Method and system for improving new energy bearing capacity of flexible power distribution network based on cooperation of light, storage and charging

Abstract

According to the method and system for improving the new energy bearing capacity of the flexible power distribution network based on light, storage and charging cooperation, the maximum new energy open capacity in each transformer area serves as a lower-layer optimization target, a lower-layer optimization model is established through the lower-layer optimization target, a power model of a multi-port energy router of an integrated energy storage device and a power model of a flexible soft switch, and the new energy bearing capacity of the flexible power distribution network is improved. Solving the lower-layer optimization model to obtain the new energy open capacity of each transformer area; taking the maximum capacity of the new energy accessed into the flexible power distribution network as an upper-layer optimization target, establishing an upper-layer optimization model by using the upper-layer optimization target, the power flow model of the flexible power distribution network and the power flow constraint condition, and solving the upper-layer optimization model to obtain a configuration scheme of the new energy in the flexible power distribution network; based on the configuration scheme of the new energy in the flexible power distribution network, when the voltage of each node in the flexible power distribution network meets the safety constraint condition and the charging load in the flexible power distribution network is maximum, the site position of the electric vehicle charging station is determined, and the photovoltaic bearing capacity of the flexible power distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy regulation and control, and specifically, relates to a method and system for improving the new energy carrying capacity of a flexible distribution network with coordinated photovoltaic storage and charging. Background Art

[0002] With the large-scale access of photovoltaic power to the distribution network, its volatility and intermittent characteristics are poorly matched with the temporal and spatial matching of electricity demand, leading to a series of problems such as voltage exceeding the limit, reverse overload of distribution transformers, and substandard power quality, which seriously threaten the safe operation of the distribution network.

[0003] In the existing technology, the introduction of flexible soft switches and energy routers provides a new solution to improve the photovoltaic carrying capacity of the distribution network. The two can work together to adjust the energy flow between distributed photovoltaic power sources and distribution network loads in real time, alleviate the impact of the volatility and intermittency of photovoltaic power generation on the operation of the power grid, and optimize the distribution of energy in the power grid according to the actual situation of photovoltaic power generation, reduce voltage fluctuations and overload phenomena, and improve the safety of the power grid. In addition, a reasonable photovoltaic storage and charging collaborative layout can significantly improve the distribution network's acceptance capacity for distributed photovoltaics by optimizing power flow and reducing system imbalances; energy storage facilities can improve voltage stability and power supply reliability by smoothing the fluctuations of photovoltaic output, and the intelligent scheduling of charging facilities can achieve load transfer and alleviate local overload pressure during peak hours. However, the existing technologies are limited to the application of a single strategy, such as dynamic network reconstruction based on energy routers, or flexible soft switch intelligent control to improve the new energy carrying capacity, or only improve the new energy carrying capacity from the collaborative layout of photovoltaic storage and charging. The coordination and cooperation between flexible soft switches, energy routers and the collaborative layout of photovoltaic storage and charging is not considered, and the layout planning of the three aspects of photovoltaic storage and charging is not fully considered. The optimal deployment location of photovoltaic storage and charging cannot be achieved, resulting in a serious underestimation of the planned installed capacity of new energy. Therefore, it is an urgent problem to comprehensively consider the coordination and cooperation of different methods to improve the acceptance capacity and fully carry out the collaborative layout planning of photovoltaic storage and charging. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for improving the new energy carrying capacity of a flexible distribution network with coordinated photovoltaic storage and charging, which can realize optimal scheduling of energy routers, energy storage charging and discharging, and charging loads, improve the photovoltaic carrying capacity of the flexible distribution network, and smooth the volatility of photovoltaic power generation.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging coordination. The flexible distribution network includes multiple substations, each of which includes a flexible soft switch and a multi-port energy router; including:

[0007] The energy storage device in the flexible distribution network is integrated into the multi-port energy router, and the power model of the multi-port energy router with integrated energy storage device is established; the power model of the flexible soft switch is established; based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, the power flow model and power flow constraint conditions of the flexible distribution network are established;

[0008] Taking the maximum open capacity of new energy in each substation as the lower-level optimization target, using the lower-level optimization target, the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, a lower-level optimization model is established, and the lower-level optimization model is solved to obtain the open capacity of new energy in each substation; based on the open capacity of new energy in each substation, taking the maximum capacity of new energy connected to the flexible distribution network as the upper-level optimization target, the upper-level optimization model is established using the upper-level optimization target, the power flow model of the flexible distribution network and the power flow constraint conditions, and the upper-level optimization model is solved to obtain the configuration plan of new energy in the flexible distribution network;

[0009] Based on the configuration scheme of renewable energy in the flexible distribution network, the site location of the electric vehicle charging station is determined when the voltage of each node in the flexible distribution network meets the safety constraints and the charging load in the flexible distribution network is the largest.

[0010] Preferably, the power model of the multi-port energy router with integrated energy storage device satisfies the following relationship:

[0011]

[0012] In the formula, and are the active power and reactive power injected or outflowed from port u of the energy router, k is the number of ports of the energy router, P es,ch , P es,dis are the charging power and discharging power of the energy storage device integrated in the energy router, and are the lower and upper limits of reactive power at port u of the energy router, P ch,max and P dis,max are the upper limit of charging efficiency and discharge efficiency of the energy storage device integrated in the energy router, S ER is the total capacity of the energy router, α ess is a 0-1 variable representing the charging and discharging state of the energy storage device integrated in the energy router, α ess =1 when the energy storage device is charged, α ess =0, the energy storage device discharges.

[0013] Preferably, the power model of the flexible soft switch satisfies the following relationship:

[0014]

[0015] In the formula, are the active power and reactive power injected or outflowed at port n of the flexible soft switch, S SOP is the capacity of the flexible soft switch, and m is the number of ports of the flexible soft switch.

[0016] Preferably, the power flow model of the flexible distribution network satisfies the following relationship:

[0017]

[0018] In the formula, are the active power and reactive power injected into node i by the traditional energy unit, are the active power and reactive power injected into node i by the new energy unit, are the active power and reactive power injected into or out of node i at port n of the flexible soft switch, are the active power and reactive power injected into or out of node i at port u of the multi-port energy router with integrated energy storage device, are the active power and reactive power of the load at node i, respectively, P ij , Q ij are the active power and reactive power of line ij respectively, r ij 、x ij are the resistance and reactance of line ij respectively, is a 0-1 variable indicating whether the line ij is opened or closed in the flexible distribution network operation scenario w. When line ij is closed, When line ij is disconnected, when w=0, the flexible distribution network is in normal operation, w=1,2,…,N c When the flexible distribution network is in different fault scenarios, N c is the number of fault scenarios, N L (i) is the set of nodes connected to node i through lines, is the square value of the current in line ij;

[0019] Flexible distribution network operation scenarios include: normal operation scenario, N-1 fault scenario and N-2 fault scenario.

[0020] Preferably, the power flow constraint condition of the flexible distribution network satisfies the following relationship:

[0021]

[0022] U i,min <U i <U i,max (7)

[0023] In the formula, is the current of line ij in the flexible distribution network operation scenario w, are the lower and upper limits of the current of line ij under the flexible distribution network operation scenario w, U i is the voltage at node i, U i,min , U i,max are the lower and upper limits of the voltage at node i respectively;

[0024] P GT,min ≤P GT ≤P GT,max (8)

[0025] P P2G,min ≤P P2G ≤P P2G,max (9)

[0026] Where P GT,min and P GT,max are the lower and upper limits of the gas turbine output power, respectively, P GT is the output power of the gas turbine, P P2G,min and P P2G,max are the lower and upper limits of the output power of the power-to-gas equipment, P P2G is the output power of the power-to-gas device.

[0027] Preferably, the lower layer optimization objective satisfies the following relationship:

[0028]

[0029] In the formula, is the power required by the load in the area, is the output power of the new energy unit μ, ρ is the number of loads in the area, and κ is the number of new energy units in the area.

[0030] Preferably, the upper layer optimization objective satisfies the following relationship:

[0031]

[0032] In the formula, is the open capacity of new energy at node i in the flexible distribution network, N pv It is the number of nodes connected to the renewable energy units in the flexible distribution network.

[0033] Preferably, by solving the lower optimization model, the power allocation scheme of each port of the flexible soft switch in each substation and the power allocation scheme of each port of the multi-port energy router are also obtained;

[0034] Based on the power model of the multi-port energy router with integrated energy storage device, the capacity configuration scheme and access location of the energy storage device in the flexible distribution network are determined according to the power allocation scheme of each port of the multi-port energy router.

[0035] Preferably, by solving the upper-layer optimization model, the configuration scheme of the new energy obtained includes access nodes and planned capacity of the newly added new energy, and capacity adjustment scheme of the existing new energy.

[0036] Preferably, based on the configuration scheme of the new energy in the flexible distribution network, when the voltage of each node in the flexible distribution network meets the safety constraint condition and the charging load in the flexible distribution network is the largest, the site location of the electric vehicle charging station is determined, including:

[0037] Obtain the predicted value of charging load in the flexible distribution network to determine the number of charging stations to be built;

[0038] The chance-constrained programming method is used to establish a model that characterizes the maximum charging load in the flexible distribution network, satisfying the following relationship:

[0039]

[0040] In the formula, Z is the maximum charging load demand in the flexible distribution network, I is the set of charging points generated by electric vehicles, Q is the set of candidate sites for charging stations, and h is c is the charging load of charging point c, N is the number of charging stations to be built, Z c is the state variable of the charging point. If the electric vehicle generating the charging point c can use the remaining power to reach the candidate site q of the charging station, it means that the candidate site q of the charging station can cover the charging point c, then Z c =1, otherwise Z c =0,X q is the state variable of the candidate site q of the charging station. If the candidate site q of the charging station is used to build a charging station, then X q =1, otherwise X q =0;

[0041] The probability distribution method is used to establish a model to characterize the voltage of each node in the flexible distribution network to meet the safety constraints, which satisfies the following relationship:

[0042] Pr{U i,min ≤U i ≤U i,max}≥β V (16)

[0043] In the formula, Pr{} is the probability of the event being true, U i,min , U i,max are the lower and upper limits of the voltage at node i, U i is the voltage at node i, βV is the confidence level value of the node voltage safety constraint;

[0044] The safety constraints of the voltage at each node in the flexible distribution network are converted into linear constraints to satisfy the following relationship:

[0045] U i,min -|U i,s |≤K·Z vmin,s (17)

[0046] |U i,s |-U i,max ≤K·Z vmax,s (18)

[0047]

[0048] Z vmin,s ,Z vmax,s ∈{0,1},s=1,2,…,N s (twenty one)

[0049] Where N s is the number of samples, U i,s is the sth sample of the voltage at node i, Z vmin,s is a state variable that indicates whether the sth sampling sample of the node voltage exceeds the lower limit. vmin,s = 0, the node voltage sth sampling sample does not exceed the lower limit, when Z vmin,s = 1, the node voltage sth sampling sample exceeds the lower limit, Z vmax,s is a state variable that indicates whether the sth sampling sample of the node voltage exceeds the upper limit. vmax,s = 0, the node voltage sth sampling sample does not exceed the upper limit, when Z vmax,s = 1, the node voltage sth sampling sample exceeds the upper limit, K is positive and makes Z vmin,s , Z vmax,s When is 1, equations (19) and (20) hold true;

[0050] The model representing the maximum charging load in the flexible distribution network and the linear constraints are jointly iteratively solved to obtain the optimal site of the charging station as the site location of the electric vehicle charging station.

[0051] The present invention also proposes a system for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging coordination. The flexible distribution network includes multiple substations, each of which includes a flexible soft switch and a multi-port energy router; including:

[0052] A model building module is used to integrate the energy storage device in the flexible distribution network into the multi-port energy router and establish a power model of the multi-port energy router with integrated energy storage device; establish a power model of the flexible soft switch; based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, establish a power flow model and power flow constraint conditions of the flexible distribution network;

[0053] The photovoltaic storage coordination module is used to take the maximum open capacity of new energy in each substation as the lower-level optimization target, and use the lower-level optimization target, the power model of the multi-port energy router with integrated energy storage device, and the power model of the flexible soft switch to establish a lower-level optimization model, solve the lower-level optimization model, and obtain the open capacity of new energy in each substation; based on the open capacity of new energy in each substation, take the maximum capacity of new energy connected to the flexible distribution network as the upper-level optimization target, and use the upper-level optimization target, the power flow model of the flexible distribution network, and the power flow constraint conditions to establish an upper-level optimization model, solve the upper-level optimization model, and obtain the configuration plan of new energy in the flexible distribution network;

[0054] The photovoltaic storage and charging coordination module is used to determine the site location of the electric vehicle charging station based on the configuration plan of new energy in the flexible distribution network when the voltage of each node in the flexible distribution network meets the safety constraints and the charging load in the flexible distribution network is the largest.

[0055] The present invention is also a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0056] The present invention is also a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0057] The beneficial effects of the present invention are that, compared with the prior art, at least the method proposed in the present invention realizes the improvement of the photovoltaic carrying capacity of the flexible distribution network with layered coordinated transfer of multiple substations, and establishes a two-layer optimization model for improving the photovoltaic carrying capacity of the distribution network by taking the maximum capacity of distributed photovoltaic access to the distribution network as the optimization target, wherein the upper-layer optimization model optimizes the power allocation scheme of each port of the interconnected equipment under the condition of coordinated transfer of multiple substations based on the internal power flexible regulation mechanism of the flexible soft switch and the energy router; the lower-layer optimization model optimizes the multi-energy transfer scheme within a single substation based on the power transfer results between substations, taking into account the coordinated mutual assistance capabilities of multiple energy sources such as electricity and gas.

[0058] Based on the determined power allocation scheme for each port of interconnected equipment under the conditions of coordinated transfer of multiple substations and the multi-energy transfer scheme within a single substation, a coordinated layout plan for photovoltaic storage and charging in a flexible interconnected distribution network considering source-load uncertainty is proposed. By analyzing the temporal and spatial distribution characteristics of photovoltaic power generation, energy storage charging and discharging characteristics and charging load demand in different substations, the impact of layered coordinated transfer of multiple substations on the photovoltaic carrying capacity of the distribution network is considered. Based on chance constrained planning, an optimization model and constraint conditions for the location of charging stations under source-load uncertainty are constructed, and converted into a mixed integer programming model for solution, ultimately obtaining the optimal deployment locations of photovoltaic, energy storage and electric vehicle charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a topological diagram of a distribution network including an energy router and a flexible soft switch in an embodiment of the present invention;

[0060] Figure 2 It is a flow chart of the method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging coordination proposed in the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0062] The present invention proposes a method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging synergy. The flexible distribution network includes multiple substations, each of which includes a flexible soft switch and a multi-port energy router. The multi-port energy router with integrated energy storage device proposed by the present invention is connected in the flexible distribution network topology as follows: Figure 1 As shown, the substation includes 66 nodes, wherein node 66 is connected to the gas turbine GT, node 55 is connected to the power-to-gas device P2G, and nodes 22, 25 and 55 are all connected to the flexible soft switch SOP. Figure 1 The energy router includes 4 ports, among which port 1 is connected to node 33, port 2 is connected to node 18, port 3 is connected to node 51, and port 4 is connected to node 58; the energy storage device integrated in the multi-port energy router can provide support for continuous energy supply, energy quality control and energy scheduling management, and reasonable arrangement of energy storage charging and discharging plans can realize flexible expansion of power distribution equipment in the substation area.

[0063] like Figure 2 As shown, the improvement methods include:

[0064] Step 1: Integrate the energy storage device in the flexible distribution network into the multi-port energy router, and establish a power model of the multi-port energy router with integrated energy storage device; establish a power model of the flexible soft switch; based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, establish a flow model and flow constraints of the flexible distribution network.

[0065] Specifically, the power model of the multi-port energy router integrated with the energy storage device satisfies the following relationship:

[0066]

[0067] In the formula, and are the active power and reactive power injected or outflowed from port u of the energy router, k is the number of ports of the energy router, P es,ch , P es,dis are the charging power and discharging power of the energy storage device integrated in the energy router, and are the lower and upper limits of reactive power at port u of the energy router, P ch,max and P dis,max are the upper limit of charging efficiency and discharge efficiency of the energy storage device integrated in the energy router, S ER is the total capacity of the energy router, α ess is a 0-1 variable representing the charging and discharging state of the energy storage device integrated in the energy router, α ess =1 when the energy storage device is charged, α ess =0, the energy storage device discharges.

[0068] In actual operation, it is necessary to consider the power loss of the energy storage device during energy conversion and the service life of the energy storage device. The constraints of the energy storage device integrated in the energy router satisfy the following relationship:

[0069] SOC min ≤SOC≤SOC max (2)

[0070] In the formula, SOC max and SOC min They are the upper and lower limits of the state of charge of the energy storage device respectively, and SOC is the state of charge of the energy storage device.

[0071] Specifically, in addition to satisfying the general node constraints, the node where the SOP is located ignores the power loss of the SOP itself, and the power model of the flexible soft switch satisfies the following relationship:

[0072]

[0073] In the formula, are the active power and reactive power injected or outflowed at port n of the flexible soft switch, S SOP is the capacity of the flexible soft switch, and m is the number of ports of the flexible soft switch.

[0074] Based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, the power flow model of the flexible distribution network is established, which satisfies the following relationship:

[0075]

[0076] In the formula, are the active power and reactive power injected into node i by the traditional energy unit, are the active power and reactive power injected into node i by the new energy unit, are the active power and reactive power injected into or out of node i at port n of the flexible soft switch, are the active power and reactive power injected into or out of node i at port u of the multi-port energy router with integrated energy storage device, are the active power and reactive power of the load at node i, respectively, P ij , Q ij are the active power and reactive power of line ij respectively, r ij 、x ij are the resistance and reactance of line ij respectively, is a 0-1 variable indicating whether the line ij is opened or closed in the flexible distribution network operation scenario w. When line ij is closed, When line ij is disconnected, when w=0, the flexible distribution network is in normal operation, w=1,2,…,N c When the flexible distribution network is in different fault scenarios, N c is the number of fault scenarios, N L (i) is the set of nodes connected to node i through lines, is the square value of the current in line ij;

[0077] In the embodiment, the flexible distribution network operation scenarios include but are not limited to: normal operation scenario, N-1 fault scenario and N-2 fault scenario;

[0078] The power flow constraint condition of the flexible distribution network satisfies the following relationship:

[0079]

[0080] U i,min <U i <U i,max (7)

[0081] In the formula, is the current of line ij in the flexible distribution network operation scenario w, are the lower and upper limits of the current of line ij under the flexible distribution network operation scenario w, U i is the voltage at node i, U i,min , U i,max are the lower and upper limits of the voltage at node i respectively;

[0082] P GT,min ≤P GT ≤P GT,max (8)

[0083] P P2G,min ≤P P2G ≤P P2G,max (9)

[0084] Where P GT,min and P GT,max are the lower and upper limits of the gas turbine output power, respectively, P GT is the output power of the gas turbine, P P2G,min and P P2G,max are the lower and upper limits of the output power of the power-to-gas equipment, P P2G is the output power of the power-to-gas device.

[0085] In the embodiment, based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, the DistFlow power flow model suitable for the radial network is optimized, and a 0-1 variable representing the line opening or closing state under the operation state of the flexible distribution network is introduced, so that the power flow structure of the distribution network with flexible topology structure is more applicable; based on the optimized substation power flow model and power flow constraint conditions, not only the power flow is optimized by the power transfer capability of the flexible soft switch and the multi-port energy router outside the substation, and the voltage level is improved to enhance the new energy carrying capacity of the distribution network, but also the coupling of multiple energy sources such as electricity and gas is realized to realize the transfer between different energy sources, so as to coordinate and enhance the new energy carrying capacity of the distribution network.

[0086] The present invention introduces new energy carrying capacity improvement methods such as dynamic network reconstruction and flexible soft switch intelligent control into the flexible distribution network through the power model of the multi-port energy router integrated with the energy storage device and the power model of the flexible soft switch. The power flow model and power flow constraint conditions of the substation are established based on the power model of the multi-port energy router integrated with the energy storage device and the power model of the flexible soft switch, thereby achieving coordination and cooperation between new energy carrying capacity improvement methods such as dynamic network reconstruction and flexible soft switch intelligent control, and avoiding the problem of underestimation of new energy installed capacity planning caused by a single improvement method.

[0087] Step 2: Taking the maximum open capacity of new energy in each substation as the lower-level optimization target, using the lower-level optimization target, the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, establish a lower-level optimization model, solve the lower-level optimization model, and obtain the open capacity of new energy in each substation; based on the open capacity of new energy in each substation, taking the maximum capacity of new energy connected to the flexible distribution network as the upper-level optimization target, using the upper-level optimization target, the flow model and flow constraints of the flexible distribution network to establish an upper-level optimization model, solve the upper-level optimization model, and obtain the configuration plan of new energy in the flexible distribution network.

[0088] The new energy open capacity of each substation obtained by solving the lower-level optimization model is the maximum capacity of new energy that can be connected when the multi-port energy router and the flexible soft switch realize the energy synergy and mutual assistance of each node in the substation, realizing the improvement of the new energy carrying capacity in each substation under the coordination and cooperation between the new energy carrying capacity improvement methods. By solving the lower-level optimization model, the power allocation scheme of each port of the flexible soft switch in each substation and the power allocation scheme of each port of the multi-port energy router are obtained. Based on the power model of the multi-port energy router with integrated energy storage device, it can be seen that the power allocation scheme of each port of the multi-port energy router is actually determined. The capacity configuration scheme and access position of the energy storage device in the flexible distribution network, and further obtain the new energy access node scheme and capacity configuration scheme in each substation based on the above power allocation scheme.

[0089] Specifically, the lower-level optimization objective satisfies the following relationship:

[0090]

[0091] In the formula, is the power required by the load in the area, is the output power of the new energy unit μ, ρ is the number of loads in the area, and κ is the number of new energy units in the area;

[0092] Specifically, the upper-level optimization objective satisfies the following relationship:

[0093]

[0094] In the formula, is the open capacity of new energy at node i in the flexible distribution network, N pv It is the number of nodes connected to the renewable energy units in the flexible distribution network.

[0095] The maximum capacity of new energy connected to the flexible distribution network means that the flexible distribution network's carrying capacity for new energy is maximized. In addition, the nodes to which new energy units are connected are different, and the power flow of the flexible distribution network is also different. Therefore, when solving the upper-level optimization model, the new energy configuration plan obtained includes the access nodes and planned capacity of new energy, and the capacity adjustment plan of existing new energy. Based on the determined new energy configuration plan, not only the new energy carrying capacity of the flexible distribution network is improved, but also the coordinated layout of photovoltaic and storage is realized, which can serve as the basis for the coordinated layout of photovoltaic, storage and charging.

[0096] Step 3: Based on the configuration scheme of renewable energy in the flexible distribution network, when the voltage of each node in the flexible distribution network meets the safety constraint condition and the charging load in the flexible distribution network is the largest, determine the site location of the electric vehicle charging station.

[0097] On the basis of the configuration plan of new energy in the flexible distribution network, the synergy of photovoltaic storage and charging is considered, that is, the coordinated power distribution between electric vehicle charging load and photovoltaic storage is further realized; on the basis of the configuration plan of new energy in the flexible distribution network, the location of electric vehicle charging stations is optimized to further improve the photovoltaic carrying capacity of the distribution network.

[0098] Specifically, step 3 includes:

[0099] Step 3.1, obtaining the predicted value of charging load in the flexible distribution network to determine the number of charging stations to be built;

[0100] In the embodiment, the changing pattern of traffic density in different time periods and different areas is generated based on traffic flow data, and the charging habits of users in different time periods and different areas are identified by using the K-means clustering method, and the daily load curve of electric vehicles is generated to obtain the medium- and long-term load forecast results of the charging load, and the number N of charging stations to be built is calculated based on the charging load forecast results.

[0101] Step 3.2, the chance-constrained programming method is used to establish a model that characterizes the maximum charging load in the flexible distribution network, satisfying the following relationship:

[0102]

[0103] In the formula, Z is the maximum charging load demand in the flexible distribution network, I is the set of charging points generated by electric vehicles, Q is the set of candidate sites for charging stations, and h is c is the charging load of charging point c, N is the number of charging stations to be built, Z c is the state variable of the charging point. If the electric vehicle generating the charging point c can use the remaining power to reach the candidate site q of the charging station, it means that the candidate site q of the charging station can cover the charging point c, then Z c =1, otherwise Z c =0,X qis the state variable of the candidate site q of the charging station. If the candidate site q of the charging station is used to build a charging station, then X q =1, otherwise X q =0.

[0104] Step 3.3, a probability distribution method is used to establish a model that characterizes the voltage of each node in the flexible distribution network to meet the safety constraints, satisfying the following relationship:

[0105] Pr{U i,min ≤U i ≤U i,max}≥β V (16)

[0106] In the formula, Pr{} is the probability of the event being true, U i,min , U i,max are the lower and upper limits of the voltage at node i, U i is the voltage at node i, β V is the confidence level value of the node voltage safety constraint.

[0107] The safety constraints of the voltage at each node in the flexible distribution network are converted into linear constraints to satisfy the following relationship:

[0108] U i,min -|U i,s |≤K·Z vmin,s (17)

[0109] |U i,s |-U i,max ≤K·Z vmax,s (18)

[0110]

[0111] Z vmin,s ,Z vmax,s ∈{0,1},s=1,2,…,N s (twenty one)

[0112] Where N s is the number of samples, U i,s is the sth sample of the voltage at node i, Z vmin,s is a state variable that indicates whether the sth sampling sample of the node voltage exceeds the lower limit. vmin,s = 0, the node voltage sth sampling sample does not exceed the lower limit, when Z vmin,s = 1, the node voltage sth sampling sample exceeds the lower limit, Z vmax,s is a state variable that indicates whether the sth sampling sample of the node voltage exceeds the upper limit. vmax,s = 0, the node voltage sth sampling sample does not exceed the upper limit, when Zvmax,s = 1, the node voltage sth sampling sample exceeds the upper limit, K is positive and makes Z vmin,s , Z vmax,s When is 1, equations (19) and (20) must hold.

[0113] In step 3.4, the model representing the maximum charging load in the flexible distribution network and the linear constraint conditions are jointly iteratively solved to obtain the optimal site of the charging station as the site location of the electric vehicle charging station.

[0114] In the embodiment, the low-voltage area test example adopts an improved IEEE66 node system, and sets 4 different schemes to compare and analyze the distributed photovoltaic carrying capacity evaluation method of the distribution network: Scheme 1 is the method proposed by the present invention; Scheme 2 does not consider the multi-energy transfer within a single area, but only considers the coordinated transfer of multiple areas; Scheme 3 does not consider the coordinated transfer of multiple areas, but only considers the multi-energy transfer within a single area; Scheme 4 does not consider the coordinated transfer of multiple areas and the multi-energy transfer within a single area. The distributed photovoltaic carrying capacity of the distribution network obtained by simulating the above 4 different schemes is shown in Table 1.

[0115] Table 1 Comparative analysis of different distribution network carrying capacity improvement methods

[0116]

[0117] The simulation results show that the method proposed in the present invention comprehensively considers the power transfer in multiple areas and the multi-energy transfer in a single area, and improves the power grid flow distribution in real time by optimizing the power flow path, so that the output of the photovoltaic power generation system is more evenly distributed in the entire power grid. The proposed method can improve the carrying capacity of the distribution network by more than 15.18%.

[0118] The present invention also proposes a system for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging coordination. The flexible distribution network includes multiple substations, each of which includes a flexible soft switch and a multi-port energy router; including:

[0119] A model building module is used to integrate the energy storage device in the flexible distribution network into the multi-port energy router and establish a power model of the multi-port energy router with integrated energy storage device; establish a power model of the flexible soft switch; based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, establish a power flow model and power flow constraint conditions of the flexible distribution network;

[0120] The photovoltaic storage coordination module is used to take the maximum open capacity of new energy in each substation as the lower-level optimization target, and use the lower-level optimization target, the power model of the multi-port energy router with integrated energy storage device, and the power model of the flexible soft switch to establish a lower-level optimization model, solve the lower-level optimization model, and obtain the open capacity of new energy in each substation; based on the open capacity of new energy in each substation, take the maximum capacity of new energy connected to the flexible distribution network as the upper-level optimization target, and use the upper-level optimization target, the power flow model of the flexible distribution network, and the power flow constraint conditions to establish an upper-level optimization model, solve the upper-level optimization model, and obtain the configuration plan of new energy in the flexible distribution network;

[0121] The photovoltaic storage and charging coordination module is used to determine the site location of the electric vehicle charging station based on the configuration plan of new energy in the flexible distribution network when the voltage of each node in the flexible distribution network meets the safety constraints and the charging load in the flexible distribution network is the largest.

[0122] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0123] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0124] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0125] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging synergy, wherein the flexible distribution network includes multiple substations, each of which includes a flexible soft switch and a multi-port energy router; characterized in that: include: Integrate the energy storage device in the flexible distribution network into the multi-port energy router, and establish the power model of the multi-port energy router with integrated energy storage device; Establish a power model for flexible soft switching; Based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, the power flow model and power flow constraint conditions of the flexible distribution network are established; Taking the maximum open capacity of new energy in each substation as the lower-level optimization target, using the lower-level optimization target, the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, a lower-level optimization model is established, and the lower-level optimization model is solved to obtain the open capacity of new energy in each substation; based on the open capacity of new energy in each substation, taking the maximum capacity of new energy connected to the flexible distribution network as the upper-level optimization target, the upper-level optimization model is established using the upper-level optimization target, the power flow model of the flexible distribution network and the power flow constraint conditions, and the upper-level optimization model is solved to obtain the configuration plan of new energy in the flexible distribution network; Based on the configuration scheme of renewable energy in the flexible distribution network, the site location of the electric vehicle charging station is determined when the voltage of each node in the flexible distribution network meets the safety constraints and the charging load in the flexible distribution network is the largest.

2. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 1 is characterized in that: The power model of a multi-port energy router with integrated energy storage device satisfies the following relationship: Where P u ER and are the active power and reactive power injected or outflowed from port u of the energy router, k is the number of ports of the energy router, P es,ch , P es,dis are the charging power and discharging power of the energy storage device integrated in the energy router, and are the lower and upper limits of reactive power at port u of the energy router, P ch,max and P dis,max are the upper limit of charging efficiency and discharge efficiency of the energy storage device integrated in the energy router, S ER is the total capacity of the energy router, α ess is a 0-1 variable representing the charging and discharging state of the energy storage device integrated in the energy router, α ess =1 when the energy storage device is charged, α ess =0, the energy storage device discharges.

3. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging coordination according to claim 1 is characterized in that: The power model of the flexible soft switch satisfies the following relationship: Where P n SOP , Q n SOP are the active power and reactive power injected or outflowed at port n of the flexible soft switch, S SOP is the capacity of the flexible soft switch, and m is the number of ports of the flexible soft switch.

4. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 1 is characterized in that: The power flow model of the flexible distribution network satisfies the following relationship: Where P i g , are respectively the active power and reactive power injected into node i by the traditional energy unit, P i pv , are the active power and reactive power injected into node i by the new energy unit, are the active power and reactive power injected into or out of node i at port n of the flexible soft switch, are the active power and reactive power injected into or out of node i by port u of the multi-port energy router with integrated energy storage device, P i d , are the active power and reactive power of the load at node i, respectively, P ij , Q ij are the active power and reactive power of line ij respectively, r ij 、x ij are the resistance and reactance of line ij respectively, is a 0-1 variable indicating whether the line ij is opened or closed in the flexible distribution network operation scenario w. When line ij is closed, When line ij is disconnected, when w=0, the flexible distribution network is in normal operation, w=1,2,…,N c When the flexible distribution network is in different fault scenarios, N c is the number of fault scenarios, N L (i) is the set of nodes connected to node i through lines, is the square value of the current in line ij; Flexible distribution network operation scenarios include: normal operation scenario, N-1 fault scenario and N-2 fault scenario.

5. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 4 is characterized in that: The power flow constraint condition of the flexible distribution network satisfies the following relationship: IN i,min <In i <In i,max (7) In the formula, is the current of line ij in the flexible distribution network operation scenario w, are the lower and upper limits of the current of line ij under the flexible distribution network operation scenario w, U i is the voltage at node i, U i,min , U i,max are the lower and upper limits of the voltage at node i respectively; P GT,min ≤P GT ≤P GT,max (8) P P2G,min ≤P P2G ≤P P2G,max (9) Where P GT,min and P GT,max are the lower and upper limits of the gas turbine output power, respectively, P GT is the output power of the gas turbine, P P2G,min and P P2G,max are the lower and upper limits of the output power of the power-to-gas equipment, P P2G is the output power of the power-to-gas device.

6. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 1 is characterized in that: The lower-level optimization objective satisfies the following relationship: In the formula, is the power required by the load in the area, is the output power of the new energy unit μ, ρ is the number of loads in the area, and κ is the number of new energy units in the area.

7. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 1 is characterized in that: The upper optimization objective satisfies the following relationship: In the formula, is the open capacity of new energy at node i in the flexible distribution network, N pv It is the number of nodes connected to the renewable energy units in the flexible distribution network.

8. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 1 is characterized in that: By solving the lower optimization model, the power allocation scheme of each port of the flexible soft switch in each substation and the power allocation scheme of each port of the multi-port energy router are also obtained; Based on the power model of the multi-port energy router with integrated energy storage device, the capacity configuration scheme and access location of the energy storage device in the flexible distribution network are determined according to the power allocation scheme of each port of the multi-port energy router.

9. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 8 is characterized in that: By solving the upper-level optimization model, the configuration plan of new energy sources is obtained, including the access nodes and planned capacity of new energy sources, and the capacity adjustment plan of existing new energy sources.

10. The method for improving the new energy carrying capacity of a flexible distribution network with photovoltaic, storage and charging coordination according to claim 1 is characterized in that: Based on the configuration scheme of new energy in the flexible distribution network, when the voltage of each node in the flexible distribution network meets the safety constraint conditions and the charging load in the flexible distribution network is the largest, the site location of the electric vehicle charging station is determined, including: Obtain the predicted value of charging load in the flexible distribution network to determine the number of charging stations to be built; The chance-constrained programming method is used to establish a model that characterizes the maximum charging load in the flexible distribution network, satisfying the following relationship: In the formula, Z is the maximum charging load demand in the flexible distribution network, I is the set of charging points generated by electric vehicles, Q is the set of candidate sites for charging stations, and h is c is the charging load of charging point c, N is the number of charging stations to be built, Z c is the state variable of the charging point. If the electric vehicle generating the charging point c can use the remaining power to reach the candidate site q of the charging station, it means that the candidate site q of the charging station can cover the charging point c, then Z c =1, otherwise Z c =0,X q is the state variable of the candidate site q of the charging station. If the candidate site q of the charging station is used to build a charging station, then X q =1, otherwise X q =0; The probability distribution method is used to establish a model to characterize the voltage of each node in the flexible distribution network to meet the safety constraints, which satisfies the following relationship: Pr{U i,min ≤U i ≤U i,max }≥β V (16) In the formula, Pr{} is the probability of the event being true, U i,min , U i,max are the lower and upper limits of the voltage at node i, U i is the voltage at node i, β V is the confidence level value of the node voltage safety constraint; The safety constraints of the voltage at each node in the flexible distribution network are converted into linear constraints to satisfy the following relationship: U i,min -|U i,s |≤K·Z vmin,s (17) |U i,s |-U i,max ≤K·Z vmax,s (18) Z vmin,s ,Z vmax,s ∈{0,1},s=1,2,…,N s (21) Where N s is the number of samples, U i,s is the sth sample of the voltage at node i, Z vmin,s is a state variable that indicates whether the sth sampling sample of the node voltage exceeds the lower limit. vmin,s = 0, the node voltage sth sampling sample does not exceed the lower limit, when Z vmin,s = 1, the node voltage sth sampling sample exceeds the lower limit, Z vmax,s is a state variable that indicates whether the sth sampling sample of the node voltage exceeds the upper limit. vmax,s = 0, the node voltage sth sampling sample does not exceed the upper limit, when Z vmax,s = 1, the node voltage sth sampling sample exceeds the upper limit, K is positive and makes Z vmin,s , Z vmax,s When is 1, equations (19) and (20) hold true; The model representing the maximum charging load in the flexible distribution network and the linear constraints are jointly iteratively solved to obtain the optimal site of the charging station as the site location of the electric vehicle charging station.

11. A system for improving the new energy carrying capacity of a flexible distribution network with photovoltaic storage and charging synergy, wherein the flexible distribution network includes multiple substations, each of which includes a flexible soft switch and a multi-port energy router; characterized in that: include: A model building module is used to integrate the energy storage device in the flexible distribution network into the multi-port energy router and establish a power model of the multi-port energy router with integrated energy storage device; establish a power model of the flexible soft switch; based on the power model of the multi-port energy router with integrated energy storage device and the power model of the flexible soft switch, establish a power flow model and power flow constraint conditions of the flexible distribution network; The photovoltaic storage coordination module is used to take the maximum open capacity of new energy in each substation as the lower-level optimization target, and use the lower-level optimization target, the power model of the multi-port energy router with integrated energy storage device, and the power model of the flexible soft switch to establish a lower-level optimization model, solve the lower-level optimization model, and obtain the open capacity of new energy in each substation; based on the open capacity of new energy in each substation, take the maximum capacity of new energy connected to the flexible distribution network as the upper-level optimization target, and use the upper-level optimization target, the power flow model of the flexible distribution network, and the power flow constraint conditions to establish an upper-level optimization model, solve the upper-level optimization model, and obtain the configuration plan of new energy in the flexible distribution network; The photovoltaic storage and charging coordination module is used to determine the site location of the electric vehicle charging station based on the configuration plan of new energy in the flexible distribution network when the voltage of each node in the flexible distribution network meets the safety constraints and the charging load in the flexible distribution network is the largest.

12. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 10.

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

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