Mobile energy storage day-ahead scheduling method considering power-traffic coupling

Abstract

The invention discloses a mobile energy storage day-ahead scheduling method considering power-traffic coupling. The method comprises the steps that traffic flow distribution is obtained based on a traffic flow balance optimization model, then a space-time expansion traffic network is constructed, and the optimal passing time of mobile energy storage equipment is obtained based on an optimal path search model. And then obtaining the total operation cost of the traffic-power coupling network after rescheduling based on the optimization operation model and the rescheduling optimization model, and finally determining an optimal day-ahead scheduling scheme based on a scheduling scheme evaluation model. According to the method, the operation requirements of the power distribution network under uncertainty, the uncertain distribution condition of the traffic flow in the traffic network and the influence of the uncertain distribution condition on the mobile energy storage traffic behavior are considered, and collaborative optimization operation of the mobile energy storage and power-traffic coupling network under an uncertain scene is considered; and the mobile energy storage day-ahead scheduling under the power-traffic coupling network is realized.

Description

Technical Field

[0001] The present invention relates to a mobile energy storage day-ahead scheduling method, which relates to the field of power system scheduling and operation, and specifically relates to a mobile energy storage day-ahead scheduling method considering the power-traffic coupling. Background Art

[0002] As a new type of energy storage system, mobile energy storage can complete the transfer of electrical energy in time and space, and its application in the power system significantly improves the regulation ability of the power system. At the same time, mobile energy storage moves in the transportation network and provides services for the power network. Its operation is affected by both the receiving-end transportation network and the power network. On the one hand, traffic jams under the uncertainty of traffic flow may affect the mobile energy storage scheduling scheme in some scenarios, and the mobile energy storage cannot reach the target site in time, resulting in the inability to achieve the power services in the mobile energy storage plan; on the other hand, the charging load of electric vehicles may change the load distribution of the power system, further affecting the service objectives of mobile energy storage; in this context, it is necessary to study the traffic flow distribution of electric vehicles and fuel vehicles considering uncertainty in the transportation network, and study the day-ahead scheduling optimization method of mobile energy storage from the perspective of a power-traffic coupling network considering uncertainty. Summary of the Invention

[0003] In order to solve the problems in the background art, the present invention provides a mobile energy storage day-ahead scheduling method considering the power-traffic coupling.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The mobile energy storage day-ahead scheduling method considering the power-traffic coupling of the present invention includes:

[0006] S1: Under different time-sequence operation prediction scenarios of a traffic-power coupling network including mobile energy storage devices, establish a traffic flow equilibrium optimization model, input the period traffic travel demand into the traffic flow equilibrium optimization model under each time-sequence operation prediction scenario, and output the traffic flow distribution of the traffic-power coupling network after processing.

[0007] S2: Based on the traffic flow distribution of the traffic-power coupling network, construct a spatio-temporal extended traffic network, and establish an optimal path search model for the spatio-temporal extended traffic network. Input the movement plan of the spatio-temporal extended traffic network into the optimal path search model under each time-sequence operation prediction scenario, and output the optimal passing time of the mobile energy storage device after processing.

[0008] S3: Establish an optimal operation model and a rescheduling optimization model for the transportation - power coupling network. Input the optimal passing time of the mobile energy storage device in each time - series operation prediction scenario into the optimal operation model. After processing, output the day - ahead scheduling candidate plan of the mobile energy storage device, and then input it into the rescheduling optimization model. After processing, output the total operation cost of the transportation - power coupling network after rescheduling in the day - ahead scheduling candidate plan.

[0009] S4: Establish a scheduling plan evaluation model for the transportation - power coupling network. Input the total operation cost of the transportation - power coupling network after rescheduling in the day - ahead scheduling candidate plan in each time - series operation prediction scenario into the scheduling plan evaluation model. After processing, output the optimal day - ahead scheduling plan of the mobile energy storage device, and conduct the day - ahead scheduling of the mobile energy storage device in each time - series operation prediction scenario.

[0010] In the step S1 described above, the power - transportation coupling network is composed of the coupling of a transportation network and a distribution network. The distribution network includes power nodes connected by each transmission line. The distribution network also includes several new - energy power generation devices, controllable distributed generating units, and mobile energy storage stations connected to the power nodes. The new - energy power generation devices include wind power devices and photovoltaic devices. The controllable distributed generating units include distributed gas turbine units. Each mobile energy storage device moves in the transportation network. After the mobile energy storage device arrives at each mobile energy storage station, it charges and discharges to achieve the transmission of energy in time and space. At the same time, several electric vehicles and fuel vehicles move in the transportation network, generating traffic flow distribution. Therefore, the operation of the mobile energy storage device will be affected and restricted by the traffic flow on each road in the transportation network; there are multiple uncertainties in the operation of the power - transportation coupling network, including the uncertainty of new - energy output and power load in the power grid, as well as the uncertainty of traffic flow in the transportation network. Therefore, multiple time - series operation prediction scenarios of the power - transportation coupling network are used to describe these uncertainties; the transportation network includes traffic nodes connected by each traffic road.

[0011] In the step S1 described above, the traffic flow equilibrium optimization model is specifically as follows:

[0012]

[0013] Among them, is the optimization cost target in the ω - th time - series operation prediction scenario, and are the travel - time cost and charging cost in the ω - th time - series operation prediction scenario respectively; κ is the cost factor of commuting time; and are the non - charging road set and charging road set in the traffic road set respectively, x ω,a1,t and x ω,a2,tare the traffic flows on the a1-th non-charging road and the a2-th charging road during the t-th period in the ω-th time-series operation prediction scenario, respectively; and are the commuting times on the a1-th non-charging road and the a2-th charging road during the t-th period in the ω-th time-series operation prediction scenario, respectively, where z is the traffic flow condition; E B is the average charging demand of electric vehicles; is the charging power cost parameter on the a2-th charging road; the cost can be specifically measured by power.

[0014] The constraint conditions of the traffic flow equilibrium optimization model are as follows:

[0015] a) Road commuting time constraint:

[0016]

[0017]

[0018] Among them, is the commuting time on the a1-th non-charging road without traffic flow; and are the traffic flow capacities on the a1-th non-charging road and the a2-th charging road, respectively; ε is the average charging efficiency of mobile energy storage devices; is the longest waiting time on the a2-th charging road.

[0019] b) Road traffic flow constraint:

[0020]

[0021] Among them, x ω,a,t is the traffic flow on the a-th traffic road during the t-th period in the ω-th time-series operation prediction scenario; M od is the set of origin-destination traffic node pairs; and are the sets of available paths for electric vehicles and fuel vehicles to travel from the r-th origin traffic node to the s-th destination traffic node, respectively; is the road indication parameter of the traffic network, is a 0-1 variable. When the l-th path from the r-th origin traffic node to the s-th destination traffic node passes through the a-th traffic road, and when the l-th path from the r-th origin traffic node to the s-th destination traffic node does not pass through the a-th traffic road; and are respectively the traffic flows on the l-th path from the r-th starting traffic node to the s-th destination traffic node of electric vehicles and fuel vehicles during the t-th period in the ω-th time-series operation prediction scenario; and are respectively the travel demand flows of electric vehicles and fuel vehicles from the r-th starting traffic node to the s-th destination traffic node during the t-th period in the ω-th time-series operation prediction scenario; N T is the set of operation periods of the power-transportation coupling network.

[0022] The traffic demand for each period includes the travel demand flows of electric vehicles and fuel vehicles between every two traffic nodes during each period in each time-series operation prediction scenario; the traffic flow distribution of the traffic-power coupling network includes the traffic flows on each traffic road during each period in each time-series operation prediction scenario.

[0023] In the step S2 mentioned above, the optimal path search model of the spatio-temporal extended traffic network is specifically as follows:

[0024]

[0025]

[0026] where y and y aω are respectively the set of passing variables and the -th passing variable therein, are respectively the -th passing variables in the set of passing variables y, is a 0-1 variable. When , the -th traffic road of the spatio-temporal extended traffic network is selected as the passing road. When , the -th traffic road of the spatio-temporal extended traffic network is not selected as the passing road; is the passing time of the k-th mobile energy storage device from the i-th mobile energy storage site to the j-th mobile energy storage site during the t-th time period in the ω-th time-series operation prediction scenario; is the set of traffic roads of the spatio-temporal extended traffic network; is the passing time of the -th traffic road of the spatio-temporal extended traffic network in the ω-th time-series operation prediction scenario, is the traffic flow of the -th traffic road of the spatio-temporal extended traffic network in the ω-th time-series operation prediction scenario; N T is the set of operation periods; and They are respectively the virtual traffic road set and the physical traffic road set of the spatio-temporal extended traffic network in the t-th time period; is the length of the k-th traffic road; is the passing speed of the k-th mobile energy storage device on the k-th traffic road, is the free passing speed of the k-th mobile energy storage device on the k-th traffic road, that is, the passing speed when there are no other mobile energy storage devices on the current traffic road; is the capacity of the k-th physical traffic road; is the traffic road set of the t-th time period of the spatio-temporal extended traffic network under the ω-th time sequence operation prediction scenario; is the passing time of the k-th traffic road of the spatio-temporal extended traffic network, is the traffic flow of the k-th traffic road of the spatio-temporal extended traffic network; T s is the duration of the operation period of the power-transportation coupling network; the passing time of the mobile energy storage device on each virtual traffic road is 0, while the passing time of the mobile energy storage device on the physical traffic road is determined by the road length, capacity, traffic flow, and passing speed. Δ is the node-road incidence matrix of the spatio-temporal extended traffic network. In the node-road incidence matrix Δ, when is true, then the k-th traffic road of the spatio-temporal extended traffic network leads to the r-th traffic node, is the node-road incidence variable in the node-road incidence matrix Δ. When is true, then the k-th traffic road of the spatio-temporal extended traffic network departs from the r-th traffic node; Λ rs is the origin-destination node vector, Λ rs indicates the travel demand of the mobile energy storage device from the r-th traffic node to the s-th traffic node; in the origin-destination node vector Λ rs , when Λ rs (r) = 1 and Λ rs (s) = -1, then the r-th traffic node is the destination node and the s-th traffic node is the origin node. When Λ rs (r) = -1 and Λ rs (s) = 1, then the r-th traffic node is the origin node and the s-th traffic node is the destination node.

[0027] The movement plan of the spatio-temporal extended transportation network includes the traffic flow of each traffic road in the spatio-temporal extended transportation network under each time-series operation prediction scenario; the optimal passing time of the mobile energy storage device includes the passing time of each mobile energy storage device moving between every two mobile energy storage stations in each time period under each time-series operation prediction scenario.

[0028] In step S3 described above, for each time-series operation prediction scenario, the optimal operation model of the traffic-electricity coupling network is specifically as follows:

[0029]

[0030] Among them, C φ is the total operation cost of the traffic-electricity coupling network in the day-ahead scheduling candidate plan φ under the current time-series operation prediction scenario, and are respectively the operation cost of the mobile energy storage device, the cost of the power input from the transmission network to the distribution network via the substation, and the generation operation cost of the controllable distributed power generation unit in the day-ahead scheduling candidate plan φ; N T is the set of operation time periods, and N m is the set of mobile energy storage devices; CP k and FC k are respectively the unit charge-discharge cost and unit fuel cost of the kth mobile energy storage device; γ is the aging coefficient of the energy storage battery of the mobile energy storage device after linear approximation; N s is the set of mobile energy storage stations; and are respectively the charging and discharging powers of the kth mobile energy storage device at the ith mobile energy storage station in the tth time period in the day-ahead scheduling candidate plan φ; Dist max is the maximum value of the distances between each mobile energy storage station; ζ φ,k,t is the displacement indication variable of the kth mobile energy storage device in the tth time period in the day-ahead scheduling candidate plan φ, and ζ φ,k,t is a 0-1 variable. When ζ φ,k,t = 1, it means that the kth mobile energy storage device has movement in the tth time period in the day-ahead scheduling candidate plan φ. When ζ φ,k,t = 0, it means that the kth mobile energy storage device has no movement in the tth time period in the day-ahead scheduling candidate plan φ; EP t is the electricity cost parameter in the tth time period; is the power transmitted from the substation to the distribution network in the tth time period in the day-ahead scheduling candidate plan φ; T s is the duration of the operation time period of the power-traffic coupling network; N g is the set of controllable distributed power generation units in the distribution network; is the unit generation cost of the gth controllable distributed power generation unit; is the power output of the g-th controllable distributed power generation unit in the t-th time period of the day-ahead scheduling candidate plan φ.

[0031] The optimal operation model of the transportation-power coupling network is based on the operation constraints of the distribution network and the operation constraints of mobile energy storage devices, as follows:

[0032] a) Operation constraints of the distribution network:

[0033] P φ,m+1,t = P φ,m,t - p φ,m+1,t

[0034] Q φ,m+1,t = Q φ,m,t - q φ,m+1,t

[0035]

[0036]

[0037]

[0038]

[0039] Among them, P φ,m+1,t and Q φ,m+1,t are the active and reactive power flows of the transmission line between the (m + 1)-th power node and the (m + 2)-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively. P φ,m,t and Q φ,m,t are the active and reactive power flows of the transmission line between the m-th power node and the (m + 1)-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively. p φ,m+1,t and q φ,m+1,t are the active power and reactive power of the (m + 1)-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively. p φ,m,t and q φ,m,t are the active power and reactive power of the m-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively; and are the active and reactive load powers of the m-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively; and are the active and reactive load shedding powers of the m-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively; and are the sets of new energy power generation units and controllable distributed power generation units connected to the m-th power node in the distribution network, respectively; and are the active and reactive power dispatch generation output powers of the b-th new energy generating unit in the t-th time period of the day-ahead dispatch candidate plan φ, respectively; and are the active and reactive power dispatch generation output powers of the g-th controllable distributed generating unit in the t-th time period of the day-ahead dispatch candidate plan φ, respectively; and are the dispatch active power and reactive power of the k-th mobile energy storage device at the m-th power node in the t-th time period of the day-ahead dispatch candidate plan φ; E B is the average charging demand of a single electric vehicle; is the set of charging roads of the transportation network connected to the m-th power node in the distribution network, x φ,a2,t is the traffic flow of the a2-th charging road in the t-th time period of the day-ahead dispatch candidate plan φ; h and H are the indicator bit and the total number of linearized edges in the polygon linearization method, respectively; is the upper limit of the transmission capacity of the transmission line from the m-th power node to the m + 1-th power node; V φ,m,t and V φ,1,t are the voltage amplitudes at the m-th power node and the 1-st power node in the t-th time period of the day-ahead dispatch candidate plan φ, respectively, V φ,m+1,t is the voltage amplitude at the m + 1-th power node in the t-th time period of the day-ahead dispatch candidate plan φ; R m and X m are the resistance and reactance of the transmission line between the m-th power node and the m + 1-th power node, respectively; V m and are the lower limit and upper limit of the voltage of the m-th power node, respectively.

[0040] b) Operating constraint conditions of mobile energy storage devices:

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] Among them, and are the charging and discharging powers of the k-th mobile energy storage device at the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ, respectively; and are the active and reactive powers of the k-th mobile energy storage device at the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ, respectively; z φ,k,i,t is the connection indication variable between the k-th mobile energy storage device and the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ, z φ,k,i,t is a 0-1 variable. When z φ,k,i,t = 1, the k-th mobile energy storage device is connected to the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ. When z φ,k,i,t = 0, the k-th mobile energy storage device is not connected to the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ; and are the maximum charging and discharging powers of the k-th mobile energy storage device, respectively; is the maximum reactive power output of the k-th mobile energy storage device; is the rated power of the k-th mobile energy storage device; SOC φ,k,t and SOC φ,k,t+1 are the state of charge of the k-th mobile energy storage during the t-th and t + 1-th time periods in the day-ahead scheduling candidate scheme φ, respectively; is the rated battery capacity of the k-th mobile energy storage device; and are the charging and discharging efficiencies of the k-th mobile energy storage device, respectively; SOC k and are the lower and upper limits of the state of charge SOC k,t of the k-th mobile energy storage device during the t-th time period, respectively; z φ,k,i,t 、z φ,k,j,t+Δτ and z φ,k,i,t+1 are the connection indication variables between the k-th mobile energy storage device and the i-th mobile energy storage site during the t-th, t + Δτ-th and t + 1-th time periods in the day-ahead scheduling candidate scheme φ, respectively. z φ,k,i,t is a 0-1 variable, and Δτ is an auxiliary variable for the passing time of the mobile energy storage; is the passing time from the i-th mobile energy storage site to the j-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ.

[0052] Determine the day-ahead scheduling candidate plan φ of the mobile energy storage device according to the connection indication variables between each mobile energy storage device and each mobile energy storage site during each period in

[0053] In the step S3 described above, the rescheduling optimization model of the traffic-electricity coupling network is specifically as follows:

[0054]

[0055]

[0056] Among them, is the total operating cost of the rescheduled traffic-electricity coupling network in the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate plan φ, and are respectively the operating cost of the mobile energy storage device, the cost of the power input from the transmission network to the distribution network via the substation, and the generation operating cost of the controllable distributed generation unit in the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate plan φ; and are respectively the charging and discharging powers of the k-th mobile energy storage device at the i-th mobile energy storage site during the t-th period in the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate plan φ; is the displacement indication variable of the k-th mobile energy storage device during the t-th period in the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate plan φ; is the power transmitted from the substation to the distribution network during the t-th period in the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate plan φ; is the power output of the g-th controllable distributed generation unit during the t-th period in the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate plan φ; is the connection indication variable between the k-th mobile energy storage device and the i-th mobile energy storage site in the rescheduling of the day-ahead scheduling candidate plan φ in the ω-th time-series operation prediction scenario, is the connection indication variable between the k-th mobile energy storage device and the i-th mobile energy storage site during the (t + Δτ)-th period in the rescheduling of the day-ahead scheduling candidate plan φ in the ω-th time-series operation prediction scenario; is the passing time of the k-th mobile energy storage device from the i-th mobile energy storage site to the j-th mobile energy storage site during the t-th period in the ω-th time-series operation prediction scenario.

[0057] The total operating cost of the rescheduled traffic - power coupled network in the day - ahead scheduling candidate solution is the total operating cost of the rescheduled traffic - power coupled network in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate solution φ.

[0058] In step S4 described above, the scheduling scheme evaluation model is specifically as follows:

[0059]

[0060] Among them, C * is the scheduling scheme evaluation parameter of the day - ahead scheduling candidate solution φ; Ψ is the set of N ω day - ahead scheduling candidate solutions φ, and Ω is the set of N time - series operation prediction scenarios; σ ω is the scenario probability of the ω - th time - series operation prediction scenario; is the total operating cost of the rescheduled traffic - power coupled network in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate solution φ; N T is the set of operating time periods of the power - traffic coupled network; N R and N b are the sets of new - energy generating units and power nodes respectively; is the maximum power generation capacity of the b - th new - energy generating unit in the t - th time period in the ω - th time - series operation prediction scenario, is the power generation of the b - th new - energy generating unit in the t - th time period after scheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate solution φ; and are the upper limits of the expected new - energy curtailment and the expected load shedding amount that can be tolerated for the corresponding day - ahead scheduling candidate solution respectively; is the active load shedding power of the m - th power node in the t - th time period of the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate solution φ.

[0061] When the scheduling scheme evaluation parameter C * of the day - ahead scheduling candidate solution φ is the smallest, the optimal day - ahead scheduling scheme in the day - ahead scheduling candidate solution φ is obtained.

[0062] The electronic device of the present invention includes: a memory and a processor that are coupled to each other. Among them, the memory stores program data, and the processor calls the program data to execute the method as described above.

[0063] The computer - readable storage medium of the present invention stores program data thereon, and when the program data is executed by a processor, the method as described above is implemented.

[0064] The power-transportation coupling optimization operation model and rescheduling model of the method of the present invention considering mobile energy storage can achieve the day-ahead optimal scheduling of mobile energy storage considering the influence of uncertain traffic flow and the operation requirements of the distribution network, and minimize the expected operation cost of the distribution network under uncertainty.

[0065] The traffic flow equilibrium optimization model of the present invention can optimize the traffic flow distribution and electric vehicle charging load distribution of each road in each time period in the transportation network based on the predicted travel demand values in each time period in the transportation network, so as to minimize the travel cost of the entire transportation network.

[0066] The optimal path search model of the mobile energy storage of the present invention can optimize the fastest path and the corresponding commuting time between each mobile energy storage site based on the traffic flow distribution of each road in the transportation network.

[0067] The beneficial effects of the present invention are:

[0068] The method of the present invention realizes the optimal scheduling of mobile energy storage considering the coupling relationship in the power network and the transportation network, considers the influence of the traffic flow distribution in the transportation network on the moving time of the mobile energy storage between mobile energy storage sites, and the influence of the electric vehicle charging load on the power load distribution of the distribution network, can avoid traffic jams in some areas and at some times in the transportation network resulting in the mobile energy storage being unable to reach the target site in time, ensure that the mobile energy storage power service can be realized on time, and fully meet the operation requirements of the distribution network.

[0069] The present invention considers the operation requirements of the distribution network under uncertainty, as well as the uncertain distribution of traffic flow in the transportation network and its influence on the passing behavior of mobile energy storage, and considers the coordinated optimal operation of mobile energy storage and the power-transportation coupling network under uncertain scenarios, realizing the day-ahead scheduling of mobile energy storage under the power-transportation coupling network. Description of the Drawings

[0070] Figure 1 It is a flow chart of the method of the present invention. Detailed Embodiments

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given in conjunction with the drawings in the specification.

[0072] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0073] Such as Figure 1As shown in the figure, the day-ahead scheduling method of mobile energy storage considering power-transportation coupling of the present invention is as follows:

[0074] S1: Under different time-sequence operation prediction scenarios of the transportation-power coupling network including mobile energy storage devices, establish a traffic flow equilibrium optimization model. Input the traffic demand in each time period into the traffic flow equilibrium optimization model under each time-sequence operation prediction scenario, and output the traffic flow distribution of the transportation-power coupling network after processing. The power-transportation coupling network is composed of the coupling of a transportation network and a distribution network. The distribution network includes power nodes connected by various transmission lines. The distribution network also includes a number of new energy generation devices, controllable distributed generating units, and mobile energy storage sites connected to the power nodes. The new energy generation devices include wind power devices and photovoltaic devices. The controllable distributed generating units include distributed gas turbines. Each mobile energy storage device moves in the transportation network. After the mobile energy storage device arrives at each mobile energy storage site, it charges and discharges to achieve the transmission of energy in time and space. At the same time, a number of electric vehicles and fuel vehicles move in the transportation network to generate a traffic flow distribution. Therefore, the operation of the mobile energy storage device will be affected and restricted by the traffic flow on each road in the transportation network; the operation of the power-transportation coupling network has multiple uncertainties, including the uncertainty of new energy output and power load in the power grid, as well as the uncertainty of traffic flow in the transportation network. Therefore, multiple time-sequence operation prediction scenarios of the power-transportation coupling network are used to describe these uncertainties; the transportation network includes traffic nodes connected by various traffic roads.

[0075] The new energy generating units in the distribution network include wind power devices and photovoltaic devices. Obtain the historical data of the output of the new energy generating units, the historical data of the distribution network load, and the historical data of the traffic flow distribution of the transportation network, so as to calculate the probability distributions of new energy output, distribution network load, and traffic flow distribution. On this basis, use the Latin hypercube simulation method to simulate and generate a large number of operation prediction scenarios of the power-transportation coupling network including new energy output, distribution network load, and traffic flow distribution. The operation prediction scenarios at different time periods are the different time-sequence operation prediction scenarios of the transportation-power coupling network.

[0076] The traffic flow equilibrium optimization model is as follows:

[0077]

[0078] Among them, is the optimization cost objective under the ω-th time-sequence operation prediction scenario, and are the travel time cost and charging cost under the ω-th time-sequence operation prediction scenario respectively; κ is the cost factor of commuting time; and They are the non - charging road set and the charging road set in the traffic road set respectively, x ω,a1,t and x ω,a2,t are the traffic flows on the a1 - th non - charging road and the a2 - th charging road respectively in the t - th time period under the ω - th time - series operation prediction scenario; and are the commuting times on the a1 - th non - charging road and the a2 - th charging road respectively in the t - th time period under the ω - th time - series operation prediction scenario, z is the traffic flow situation; E B is the average charging demand of electric vehicles; is the charging power cost parameter on the a2 - th charging road; the cost can be specifically measured by power.

[0079] The constraint conditions of the traffic flow equilibrium optimization model are as follows:

[0080] a) Road commuting time constraint:

[0081]

[0082] Among them, is the commuting time on the a1 - th non - charging road without traffic flow; and are the traffic flow capacities on the a1 - th non - charging road and the a2 - th charging road respectively; ε is the average charging efficiency of the mobile energy storage device; is the longest waiting time on the a2 - th charging road.

[0083] b) Road traffic flow constraint:

[0084]

[0085] Among them, x ω,a,t is the traffic flow on the a - th traffic road in the t - th time period under the ω - th time - series operation prediction scenario; M od is the set of origin - destination traffic node pairs; and are the available path sets of electric vehicles and fuel vehicles from the r - th origin traffic node to the s - th destination traffic node respectively; is the road indication parameter of the traffic network, is a 0 - 1 variable. When , the l - th path from the r - th origin traffic node to the s - th destination traffic node passes through the a - th traffic road. When , the l - th path from the r - th origin traffic node to the s - th destination traffic node does not pass through the a - th traffic road; and are respectively the traffic flows on the l-th path where electric vehicles and fuel vehicles travel from the r-th starting traffic node to the s-th arriving traffic node within the t-th time period under the ω-th time-series operation prediction scenario; and are respectively the travel demand flows of electric vehicles and fuel vehicles from the r-th starting traffic node to the s-th arriving traffic node within the t-th time period under the ω-th time-series operation prediction scenario; N T is the set of operation time periods of the power-transportation coupling network.

[0086] The traffic travel demand volume in each time period includes the travel demand flows of electric vehicles and fuel vehicles between every two traffic nodes within each time period under each time-series operation prediction scenario; the traffic flow distribution of the traffic-power coupling network includes the traffic flows on each traffic road within each time period under each time-series operation prediction scenario.

[0087] By operating the traffic flow equilibrium optimization model for each scenario and each time period, the traffic flow distribution on each traffic road in the traffic network under each scenario can be obtained is the traffic flow distribution of the power-transportation coupling network under the ω-th time-series operation prediction scenario.

[0088] S2: Based on the traffic flow distribution of the traffic-power coupling network, construct a spatio-temporal extended traffic network, and establish an optimal path search model for the spatio-temporal extended traffic network. Input the movement plan of the spatio-temporal extended traffic network into the optimal path search model under each time-series operation prediction scenario, and after processing, output the optimal passing time of the mobile energy storage device. For the traffic flow distribution of the power-transportation coupling network under the ω-th time-series operation prediction scenario are respectively the traffic flow distributions of the power-transportation coupling network within the 1st, 2nd, …, t-th, …, N T time periods under the ω-th time-series operation prediction scenario, x ω,t = [x ω,1,t , x ω,2,t , …, x ω,a,t , …, x ω,A,t , x ω,1,t , x ω,2,t , …, x ω,a,t , …, x ω,A,t are respectively the traffic flows on the 1st, 2nd, …, a-th, …, A-th traffic roads within the t-th time period under the ω-th time-series operation prediction scenario; Based on the traffic flow distribution of the power-transportation coupling network under the ω-th time-series operation prediction scenario First, copy the traffic network G for N T + 1 layers; then, for the first N TLayer, construct virtual traffic roads between every two replicated identical traffic nodes between every two adjacent traffic networks G. Meanwhile, the last N T +1 layer serves as a virtual final arrival layer. Each traffic node in the final arrival layer is linked to the replicated identical traffic nodes in the previous 1st to Nth T layers, and finally construct a spatio-temporal extended traffic network including N T layers and a virtual final arrival layer G = [T N , T A , T N and T A are respectively the set of traffic nodes and the set of traffic roads of the traffic network. and are respectively the set of traffic nodes and the set of traffic roads of the spatio-temporal extended traffic network. and are respectively the set of traffic nodes and the set of traffic roads of the t-th time period corresponding layer of the spatio-temporal extended traffic network.

[0089] The optimal path search model of the spatio-temporal extended traffic network is specifically as follows:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] Δ·y = Λ rs

[0096] where y and are respectively the set of passing variables and the -th passing variable in it. are respectively the -th passing variables in the set of passing variables y. is a 0-1 variable. When , then the -th traffic road of the spatio-temporal extended traffic network is selected as the passing road. When , then the -th traffic road of the spatio-temporal extended traffic network is not selected as the passing road; is the travel time of the k-th mobile energy storage device from the i-th mobile energy storage site to the j-th mobile energy storage site in the t-th time period under the ω-th time-series operation prediction scenario; is the set of traffic roads in the spatio-temporal extended transportation network; is the travel time of the -th traffic road in the spatio-temporal extended transportation network under the ω-th time-series operation prediction scenario, and is the traffic flow of the T -th traffic road in the spatio-temporal extended transportation network under the ω-th time-series operation prediction scenario; N and are respectively the set of virtual traffic roads and the set of physical traffic roads in the spatio-temporal extended transportation network in the t-th time period; is the length of the -th traffic road; is the travel speed of the k-th mobile energy storage device on the -th traffic road, and is the free travel speed of the k-th mobile energy storage device on the -th traffic road, that is, the travel speed when there are no other mobile energy storage devices on the current traffic road; is the capacity of the -th actual traffic road; is the set of traffic roads in the spatio-temporal extended transportation network in the t-th time period under the ω-th time-series operation prediction scenario; is the travel time of the -th traffic road in the spatio-temporal extended transportation network, and is the traffic flow of the s -th traffic road in the spatio-temporal extended transportation network; T The duration of the operation period of the power-transportation coupling network is; the travel time of the mobile energy storage device on each virtual traffic road is 0, while the travel time of the mobile energy storage device on the physical traffic road is determined by the road length and capacity, traffic flow, and travel speed. Δ is the node-road incidence matrix of the spatio-temporal extended transportation network. In the node-road incidence matrix Δ, when is true, then the -th traffic road in the spatio-temporal extended transportation network goes to the r-th traffic node, is the node-road incidence variable in the node-road incidence matrix Δ. When is true, then the -th traffic road in the spatio-temporal extended transportation network leaves the r-th traffic node; Λ rs is the origin-destination node vector, and Λ rs indicates the travel demand of the mobile energy storage device from the r-th traffic node to the s-th traffic node; in the origin-destination node vector Λrs In it, when Λ rs (r) = 1 and Λ rs (s) = -1, then the r-th traffic node is the arrival node and the s-th traffic node is the starting node. When Λ rs (r) = -1 and Λ rs (s) = 1, then the r-th traffic node is the starting node and the s-th traffic node is the arrival node.

[0097] The movement plan of the spatio-temporal extended traffic network includes the traffic flow of each traffic road in the spatio-temporal extended traffic network under each time-series operation prediction scenario; the optimal passing time of the mobile energy storage device includes the passing time of each mobile energy storage device moving between every two mobile energy storage stations in each time period under each time-series operation prediction scenario.

[0098] S3: Establish an optimal operation model and a rescheduling optimization model for the traffic-electricity coupling network. Input the optimal passing time of the mobile energy storage device under each time-series operation prediction scenario into the optimal operation model. After processing, output the day-ahead scheduling candidate plan of the mobile energy storage device, and then input it into the rescheduling optimization model. After processing, output the total operation cost of the traffic-electricity coupling network after rescheduling in the day-ahead scheduling candidate plan. For each time-series operation prediction scenario, the optimal operation model of the traffic-electricity coupling network is specifically as follows:

[0099]

[0100] Among them, C φ is the total operation cost of the traffic-electricity coupling network in the day-ahead scheduling candidate plan φ under the current time-series operation prediction scenario. and are respectively the operation cost of the mobile energy storage device, the cost of the power input from the transmission network to the distribution network via the substation, and the power generation operation cost of the controllable distributed generator set in the day-ahead scheduling candidate plan φ; N T is the set of operation time periods, and N m is the set of mobile energy storage devices; CP k and FC k are respectively the unit charge-discharge cost and the unit fuel cost of the k-th mobile energy storage device; γ is the aging coefficient of the energy storage battery of the mobile energy storage device after linear approximation; N s is the set of mobile energy storage stations; and are respectively the charging and discharging powers of the k-th mobile energy storage device at the i-th mobile energy storage station in the t-th time period in the day-ahead scheduling candidate plan φ; Dist max is the maximum value of the distances between each mobile energy storage station; ζ φ,k,t$\zeta_{k,t}^{\varphi}$ is the displacement indicator variable of the $k$-th mobile energy storage device in the $t$-th time period of the day-ahead scheduling candidate plan $\varphi$. φ,k,t $\delta_{k,t}^{\varphi}$ φ,k,t is a 0-1 variable. When $\zeta_{k,t}^{\varphi}$ φ,k,t $ = 1$, it means that the $k$-th mobile energy storage device in the day-ahead scheduling candidate plan $\varphi$ moves in the $t$-th time period. When $\zeta_{k,t}^{\varphi}$ t $ = 0$, it means that the $k$-th mobile energy storage device in the day-ahead scheduling candidate plan $\varphi$ does not move in the $t$-th time period; $EP_{t}$ is the power transmitted from the substation to the distribution network in the $t$-th time period of the day-ahead scheduling candidate plan $\varphi$; $T$ s is the duration of the operation period of the power-transportation coupling network; $N$ g is the set of controllable distributed generation units in the distribution network; $c_{g}$ is the unit power generation cost of the $g$-th controllable distributed generation unit;

[0101] The optimal operation model of the transportation-power coupling network is based on the operation constraints of the distribution network and the operation constraints of mobile energy storage devices, as follows:

[0102] a) Operation constraints of the distribution network:

[0103] $P_{m + 1,m + 2,t}^{\varphi}$ φ,m+1,t $ = P_{m,m + 1,t}^{\varphi}$ φ,m,t $ - p_{m + 1,t}^{\varphi}$ φ,m+1,t

[0104] $Q_{m + 1,m + 2,t}^{\varphi}$ φ,m+1,t $ = Q_{m,m + 1,t}^{\varphi}$ φ,m,t $ - q_{m + 1,t}^{\varphi}$ φ,m+1,t

[0105]

[0106] where $P_{m + 1,m + 2,t}^{\varphi}$ φ,m+1,t and $Q_{m + 1,m + 2,t}^{\varphi}$ φ,m+1,t are the active and reactive power flows of the transmission line between the $(m + 1)$-th power node and the $(m + 2)$-th power node in the $t$-th time period of the day-ahead scheduling candidate plan $\varphi$ respectively. $P_{m,m + 1,t}^{\varphi}$ φ,m,t and $Q_{m,m + 1,t}^{\varphi}$ φ,m,t are the active and reactive power flows of the transmission line between the $m$-th power node and the $(m + 1)$-th power node in the $t$-th time period of the day-ahead scheduling candidate plan $\varphi$ respectively. $p_{m + 1,t}^{\varphi}$ φ,m+1,t and $q_{m + 1,t}^{\varphi}$ φ,m+1,t are the active power and reactive power of the $(m + 1)$-th power node in the $t$-th time period of the day-ahead scheduling candidate plan $\varphi$ respectively. $p_{m,t}^{\varphi}$ φ,m,t and $q_{m,t}^{\varphi}$ φ,m,t are the active power and reactive power of the $m$-th power node in the $t$-th time period of the day-ahead scheduling candidate plan $\varphi$ respectively; and are the active and reactive load powers of the m-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively; and are the active and reactive load shedding powers of the m-th power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively; and are the sets of new energy generating units and controllable distributed generating units connected to the m-th power node in the distribution network, respectively; and are the active and reactive scheduled power generation output powers of the b-th new energy generating unit in the t-th time period of the day-ahead scheduling candidate plan φ, respectively; and are the active and reactive scheduled power generation output powers of the g-th controllable distributed generating unit in the t-th time period of the day-ahead scheduling candidate plan φ, respectively; and are the scheduled active power and reactive power of the k-th mobile energy storage device at the m-th power node in the t-th time period of the day-ahead scheduling candidate plan φ; E B is the average charging demand of a single electric vehicle; is the set of charging roads of the transportation network connected to the m-th power node in the distribution network, x φ,a2,t is the traffic flow of the a2-th charging road in the t-th time period of the day-ahead scheduling candidate plan φ; h and H are the indicator bit and the total number of linearized edges in the polygon linearization method, respectively; is the upper limit of the transmission capacity of the transmission line from the m-th power node to the m + 1-th power node; V φ,m,t and V φ,1,t are the voltage amplitudes at the m-th power node and the 1-st power node in the t-th time period of the day-ahead scheduling candidate plan φ, respectively, V φ,m+1,t is the voltage amplitude at the m + 1-th power node in the t-th time period of the day-ahead scheduling candidate plan φ; R m and X m are the resistance and reactance of the transmission line from the m-th power node to the m + 1-th power node, respectively; V m and are the lower limit and upper limit of the voltage of the m-th power node, respectively.

[0107] b) Operating constraint conditions of mobile energy storage devices:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] Among them, and are the charging and discharging powers of the k-th mobile energy storage device at the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ, respectively; and are the active and reactive power dispatches of the k-th mobile energy storage device at the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ, respectively; z φ,k,i,t is the connection indicator variable between the k-th mobile energy storage device and the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ, z φ,k,i,t is a 0-1 variable. When z φ,k,i,t = 1, then the k-th mobile energy storage device is connected to the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ. When z φ,k,i,t = 0, then the k-th mobile energy storage device is not connected to the i-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate scheme φ; and are the maximum charging and discharging powers of the k-th mobile energy storage device, respectively; is the maximum reactive power output of the k-th mobile energy storage device; is the rated power of the k-th mobile energy storage device; SOC φ,k,t and SOC φ,k,t+1 are the state of charge of the k-th mobile energy storage during the t-th and t+1-th time periods in the day-ahead scheduling candidate scheme φ, respectively; is the rated battery capacity of the k-th mobile energy storage device; and are the charging and discharging efficiencies of the k-th mobile energy storage device, respectively; SOC k and are the state of charge SOC of the k-th mobile energy storage device during the t-th time period, respectively k,tThe lower and upper limits; z φ,k,i,t , z φ,k,j,t+Δτ and z φ,k,i,t+1 are the connection indicator variables between the k-th mobile energy storage device and the i-th mobile energy storage site during the t-th, t+Δτ-th, and t+1-th time periods in the day-ahead scheduling candidate plan φ respectively, and z φ,k,i,t is a 0-1 variable, and Δτ is an auxiliary variable for the passing time of the mobile energy storage is the passing time from the i-th mobile energy storage site to the j-th mobile energy storage site during the t-th time period in the day-ahead scheduling candidate plan φ

[0119] Determine the day-ahead scheduling candidate plan φ of the mobile energy storage device according to the connection indicator variables between each mobile energy storage device and each mobile energy storage site during each time period

[0120] The content considering the impact of traffic flow in the scenario on the operation of the mobile energy storage device is as follows

[0121] For the rescheduling of the day-ahead scheduling candidate plan φ under the ω-th time-series operation prediction scenario, initialize the setting Then, based on the time-series traffic flow distribution under the ω-th time-series operation prediction scenario, evaluate the implementation of the day-ahead scheduling candidate plan φ by the traffic flow to obtain the updated

[0122]

[0123] If there is That is, the commuting time of the k-th mobile energy storage device from the i-th mobile energy storage site to the j-th mobile energy storage site at time t in the ω-th time-series operation prediction scenario is greater than the corresponding commuting time in the ω-th time-series operation prediction scenario. It is considered that the day-ahead candidate scheduling plan φ is affected and cannot be fully realized in the ω-th time-series operation prediction scenario. Therefore, The value of is re-evaluated. For example, the scheduling result of the day-ahead scheduling candidate plan c obtained in the time-series operation prediction scenario is: the k-th mobile energy storage device spends 2 time periods from the i-th mobile energy storage site to the j-th mobile energy storage site at time t, then there is

[0124] z c,k,i,t =1, z c,k,i,t+1 =0, z c,k,i,t+2 =0, z c,k,i,t+3 =1

[0125] In the day-ahead scheduling candidate plan d obtained in the time-series operation prediction scenario, the same travel behavior takes 3 time periods, then it needs to be corrected to

[0126]

[0127] The rescheduling optimization model of the transportation - power coupling network is as follows:

[0128]

[0129] Among them, is the total operating cost of the rescheduled transportation - power coupling network in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ, and are respectively the operating cost of the mobile energy storage device, the cost of the power input from the transmission network to the distribution network via the substation, and the power generation operating cost of the controllable distributed generation unit in the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ; and are respectively the charging and discharging powers of the k - th mobile energy storage device at the i - th mobile energy storage site in the t - th time period in the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ; is the displacement indicator variable of the k - th mobile energy storage device in the t - th time period in the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ; is the power transmitted from the substation to the distribution network in the t - th time period in the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ; is the power output of the g - th controllable distributed generation unit in the t - th time period in the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ; is the connection indicator variable between the k - th mobile energy storage device and the i - th mobile energy storage site in the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ, is the connection indicator variable between the k - th mobile energy storage device and the i - th mobile energy storage site in the (t + Δτ) - th time period in the rescheduling in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ; is the travel time of the k - th mobile energy storage device from the i - th mobile energy storage site to the j - th mobile energy storage site in the t - th time period in the ω - th time - series operation prediction scenario.

[0130] The total operating cost of the rescheduled transportation - power coupling network in the day - ahead scheduling candidate plan is the total operating cost of the rescheduled transportation - power coupling network in the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate plan φ

[0131] The rescheduling optimization model of the transportation - power coupling network is based on the operating constraints of the distribution network and the operating constraints of the mobile energy storage device, as follows:

[0132] a) Operating constraints of the distribution network:

[0133]

[0134] Among them, and are respectively the active and reactive power flows of the transmission line between the (m + 1)-th power node and the (m + 2)-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the active and reactive power flows of the transmission line between the m-th power node and the (m + 1)-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the active power and reactive power of the (m + 1)-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the active power and reactive power of the m-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ; and are respectively the active and reactive load powers of the m-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the active and reactive load shedding powers of the m-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the active and reactive scheduled power generation output powers of the b-th new energy generating unit within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the active and reactive scheduled power generation output powers of the g-th controllable distributed generating unit within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the scheduled active power and reactive power of the k-th mobile energy storage device at the m-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. and are respectively the voltage amplitudes at the m-th power node and the 1-st power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ. is the voltage amplitude at the (m + 1)-th power node within the t-th period of the rescheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ.

[0135] b) Operational constraints of mobile energy storage devices:

[0136]

[0137] Among them, and are the charging and discharging powers of the k-th mobile energy storage device at the i-th mobile energy storage site during the t-th time period in the ω-th time-series operation prediction scenario of the day-ahead scheduling candidate solution φ, respectively. and are the active and reactive powers of the k-th mobile energy storage device at the i-th mobile energy storage site during the t-th time period in the ω-th time-series operation prediction scenario of the day-ahead scheduling candidate solution φ, respectively. is the connection indicator variable between the k-th mobile energy storage device and the i-th mobile energy storage site during the t-th time period in the ω-th time-series operation prediction scenario of the day-ahead scheduling candidate solution φ. and are the state of charge of the k-th mobile energy storage during the t-th and t+1-th time periods in the ω-th time-series operation prediction scenario of the day-ahead scheduling candidate solution φ, respectively.

[0138] S4: Establish an evaluation model for the scheduling scheme of the transportation-electricity coupling network. In each time-series operation prediction scenario, input the total operation cost of the re-scheduled transportation-electricity coupling network in the day-ahead scheduling candidate solution into the scheduling scheme evaluation model. After processing, output the optimal day-ahead scheduling scheme of the mobile energy storage device, and perform the day-ahead scheduling of the mobile energy storage device in each time-series operation prediction scenario.

[0139] The specific form of the scheduling scheme evaluation model is as follows:

[0140]

[0141] Among them, C * is the scheduling scheme evaluation parameter of the day-ahead scheduling candidate solution φ; Ψ is the set of N ω day-ahead scheduling candidate solutions φ, and Ω is the set of N time-series operation prediction scenarios; σ ω is the scenario probability of the ω-th time-series operation prediction scenario; is the total operation cost of the re-scheduled transportation-electricity coupling network in the ω-th time-series operation prediction scenario of the day-ahead scheduling candidate solution φ; N T is the set of operation time periods of the power-transportation coupling network; N R and N b are the sets of new energy power generation units and power nodes, respectively; is the maximum power generation capacity of the b-th new energy power generation unit during the t-th time period in the ω-th time-series operation prediction scenario, is the power generation power of the b-th new energy generating unit in the t-th period after scheduling in the ω-th time-series operation prediction scenario of the day-ahead scheduling candidate plan φ; and are respectively the upper limit of the expected new energy curtailment amount and the upper limit of the expected load shedding amount corresponding to the tolerable day-ahead scheduling candidate plan; is the active load shedding power of the m-th power node in the t-th period of rescheduling in the ω-th time-series operation prediction scenario of the day-ahead scheduling candidate plan φ.

[0142] In the scheduling scheme evaluation parameter C of the day-ahead scheduling candidate plan φ * is the smallest, the optimal day-ahead scheduling plan in the day-ahead scheduling candidate plan φ is obtained.

[0143] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. The present application is described according to the flowcharts of the methods, systems, and computer program products of the embodiments of the present application.

[0144] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concepts. Therefore, the present invention is intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. In this way, if these modifications and variations of the present application fall within the scope of the equivalent technology of the present invention, the present application also intends to include these modifications and variations.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A day-ahead scheduling method for mobile energy storage considering the power-transportation coupling, characterized in that Including: S1: Under different time-series operation prediction scenarios of a transportation-electricity coupling network including mobile energy storage devices, establish a traffic flow equilibrium optimization model. Input the period traffic travel demand into the traffic flow equilibrium optimization model under each time-series operation prediction scenario, and after processing, output the traffic flow distribution of the transportation-electricity coupling network. S2: Based on the traffic flow distribution of the transportation-electricity coupling network, construct a spatio-temporal extended traffic network, and establish an optimal path search model for the spatio-temporal extended traffic network. Input the movement plan of the spatio-temporal extended traffic network into the optimal path search model under each time-series operation prediction scenario, and after processing, output the optimal passing time of the mobile energy storage device. S3: Establish an optimal operation model and a rescheduling optimization model for the transportation-electricity coupling network. Input the optimal passing time of the mobile energy storage device under each time-series operation prediction scenario into the optimal operation model, and after processing, output the day-ahead scheduling candidate plan of the mobile energy storage device, and then input it into the rescheduling optimization model. After processing, output the total operation cost of the transportation-electricity coupling network after rescheduling in the day-ahead scheduling candidate plan. S4: Establish a scheduling plan evaluation model for the transportation-electricity coupling network. Input the total operation cost of the transportation-electricity coupling network after rescheduling in the day-ahead scheduling candidate plan under each time-series operation prediction scenario into the scheduling plan evaluation model, and after processing, output the optimal day-ahead scheduling plan of the mobile energy storage device, and perform the day-ahead scheduling of the mobile energy storage device under each time-series operation prediction scenario.

2. The day-ahead scheduling method for mobile energy storage considering the power-transportation coupling according to claim 1, characterized in that: In the step S1 described above, the power-transportation coupling network is composed of the coupling of a transportation network and a distribution network. The distribution network includes power nodes connected by each transmission line. The distribution network also includes several new energy power generation devices, controllable distributed generating units, and mobile energy storage sites connected to the power nodes. Each mobile energy storage device moves in the transportation network. After the mobile energy storage device arrives at each mobile energy storage site, it charges and discharges. A number of electric vehicles and fuel vehicles move in the transportation network to generate traffic flow distribution; the transportation network includes traffic nodes connected by each traffic road.

3. The day-ahead scheduling method for mobile energy storage considering power-transport coupling according to claim 2, characterized in that: In the step S1 described above, the traffic flow equilibrium optimization model is specifically as follows: Among them, is the optimization cost objective under the ω-th timing operation prediction scenario, and are the travel time cost and charging cost under the ω-th timing operation prediction scenario respectively; κ is the cost factor of the commuting time; and are the non-charging road set and the charging road set in the traffic road set respectively, x ω,a1,t and x ω,a2,t are the traffic flows on the a1-th non-charging road and the a2-th charging road in the t-th period under the ω-th timing operation prediction scenario respectively; and are the commuting times on the a1-th non-charging road and the a2-th charging road in the t-th period under the ω-th timing operation prediction scenario respectively, z is the traffic flow condition; E B is the average charging demand of the electric vehicle; is the charging power cost parameter on the a2-th charging road. The constraint conditions of the traffic flow equilibrium optimization model are as follows: a) Road commuting time constraint: Among them, is the commuting time on the a1th non-charging road under the condition of no traffic flow; and are the traffic flow capacities on the a1th non-charging road and the a2th charging road respectively; ε is the average charging efficiency of the mobile energy storage device; is the longest waiting time on the a2th charging road; b) Road traffic flow constraint: where x ω,a,t is the traffic flow on the ath traffic road in the tth period under the ωth time-series operation prediction scenario; M od is the set of origin-destination traffic node pairs; and are respectively the sets of available paths for electric vehicles and fuel vehicles to travel from the rth origin traffic node to the sth destination traffic node; is the road indication parameter of the transportation network. When , the lth path from the rth origin traffic node to the sth destination traffic node passes through the ath traffic road. When , the lth path from the rth origin traffic node to the sth destination traffic node does not pass through the ath traffic road; and are respectively the traffic flows on the lth paths where electric vehicles and fuel vehicles are located from the rth origin traffic node to the sth destination traffic node in the tth period under the ωth time-series operation prediction scenario; and are respectively the travel demand flows of electric vehicles and fuel vehicles from the rth origin traffic node to the sth destination traffic node in the tth period under the ωth time-series operation prediction scenario; N T is the set of operation periods of the power-transportation coupling network; The period traffic travel demand includes the travel demand flow of electric vehicles and fuel vehicles between every two traffic nodes in each period under each time-series operation prediction scenario; the traffic flow distribution of the transportation-electricity coupling network includes the traffic flow on each traffic road in each period under each time-series operation prediction scenario.

4. The day-ahead scheduling method for mobile energy storage considering power-transportation coupling according to claim 2, wherein: In the said step S2, for the traffic flow distribution of the power-transportation coupling network under the ω-th time-series operation prediction scenario They are respectively the traffic flow distributions of the power-transportation coupling network under the ω-th time-series operation prediction scenario in the 1st, 2nd, …, t-th, …, N-th T time periods, and x ω,t =[x ω,1,t , x ω,2,t , …, x ω,a,t , …, x ω,A,t , where x ω,1,t , x ω,2,t , …, x ω,a,t , …, x ω,A,t are respectively the traffic flows on the 1st, 2nd, …, a-th, …, A-th traffic roads in the t-th time period under the ω-th time-series operation prediction scenario; Traffic flow distribution based on the power-transportation coupled network under the ω-th time-series operation prediction scenario First, copy the transportation network G to N T +1 layers; then, for the first to the Nth T layers, construct virtual transportation roads connecting every two identical copied transportation nodes between every two adjacent transportation networks G. At the same time, the last N T +1 layer serves as a virtual final arrival layer, and each transportation node in the final arrival layer is linked to the identical copied transportation nodes in the first to the Nth T layers. Finally, construct a spatio-temporal extended transportation network containing N T layers and a virtual final arrival layer 5. The day-ahead scheduling method for mobile energy storage considering the power-transportation coupling according to claim 2, wherein: In the step S2 described above, the optimal path search model of the spatio-temporal extended traffic network is specifically as follows: Among them, y and are respectively the set of traffic variables and the th traffic variable therein. When y a = 1, the th traffic road of the spatio-temporal extended traffic network is selected as the passing road. When , the th traffic road of the spatio-temporal extended traffic network is not selected as the passing road; is the passing time of the kth mobile energy storage device moving from the ith mobile energy storage site to the jth mobile energy storage site in the tth time period under the ωth time-series operation prediction scenario; is the set of traffic roads of the spatio-temporal extended traffic network; is the passing time of the th traffic road of the spatio-temporal extended traffic network under the ωth time-series operation prediction scenario, is the traffic flow of the th traffic road of the spatio-temporal extended traffic network under the ωth time-series operation prediction scenario; N T is the set of operation time periods; and are respectively the set of virtual traffic roads and the set of physical traffic roads of the spatio-temporal extended traffic network in the tth time period; L a is the length of the th traffic road; is the passing speed of the kth mobile energy storage device on the th traffic road, is the free passing speed of the kth mobile energy storage device on the th traffic road; is the capacity of the th actual traffic road; is the set of traffic roads of the spatio-temporal extended traffic network in the tth time period under the ωth time-series operation prediction scenario; is the passing time of the th traffic road of the spatio-temporal extended traffic network, is the traffic flow of the th traffic road of the spatio-temporal extended traffic network; T s is the duration of the operation time period of the power-transportation coupling network; Δ is the node-road incidence matrix of the spatio-temporal extended traffic network. In the node-road incidence matrix Δ, when , the th traffic road of the spatio-temporal extended traffic network leads to the rth traffic node, is the node-road incidence variable in the node-road incidence matrix Δ. When , the th traffic road of the spatio-temporal extended traffic network departs from the rth traffic node; Λ rs is the start - arrival node vector. In the start - arrival node vector Λ rs when Λ rs (r) = 1 and Λ rs (s) = - 1, then the r - th traffic node is the arrival node and the s - th traffic node is the start node. When Λ rs (r) = - 1 and Λ rs (s) = 1, then the r - th traffic node is the start node and the s - th traffic node is the arrival node; The movement plan of the spatio-temporal extended traffic network includes the traffic flow of each traffic road of the spatio-temporal extended traffic network under each time-series operation prediction scenario; the optimal passing time of the mobile energy storage device includes the passing time of each mobile energy storage device moving between every two mobile energy storage sites in each time period under each time-series operation prediction scenario.

6. The day-ahead scheduling method of mobile energy storage considering power-transportation coupling according to claim 2, characterized in that: In step S3, for each time series operation prediction scenario, the optimal operation model of the traffic-electricity coupled network is specifically as follows: Among them, C φ is the total operation cost of the transportation - power coupling network in the day - ahead scheduling candidate scenario φ for the current time series operation prediction, and are respectively the operation cost of the mobile energy storage device, the cost of the power input from the transmission network to the distribution network via the substation, and the generation operation cost of the controllable distributed generator set in the day - ahead scheduling candidate scenario φ; N T is the set of operation time periods, and N m is the set of mobile energy storage devices; CP k and FC k are respectively the unit charge - discharge cost and the unit fuel cost of the k - th mobile energy storage device; γ is the aging coefficient of the energy storage battery of the mobile energy storage device after linear approximation; N s is the set of mobile energy storage sites; and are respectively the charging and discharging power of the k - th mobile energy storage device at the i - th mobile energy storage site in the t - th time period in the day - ahead scheduling candidate scenario φ; Dist max is the maximum value of the distances between mobile energy storage sites; ζ φ,k,t is the displacement indicator variable of the k - th mobile energy storage device in the t - th time period in the day - ahead scheduling candidate scenario φ. When ζ φ,k,t = 1, the k - th mobile energy storage device in the day - ahead scheduling candidate scenario φ moves in the t - th time period. When ζ φ,k,t = 0, the k - th mobile energy storage device in the day - ahead scheduling candidate scenario φ does not move in the t - th time period; EP t is the power cost parameter in the t - th time period; is the power transmitted from the substation to the distribution network in the t - th time period in the day - ahead scheduling candidate scenario φ; T s is the duration of the operation time period of the power - transportation coupling network; N g is the set of controllable distributed generator sets in the distribution network; is the unit generation cost of the g - th controllable distributed generator set; is the power output of the g - th controllable distributed generator set in the t - th time period in the day - ahead scheduling candidate scenario φ; The optimal operation model of the traffic-electricity coupled network is based on the operation constraints of the distribution network and the operation constraints of mobile energy storage devices, and is specifically as follows: a) Operation constraints of the distribution network: P φ,m+1,t = P φ,m,t - p φ,m+1,t Q φ,m+1,t = Q φ,m,t - q φ,m+1,t Among them, P φ,m+1,t and Q φ,m+1,t are the active and reactive power flows of the transmission line between the (m + 1)-th power node and the (m + 2)-th power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively. P φ,m,t and Q φ,m,t are the active and reactive power flows of the transmission line between the m-th power node and the (m + 1)-th power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively. p φ,m+1,t and q φ,m+1,t are the active power and reactive power of the (m + 1)-th power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively. p φ,m,t and q φ,m,t are the active power and reactive power of the m-th power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively; and are the active and reactive load powers of the m-th power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively; and are the active and reactive load shedding powers of the m-th power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively; and are the sets of new energy generating units and controllable distributed generating units connected to the m-th power node in the distribution network, respectively; and are the active and reactive power dispatch generation output powers of the b-th new energy generating unit in the t-th time period of the day-ahead scheduling candidate solution φ, respectively; and are the active and reactive power dispatch generation output powers of the g-th controllable distributed generating unit in the t-th time period of the day-ahead scheduling candidate solution φ, respectively; and are the dispatch active power and reactive power of the k-th mobile energy storage device at the m-th power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively; E B is the average charging demand of a single electric vehicle; is the set of charging roads of the transportation network connected to the m-th power node in the distribution network. x φ,a2,t is the traffic flow of the a2-th charging road in the t-th time period of the day-ahead scheduling candidate solution φ; h and H are the indicator bit and the total number of linearized edges in the polygon linearization method, respectively; is the upper limit of the transmission capacity of the transmission line from the m-th power node to the (m + 1)-th power node; V φ,m,t and V φ,1,t are the voltage amplitudes at the m-th power node and the 1-st power node in the t-th time period of the day-ahead scheduling candidate solution φ, respectively. V φ,m+1,t is the voltage magnitude at the (m + 1)-th power node during the t-th time period in the current scheduling candidate solution φ; R m and X m are the resistance and reactance of the transmission line from the m-th power node to the (m + 1)-th power node respectively; V m and are the lower and upper limits of the voltage at the m-th power node respectively; b) Operation constraints of mobile energy storage devices: Among them, and are respectively the charging and discharging powers of the k-th mobile energy storage device at the i-th mobile energy storage site in the t-th time period in the day-ahead scheduling candidate scheme φ; and are respectively the active power and reactive power of the k-th mobile energy storage device scheduled at the i-th mobile energy storage site in the t-th time period in the day-ahead scheduling candidate scheme φ; z φ,k,i,t is the connection indication variable between the k-th mobile energy storage device and the i-th mobile energy storage site in the t-th time period in the day-ahead scheduling candidate scheme φ. When z φ,k,i,t = 1, then the k-th mobile energy storage device is connected to the i-th mobile energy storage site in the t-th time period in the day-ahead scheduling candidate scheme φ. When z φ,k,i,t = 0, then the k-th mobile energy storage device is not connected to the i-th mobile energy storage site in the t-th time period in the day-ahead scheduling candidate scheme φ; and are respectively the maximum charging and discharging powers of the k-th mobile energy storage device; is the maximum reactive power output of the k-th mobile energy storage device; is the rated power of the k-th mobile energy storage device; SOC φ,k,t and SOC φ,k,t+1 are respectively the state of charge of the k-th mobile energy storage in the t-th and t + 1 time periods in the day-ahead scheduling candidate scheme φ; is the rated battery capacity of the k-th mobile energy storage device; and are respectively the charging and discharging efficiencies of the k-th mobile energy storage device; SOC k and are respectively the lower and upper limits of the state of charge SOC k,t of the k-th mobile energy storage device in the t-th time period; z φ,k,i,t , z φ,k,j,t+Δτ and z φ,k,i,t+1 are respectively the connection indication variables between the k-th mobile energy storage device and the i-th mobile energy storage site in the t-th, t + Δτ and t + 1 time periods in the day-ahead scheduling candidate scheme φ, where Δτ is an auxiliary variable of the mobile energy storage passing time; is the passing time from the i-th mobile energy storage site to the j-th mobile energy storage site in the t-th time period in the day-ahead scheduling candidate scheme φ; According to the connection indication variables between each mobile energy storage device and each mobile energy storage site within each time period, the day-ahead scheduling candidate scheme φ of the mobile energy storage device is determined.

7. The day-ahead scheduling method for mobile energy storage considering power-transport coupling according to claim 6, characterized in that: In step S3, the rescheduling optimization model of the traffic-electricity coupled network is specifically as follows: Among them, is the total operation cost of the traffic - power coupling network rescheduled under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ, and are respectively the operation cost of the mobile energy storage device rescheduled under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ, the cost of the power input from the transmission network to the distribution network via the substation, and the generation operation cost of the controllable distributed generation unit; and are respectively the charging and discharging powers of the k - th mobile energy storage device at the i - th mobile energy storage site within the t - th time period under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ; is the displacement indicator variable of the k - th mobile energy storage device within the t - th time period under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ; is the power transmitted from the substation to the distribution network within the t - th time period under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ; is the power output of the g - th controllable distributed generation unit within the t - th time period under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ; is the connection indicator variable between the k - th mobile energy storage device and the i - th mobile energy storage site under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ for rescheduling, is the connection indicator variable between the k - th mobile energy storage device and the i - th mobile energy storage site within the (t + Δτ) - th time period under the ω - th time - series operation prediction scenario in the day - ahead scheduling candidate scheme φ for rescheduling; is the passing time of the k - th mobile energy storage device from the i - th mobile energy storage site to the j - th mobile energy storage site within the t - th time period under the ω - th time - series operation prediction scenario. The total operating cost of the re-scheduled traffic-electricity coupled network in the day-ahead scheduling candidate solution is the total operating cost of the re-scheduled traffic-electricity coupled network under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate solution φ.

8. The day-ahead scheduling method for mobile energy storage considering the power-transportation coupling according to claim 2, wherein: In step S4, the scheduling scheme evaluation model is specifically as follows: Among them, C * is the scheduling scheme evaluation parameter of the day-ahead scheduling candidate scheme φ; Ψ is the set of N ω day-ahead scheduling candidate schemes φ, and Ω is the set of N time-series operation prediction scenarios; σ ω is the scenario probability of the ω-th time-series operation prediction scenario; is the total operation cost of the re-scheduled traffic-electricity coupling network under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ; N T is the set of operation time periods of the power-traffic coupling network; N R and N b are the sets of new energy generating units and power nodes respectively; is the maximum power generation capacity of the b-th new energy generating unit in the t-th time period under the ω-th time-series operation prediction scenario, is the power generation capacity of the b-th new energy generating unit in the t-th time period after scheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ; and are the upper limits of the expected new energy curtailment and the expected load shedding respectively; is the active load shedding power of the m-th power node in the t-th time period of the re-scheduling under the ω-th time-series operation prediction scenario in the day-ahead scheduling candidate scheme φ; When the scheduling scheme evaluation parameter C of the day-ahead scheduling candidate scheme φ is at its minimum, the optimal day-ahead scheduling scheme in the day-ahead scheduling candidate scheme φ is obtained. * ​ 9. An electronic device, characterized in that, Including: A mutually coupled memory and a processor, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1-8.

10. A computer-readable storage medium having program data stored thereon, characterized in that, When the program data is executed by the processor, the method according to any one of claims 1-8 is implemented.

Images