Two-stage active and reactive collaborative optimization loss reduction method and device for power distribution network

Abstract

The invention discloses a two-stage active and reactive collaborative optimization loss reduction method and device for a power distribution network, and the method comprises the steps: constructing an active non-cooperative game model according to an active power optimization target; solving the active non-cooperative game model through an optimal response dynamic algorithm to obtain an active scheduling scheme, and issuing the active scheduling scheme to a reactive power optimization stage; constructing a reactive cooperative game model according to the reactive power optimization target; and solving the reactive cooperative game model by setting a numerical optimization algorithm, and outputting the optimal reactive output and income distribution of each main body. According to the invention, the system efficiency can be improved while the line loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid losses, and in particular to a method and device for two-stage active and reactive collaborative optimization loss reduction in a distribution network. Background Art

[0002] The widespread integration of various flexible resource entities, such as distributed generation (DG) operators, virtual power plant (VPP) operators, and reactive power compensation service providers, has significantly changed the structure and operation of distribution networks. Under the traditional centralized dispatch model, the control rights of each entity rested with the distribution network operator, whose power output was centrally dispatched by the distribution network, with the dispatch results distributed to each entity in the form of control instructions for execution. However, with the development of the electricity market, the dispatch authority of various distributed flexible resources has gradually shifted from the distribution network operator to independent DG operators, VPP operators, and reactive power compensation service providers. In this market environment, each entity has different interests and pursues its own optimization objectives and output plans. For example, DG operators aim to maximize power generation revenue; VPP operators aim to benefit from participating in demand response by optimizing the dispatch of various flexible load resources; and reactive power compensation service providers strive to provide reactive power compensation services to maintain voltage security and maximize economic returns. However, without coordination, these independent optimization objectives and output plans can significantly increase the randomness of distribution network power flows, trigger voltage fluctuations, increase various operational risks in the system, and significantly reduce economic efficiency.

[0003] Distribution line losses refer to the energy losses caused by factors such as line resistance and transformer losses during the transmission and distribution of electricity. In traditional distribution networks, line losses are primarily determined by source-load power, power flow distribution, and the physical characteristics of the distribution network. However, in an environment with widespread integration of multiple entities, such as distributed generation (DGs), the causes and characteristics of distribution network line losses change significantly. In addition to the significant impact of DG location, capacity, and operating mode on line losses, the competitive dynamics between these entities also have a profound impact. In this scenario, each entity pursues its own interests, and this independent decision-making can lead to suboptimal operation of the overall system. DG operators may maximize output power during peak electricity prices without considering the resulting localized power flow overload and increased line losses. Similarly, virtual power plant operators may adjust load based on their own demand response strategies without considering the impact of these adjustments on the overall distribution network power flow. Independent decisions made by reactive power compensation service providers can, in certain circumstances, increase localized reactive power surpluses or shortages, thereby impacting voltage levels and line losses. In order to avoid non-optimal operation of the distribution network caused by independent decision-making of each subject, a coordinated optimization strategy must be adopted to find the equilibrium point among multiple subjects through game theory methods to achieve optimal operation of the overall system, reduce line losses and ensure voltage stability.

[0004] Currently, although some research has achieved certain results in the calculation and optimization of distribution network line losses, some shortcomings still exist. First, most studies focus on the optimization of active or reactive power alone, ignoring the mechanism by which the synergistic effect between the two affects distribution network line losses. Furthermore, existing research focuses on distribution networks and single-access scenarios, failing to fully consider the impact of the game-playing behavior of multiple entities, such as distributed power generation operators, virtual power plant operators, and reactive power compensation service providers, on line losses in different scenarios. This makes it difficult to formulate a coordinated active / reactive loss reduction strategy under this multi-agent game, making it difficult to achieve optimal line loss control in practical applications.

[0005] Therefore, how to invent a method for coordinated optimization of active and reactive power to reduce line losses while improving system efficiency has become an urgent problem to be solved. Summary of the Invention

[0006] To this end, the present invention provides a two-stage active and reactive collaborative optimization loss reduction method and device for a distribution network. In the first stage, a non-cooperative game model is adopted to seek the best active scheduling strategy among distributed power sources, distribution network operators and virtual power plant operators to achieve the comprehensive optimization of economic benefits and line loss minimization; in the second stage, based on the optimization results of the first stage, a cooperative game model is further adopted to optimize reactive power between distributed power sources and reactive compensation devices, further reduce line losses and ensure that the voltage does not exceed the limit.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a two-stage active and reactive coordinated optimization loss reduction method for a distribution network, comprising:

[0008] According to the active power optimization goal, an active power non-cooperative game model is constructed;

[0009] Solving the active power non-cooperative game model through the best response dynamic algorithm to obtain an active power scheduling plan, and sending the active power scheduling plan to the reactive power optimization stage;

[0010] According to the reactive power optimization goal, a reactive power cooperation game model is constructed;

[0011] By setting a numerical optimization algorithm, the reactive cooperation game model is solved and the optimal reactive output and benefit distribution of each entity are output.

[0012] As a preferred solution for a two-stage active and reactive power coordinated optimization loss reduction method for a distribution network, the active power optimization objectives include: the optimization objectives of a distributed power generation operator and the optimization objectives of a virtual power plant operator; the optimization objective of the distributed power generation operator is to maximize its own power generation revenue; the optimization objective of the virtual power plant operator is to maximize its own demand response revenue;

[0013] The function expression of the optimization objective of the distributed power operator is:

[0014]

[0015] Where U DG,i is the node voltage of the distributed power supply; P DG,i is the decision variable of the distributed power operator; C DG,i (P DG,i ) is the generation cost function of distributed generation operator i; π DG The electricity price is RMB / kWh;

[0016] The function expression of the optimization objective of the virtual power plant operator is:

[0017]

[0018] Where U VPP,j is the node voltage of the virtual power plant; ΔP VPP,j is the decision variable of the virtual power plant operator; C VPP,j (ΔP VPP,j ) is the regulation cost function of virtual power plant operator j; π VPP It is the demand response incentive electricity price, in yuan / kWh.

[0019] As a preferred solution for a two-stage active and reactive power coordinated optimization loss reduction method for a distribution network, the reactive power optimization objective is to minimize the total active line loss of the distribution network while ensuring safe and stable operation of the distribution network. The expression of the reactive power optimization objective is:

[0020]

[0021] Where L is the set of all lines in the distribution network; R kl is the resistance of the circuit (k, l); I kl is the current of line (k, l); {Q i} is the collection of all distributed power sources in the distribution network; N is the subject participating in the game; P loss is the total active line loss of the distribution network.

[0022] As a preferred solution for a two-stage active and reactive power coordinated optimization loss reduction method for a distribution network, the steps for solving the active non-cooperative game model are as follows:

[0023] Setting initial strategies based on the optimization objectives of the distributed generation operator and the virtual power plant operator;

[0024] For distributed power generation operators and virtual power plant operators, iterative solutions are performed to obtain the optimization strategies of distributed power generation operators and virtual power plant operators respectively;

[0025] Perform power flow calculations based on the optimization strategies of the distributed power generation operator and the virtual power plant operator to check whether the node voltage and line power meet the constraints; if so, proceed to the next step; if not, perform the optimization again to obtain a new optimization strategy;

[0026] According to the initial strategy, respectively calculating the main strategy changes of the distributed power operator and the virtual power plant operator to obtain the main strategy changes of the distributed power operator and the virtual power plant operator;

[0027] It is judged whether the change in the main strategy of the distributed power operator and the main strategy of the virtual power plant operator meet the set threshold; if the change in the strategy is less than the set threshold, the iteration converges and the final strategy is output; if the change in the strategy is not less than the set threshold, the iteration solution optimization is continued.

[0028] As a preferred solution for a two-stage active and reactive collaborative optimization loss reduction method for a distribution network, the steps for solving the reactive cooperative game model are as follows:

[0029] Based on the set constraints, a reactive power optimization model is constructed with the goal of minimizing the total active power line loss;

[0030] For the alliance formed by the cooperation of the game players, the reactive power optimization problem is solved to obtain the corresponding total bus loss; the alliance value is calculated by setting the value calculation formula;

[0031] By setting the Shapley value calculation formula, the Shapley value of the game subject is calculated;

[0032] Allocate the total income of the alliance to the game entities according to the Shapley value;

[0033] Check whether the profit distribution meets the core conditions; if the core conditions are met, output the optimal reactive power output and profit distribution of the game subject; if the core conditions are not met, continue the iterative calculation.

[0034] The present invention further provides a two-stage active and reactive collaborative optimization loss reduction device for a distribution network, which is based on the above two-stage active and reactive collaborative optimization loss reduction method for a distribution network, comprising:

[0035] Active power non-cooperative game model construction module, used to construct an active power non-cooperative game model according to the active power optimization target;

[0036] An active non-cooperative game model solving module is used to solve the active non-cooperative game model through the best response dynamic algorithm to obtain an active scheduling plan, and send the active scheduling plan to the reactive power optimization stage;

[0037] Reactive power cooperation game model construction module, used to construct a reactive power cooperation game model according to the reactive power optimization target;

[0038] The reactive cooperation game model solving module is used to solve the reactive cooperation game model by setting a numerical optimization algorithm, and output the optimal reactive power output and benefit distribution of each subject.

[0039] As a preferred solution for a two-stage active and reactive power collaborative optimization loss reduction device for a distribution network, in the active non-cooperative game model construction module, the active power optimization objectives include: the optimization objectives of the distributed power generation operator and the optimization objectives of the virtual power plant operator; the optimization objective of the distributed power generation operator is to maximize its own power generation revenue; the optimization objective of the virtual power plant operator is to maximize its own demand response revenue;

[0040] The function expression of the optimization objective of the distributed power operator is:

[0041]

[0042] Where U DG,i is the node voltage of the distributed power supply; P DG,i is the decision variable of the distributed power operator; C DG,i (P DG,i ) is the generation cost function of distributed generation operator i; π DG The electricity sales price is RMB / kWh.

[0043] The function expression of the optimization objective of the virtual power plant operator is:

[0044]

[0045] Where U VPP,j is the node voltage of the virtual power plant; ΔP VPP,j is the decision variable of the virtual power plant operator; C VPP,j (ΔP VPP,j ) is the regulation cost function of virtual power plant operator j; π VPP It is the demand response incentive electricity price, in yuan / kWh.

[0046] As a preferred solution for a two-stage active and reactive power coordinated optimization loss reduction device for a distribution network, in the reactive power cooperation game model construction module, the reactive power optimization objective is to minimize the total active line loss of the distribution network while ensuring the safe and stable operation of the distribution network. The expression of the reactive power optimization objective is:

[0047]

[0048] Where L is the set of all lines in the distribution network; R kl is the resistance of the circuit (k, l); I kl is the current of line (k, l); {Q i} is the collection of all distributed power sources in the distribution network; N is the subject participating in the game; P loss is the total active line loss of the distribution network.

[0049] As a preferred solution for a two-stage active and reactive collaborative optimization loss reduction device for a distribution network, in the active non-cooperative game model solving module, the active non-cooperative game model solving submodule includes:

[0050] An initial strategy setting submodule is used to set the initial strategy according to the optimization objectives of the distributed power generation operator and the optimization objectives of the virtual power plant operator;

[0051] The optimization strategy acquisition submodule is used to perform iterative solutions for distributed power operators and virtual power plant operators, respectively, to obtain the optimization strategies of distributed power operators and virtual power plant operators;

[0052] The constraint condition judgment and processing submodule is used to perform power flow calculation according to the optimization strategy of the distributed power supply operator and the optimization strategy of the virtual power plant operator, and check whether the node voltage and line power meet the constraint conditions; if the constraint conditions are met, the next step is carried out; if the constraint conditions are not met, the optimization is re-solved to obtain a new optimization strategy;

[0053] A strategy change calculation submodule is used to calculate the main strategy changes of the distributed power operator and the virtual power plant operator according to the initial strategy, and obtain the main strategy changes of the distributed power operator and the virtual power plant operator;

[0054] The convergence judgment and processing submodule is used to judge whether the change in the main strategy of the distributed power operator and the main strategy of the virtual power plant operator meet the set threshold; if the change in the strategy is less than the set threshold, the iterative convergence is performed and the final strategy is output; if the change in the strategy is not less than the set threshold, the iterative solution optimization is continued.

[0055] As a preferred solution for a two-stage active and reactive collaborative optimization loss reduction device for a distribution network, in the reactive cooperative game model solving module, the reactive cooperative game model solving submodule includes:

[0056] The reactive power optimization model construction submodule is used to construct a reactive power optimization model based on set constraints and with the goal of minimizing total active power line loss;

[0057] The bus loss and alliance value calculation and acquisition submodule is used to solve the reactive power optimization problem of the alliance formed by the participating game entities and obtain the corresponding bus loss; by setting the value calculation formula, the alliance value is calculated;

[0058] The Shapley value calculation submodule is used to calculate the Shapley value of the game subject by setting the Shapley value calculation formula;

[0059] A total income distribution submodule, used to distribute the total income of the alliance to the game entities according to the Shapley value;

[0060] The profit distribution judgment and processing submodule is used to check whether the profit distribution meets the core conditions; if the core conditions are met, the optimal reactive power output and profit distribution of the game subject are output; if the core conditions are not met, the iterative calculation continues.

[0061] The present invention has the following advantages: the present invention constructs an active non-cooperative game model according to the active power optimization target; solves the active non-cooperative game model through the best response dynamic algorithm to obtain an active scheduling plan, and sends the active scheduling plan to the reactive power optimization stage; the solving steps of the active non-cooperative game model are: setting an initial strategy according to the optimization target of the distributed power operator and the optimization target of the virtual power plant operator; performing iterative solutions for the distributed power operator and the virtual power plant operator respectively, and obtaining the optimization strategy of the distributed power operator and the optimization strategy of the virtual power plant operator respectively; performing flow calculation according to the optimization strategy of the distributed power operator and the optimization strategy of the virtual power plant operator Calculate and check whether the node voltage and line power meet the constraints; if the constraints are met, proceed to the next step; if the constraints are not met, re-solve and optimize to obtain a new optimization strategy; according to the initial strategy, calculate the main strategy changes of the distributed power operator and the virtual power plant operator respectively, and obtain the main strategy changes of the distributed power operator and the virtual power plant operator; judge whether the main strategy changes of the distributed power operator and the virtual power plant operator meet the set threshold; if the strategy changes are less than the set threshold, iterate and converge, and output the final strategy; if the strategy changes are not less than the set threshold, continue to iterate and optimize. According to the reactive power optimization target, construct a reactive cooperation game model; solve the reactive cooperation game model by setting a numerical optimization algorithm, and output the optimal reactive output and profit distribution of each entity. The steps for solving the reactive cooperative game model are as follows: based on the set constraints, with the goal of minimizing the total active power line loss, a reactive optimization model is constructed; for the alliance formed by the cooperation of the game entities, the reactive optimization problem is solved to obtain the corresponding total power loss; by setting a value calculation formula, the alliance value is calculated; by setting a Shapley value calculation formula, the Shapley value of the game entity is calculated; the total income of the alliance is distributed to the game entity according to the Shapley value; whether the income distribution meets the core conditions is checked; if the core conditions are met, the optimal reactive output and income distribution of the game entity are output; if the core conditions are not met, the iterative calculation is continued. In the first stage of the present invention, a non-cooperative game model is adopted to seek the best active power scheduling strategy between distributed power sources, distribution network operators and virtual power plant operators to achieve the comprehensive optimization of economic benefits and line loss minimization; in the second stage, based on the optimization results of the first stage, a cooperative game model is further adopted to optimize reactive power between distributed power sources and reactive compensation devices, further reduce line losses and ensure that the voltage does not exceed the limit. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0063] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0064] Figure 1 This is a flow chart of a two-stage active and reactive collaborative optimization loss reduction method for a distribution network provided in Example 1 of the present invention;

[0065] Figure 2 This is a schematic diagram of the architecture of a multi-agent game loss reduction method in a two-stage active and reactive collaborative optimization loss reduction method for a distribution network provided in Example 1 of the present invention;

[0066] Figure 3 This is a schematic diagram of an IEEE 33-node power distribution system in a possible embodiment provided in Embodiment 1 of the present invention;

[0067] Figure 4 This is a schematic diagram of DG and VPP output in a possible embodiment provided in Example 1 of the present invention;

[0068] Figure 5 This is a schematic diagram of the DG reactive output and the reactive compensation amount of the reactive compensation service provider in a possible embodiment provided in Example 1 of the present invention;

[0069] Figure 6 This is a schematic diagram showing a comparison of line losses before and after gaming in a possible embodiment provided in Example 1 of the present invention;

[0070] Figure 7 This is a schematic diagram of the architecture of a two-stage active and reactive collaborative optimization and loss reduction device for a distribution network provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0071] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. It is apparent that the described embodiments are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0072] Example 1

[0073] See also Figure 1 and Figure 2 Embodiment 1 of the present invention provides a two-stage active and reactive coordinated optimization loss reduction method for a distribution network, comprising the following steps:

[0074] S1. Construct an active power non-cooperative game model based on the active power optimization goal;

[0075] S2. Solve the active power non-cooperative game model using the best response dynamic algorithm to obtain an active power scheduling plan, and send the active power scheduling plan to the reactive power optimization stage;

[0076] S3. Construct a reactive power cooperation game model based on the reactive power optimization goal;

[0077] S4. Solve the reactive cooperation game model by setting a numerical optimization algorithm, and output the optimal reactive output and benefit distribution of each entity.

[0078] In this example, the impact of multi-agent game play on distribution network line losses is primarily due to the independent decisions made by each entity (distributed power generation, virtual power plant operators, and reactive power compensation service providers) based on their own interests. This uncoordinated decision-making behavior can lead to uneven power flow distribution in the distribution network, thereby increasing line losses.

[0079] Distribution network operator: As a distribution network operator, with the goal of safe and economical operation of the power grid, it changes the power flow distribution in the network by adjusting the output of adjustable flexible resources and the power of interconnection lines within its jurisdiction to achieve safe, economical and loss-reducing operation of the power grid.

[0080] Distributed power generation operators: As operators of distributed generation equipment, they sell as much electricity as possible to the distribution network to maximize profits. However, the randomness and volatility of this energy can affect the safe and economic operation of the distribution network, such as increased line losses and larger voltage fluctuations.

[0081] Virtual power plant operators adjust their electricity consumption plans based on electricity price incentives, shifting load curves and supporting the dispatching and operation of distribution networks. This behavior not only directly affects the supply and demand balance in the electricity market but also has a direct impact on the power flow distribution of the distribution network.

[0082] Reactive power compensation service providers: Their independent decisions may, in certain circumstances, lead to localized surpluses or shortages of reactive power, impacting voltage levels and line losses. This surplus or shortage of reactive power can cause voltage fluctuations, impacting the stable operation of the power grid.

[0083] In the electricity market, these stakeholders independently optimize their own interests, influencing and constraining each other. Each stakeholder's benefits are influenced by the decision variables of other stakeholders, necessitating the use of game theory to resolve these conflicts and optimize the overall system's operation.

[0084] In this embodiment, in step S1, an active power non-cooperative game model is constructed according to the active power optimization target;

[0085] Specifically, distributed power generation operators, distribution network operators, and virtual power plant operators act as game participants, each pursuing the maximization of their own utility. Based on the optimization goals of the game players, an active non-cooperative game model is constructed;

[0086] Among them, Distributed Generation Operators, denoted as set N DG , where each element represents a distributed power generation operator, responsible for controlling the active power output of its distributed power generation, with the goal of maximizing its own power generation revenue. Virtual Power Plant Operators (VPOs), denoted as set N VPP , where each element represents a virtual power plant operator that participates in demand response by adjusting controllable loads, with the goal of maximizing demand response benefits. The subject set can be expressed as:

[0087] N=N DG ∪N VPP (1)

[0088] The decision variable of the distributed generation operator is P DG,i , represents the active power output of distributed power operator i, and the output constraint space is The decision variable for the virtual power plant operator is ΔP VPP,j , represents the active power regulation of virtual power plant operator j, and the output constraint space is

[0089] Both parties employ a non-cooperative game, where each agent makes independent decisions, pursuing its own interests without considering cooperation with other agents. Each agent, knowing the strategies of the other players, optimizes its own strategy to maximize its own gains. The solution to the game is the set of all agent strategies, known as the Nash equilibrium. At this equilibrium, no agent can unilaterally change its strategy to achieve higher gains.

[0090] The optimization goal of the distributed power generation operator is to maximize its own power generation revenue; the function expression of the optimization goal of the distributed power generation operator is:

[0091]

[0092] Where U DG,i is the node voltage of the distributed power supply; P DG,i is the decision variable of the distributed power operator; C DG,i (P DG,i ) is the power generation cost function of distributed power operator i, which usually takes the form of a quadratic function; DG The electricity price is RMB / kWh:

[0093]

[0094] Where a i 、b i 、c i is the corresponding cost coefficient.

[0095] The constraints are:

[0096]

[0097] Where, is the minimum value of the decision variable of the distributed generation operator; is the maximum value of the decision variable of the distributed generation operator.

[0098] In this embodiment, the optimization goal of the virtual power plant operator is to maximize its own demand response benefits; the function expression of the optimization goal of the virtual power plant operator is:

[0099]

[0100] Where U VPP,j is the node voltage of the virtual power plant; ΔP VPP,j is the decision variable of the virtual power plant operator; C VPP,j (ΔP VPP,j) is the regulation cost function of virtual power plant operator j, which usually takes the form of a quadratic function; VPP The demand response incentive electricity price is in yuan / kWh:

[0101]

[0102] Where, d i 、e i 、f i is the corresponding cost coefficient

[0103] The constraints are:

[0104]

[0105] Where, is the minimum value of the decision variable of the virtual power plant operator; is the maximum value of the decision variable of the virtual power plant operator;

[0106] The voltage amplitude of each node in the distribution network must be kept within a specified range. For node k∈β (β represents the set of all nodes), the voltage constraint can be expressed as:

[0107]

[0108] Where V k is the voltage amplitude of node k; and They are the lower and upper limits of the node voltage, respectively, and are usually set to 0.95pu and 1.05pu.

[0109] In this embodiment, to prevent line overload and ensure safe operation of the line, it is necessary to limit the transmission power or current of the line. The line capacity constraint is that for line (k, l)∈L (L is the set of lines), the transmission of active power and reactive power cannot exceed its rated capacity; the active and reactive power constraints are:

[0110]

[0111] Where, P kl and Q kl are the active and reactive powers on line (k,l) respectively. 'max' represents the active and reactive capacity of the line.

[0112] In this embodiment, the Distribution Network Operator (DNO) does not directly participate in the game, and its goal is to minimize the total loss of the distribution network. DG and π VPP To indirectly influence the decision-making of distributed power sources and virtual power plants.

[0113] In this embodiment, in step S2, the active non-cooperative game model is solved by the best response dynamic algorithm to obtain an active power scheduling plan, and the active power scheduling plan is sent to the reactive power optimization stage;

[0114] Specifically, the best response function of each subject is constructed first: for distributed power operator i, its best response function is to solve its own optimal strategy based on the fixed strategies of other subjects. Make U DG,i Maximize; for virtual power plant operator j, its best response function is to solve its own optimal strategy based on the fixed strategies of other entities Make U VPP,j Then, an iterative algorithm based on the best response is used to solve the Nash equilibrium.

[0115] The steps for solving the active non-cooperative game model are:

[0116] S21. setting an initial strategy based on the optimization objectives of the distributed power generation operator and the virtual power plant operator;

[0117] The initial strategy expression is:

[0118]

[0119] Where, is the initial decision variable of the distributed generation operator; are the initial decision variables of the virtual power plant operator.

[0120] S22, performing iterative solutions for distributed power operators and virtual power plant operators, respectively, to obtain the optimization strategies of distributed power operators and virtual power plant operators;

[0121] Specifically, for each distributed power operator i, fix the strategies of other entities and solve the optimization problem shown in formulas (2)-(4):

[0122] Because C DG,i (P DG,i ) is a quadratic function. The above problem is a one-dimensional convex optimization problem, and the optimal solution can be obtained by differentiation:

[0123]

[0124] The solution is:

[0125]

[0126] like If the output limit is met, this value is used; if If the limit is exceeded, the corresponding upper and lower limits are taken.

[0127] For each virtual power plant operator j: fix the strategies of other entities and solve the optimization problem shown in formulas (5)-(7). Similarly, C VPP,i (ΔP VPP,j ) is a quadratic function. The above problem is a one-dimensional convex optimization problem. The optimal solution is obtained by derivation:

[0128]

[0129] The solution is:

[0130]

[0131] If satisfied If the adjustment limit is met, this value is used; If the limit is exceeded, the corresponding upper and lower limits are taken.

[0132] S23. Perform power flow calculation according to the optimization strategy of the distributed power supply operator and the optimization strategy of the virtual power plant operator to check whether the node voltage and line power meet the constraints; if so, proceed to the next step; if not, perform the optimization again to obtain a new optimization strategy;

[0133] Specifically, the strategy is updated according to the optimization strategy of the distributed power supply operator and the optimization strategy of the virtual power plant operator:

[0134]

[0135] Check the network security constraints shown in formulas (8)-(10): After updating the strategy, perform power flow calculations to check whether the node voltage, line power, etc. meet the constraints. If not, adjust the strategy.

[0136] S24. Calculate the main strategy changes of the distributed power generation operator and the virtual power plant operator respectively according to the initial strategy to obtain the main strategy changes of the distributed power generation operator and the virtual power plant operator;

[0137] Among them, the change of each subject's strategy is calculated:

[0138]

[0139] Where ΔP DG,i is the change in the distributed power operator's strategy; Δ(ΔP VPP,j ) is the change in the strategy of the virtual power plant operator.

[0140] S25. Determine whether the change in the distributed power supply operator's main strategy and the change in the virtual power plant operator's main strategy meet the set threshold; if the strategy change is less than the set threshold, iterate and converge, and output the final strategy; if the strategy change is not less than the set threshold, continue to iterate and solve the optimization.

[0141] Specifically, if the policy changes of all agents are less than a preset threshold ε, the iteration converges and the final policy is output. If convergence is not reached, k=k+1 is set and the process returns to step S22 to continue iteration.

[0142] In this embodiment, in each iteration, it is necessary to ensure that the agent's strategy meets its physical constraints and network security constraints; at the same time, since the optimal response of each agent is a convex optimization problem and the objective function is a concave function with respect to the strategy variable, the iterative algorithm has good convergence; although the agents each optimize their own benefits, it is necessary to guide their decisions through mechanisms such as electricity prices to facilitate the overall optimization of the distribution network.

[0143] In this embodiment, in step S3, a reactive power cooperation game model is constructed according to the reactive power optimization target;

[0144] Specifically, in the second-stage reactive power cooperation game model, the players involved in the game include: distributed power generation operators with reactive power regulation capabilities and reactive power compensation service providers. The set of players can be expressed as:

[0145] N=N DG ∪N QC (19)

[0146] The decision variable of the distributed generation operator is Q DG,i , represents the active power output of distributed power operator i, and the output constraint space is The decision variable of the reactive power compensation service provider is ΔQ C,j , represents the reactive power regulation of reactive power compensation service provider j, and the output constraint space is Cooperative Game refers to the cooperation among entities to form an alliance and jointly optimize reactive power distribution to minimize line losses.

[0147] Defining the Alliance The value function v(S) represents the line loss reduction that alliance S can obtain through cooperation:

[0148]

[0149] Where, Baseline loss when no subject participates in reactive power regulation; Optimize the bus loss after reactive power output for alliance S cooperation.

[0150] The goal of alliance N is to minimize the total active line loss of the distribution network while ensuring the safety and stable operation of the network. The optimization problem can be expressed as:

[0151]

[0152] Where L is the set of all lines in the distribution network; R kl is the resistance of the circuit (k, l); I kl is the current of line (k, l); {Q i} is the collection of all distributed power sources in the distribution network; N is the subject participating in the game; P loss is the total active line loss of the distribution network.

[0153] The constraints are:

[0154]

[0155] Where Q i Provide reactive power to distributed power generation operators; The minimum reactive power output of the distributed power generation operator; It is the maximum reactive power output of the distributed power generation operator.

[0156] In this embodiment, in step S4, the reactive cooperation game model is solved by setting a numerical optimization algorithm, and the optimal reactive output and profit distribution of each entity is output.

[0157] Specifically, the steps for solving the reactive cooperation game model are:

[0158] S41. Based on the set constraints, a reactive power optimization model is constructed with the goal of minimizing the total active power line loss;

[0159] Among them, the optimization variable is the reactive power output of all entities {Q i}i∈N.

[0160] S42. Solve the reactive power optimization problem for the alliance formed by the cooperative game participants to obtain the corresponding total bus loss; calculate the alliance value by setting a value calculation formula;

[0161] Specifically, for all possible alliances Solve the reactive power optimization problem and get the corresponding bus loss The alliance value is calculated using formula (20).

[0162] S43. Calculate the Shapley value of the game subject by setting a Shapley value calculation formula;

[0163] Specifically, for each subject i∈N, calculate its Shapley value Φi , which represents its average marginal contribution to the alliance:

[0164]

[0165] Where |S| is the number of players in the alliance S; |N| is the total number of all entities.

[0166] Since the number of alliances grows exponentially with the number of entities, approximate methods such as Monte Carlo simulation can be used to calculate the Shapley value.

[0167] S44. Allocating the total income of the alliance to the game entities according to the Shapley value;

[0168] Specifically, the total revenue v(N) of the alliance is calculated according to the Shapley value Φ i Assigned to each entity:

[0169] R i =Φ i v(N)(25)

[0170] Where R i For the benefit of each entity.

[0171] S45. Check whether the profit distribution meets the core conditions; if the core conditions are met, output the optimal reactive power output and profit distribution of the game subject; if the core conditions are not met, continue the iterative calculation.

[0172] Specifically, check whether the income distribution meets the core conditions:

[0173]

[0174] If the conditions are met, the cooperation is stable, and the optimal reactive power output and profit distribution of the game players are output; if the core conditions are not met, the iterative calculation continues.

[0175] In this embodiment, since the reactive power optimization problem is a nonlinear programming problem, it can be solved using numerical optimization methods such as the interior point method and gradient descent method. The number of alliances grows exponentially with the number of entities. For a large number of entities, an approximate algorithm is needed to simplify the calculation.

[0176] In a possible embodiment, an example of two-stage active and reactive coordinated optimization loss reduction in a power distribution system is provided as follows:

[0177] like Figure 3 As shown, the calculation example uses an IEEE 33-node distribution system, consisting of 33 nodes and 32 lines with a rated voltage of 12.66 kV. The total active load is approximately 3715 kW, and the total reactive load is approximately 2300 kVar. The trunk lines have a tree-like structure with some branch lines connected, reflecting typical distribution network characteristics.

[0178] The DG installation nodes and adjustable capacities are shown in Table 1, and the corresponding power generation cost coefficients are shown in Table 2.

[0179] DG Number Access Node Maximum active output (kW) Maximum reactive power output (kVar) 1 14 200 80 2 25 150 60 3 30 100 40

[0180] Table 1 DG installation nodes and capacity

[0181] DG Number <![CDATA[a (yuan / kW 2 h)]]> b(yuan / kWh) c(yuan / h) 1 0.0002 0.4 0 2 0.00015 0.35 0 3 0.00018 0.38 0

[0182] Table 2 Power generation cost coefficients at DG installation nodes

[0183] The VPP access nodes and adjustable active capacity are shown in Table 3. The cost coefficients are d = 0.0001, e = 0.00012, and f = 0.00015.

[0184] VPP Number Access Node Adjustable power (kW) 1 5 100 2 18 150 3 27 100

[0185] Table 3 VPP access nodes and adjustable active power

[0186] The access nodes and adjustable reactive power of reactive compensation service providers are shown in Table 4, and the corresponding cost coefficients are 0.0001, 0.00015, and 0.0002, respectively.

[0187] QC Number Access Node Compensable reactive power (kVar) 1 33 100 2 18 120 3 22 150

[0188] Table 4 QC access nodes and adjustable reactive power

[0189] After the first stage of active non-cooperative game, the output of each DG and the VPP adjustment power are as follows: Figure 4 shown.

[0190] After the second stage of reactive cooperation game, the reactive power output of each DG and the reactive power adjusted by the reactive compensation service provider are as follows: Figure 5 shown.

[0191] The comparison of line loss before and after the two-stage game is as follows: Figure 6 As shown in the figure, the network losses in the first and second phases decreased by 4.9% and 10.8% respectively, and the distribution network line losses were significantly improved.

[0192] In summary, the present invention constructs an active non-cooperative game model according to the active power optimization target; solves the active non-cooperative game model through the best response dynamic algorithm to obtain an active scheduling plan, and sends the active scheduling plan to the reactive power optimization stage; the solving steps of the active non-cooperative game model are: setting an initial strategy according to the optimization target of the distributed power operator and the optimization target of the virtual power plant operator; performing iterative solutions for the distributed power operator and the virtual power plant operator respectively, and obtaining the optimization strategy of the distributed power operator and the optimization strategy of the virtual power plant operator respectively; performing flow calculation according to the optimization strategy of the distributed power operator and the optimization strategy of the virtual power plant operator, and checking Check whether the node voltage and line power meet the constraints; if they do, proceed to the next step; if they do not, re-solve and optimize to obtain a new optimization strategy; based on the initial strategy, calculate the main strategy changes of the distributed power operator and the virtual power plant operator respectively to obtain the main strategy changes of the distributed power operator and the virtual power plant operator; judge whether the main strategy changes of the distributed power operator and the virtual power plant operator meet the set threshold; if the strategy changes are less than the set threshold, iterate to converge and output the final strategy; if the strategy changes are not less than the set threshold, continue iterative optimization. According to the reactive power optimization target, construct a reactive cooperation game model; solve the reactive cooperation game model by setting a numerical optimization algorithm, and output the optimal reactive output and profit distribution of each entity. The steps for solving the reactive cooperative game model are as follows: based on the set constraints, with the goal of minimizing the total active power line loss, a reactive optimization model is constructed; for the alliance formed by the cooperation of the game entities, the reactive optimization problem is solved to obtain the corresponding total power loss; by setting a value calculation formula, the alliance value is calculated; by setting a Shapley value calculation formula, the Shapley value of the game entity is calculated; the total income of the alliance is distributed to the game entity according to the Shapley value; whether the income distribution meets the core conditions is checked; if the core conditions are met, the optimal reactive output and income distribution of the game entity are output; if the core conditions are not met, the iterative calculation is continued. In the first stage of the present invention, a non-cooperative game model is adopted to seek the best active power scheduling strategy between distributed power sources, distribution network operators and virtual power plant operators to achieve the comprehensive optimization of economic benefits and line loss minimization; in the second stage, based on the optimization results of the first stage, a cooperative game model is further adopted to optimize reactive power between distributed power sources and reactive compensation devices, further reduce line losses and ensure that the voltage does not exceed the limit.

[0193] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0194] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0195] Example 2

[0196] See also Figure 7 Embodiment 2 of the present invention further provides a two-stage active and reactive power coordinated optimization loss reduction device for a distribution network, comprising:

[0197] Active power non-cooperative game model construction module 001, used to construct an active power non-cooperative game model according to the active power optimization target;

[0198] Active power non-cooperative game model solving module 002 is used to solve the active power non-cooperative game model through the best response dynamic algorithm to obtain an active power scheduling plan, and send the active power scheduling plan to the reactive power optimization stage;

[0199] Reactive power cooperation game model construction module 003, used to construct a reactive power cooperation game model according to the reactive power optimization target;

[0200] The reactive cooperation game model solving module 004 is used to solve the reactive cooperation game model by setting a numerical optimization algorithm, and output the optimal reactive power output and benefit distribution of each entity.

[0201] In this embodiment, in the active power non-cooperative game model construction module 001, the active power optimization objectives include: the optimization objectives of the distributed power generation operator and the optimization objectives of the virtual power plant operator; the optimization objective of the distributed power generation operator is to maximize its own power generation revenue; the optimization objective of the virtual power plant operator is to maximize its own demand response revenue;

[0202] The function expression of the optimization objective of the distributed power operator is:

[0203]

[0204] Where U DG,i is the node voltage of the distributed power supply; P DG,i is the decision variable of the distributed power operator; C DG,i (P DG,i ) is the generation cost function of distributed generation operator i; π DG The electricity sales price is RMB / kWh.

[0205] The function expression of the optimization objective of the virtual power plant operator is:

[0206]

[0207] Where U VPP,j is the node voltage of the virtual power plant; ΔP VPP,j is the decision variable of the virtual power plant operator; C VPP,j (ΔP VPP,j ) is the regulation cost function of virtual power plant operator j; π VPP It is the demand response incentive electricity price, in yuan / kWh.

[0208] In this embodiment, in the reactive power cooperation game model construction module 003, the reactive power optimization objective is to minimize the total active line loss of the distribution network while ensuring the safe and stable operation of the distribution network. The expression of the reactive power optimization objective is:

[0209]

[0210] Where L is the set of all lines in the distribution network; R kl is the resistance of the circuit (k, l); I kl is the current of line (k, l); {Q i} is the collection of all distributed power sources in the distribution network; N is the subject participating in the game; P loss is the total active line loss of the distribution network.

[0211] In this embodiment, in the active non-cooperative game model solving module 002, the active non-cooperative game model solving submodule includes:

[0212] The initial strategy setting submodule 021 is used to set the initial strategy according to the optimization objectives of the distributed power generation operator and the optimization objectives of the virtual power plant operator;

[0213] The optimization strategy acquisition submodule 022 is used to perform iterative solutions for distributed power operators and virtual power plant operators, respectively, to obtain the optimization strategies of distributed power operators and virtual power plant operators;

[0214] The constraint condition judgment and processing submodule 023 is used to perform power flow calculation based on the optimization strategy of the distributed power supply operator and the optimization strategy of the virtual power plant operator, and check whether the node voltage and line power meet the constraint conditions; if the constraint conditions are met, the next step is carried out; if the constraint conditions are not met, the optimization is re-solved to obtain a new optimization strategy;

[0215] The strategy change calculation submodule 024 is used to calculate the main strategy changes of the distributed power operator and the virtual power plant operator according to the initial strategy, and obtain the main strategy changes of the distributed power operator and the virtual power plant operator;

[0216] The convergence judgment and processing submodule 025 is used to judge whether the change in the main strategy of the distributed power operator and the main strategy of the virtual power plant operator meet the set threshold; if the change in the strategy is less than the set threshold, the iterative convergence is performed and the final strategy is output; if the change in the strategy is not less than the set threshold, the iterative solution optimization is continued.

[0217] In this embodiment, in the reactive cooperation game model solving module 004, the reactive cooperation game model solving submodule includes:

[0218] The reactive power optimization model construction submodule 041 is used to construct a reactive power optimization model based on set constraints and with the goal of minimizing total active power line loss;

[0219] The total bus loss and alliance value calculation and acquisition submodule 042 is used to solve the reactive power optimization problem of the alliance formed by the game entities and obtain the corresponding total bus loss; and calculate the alliance value by setting a value calculation formula;

[0220] The Shapley value calculation submodule 043 is used to calculate the Shapley value of the game subject by setting the Shapley value calculation formula;

[0221] The total income distribution submodule 044 is used to distribute the total income of the alliance to the game entities according to the Shapley value;

[0222] The profit distribution judgment and processing submodule 045 is used to check whether the profit distribution meets the core conditions; if the core conditions are met, the optimal reactive power output and profit distribution of the game subject are output; if the core conditions are not met, the iterative calculation is continued.

[0223] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and no further details will be given here.

[0224] Example 3

[0225] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a two-stage active and reactive collaborative optimization loss reduction method for a distribution network is stored. The program code includes instructions for executing embodiment 1 or any possible implementation method of a two-stage active and reactive collaborative optimization loss reduction method for a distribution network.

[0226] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0227] Example 4

[0228] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0229] The processor and the memory communicate with each other through a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a two-stage active and reactive collaborative optimization loss reduction method for a distribution network according to embodiment 1 or any possible implementation thereof.

[0230] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0231] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0232] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0233] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A two-stage active and reactive collaborative optimization loss reduction method for distribution network, characterized in that: include: According to the active power optimization goal, an active power non-cooperative game model is constructed; Solving the active power non-cooperative game model through the best response dynamic algorithm to obtain an active power scheduling plan, and sending the active power scheduling plan to the reactive power optimization stage; According to the reactive power optimization goal, a reactive power cooperation game model is constructed; By setting a numerical optimization algorithm, the reactive cooperation game model is solved and the optimal reactive output and benefit distribution of each entity are output.

2. A two-stage active and reactive coordinated optimization loss reduction method for distribution network according to claim 1, characterized in that: The active power optimization objectives include: the optimization objectives of the distributed power generation operator and the optimization objectives of the virtual power plant operator; the optimization objective of the distributed power generation operator is to maximize its own power generation revenue; the optimization objective of the virtual power plant operator is to maximize its own demand response revenue; The function expression of the optimization objective of the distributed power operator is: Where U DG,i is the node voltage of the distributed power supply; P DG,i is the decision variable of the distributed power operator; C DG,i (P DG,i ) is the generation cost function of distributed generation operator i; π DG The electricity price is RMB / kWh; The function expression of the optimization objective of the virtual power plant operator is: Where U VPP,j is the node voltage of the virtual power plant; ΔP VPP,j is the decision variable of the virtual power plant operator; C VPP,j (ΔP VPP,j ) is the regulation cost function of virtual power plant operator j; π VPP It is the demand response incentive electricity price, in yuan / kWh.

3. A two-stage active and reactive power coordinated optimization loss reduction method for distribution network according to claim 2, characterized in that: The reactive power optimization objective is to minimize the total active line loss of the distribution network while ensuring safe and stable operation of the distribution network. The expression of the reactive power optimization objective is: Where L is the set of all lines in the distribution network; R kl is the resistance of the circuit (k, l); I kl is the current of line (k, l); {Q i } is the collection of all distributed power sources in the distribution network; N is the subject participating in the game; P loss is the total active line loss of the distribution network.

4. A two-stage active and reactive coordinated optimization loss reduction method for distribution network according to claim 3, characterized in that: The steps for solving the active non-cooperative game model are: Setting initial strategies based on the optimization objectives of the distributed generation operator and the virtual power plant operator; For distributed power generation operators and virtual power plant operators, iterative solutions are performed to obtain the optimization strategies of distributed power generation operators and virtual power plant operators respectively; Perform power flow calculations based on the optimization strategies of the distributed power generation operator and the virtual power plant operator to check whether node voltage and line power meet constraints; if so, proceed to the next step; If the constraints are not met, the optimization is performed again to obtain a new optimization strategy; According to the initial strategy, respectively calculating the main strategy changes of the distributed power operator and the virtual power plant operator to obtain the main strategy changes of the distributed power operator and the virtual power plant operator; It is judged whether the change in the main strategy of the distributed power operator and the main strategy of the virtual power plant operator meet the set threshold; if the change in the strategy is less than the set threshold, the iteration converges and the final strategy is output; if the change in the strategy is not less than the set threshold, the iteration solution optimization is continued.

5. A two-stage active and reactive coordinated optimization loss reduction method for distribution network according to claim 4, characterized in that: The steps for solving the reactive cooperation game model are: Based on the set constraints, a reactive power optimization model is constructed with the goal of minimizing the total active power line loss; Solve the reactive power optimization problem for the alliance formed by the cooperation of the game players and obtain the corresponding total bus loss; By setting the value calculation formula, the alliance value is calculated; By setting the Shapley value calculation formula, the Shapley value of the game subject is calculated; Allocate the total income of the alliance to the game entities according to the Shapley value; Check whether the profit distribution meets the core conditions; if the core conditions are met, output the optimal reactive power output and profit distribution of the game subject; if the core conditions are not met, continue the iterative calculation.

6. A two-stage active and reactive collaborative optimization loss reduction device for a distribution network, adopting a two-stage active and reactive collaborative optimization loss reduction method for a distribution network according to any one of claims 1 to 5, characterized in that: include: Active power non-cooperative game model construction module, used to construct an active power non-cooperative game model according to the active power optimization target; An active non-cooperative game model solving module is used to solve the active non-cooperative game model through the best response dynamic algorithm to obtain an active scheduling plan, and send the active scheduling plan to the reactive power optimization stage; Reactive power cooperation game model construction module, used to construct a reactive power cooperation game model according to the reactive power optimization target; The reactive cooperation game model solving module is used to solve the reactive cooperation game model by setting a numerical optimization algorithm, and output the optimal reactive power output and benefit distribution of each subject.

7. A two-stage active and reactive power coordinated optimization loss reduction device for a distribution network according to claim 6, characterized in that: In the active non-cooperative game model construction module, the active power optimization objectives include: the optimization objectives of the distributed power generation operator and the optimization objectives of the virtual power plant operator; the optimization objective of the distributed power generation operator is to maximize its own power generation revenue; the optimization objective of the virtual power plant operator is to maximize its own demand response revenue; The function expression of the optimization objective of the distributed power operator is: Where U DG,i is the node voltage of the distributed power supply; P DG,i is the decision variable of the distributed power operator; C DG,i (P DG,i ) is the generation cost function of distributed generation operator i; π DG The electricity price is RMB / kWh; The function expression of the optimization objective of the virtual power plant operator is: Where U VPP,j is the node voltage of the virtual power plant; ΔP VPP,j is the decision variable of the virtual power plant operator; C VPP,j (ΔP VPP,j ) is the regulation cost function of virtual power plant operator j; π VPP It is the demand response incentive electricity price, in yuan / kWh.

8. A two-stage active and reactive power coordinated optimization loss reduction device for a distribution network according to claim 7, characterized in that: In the reactive power cooperation game model construction module, the reactive power optimization objective is to minimize the total active line loss of the distribution network while ensuring the safe and stable operation of the distribution network. The expression of the reactive power optimization objective is: Where L is the set of all lines in the distribution network; R kl is the resistance of the circuit (k, l); I kl is the current of line (k, l); {Q i } is the collection of all distributed power sources in the distribution network; N is the subject participating in the game; P loss is the total active line loss of the distribution network.

9. A two-stage active and reactive power coordinated optimization loss reduction device for a distribution network according to claim 8, characterized in that: In the active non-cooperative game model solving module, the active non-cooperative game model solving submodule includes: An initial strategy setting submodule is used to set the initial strategy according to the optimization objectives of the distributed power generation operator and the optimization objectives of the virtual power plant operator; The optimization strategy acquisition submodule is used to perform iterative solutions for distributed power operators and virtual power plant operators, respectively, to obtain the optimization strategies of distributed power operators and virtual power plant operators; The constraint condition judgment and processing submodule is used to perform power flow calculation according to the optimization strategy of the distributed power supply operator and the optimization strategy of the virtual power plant operator, and check whether the node voltage and line power meet the constraint conditions; if the constraint conditions are met, the next step is carried out; if the constraint conditions are not met, the optimization is re-solved to obtain a new optimization strategy; A strategy change calculation submodule is used to calculate the main strategy changes of the distributed power operator and the virtual power plant operator according to the initial strategy, and obtain the main strategy changes of the distributed power operator and the virtual power plant operator; The convergence judgment and processing submodule is used to judge whether the change in the main strategy of the distributed power operator and the main strategy of the virtual power plant operator meet the set threshold; if the change in the strategy is less than the set threshold, the iterative convergence is performed and the final strategy is output; if the change in the strategy is not less than the set threshold, the iterative solution optimization is continued.

10. A two-stage active and reactive power coordinated optimization loss reduction device for a distribution network according to claim 9, characterized in that: In the reactive cooperation game model solving module, the reactive cooperation game model solving submodule includes: The reactive power optimization model construction submodule is used to construct a reactive power optimization model based on set constraints and with the goal of minimizing total active power line loss; The bus loss and alliance value calculation and acquisition submodule is used to solve the reactive power optimization problem of the alliance formed by the participating game entities and obtain the corresponding bus loss; by setting the value calculation formula, the alliance value is calculated; The Shapley value calculation submodule is used to calculate the Shapley value of the game subject by setting the Shapley value calculation formula; A total income distribution submodule, used to distribute the total income of the alliance to the game entities according to the Shapley value; The profit distribution judgment and processing submodule is used to check whether the profit distribution meets the core conditions; if the core conditions are met, the optimal reactive power output and profit distribution of the game subject are output; if the core conditions are not met, the iterative calculation continues.

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