A model and method for multi-objective optimization scheduling of virtual power plants participating in distribution network

Abstract

The present invention discloses a model and method for multi-objective optimization scheduling of a virtual power plant participating in a distribution network. The model is a multi-objective optimization scheduling model. The multi-objective optimization scheduling model is based on the construction of a node virtual machine group. The node virtual machine group in the virtual power plant in the substation is called on the basis of the distribution network objective function. The construction of the node virtual machine group includes the following steps: S1, using a first-order ETP model to describe the thermal dynamic characteristics of a building, calculating its adjustable potential, and using it to form a house air conditioning energy storage model by analogy with energy storage, S2, modeling the electric energy storage of each node, S3, modeling the nodes with distributed photovoltaics in the virtual power plant in the substation, calculating their active and reactive adjustable potentials, and S4, each node sums the adjustable potential of its distributed resources. The model and method of the present invention realize full utilization of resources, help improve the utilization rate of equipment, reduce investment in expansion and transformation, and reduce investment in line transformation.

Description

Technical Field

[0001] The present invention relates to the technical field of power system distribution network optimization dispatching, and in particular to a model and method for multi-objective optimization dispatching of a distribution network in which a virtual power plant participates. Background Art

[0002] In recent years, with the development of the economy, the characteristics of power load have deteriorated, the load growth rate has been higher than the electricity growth rate, and line overloads are prone to occur in some hot spots. In addition, with the development of distributed photovoltaics, the power supply structure of the power grid has changed, and the operation mode of the distribution network has also changed accordingly. Some nodes are prone to voltage over-limit problems.

[0003] With the development of virtual power plant (VPP) technology, distributed resources on the user side can participate in the operation of the distribution network by aggregating and building a virtual power plant. Since distributed photovoltaics are equipped with reactive equipment, they can form a virtual power plant with both active and reactive regulation capabilities together with other adjustable active load resources. How to optimize the use of virtual power plant resources based on the actual power supply area to solve line overloads and achieve voltage adjustment and other multi-objective optimization has not been well solved. Existing studies are mostly based on intelligent algorithms or configuration weights to solve multi-objective optimization problems, which have problems such as low solution efficiency and poor quality of Pareto optimal solutions. Summary of the invention

[0004] The purpose of the present invention is to provide a model and method for multi-objective optimization scheduling of virtual power plants participating in distribution networks, so as to optimize the calling of distributed resources within the substation area with higher solution efficiency, reduce the peak-to-valley difference rate, alleviate the problem of line overload in some areas, and assist in voltage adjustment of the power system.

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

[0006] A virtual power plant participates in a multi-objective optimization scheduling model for a distribution network, the model being a multi-objective optimization scheduling model, the multi-objective optimization scheduling model being based on the construction of a node virtual machine group, and calling a node virtual machine group in a substation virtual power plant based on a distribution network objective function;

[0007] The construction of the node virtual machine group includes the following steps:

[0008] S1. Use the first-order ETP model to describe the thermal dynamic characteristics of the building, calculate its adjustable potential, and use it as an analogy for energy storage to form a house air conditioning energy storage model;

[0009] S2. Construct a model for the electric energy storage of each node;

[0010] S3. Build a model for nodes with distributed photovoltaic power in the virtual power plant in the substation area and calculate their active and reactive adjustable potential;

[0011] S4. Each node adds up the regulation potential of its distributed resources to form a node virtual machine group.

[0012] Furthermore, the construction of the multi-objective compensation model of the virtual power plant in the substation area includes the following steps:

[0013] Step 1: Based on the integrated energy service company and other aggregators, the virtual machine groups of each node in the virtual power plant of the substation are aggregated to build a virtual power plant, and the dispatching instructions of the superior dispatching center are received to arrange the output of the virtual machine groups of each node;

[0014] Step 2: Select a linear market price model that depends on the total power demand of the node to represent the electricity price of the node virtual machine group.

[0015] Furthermore, the objective function includes minimizing the call cost, minimizing the power flow imbalance and minimizing the voltage deviation.

[0016] Furthermore, the objective function is to minimize the cost of calling the virtual power plant. The virtual power plant is composed of a group of virtual machines at each node. When calling the virtual power plant to participate in the optimization operation of the power system, the objective function is to minimize the cost of calling the virtual power plant resources:

[0017]

[0018] Where, subscript k represents the node number of the virtual machine group, N k Indicates the virtual total number of nodes; c re,k (t) is the reactive power unit price of the calling node virtual machine group.

[0019] Furthermore, the objective function is to minimize the flow imbalance as follows. The load density in some hot spots is high, which makes the distribution network flow distribution uneven. When disturbance occurs, the lines with higher load rates are prone to overload and cause system failure. The flow in the distribution network area can be balanced by calling virtual power plant resources, with the goal of minimizing the flow imbalance, that is,

[0020]

[0021]

[0022] Where B(t) is the unbalanced degree of power flow in the distribution network area; H 0 (t) and H(t) are the power flow entropy of the distribution network area before and after the virtual power plant participates in the dispatching operation; H max is the maximum value of power flow entropy.

[0023] Furthermore, the objective function is to minimize the voltage deviation. The calculation is as follows: some nodes in the distribution network area have high requirements for voltage quality. By calling virtual power plant resources to improve the voltage level of the distribution network, the optimization goal is to minimize the voltage deviation of some nodes within the scheduling period.

[0024]

[0025] Where U i (t) is the voltage of node i after the virtual power plant performs voltage regulation; U ref It is the reference voltage of the distribution network.

[0026] Furthermore, the peak-to-valley difference of the distribution network load is too large, and the substation may be overloaded during the peak load period. The virtual power plant is called to keep the peak-to-valley difference rate of the distribution network area at a certain level.

[0027]

[0028] Where R is the peak-to-valley difference rate of the daily load in the distribution network partition; R max is the maximum peak-to-valley difference rate allowed;

[0029] The distribution network should satisfy the following power flow constraints

[0030]

[0031]

[0032] Where P ij (t) and Q ij (t) is the active and reactive power flowing from node i to node j; r ij and x ij are the resistance and reactance of the line between nodes i and j respectively; P j (t) and Q j (t) are the active power and reactive power flowing into node j respectively; u(j) is the set of nodes from which power flows to node j; v(j) is the set of nodes to which power flows to node j;

[0033] The voltage of each node in the distribution network should satisfy the following constraints

[0034]

[0035] Where U i (t) is the voltage of node i; and are the lower and upper limits of the voltage amplitude at node i, respectively;

[0036] The line transmission power should meet the following constraints

[0037]

[0038] In the formula, is the maximum value of the transmission power of line l;

[0039] The output of the distribution network virtual power plant should meet the following constraints

[0040]

[0041] P vpp,k (t) = P vir,k (t)-ΔP ch / dis,k (t)+ΔP pv,k (t)

[0042]

[0043] In the formula, S vpp (t) is the apparent power of the virtual power plant; ΔP ch / dis,k (t) and ΔP pv,k (t) are the internal energy storage and photovoltaic output power adjustment of the node virtual machine group; Q vpp,k (t) is the reactive power output of the node virtual machine group;

[0044] The power flow entropy of the distribution network is shown as follows:

[0045]

[0046] In the formula, μ l (t) is the load rate of line l; is the power flow value of the current line l; L is the total number of lines;

[0047] Given a constant column A = {a1, a2, ..., a f ,…,a z}, l f Indicates the load factor μ l ∈(a f ,a f+1 ], and the number of lines in each load range is proportional to:

[0048]

[0049]

[0050] In the formula, C is a constant, which is taken as ln10. When the load rates of all branches in the system are in the same interval, H(t) is zero. At this time, the power flow distribution of the system is in the most balanced state, that is, the power flow carried by the line is proportional to its capacity; when the load rates of all lines in the system are not in the same interval, H(t) reaches the maximum value:

[0051]

[0052] A method for a virtual power plant to participate in a multi-objective optimization scheduling model for a distribution network, the method comprising analyzing the dispatchable potential of internal resources of a node virtual machine group, a multi-objective optimization model based on distribution network optimization, and a multi-objective model solving method based on AUGMECON2.

[0053] Furthermore, the solution method comprises the following steps:

[0054] A. Second-Order Cone Relaxation

[0055] Since the non-convexity of the branch flow equation makes this problem difficult to solve, this patent transforms the branch flow equation into a second-order cone form:

[0056]

[0057]

[0058] The branch power flow equation is transformed into a linear equation system by replacing equivalent variables in the above formula, and the above equality constraints are relaxed as follows:

[0059]

[0060] Existing literature has derived and proved that when the node load has no upper bound and the objective function is a strictly increasing function of the branch current, the above relaxation is accurate and can be equivalently converted into a standard second-order cone form as follows:

[0061]

[0062] B. Multi-objective optimization method AUGMECON2

[0063] This patent adopts the improved augmented ε-constraint method (AUGMECON2) to solve the proposed multi-objective optimization problem. AUGMECON2 requires a payoff matrix containing the optimal value and the worst value of each objective function. The worst value in the matrix is ​​the upper bound of the minimization problem, and the difference between the worst value and the optimal value is the value range; the optimal value can be simply taken as the optimal value when each objective function is optimized independently; the worst value can be obtained through the following dictionary optimization:

[0064] ① Optimize the objective function C separately and record its optimal value

[0065] ② Add constraints Optimize the objective function B separately under the condition of That is the worst value of B in the payment matrix;

[0066] ③ Add constraints and Optimize the objective function ΔU separately under the condition of This is the worst value of ΔU in the payment matrix;

[0067] The objective function and constraints based on the multi-objective optimization model of this patent are as follows:

[0068] min[C+eps×(s B / r B +0.1×s ΔU / r ΔU )]

[0069]

[0070] Where eps is a small constant (usually around 10 -6 ~10 -3 );S B and S ΔU is an additional variable; r B and r ΔU is the range of each objective function; e B and e ΔU is the parameter for a specific iteration in the heuristic random search method; Ub B and Ub ΔU are the upper bounds of the two objective functions respectively; g B and g ΔU The objective function range r is B and r ΔU The number of intervals divided; n B and n ΔU are counters of the two objective functions respectively.

[0071] Beneficial effects of the present invention:

[0072] 1. The multi-objective optimization scheduling model of the present invention realizes full utilization of resources. The use of VPP can adjust the peak-to-valley difference rate of the load carried by the substation in the power supply area, which helps to improve the utilization rate of equipment and reduce the investment in expansion and transformation. The VPP optimization scheduling can improve the power flow distribution in hot spots, avoid line overload, power supply shortage and other phenomena, and reduce line transformation investment. The use of VPP voltage regulation can complete voltage adjustment based on existing resources and reduce investment in reactive power sources;

[0073] 2. The method for solving the multi-objective optimization scheduling model of the present invention adopts the AUGMECON2 method to solve the multi-objective optimization model, which can improve the quality of the Pareto optimal solution and improve the solution efficiency by avoiding redundant iterations. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present invention will be further described below in conjunction with the accompanying drawings.

[0075] Figure 1 It is a schematic diagram of the virtual power plant architecture of the present invention;

[0076] Figure 2 It is a schematic diagram of solving the multi-objective optimization model of the present invention. DETAILED DESCRIPTION

[0077] 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.

[0078] A multi-objective optimization scheduling model for virtual power plants participating in distribution networks. The model is a multi-objective optimization scheduling model. The multi-objective optimization scheduling model is based on the construction of node virtual machine groups. The node virtual machine groups in the substation virtual power plant are called on the basis of minimizing the calling cost, minimizing the flow imbalance and minimizing the voltage deviation.

[0079] Building a node VM group includes the following steps:

[0080] S1. Use the first-order ETP model to describe the thermal dynamic characteristics of the building, calculate its adjustable potential, and use it as an analogy for energy storage to form a house air conditioning energy storage model;

[0081] The load of the air-conditioned room is dynamically balanced, and the first-order ETP model is used to describe the thermal dynamic characteristics of the building, which is specifically expressed as:

[0082]

[0083] Q air (t) = P air (t)COP

[0084] Where, T out (t) and T in (t) are outdoor and indoor temperatures, respectively; R and C a are the equivalent thermal resistance and thermal capacity respectively; Q in (t) is the heat dissipation power of indoor personnel and equipment; Q air (t) is the cooling power of the air conditioner; P air (t) is the electrical power of the air conditioner; COP is the energy efficiency ratio.

[0085] Taking into account the slow dynamic process of heat dissipation and temperature change in the air-conditioned room, the heat balance equation expressed by the differential equation can be differentiated to achieve a simple and effective solution to the air-conditioning load problem.

[0086]

[0087] It can be seen from the above formula that the indoor temperature of the air-conditioned room has the characteristics of energy storage. When the air-conditioning load is set at the comfortable state temperature T set When the air conditioner is running, the indoor temperature can be considered to be constant. At this time, the power consumption of the air conditioner is the baseline load, that is,

[0088]

[0089] When the power consumption deviates from the baseline load, the air conditioner can be considered to be performing virtual power generation, i.e.

[0090] P vir (t) = P baseline (t)-P air (t)

[0091] T min (t)≤T in (t)≤T max (t)

[0092] P min (t)≤P vir (t)≤P max (t)

[0093]

[0094]

[0095] Where, T min (t) and T max (t) are the minimum and maximum temperatures of the indoor operation. vir When (t)>0, the air conditioner reduces the power load and presents a virtual power generation state to the outside world, that is,

[0096]

[0097] In the formula, C a T in Item (t) is the energy unit, As a unit of power, it can be analogized to form air conditioning energy storage as shown in the following formula.

[0098]

[0099] S air (t) = C a Tin (t)

[0100]

[0101] S2. Construct a model for the electric energy storage of each node;

[0102] The main constraints for electric energy storage are as follows:

[0103] S ES (t+1)=S ES (t)+η(t)P ch / dis (t)Δt

[0104]

[0105]

[0106]

[0107] In the formula, S ES (t) is the energy stored; is the upper limit of energy storage capacity; P ch / dis (t) is the charging and discharging power of energy storage; and are the maximum charging and discharging powers of energy storage respectively; η ch and η dis are the charge and discharge efficiencies, respectively.

[0108] S3. Build a model for nodes with distributed photovoltaic power in the virtual power plant in the substation area and calculate their active and reactive adjustable potential;

[0109] Each node virtual machine group optimizes the scheduling of photovoltaic active and reactive output according to the maximum output forecast information of distributed photovoltaic power generation it manages. The active and reactive output constraints are mainly

[0110]

[0111] Where P PV (t) and They are the active output and maximum predicted output of the photovoltaic power generation unit respectively.

[0112] 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:

[0113]

[0114] In the formula, is the maximum reactive power regulation capacity; S inv It is the capacity of the inverter, which is approximately 1.0 to 1.1 times of the rated active capacity.

[0115] S4. Each node adds up the regulation potential of its distributed resources to form a node virtual machine group.

[0116] The construction of a multi-objective compensation model for a virtual power plant in a substation area includes the following steps:

[0117] Step 1: Aggregators such as integrated energy service companies aggregate the virtual machine groups of each node in the virtual power plant of the substation to build a virtual power plant, and receive dispatch instructions from the superior dispatch center to arrange the output of the virtual machine groups of each node.

[0118] like Figure 1 As shown in the figure, a virtual power plant is constructed using the distributed adjustable resources under the distribution network power supply substation to achieve optimal regulation of the distribution network in the substation.

[0119] Step 2: Select a linear market price model that depends on the total power demand of the node to represent the electricity price of the node virtual machine group.

[0120] The electricity price model is as follows:

[0121] c(t)=c0(t)+β|P vpp (t)|

[0122] Where c(t) is the node electricity price for calling the virtual power plant output; c0(t) is the basic electricity price; β is the sensitivity coefficient of the active output of the node virtual machine group to the node electricity price; P vpp (t) is the active power output of the node virtual machine group, and the node virtual machine group can be adjusted in both positive and negative directions.

[0123] The multi-objective application model of the virtual power plant calls on the distributed resources of the distribution network to regulate the flow and voltage within the distribution network, and makes the peak-to-valley difference of the load carried by the substation in the substation area meet certain requirements, thereby alleviating the overload problem of the substation, delaying its expansion and transformation, and improving equipment utilization.

[0124] The calculation of each objective function of the multi-objective optimization scheduling model is as follows:

[0125] ① Minimum call cost

[0126] like Figure 1 As shown in the figure, the virtual power plant is composed of a group of virtual machines at each node. When the virtual power plant is called to participate in the optimization operation of the power system, the objective function is to minimize the resource cost of calling the virtual power plant:

[0127]

[0128] Where, subscript k represents the node number of the virtual machine group, N k Indicates the virtual total number of nodes; c re,k(t) is the reactive power unit price of the calling node virtual machine group.

[0129] ② The imbalance of distribution network flow is minimal

[0130] The high load density in some hot spots makes the distribution network flow distribution uneven. 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 plant resources, with the goal of minimizing the flow imbalance.

[0131]

[0132]

[0133] Where B(t) is the unbalanced degree of power flow in the distribution network area; H 0 (t) and H(t) are the power flow entropy of the distribution network area before and after the virtual power plant participates in the dispatching operation; H max is the maximum value of power flow entropy.

[0134] ③The distribution network voltage deviation is minimal

[0135] Some nodes in the distribution network area have high requirements for voltage quality. By calling virtual power plant resources to improve the voltage level of the distribution network, the optimization goal is to minimize the voltage deviation of some nodes within the dispatch cycle.

[0136]

[0137] Where U i (t) is the voltage of node i after the virtual power plant performs voltage regulation; U ref It is the reference voltage of the distribution network.

[0138] ④Constraints

[0139] The peak-to-valley difference in distribution network load is too large, and the substation may be overloaded during peak load periods. In order to solve the overload problem, the substation needs to be expanded, which will result in large-scale investment and the use of virtual power plants to keep the peak-to-valley difference in the distribution network area at a certain level.

[0140]

[0141] Where R is the peak-to-valley difference rate of the daily load in the distribution network partition; R max is the maximum peak-to-valley difference allowed.

[0142] The distribution network should meet the following power flow constraints

[0143]

[0144]

[0145] Where Pij (t) and Q ij (t) is the active and reactive power flowing from node i to node j; r ij and x ij are the resistance and reactance of the line between nodes i and j respectively; P j (t) and Q j (t) are the active power and reactive power flowing into node j respectively; u(j) is the set of nodes from which power flows to node j; v(j) is the set of nodes to which power flows to node j.

[0146] The voltage of each node in the distribution network should satisfy the following constraints

[0147]

[0148] Where U i (t) is the voltage of node i; and are the lower and upper limits of the voltage amplitude at node i respectively.

[0149] The line transmission power should meet the following constraints

[0150]

[0151] In the formula, is the maximum value of the transmission power of line l.

[0152] The output of the distribution network virtual power plant should meet the following constraints

[0153]

[0154] P vpp,k (t) = P vir,k (t)-ΔP ch / dis,k (t)+ΔP pv,k (t)

[0155]

[0156] In the formula, S vpp (t) is the apparent power of the virtual power plant; ΔP ch / dis,k (t) and ΔP pv,k (t) are the internal energy storage and photovoltaic output power adjustment of the node virtual machine group; Q vpp,k (t) is the reactive power output of the node virtual machine group.

[0157] The power flow entropy of the distribution network is shown as follows:

[0158]

[0159] In the formula, μ l (t) is the load rate of line l; is the power flow value of the current line l; L is the total number of lines.

[0160] Given a constant column A = {a1, a2, ..., a f ,…,a z}, l f Indicates the load factor μ l ∈(a f ,a f+1 ], and the number of lines in each load range is proportional to:

[0161]

[0162]

[0163] In the formula, C is a constant, which is taken as ln10. When the load rates of all branches in the system are in the same interval, H(t) is zero. At this time, the power flow distribution of the system is in the most balanced state, that is, the power flow carried by the line is proportional to its capacity; when the load rates of all lines in the system are not in the same interval, H(t) reaches the maximum value:

[0164]

[0165] A method for a virtual power plant to participate in a multi-objective optimization scheduling model of a distribution network, the method includes analyzing the dispatchable potential of internal resources of a node virtual machine group, a multi-objective optimization model based on distribution network optimization, and a multi-objective model solving method based on AUGMECON2.

[0166] The method of solving the multi-objective model using the improved augmented ε-constraint method (AUGMECON2) includes the following steps:

[0167] A. Second-Order Cone Relaxation

[0168] Since the non-convexity of the branch flow equation makes this problem difficult to solve, this patent transforms the branch flow equation into a second-order cone form:

[0169]

[0170]

[0171] The branch power flow equation is transformed into a linear equation system by replacing equivalent variables in the above formula, and the above equality constraints are relaxed as follows:

[0172]

[0173] Existing literature has derived and proved that when the node load has no upper bound and the objective function is a strictly increasing function of the branch current, the above relaxation is accurate and can be equivalently converted into a standard second-order cone form as follows:

[0174]

[0175] B. Multi-objective optimization method AUGMECON2

[0176] This patent uses the improved augmented ε-constraint method (AUGMECON2) to solve the proposed multi-objective optimization problem. AUGMECON2 requires a payoff matrix containing the optimal and worst values ​​of each objective function. The worst value in the matrix is ​​the upper bound of the minimization problem, and the difference between the worst and optimal values ​​is the value range. The optimal value can be simply taken as the optimal value when each objective function is optimized independently; the worst value can be obtained through the following dictionary optimization:

[0177] ① Optimize the objective function C separately and record its optimal value

[0178] ② Add constraints Optimize the objective function B separately under the condition of That is the worst value of B in the payment matrix;

[0179] ③ Add constraints and Optimize the objective function ΔU separately under the condition of This is the worst value of ΔU in the payment matrix.

[0180] like Figure 2 As shown, the objective function and constraints based on the multi-objective optimization model of this patent are shown as follows:

[0181] min[C+eps×(s B / r B +0.1×s ΔU / r ΔU )]

[0182]

[0183] Where eps is a small constant (usually around 10 -6 ~10 -3 );S B and S ΔU is an additional variable; r B and r ΔU is the range of each objective function; e B and e ΔU is the parameter for a specific iteration in the heuristic random search method; Ub B and Ub ΔU are the upper bounds of the two objective functions respectively; g B and g ΔUThe objective function range r is B and r ΔU The number of intervals divided; n B and n ΔU are counters of the two objective functions respectively.

[0184] By analyzing the active and reactive regulation potential of adjustable load resources such as distributed photovoltaic, energy storage and house virtual energy storage on the user side, a node virtual group is formed. All node virtual groups in a virtual power plant in a power supply substation are used to construct a virtual power plant. The objective functions are to minimize the calling cost, minimize the imbalance of power flow in the distribution network area and minimize the node voltage deviation. Multi-objective optimization scheduling is achieved under the premise of meeting the peak-to-valley rate regulation requirements of the substation in the substation area. The multi-objective optimization model is solved by using the AUGMECON2 method. This method can improve the quality of the Pareto optimal solution and improve the solution efficiency by avoiding redundant iterations.

[0185] 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.

[0186] 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 virtual power plant participating in the multi-objective optimization scheduling model of distribution network, characterized in that: The model is a multi-objective optimization scheduling model, which is based on the construction of node virtual machine groups and calls node virtual machine groups in the virtual power plant of the substation on the basis of the distribution network objective function; The construction of the node virtual machine group includes the following steps: S1. Use the first-order ETP model to describe the thermal dynamic characteristics of the building, calculate its adjustable potential, and use it as an analogy for energy storage to form a house air conditioning energy storage model; S2. Construct a model for the electric energy storage of each node; S3. Build a model for nodes with distributed photovoltaic power in the virtual power plant in the substation area and calculate their active and reactive adjustable potential; S4. Each node adds up the adjustment potential of its distributed resources to form a node virtual machine group; The multi-objective compensation model of the virtual power plant in the substation area is constructed by the following steps: Step 1: Based on the integrated energy service company and other aggregators, the virtual machine groups of each node in the virtual power plant of the substation are aggregated to build a virtual power plant, and the dispatching instructions of the superior dispatching center are received to arrange the output of the virtual machine groups of each node; Step 2: Select a linear market price model that depends on the total power demand of the node to represent the electricity price of the node virtual machine group; The objective function includes minimizing the call cost, minimizing the power flow imbalance and minimizing the voltage deviation; The objective function is to minimize the cost of calling. The virtual power plant is composed of a group of virtual machines at each node. When calling the virtual power plant to participate in the optimization operation of the power system, the objective function is to minimize the cost of calling the virtual power plant resources: Where, subscript k represents the node number of the virtual machine group, N k Indicates the virtual total number of nodes; c re,k (t) is the reactive power unit price of the calling node virtual machine group; The objective function is to minimize the flow imbalance. The calculation is as follows. The load density in some hot spots is high, which makes the distribution network flow distribution uneven. When disturbance occurs, the lines with higher load rates are prone to overload and cause system failure. The flow in the distribution network area can be balanced by calling virtual power plant resources, with the goal of minimizing the flow imbalance, that is, Where B(t) is the unbalanced degree of power flow in the distribution network area; H 0 (t) and H(t) are the power flow entropy of the distribution network area before and after the virtual power plant participates in the dispatching operation; H max is the maximum value of the power flow entropy; The objective function is to minimize the voltage deviation. The calculation is as follows: some nodes in the distribution network area have high requirements for voltage quality. By calling virtual power plant resources to improve the voltage level of the distribution network, the optimization goal is to minimize the voltage deviation of some nodes within the dispatch cycle. Where U i (t) is the voltage of node i after the virtual power plant performs voltage regulation; U ref It is the reference voltage of the distribution network; The peak-to-valley difference of the distribution network load is too large. During peak load periods, the substation may be overloaded. The virtual power plant is called to keep the peak-to-valley difference rate of the distribution network load at a certain level. Where R is the peak-to-valley difference rate of the daily load in the distribution network partition; R max is the maximum peak-to-valley difference rate allowed; The distribution network should satisfy the following power flow constraints Where P ij (t) and Q ij (t) is the active and reactive power flowing from node i to node j; r ij and x ij are the resistance and reactance of the line between nodes i and j respectively; P j (t) and Q j (t) are the active power and reactive power flowing into node j respectively; u(j) is the set of nodes from which power flows to node j; v(j) is the set of nodes to which power flows to node j; The voltage of each node in the distribution network should satisfy the following constraints Where U i (t) is the voltage of node i; and are the lower and upper limits of the voltage amplitude at node i, respectively; The line transmission power should satisfy the following constraints -P l max ≤P l 0 (t)≤P l max Where P l max is the maximum value of the transmission power of line l; The output of the distribution network virtual power plant should meet the following constraints P vpp,k (t)=P vir,k (t)-ΔP ch / dis,k (t)+ΔP pv,k (t) In the formula, S vpp (t) is the apparent power of the virtual power plant; ΔP ch / dis,k (t) and ΔP pv,k (t) are the internal energy storage and photovoltaic output power adjustment of the node virtual machine group; Q vpp,k (t) is the reactive power output of the node virtual machine group; The power flow entropy of the distribution network is shown as follows: In the formula, μ l (t) is the load rate of line l; P l 0 (t) is the current power flow value of line l; L is the total number of lines; Given a constant column A = {a1, a2, ..., a f ,...,a z }, l f Indicates the load rate μ1∈(a f ,a f+1 ], and the number of lines in each load range is proportional to: In the formula, C is a constant, which is taken as ln10. When the load rates of all branches in the system are in the same interval, H(t) is zero. At this time, the power flow distribution of the system is in the most balanced state, that is, the power flow carried by the line is proportional to its capacity; when the load rates of all lines in the system are not in the same interval, H(t) reaches the maximum value:

2. A method for a virtual power plant to participate in a multi-objective optimization scheduling model of a distribution network according to claim 1, characterized in that: The method includes analyzing the schedulable potential of internal resources of a node virtual machine group, a multi-objective optimization model based on distribution network optimization, and a multi-objective model solving method based on AUGMECON2.

3. The method for a virtual power plant to participate in a multi-objective optimization scheduling model of a distribution network according to claim 2, characterized in that: The solution method comprises the following steps: A. Second-Order Cone Relaxation Since the non-convexity of the branch flow equation makes the problem difficult to solve, the branch flow equation is transformed into a second-order cone form: The branch power flow equation is transformed into a linear equation system by replacing equivalent variables in the above formula, and the above equality constraints are relaxed as follows: When the node load has no upper bound and the objective function is a strictly increasing function of the branch current, the above relaxation is accurate and can be equivalently converted into the standard second-order cone form as follows: B. Multi-objective optimization method AUGMECON2 The improved augmented ε-constraint method is used to solve the proposed multi-objective optimization problem. AUGMECON2 requires a payoff matrix containing the optimal value and the worst value of each objective function. The worst value in the matrix is ​​the upper bound of the minimization problem, and the difference between the worst value and the optimal value is the value range; the optimal value can be simply taken as the optimal value when each objective function is optimized independently; the worst value can be obtained through the following dictionary optimization: ① Optimize the objective function C separately and record its optimal value ② Add constraints Optimize the objective function B separately under the condition of That is the worst value of B in the payment matrix; ③ Add constraints and Optimize the objective function ΔU separately under the condition of This is the worst value of ΔU in the payment matrix; The objective function and constraints based on the multi-objective optimization model are shown as follows: min[C+eps×(s B / r B +0.1×s ΔU / r ΔU )] In the formula, eps is a small constant, usually around 10 -6 ~10 -3 ;s B and ΔU is an additional variable; r B and r ΔU is the range of each objective function; e B and e ΔU is the parameter for a specific iteration in the heuristic random search method; Ub B and Ub ΔU are the upper bounds of the two objective functions respectively; g B and g ΔU The objective function range r is B and r ΔU The number of intervals divided; n B and n ΔU are counters of the two objective functions respectively.

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