A collaborative optimization operation method for multi-region virtual power plants based on zone autonomy

Abstract

The present invention discloses a method for collaborative optimization operation of multi-region virtual power plants based on partition autonomy, which belongs to the technical field of power system operation; the operation method comprises: firstly analyzing the adjustment potential of terminal adjustable resources; then constructing a distribution network virtual power plant centralized control center based on the terminal adjustable resources in the distribution network area; the distribution network virtual power plant centralized control center connects to the main network dispatching center upward and receives dispatching instructions, and receives the operation information of the terminal adjustable resources downward and issues control instructions; then the main network dispatching center combines the unit output according to the adjustable potential of the virtual power plant and the power generation capacity of the conventional units, determines the output of the virtual power plant and issues dispatching instructions to it; optimizes the call of the adjustable resources in the distribution network area with a faster solution speed, solves the distribution network operation problems in a targeted manner, and realizes the coordinated operation of multi-region virtual power plants based on partition autonomy.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation, and specifically relates to a multi-region virtual power plant collaborative optimization operation method based on zone autonomy. Background Art

[0002] In recent years, with the development of the economy, the characteristics of power load have deteriorated, the peak-to-valley difference of load has increased, and the excessive load density in some hot spots has easily caused line overload, resulting in increased investment in power equipment; at the same time, the development of distributed photovoltaics has changed the power structure of the power grid, and the operation mode of the distribution network has changed accordingly, and some nodes are prone to voltage problems.

[0003] With the development of virtual power plant (VPP) technology, user-side adjustable resources can participate in the operation of distribution networks by aggregating and building virtual power plants. However, virtual power plant resources are terminal resources. If traditional centralized solution methods are used, it will bring problems such as heavy communication burden and slow solution speed. Therefore, how to call virtual power plants to achieve partition autonomy and fast solution for distribution network operation problems in different actual power supply partitions has not been well solved. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for collaborative optimization operation of a multi-region virtual power plant based on partition autonomy, which optimizes the call of adjustable resources within the distribution network area with a faster solution speed, solves distribution network operation problems in a targeted manner, and realizes coordinated operation of a multi-region virtual power plant based on partition autonomy.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A multi-region virtual power plant collaborative optimization operation method based on partition autonomy, wherein the system operation architecture of the virtual power plant includes: terminal adjustable resources, a distribution network virtual power plant centralized control center and a main network dispatching center; the operation method includes the following steps:

[0007] S1, conduct regulation potential analysis on terminal adjustable resources;

[0008] S2, build a distribution network virtual power plant centralized control center based on the terminal adjustable resources in the distribution network area; the distribution network virtual power plant centralized control center connects to the main grid dispatching center and receives dispatching instructions, receives the operation information of the terminal adjustable resources and issues control instructions;

[0009] S3, the main grid dispatching center combines the unit output according to the adjustable potential of the virtual power plant and the power generation capacity of conventional units, determines the output of the virtual power plant and issues dispatching instructions to it.

[0010] Furthermore, the terminal adjustable resources include: air-conditioning load, electric bus charging station, electric energy storage and distributed photovoltaic.

[0011] Furthermore, the adjustment potential of the terminal adjustable resources is as follows:

[0012] 1) Air conditioning load:

[0013] P t AC,DR =P t AC,base -P t AC

[0014] Where P t AC is the electrical power of the air conditioner; P t AC,DR is the regulating potential of air conditioning; P t AC,base The air conditioning power for optimal comfort;

[0015] 2) Electric bus charging station:

[0016]

[0017] Where P t EV,DR The regulation potential for charging stations; Charging power for buses; The charging power when not participating in the virtual power plant regulation is:

[0018] 3) Electrical energy storage:

[0019]

[0020] Where P t ES,DR The regulating potential for energy storage; and are the charging and discharging power of energy storage respectively; and are respectively the charging and discharging power of the electric energy storage when it does not participate in the regulation of the virtual power plant;

[0021] 4) Distributed photovoltaic:

[0022]

[0023] In the formula, is the adjustable potential of distributed photovoltaics; P pv,t and Q pv,t They are respectively the active and reactive outputs of distributed photovoltaics; and They are respectively the active and reactive outputs when distributed photovoltaics do not participate in the regulation of virtual power plants.

[0024] Furthermore, the output of the virtual power plant in the distribution network area is:

[0025]

[0026] In the formula, The apparent power output of the virtual power plant in the distribution network area, and They are the active power and reactive power output of the virtual power plant in the distribution network area respectively.

[0027] Furthermore, the objective function of the operation of the distribution network virtual power plant centralized control center includes:

[0028] f j =μ1F vpp,j +μ2F p-v,j +μ3F pf,j +μ4F V,j

[0029] μ1+μ2+μ3+μ4=1

[0030] The four objective functions of minimizing system operation cost, minimizing load fluctuation variance, minimizing power flow imbalance and minimizing voltage deviation are as follows:

[0031]

[0032]

[0033]

[0034]

[0035] In the above formulas, F vpp,j is the system operating cost, F p-v,j is the load fluctuation variance, F pf,j is the power flow imbalance, F V,j is the voltage deviation, μ1~μ4 are the weight coefficients corresponding to the four objective functions; is the power received by the distribution network from the main grid, γ t is the unit cost of calling a virtual power plant, is the load in the distribution network area, η t The price of electricity sold by the distribution network; For the imbalance of the flow, and H j,t They are the power flow entropy of the distribution network area before and after the virtual power plant participates in the dispatch, is the maximum value of power flow entropy; Θ j is the node set in the distribution network area, k is the node number in the distribution network area, U j,k,t is the node voltage, U j,refIt is the reference voltage of the distribution network area.

[0036] Furthermore, the operating constraints of the distribution network virtual power plant centralized control center are:

[0037] 1) Power balance constraints:

[0038]

[0039]

[0040] In the formula, The power load of the distribution network area when it does not participate in the regulation of the virtual power plant;

[0041] 2) Transmission power constraints between main and distribution networks:

[0042]

[0043] 3) Virtual power plant output constraints:

[0044]

[0045] In the formula, and are the minimum and maximum outputs of the virtual power plant, respectively;

[0046] 4) Power flow entropy constraint:

[0047]

[0048] In the formula, is the load factor of line l, is the power flow value of line l, L j is the total number of lines;

[0049] Given a constant column A j ={a j,1 ,a j,2 ,...,a j,f ,...,a j,z}, Indicates load factor The number of transmission lines is proportional to the number of lines in each load range:

[0050]

[0051]

[0052] Where C is a constant; when the load rates of all branches in the system are in the same range, H j,tWhen the load rate of all lines in the system is not in the same range, H j,t It reaches the maximum value:

[0053]

[0054] 5) Voltage constraints:

[0055]

[0056] In the formula, and are the lower and upper limits of the node voltage amplitude respectively;

[0057] 6) Distribution network flow constraints:

[0058]

[0059]

[0060] Where P j,mn,t and Q j,mn,t are the active and reactive power flowing from node m to node n in the distribution network area; r j,mn and x j,mn are the resistance and reactance of the line between nodes m and n respectively; P j,n,t and Q j,n,t are the active power and reactive power flowing into node n respectively; u(n) j is the set of nodes from which power flows to node n; g∈v(n) j is the set of nodes to which the power of node n flows;

[0061] 7) Line transmission capacity constraints:

[0062]

[0063] Furthermore, the main network dispatching center aims to minimize the operating cost, and its objective function is:

[0064] minC grid =C G -C sell

[0065]

[0066]

[0067] In the above formula, C G is the cost of electricity generated by the generator, C sell is the revenue from electricity sales, C vppis the cost of calling the virtual power plant; T is the dispatch period, I is the set of conventional generators, and J is the set of distribution networks; the subscript t represents the time period t, i represents conventional generator i, and j represents distribution network j; P G,i,t is the output of the conventional unit, a i , b i and c i is the corresponding cost coefficient; is the power transmitted from the main network to the distribution network, λ t For electricity price.

[0068] Furthermore, the constraints for the operation of the main network dispatching center are:

[0069] 1) Power balance constraints:

[0070]

[0071] 2) Output constraints of conventional units:

[0072]

[0073]

[0074] In the formula, and are the upper and lower limits of the unit’s output respectively, and are the downward and upward climbing rates of the unit, respectively, and Δt is an operating period;

[0075] 3) Transmission power constraints between main and distribution networks:

[0076]

[0077] In the formula, and They are the minimum and maximum values ​​of the transmission power between the main network and the distribution network, respectively.

[0078] Furthermore, the target cascade method is used to transform the model;

[0079] The distribution network objective function is expressed as:

[0080]

[0081] In the formula, is the value after optimization of the main network; ω j,t and θ j,t are the multipliers of the linear and quadratic terms of the Lagrange penalty function respectively;

[0082] The main network objective function is expressed as:

[0083]

[0084] In the formula, This is the value after network optimization.

[0085] Furthermore, the target cascade method is used to solve the problem as follows:

[0086] Step 21: Input the main network, distribution network parameters and equipment parameters, set the initial values ​​of coupling variables and penalty function multipliers, and set the number of iterations z = 1;

[0087] Step 22: Each distribution network area solves its own optimization problem based on the distribution network objective function containing the Lagrange penalty function term and the constraints of each distribution network, and converts the solved distribution network receiving power into The data is passed to the main grid dispatching layer, and the distribution grid can solve the problems in parallel to speed up the calculation.

[0088] Step 23: After the main network receives the data transmitted by the distribution network, it optimizes and solves the main network objective function containing the Lagrange penalty function term and the main network constraint conditions, and converts the obtained main network transmission power Delivered to the distribution network;

[0089] Step 24: Determine whether the convergence condition is met according to the following formula:

[0090]

[0091]

[0092] Where ε1 and ε2 are convergence precisions; if both are satisfied, the iteration process is terminated and the result is output; otherwise, the Lagrange penalty function multiplier is updated according to the following formula, z=z+1, and the solution is returned to step 22 to continue;

[0093]

[0094] Among them, ω j,t and θ j,t The initial value of is a constant.

[0095] Beneficial effects of the present invention:

[0096] 1. The virtual power plant of the present invention uses existing demand-side resources. The use of virtual power plant technology can improve the utilization rate of equipment, improve the power supply quality of the distribution network, and achieve full utilization of resources;

[0097] 2. Each sub-area distribution network of the present invention can set weights for different targets according to the region's own load to achieve local optimization, thereby improving the distribution network power supply problem in a more targeted manner;

[0098] 3. The present invention adopts the target cascade analysis method for solving. Only power information is transmitted interactively between the main network and the distribution network, and multiple distribution networks can perform parallel calculations, which reduces the communication burden of the main network and improves the calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0100] Figure 1 It is a schematic diagram of the system operation architecture of the virtual power plant of the present invention;

[0101] Figure 2 It is a schematic diagram of constructing a distribution network virtual power plant according to an embodiment of the present invention;

[0102] Figure 3 is a schematic diagram of the network system structure of an embodiment of the present invention;

[0103] Figure 4 It is a flow chart for solving the collaborative optimization operation of multi-region virtual power plants based on zone autonomy in the present invention. DETAILED DESCRIPTION

[0104] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0105] like Figure 1-2 As shown in the figure, the system operation architecture of the virtual power plant includes three parts: terminal adjustable resources, distribution network virtual power plant centralized control center and main network dispatching center;

[0106] A multi-region virtual power plant collaborative optimization operation method based on zone autonomy includes the following steps:

[0107] S1, conduct regulation potential analysis on terminal adjustable resources;

[0108] S2, build a distribution network virtual power plant centralized control center based on the terminal adjustable resources in the distribution network area; the distribution network virtual power plant centralized control center connects to the main grid dispatching center and receives dispatching instructions, receives the operation information of the terminal adjustable resources and issues control instructions;

[0109] S3, the main grid dispatching center combines the unit output according to the adjustable potential of the virtual power plant and the power generation capacity of conventional units, determines the output of the virtual power plant and issues dispatching instructions to it.

[0110] In S1, the terminal adjustable resources include: air conditioning load, electric bus charging station, electric energy storage and distributed photovoltaic; among them, the adjustment potential of each adjustable resource is analyzed as follows:

[0111] 1) Air conditioning load;

[0112] When the air conditioning load is used as a virtual power plant resource, the load reduction is mainly achieved by adjusting the indoor temperature. This patent uses a first-order ETP model to describe the thermal dynamic model of the building. Assuming that the outdoor temperature remains unchanged within a scheduling period Δt, the indoor temperature change can be expressed as:

[0113]

[0114] Where C and R are the thermal capacity and thermal resistance of the building respectively; T t in and T t out are the indoor and outdoor temperatures respectively; P t AC is the electrical power of the air conditioner, and COP is the cooling efficiency of the air conditioner;

[0115] In order to meet the heating comfort of indoor personnel, the indoor temperature should meet the following requirements:

[0116] T t in,min ≤T t in ≤T t in,max

[0117] Where, T t in,min and T t in,max are the minimum and maximum indoor temperatures allowed for comfort, respectively;

[0118] The electric power of the air conditioner should meet the following constraints:

[0119]

[0120] In the formula, is the rated power of the air conditioner;

[0121] The air conditioning load regulation potential is:

[0122] P t AC,DR =P t AC,base -Pt AC

[0123] Where P t AC,DR is the regulating potential of air conditioning; P t AC,base The air conditioning power for optimal comfort.

[0124] 2) Electric bus charging stations;

[0125] Electric buses are operated during the day and charged at night. Considering that the operating hours of buses on different routes are not much different, the present invention assumes that buses of the same model have the same charge state during operation. When the electric bus charging station participates in the virtual power plant regulation, it mainly transfers the load by adjusting its charging power in different time periods. In order to analyze its external regulation potential, the entire charging station is regarded as a whole, that is,

[0126]

[0127]

[0128]

[0129]

[0130] In the formula, Charging power for buses; Minimum power to prevent the vehicle from entering sleep mode; is the maximum charging power; is the charging efficiency; S EV,t The power of the bus battery; is the maximum storage capacity of the battery; T dis , T ch and T ch_end They represent the bus operation period, night charging period and charging end period respectively; The minimum charge level that the battery needs to meet when charging is completed;

[0131] The regulation potential of the charging station is

[0132]

[0133] Where P t EV,DR For the regulation potential of the charging station, It is the charging power when not participating in the virtual power plant regulation.

[0134] 3) Electrical energy storage;

[0135] Electric energy storage is the most flexible resource and can participate in the regulation of multiple scenarios such as peak shaving and valley filling. The main constraints on electric energy storage are as follows:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] In the formula, S ES,t Energy for storage; The upper limit of energy storage capacity; is the charging power of the energy storage, is the discharge power of energy storage; and are the maximum charging and discharging power of energy storage, respectively; and are the charging and discharging efficiencies, respectively;

[0142] The adjustable potential of electrical energy storage is

[0143]

[0144] Where P t ES,DR The regulation potential of energy storage, and They are respectively the charging and discharging power of the electric energy storage when it does not participate in the regulation of the virtual power plant.

[0145] 4) Distributed photovoltaic

[0146] The virtual power plant can optimize the active and reactive output of photovoltaic power generation according to the maximum output forecast information of distributed photovoltaic power generation. The active and reactive output constraints are mainly

[0147]

[0148] Where P pv,t and They are respectively the active output and the maximum predicted output of the photovoltaic power generation unit;

[0149] Photovoltaic inverters can use their own reactive power control function to provide voltage support for the distribution system. The relationship between the adjustable reactive power range and the inverter capacity is:

[0150]

[0151]

[0152] In the formula, Q pv,t Reactive power output for distributed photovoltaics; is the maximum reactive power regulation capacity; S inv is the capacity of the inverter, which is about 1.0 to 1.1 times the rated active capacity;

[0153] The adjustable potential of distributed photovoltaic is

[0154]

[0155] In the formula, For the adjustable potential of distributed photovoltaics, and They are respectively the active and reactive outputs when distributed photovoltaics do not participate in the regulation of virtual power plants.

[0156] In S3, the output of the distribution network virtual power plant is as follows:

[0157] By adding up the above-mentioned resource regulation potentials in the distribution network area, the output of the virtual power plant in the distribution network area can be obtained as follows:

[0158]

[0159] In the formula, The apparent power output of the virtual power plant in the distribution network area, and They are the active power and reactive power output of the virtual power plant in the distribution network area respectively.

[0160] The objective function and constraints of the distribution network virtual power plant centralized control center in the operation architecture are as follows:

[0161] (1) Objective function of the distribution network virtual power plant control center;

[0162] Different distribution network areas have different load characteristics, and the scenarios that require virtual power plants to adjust are also different; the system operation cost F vpp,j Minimum, load fluctuation variance F p-v,j Minimum, power flow imbalance F pf,j Minimum and voltage deviation F V,j The minimum four objective functions, different distribution network areas determine the regulation requirements through weights, that is, the distribution network optimization objective function is:

[0163] f j =μ1F vpp,j +μ2F p-v,j +μ3F pf,j +μ4F V,j

[0164] μ1+μ2+μ3+μ4=1

[0165] In the formula, μ1~μ4 are the weight coefficients corresponding to the four objective functions;

[0166] A. Optimal cost;

[0167] The operating cost of the distribution network mainly includes the cost of purchasing electricity from the main grid, the cost of calling load-side resources and the income from selling electricity, that is,

[0168]

[0169] B. The load fluctuation variance is minimal;

[0170] When the peak-to-valley difference of the distribution network load is large, the substation needs to be expanded to solve the power supply gap problem, which will result in large-scale investment. However, this peak load only exists for a very short time in a year, which will reduce the utilization rate of equipment. The use of virtual power plants can reduce the peak-to-valley difference in the region, thereby improving the utilization rate of equipment.

[0171]

[0172] C. The imbalance of the current is minimal;

[0173] When there are hot spots with high load density in the distribution network, the flow distribution will be unbalanced. When disturbance occurs, the lines with higher load rate are prone to overload and cause system failure. The flow in the distribution network area can be balanced by calling virtual power plants, with the goal of minimizing the flow imbalance, that is,

[0174]

[0175] D. Minimum voltage deviation;

[0176] The access of distributed power sources will cause voltage over-limit problems in some distribution network areas. By calling virtual power plant resources, the voltage level of the distribution network can be improved, with the minimum voltage deviation of some nodes within the scheduling cycle as the optimization goal, that is,

[0177]

[0178] In the above formulas, is the power received by the distribution network from the main grid, γ t is the unit cost of calling a virtual power plant, is the load in the distribution network area, η t The price of electricity sold by the distribution network; For the imbalance of the flow, and H j,t They are the power flow entropy of the distribution network area before and after the virtual power plant participates in the dispatch, is the maximum value of power flow entropy; Θ j is the node set in the distribution network area, k is the node number in the distribution network area, Uj,k,t is the node voltage, U j,ref It is the reference voltage of the distribution network area.

[0179] (2) Constraints;

[0180] A. Power balance constraints:

[0181]

[0182]

[0183] In the formula, The power load of the distribution network area when it does not participate in the regulation of the virtual power plant;

[0184] B. Transmission power constraints between main and distribution networks:

[0185]

[0186] C. Virtual power plant output constraints:

[0187]

[0188] In the formula, and are the minimum and maximum outputs of the virtual power plant, respectively;

[0189] D. Power flow entropy constraint:

[0190]

[0191] In the formula, is the load factor of line l, is the power flow value of line l, L j is the total number of lines;

[0192] Given a constant column A j ={a j,1 ,a j,2 ,...,a j,f ,...,a j,z}, Indicates load factor The number of transmission lines is proportional to the number of lines in each load range:

[0193]

[0194]

[0195] Where, C is a constant, which is taken as ln10 in the present invention; when the load rates of all branches in the system are in the same range, H j,tWhen the load rate of all lines in the system is not in the same range, H j,t It reaches the maximum value:

[0196]

[0197] E. Voltage Constraints:

[0198]

[0199] In the formula, and are the lower and upper limits of the node voltage amplitude respectively;

[0200] F. Distribution network flow constraints:

[0201]

[0202]

[0203] Where P j,mn,t and Q j,mn,t are the active and reactive power flowing from node m to node n in the distribution network area; r j,mn and x j,mn are the resistance and reactance of the line between nodes m and n respectively; P j,n,t and Q j,n,t are the active power and reactive power flowing into node n respectively; u(n) j is the set of nodes from which power flows to node n; g∈v(n) j is the set of nodes to which the power of node n flows;

[0204] G. Line transmission capacity constraints:

[0205]

[0206] The objective function and constraints of the operation of the main network dispatch center in the operation architecture are as follows:

[0207] (1) Objective function of the main grid dispatching center;

[0208] The main network aims to minimize the operating cost, and its objective function is

[0209] minC grid =C G -C sell

[0210] In the formula, C G is the cost of electricity generated by the generator, C sell is the revenue from electricity sales, C vppThe cost of deploying virtual power plants;

[0211]

[0212]

[0213] Where T is the dispatch period, I is the set of conventional generators, and J is the set of distribution networks; subscript t represents time period t, i represents conventional generator i, and j represents distribution network j; P G,i,t is the output of the conventional unit, a i , b i and c i is the corresponding cost coefficient; is the power transmitted from the main network to the distribution network, λ t For electricity price.

[0214] (2) Constraints;

[0215] A. Power balance constraints:

[0216]

[0217] B. Output constraints of conventional units:

[0218]

[0219]

[0220] In the formula, and are the upper and lower limits of the unit’s output respectively, and are the downward and upward climbing rates of the unit, respectively, and Δt is an operating period;

[0221] C. Transmission power constraints between main and distribution networks:

[0222]

[0223] In the formula, and They are the minimum and maximum values ​​of the transmission power between the main network and the distribution network, respectively.

[0224] like Figure 3 As shown in the figure, the main grid contains two 110kV distribution network areas and two 35kV distribution network areas. The solution steps for realizing the optimal operation of the multi-region virtual power plant system based on partition autonomy using the target cascade method are as follows:

[0225] Step 1: Model transformation;

[0226] From the above model, it can be seen that the transmission power from the main network to the distribution network and the power received by the distribution network from the main grid is a coupled variable with the following constraints:

[0227]

[0228] When solving the economic dispatch plan, the main grid will transmit power Optimized value It is passed to the distribution network in the form of parameters; while optimizing its own objective function, the distribution network needs to consider the main network transmission power. and distribution network receiving power The Lagrange penalty function is added to the distribution network objective function to represent the distribution network receiving power. Transmission power after optimization with the main network The deviation of , then the distribution network objective function can be expressed as:

[0229]

[0230] In the formula, ω j,t and θ j,t are the multipliers of the linear and quadratic terms of the Lagrange penalty function respectively;

[0231] Similarly, the main network is connected to J distribution networks, and J Lagrangian penalty functions are added to the objective function of the main network to represent the transmission power of the main network Optimized receiving power for each distribution network The deviation of , then the objective function of the main network can be expressed as:

[0232]

[0233] Step 2: Model solution;

[0234] like Figure 4 As shown in the figure, the solution process of the collaborative optimization operation of multi-region virtual power plants based on partition autonomy is as follows:

[0235] Step 21: Input the main network, distribution network parameters and equipment parameters, set the coupling variables (i.e. the main network transmission power and the distribution network receiving power) and the initial value of the penalty function multiplier, and set the number of iterations z = 1;

[0236] Step 22: Each distribution network area solves its own optimization problem based on the distribution network objective function containing the Lagrange penalty function term and the constraints of each distribution network, and converts the solved distribution network receiving power into The data is passed to the main grid dispatching layer, and the distribution grid can solve the problems in parallel to speed up the calculation.

[0237] Step 23: After the main network receives the data transmitted by the distribution network, it optimizes and solves the main network objective function containing the Lagrange penalty function term and the main network constraint conditions, and converts the obtained main network transmission power Delivered to the distribution network;

[0238] Step 24: Determine whether the convergence condition is met according to the following formula:

[0239]

[0240]

[0241] Where ε1 and ε2 are convergence precisions; if both are satisfied, the iteration process is terminated and the result is output; otherwise, the Lagrange penalty function multiplier is updated according to the following formula, z=z+1, and the solution is returned to step 22 to continue;

[0242]

[0243] Among them, ω j,t and θ j,t The initial value of is generally a small constant.

[0244] It should be noted that, although the operations of the method of the present invention are described in a specific order in the above embodiments and the accompanying drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0245] The method for collaborative optimization operation of multi-region virtual power plants based on zoned autonomy proposed in the present invention optimizes the calling of existing adjustable resources in the distribution network area, and specifically solves the problems of excessive peak-to-valley difference, excessive power flow imbalance and excessive voltage deviation in distribution network operation. The target cascade analysis method is used to achieve the optimization solution of coordinated operation of multi-region virtual power plants based on zoned autonomy at a faster solution speed.

[0246] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0247] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for collaborative optimization operation of a multi-region virtual power plant based on partition autonomy, characterized in that: The system operation architecture of the virtual power plant includes: terminal adjustable resources, a distribution network virtual power plant centralized control center and a main network dispatching center; the operation method includes the following steps: S1, conduct regulation potential analysis on terminal adjustable resources; S2, build a distribution network virtual power plant centralized control center based on the terminal adjustable resources in the distribution network area; the distribution network virtual power plant centralized control center connects to the main grid dispatching center and receives dispatching instructions, receives the operation information of the terminal adjustable resources and issues control instructions; S3, the main grid dispatching center combines the unit output according to the adjustable potential of the virtual power plant and the power generation capacity of the conventional units, determines the output of the virtual power plant and issues dispatching instructions to it; The terminal adjustable resources include: air conditioning load, electric bus charging station, electric energy storage and distributed photovoltaic; The regulation potential of the terminal adjustable resources is as follows: 1) Air conditioning load: P t AC,DR =P t AC,base -P t AC Where P t AC is the electrical power of the air conditioner; P t AC,DR is the regulating potential of air conditioning; P t AC,base The power of air conditioner when it is in the best comfort state; 2) Electric bus charging station: Where P t EV,DR The regulation potential for charging stations; Charging power for buses; The charging power when not participating in the virtual power plant regulation is: 3) Electrical energy storage: Where P t ES,DR The regulating potential for energy storage; and are the charging and discharging power of energy storage respectively; and are respectively the charging and discharging power of the electric energy storage when it does not participate in the regulation of the virtual power plant; 4) Distributed photovoltaic: In the formula, is the adjustable potential of distributed photovoltaics; P pv,t and Q pv,t They are respectively the active and reactive outputs of distributed photovoltaics; and They are respectively the active and reactive outputs of distributed photovoltaics when they do not participate in the regulation of virtual power plants; The output of the virtual power plant in the distribution network area is: In the formula, The apparent power output of the virtual power plant in the distribution network area, and They are respectively the active power and reactive power output of the virtual power plant in the distribution network area; The objective function of the distribution network virtual power plant centralized control center is as follows: min F j =μ1F vpp,j +μ2F p-v,j +μ3F pf,j +μ4F V,j μ1+μ2+μ3+μ4=1 The four objective functions of minimizing system operation cost, minimizing load fluctuation variance, minimizing power flow imbalance and minimizing voltage deviation are as follows: In the above formulas, F vpp,j is the system operating cost, F p-v,j is the load fluctuation variance, F pf,j is the power flow imbalance, F V,j is the voltage deviation, μ1~μ4 are the weight coefficients corresponding to the four objective functions; is the power received by the distribution network from the main grid, γ t is the unit cost of calling a virtual power plant, is the load in the distribution network area, η t The price of electricity sold by the distribution network; For the imbalance of the flow, and H j,t They are the power flow entropy of the distribution network area before and after the virtual power plant participates in the dispatch, is the maximum value of power flow entropy; Θ j is the node set in the distribution network area, k is the node number in the distribution network area, U j,k,t is the node voltage, U j,ref It is the reference voltage of the distribution network area; The main network dispatching center aims to minimize the operating cost, and its objective function is: minC grid =C G -C sell In the above formula, C G is the cost of electricity generated by the generator, C sell is the revenue from electricity sales, C vpp is the cost of calling the virtual power plant; T is the dispatch period, I is the set of conventional generators, and J is the set of distribution networks; the subscript t represents the time period t, i represents conventional generator i, and j represents distribution network j; P G,i,t is the output of the conventional unit, a i , b i and c i is the corresponding cost coefficient; is the power transmitted from the main network to the distribution network, λ t For electricity prices; The target cascade method is used to transform the model; The distribution network objective function is expressed as: In the formula, is the value after optimization of the main network; ω j,t and θ j,t are the multipliers of the linear and quadratic terms of the Lagrange penalty function respectively; The main network objective function is expressed as: In the formula, It is the value after the distribution network is optimized; The solution steps using the target cascade method are: Step 21: Input the main network, distribution network parameters and equipment parameters, set the initial values ​​of coupling variables and penalty function multipliers, and set the number of iterations z = 1; Step 22: Each distribution network area solves its own optimization problem based on the distribution network objective function containing the Lagrange penalty function term and the constraints of each distribution network, and converts the solved distribution network receiving power into The data is passed to the main grid dispatching layer, and the distribution grid can solve the problems in parallel to speed up the calculation. Step 23: After the main network receives the data transmitted by the distribution network, it optimizes and solves the main network objective function containing the Lagrange penalty function term and the main network constraint conditions, and converts the obtained main network transmission power Delivered to the distribution network; Step 24: Determine whether the convergence condition is met according to the following formula: Where ε1 and ε2 are convergence precisions; if both are satisfied, the iteration process is terminated and the result is output; otherwise, the Lagrange penalty function multiplier is updated according to the following formula, z=z+1, and the solution is returned to step 22 to continue; Among them, ω j,t and θ j,t The initial value of is a constant.

2. A method for collaborative optimization operation of a multi-region virtual power plant based on partition autonomy according to claim 1, characterized in that: The operating constraints of the distribution network virtual power plant centralized control center are: 1) Power balance constraints: In the formula, The power load of the distribution network area when it does not participate in the regulation of the virtual power plant; 2) Transmission power constraints between main and distribution networks: 3) Virtual power plant output constraints: In the formula, and are the minimum and maximum outputs of the virtual power plant, respectively; 4) Power flow entropy constraint: In the formula, is the load factor of line l, is the power flow value of line l, L j is the total number of lines; Given a constant column A j ={a j,1 ,a j,2 ,...,a j,f ,...,a j,z }, Indicates load factor The number of transmission lines is proportional to the number of lines in each load range: Where C is a constant; when the load rates of all branches in the system are in the same range, H j,t When the load rate of all lines in the system is not in the same range, H j,t It reaches the maximum value: 5) Voltage constraint: In the formula, and are the lower and upper limits of the node voltage amplitude respectively; 6) Distribution network flow constraints: Where P j,mn,t and Q j,mn,t are the active and reactive power flowing from node m to node n in the distribution network area; r j,mn and x j,mn are the resistance and reactance of the line between nodes m and n respectively; P j,n,t and Q j,n,t are the active power and reactive power flowing into node n respectively; u(n) j is the set of nodes from which power flows to node n; g∈v(n) j is the set of nodes to which the power of node n flows; 7) Line transmission capacity constraints:

3. A method for collaborative optimization operation of a multi-region virtual power plant based on partition autonomy according to claim 2, characterized in that: The constraints for the operation of the main network dispatching center are: 1) Power balance constraints: 2) Output constraints of conventional units: In the formula, and are the upper and lower limits of the unit’s output respectively, and are the downward and upward climbing rates of the unit, respectively, and Δt is an operating period; 3) Transmission power constraints between main and distribution networks: In the formula, and They are the minimum and maximum values ​​of the transmission power between the main network and the distribution network, respectively.

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