CN116934365A - Semi-dynamic traffic flow-based method for participating in energy market bidding by charging station operators - Google Patents
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Abstract
The application relates to a semi-dynamic traffic flow-based bidding method for a charging station operator to participate in an energy market, belongs to the field of electric power market bidding, and particularly relates to a bidding model for an electric vehicle charging station operator to participate in a day-ahead energy market. The method comprises the following specific steps: step 1, establishing a double-layer bidding model based on market clearing in a power distribution network, and obtaining node marginal prices of power distribution network nodes where charging stations are located; step 2, establishing a semi-dynamic traffic flow distribution model of a traffic network according to the charging prices of all charging stations, and obtaining the charging load of all charging stations based on a user balancing principle; and step 3, mutually iterating a double-layer bidding model in the power distribution network and a semi-dynamic traffic flow distribution model in the traffic network, and finally obtaining an optimal bidding strategy based on the fixed point theory. The method of the application is based on the uncertainty of the original load of the power grid and the uncertainty of the traffic demand in the traffic network fully in the bidding process, so that the finally obtained bidding strategy is more reasonable and has better guiding function.
Description
Technical Field
The application relates to the field of bidding of electric power markets, in particular to a bidding model for an electric vehicle charging station operator to participate in a day-ahead energy market.
Background
Electric vehicles have become a promising alternative to fuel vehicles due to their eco-friendly and cost-effective advantages. However, the large-scale popularization of electric vehicles greatly increases the charging load of the electric vehicles in the power distribution network, and the power distribution network has the risk of overload during the load peak period. In addition, the location of the charging station along the traffic network can affect the route and travel time of the electric vehicle that needs to be charged.
As a transportation means, meeting the travel demands of users is a basic function of electric automobiles. Traffic characteristics such as user travel demands, road network structures, road conditions and the like can fundamentally determine user charging demands and schedulable spaces, so that the operation of a power grid is affected. On the other hand, factors such as charging facility layout, charging price and the like also influence the charging decision of users, thereby influencing traffic flow distribution and traffic network operation. The development and popularization of electric automobiles lead to the gradual formation of a fused electric-traffic system by the interaction and close coupling of a power grid and a traffic network.
The electric automobile charging station operator is as electric power market participant, need estimate the charging demand of each moment in the next day before the day, purchase electric quantity through market before the day, purchase the electric power price and obtain through simulation market clearance, in addition, charging station operator still need set up suitable charging price to obtain the income that charges. In addition, the original load in the distribution network and the travel demands of users in the traffic network are all uncertain.
Thus, in order to obtain higher yields, charging station operators need to be fully based on the coupling characteristics of the distribution network and the traffic network when participating in day-ahead market bidding, while based on the uncertainty of the original load of the distribution network in simulated market clearing, and based on the uncertainty of traffic demands in a semi-dynamic traffic flow distribution model.
Disclosure of Invention
Aiming at the fact that the coupling characteristics of a power distribution network and a traffic network are ignored in the bidding process of the current charging station operators, the application provides a method for participating in daily market bidding of the charging station operators based on semi-dynamic traffic flow, and the charging load of the charging station is modeled based on a user balancing principle.
The application adopts the following technical scheme:
step 1, establishing a double-layer bidding model based on market clearing in a power distribution network, wherein an upper layer model of the double-layer bidding model sets charging price capable of obtaining maximum benefit with the aim of maximizing charging station benefit; the lower model of the double-layer bidding model performs market clearing with the minimum running cost of the electric power system as a target, and node marginal prices of the power distribution network nodes where the charging stations are located are obtained;
step 2, establishing a semi-dynamic traffic flow distribution model of a traffic network according to the charging prices of all charging stations, and obtaining the charging load of all charging stations based on a user balancing principle;
and step 3, mutually iterating a double-layer bidding model in the power distribution network and a semi-dynamic traffic flow distribution model in the traffic network, and finally obtaining an optimal bidding strategy based on the fixed point theory.
Preferably, the objective function of the upper layer model of the double-layer bidding model of the power distribution network in the step 1 is specifically:
the constraint conditions include:
wherein: t is a time period set; n is a charging station set;the charging price of the charging station n at the time t is set; />The electricity purchase price of the charging station n at the time t is obtained; />Charging load at time t under field Jing for charging station n; />And->Respectively is charged withA lower limit and an upper limit of the charging price of the station n at the time t; the N represents the total number of charging stations; c (C) t The average charging price at time t for a plurality of charging stations.
Preferably, the lower model of the power distribution network double-layer bidding model in the step 1 is a market clearing model, and the objective function is:
wherein: pi ω Is scene omega probability; n (N) ω A scene set of the basic load of the power grid; psi phi type N The node set is a power distribution network node set; pi (0) is a sub-node set of the upper power grid nodes of the power distribution network; a, a j The unit power generation cost of the generator connected with the node j;the power generation power of the generator connected with the node j at the moment t under the scene omega is obtained; />The electricity purchasing price of the power distribution network and the upper power grid at the time t is obtained; p (P) 0j,t,ω For branch 0j, at time t under field Jing.
Further preferably, the objective function of the lower layer model of the double-layer bidding model of the power distribution network for the market clearing model comprises four types of constraint conditions, specifically:
1) Power balance constraint:
wherein: p (P) ij,t,ω And Q ij,t,ω Respectively the active power and the reactive power of a power distribution network branch ij at the moment t under the scene omega; r is (r) ij And x ij The resistance and the reactance of the distribution network branch ij are respectively;and->Respectively obtaining active power and reactive power of a generator at a node j of the power distribution network at a moment t under a scene omega; />And->Respectively an active load and a reactive load at a node j of the power distribution network at a moment t under a scene omega; l (L) ij,t,ω The current square of a power distribution network branch ij at the moment t under the scene omega is given; />Charging load for the electric automobile at the node j of the power distribution network at the moment t; />The node marginal electricity price of the power distribution network node j at the moment t under the scene omega is set; the node marginal price at the node connected to the charging station is superimposed by probability +.>The charged electricity purchasing price can be obtained
2) Node voltage constraint:
wherein: v i,t,ω The node voltage square of the power distribution network node j at the moment t under the scene omega is obtained;
3) Branch tidal current constraint:
4) Decision variable upper and lower limit constraints:
wherein: i vandthe node voltage square lower limit and the node voltage square upper limit of the power distribution network node i are respectively; />The upper limit of the square of the current of the distribution network branch ij is set; i P g and->Respectively the active power of the generators at the node i of the power distribution networkA lower and upper power limit; />And->The lower limit and the upper limit of the reactive power of the generator at the node i of the power distribution network are respectively defined.
Preferably, the objective function of the semi-dynamic traffic flow distribution model in step 2 is:
wherein: w is a traffic demand set; y is a decision variable set of a semi-dynamic traffic flow distribution model;and->The minimum passing cost of the fuel vehicle and the electric vehicle from the starting point r to the ending point s at the moment t; />And->The corrected traffic demand for the fuel vehicle and the electric vehicle from the start point r to the end point s at the time t.
Further preferably, constraints of the semi-dynamic traffic flow distribution model are as follows:
1) Road traffic constraints
Wherein: x is x a,t The traffic flow of the path a at the time t is;and->The association coefficients of the optional path k and the road a of the fuel vehicle and the electric vehicle from the starting point r to the end point s respectively show that the path k and the road a are included when the association coefficients are 1, otherwise, the path k does not include the road a;
2) Road traffic time constraint
Wherein:zero traffic transit time for regular path a; />Passing capacity for charging path a; />The charging time of the electric vehicle at the charging station; />The charging capacity of the charging station; />A regular path set, a charging station path set, and a charging station bypass path set, respectively.
3) Traffic demand range constraints
Wherein:a traffic demand predicted value from a starting point r to a terminal point s at a time t; q r,s,t The actual value of the traffic demand from the starting point r to the ending point s at the moment t; />Andζ r,s,t respectively an upper bound and a lower bound of the prediction error; />And->A variable between 0 and 1 for controlling the up-down deviation ratio of the actual traffic demand; Γ is a robust parameter used to control the conservation of the robust model.
4) Fuel vehicle traffic demand correction constraints
Wherein:is the initial traffic demand for the fuel vehicle to travel from the start r to the end s at time t.
5) Electric vehicle traffic demand correction constraint
Wherein:the initial traffic demand for the electric vehicle to travel from the start point r to the end point s at time t.
6) Fuel vehicle and electric vehicle passing cost constraint
Wherein: alpha is the cost per unit time;the charging price of the charging station n at the time t is set; e (E) B Is the charging capacity of a single electric vehicle.
The charging load of a charging station is related to the traffic flow of the corresponding charging station path
Preferably, the lower model in the first step is based on the uncertainty of the original load of the power distribution network, and the uncertainty is processed by adopting random optimization based on scenes.
Preferably, the traffic network semi-dynamic traffic flow distribution model in the step 2 is based on the uncertainty of traffic network traffic demands, and adopts robust optimization to process the uncertainty.
The application has the advantages that compared with the prior art,
1, the method for participating in market bidding in the day-ahead by the charging station operator fully bases on the coupling characteristics of the power grid and the traffic network, and models the charging load of the charging station based on a semi-dynamic traffic flow model.
2, the method of the application is based on the uncertainty of the original load of the power grid and the uncertainty of the traffic demand in the traffic network fully in the bidding process, so that the finally obtained bidding strategy is more reasonable and has good guiding function.
The application prepares the energy market price based on the actual network topology graph and the distribution topology graph and the benefits and the cost of each main body, has extremely high flexibility and adaptability, and can adapt to the abrupt change of the market and the change of the policy by changing the corresponding parameters.
Drawings
FIG. 1 is a flow chart of a method of participating in energy market bidding by a charging station operator based on semi-dynamic traffic flow;
FIG. 2 is a topology of a node traffic network;
FIG. 3 is a path parameter of a traffic network;
FIG. 4 is a topological structure diagram of a power distribution network;
FIG. 5 is a plot of revenue and cost for each principal;
fig. 6 is a charge price and charge load of two charging stations.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments of the application are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present application.
The application adopts the following technical scheme:
step 1, establishing a double-layer bidding model based on market clearing in a power distribution network, wherein an upper layer model of the double-layer bidding model sets charging price capable of obtaining maximum benefit with the aim of maximizing charging station benefit; the lower model of the double-layer bidding model performs market clearing with the minimum running cost of the electric power system as a target, and node marginal prices of the power distribution network nodes where the charging stations are located are obtained;
step 2, establishing a semi-dynamic traffic flow distribution model of a traffic network according to the charging prices of all charging stations, and obtaining the charging load of all charging stations based on a user balancing principle;
and step 3, mutually iterating a double-layer bidding model in the power distribution network and a semi-dynamic traffic flow distribution model in the traffic network, and finally obtaining an optimal bidding strategy based on the fixed point theory.
Preferably, the objective function of the upper layer model of the double-layer bidding model of the power distribution network in the step 1 is specifically:
the constraint conditions include:
wherein: t is a time period set; n is a charging station set;the charging price of the charging station n at the time t is set; />The electricity purchase price of the charging station n at the time t is obtained; />Charging load at time t under field Jing for charging station n; />And->The charging price lower limit and the charging price upper limit of the charging station n at the time t are respectively; n represents the total number of charging stations; c (C) t The average charging price at time t for a plurality of charging stations.
Scene omega some typical scenes describe possible outcomes of an indefinite quantity. For example, 60% of the highest air temperature on a certain day may be 30 degrees and 40% may be 29 degrees, which may be considered two scenarios. If the highest air temperature on the day is required to be 29.6, the expected values of two scenes are considered.
Preferably, the lower model of the power distribution network double-layer bidding model in the step 1 is a market clearing model, and the objective function is:
wherein: pi ω Is scene omega probability; n (N) ω A scene set of the basic load of the power grid; psi phi type N The node set is a power distribution network node set; pi (0) is a sub-node set of the upper power grid nodes of the power distribution network; a, a j The unit power generation cost of the generator connected with the node j;the power generation power of the generator connected with the node j at the moment t under the scene omega is obtained; />The electricity purchasing price of the power distribution network and the upper power grid at the time t is obtained; p (P) 0j,t,ω For branch 0j, at time t under field Jing.
Further preferably, the objective function of the lower layer model of the double-layer bidding model of the power distribution network for the market clearing model comprises four types of constraint conditions, specifically:
1) Power balance constraint:
wherein: p (P) ij,t,ω And Q ij,t,ω Respectively the active power and the reactive power of a power distribution network branch ij at the moment t under the scene omega; r is (r) ij And x ij The resistance and the reactance of the distribution network branch ij are respectively;and->Respectively obtaining active power and reactive power of a generator at a node j of the power distribution network at a moment t under a scene omega; />And->Respectively an active load and a reactive load at a node j of the power distribution network at a moment t under a scene omega; l (L) ij,t,ω The current square of a power distribution network branch ij at the moment t under the scene omega is given; />Charging load for the electric automobile at the node j of the power distribution network at the moment t; />The node marginal electricity price of the power distribution network node j at the moment t under the scene omega is set; the node marginal price at the node connected to the charging station is superimposed by probability +.>The charged electricity purchasing price can be obtained
2) Node voltage constraint:
wherein: v i,t,ω The node voltage square of the power distribution network node j at the moment t under the scene omega is obtained;
3) Branch tidal current constraint:
4) Decision variable upper and lower limit constraints:
wherein: v i Andthe node voltage square lower limit and the node voltage square upper limit of the power distribution network node i are respectively; />The upper limit of the square of the current of the distribution network branch ij is set; p (P) i g And->The lower limit and the upper limit of the active power of the generator at the node i of the power distribution network are respectively set; />And->The lower limit and the upper limit of the reactive power of the generator at the node i of the power distribution network are respectively defined.
Preferably, the objective function of the semi-dynamic traffic flow distribution model in step 2 is:
wherein: w is a traffic demand set; y is a decision variable set of a semi-dynamic traffic flow distribution model;and->The minimum passing cost of the fuel vehicle and the electric vehicle from the starting point r to the ending point s at the moment t; />And->The corrected traffic demand for the fuel vehicle and the electric vehicle from the start point r to the end point s at the time t.
Further preferably, constraints of the semi-dynamic traffic flow distribution model are as follows:
1) Road traffic constraints
Wherein: x is x a,t The traffic flow of the path a at the time t is;and->The association coefficients of the optional path k and the road a of the fuel vehicle and the electric vehicle from the starting point r to the end point s respectively show that the path k and the road a are included when the association coefficients are 1, otherwise, the path k does not include the road a;
2) Road traffic time constraint
Wherein:zero traffic transit time for regular path a; />Passing capacity for charging path a; />The charging time of the electric vehicle at the charging station; />The charging capacity of the charging station; />A regular path set, a charging station path set, and a charging station bypass path set, respectively.
3) Traffic demand range constraints
Wherein:a traffic demand predicted value from a starting point r to a terminal point s at a time t; q r,s,t For t time from start r to endThe actual value of traffic demand at point s; />Andζ r,s,t respectively an upper bound and a lower bound of the prediction error; />And->A variable between 0 and 1 for controlling the up-down deviation ratio of the actual traffic demand; Γ is a robust parameter used to control the conservation of the robust model.
4) Fuel vehicle traffic demand correction constraints
Wherein:is the initial traffic demand for the fuel vehicle to travel from the start r to the end s at time t.
5) Electric vehicle traffic demand correction constraint
Wherein:the initial traffic demand for the electric vehicle to travel from the start point r to the end point s at time t.
6) Fuel vehicle and electric vehicle passing cost constraint
Wherein: alpha is the cost per unit time;the charging price of the charging station n at the time t is set; e (E) B Is the charging capacity of a single electric vehicle.
The charging load of a charging station is related to the traffic flow of the corresponding charging station path
Preferably, the lower model in the first step is based on the uncertainty of the original load of the power distribution network, and the uncertainty is processed by adopting random optimization based on scenes.
Preferably, the traffic network semi-dynamic traffic flow distribution model in the step 2 is based on the uncertainty of traffic network traffic demands, and adopts robust optimization to process the uncertainty.
We use a simple distribution-traffic coupling network for simulation analysis. The topology of the traffic network is shown in fig. 2. The traffic network comprises 3 nodes, 4 paths and two charging stations, wherein the upper limit and the lower limit of the charging price of the charging stations are respectively 200$/MWh and 160$/MWh, and the average charging price of the two charging stations is 180$/MWh. The standard value of traffic network flow is 50/h, the charging capacity of the charging station is 3, and the charging time of each electric automobileThe path parameters of the traffic network are shown in fig. 3.
The topology of the distribution network is shown in fig. 4. Line impedance values are noted on each branch. The reference value of the power distribution network is 5MVA, the upper limit and the lower limit of node voltage are respectively 1.05 and 0.9, and the node voltage of the balance node is 1.04. Two generators in the distribution network have the same parameters.a j =140$/MWh. The purchase price of the distribution network from the upper power network is +.>
The benefits and costs of the charging station, the distribution system operators and the owners of the electric vehicles can be obtained through calculation, and are shown in fig. 5.
The charge prices and charge loads of the two charging stations are shown in fig. 6. We can find that the charging station operator will obtain the maximum profit by adjusting the charging price such that the charging load for each time period is equally distributed between the two charging stations. When the charging load is at a lower level, the two charging stations will be set with differentiated charging prices, and when the charging load is at a higher level, the charging prices of the two charging stations are equal, namely 180$/MWh.
Compared with the prior art, the application has the beneficial effects that:
1, the method for participating in market bidding in the day-ahead by the charging station operator fully bases on the coupling characteristics of the power grid and the traffic network, and models the charging load of the charging station based on a semi-dynamic traffic flow model.
2, the method of the application is based on the uncertainty of the original load of the power grid and the uncertainty of the traffic demand in the traffic network fully in the bidding process, so that the finally obtained bidding strategy is more reasonable and has good guiding function.
The application prepares the energy market price based on the actual network topology graph and the distribution topology graph and the benefits and the cost of each main body, has extremely high flexibility and adaptability, and can adapt to the abrupt change of the market and the change of the policy by changing the corresponding parameters.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (10)
1. A charging station operator participation energy market bidding method based on semi-dynamic traffic flow is characterized in that:
step 1, establishing a double-layer bidding model based on market clearing in a power distribution network, wherein an upper layer model of the double-layer bidding model sets charging price capable of obtaining maximum benefit with the aim of maximizing charging station benefit; the lower model of the double-layer bidding model performs market clearing with the minimum running cost of the electric power system as a target, and node marginal prices of the power distribution network nodes where the charging stations are located are obtained;
step 2, establishing a semi-dynamic traffic flow distribution model of a traffic network according to the charging prices of all charging stations, and obtaining the charging load of all charging stations based on a user balancing principle;
and step 3, mutually iterating a double-layer bidding model in the power distribution network and a semi-dynamic traffic flow distribution model in the traffic network, and finally obtaining an optimal bidding strategy based on the fixed point theory.
2. The method for participating in energy market bidding by a charging station operator based on semi-dynamic traffic flow according to claim 1, wherein the upper model objective function of the power distribution network double-layer bidding model in step 1 is specifically:
the constraint conditions include:
wherein: t is a time period set; n is a charging station set;the charging price of the charging station n at the time t is set; />The electricity purchase price of the charging station n at the time t is obtained; />Charging load at time t under field Jing for charging station n; />And->The charging price lower limit and the charging price upper limit of the charging station n at the time t are respectively; the N represents the total number of charging stations; c (C) t The average charging price at time t for a plurality of charging stations.
3. The method for participating in energy market bidding by a charging station operator based on semi-dynamic traffic flow according to claim 1, wherein the lower model of the power distribution network double-layer bidding model in step 1 is a market clearing model, and the objective function is:
wherein: pi ω Is scene omega probability; n (N) ω A scene set of the basic load of the power grid; psi phi type N The node set is a power distribution network node set; pi (0) is a sub-node set of the upper power grid nodes of the power distribution network; a, a j The unit power generation cost of the generator connected with the node j;the power generation power of the generator connected with the node j at the moment t under the scene omega is obtained; />The electricity purchasing price of the power distribution network and the upper power grid at the time t is obtained; p (P) 0j,t,ω For branch 0j, at time t under field Jing.
4. The method for participating in energy market bidding by a charging station operator based on semi-dynamic traffic flow according to claim 3, wherein the objective function of the lower model of the power distribution network double-layer bidding model comprises four types of constraint conditions, specifically:
1) Power balance constraint:
wherein: p (P) ij,t,ω And Q ij,t,ω Respectively the active power and the reactive power of a power distribution network branch ij at the moment t under the scene omega; r is (r) ij And x ij The resistance and the reactance of the distribution network branch ij are respectively;and->Respectively obtaining active power and reactive power of a generator at a node j of the power distribution network at a moment t under a scene omega; />And->Respectively an active load and a reactive load at a node j of the power distribution network at a moment t under a scene omega; l (L) ij,t,ω The current square of a power distribution network branch ij at the moment t under the scene omega is given; />Charging load for the electric automobile at the node j of the power distribution network at the moment t; />The node marginal electricity price of the power distribution network node j at the moment t under the scene omega is set; the node marginal price at the node connected to the charging station is superimposed by probability +.>The charging purchase price can be obtained>
2) Node voltage constraint:
wherein: v i,t,ω The node voltage square of the power distribution network node j at the moment t under the scene omega is obtained;
3) Branch tidal current constraint:
4) Decision variable upper and lower limit constraints:
wherein:v i andthe node voltage square lower limit and the node voltage square upper limit of the power distribution network node i are respectively; />The upper limit of the square of the current of the distribution network branch ij is set;P i g and->The lower limit and the upper limit of the active power of the generator at the node i of the power distribution network are respectively set; />And->The lower limit and the upper limit of the reactive power of the generator at the node i of the power distribution network are respectively defined.
5. The method of claim 1, wherein the objective function of the semi-dynamic traffic flow distribution model in step 2 is:
wherein: w is a traffic demand set; y is a decision variable set of a semi-dynamic traffic flow distribution model;and->The minimum passing cost of the fuel vehicle and the electric vehicle from the starting point r to the ending point s at the moment t; />And->Repair for t-moment fuel vehicle and electric vehicle running from starting point r to ending point sThe traffic demand is right after.
6. The method of claim 1, wherein the constraints of the semi-dynamic traffic flow distribution model are as follows:
1) Road traffic constraints
Wherein: x is x a,t The traffic flow of the path a at the time t is;and->The association coefficients of the optional path k and the road a of the fuel vehicle and the electric vehicle from the starting point r to the end point s respectively show that the path k and the road a are included when the association coefficients are 1, otherwise, the path k does not include the road a;
2) Road traffic time constraint
Wherein:zero traffic transit time for regular path a; />Passing capacity for charging path a; />The charging time of the electric vehicle at the charging station; />The charging capacity of the charging station; />Respectively a conventional path set, a charging station path set and a charging station bypass path set;
3) Traffic demand range constraints
Wherein:a traffic demand predicted value from a starting point r to a terminal point s at a time t; q r,s,t The actual value of the traffic demand from the starting point r to the ending point s at the moment t; />Andζ r,s,t respectively an upper bound and a lower bound of the prediction error; />And->A variable between 0 and 1 for controlling the up-down deviation ratio of the actual traffic demand; Γ is a robust parameter used to control a robust modelConservation;
4) Fuel vehicle traffic demand correction constraints
Wherein:an initial traffic demand for the fuel vehicle to travel from a start point r to an end point s at time t;
5) Electric vehicle traffic demand correction constraint
Wherein:the initial traffic demand for the electric vehicle to travel from the start point r to the end point s at time t.
6) Fuel vehicle and electric vehicle passing cost constraint
Wherein: alpha is the cost per unit time;the charging price of the charging station n at the time t is set; e (E) B The charging capacity of the single electric vehicle;
the charging load of a charging station is related to the traffic flow of the corresponding charging station path
7. The method of claim 1, wherein the underlying model in step one is based on uncertainty of the original load of the distribution network, and a scene-based stochastic optimization is used to handle such uncertainty.
8. The semi-dynamic traffic flow-based charging station operator participation energy market bidding method according to claim 1, wherein the traffic network semi-dynamic traffic flow distribution model in step 2 is based on uncertainty of traffic network traffic demands, and robust optimization is adopted to handle such uncertainty.
9. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-8.
10. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-8.
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