Electric vehicle charging station commutation response method

Abstract

The invention relates to the field of electric vehicle charging station optimization operation, and discloses an electric vehicle charging station commutation response method comprising the following steps: obtaining basic operation parameters of a power distribution network and a charging station, and building a power distribution network three-phase optimal power flow model based on an AC optimal power flow; establishing a charging station commutation operation optimization model taking the minimum electricity purchase cost as a target; based on the three-phase optimal power flow model of the power distribution network and the charging station commutation operation optimization model, establishing an interaction mechanism of electricity price clearing power adjustment between the charging stations of the power distribution network; and solving the interaction mechanism to obtain the number of the charging piles in the charging station distributed on each phase of the alternating-current three phases, so that the lowest electricity purchasing cost of the charging station can be realized. Under the excitation of the three-phase DLMP of the power distribution network, the commutation operation of each charging pile is optimized, and the operation cost of the power distribution network is reduced while the power purchase cost of the power distribution network is reduced.

Description

Technical Field

[0001] The invention relates to the field of optimized operation of electric vehicle charging stations, and in particular to a phase change response method of electric vehicle charging stations. Background Art

[0002] As the proportion of renewable energy generation connected to the distribution network increases, its output volatility and uncertainty will pose a serious threat to the three-phase balance of the power grid. At the same time, the single-phase access of residential loads and their power imbalance will also aggravate the three-phase imbalance of the power grid. At present, some studies have proposed a method for constructing a three-phase power flow model for the distribution network, and used the three-phase voltage imbalance constraint to stimulate the three-phase distribution node marginal price (Distribution LocationalMarginal Pricing, DLMP) of each node in the distribution network. The three-phase DLMP can not only reflect the imbalance of power between phases of each distribution node, but also serve as a market signal to drive various types of flexible resources or load aggregates to adjust their electricity consumption behavior to reduce their own electricity costs and respond to the three-phase balance regulation of the distribution network.

[0003] At present, electric vehicles are developing rapidly. At present, the penetration rate of new energy electric vehicles in China has exceeded 40%, and it is showing a trend of gradual increase. In addition, they are charged and replenished in various charging stations. The proportion of charging load in the total load of the power grid has also increased year by year, becoming an increasingly important factor affecting the safe and stable operation of the power grid. Among the existing charging stations, there is a certain proportion of AC charging stations, most of which use 7kW single-phase AC charging piles. The proportion of each phase power in the charging station will have a great impact on the three-phase imbalance of the power grid. By reasonably adjusting the phase sequence of the charging piles connected to the distribution network, it is helpful to reduce the three-phase imbalance of the distribution network. In the existing related methods, AC charging piles are mostly based on three-phase electricity meters to judge the degree of three-phase imbalance, and perform corresponding phase switching operations by setting thresholds. This method cannot play the incentive role of market prices, nor can it measure the economic value of charging stations performing phase switching operations to reduce the three-phase imbalance of the distribution network. Summary of the invention

[0004] The present invention aims to at least solve the technical problem that the existing technology for alleviating the three-phase imbalance of the distribution network cannot reduce the power purchase cost itself and the operation cost of the distribution network.

[0005] To this end, an object of the present invention is to provide a phase change response method for an electric vehicle charging station, comprising the following steps:

[0006] The following steps are involved:

[0007] Obtain the basic operating parameters of the distribution network and charging stations, and establish a three-phase optimal power flow model for the distribution network based on AC optimal power flow;

[0008] Establish a charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost;

[0009] Based on the three-phase optimal power flow model of the distribution network and the phase-change operation optimization model of the charging station, an interactive mechanism for price-clearing power adjustment between charging stations in the distribution network is established;

[0010] By solving the interaction mechanism and obtaining the number of charging piles in the charging station distributed on each phase of the three-phase AC, the lowest electricity purchase cost of the charging station can be achieved.

[0011] Furthermore, based on the three-phase optimal power flow model of the distribution network and the phase-change operation optimization model of the charging station, an interactive mechanism for adjusting the power of electricity price clearing between the charging stations in the distribution network is established, and the interactive mechanism includes:

[0012] The distribution network clears the three-phase DLMP and releases it to the charging station. The charging station performs phase switching operations according to the three-phase DLMP to adjust the power of each phase until the charging station no longer performs phase switching operations.

[0013] Furthermore, the expression of the interaction mechanism is as follows:

[0014]

[0015]

[0016] Among them, f EVCSO is the electricity purchase cost of the charging station connected to the distribution network node j; Indicates the node number set of the charging station connected to the distribution network; It represents the three-phase power purchase DLMP of the charging station; Indicates the total charging power of the charging station; is the active power of the charging load from the charging station on phase φ at the distribution network node j; Indicates the number of working charging piles connected to this phase; Indicates the power of a single working charging pile; N j Represents the number of charging piles; Φ represents the set of three-phase phase sequences of AC power.

[0017] Furthermore, the interaction mechanism is solved to obtain the number of charging piles in the charging station distributed on each phase of the three-phase AC, which can achieve the lowest electricity purchase cost of the charging station, including:

[0018] Relax the charging power of each phase in the charging station into a continuous variable;

[0019] Convert the relaxed interaction mechanism into a variational inequality;

[0020] The variational inequality is solved based on the outer gradient algorithm to obtain the number of charging piles in the charging station distributed on each phase of the AC three-phase, which can achieve the lowest electricity purchase cost for the charging station.

[0021] Furthermore, the charging power of each phase in the charging station is relaxed into a continuous variable, and the model is established as follows:

[0022]

[0023] Furthermore, the relaxed interaction mechanism is converted into a variational inequality, which is as follows:

[0024]

[0025] Among them, Ω EVCSO is the feasible domain.

[0026] Furthermore, the variational inequality is solved based on the outer gradient algorithm to obtain the number of charging piles in the charging station distributed on each phase of the AC three-phase, which is used for the phase switching operation of the charging station, including:

[0027] Step 1: Select the initial iteration point Select each iteration step size 0<γ<1, set the iteration convergence error ε>0, and record the initial number of iterations η=0;

[0028] Step 2: Calculate the current three-phase charging load Clearing three-phase DLMP under Get the feasible region Ω EVCSO The predicted projection point on in, Indicates that in the feasible region Ω EVCSO The Euclidean norm mapping in is specifically represents the projection point;

[0029] Step 3: Calculate the predicted projection point Clearing three-phase DLMP By in the feasible domain Ω EVCSO Project upward to obtain the next iterative update solution

[0030] Step 4: Determine whether the iterative convergence conditions are met If satisfied, output the current As the optimal three-phase charging load distribution result of the charging station; if it is not satisfied, return to the second step and iterate the calculation again.

[0031] Furthermore, the fourth step: determining whether the iterative convergence condition is met If satisfied, output the current As the optimal three-phase charging load distribution result of the charging station, the lowest electricity purchase cost of the charging station can be achieved; if it is not satisfied, return to the second step and iterate the calculation again, including:

[0032] The optimal three-phase charging load distribution result of the charging station is rounded using the following formula:

[0033]

[0034] Among them, round() represents rounding operation.

[0035] Further, a three-phase optimal power flow model of a distribution network of AC optimal power flow, the three-phase optimal power flow model of the distribution network is as follows;

[0036]

[0037] Among them, f DSO The operating cost of the distribution network; The cost of purchasing electricity from the upper grid; The cost of using micro gas turbine to generate electricity for itself; φ and They are the electricity price and active power purchased by the distribution network from the φ-phase of the upper grid respectively; and are the cost and active power of MT power generation on phase φ of distribution network node j; Φ represents the set of three-phase phase sequence of AC power; Ω MT It represents the node set of MT connected to the distribution network; It is the reactive power when the distribution network purchases electricity from the φ phase of the upper power grid; and They represent the active and reactive power transmitted from the first node to node k in the distribution network respectively; and They represent the active and reactive power on the φ phase of the transmission line ij in the distribution network respectively; and They represent the active and reactive power losses on the φ phase of the transmission line ij in the distribution network respectively; is the reactive power generated by MT on phase φ of distribution network node j; and They represent the active and reactive power of other loads on phase φ at node j of the distribution network respectively; A is the active power of the charging load from the charging station on phase φ at the distribution network node j; j represents the set of first node serial numbers of the line with node j as the end node in the distribution network; k:j→k represents the set of last node serial numbers of the line with node j as the first node in the distribution network; and They represent the resistance / reactance matrix coefficients on the φ phase between the transmission lines ij respectively; and Respectively represent the active / reactive power matrix on each phase between transmission lines ij; is the voltage per unit value on phase φ at node j of the distribution network The square of and are the lower and upper limits of the voltage amplitude of the distribution network node j respectively; V ref P is the reference voltage value of the substation node connected to the first node of the distribution network; max With Q max The maximum limits of active and reactive power amplitudes purchased for the first node respectively; and are the lower and upper limits of the active power of MT power generation on phase φ at node j; cosα j is the power factor of the MT accessing node j; K v is the three-phase voltage imbalance threshold; C1 represents the set of serial numbers of the other nodes of all transmission lines connected to the first node in the distribution network; represents the active power on the φ phase of the transmission line jk in the distribution network; It represents the reactive power on the φ phase of the transmission line jk in the distribution network; is the per unit voltage value of phase φ at node i in the distribution network The square of It represents the voltage per unit value of the first node of the distribution network The square of

[0038] is the dual multiplier corresponding to the power balance constraint of each node in the distribution network; the three-phase DLMP of each node as the clearing of the distribution network is expressed as φ and Indicates different phase sequences of the three-phase AC.

[0039] Furthermore, a charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost is established. The charging station commutation operation optimization model is as follows:

[0040]

[0041] Among them, f EVCSO is the electricity purchase cost of the charging station connected to the distribution network node j; Indicates the node number set of the charging station connected to the distribution network; It represents the three-phase power purchase DLMP of the charging station; Indicates the total charging power of the charging station; The active power of the charging load from the charging station on phase φ at the distribution network node j; Indicates the number of working charging piles connected to this phase; Indicates the power of a single working charging pile; total N j Indicates the number of charging piles in operation.

[0042] The present invention provides an optimized operation method of an integrated energy system considering multi-mode hydrogen transportation, which has the following beneficial effects:

[0043] The present invention is based on the three-phase DLMP of the distribution network, optimizes the commutation operation of the charging pile with the goal of minimizing the electricity purchase cost of the charging station, and proposes an interactive mechanism of price clearing-power adjustment between the distribution network and the charging station driven by the three-phase electricity price of the distribution network; then, the integer variables representing the commutation operation of the charging pile in the charging station optimization model are relaxed into continuous variables, and the interaction mechanism problem after relaxation is converted into a variational inequality problem; finally, the optimal allocation strategy of the three-phase charging power of the charging station under the interactive mechanism is obtained based on the external gradient algorithm, and the power under the strategy is rounded to obtain the charging pile commutation operation strategy that meets the engineering application requirements.

[0044] The present invention discloses a phase change response method for an electric vehicle charging station driven by a three-phase electricity price of a distribution network, which enables the charging station to optimize the phase change operation of each charging pile under the stimulation of the three-phase DLMP of the distribution network, thereby reducing the power purchase cost of the charging station and the operation cost of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 The present invention is a flow chart of a phase change response method of an electric vehicle charging station. DETAILED DESCRIPTION

[0047] Various aspects and features of the present invention are described herein with reference to the accompanying drawings.

[0048] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of the embodiments. Other modifications within the scope and spirit of the present invention will occur to those skilled in the art.

[0049] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the general description of the invention given above and the detailed description of the embodiments given below, serve to explain the principles of the invention.

[0050] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0051] It should also be understood that, although the invention has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the invention that have the characteristics recited in the claims and are therefore within the scope of protection defined thereby.

[0052] The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0053] Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present invention, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present invention with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present invention in a variety of ways with substantially any suitable detailed structure.

[0054] In the description of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0055] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0056] Example

[0057] like Figure 1 As shown, this embodiment provides a phase change response method for an electric vehicle charging station, comprising the following steps:

[0058] Step S1: Obtain basic operating parameters of the distribution network and the charging station, and establish a three-phase optimal power flow model of the distribution network based on AC optimal power flow;

[0059] Step S2: Establishing a charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost;

[0060] Step S3: Based on the three-phase optimal power flow model of the distribution network and the phase-change operation optimization model of the charging station, an interactive mechanism for power adjustment of electricity price clearing between charging stations in the distribution network is established;

[0061] Step S4: Solving the interaction mechanism to obtain the number of charging piles in the charging station distributed on each phase of the three-phase AC, so as to achieve the lowest electricity purchase cost for the charging station.

[0062] According to another specific embodiment of the present invention, on the basis of step S1, basic operating parameters of the distribution network and the charging station are obtained, and a three-phase optimal power flow model of the distribution network based on the AC optimal power flow is established. The three-phase optimal power flow model of the distribution network is as follows;

[0063]

[0064]

[0065] Among them, f DSO The operating cost of the distribution network; The cost of purchasing electricity from the upper grid; The cost of using micro gas turbine to generate electricity for itself; φ and They are the electricity price and active power purchased by the distribution network from the φ-phase of the upper grid respectively; and are the cost and active power of MT power generation on phase φ of distribution network node j; Φ represents the set of three-phase phase sequence of AC power; Ω MT It represents the node set of MT connected to the distribution network; It is the reactive power when the distribution network purchases electricity from the φ phase of the upper power grid; and They represent the active and reactive power transmitted from the first node to node k in the distribution network respectively; and They represent the active and reactive power on the φ phase of the transmission line ij in the distribution network respectively; and They represent the active and reactive power losses on the φ phase of the transmission line ij in the distribution network respectively; is the reactive power generated by MT on phase φ of distribution network node j; and They represent the active and reactive power of other loads on phase φ at node j of the distribution network respectively; A is the active power of the charging load from the charging station on phase φ at the distribution network node j; j represents the set of first node serial numbers of the line with node j as the end node in the distribution network; k:j→k represents the set of last node serial numbers of the line with node j as the first node in the distribution network; and They represent the resistance / reactance matrix coefficients on the φ phase between the transmission lines ij respectively; and Respectively represent the active / reactive power matrix on each phase between transmission lines ij; is the voltage per unit value on phase φ at node j of the distribution network The square of and are the lower and upper limits of the voltage amplitude of the distribution network node j respectively; V ref P is the reference voltage value of the substation node connected to the first node of the distribution network; max With Q max The maximum limits of active and reactive power amplitudes purchased for the first node respectively; and are the lower and upper limits of the active power of MT power generation on phase φ at node j; cosα j is the power factor of the MT accessing node j; K v is the three-phase voltage imbalance threshold; C1 represents the set of serial numbers of the other nodes of all transmission lines connected to the first node in the distribution network; represents the active power on the φ phase of the transmission line jk in the distribution network; It represents the reactive power on the φ phase of the transmission line jk in the distribution network; is the per unit voltage value of phase φ at node i in the distribution network The square of It represents the voltage per unit value of the first node of the distribution network The square of

[0066] is the dual multiplier corresponding to the power balance constraint of each node in the distribution network; the three-phase DLMP of each node as the clearing of the distribution network is expressed as φ and Indicates different phase sequences of the three-phase AC.

[0067] Formula (1) is the objective function of the distribution network optimization model. Formula (2) and Formula (3) respectively represent the active / reactive power balance constraints of the first node connecting the distribution network to the upper power grid. Formula (4) and Formula (5) respectively represent the active / reactive power balance constraints on the φ phase at node j in the distribution network. Formula (6) is the voltage drop constraint of the three-phase distribution network. Formula (7) is the voltage amplitude constraint of the distribution network node. Formula (8) is the voltage amplitude constraint of the first node of the distribution network. Formulas (9) and (10) are the constraints on the purchase of active / reactive power by the first node of the distribution network. Formulas (11) and (12) are the active / reactive output constraints of MT. Formula (13) is the three-phase voltage imbalance constraint of the distribution network.

[0068] According to another specific embodiment of the present invention, on the basis of step S2, a charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost is established. The charging station commutation operation optimization model is as follows:

[0069]

[0070] Among them, f EVCSO is the electricity purchase cost of the charging station connected to the distribution network node j; Indicates the node number set of the charging station connected to the distribution network; It represents the three-phase power purchase DLMP of the charging station; Indicates the total charging power of the charging station; Indicates the number of working charging piles connected to this phase; Indicates the power of a single working charging pile; total N j Indicates the number of charging piles in operation.

[0071] According to another specific embodiment of the present invention, on the basis of step S3, based on the three-phase optimal power flow model of the distribution network and the commutation operation optimization model of the charging station, an interactive mechanism for adjusting the power of electricity price clearing between the charging stations in the distribution network is established, and the interactive mechanism includes:

[0072] The distribution network clears the three-phase DLMP and releases it to the charging station. The charging station performs phase switching operations according to the three-phase DLMP to adjust the power of each phase until the charging station no longer performs phase switching operations.

[0073] According to another specific embodiment of the present invention, based on the above embodiment, the expression of the interaction mechanism is as follows:

[0074]

[0075] According to another specific embodiment of the present invention, based on the step S4, the interaction mechanism is solved to obtain the number of charging piles in the charging station distributed on each phase of the three-phase AC, so as to achieve the lowest electricity purchase cost of the charging station, including:

[0076] Relax the charging power of each phase in the charging station into a continuous variable;

[0077] Convert the relaxed interaction mechanism into a variational inequality;

[0078] The variational inequality is solved based on the outer gradient algorithm to obtain the number of charging piles in the charging station distributed on each phase of the AC three-phase, which can achieve the lowest electricity purchase cost for the charging station.

[0079] According to another specific embodiment of the present invention, on the basis of the above embodiment, the charging power of each phase in the charging station is relaxed into a continuous variable, and the model is established as follows:

[0080]

[0081]

[0082] According to another specific embodiment of the present invention, based on the above embodiment, the relaxed interaction mechanism is converted into a variational inequality, which is as follows:

[0083]

[0084] Among them, Ω EVCSO is the feasible domain.

[0085] According to another specific embodiment of the present invention, on the basis of the above embodiment, the variational inequality is solved based on the external gradient algorithm to obtain the number of charging piles in the charging station distributed on each phase of the AC three-phase, which is used for the phase switching operation of the charging station, including:

[0086] Step 1: Select the initial iteration point Select each iteration step size 0<γ<1, set the iteration convergence error ε>0, and record the initial number of iterations η=0;

[0087] Step 2: Calculate the current three-phase charging load Clearing three-phase DLMP under Get the feasible region Ω EVCSO The predicted projection point on in, Indicates that in the feasible region Ω EVCSO The Euclidean norm mapping in is specifically represents the projection point;

[0088] Step 3: Calculate the predicted projection point Clearing three-phase DLMP By in the feasible domain Ω EVCSOProject upward to obtain the next iterative update solution

[0089] Step 4: Determine whether the iterative convergence conditions are met If satisfied, output the current As the optimal three-phase charging load distribution result of the charging station; if it is not satisfied, return to the second step and iterate the calculation again.

[0090] According to another specific embodiment of the present invention, based on the above embodiment, the fourth step: judging whether the iterative convergence condition is met If satisfied, output the current As the optimal three-phase charging load distribution result of the charging station, the lowest electricity purchase cost of the charging station can be achieved; if it is not satisfied, return to the second step and iterate the calculation again, including:

[0091] The optimal three-phase charging load distribution result of the charging station is rounded using the following formula:

[0092]

[0093] Among them, round() represents rounding operation.

[0094] The present invention provides an optimized operation method of an integrated energy system considering multi-mode hydrogen transportation, which has the following beneficial effects:

[0095] The present invention is based on the three-phase DLMP of the distribution network, optimizes the commutation operation of the charging pile with the goal of minimizing the electricity purchase cost of the charging station, and proposes an interactive mechanism of price clearing-power adjustment between the distribution network and the charging station driven by the three-phase electricity price of the distribution network; then, the integer variables representing the commutation operation of the charging pile in the charging station optimization model are relaxed into continuous variables, and the interaction mechanism problem after relaxation is converted into a variational inequality problem; finally, the optimal allocation strategy of the three-phase charging power of the charging station under the interactive mechanism is obtained based on the external gradient algorithm, and the power under the strategy is rounded to obtain the charging pile commutation operation strategy that meets the engineering application requirements.

[0096] The present invention discloses a phase change response method for an electric vehicle charging station driven by a three-phase electricity price of a distribution network, which enables the charging station to optimize the phase change operation of each charging pile under the stimulation of the three-phase DLMP of the distribution network, thereby reducing the power purchase cost of the charging station and the operation cost of the distribution network.

[0097] An embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the embodiment of the present disclosure is implemented, including the following steps:

[0098] Step S1: Obtain the basic operating parameters of the distribution network and the charging station, and establish a three-phase optimal power flow model of the distribution network based on AC optimal power flow;

[0099] Step S2: Establishing a charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost;

[0100] Step S3: Based on the three-phase optimal power flow model of the distribution network and the phase-change operation optimization model of the charging station, an interactive mechanism for price-clearing power adjustment between charging stations in the distribution network is established;

[0101] Step S4: Solve the interaction mechanism to obtain the number of charging piles in the charging station distributed on each phase of the AC three-phase, so as to achieve the lowest electricity purchase cost for the charging station.

[0102] Furthermore, when the computer program is executed by a processor, other methods provided by the embodiments of the present disclosure are implemented.

[0103] An embodiment of the present disclosure provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method provided in any embodiment of the present disclosure when executing the computer program in the memory. Exemplarily, the steps of the electronic device computer program are as follows:

[0104] Step S1: Obtain the basic operating parameters of the distribution network and the charging station, and establish a three-phase optimal power flow model of the distribution network based on AC optimal power flow;

[0105] Step S2: Establishing a charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost;

[0106] Step S3: Based on the three-phase optimal power flow model of the distribution network and the phase-change operation optimization model of the charging station, an interactive mechanism for price-clearing power adjustment between charging stations in the distribution network is established;

[0107] Step S4: Solve the interaction mechanism to obtain the number of charging piles in the charging station distributed on each phase of the AC three-phase, so as to achieve the lowest electricity purchase cost for the charging station.

[0108] Furthermore, the processor also executes the computer program in the above embodiment.

[0109] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0110] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0111] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0112] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0114] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, constitute a limitation on the unit itself.

[0116] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0117] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0118] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0119] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0120] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0121] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A phase change response method for an electric vehicle charging station, characterized in that: The following steps are involved: Obtain the basic operating parameters of the distribution network and charging stations, and establish a three-phase optimal power flow model for the distribution network based on AC optimal power flow; Establish a charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost; Based on the three-phase optimal power flow model of the distribution network and the phase-change operation optimization model of the charging station, an interactive mechanism for price-clearing power adjustment between charging stations in the distribution network is established; By solving the interaction mechanism and obtaining the number of charging piles in the charging station distributed on each phase of the three-phase AC, the lowest electricity purchase cost of the charging station can be achieved.

2. The electric vehicle charging station commutation response method according to claim 1, characterized in that: Based on the three-phase optimal power flow model of the distribution network and the phase-change operation optimization model of the charging station, an interactive mechanism for adjusting the power of electricity price clearing between charging stations in the distribution network is established, and the interactive mechanism includes: The distribution network clears the three-phase DLMP and releases it to the charging station. The charging station performs phase switching operations according to the three-phase DLMP to adjust the power of each phase until the charging station no longer performs phase switching operations.

3. The electric vehicle charging station commutation response method according to claim 2, characterized in that: The expression of the interaction mechanism is as follows: Among them, f EVCSO is the electricity purchase cost of the charging station connected to the distribution network node j; Indicates the node number set of the charging station connected to the distribution network; represents the three-phase power purchase DLMP of the charging station; P j EV Represents the total charging power of the charging station; P j EV,φ is the active power of the charging load from the charging station on phase φ at the distribution network node j; Indicates the number of working charging piles connected to this phase; Indicates the power of a single working charging pile; N j Represents the number of charging piles; Φ represents the set of three-phase phase sequences of AC power.

4. The electric vehicle charging station commutation response method according to claim 3, characterized in that: Solving the interaction mechanism to obtain the number of charging piles in the charging station distributed on each phase of the three-phase AC can achieve the lowest electricity purchase cost for the charging station, including: Relax the charging power of each phase in the charging station into a continuous variable; Convert the relaxed interaction mechanism into a variational inequality; The variational inequality is solved based on the outer gradient algorithm to obtain the number of charging piles in the charging station distributed on each phase of the AC three-phase, which can achieve the lowest electricity purchase cost for the charging station.

5. The electric vehicle charging station commutation response method according to claim 4, characterized in that: The charging power of each phase in the charging station is relaxed into a continuous variable, and the model is established as follows:

6. The electric vehicle charging station commutation response method according to claim 5, characterized in that: The relaxed interaction mechanism is converted into a variational inequality, which is as follows: Among them, Ω EVCSO is the feasible domain.

7. The electric vehicle charging station commutation response method according to claim 6, characterized in that: Solving the variational inequality based on the external gradient algorithm to obtain the number of charging piles in the charging station distributed on each phase of the three-phase AC is used for the phase switching operation of the charging station, including: The first step: select the initial iteration point P j EV,φ,0 ∈Ω EVCSO , select the iteration step size of each time 0<γ<1, set the iteration convergence error ε>0, and record the initial number of iterations η=0; Step 2: Calculate the current three-phase charging load P j EV,φ,η Clearing three-phase DLMP under Get the feasible region Ω EVCSO The predicted projection point on in, Indicates that in the feasible region Ω EVCSO The Euclidean norm mapping in is specifically x∈Ω EVCSO , represents the projection point; Step 3: Calculate the predicted projection point Clearing three-phase DLMP By in the feasible domain Ω EVCSO Project upward to obtain the next iterative update solution Step 4: Determine whether the iterative convergence condition ‖P is met j EV,φ,η+1 -P j EV,φ,η ‖2≤ε, if satisfied, output P at this time j EV,φ,η+1 As the optimal three-phase charging load distribution result of the charging station; if it is not satisfied, return to the second step and iterate the calculation again.

8. The electric vehicle charging station commutation response method according to claim 6, characterized in that: Step 4: Determine whether the iterative convergence condition ‖P is met j EV,φ,η+1 -P j EV,φ,η ‖2≤ε, if satisfied, output P at this time j EV,φ,η+1 As the optimal three-phase charging load distribution result of the charging station, the lowest electricity purchase cost of the charging station can be achieved; If not satisfied, return to the second step and iterate the calculation again, including: The optimal three-phase charging load distribution result of the charging station is rounded using the following formula: Among them, round() represents rounding operation.

9. The electric vehicle charging station commutation response method according to claim 1, characterized in that: Obtain the basic operating parameters of the distribution network and the charging station, and establish a three-phase optimal power flow model of the distribution network based on the AC optimal power flow. The three-phase optimal power flow model of the distribution network is as follows; Among them, f DSO The operating cost of the distribution network; The cost of purchasing electricity from the upper grid; The cost of using micro gas turbine to generate electricity for itself; φ and They are the electricity price and active power purchased by the distribution network from the φ-phase of the upper grid respectively; With P j MT,φ are the cost and active power of MT power generation on phase φ of distribution network node j; Φ represents the set of three-phase phase sequence of AC power; Ω MT It represents the node set of MT connected to the distribution network; It is the reactive power when the distribution network purchases electricity from the φ phase of the upper power grid; and They represent the active and reactive power transmitted from the first node to node k in the distribution network respectively; and They represent the active and reactive power on the φ phase of the transmission line ij in the distribution network respectively; and They represent the active and reactive power losses on the φ phase of the transmission line ij in the distribution network respectively; is the reactive power generated by MT on phase φ of distribution network node j; P j D,φ and They represent the active and reactive power of other loads on phase φ at distribution network node j respectively; P j EV,φ A is the active power of the charging load from the charging station on phase φ at the distribution network node j; j represents the set of first node serial numbers of the line with node j as the end node in the distribution network; k:j→k represents the set of last node serial numbers of the line with node j as the first node in the distribution network; and They represent the resistance / reactance matrix coefficients on the φ phase between the transmission lines ij respectively; and Respectively represent the active / reactive power matrix on each phase between transmission lines ij; is the voltage per unit value V on the φ phase at the distribution network node j j φ The square of V j min,φ With V j max,φ are the lower and upper limits of the voltage amplitude of the distribution network node j respectively; V ref P is the reference voltage value of the substation node connected to the first node of the distribution network; max With Q max are the maximum limits of active and reactive power amplitudes purchased for the first node respectively; P j MT,φ,min With P j MT,φ,max are the lower and upper limits of the active power of MT power generation on phase φ at node j; cosα j is the power factor of the MT accessing node j; K v is the three-phase voltage imbalance threshold; C1 represents the set of serial numbers of the other nodes of all transmission lines connected to the first node in the distribution network; represents the active power on the φ phase of the transmission line jk in the distribution network; It represents the reactive power on the φ phase of the transmission line jk in the distribution network; is the voltage per unit value V on phase φ at node i of the distribution network i φ The square of It represents the voltage per unit value of the first node of the distribution network The square of is the dual multiplier corresponding to the power balance constraint of each node in the distribution network; the three-phase DLMP of each node as the clearing of the distribution network is expressed as φ and Indicates different phase sequences of the three-phase AC.

10. The electric vehicle charging station commutation response method according to claim 1, characterized in that: A charging station commutation operation optimization model with the goal of minimizing the electricity purchase cost is established. The charging station commutation operation optimization model is as follows: Among them, f EVCSO is the electricity purchase cost of the charging station connected to the distribution network node j; Indicates the node number set of the charging station connected to the distribution network; represents the three-phase power purchase DLMP of the charging station; P j EV Indicates the total charging power of the charging station; The active power of the charging load from the charging station on phase φ at the distribution network node j; Indicates the number of working charging piles connected to this phase; Indicates the power of a single charging pile in operation; total N j Indicates the number of charging piles in operation.

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