Method and system for formulating charging and discharging prices of optical storage and charging micro-grid with consideration of distribution and micro-grid coordination

Abstract

The invention discloses an optical storage and charging micro-grid charging and discharging price formulating method considering distribution and micro-grid coordination, and belongs to the technical field of power systems. The method comprises the following steps: describing probability density functions of the initial charging time, the charging duration and the daily mileage of the electric private car, and simulating the charging load of the electric car by adopting Monte Carlo sampling; the method comprises the following steps: modeling photovoltaic, energy storage and electric vehicle elements in the optical storage and charging micro-grid, considering distribution and micro-grid cooperative operation, constructing a master-slave game architecture taking the optical storage and charging micro-grid as a leader and an electric vehicle user as a follower, and establishing a master-slave game double-layer optimization model of dynamic charging and discharging prices; according to the method, the uncertainty of photovoltaic output and electric vehicle load is considered, the schedulable potential of internal resources of the photovoltaic storage and charging micro-grid is fully excavated, interaction with the power distribution network is achieved, the charging and discharging behaviors of the electric vehicle are guided according to the charging and discharging price, the operation efficiency of the photovoltaic storage and charging micro-grid can be effectively improved, and the operation burden of the power distribution network is relieved.

Description

Technical Field

[0001] The present invention relates to a method and system for formulating charging and discharging prices for a photovoltaic storage and charging microgrid taking into account the coordination between distribution and microgrid, and belongs to the technical field of power systems. Background Art

[0002] Some analysts believe [1] that microgrids can complement existing power grids and form a new clean and flexible system to improve the cleanliness and reliability of power supply. However, with the continuous advancement of the "dual carbon" goal, the number and scale of microgrids are growing, and the demand for microgrid access to the Internet is inevitably increasing, which poses a challenge to the flexible operation of distribution networks. In order to cope with the challenge of insufficient operational flexibility, the distribution network is equipped with flexible resource equipment such as energy storage at important nodes. By rationally charging and discharging energy storage, the fluctuation of wind and solar loads can be smoothed. When the flexibility of the microgrid is insufficient, the distribution network provides flexible resources (i.e., active power regulation capability) to the microgrid through interconnection lines to promote the local consumption of wind and solar power. At the same time, the configuration of corresponding flexible resource equipment such as batteries and flexible loads in the microgrid can also provide certain supplements to the distribution network through interconnection lines when the flexibility of the distribution network is insufficient, and play a supporting role when the distribution network is heavily loaded. Therefore, at this stage, the planning and operation of the distribution network and microgrids have put forward the need for coordination to enhance the flexibility of both parties.

[0003] However, microgrids and distribution networks usually do not belong to the same interest subject, and the interest relationship between the two needs to be considered separately during the planning process. At the same time, due to the power transmission function of the interconnection line, when the distribution network and the microgrid are operated and dispatched, their respective operation constraints have a coupling relationship, and the traditional planning method cannot directly achieve the coordinated operation of the distribution network and the microgrid. Therefore, in the operation stage, reasonable operation goals that meet the interests of both parties should be formulated to meet the coupling relationship of the interconnection line between the two, so as to obtain a feasible coordinated operation plan.

[0004] As for the research on economic optimization dispatch of photovoltaic storage and charging microgrids, as early as the 1970s, developed countries such as the United States and Japan began to attach importance to the research and development of electric vehicles. The United States started the earliest in the research of electric vehicle charging stations, and its technology is relatively mature and its market share is relatively high. The number of electric vehicles and supporting charging stations is far ahead of other countries [2]. Japan has spent a lot of energy and money to put new energy vehicles into the market and build a large number of electric vehicle charging stations to meet the demand. So far, there are more than 3,000 electric vehicle charging stations in Tokyo alone [3]. In order to ensure that electric vehicles can be sold abroad, Japanese automobile manufacturers will also build supporting electric vehicle charging stations in the sales area.

[0005] In recent years, with the research on photovoltaic-storage DC microgrids, photovoltaic-storage charging and discharging stations that combine photovoltaic power generation with electric vehicle charging and discharging stations have become a hot topic of research. As early as 2009, Toyota Automatic Textile Co., Ltd. of Japan built a photovoltaic-storage electric vehicle charging demonstration station. The charging station includes a photovoltaic power generation unit and an energy storage unit, which can realize photovoltaic power generation and energy storage system to charge electric vehicles together[4]. In 2010, the Electric Power Research Center of the United States also built the first electric vehicle charging station that uses photovoltaics as an auxiliary power source through a collaboration with several companies[5].

[0006] Photovoltaic storage charging and discharging stations have also developed rapidly in my country in recent years. In 2010, the first photovoltaic storage electric vehicle charging station in my country, built by Jiangxi Electric Power Company, was established in Yichun, Jiangxi Province, and has now started trial operation[6]. In 2015, the first photovoltaic storage charging station project in Qinghai Province was established. As an annual scientific and technological support project of the Qinghai Provincial Department of Science and Technology, the project has a total investment of about RMB 2 million and can basically meet the charging needs of electric vehicles of any brand in the world. The charging station integrates a number of advanced technologies such as photovoltaic modules, smart charging pile modules, and energy storage battery modules, solving the problem of being unable to charge electric vehicles due to insufficient photovoltaic output at night[7]. The photovoltaic storage and charging integrated demonstration project built by State Grid Electric Vehicle Co., Ltd. at the Xuguantun Service Area of ​​the Tianjin Section of the Beijing-Tianjin-Tangshan Expressway makes full use of the space resources and existing power distribution facilities of the service area to create a green energy service area.

[0007] Research on the coordinated control mode of microgrid distribution network, in terms of coordinated operation of distribution network, mainly focuses on the interactive operation mechanism of source-grid-load, coordinated optimization technology of distribution network with high penetration rate of distributed power source, coordinated control technology of distribution network based on multi-agent[8], bidding mechanism and game method of multiple subjects of distribution network[9]. However, there are relatively few studies on the coordinated operation of microgrid and distribution network. Reference

[10] established a decomposition coordination optimization model of multi-microgrid access to active distribution network and proposed a decomposition coordination algorithm with coupling characteristics.

[0008] [1] Zhao Wei. Research on Solar Photovoltaic Grid-connected Power Generation[D]. Hefei: Hefei University of Technology, 2003.

[0009] [2] Ding Ming, Tian Longgang, Pan Hao, et al. Research on operation control strategy of AC / DC hybrid microgrid [J]. Power System Protection and Control, 2015, 43(9): 1-8.

[0010] [3] Li Yulong, Tang Gang, Wang Ke, et al. Design of lithium battery management system in energy storage system [J]. Dongfang Electric Review, 2015, 116(29): 5-10.

[0011] [4] Zhao Siyuan, Yu Qingguang, Huang Jie. Design and implementation of energy storage converter based on photovoltaic microgrid [J]. Power Supply Technology, 2017, 40(7): 1031-1034.

[0012] [5] Liu Ya. Research and Design of Three-phase Energy Storage Converter[D]. Inner Mongolia: Inner Mongolia University of Science and Technology, 2016.

[0013] [6] Li Fusheng, Li Ruisheng, Zhou Fengquan. Microgrid Technology and Engineering Application[M]. Beijing: China Electric Power Press, 2013.

[0014] [7] Li Guo, Zhang Zhisheng, Wen Lingyun. Planning of charging and battery swapping network for electric vehicles under battery swapping mode[J]. Power System Protection and Control, 2013, 41(20): 93-98.

[0015] [8] Liu Lianguang, Pan Mingming, Tian Shiming, et al. Non-cooperative game method for power sales competition considering multiple entities of source, grid and load[J]. Proceedings of the CSEE, 2017, 37(6): 1618-1625.

[0016] [9] Sun Jun, Fu Rong, Chen Xi, et al. Research on decomposition and coordination optimization strategy of multiple microgrids connected to active distribution network [J]. Electrical Appliances and Energy Efficiency Management Technology, 2017 (18): 48-55.

[0017]

[10] Ai Xin, Xu Jiajia. Research on coordinated operation mode of microgrid and distribution network based on interactive dispatch[J]. Power System Protection and Control, 2013, 41(1): 143-149. Summary of the invention

[0018] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for formulating charging and discharging prices of a photovoltaic storage and charging microgrid that takes into account the coordination of distribution and microgrids. Under the premise of taking into account the coordination of distribution and microgrids, the resource coordination capability within the photovoltaic storage and charging microgrid is fully exploited to improve the operating efficiency of the photovoltaic storage and charging microgrid.

[0019] In order to achieve the above purpose / solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0020] A first aspect: a method for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid, the method comprising:

[0021] Step 1: Describe the probability density function of the starting charging time, expected charging time, and daily mileage of electric private cars, and use Monte Carlo sampling to simulate the travel characteristics and charging needs of electric vehicles;

[0022] Step 2: Based on the travel characteristics and charging demand of electric vehicles, considering the coordinated operation of distribution and microgrid, a master-slave game two-layer optimization model for dynamic charging and discharging price formulation is constructed. The master-slave game two-layer optimization model includes an upper-layer optimization operation model with the leader photovoltaic storage charging microgrid as the main body and a lower-layer optimization scheduling model with the follower electric private car as the main body;

[0023] Among them, the upper-level optimization operation model outputs the charging and discharging electricity price with the maximum objective function and constraints, and the lower-level optimization scheduling model determines the charging and discharging plan according to the electricity price released by the upper layer and the minimum lower-level objective function and constraints, and returns the output to the upper-level optimization operation model, and iterates the solution until the upper-level optimization operation model outputs the optimal charging and discharging electricity price according to the charging and discharging plan determined by the lower-level optimization scheduling model.

[0024] Optionally, in step 1,

[0025] The time it takes for electric private cars to complete their daily trips and start charging satisfies the normal distribution: ; In the formula, f s (t) is the probability density function of the starting charging time of electric private cars, μ s is the expected value, σ s is the standard deviation, is a natural constant, For charging time;

[0026] The expected charging time of electric private cars satisfies the normal distribution: ; In the formula, f p (t) is the probability density function of the expected charging time of electric private cars, μ p is the expected value, σ p is the standard deviation;

[0027] The daily mileage of electric private cars follows a log-normal distribution: ;

[0028] Where, s is the mileage, in km; μ D and σ D are the expected value and standard deviation respectively;

[0029] At the end of the trip, the electric vehicle's state of charge is: ;

[0030] In the formula, s is the mileage, s max is the maximum cruising range, S before S is the state of charge after the last charge is completed. afterTo end the last trip of the charge state;

[0031] Charging time T to meet travel needs s It can be obtained by the following formula: ;

[0032] In the formula, E is the battery capacity of the electric car, P is the charging power of the electric car, and S is the desired state of charge, which is S before Or to protect the maximum state of charge of the battery life, if the charging time to meet travel needs is longer than the expected charging time, the electric private car can participate in the internal scheduling of the photovoltaic storage and charging microgrid; if the charging time to meet travel needs is less than or equal to the expected charging time, the electric private car will be charged at the maximum charging power until it leaves the photovoltaic storage and charging station.

[0033] Optionally, the upper layer optimization operation model includes:

[0034] The upper layer is the objective function of the optimization operation model with the leader - the photovoltaic storage and charging microgrid as the main body;

[0035] The upper layer is the constraint conditions of the optimization operation model with the leader - the photovoltaic storage and charging microgrid as the main body.

[0036] Optionally, the objective function of the upper layer optimization operation model is as follows: ;

[0037] Where: T is the set of scheduling time periods; , is the charging and discharging price of electric vehicles during period t; The cost of energy storage charging and discharging loss; The purchase and sale price of electricity from the distribution network during period t; The cost of abandoned light; , is the total charging and discharging power of the electric vehicle during period t; , is the energy storage charging and discharging power during period t; , is the electricity purchase and sales price of the distribution network during period t; , They are the maximum photovoltaic output and actual output at time t respectively.

[0038] Optionally, the constraints of the upper-level optimization operation model include charging and discharging price constraints, energy storage charging and discharging power, charging and discharging state, capacity constraints, photovoltaic storage and charging microgrid power balance constraints, and photovoltaic output constraints.

[0039] Optionally, the lower layer optimization scheduling model includes:

[0040] The lower layer is the objective function of the optimization operation model with the follower - the electric private car as the main body;

[0041] The constraints of the optimization operation model in the lower layer are based on the followers - electric private cars.

[0042] Optionally, the objective function of the lower-layer optimization scheduling model is as follows: ;

[0043] Where: T is the set of scheduling time periods, I is , is the charging and discharging price of electric vehicles during period t; , is the total charging and discharging power of the i-th electric vehicle in period t.

[0044] Optionally, the constraints of the lower-level optimization scheduling model include electric vehicle charging and discharging state constraints, electric vehicle charging and discharging power constraints, electric vehicle capacity constraints and user charging cost constraints.

[0045] Optionally, the photovoltaic storage and charging microgrid includes a photovoltaic system, an energy storage system, a charging pile, and an external electric private car.

[0046] A second aspect: A system for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid, the system comprising:

[0047] The load module is used to characterize the probability density function of the starting charging time, expected charging time, and daily mileage of electric private cars, and uses Monte Carlo sampling to simulate the travel characteristics and charging needs of electric vehicles;

[0048] The scheduling module is used to build a master-slave game two-layer optimization model for dynamic charging and discharging price formulation based on the travel characteristics and charging needs of electric vehicles, taking into account the coordinated operation of distribution and microgrids. The master-slave game two-layer optimization model includes an upper-layer optimization operation model with the leader photovoltaic storage charging microgrid as the main body and a lower-layer optimization scheduling model with the follower electric private cars as the main body;

[0049] Among them, the upper-level optimization operation model outputs the charging and discharging electricity price with the maximum objective function and constraints, and the lower-level optimization scheduling model determines the charging and discharging plan according to the electricity price released by the upper layer and the minimum lower-level objective function and constraints, and returns the output to the upper-level optimization operation model, and iterates the solution until the upper-level optimization operation model outputs the optimal charging and discharging electricity price according to the charging and discharging plan determined by the lower-level optimization scheduling model.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention takes into account the coordination between the distribution network and the photovoltaic storage and charging microgrid. The photovoltaic storage and charging microgrid realizes interaction with the power grid through price-based demand response, and guides the orderly charging of electric vehicles through a master-slave game, thereby reducing the user's charging costs as much as possible, improving the operating efficiency of the photovoltaic storage and charging microgrid, and enhancing the linkage and interactivity between the distribution network and the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a method for formulating charging and discharging prices of a photovoltaic storage microgrid considering the coordination of distribution and microgrid in an embodiment of the present invention;

[0052] Figure 2 Schematic diagram of the master-slave game model of the present invention is shown in FIG. DETAILED DESCRIPTION

[0053] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0054] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0055] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances. Example

[0056] like Figure 1-Figure 2 As shown, a method for formulating charging and discharging prices of a photovoltaic storage microgrid considering the coordination of distribution and microgrid is disclosed, which is as follows:

[0057] Step 1: Describe the probability density function of the starting charging time, expected charging time, and daily mileage of electric private cars, and use Monte Carlo sampling to simulate the travel characteristics and charging needs of electric vehicles;

[0058] Step 2: Based on the travel characteristics and charging demand of electric vehicles, considering the coordinated operation of distribution and microgrid, a master-slave game two-layer optimization model for dynamic charging and discharging price formulation is constructed. The master-slave game two-layer optimization model includes an upper-layer optimization operation model with the leader photovoltaic storage charging microgrid as the main body and a lower-layer optimization scheduling model with the follower electric private car as the main body;

[0059] Among them, the upper-level optimization operation model outputs the charging and discharging electricity price with the maximum objective function and constraints, and the lower-level optimization scheduling model determines the charging and discharging plan according to the electricity price released by the upper layer and the minimum lower-level objective function and constraints, and returns the output to the upper-level optimization operation model, and iterates the solution until the upper-level optimization operation model outputs the optimal charging and discharging electricity price according to the charging and discharging plan determined by the lower-level optimization scheduling model.

[0060] The probability density functions of the starting charging time, charging time, and daily mileage of the electric private car are as follows:

[0061] In step 1,

[0062] The time it takes for electric private cars to complete their daily trips and start charging satisfies the normal distribution: ;

[0063] In the formula, f s (t) is the probability density function of the starting charging time of electric private cars, μ s is the expected value, σ s is the standard deviation, is a natural constant, For charging time;

[0064] The expected charging time of electric private cars satisfies the normal distribution: ;

[0065] In the formula, f p (t) is the probability density function of the expected charging time of electric private cars, μ p is the expected value, σ p is the standard deviation;

[0066] The daily mileage of electric private cars follows a log-normal distribution: ;

[0067] Where, s is the mileage, in km; μD and σ D are the expected value and standard deviation respectively;

[0068] At the end of the trip, the electric vehicle's state of charge is: ;

[0069] In the formula, s is the mileage, s max is the maximum cruising range, S before S is the state of charge after the last charge is completed. after To end the last trip of the charge state;

[0070] Charging time T to meet travel needs s It can be obtained by the following formula: ;

[0071] In the formula, E is the battery capacity of the electric car, P is the charging power of the electric car, and S is the desired state of charge, which is S before Or to protect the maximum state of charge of the battery life, if the charging time to meet travel needs is longer than the expected charging time, the electric private car can participate in the internal scheduling of the photovoltaic storage and charging microgrid; if the charging time to meet travel needs is less than or equal to the expected charging time, the electric private car will be charged at the maximum charging power until it leaves the photovoltaic storage and charging station.

[0072] In the embodiment of the present application, the internal resources of the photovoltaic storage and charging microgrid are composed of: a photovoltaic system, an energy storage system, a charging pile, and external electric private cars.

[0073] In this embodiment, during the specific implementation process, a master-slave game two-layer optimization model with the photovoltaic storage and charging microgrid as the leader and the electric private car users as the followers is specifically as follows:

[0074] Establish an optimized operation model with the leader - photovoltaic storage and charging microgrid as the main body;

[0075] Establish an optimization dispatching model with followers - electric private cars as the main body at the bottom layer;

[0076] In this embodiment, during the specific implementation process, the optimization operation model with the leader-photovoltaic storage and charging microgrid as the main body includes:

[0077] The upper layer is the objective function of the optimization operation model with the leader - the photovoltaic storage and charging microgrid as the main body;

[0078] The upper layer is the constraint conditions of the optimization operation model with the leader - the photovoltaic storage and charging microgrid as the main body.

[0079] In this embodiment, the objective function of the upper layer optimization operation model with the leader-photovoltaic storage and charging microgrid as the main body is as follows:

[0080] Where: T is the set of scheduling time periods; , is the charging and discharging price of electric vehicles during period t; The cost of energy storage charging and discharging loss; is the purchase and sale price of electricity on the distribution network during period t. , is the total charging and discharging power of the electric vehicle during period t; , is the energy storage charging and discharging power during period t; , is the electricity purchase and sales price of the distribution network during period t.

[0081] In this embodiment, during the specific implementation process, the constraints of the upper layer optimization operation model with the leader-photovoltaic storage and charging microgrid as the main body include:

[0082] Charge and discharge price constraints: ; ;

[0083] In the formula, , is the charging and discharging electricity price, For the electricity purchase price of the distribution network, , It is the upper and lower limits of charging and discharging electricity prices.

[0084] Energy storage charging and discharging power, charging and discharging status, and capacity constraints: ; ; ; ; ;

[0085] In the formula, , is the charging and discharging state of energy storage at time t, , is the charging and discharging power of energy storage at time t, , is the maximum charging and discharging power of energy storage, is the energy storage capacity at time t, , is the charging and discharging efficiency of energy storage, is the initial charge state of energy storage, , It is the upper and lower limits of the energy storage charge state.

[0086] Power balance constraints of photovoltaic storage and charging microgrid: ;

[0087] To exchange electricity with the distribution network, For the actual output of photovoltaic is the energy storage discharge power, is the total discharge power of the electric vehicle; Charging power for energy storage; The total charging power of the electric vehicle.

[0088] Photovoltaic output constraints: ;

[0089] The maximum output of photovoltaic power.

[0090] In the specific implementation process of this embodiment, the optimization scheduling model with the follower-electric private car as the main body includes:

[0091] The lower layer is the objective function of the optimization operation model with the follower - the electric private car as the main body;

[0092] The constraints of the optimization operation model in the lower layer are based on the followers - electric private cars.

[0093] The objective function of the optimization operation model in the lower layer with the follower - electric private car as the main body is: ;

[0094] Where: T is the set of scheduling time periods; I is the set of electric vehicle users; , is the charging and discharging price of electric vehicles during period t; , is the total charging and discharging power of the i-th electric vehicle in period t;

[0095] The constraints of the optimization operation model with followers - electric private cars as the main body in the lower layer include:

[0096] Electric vehicle charging and discharging state constraints ;

[0097] In the formula, , is the charging and discharging status of the i-th electric vehicle.

[0098] Electric vehicle charging and discharging power constraints ;

[0099] In the formula, , is the charging and discharging power of the i-th electric vehicle; , It is the maximum power for charging and discharging electric vehicles.

[0100] Electric vehicle energy storage battery capacity constraints: ; ;

[0101] In the formula, is the battery capacity of the i-th electric car, , is the charging and discharging efficiency of electric vehicles, The amount of electricity required for charging the i-th electric vehicle this time; , is the exit and start charging time of the i-th electric vehicle.

[0102] User charging fee constraints ;

[0103] In the formula, The charging cost of electric vehicles after participating in the response, It is the charging cost of electric vehicles before participating in the response.

[0104] Example 2

[0105] A system for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid, the system comprising:

[0106] The load module is used to characterize the probability density function of the starting charging time, charging time, and daily mileage of electric private cars, and uses Monte Carlo sampling to simulate the travel characteristics and charging needs of electric vehicles;

[0107] The scheduling module is used to build a master-slave game two-layer optimization model of dynamic charging and discharging prices based on the travel characteristics and charging needs of electric vehicles, taking into account the coordinated operation of distribution and microgrids. The master-slave game two-layer optimization model includes an upper-layer optimization operation model with the leader photovoltaic storage charging microgrid as the main body and a lower-layer optimization scheduling model with the follower electric private cars as the main body;

[0108] Among them, the upper-level optimization operation model outputs the charging and discharging electricity price with the maximum objective function and constraints, and the lower-level optimization scheduling model determines the charging and discharging plan according to the electricity price released by the upper layer with the minimum lower-level objective function and constraints, until the upper-level optimization operation model outputs the charging and discharging electricity price according to the charging and discharging plan determined by the lower-level optimization scheduling model.

[0109] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0110] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0111] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0112] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0113] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0114] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for formulating charging and discharging prices for a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid, characterized in that: The method comprises: Step 1: Describe the probability density function of the starting charging time, expected charging time, and daily mileage of electric private cars, and use Monte Carlo sampling to simulate the travel characteristics and charging needs of electric vehicles; Step 2: Based on the travel characteristics and charging demand of electric vehicles, considering the coordinated operation of distribution and microgrid, a master-slave game two-layer optimization model for dynamic charging and discharging price formulation is constructed. The master-slave game two-layer optimization model includes an upper-layer optimization operation model with the leader photovoltaic storage charging microgrid as the main body and a lower-layer optimization scheduling model with the follower electric private car as the main body; Among them, the upper-level optimization operation model outputs the charging and discharging electricity price with the maximum objective function and constraints, and the lower-level optimization scheduling model determines the charging and discharging plan according to the electricity price released by the upper layer and the minimum lower-level objective function and constraints, and returns the output to the upper-level optimization operation model, and iterates the solution until the upper-level optimization operation model outputs the optimal charging and discharging electricity price according to the charging and discharging plan determined by the lower-level optimization scheduling model.

2. The method for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: In step 1, the time when the electric private car completes the trip and starts charging every day satisfies the normal distribution: ； In the formula, f s (t) is the probability density function of the starting charging time of electric private cars, μ s is the expected value, σ s is the standard deviation, is a natural constant, For charging time; The expected charging time of electric private cars satisfies the normal distribution: ； In the formula, f p (t) is the probability density function of the expected charging time of electric private cars, μ p is the expected value, σ p is the standard deviation; The daily mileage of electric private cars follows a log-normal distribution: ； Where, s is the mileage, in km; μ D and σ D are the expected value and standard deviation respectively; At the end of the trip, the electric vehicle's state of charge is: ； In the formula, s is the mileage, s max is the maximum cruising range, S before S is the state of charge after the last charge is completed. after To end the last trip of the charge state; Charging time T to meet travel needs s It can be obtained by the following formula: ； In the formula, E is the battery capacity of the electric car, P is the charging power of the electric car, and S is the desired state of charge, which is S before Or to protect the maximum state of charge of the battery life, if the charging time to meet travel needs is longer than the expected charging time, the electric private car can participate in the internal scheduling of the photovoltaic storage and charging microgrid; if the charging time to meet travel needs is less than or equal to the expected charging time, the electric private car will be charged at the maximum charging power until it leaves the photovoltaic storage and charging station.

3. The method for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: The upper layer optimization operation model includes: The upper layer is the objective function of the optimization operation model with the leader - the photovoltaic storage and charging microgrid as the main body; The upper layer is the constraint conditions of the optimization operation model with the leader - the photovoltaic storage and charging microgrid as the main body.

4. The method for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: The objective function of the upper optimization operation model is as follows: ； Where: T is the scheduling time period set; , is the charging and discharging price of electric vehicles during period t; The cost of energy storage charging and discharging loss; The purchase and sale price of electricity from the distribution network during period t; The cost of abandoned light; , is the total charging and discharging power of the electric vehicle during period t; , is the energy storage charging and discharging power during period t; , is the electricity purchase and sales price of the distribution network during period t; , They are the maximum photovoltaic output and actual output at time t respectively.

5. The method for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: The constraints of the upper-level optimization operation model include charging and discharging price constraints, energy storage charging and discharging power, charging and discharging status, capacity constraints, photovoltaic storage and charging microgrid power balance constraints, and photovoltaic output constraints.

6. The method for formulating charging and discharging prices of a photovoltaic storage microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: The lower layer optimization scheduling model includes: The lower layer is the objective function of the optimization operation model with the follower - the electric private car as the main body; The constraints of the optimization operation model in the lower layer are based on the followers - electric private cars.

7. The method for formulating charging and discharging prices of a photovoltaic storage microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: The objective function of the lower-level optimization scheduling model is as follows: ； Where: T is the set of scheduling time periods, I is the set of electric vehicles, , is the charging and discharging price of electric vehicles during period t; , is the total charging and discharging power of the i-th electric vehicle in period t.

8. The method for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: The constraints of the lower-level optimization scheduling model include electric vehicle charging and discharging state constraints, electric vehicle charging and discharging power constraints, electric vehicle capacity constraints and user charging cost constraints.

9. The method for formulating charging and discharging prices of a photovoltaic storage and charging microgrid considering the coordination of distribution and microgrid according to claim 1 is characterized in that: The photovoltaic storage and charging microgrid includes a photovoltaic system, an energy storage system, charging piles, and external electric private cars.

10. A charging and discharging price setting system for a photovoltaic storage microgrid considering the coordination of distribution and microgrid, characterized in that: The system comprises: The load module is used to characterize the probability density function of the starting charging time, expected charging time, and daily mileage of electric private cars, and uses Monte Carlo sampling to simulate the travel characteristics and charging needs of electric vehicles; The scheduling module is used to build a master-slave game two-layer optimization model for dynamic charging and discharging price formulation based on the travel characteristics and charging needs of electric vehicles, taking into account the coordinated operation of distribution and microgrids. The master-slave game two-layer optimization model includes an upper-layer optimization operation model with the leader photovoltaic storage charging microgrid as the main body and a lower-layer optimization scheduling model with the follower electric private cars as the main body; Among them, the upper-level optimization operation model outputs the charging and discharging electricity price with the maximum objective function and constraints, and the lower-level optimization scheduling model determines the charging and discharging plan according to the electricity price released by the upper layer and the minimum lower-level objective function and constraints, and returns the output to the upper-level optimization operation model, and iterates the solution until the upper-level optimization operation model outputs the optimal charging and discharging electricity price according to the charging and discharging plan determined by the lower-level optimization scheduling model.

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