovoltaic power generation unit switches between "connecting to the first power supply network" and "charging the energy storage unit" mutually exclusively through a power generation switching cabinet. The energy storage unit switches between charging and discharging states through an energy storage switching cabinet, and in the discharging state, it outputs mutually exclusively between the first and second power supply networks. This architecture already possesses the core technical features of this invention.

[0205] In some embodiments of the present invention, the operating results under different architectures can be summarized and compared to reflect the comprehensive technical effects of the core architecture of the present invention in terms of investment, operating costs, and annualized total costs. Examples are shown in Table 1 below.

[0206] Table 1 Summary of running results under different architectures

[0207] In some implementations, the "annualized investment" in the table can consist of the annualized investment cost of the photovoltaic power generation unit and the annualized investment cost of the energy storage unit. If the investment cost caliber used in the project is actually "converted annual investment cost" or "average annual investment cost converted over lifespan," then the corresponding result can be filled in according to the actual calculation caliber. If the result caliber used in the project is "one-time investment cost + annual operating cost," then "annualized investment" can also be understood as the investment conversion value used for annual economic comparison, without changing the technical essence.

[0208] In some embodiments of the present invention, by comparing the different architectures shown in the table under the same input data, same parameter conditions and same optimization criteria, it can be seen that the architecture of the present invention, which adopts a topology switching switch cabinet and realizes the coordinated switching operation of photovoltaic power generation unit and energy storage unit, can more effectively improve the utilization rate of distributed energy resources, reduce the system electricity purchase cost and the comprehensive annualized total cost, and its overall economic efficiency is better than the simplified comparison architecture that does not have complete topology switching capability.

[0209] Figure 3 It can display the daily aggregated results of photovoltaic power generation, service area power purchase, charging pile power purchase, energy storage discharge, service area load, charging pile load, energy storage charging, grid-connected power, and curtailed photovoltaic power.

[0210] Figure 4 It can display the power changes of service area load, charging pile load, photovoltaic power generation, service area power purchase, charging pile power purchase and energy storage discharge during a typical summer week.

[0211] Figure 5 It can display the energy storage state of charge change curve and the corresponding electricity purchase price curve during a typical summer week, so as to reflect the charging and discharging regulation relationship of the energy storage unit under time-of-use pricing conditions.

[0212] The above provides a detailed description of the energy storage capacity optimization configuration method and system for the photovoltaic-storage-charging system in highway service areas provided by this application. Specific examples have been used in this paper to illustrate the principles and implementation methods of this application. The above description of the embodiments is only for the purpose of helping to understand the concept of this application and should not be construed as a limitation on the scope of protection of this application.

Claims

1. A method for optimizing the energy storage capacity configuration of a photovoltaic-energy storage-charging system in highway service areas, characterized in that, include: Step S1: Construct a planning scenario model: Collect basic data on the power generation potential, load time series data, and system grid connection conditions of the planning area to construct a planning scenario model; Step S2: Establish a system architecture and capacity optimization configuration model: Based on the planning scenario model, determine the system components, decision variables, and objective function of the system to be optimized, and construct a capacity optimization configuration model; wherein, the system components include distributed energy units, topology switching networks, a first power supply network, and a second power supply network; the distributed energy units include photovoltaic power generation units and energy storage units; the first power supply network and the second power supply network correspond to different load types and are electrically isolated from each other; the capacity optimization configuration model uses the mutual exclusion topology switching constraints of the photovoltaic power generation units and the energy storage units under the topology switching network, as well as the power balance constraints of the first power supply network and the second power supply network, as boundary conditions; Step S3: Solve for the energy storage capacity configuration scheme: Based on the planning scenario model, use a hierarchical optimization algorithm to solve the capacity optimization configuration model to obtain the target energy storage capacity configuration scheme that satisfies the topology switching constraint and the power balance constraint.

2. The method for optimizing the energy storage capacity of a photovoltaic-energy storage-charging system in a highway service area according to claim 1, characterized in that, The power balance constraint is achieved by dividing the annual operating cycle into multiple discrete operating periods, and establishing power balance constraint equations for the first power supply network and the second power supply network for each discrete period.

3. The method for optimizing the energy storage capacity of a photovoltaic-energy storage-charging system in a highway service area according to claim 1, characterized in that, The objective function includes at least the objective of minimizing the total system cost; the total system cost includes at least the one-time investment cost of the distributed energy unit and the annual operating cost of the system, and the annual operating cost includes at least the electricity purchase cost of the first power supply network, the electricity purchase cost of the second power supply network, the operation and maintenance cost of the photovoltaic power generation unit, the operation and maintenance cost of the energy storage unit, and the photovoltaic grid connection revenue.

4. The method for optimizing the energy storage capacity of a photovoltaic-energy storage-charging system in a highway service area according to claim 3, characterized in that, In step S3, during the solution process, the power purchased by the system during the demand response period is reduced by introducing a demand response period constraint; the demand response power is determined based on the difference between the baseline power purchased through the public grid and the actual power purchased during the demand response period; the demand response revenue is determined based on the demand response power; and the demand response revenue is included in the objective function.

5. A photovoltaic-storage-charging system for highway service areas, characterized in that: include: The first power supply network is used to supply power to the first local load. It is connected to the public power grid through the first grid connection point and is equipped with an independent first power metering node. The first power supply network has the ability to transmit power bidirectionally with the public power grid. The second power supply network is used to supply power to the second local load. It is connected to the public power grid through the second grid connection point and is equipped with an independent second power metering node. The second power supply network is electrically isolated from the first power supply network and is configured to detect and limit the reverse power flowing to the public power grid through the second grid connection point. Distributed energy units include photovoltaic power generation units and energy storage units; A topology switching network includes at least one topology switching unit, which is connected to the first power supply network, the second power supply network, and the distributed energy unit respectively. It is used to receive topology switching control commands and, in response to the topology switching control commands, enable the distributed energy unit to connect to the first power supply network or the second power supply network while keeping the first power supply network and the second power supply network from forming a direct electrical connection. The control module is configured to execute the energy storage capacity optimization configuration method of the highway service area photovoltaic-storage-charging system as described in any one of claims 1 to 4, and generate a topology switching control command to be sent to the topology switching network.

6. The highway service area photovoltaic-storage-charging system according to claim 5, characterized in that, The control module is also configured to: The target energy storage capacity configuration scheme is evaluated based on economic indicators. When the evaluation result does not meet the preset requirements, the control module is triggered to recalculate the capacity optimization configuration by adjusting the parameters and / or operating strategy in the planning scenario model until the evaluation result meets the preset requirements and the final energy storage capacity configuration result is output.

7. The highway service area photovoltaic-storage-charging system according to claim 5, characterized in that, The energy storage unit includes at least two energy storage sub-units. The total energy storage capacity is allocated to each energy storage sub-unit according to a preset capacity ratio. Each energy storage sub-unit is connected to a corresponding topology switching unit in the topology switching network, so that each energy storage sub-unit can independently perform mutually exclusive topology switching between the first power supply network and the second power supply network.

8. The highway service area photovoltaic-storage-charging system according to claim 5, characterized in that, The topology switching network includes power generation switching cabinets and energy storage switching cabinets; The power generation switching cabinet is connected to the photovoltaic power generation unit and is configured to allow the electrical energy output by the photovoltaic power generation unit to switch mutually exclusively between the following two connection states: In the first connection state, the photovoltaic power generation unit is connected to the first power supply network to absorb the load within the first power supply network and to feed the remaining electrical energy back to the public power grid through the first grid connection point; In the second connection state, the photovoltaic power generation unit is connected to the energy storage unit to charge the energy storage unit; The energy storage switching cabinet is connected to the energy storage unit and is configured to enable the energy storage unit to receive electrical energy from the second power supply network and / or the photovoltaic power generation unit in the charging state, and to enable the energy output of the energy storage unit to be mutually exclusive switched between the first power supply network and the second power supply network in the discharging state.

9. The highway service area photovoltaic-storage-charging system according to claim 5, characterized in that, The control module is also configured to generate an energy storage discharge control command when it receives a demand response command from the public power grid and is in the corresponding demand response period, and send the energy storage discharge control command to the topology switching network to control the energy storage unit to discharge to the local load side within the power supply network with which it is currently electrically connected.

10. The highway service area photovoltaic-storage-charging system according to claim 5, characterized in that, The first local load is the commercial building load of the highway service area. The commercial building load includes at least the operating equipment load, which includes at least one or more of the following: lighting load, air conditioning load, catering equipment load, retail equipment load, water supply and drainage load, and monitoring load, and corresponds to the first electricity purchase price mechanism. The second local load is the load of electric vehicle charging piles, and corresponds to the second electricity purchase price mechanism.

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