Multi-time scale optimization method and device for multi-distributed resource virtual power plant

Abstract

The invention relates to a multi-time scale optimization method and device for a multi-distributed resource virtual power plant, and the method comprises the steps: building a virtual energy storage model of an electric vehicle; based on the virtual energy storage model of the electric vehicle, constructing a day-ahead optimization model by taking the lowest electricity purchase cost as a target, and solving the day-ahead optimization model to obtain optimized charging and discharging scheduling of the electric vehicle and energy storage equipment and a switching plan of a reactive power compensation device; the method comprises the following steps: constructing an intra-day optimization model by taking the minimum wind and light curtailment as a target, taking charge and discharge scheduling of an electric vehicle and energy storage equipment and a switching plan of a reactive power compensation device as input of the intra-day optimization model, and solving the intra-day optimization model to obtain an intra-day reducible load and an optimization result of the energy storage equipment. According to the method, the problems of multi-energy integration and multi-time scale optimization in the virtual power plant can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant optimization, and in particular to a multi-time scale optimization method and device for a multi-distributed resource virtual power plant. Background Art

[0002] The traditional centralized electricity production model can no longer meet the growing energy demand and environmental protection requirements. Driven by the rapid development of the energy industry and intelligent technology, the emergence of virtual power plants has solved the problems of fragmentation and volatility of distributed energy resources. As a new energy management model that integrates and utilizes multiple distributed resources such as electric vehicles, renewable energy and energy storage equipment, virtual power plants have the advantages of high flexibility and low operating costs. By coordinating and optimizing the relationship between various energy components, virtual power plants effectively improve the energy utilization efficiency of the power system, which is of great significance to promoting the large-scale access of renewable energy and realizing the intelligence of the power system.

[0003] However, existing research has the following problems: 1) The types of distributed resources in virtual power plants studied are not rich enough, and their modeling is still not perfect; 2) The optimization scenarios of virtual power plants under multiple time scales are not fully considered, and the output uncertainty problem of renewable energy cannot be effectively solved; 3) The calculation speed, calculation scale and solution efficiency of the solution algorithm used are limited, and it is difficult to meet the computational requirements of the interaction between large-scale virtual power plants and distribution networks in power flow calculations. Summary of the invention

[0004] The present invention provides a multi-time scale optimization method and device for a multi-distributed resource virtual power plant, aiming to solve the multi-energy integration and multi-time scale optimization problems in the virtual power plant.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide a multi-time scale optimization method for a multi-distributed resource virtual power plant, comprising the following steps:

[0006] Establish a virtual energy storage model for electric vehicles;

[0007] Based on the virtual energy storage model of the electric vehicle, a day-ahead optimization model is constructed with the goal of minimizing the electricity purchase cost, and the day-ahead optimization model is solved to obtain an optimized charging and discharging schedule of the electric vehicle and the energy storage device and a switching plan of the reactive compensation device;

[0008] An intraday optimization model is constructed with the goal of minimizing wind and solar power abandonment. The charging and discharging scheduling of electric vehicles and energy storage equipment and the switching plan of reactive compensation devices are used as inputs of the intraday optimization model. The intraday optimization model is solved to obtain the optimization results of the load and energy storage equipment that can be reduced during the day.

[0009] The establishment of a virtual energy storage model for electric vehicles specifically includes:

[0010] Calculate the charging power feasible region and power feasible region of each electric vehicle based on the initial power of each electric vehicle;

[0011] According to the charging power feasible domain and the power feasible domain of each electric vehicle, the charging power and power of multiple electric vehicles are aggregated into a virtual energy storage model using a relaxed Minkowski sum.

[0012] The objective function f1 of the day-ahead optimization model is expressed as: Among them, λ buy is the electricity purchase price, P G,t For electricity purchase.

[0013] The constraints of the day-ahead optimization model include:

[0014] The virtual energy storage model constraints are expressed as: Among them, P t VB and Respectively represent the power and electricity of the virtual energy storage model at time t, P t VBmin and P t VBmax are the power lower limit and power upper limit of the virtual energy storage model at time t, and are the lower and upper limits of the power of the virtual energy storage model at time t, Δt represents the time interval, represents the initial power of the kth electric vehicle when it starts charging, represents the initial power of the i-th electric car when it starts running, represents the starting time of charging for the kth electric vehicle, It is represented as the starting time of the kth electric car;

[0015] Energy storage device constraints are expressed as: Among them, P t ch and P t dis are the charging power and discharging power of the energy storage device, P ch,max and P dis,max are the maximum charging power and the maximum discharging power of the energy storage device respectively; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage device respectively; is the energy storage capacity of the energy storage device at time t; and They are the lower limit and upper limit of the energy storage capacity of the energy storage device at time t respectively;

[0016] The reactive power compensation device constraint is expressed as: in, is the reactive power output of the static reactive power compensation device at time t, Q SVC,min and Q SVC,max is the lower and upper limits of the reactive power output of the static VAR compensation device, is the reactive power output of the switched capacitor at time t, is the number of switched capacitors put into operation at time t, is the reactive output of a single group of switched capacitors at time t; V min and V max They are the lower voltage limit and the upper voltage limit respectively; is the square of the on-load tap-changing transformer ratio at time t, is the square value of the basic transformation ratio of the on-load tap-changing transformer at time t; is the square difference of the transformation ratio of adjacent taps s of the on-load tap-changing transformer, Indicates the status of the on-load tap-changing transformer;

[0017] The grid operation constraints are expressed as: Among them, U i,t and U j,t are the voltages of node i and node j at time t; P ij,t and Q ij,t are the active power and reactive power at the head end of branch ij at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t is the current flowing through branch ij at time t; p j,t and q j,t are the active load and reactive load of node j at time t respectively; u(j) is the set of child nodes of node j; and are the wind power output and photovoltaic output of node j at time t, is the active output of the virtual energy storage model at node j at time t; is the reactive power output of the static VAR compensation device at node j at time t, is the reactive power output of the virtual energy storage model at node j at time t, U i,min and U i,max are the voltage lower limit and voltage upper limit of node i, S ij,max is the maximum transmission capacity of branch ij.

[0018] The objective function f2 of the intraday optimization model is expressed as: in, is the actual wind power output of node i at time t, is the day-ahead wind power output plan for node i at time t, is the actual photovoltaic output of node j at time t, is the day-ahead PV output plan for node j at time t.

[0019] The constraints of the intraday optimization model include:

[0020] The load constraint can be reduced, expressed as: Among them, P t cut is the acceptable power reduction value at time t; P t cut,max is the maximum acceptable power reduction value at time t; is the total load power that can be reduced within time T;

[0021] Energy storage device constraints are expressed as: Among them, P t ch and P t dis are the charging power and discharging power of the energy storage device, P ch,max and P dis,max are the maximum charging power and the maximum discharging power of the energy storage device respectively; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage device respectively; is the energy storage capacity of the energy storage device at time t; and They are the lower limit and upper limit of the energy storage capacity of the energy storage device at time t respectively;

[0022] The grid operation constraints are expressed as: Among them, U i,t and U j,t are the voltages of node i and node j at time t; P ij,t and Q ij,t are the active power and reactive power at the head end of branch ij at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t is the current flowing through branch ij at time t; p j,t and q j,t are the active load and reactive load of node j at time t respectively; u(j) is the set of child nodes of node j; and are the wind power output and photovoltaic output of node j at time t, is the active output of the virtual energy storage model at node j at time t; is the acceptable power reduction value of node j at time t; is the reactive power output of the static VAR compensation device at node j at time t, is the reactive power output of the virtual energy storage model at node j at time t, U i,min and U i,max are the voltage lower limit and voltage upper limit of node i, S ij,max is the maximum transmission capacity of branch ij.

[0023] The solving of the day-ahead optimization model and the intraday optimization model specifically includes:

[0024] The day-ahead optimization model and the intraday optimization model are converted into linear equations that can be solved quickly using a second-order cone relaxation technique;

[0025] A commercial solver is used to solve the converted day-ahead optimization model and the intraday optimization model.

[0026] The second-order cone relaxation technique is used to convert the day-ahead optimization model and the intraday optimization model into linear equations that can be quickly solved, specifically:

[0027] The square of the voltage at node i at time t is defined as u i,t , the square of the voltage at node j at time t is defined as u j,t , define the square of the current flowing through branch ij at time t as i ij,t ;

[0028] will u i,t 、u j,t and i ij,t The power grid operation constraints in the day-ahead optimization model and the intra-day optimization model are introduced to convert the day-ahead optimization model and the intra-day optimization model into convex optimization problems.

[0029] The technical solution adopted by the present invention to solve the technical problem is: to provide a multi-time scale optimization device for a multi-distributed resource virtual power plant, comprising:

[0030] Establishing a module for building a virtual energy storage model for electric vehicles;

[0031] A day-ahead optimization module is used to construct a day-ahead optimization model based on the virtual energy storage model of the electric vehicle with the goal of minimizing the electricity purchase cost, and solve the day-ahead optimization model to obtain an optimized charging and discharging schedule of the electric vehicle and the energy storage device and a switching plan of the reactive compensation device;

[0032] The intraday optimization module is used to construct an intraday optimization model with the goal of minimizing wind and solar power abandonment, take the charging and discharging scheduling of electric vehicles and energy storage equipment and the switching plan of reactive compensation devices as inputs of the intraday optimization model, and solve the intraday optimization model to obtain the optimization results of the load and energy storage equipment that can be reduced during the day.

[0033] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the multi-time scale optimization method of the multi-distributed resource virtual power plant when executing the computer program.

[0034] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the multi-time scale optimization method of the multi-distributed resource virtual power plant are implemented.

[0035] Beneficial Effects

[0036] Due to the adoption of the above-mentioned technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention optimizes the energy storage equipment, reactive compensation device and electric vehicle in the day ahead, balances the output of renewable energy to minimize the cost of purchasing electricity, and optimizes the reducible load and supercapacitor energy storage equipment in the day taking into account the uncertainty of renewable energy, thereby solving the multi-energy integration and multi-time scale optimization problems in the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of a multi-time scale optimization method of a multi-distributed resource virtual power plant according to a first embodiment of the present invention;

[0038] Figure 2 is an optimized power diagram of an electric vehicle in an embodiment of the present invention;

[0039] Figure 3 is a system indicator result diagram in an embodiment of the present invention;

[0040] Figure 4 It is a diagram of power optimization results in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only intended to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0042] The first embodiment of the present invention relates to a multi-time scale optimization method for a multi-distributed resource virtual power plant, the virtual power plant to which the method is applicable includes electric vehicles, curtailable loads, energy storage devices, and renewable energy, wherein electric vehicles, as dispatchable loads, can participate in the energy dispatch and balance of the system. At the same time, curtailable loads can reduce power consumption when needed, effectively reduce load peaks, and improve system stability. In addition, energy storage devices play an important role in energy storage and dispatching, which can balance the supply and demand relationship of the power grid and cope with energy fluctuations.

[0043] like Figure 1 As shown, the multi-time scale optimization method of the multi-distributed resource virtual power plant of this embodiment includes the following steps:

[0044] Step 1: Establish a virtual energy storage model for electric vehicles.

[0045] In smart grids, with the large-scale access of electric vehicles (EVs), solving each EV individually will increase the computational load of the system. In order to simplify decision variables and effectively manage the charging behavior of EVs, a virtual energy storage model can be used to aggregate and uniformly schedule EVs. This implementation method establishes a virtual energy storage model for electric vehicles on this basis. When establishing the model, the feasible domain of charging power and the feasible domain of power of each electric vehicle are first calculated based on the initial power of each electric vehicle. The feasible domain of charging power of each electric vehicle can be calculated by judging whether the initial power of the EV reaches the power threshold. The calculation method is as follows:

[0046]

[0047] in, and They represent the upper and lower limits of the power of the kth EV at time t respectively; and are the upper and lower limits of the charging power of the kth EV, respectively, where is a negative value; and are the maximum and minimum battery power of the kth EV, is the initial power of the kth EV when it starts charging, is the power threshold of the kth EV, is the starting time of the kth EV’s driving, The starting time for charging the kth EV.

[0048] The feasible domain calculation formula of EV power is:

[0049]

[0050] in, and are the upper and lower limits of the EV’s charging power at time t, and Δt represents the time interval.

[0051] The amount of electricity of EV cannot change suddenly within the feasible domain, but the power can change suddenly. The size of the feasible domain reflects the dispatchability of EV. Based on the relaxed Minkowski sum, the energy and power of multiple EVs are aggregated into a virtual energy storage model. The flexibility of the virtual energy storage model is obtained by summing the upper and lower bounds of each EV. The calculation process is as follows:

[0052]

[0053] in, represents the set of EVs that can participate in the dispatch at time t. The above equation is a simplified representation of the relaxed Minkowski sum, and the virtual energy storage (VB) model is obtained based on the high-dimensional summation of the equation.

[0054] After the VB model is aggregated, the electric vehicles can be converted as a whole to participate in the calculation without considering the charging and discharging conditions of a single EV.

[0055] Step 2: construct a day-ahead optimization model based on the virtual energy storage model of the electric vehicle with the goal of minimizing the electricity purchase cost.

[0056] This implementation method optimizes the charging and discharging scheduling of electric vehicles and lithium battery energy storage on the day before, plans the switching state of reactive power compensation devices, and balances the output of renewable energy. Therefore, the objective function of the constructed day-ahead optimization model is to minimize the power purchase cost, which is expressed as:

[0057]

[0058] Among them, λ buy is the electricity purchase price, P G,t For electricity purchase.

[0059] The constraints of the constructed day-ahead optimization model include virtual energy storage model constraints, energy storage equipment constraints, reactive power compensation device constraints and grid operation constraints.

[0060] In addition to satisfying general energy storage charging and discharging constraints and upper and lower limit constraints, the virtual energy storage model also needs to consider the added and reduced EV energy in the same period. Therefore, the virtual energy storage model constraints can be expressed as:

[0061] P t VBmin ≤P t VB ≤P t VBmax

[0062]

[0063] Among them, P t VB and Respectively represent the power and electricity of the virtual energy storage model at time t, P t VBmin and P t VBmax are the power lower limit and power upper limit of the virtual energy storage model at time t, and are the lower and upper limits of the power of the virtual energy storage model at time t, represents the initial power of the kth electric vehicle when it starts charging, represents the initial power of the i-th electric car when it starts running, represents the starting time of charging of the kth EV, It is represented as the starting time of the kth EV’s driving;

[0064] In each iteration, the power and power feasible domain of each EV are fixed, but because the EVs involved in the dispatch are different, the virtual energy storage model needs to be recalculated in each iteration.

[0065] The energy storage device constraints can be expressed as:

[0066]

[0067] Among them, P t ch and P t dis are the charging power and discharging power of the energy storage device, P ch,max and P dis,max are the maximum charging power and the maximum discharging power of the energy storage device respectively; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage device respectively; is the energy storage capacity of the energy storage device at time t; and They are the lower limit and upper limit of the energy storage capacity of the energy storage equipment at time t respectively.

[0068] Discrete reactive power compensation devices include on-load tap changer (OLTC), capacitor banks (CB), and static var compensation (SVC). These devices play an important role in the power system and are used to regulate voltage, improve power factor, and stabilize system operation. In order to ensure the safe and stable operation of the power grid, the constraints of the reactive power compensation device can be expressed as:

[0069]

[0070] in, is the reactive power output of the static reactive power compensation device at time t, Q SVC,min and Q SVC,max is the lower and upper limits of the reactive power output of the static VAR compensation device, is the reactive power output of the switched capacitor at time t, is the number of switched capacitors put into operation at time t, is the reactive output of a single group of switched capacitors at time t; V min and V max They are the lower voltage limit and the upper voltage limit respectively; is the square of the on-load tap-changing transformer ratio at time t, is the square value of the basic transformation ratio of the on-load tap-changing transformer at time t; is the square difference of the transformation ratio of adjacent taps s of the on-load tap-changing transformer, Indicates the status of the on-load tap-changing transformer, represented by 0 and 1.

[0071] At the same time, the system also needs to meet the grid operation constraints. In this implementation, the grid operation constraints include power flow constraints, node voltage constraints and line transmission capacity constraints.

[0072] Taking the branch ij in the distribution network as an example, the power flow constraint of the branch ij can be established according to its operating status at time t as follows:

[0073]

[0074] Among them, U i,t and U j,t are the voltages of node i and node j at time t; P ij,t and Q ij,t are the active power and reactive power at the head end of branch ij at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t is the current flowing through branch ij at time t; p j,t and q j,t are the active load and reactive load of node j at time t respectively; u(j) is the set of child nodes of node j; and are the wind power output and photovoltaic output of node j at time t, is the active output of the virtual energy storage model at node j at time t; is the reactive power output of the static VAR compensation device at node j at time t, is the reactive output of the virtual energy storage model at node j at time t.

[0075] The node voltage constraint can be expressed as:

[0076] U i,min ≤U i,t ≤U i,max

[0077] Among them, U i,min and U i,max are the voltage lower limit and voltage upper limit of node i respectively.

[0078] The line transmission capacity constraint can be expressed as:

[0079]

[0080] Among them, S ij,max is the maximum transmission capacity of branch ij.

[0081] Step 3, solving the day-ahead optimization model to obtain optimized charging and discharging scheduling of electric vehicles and energy storage devices and switching plans of reactive power compensation devices.

[0082] When solving the day-ahead optimization model, this embodiment takes into account that the power flow constraint model contains quadratic forms and integer terms, constituting a mixed integer nonlinear programming problem. Therefore, it is difficult to obtain a global optimal solution using traditional optimization methods. Therefore, this embodiment uses the second-order cone relaxation technology (SOCR) to convert the original problem into a linear equation that can be solved quickly, and solves it based on the Gurobi solver, specifically:

[0083] First introduce the following variables:

[0084]

[0085] In the formula, u i,t and i ij,t are the square of node voltage and branch current respectively. I represents the set of all nodes, and L represents the set of all branches.

[0086] The variables of the original problem are replaced, that is, the above variables are introduced into the power flow constraints of the day-ahead optimization model, and the power flow constraints are converted into a convex optimization problem, as shown below:

[0087]

[0088] The Gurobi solver is then used to solve the converted day-ahead optimization model to obtain the optimized charging and discharging scheduling of electric vehicles and energy storage equipment and the switching plan of reactive compensation devices.

[0089] Step 4: Build an intraday optimization model with the goal of minimizing wind and solar power curtailment.

[0090] Considering the uncertainty of wind power and photovoltaic power generation output, it is necessary to optimize the load that can be reduced and the energy storage equipment within the day to cope with the challenges brought by energy fluctuations. The intraday optimization is based on the day before, with the minimum wind and solar abandonment as the objective function, which is expressed as:

[0091]

[0092] in, is the actual wind power output of node i at time t, is the day-ahead wind power output plan for node i at time t, is the actual photovoltaic output of node j at time t, is the day-ahead PV output plan for node j at time t.

[0093] The constraints of the intraday optimization model in this embodiment include load constraints that can be reduced, energy storage equipment constraints, and grid operation constraints.

[0094] In the power system, the curtailable load usually refers to some loads with high flexibility, such as air conditioning, heating, lighting, etc. Their loads can be adjusted according to the needs of the system. Therefore, the curtailable load constraint can be expressed as:

[0095] 0≤P t cut ≤P t cut,max

[0096]

[0097] Among them, P t cut is the acceptable power reduction value at time t; P t cut,max is the maximum acceptable power reduction value at time t; is the total load power that can be reduced within time T.

[0098] The energy storage equipment constraints of the intraday optimization model are the same as those of the day-ahead optimization model and will not be described in detail here.

[0099] The intraday optimization model must also meet the grid operation constraints. The node voltage constraints and line transmission capacity constraints of the intraday optimization model are the same as those of the day-ahead optimization model. The two optimization models differ only in the power flow constraints. The power flow constraints of the intraday optimization model are expressed as:

[0100]

[0101] Step 5, using the charging and discharging scheduling of electric vehicles and energy storage equipment and the switching plan of reactive compensation devices obtained by the day-ahead optimization model as inputs of the intraday optimization model, solving the intraday optimization model, and obtaining the optimization results of the load and energy storage equipment that can be reduced within the day.

[0102] In this step, SOCR is also used to convert the original problem into a linear equation that can be solved quickly when solving the intraday optimization model, and the solution is based on the Gurobi solver. As with the solution of the day-ahead optimization model, the following variables are first introduced:

[0103]

[0104] Substitute the above variables into the power flow constraints of the intraday optimization model and transform the power flow constraints into a convex optimization problem as follows:

[0105]

[0106] After completing the above conversion, the Gurobi solver can be used to obtain the optimization results of the load that can be reduced and the energy storage equipment that can be used within the day.

[0107] In order to verify the actual effect of this implementation method, a simulation example of the IEEE 33-node system was built for analysis. Among them, the SVC input nodes are 6, 15, and 31, and the CB input nodes are 6 and 17. At the same time, the wind farm is distributed at nodes 17 and 32, the photovoltaic power station is configured at node 25, and nodes 16 and 29 are configured with EV charging stations, with 400 and 300 EVs participating in the dispatch respectively. The load that can be reduced is distributed at nodes 7, 9, and 28, and the lithium battery-supercapacitor energy storage device is configured at node 20.

[0108] The current electric vehicle optimization results are as follows Figure 2 As shown. Figure 2 It can be seen that the capacity and power upper and lower limits of VB are mainly determined by the number of EVs participating in the dispatch at the same time. As the number of EVs being charged continues to increase, the dispatch flexibility of VB continues to increase. The charging time of the electric vehicle cluster is mainly distributed between 07:00 and 24:00, while the actual power peak is mainly at 08:00 and 22:00. The electric vehicles were partially discharged around 20:00 to compensate for the impact of the peak power consumption in the evening.

[0109] According to the method of this embodiment, the optimization results of various system indicators within the day are obtained as follows: Figure 3 As shown in Figure 2. During the period from 00:00 to 04:00 when electric vehicles were not involved in the dispatch, the wind abandonment volume and average line loss were both large. The operating power optimization results of each device in the virtual power plant within the day are shown in Figure 2. Figure 4As shown in the figure, the energy storage equipment is charged in the early morning, and the peak electricity consumption in the evening from 17:00 to 22:00 corresponds to the consumption of excess wind power. By optimizing the power of each distributed resource in the virtual power plant, the cost of electricity is reduced by 15.13% compared to no scheduling.

[0110] A second embodiment of the present invention relates to a multi-time scale optimization device for a multi-distributed resource virtual power plant, comprising:

[0111] Establishing a module for building a virtual energy storage model for electric vehicles;

[0112] A day-ahead optimization module is used to construct a day-ahead optimization model based on the virtual energy storage model of the electric vehicle with the goal of minimizing the electricity purchase cost, and solve the day-ahead optimization model to obtain an optimized charging and discharging schedule of the electric vehicle and the energy storage device and a switching plan of the reactive compensation device;

[0113] The intraday optimization module is used to construct an intraday optimization model with the goal of minimizing wind and solar power abandonment, take the charging and discharging scheduling of electric vehicles and energy storage equipment and the switching plan of reactive compensation devices as inputs of the intraday optimization model, and solve the intraday optimization model to obtain the optimization results of the load and energy storage equipment that can be reduced during the day.

[0114] The establishment module includes:

[0115] A calculation unit, used for calculating a charging power feasible region and a power feasible region of each electric vehicle based on an initial power of each electric vehicle;

[0116] The aggregation unit is used to aggregate the charging power and power of multiple electric vehicles into a virtual energy storage model by using a relaxed Minkowski sum according to the charging power feasible domain and the power feasible domain of each electric vehicle.

[0117] The objective function f1 of the day-ahead optimization model constructed by the day-ahead optimization module is expressed as: Among them, λ buy is the electricity purchase price, P G,t For electricity purchase.

[0118] The constraints of the day-ahead optimization model constructed by the day-ahead optimization module include:

[0119] The virtual energy storage model constraints are expressed as: Among them, P t VB and Respectively represent the power and electricity of the virtual energy storage model at time t, P t VBmin and P t VBmax are the power lower limit and power upper limit of the virtual energy storage model at time t, and are the lower and upper limits of the power of the virtual energy storage model at time t, Δt represents the time interval, represents the initial power of the kth electric vehicle when it starts charging, represents the initial power of the i-th electric car when it starts running, represents the starting time of charging of the kth EV, It is represented as the starting time of the kth EV’s driving;

[0120] Energy storage device constraints are expressed as: Among them, P t ch and P t dis are the charging power and discharging power of the energy storage device, P ch,max and P dis,max are the maximum charging power and the maximum discharging power of the energy storage device respectively; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage device respectively; is the energy storage capacity of the energy storage device at time t; and They are the lower limit and upper limit of the energy storage capacity of the energy storage device at time t respectively;

[0121] The reactive power compensation device constraint is expressed as: in, is the reactive power output of the static reactive power compensation device at time t, Q SVC,min and Q SVC,max is the lower and upper limits of the reactive power output of the static VAR compensation device, is the reactive power output of the switched capacitor at time t, is the number of switched capacitors put into operation at time t, is the reactive output of a single group of switched capacitors at time t; V min and V max They are the lower voltage limit and the upper voltage limit respectively; is the square of the on-load tap-changing transformer ratio at time t, is the square value of the basic transformation ratio of the on-load tap-changing transformer at time t; is the square difference of the transformation ratio of adjacent taps s of the on-load tap-changing transformer, Indicates the status of the on-load tap-changing transformer;

[0122] The grid operation constraints are expressed as: Among them, U i,t and U j,t are the voltages of node i and node j at time t; P ij,t and Qij,t are the active power and reactive power at the head end of branch ij at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t is the current flowing through branch ij at time t; p j,t and q j,t are the active load and reactive load of node j at time t respectively; u(j) is the set of child nodes of node j; and are the wind power output and photovoltaic output of node j at time t, is the active output of the virtual energy storage model at node j at time t; is the reactive power output of the static VAR compensation device at node j at time t, is the reactive power output of the virtual energy storage model at node j at time t, U i,min and U i,max are the voltage lower limit and voltage upper limit of node i, S ij,max is the maximum transmission capacity of branch ij.

[0123] The objective function f2 of the intraday optimization model constructed by the intraday optimization module is expressed as: in, is the actual wind power output of node i at time t, is the day-ahead wind power output plan for node i at time t, is the actual photovoltaic output of node j at time t, is the day-ahead PV output plan for node j at time t.

[0124] The constraints of the intraday optimization model constructed by the intraday optimization module include:

[0125] The load constraint can be reduced, expressed as: Among them, P t cut is the acceptable power reduction value at time t; P t cut,max is the maximum acceptable power reduction value at time t; is the total load power that can be reduced within time T;

[0126] Energy storage device constraints are expressed as: Among them, P t ch and P t dis are the charging power and discharging power of the energy storage device, P ch,max and P dis,max are the maximum charging power and the maximum discharging power of the energy storage device respectively; η ch and ηdis are the charging efficiency and discharging efficiency of the energy storage device respectively; is the energy storage capacity of the energy storage device at time t; and They are the lower limit and upper limit of the energy storage capacity of the energy storage device at time t respectively;

[0127] The grid operation constraints are expressed as: Among them, U i,t and U j,t are the voltages of node i and node j at time t; P ij,t and Q ij,t are the active power and reactive power at the head end of branch ij at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t is the current flowing through branch ij at time t; p j,t and q j,t are the active load and reactive load of node j at time t respectively; u(j) is the set of child nodes of node j; and are the wind power output and photovoltaic output of node j at time t, is the active output of the virtual energy storage model at node j at time t; is the acceptable power reduction value of node j at time t; is the reactive power output of the static VAR compensation device at node j at time t, is the reactive power output of the virtual energy storage model at node j at time t, U i,min and U i,max are the voltage lower limit and voltage upper limit of node i, S ij,max is the maximum transmission capacity of branch ij.

[0128] The day-ahead optimization module and the intraday optimization module both include:

[0129] A conversion unit, used for converting the day-ahead optimization model or the intraday optimization model into a linear equation that can be solved quickly by using a second-order cone relaxation technique;

[0130] A solving unit is used to solve the converted day-ahead optimization model or the intraday optimization model using a commercial solver.

[0131] The conversion unit comprises:

[0132] Define a subunit to define the square of the voltage at node i at time t as u i,t , the square of the voltage at node j at time t is defined as u j,t , define the square of the current flowing through branch ij at time t as i ij,t ;

[0133] Bring in the subunit to convert u i,t 、u j,t and i ij,t The power grid operation constraints in the day-ahead optimization model and the intra-day optimization model are introduced to convert the day-ahead optimization model and the intra-day optimization model into convex optimization problems.

[0134] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the multi-time scale optimization method of a multi-distributed resource virtual power plant of the first embodiment when executing the computer program.

[0135] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-time-scale optimization method of a multi-distributed resource virtual power plant of the first embodiment.

[0136] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-time scale optimization method for a multi-distributed resource virtual power plant, characterized in that: The following steps are involved: Establish a virtual energy storage model for electric vehicles; Based on the virtual energy storage model of the electric vehicle, a day-ahead optimization model is constructed with the goal of minimizing the electricity purchase cost, and the day-ahead optimization model is solved to obtain an optimized charging and discharging schedule of the electric vehicle and the energy storage device and a switching plan of the reactive compensation device; An intraday optimization model is constructed with the goal of minimizing wind and solar power abandonment. The charging and discharging scheduling of electric vehicles and energy storage equipment and the switching plan of reactive compensation devices are used as inputs of the intraday optimization model. The intraday optimization model is solved to obtain the optimization results of the load and energy storage equipment that can be reduced during the day.

2. The multi-time scale optimization method of a multi-distributed resource virtual power plant according to claim 1, characterized in that: The establishment of a virtual energy storage model for electric vehicles specifically includes: The charging power feasible domain and the power feasible domain of each electric vehicle are calculated based on the initial power of each electric vehicle; and the charging power and power of multiple electric vehicles are aggregated into a virtual energy storage model using a relaxed Minkowski sum according to the charging power feasible domain and the power feasible domain of each electric vehicle.

3. The multi-time scale optimization method of a multi-distributed resource virtual power plant according to claim 1, characterized in that: The objective function f1 of the day-ahead optimization model is expressed as: Among them, λ buy is the electricity purchase price, P G,t For electricity purchase.

4. The multi-time scale optimization method of a multi-distributed resource virtual power plant according to claim 1, characterized in that: The constraints of the day-ahead optimization model include: The virtual energy storage model constraints are expressed as: in, and They represent the power and electricity of the virtual energy storage model at time t, and are the lower and upper power limits of the virtual energy storage model at time t, and are the lower and upper limits of the power of the virtual energy storage model at time t, Δt represents the time interval, represents the power threshold of the kth dispatchable electric vehicle, represents the initial power of the kth electric vehicle when it starts charging, represents the initial power of the i-th electric car when it starts running, represents the starting time of charging for the kth electric vehicle, It is represented as the starting time of the kth electric vehicle; the energy storage device constraint is expressed as: in, and are the charging power and discharging power of the energy storage device, P ch ,max and P dis,max are the maximum charging power and the maximum discharging power of the energy storage device respectively; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage device respectively; is the energy storage capacity of the energy storage device at time t; and are the lower and upper limits of the energy storage capacity of the energy storage equipment at time t respectively; the reactive power compensation device constraint is expressed as: in, is the reactive power output of the static reactive power compensation device at time t, and is the lower and upper limits of the reactive power output of the static VAR compensation device, is the reactive power output of the switched capacitor at time t, is the number of switched capacitors put into operation at time t, is the reactive output of a single group of switched capacitors at time t; V min and V max They are the lower voltage limit and the upper voltage limit respectively; is the square of the on-load tap-changing transformer ratio at time t, is the square value of the basic transformation ratio of the on-load tap-changing transformer at time t; is the square difference of the transformation ratio of adjacent taps s of the on-load tap-changing transformer, Indicates the status of the on-load tap-changing transformer; The grid operation constraints are expressed as: Among them, U i,t and U j,t are the voltages of node i and node j at time t; P ij,t and Q ij,t are the active power and reactive power at the head end of branch ij at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t is the current flowing through branch ij at time t; p j,t and q j,t are the active load and reactive load of node j at time t respectively; u(j) is the set of child nodes of node j; and are the wind power output and photovoltaic output of node j at time t, is the active output of the virtual energy storage model at node j at time t; is the reactive power output of the static VAR compensation device at node j at time t, is the reactive power output of the virtual energy storage model at node j at time t, U i,min and U i,max are the voltage lower limit and voltage upper limit of node i, S ij,max is the maximum transmission capacity of branch ij.

5. The multi-time scale optimization method of a multi-distributed resource virtual power plant according to claim 1, characterized in that: The objective function f2 of the intraday optimization model is expressed as: in, is the actual wind power output of node i at time t, is the day-ahead wind power output plan for node i at time t, is the actual photovoltaic output of node j at time t, is the day-ahead PV output plan of node j at time t.

6. The multi-time scale optimization method of a multi-distributed resource virtual power plant according to claim 1, characterized in that: The constraints of the intraday optimization model include: The load constraint can be reduced, expressed as: in, is the acceptable power reduction value at time t; is the maximum acceptable power reduction value at time t; is the total load power that can be reduced within time T; Energy storage device constraints are expressed as: in, and are the charging power and discharging power of the energy storage device, P ch,max and P dis,max are the maximum charging power and the maximum discharging power of the energy storage device respectively; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage device respectively; is the energy storage capacity of the energy storage device at time t; and are the lower and upper limits of the energy storage capacity of the energy storage equipment at time t respectively; the grid operation constraints are expressed as: Among them, U i,t and U j,t are the voltages of node i and node j at time t; P ij,t and Q ij,t are the active power and reactive power at the head end of branch ij at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t is the current flowing through branch ij at time t; p j,t and q j,t are the active load and reactive load of node j at time t respectively; u(j) is the set of child nodes of node j; and are the wind power output and photovoltaic output of node j at time t, is the active output of the virtual energy storage model at node j at time t; is the acceptable power reduction value of node j at time t; is the reactive power output of the static VAR compensation device at node j at time t, is the reactive power output of the virtual energy storage model at node j at time t, U i,min and U i,max are the voltage lower limit and voltage upper limit of node i, S ij,max is the maximum transmission capacity of branch ij.

7. The multi-time scale optimization method of a multi-distributed resource virtual power plant according to claim 1, characterized in that: The solving of the day-ahead optimization model and the intraday optimization model specifically includes: The day-ahead optimization model and the intraday optimization model are converted into linear equations that can be solved quickly using a second-order cone relaxation technique; A commercial solver is used to solve the converted day-ahead optimization model and the intraday optimization model.

8. The multi-time scale optimization method of a multi-distributed resource virtual power plant according to claim 7, characterized in that: The second-order cone relaxation technique is used to convert the day-ahead optimization model and the intraday optimization model into linear equations that can be quickly solved, specifically: The square of the voltage at node i at time t is defined as u i,t , the square of the voltage at node j at time t is defined as u j,t , the square of the current flowing through branch ij at time t is defined as i ij,t ; will u i,t 、u j,t and i ij,t The power grid operation constraints in the day-ahead optimization model and the intra-day optimization model are introduced to convert the day-ahead optimization model and the intra-day optimization model into convex optimization problems.

9. A multi-time scale optimization device for a multi-distributed resource virtual power plant, characterized in that: include: Establishing a module for building a virtual energy storage model for electric vehicles; A day-ahead optimization module is used to construct a day-ahead optimization model based on the virtual energy storage model of the electric vehicle with the goal of minimizing the electricity purchase cost, and solve the day-ahead optimization model to obtain an optimized charging and discharging schedule of the electric vehicle and the energy storage device and a switching plan of the reactive compensation device; The intraday optimization module is used to construct an intraday optimization model with the goal of minimizing wind and solar power abandonment, take the charging and discharging scheduling of electric vehicles and energy storage equipment and the switching plan of reactive compensation devices as inputs of the intraday optimization model, and solve the intraday optimization model to obtain the optimization results of the load and energy storage equipment that can be reduced during the day.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the multi-time scale optimization method of a multi-distributed resource virtual power plant as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-time scale optimization method of a multi-distributed resource virtual power plant as described in any one of claims 1-8 are implemented.

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