Active power distribution network scheduling method and system considering power interaction between microgrids

Abstract

The invention discloses an active power distribution network scheduling method considering power interaction between micro-grids. The method comprises the following steps: establishing a sub-scheduling model of each micro-grid and a master scheduling model and a slave scheduling model of an active power distribution network; the slave scheduling model allocates the saved cost of the micro-grid alliance to each micro-grid according to the ratio of the sum of the interaction power of each micro-grid to the sum of the interaction power of all micro-grids in the scheduling period; the slave scheduling model and master scheduling model interaction module is used for sending the sum of the net loads of all micro-grids to the master scheduling model from the scheduling model, and the master scheduling model outputs customized electricity price; outputting a response scheme for the customized electricity price from the scheduling model; establishing an interaction relationship between the customized electricity price output by the master scheduling model and the response scheme for the customized electricity price output by the slave scheduling model based on a fixed point mapping method; and based on the master-slave game model, solving an equilibrium solution of the master scheduling model and the slave scheduling model which have an interaction relationship, and as an active power distribution network scheduling method, improving the cooperative operation efficiency among multiple subjects.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization and dispatching, and in particular, relates to an active distribution network dispatching method and system taking into account power interaction between microgrids. Background Art

[0002] With the rapid development of distributed power generation, energy storage is configured in each microgrid. In order to take into account the power fluctuation smoothing effect of the microgrid group and the cost of energy storage use, an effective method is to use shared energy storage in the microgrid group.

[0003] In the prior art, the utilization rate of energy storage is improved by configuring the capacity of long-term and short-term shared energy storage, and a leasing and scheduling model for microgrid operators and shared energy storage operators is established to promote the efficient utilization of energy storage resources and the local consumption of renewable energy. When the microgrid group operates jointly, all microgrids in the multi-microgrid system are set as energy management entities with equal status, and a cooperative game model of the multi-microgrid system is established. The goal is to maximize the benefits of the microgrid alliance, obtain the optimal interactive power and transaction price between microgrids, and adopt a cost allocation method based on Nucleolus in the cooperative game. The Shapley value is used to share the cooperative benefits to ensure fair cost sharing among members of the microgrid alliance. The two-stage robust optimization scheduling model for multiple microgrids conducts complementary competitive transactions for surplus and shortage new energy between microgrids, and introduces the VCG (Vickrey-Clarke-Groves) mechanism to deal with the behavior of microgrids falsely reporting the valuation of surplus and shortage new energy, so as to achieve efficient local consumption of new energy. In terms of the interaction between microgrids and distribution networks, distributed modeling methods are used. Microgrids and distribution networks are regarded as different stakeholders, and different optimization goals are established. Through the target cascade method, the interconnection line power is equivalent to virtual generators and virtual loads, realizing the decoupling and parallel solution of distribution network and microgrid operation. With the distribution network as the main body and the microgrid alliance as the slave, a master-slave game scheduling model is constructed. The distribution network, as the leader, sets the transaction price with the microgrid with the goal of minimizing the operating cost. The microgrid, as a follower, forms a cooperative alliance under the optimized electricity price to formulate the microgrid scheduling strategy and benefit distribution mechanism, so as to achieve a win-win situation for different stakeholders. A microgrid-distribution network coordination optimization model based on cooperative game is proposed, with bargaining theory as the core, and the goal of maximizing the interests of each party and the alliance. The interactive electricity price and interactive power are determined through negotiation, and the improved Shapley method is used to distribute the additional benefits of the alliance. In the above distributed modeling of microgrids and distribution networks, the target cascade method is used to decouple the interactive power between the two, without considering the impact of their interactive electricity prices on the operation of both parties. As for the joint operation of multiple microgrids, there is a lack of flexible and efficient methods for power interaction between microgrids. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention proposes an active distribution network scheduling method and system taking into account power interaction between microgrids, establishes a scheduling framework and strategy for a new active distribution network (ADN), and improves the collaborative operation efficiency among multiple subjects in scenarios of multi-party game and operation mechanism such as distribution system operator (DSO) and microgrid coalition (MGCO).

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

[0006] The present invention proposes an active distribution network scheduling method taking into account power interaction between microgrids. The scheduling framework of the active distribution network includes a distribution system operator and a microgrid alliance. The microgrid alliance includes multiple microgrids, including:

[0007] The operating data of each microgrid is obtained to determine the net load of each microgrid, and the constraints on the power interaction direction between the microgrids are established according to the net load of each microgrid; the sub-dispatching model of each microgrid is established by using the objective function and the constraints on the power interaction direction between the microgrids, taking the minimum penalty cost of wind and solar power abandonment of each microgrid as the objective function;

[0008] Taking the minimum comprehensive operation cost of the active distribution network as the objective function, combined with the system stability constraints, customized electricity price range constraints and customized electricity price constraints, the main dispatching model of the active distribution network is constructed;

[0009] Taking the minimization of the response operation cost of the microgrid alliance as the objective function and the operation constraints of the microgrid alliance, a slave dispatch model of the active distribution network is constructed;

[0010] The sub-dispatching model of each microgrid sends the net load, power interaction direction and wind and solar power abandonment penalty cost of each microgrid to the sub-dispatching model according to the operation data of each microgrid; the sub-dispatching model distributes the cost savings of the microgrid alliance to each microgrid according to the ratio of the sum of the interaction power of each microgrid to the sum of the interaction power of all microgrids within the dispatching period;

[0011] The slave dispatch model sends the sum of the net loads of all microgrids to the main dispatch model, and the main dispatch model outputs a customized electricity price; the slave dispatch model outputs a response plan to the customized electricity price;

[0012] Based on the fixed point mapping method, an interactive relationship is established between the customized electricity price output by the main scheduling model and the response plan to the customized electricity price output by the slave scheduling model; based on the master-slave game model, the equilibrium solution of the main scheduling model and the slave scheduling model with an interactive relationship is solved, and the equilibrium solution includes: the customized electricity price of the distribution system operator and the response plan of the microgrid alliance to the customized electricity price.

[0013] Preferably, the operation data of each microgrid includes: wind power generation power, photovoltaic power generation power, energy storage charging power, energy storage discharge power, wind abandonment rate and photovoltaic abandonment rate; the net load of each microgrid in each time period satisfies the following relationship:

[0014]

[0015] In the formula, is the net load of the i-th microgrid in period t, are the load power, wind power generation power, photovoltaic power generation power, energy storage charging power and energy storage discharging power of the i-th microgrid in period t, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, respectively.

[0016] Preferably, the constraints on the power interaction direction between microgrids are expressed as follows:

[0017]

[0018] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t, and N is the number of microgrids.

[0019] Preferably, the objective function of the sub-dispatching model of the i-th microgrid satisfies the following relationship:

[0020]

[0021] In the formula, is the penalty cost of wind and solar power abandonment in the i-th microgrid, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

[0022] Preferably, the comprehensive operating cost of the active distribution network satisfies the following relationship:

[0023]

[0024] In the formula, C DSO is the comprehensive operating cost of the active distribution network, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of electricity purchased by the distribution system operator from the upper grid. Penalty costs for voltage exceeding limit and branch power exceeding limit.

[0025] Preferably, the transaction cost between the distribution system operator and the microgrid alliance satisfies the following relationship:

[0026]

[0027] In the formula, are the purchase and sale electricity prices for period t set by the distribution system operator, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively, N is the number of microgrids, and T is the total number of time periods in the dispatch cycle;

[0028] The cost of electricity purchased by the distribution system operator from the upper-level power grid satisfies the following relationship:

[0029]

[0030] In the formula, is the power purchased by the distribution system operator from the upper grid during period t, λ sell The electricity price sold by the upper power grid;

[0031] The penalty costs for voltage over-limit and branch power over-limit satisfy the following relationship:

[0032]

[0033] In the formula, β U , β L are the voltage over-limit penalty coefficient and the branch power over-limit penalty coefficient, U k is the voltage at node k, are the upper and lower limits of the voltage at node k, respectively, l is the power of branch l, is the upper power limit of branch l, and L is the number of branches.

[0034] Preferably, the system stability constraints include: power flow constraints, branch power constraints, and node voltage constraints;

[0035] Customize the electricity price range constraint to satisfy the following relationship:

[0036]

[0037] In the formula, are the electricity purchase and sales prices of the distribution system operator in period t, respectively, buy,p , buy,f , buy,v are the electricity purchase prices set by distribution system operators during peak, flat and valley periods, respectively, sell,p , sell,f , sell,v are the electricity prices customized by distribution system operators during peak, flat and valley periods, respectively. p , T f , T v They are peak, flat and valley time periods respectively;

[0038] The constraints for customized electricity prices for electricity sales are as follows:

[0039]

[0040] In the formula, The upper limit of the average value of the electricity price set by the distribution system operator during the dispatch period.

[0041] Preferably, the response operation cost of the microgrid alliance satisfies the following relationship:

[0042]

[0043] In the formula, C MGCO The responsive operating costs of the microgrid alliance, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of gas turbine power generation participating in the microgrid alliance is is the energy storage usage cost of participating in the response in the microgrid alliance, Penalty costs for wind and solar curtailment in microgrid alliances.

[0044] Preferably, the power generation cost of the gas turbines participating in the response in the microgrid alliance satisfies the following relationship:

[0045]

[0046] In the formula, is the power generation of the gas turbine participating in the response in the i-th microgrid, a MT,i 、b MT,i 、c MT,i are the cost coefficients of gas turbine power generation participating in the response in the i-th microgrid, P is the total number of pollutant types, is the unit emission cost of the p-th type of pollutant, is the emission of the pth type of pollutant corresponding to the power generation of the gas turbine participating in the response in the i-th microgrid, N is the number of microgrids, and T is the total number of time periods in the scheduling cycle;

[0047] The energy storage usage cost participating in the response in the microgrid alliance satisfies the following relationship:

[0048]

[0049] In the formula, γ ES is the charging and discharging cost per unit power of energy storage, are respectively the charging power and discharging power of the energy storage participating in the response of the i-th microgrid in period t;

[0050] The penalty cost for wind and solar power abandonment in the microgrid alliance satisfies the following relationship:

[0051]

[0052] In the formula, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

[0053] Preferably, the operating constraints of the microgrid alliance include:

[0054] 1) The power consumption of the microgrid must satisfy the following relationship:

[0055]

[0056] In the formula, are the upper limit of power purchase and power sale of the i-th microgrid, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively;

[0057] 2) The power purchased and sold by the microgrid and the power mutual assistance between microgrids are transmitted through the microgrid interconnection line. The transmission power constraint of the microgrid interconnection line satisfies the following relationship:

[0058]

[0059] Where: is the upper limit of transmission power of the i-th microgrid tie line, is the sum of the power transmitted from other microgrids to the i-th microgrid, is the sum of the power transmitted from the i-th microgrid to other microgrids;

[0060] 3) The direction constraint of power interaction between microgrids satisfies the following relationship:

[0061]

[0062] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t;

[0063] 4) The power generation constraint of the gas turbine satisfies the following relationship:

[0064]

[0065] In the formula, are the upper and lower limits of the power generation of the gas turbine in the i-th microgrid respectively;

[0066] 5) Power balance constraint, satisfying the following relationship:

[0067]

[0068] In the formula, is the net load of the i-th microgrid in period t;

[0069] 6) Energy storage charging and discharging constraints satisfy the following relationship:

[0070]

[0071] In the formula, is the upper limit of energy storage charging and discharging power of the i-th microgrid in period t;

[0072] 7) Wind and solar power abandonment constraints satisfy the following relationship:

[0073]

[0074] In the formula, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, respectively.

[0075] Preferably, the cost savings of all microgrids are allocated to each microgrid, satisfying the following relationship:

[0076]

[0077] Where, ΔF MGi is the revenue of the i-th microgrid, is the cost saving of the allocated microgrid alliance, C CO,MGi , C NOCO,MGi are the cost of the i-th microgrid when it cooperates with other microgrids and the cost of non-cooperative operation, respectively. To save costs for the microgrid alliance,MGi is the cost saving allocation coefficient corresponding to the i-th microgrid, is the interactive power of the i-th microgrid in period t, is the sum of the interactive power of the i-th microgrid in the scheduling period, is the sum of the interactive powers of all microgrids in the scheduling period, N is the number of microgrids, and T is the total number of time periods in the scheduling period.

[0078] Preferably, the interactive relationship between the master scheduling model and the slave scheduling model is established based on a fixed point mapping method; including:

[0079] The customized electricity price output by the main dispatch model is mapped to the electricity consumption response output by the dispatch model, satisfying the following relationship:

[0080]

[0081] In the formula, is the customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D A () is the mapping function, is the mapping of the set of customized electricity prices for the distribution system operator in period t;

[0082] The power consumption response output from the dispatch model is mapped to the updated customized electricity price output by the main dispatch model, satisfying the following relationship:

[0083]

[0084] In the formula, is the updated customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D B () is the mapping function, It is a mapping of the set of response plans of the microgrid alliance to the customized electricity price in period t.

[0085] Preferably, the master-slave game model is represented by the following set:

[0086] S={H;ρ DSO ; δ MGCO ; C DSO ; C MGCO}

[0087] Among them, the distribution system operator and the microgrid alliance constitute the set of game participants H, and the decision variable of the distribution system operator is the set of customized electricity prices of the distribution system operator in period t: satisfy The decision variables of the microgrid alliance are the set of response plans of the microgrid alliance to the customized electricity price in period t: satisfy Distribution system operators and microgrid alliances have their own objective functions C DSO , C MGCO , are the electricity purchase and sales prices of the distribution system operator in period t, respectively. DSO is the comprehensive operating cost of the active distribution network, C MGCO Responsive operating costs for microgrid alliances.

[0088] The present invention also proposes an active distribution network dispatching system taking into account power interaction between microgrids, comprising:

[0089] The sub-dispatching model establishment module is used to obtain the operating data of each microgrid to determine the net load of each microgrid, and establish the constraints of the power interaction direction between each microgrid according to the net load of each microgrid; taking the minimum penalty cost of wind and solar abandonment of each microgrid as the objective function, and using the objective function and the constraints of the power interaction direction between each microgrid, establish the sub-dispatching model of each microgrid;

[0090] The main dispatch model establishment module is used to build the main dispatch model of the active distribution network with the minimum comprehensive operation cost of the active distribution network as the objective function, and the system stability constraints, customized electricity price range constraints and customized electricity price constraints;

[0091] A module for establishing a slave dispatch model is used to construct a slave dispatch model of the active distribution network with the minimum response operation cost of the microgrid alliance as the objective function and the operation constraints of the microgrid alliance;

[0092] The slave dispatch model and sub-dispatching model interaction module is used for the sub-dispatching model of each microgrid. According to the operation data of each microgrid, the net load, power interaction direction and wind and solar power abandonment penalty cost of each microgrid are sent to the slave dispatch model; the slave dispatch model distributes the cost savings of the microgrid alliance to each microgrid according to the ratio of the sum of the interaction power of each microgrid to the sum of the interaction power of all microgrids within the dispatch period;

[0093] The module for interacting with the slave dispatching model and the master dispatching model is used to send the sum of the net loads of all microgrids from the slave dispatching model to the master dispatching model, and the master dispatching model outputs a customized electricity price; the slave dispatching model outputs a response plan to the customized electricity price; an interactive relationship between the customized electricity price output by the master dispatching model and the response plan to the customized electricity price output by the slave dispatching model is established based on a fixed point mapping method; based on a master-slave game model, an equilibrium solution of the master dispatching model and the slave dispatching model having an interactive relationship is solved, and the equilibrium solution includes: the customized electricity price of the distribution system operator and the response plan of the microgrid alliance to the customized electricity price.

[0094] In the sub-dispatching model establishment module, the operating data of each microgrid includes: wind power generation power, photovoltaic power generation power, energy storage charging power, energy storage discharge power, wind power abandonment rate and photovoltaic power abandonment rate; the net load of each microgrid in each time period satisfies the following relationship:

[0095]

[0096] In the formula, is the net load of the i-th microgrid in period t, are the load power, wind power generation power, photovoltaic power generation power, energy storage charging power and energy storage discharging power of the i-th microgrid in period t, are the wind power abandonment rate and solar power abandonment rate of the i-th microgrid in period t, respectively;

[0097] The constraints on the power interaction direction between microgrids are expressed as follows:

[0098]

[0099] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t, N is the number of microgrids;

[0100] The objective function of the sub-dispatching model of the i-th microgrid satisfies the following relationship:

[0101]

[0102] In the formula, is the penalty cost of wind and solar power abandonment in the i-th microgrid, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

[0103] In the main dispatch model establishment module, the comprehensive operation cost of the active distribution network satisfies the following relationship:

[0104]

[0105] In the formula, C DSO is the comprehensive operating cost of the active distribution network, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of electricity purchased by the distribution system operator from the upper grid. Penalty costs for voltage over-limit and branch power over-limit;

[0106] The transaction cost between the distribution system operator and the microgrid alliance satisfies the following relationship:

[0107]

[0108] In the formula, are the purchase and sale electricity prices for period t set by the distribution system operator, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively, N is the number of microgrids, and T is the total number of time periods in the dispatch cycle;

[0109] The cost of electricity purchased by the distribution system operator from the upper-level power grid satisfies the following relationship:

[0110]

[0111] In the formula, is the power purchased by the distribution system operator from the upper grid during period t, λ sell The electricity price sold by the upper power grid;

[0112] The penalty costs for voltage over-limit and branch power over-limit satisfy the following relationship:

[0113]

[0114] In the formula, β U , β L are the voltage over-limit penalty coefficient and the branch power over-limit penalty coefficient, U k is the voltage at node k, are the upper and lower limits of the voltage at node k, respectively, l is the power of branch l, is the upper power limit of branch l, L is the number of branches;

[0115] System stability constraints include: power flow constraints, branch power constraints, and node voltage constraints;

[0116] Customize the electricity price range constraint to satisfy the following relationship:

[0117]

[0118] In the formula, are the electricity purchase and sales prices of the distribution system operator in period t, respectively, buy,p , buy,f , buy,v are the electricity purchase prices set by distribution system operators during peak, flat and valley periods, respectively,sell,p , sell,f , sell,v are the electricity prices customized by distribution system operators during peak, flat and valley periods, respectively. p , T f , T v They are peak, flat and valley time periods respectively;

[0119] The constraints for customized electricity prices for electricity sales are as follows:

[0120]

[0121] In the formula, The upper limit of the average value of the electricity price set by the distribution system operator during the dispatch period.

[0122] From the dispatch model building module, the response operation cost of the microgrid alliance satisfies the following relationship:

[0123]

[0124] In the formula, C MGCO The responsive operating costs of the microgrid alliance, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of gas turbine power generation participating in the microgrid alliance is is the energy storage usage cost of participating in the response in the microgrid alliance, Penalty costs for wind and solar curtailment in microgrid alliances;

[0125] The power generation cost of gas turbines participating in the microgrid alliance satisfies the following relationship:

[0126]

[0127] In the formula, is the power generation of the gas turbine participating in the response in the i-th microgrid, a MT,i 、b MT,i 、c MT,i are the cost coefficients of gas turbine power generation participating in the response in the i-th microgrid, P is the total number of pollutant types, is the unit emission cost of the p-th type of pollutant, is the emission of the pth type of pollutant corresponding to the power generation of the gas turbine participating in the response in the i-th microgrid, N is the number of microgrids, and T is the total number of time periods in the scheduling cycle;

[0128] The energy storage usage cost participating in the response in the microgrid alliance satisfies the following relationship:

[0129]

[0130] In the formula, γ ES is the charging and discharging cost per unit power of energy storage, are respectively the charging power and discharging power of the energy storage participating in the response of the i-th microgrid in period t;

[0131] The penalty cost for wind and solar power abandonment in the microgrid alliance satisfies the following relationship:

[0132]

[0133] In the formula, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t respectively;

[0134] The operating constraints of the microgrid alliance include:

[0135] 1) The power consumption of the microgrid must satisfy the following relationship:

[0136]

[0137] In the formula, are the upper limit of power purchase and power sale of the i-th microgrid, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively;

[0138] 2) The power purchased and sold by the microgrid and the power mutual assistance between microgrids are transmitted through the microgrid interconnection line. The transmission power constraint of the microgrid interconnection line satisfies the following relationship:

[0139]

[0140] Where: is the upper limit of transmission power of the i-th microgrid tie line, is the sum of the power transmitted from other microgrids to the i-th microgrid, is the sum of the power transmitted from the i-th microgrid to other microgrids;

[0141] 3) The direction constraint of power interaction between microgrids satisfies the following relationship:

[0142]

[0143] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t;

[0144] 4) The power generation constraint of the gas turbine satisfies the following relationship:

[0145]

[0146] In the formula, are the upper and lower limits of the power generation of the gas turbine in the i-th microgrid respectively;

[0147] 5) Power balance constraint, satisfying the following relationship:

[0148]

[0149] In the formula, is the net load of the i-th microgrid in period t;

[0150] 6) Energy storage charging and discharging constraints satisfy the following relationship:

[0151]

[0152] In the formula, is the upper limit of energy storage charging and discharging power of the i-th microgrid in period t;

[0153] 7) Wind and solar power abandonment constraints satisfy the following relationship:

[0154]

[0155] In the formula, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, respectively.

[0156] 24. The active distribution network dispatching system considering power interaction between microgrids according to claim 14, characterized in that:

[0157] From the interaction module between the dispatch model and the sub-dispatching model, the cost savings of all microgrids are allocated to each microgrid, satisfying the following relationship:

[0158]

[0159] Where, ΔF MGi is the revenue of the i-th microgrid, is the cost saving of the allocated microgrid alliance, C CO,MGi , C NOCO,MGi are the cost of the i-th microgrid when it cooperates with other microgrids and the cost of non-cooperative operation, respectively. To save costs for the microgrid alliance, MGi is the cost saving allocation coefficient corresponding to the i-th microgrid, is the interactive power of the i-th microgrid in period t, is the sum of the interactive power of the i-th microgrid in the scheduling period, is the sum of the interactive powers of all microgrids in the scheduling period, N is the number of microgrids, and T is the total number of time periods in the scheduling period.

[0160] In the interaction module between the slave scheduling model and the master scheduling model, the interaction relationship between the master scheduling model and the slave scheduling model is established based on the fixed point mapping method; including:

[0161] The customized electricity price output by the main dispatch model is mapped to the electricity consumption response output by the dispatch model, satisfying the following relationship:

[0162]

[0163] In the formula, is the customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D A () is the mapping function, is the mapping of the set of customized electricity prices for the distribution system operator in period t;

[0164] The power consumption response output from the dispatch model is mapped to the updated customized electricity price output by the main dispatch model, satisfying the following relationship:

[0165]

[0166] In the formula, is the updated customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D B () is the mapping function, It is a mapping of the set of response plans of the microgrid alliance to the customized electricity price in period t.

[0167] The master-slave game model is represented by the following set:

[0168] S={H;ρ DSO ; δ MGCO ; C DSO ; C MGCO}

[0169] Among them, the distribution system operator and the microgrid alliance constitute the set of game participants H, and the decision variable of the distribution system operator is the set of customized electricity prices of the distribution system operator in period t: satisfy The decision variables of the microgrid alliance are the set of response plans of the microgrid alliance to the customized electricity price in period t: satisfy Distribution system operators and microgrid alliances have their own objective functions CDSO , C MGCO , are the electricity purchase and sales prices of the distribution system operator in period t, respectively. DSO is the comprehensive operating cost of the active distribution network, C MGCO Responsive operating costs for microgrid alliances.

[0170] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0171] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0172] The beneficial effects of the present invention are that, compared with the prior art, at least the following are included: the method proposed in the present invention solves the pricing and optimization problems of multiple subjects in the active distribution network dispatching framework, effectively improves the coordination between the subjects, and prompts them to form a reasonable competition and cooperation relationship in the market, and finally realizes the optimal allocation of resources; effectively guides the operation strategy of MGCO, making it more responsive to changes in electricity prices, which not only improves the stability of the power grid, but also provides a guarantee for the rationality of electricity prices, and helps to form a healthier market competition environment; the distributed iterative algorithm based on fixed point mapping significantly improves the solution efficiency, making it possible to quickly obtain a feasible dispatching plan in a complex power market environment.

[0173] The present invention comprehensively considers the power mutual assistance between microgrids and the wind and solar power abandonment rate, and adds the penalty cost of wind and solar power abandonment, which can further improve the local consumption of new energy, reduce the power backflow to the superior power grid, and solve the problem of reverse heavy overload; and in actual operation, power companies and energy management departments can set the upper limit or target value of the wind and solar power abandonment rate according to policy goals and technical means to maximize the use of renewable energy; the new benefit distribution mechanism determines the contribution of each microgrid to the alliance according to the power interaction ratio between microgrids, and reasonably distributes the benefits generated by the cooperation based on these contributions. This mechanism improves the efficiency and rationality of benefit distribution, encourages each microgrid to actively participate in the cooperation, and thus enhances the overall stability and economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0174] Figure 1 Schematic diagram of a master-slave game scheduling framework of an active distribution network including a microgrid in an embodiment of the present invention;

[0175] Figure 2 It is a flow chart of the active distribution network dispatching method taking into account the power interaction between microgrids proposed by the present invention;

[0176] Figure 3is a structural diagram of an improved IEEE33-node ADN system in an embodiment of the present invention;

[0177] Figure 4 is the output diagram of each ADN device in the embodiment of the present invention, Figure 4 In the figure, (a) is Example 1, (b) is Example 2, and (c) is Example 3;

[0178] Figure 5 is a voltage distribution diagram of some ADN nodes in an embodiment of the present invention;

[0179] Figure 6 is a diagram of ADN network loss in an embodiment of the present invention;

[0180] Figure 7 is a diagram of the output of each device in the microgrid 1 in the embodiment of the present invention, Figure 7 In the figure, (a) is Example 1, (b) is Example 2;

[0181] Figure 8 is a diagram of the output of each device in the microgrid 2 in the embodiment of the present invention, Figure 7 In the figure, (a) is Example 1, (b) is Example 2;

[0182] Fig. 9 is a diagram of the output of each device in the microgrid 3 in the embodiment of the present invention, Figure 7 In the figure, (a) is Example 1, (b) is Example 2;

[0183] Fig.10 It is a power interaction diagram between microgrids in an embodiment of the present invention. DETAILED DESCRIPTION

[0184] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0185] The dispatching framework of the new active distribution network (ADN) is as follows: Figure 1As shown in the figure, it includes: distribution system operator (DSO) and microgrid coalition (MGCO); because the distribution system operator DSO, microgrid MG, and microgrid coalition MGCO belong to different operating entities when the system is running, the decision-making ability and market position of DSO are higher, and the status of MGCO and MG is lower, and the master-slave hierarchical characteristics are obvious; the MGs have equal status and belong to a cooperative relationship, and MG1, MG2, ..., MGn interact with each other in power to form MGCO.

[0186] Based on the dispatching framework of the new active distribution network, the present invention proposes an active distribution network dispatching method taking into account the power interaction between microgrids. The dispatching framework of the active distribution network includes a distribution system operator and a microgrid alliance. The microgrid alliance includes multiple microgrids, such as Figure 2 As shown, the scheduling method includes:

[0187] Step 1: Obtain the operating data of each microgrid to determine the net load of each microgrid. The operating data of each microgrid includes: wind power generation power, photovoltaic power generation power, energy storage charging power, energy storage discharge power, wind abandonment rate and solar abandonment rate; establish constraints on the direction of power interaction between microgrids according to the net load of each microgrid; take the minimum penalty cost of wind and solar abandonment of each microgrid as the objective function, and use the objective function and the constraints on the direction of power interaction between microgrids to establish a sub-dispatching model for each microgrid.

[0188] After each microgrid absorbs the power generation of renewable energy and the charging and discharging of energy storage, the net load of each microgrid in each time period is obtained, which satisfies the following relationship:

[0189]

[0190] In the formula, is the net load of the i-th microgrid in period t, are the load power, wind power generation power, photovoltaic power generation power, energy storage charging power and energy storage discharging power of the i-th microgrid in period t, are the wind power abandonment rate and solar power abandonment rate of the i-th microgrid in period t, respectively;

[0191] The wind and solar power abandonment rates of microgrids are introduced into the net load to promote the local consumption of new energy.

[0192] When the net load of the i-th microgrid in period t is When the net load of the i-th microgrid in period t is less than zero, the microgrid is judged to be short of power. When it is greater than zero, the microgrid surplus power is determined; in MGCO, priority is given to controlling the power mutual assistance between the surplus power microgrid and the power-deficient microgrid. The surplus power microgrid can transmit electric energy that does not exceed its own surplus power to the power-deficient microgrid. Similarly, the power-deficient microgrid can receive electric energy that does not exceed its own power-deficient power from the surplus power microgrid. It is stipulated that the mutual assistance power cannot be reversed, so as to improve the efficiency of power mutual assistance. The constraints on the power interaction direction between the microgrids are expressed as follows:

[0193]

[0194] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t, and N is the number of microgrids.

[0195] The objective function of the sub-dispatching model of the i-th microgrid satisfies the following relationship:

[0196]

[0197] In the formula, is the penalty cost of wind and solar power abandonment in the i-th microgrid, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

[0198] Step 2: Taking the minimization of the comprehensive operating cost of the active distribution network as the objective function, and combining it with the system stability constraints, customized electricity price range constraints, and customized electricity price constraints for electricity sales, a main dispatching model for the active distribution network is constructed.

[0199] Specifically, the DSO-oriented main dispatching model takes the minimum comprehensive operating cost of the active distribution network as the objective function, where the comprehensive operating cost of the active distribution network satisfies the following relationship:

[0200]

[0201] In the formula, C DSO is the comprehensive operating cost of the active distribution network, is the transaction cost between DSO and MGCO, is the cost of electricity purchased by DSO from the upper grid, Penalty costs for voltage exceeding limit and branch power exceeding limit.

[0202] The present invention introduces penalty costs of voltage over-limit and branch power over-limit in the objective function to reduce voltage fluctuation and deviation of the distribution network, while reducing the risk of branch damage and network loss.

[0203] Among them, the transaction cost between DSO and MGCO satisfies the following relationship:

[0204]

[0205] In the formula, are the purchase and sale electricity prices set by DSO for period t, are the purchased and sold power of the i-th microgrid and DSO, respectively, N is the number of microgrids, and T is the total number of time periods in the dispatch cycle;

[0206] The cost of electricity purchased by DSO from the upper-level power grid satisfies the following relationship:

[0207]

[0208] In the formula, is the power purchased by DSO from the upper grid during period t, λ sell The electricity price sold by the upper power grid;

[0209] The penalty costs for voltage over-limit and branch power over-limit satisfy the following relationship:

[0210]

[0211] In the formula, β U , β L are the voltage over-limit penalty coefficient and the branch power over-limit penalty coefficient, U k is the voltage at node k, are the upper and lower limits of the voltage at node k, respectively, l is the power of branch l, is the upper power limit of branch l, and L is the number of branches.

[0212] Specifically, system stability constraints include, but are not limited to: power flow constraints, branch power constraints, node voltage constraints;

[0213] In order to make the time-of-use electricity price customized by DSO reasonable and reduce the fluctuation and shock of electricity price, the customized electricity price of DSO is subject to interval constraints to satisfy the following relationship:

[0214]

[0215] In the formula, are the electricity purchase and sales prices of DSO in period t, respectively, buy,p , buy,f , buy,vare the electricity purchase prices of DSO in peak, flat and valley periods, respectively, sell,p , sell,f , sell,v are the customized electricity prices of DSO during peak, flat and valley periods, respectively. p , T f , T v They are peak, flat and valley time periods respectively;

[0216] In the embodiment, the electricity purchase price of the DSO in the peak, flat and valley periods is 80% of the electricity sales price in the peak, flat and valley periods respectively.

[0217] At the same time, in order to prevent DSO from maliciously raising the electricity price for its own benefit, the DSO's customized electricity price constraints are as follows:

[0218]

[0219] In the formula, It is the upper limit of the average value of the DSO's customized electricity sales price during the dispatch period.

[0220] Step 3: Taking the minimization of the response operation cost of the microgrid alliance as the objective function and the operation constraints of the microgrid alliance, a slave dispatch model of the active distribution network is constructed.

[0221] Specifically, the MGCO-oriented slave dispatch model takes the minimum total response operation cost of the microgrid alliance as the objective function, where the response operation cost of the microgrid alliance satisfies the following relationship:

[0222]

[0223] In the formula, C MGCO The responsive operating costs of the microgrid alliance, is the transaction cost between DSO and MGCO, is the gas turbine power generation cost participating in the response in MGCO, is the cost of energy storage for response in MGCO, It is the penalty cost for wind and solar power curtailment in MGCO.

[0224] The present invention introduces penalty costs for wind and solar power abandonment into the response operation costs of the microgrid alliance, which can not only improve the local consumption of new energy, reduce power backflow to the superior power grid, and help solve the problem of reverse heavy overload; but also can effectively replace fossil energy consumption, help achieve emission reduction targets, and promote environmental protection and the transformation of energy structure.

[0225] Taking into account the power mutual assistance between microgrids and the wind and solar power abandonment rate, and adding the penalty cost for wind and solar power abandonment, we can further improve the local consumption of new energy, reduce the power backflow to the upper grid, and solve the problem of reverse overload; and in actual operation, power companies and energy management departments can set the upper limit or target value of wind and solar power abandonment rate according to policy goals and technical means to maximize the use of renewable energy. Improve the local consumption of new energy and solve the problem of reverse overload.

[0226] Among them, the power generation cost of the gas turbine participating in the response in MGCO satisfies the following relationship:

[0227]

[0228] In the formula, is the power generation of the gas turbine participating in the response in the i-th microgrid, a MT,i 、b MT,i 、c MT,i are the cost coefficients of gas turbine power generation participating in the response in the i-th microgrid, P is the total number of pollutant types, is the unit emission cost of the p-th type of pollutant, is the emission of the pth type of pollutant corresponding to the power generation of the gas turbine participating in the response in the i-th microgrid, N is the number of microgrids, and T is the total number of time periods in the scheduling cycle;

[0229] The energy storage cost participating in the response in MGCO satisfies the following relationship:

[0230]

[0231] In the formula, γ ES is the charging and discharging cost per unit power of energy storage, are respectively the charging power and discharging power of the energy storage participating in the response of the i-th microgrid in period t;

[0232] The penalty cost for wind and solar power abandonment in MGCO satisfies the following relationship:

[0233]

[0234] In the formula, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind power abandonment rate and solar power abandonment rate of the i-th microgrid in period t, respectively;

[0235] Specifically, MGCO's operating constraints include:

[0236] 1) The power consumption of the microgrid must satisfy the following relationship:

[0237]

[0238] In the formula, are the upper limit of power purchase and power sale of the i-th microgrid respectively;

[0239] 2) The power purchased and sold by the microgrid and the power mutual assistance between microgrids are transmitted through the microgrid interconnection line. The transmission power constraint of the microgrid interconnection line satisfies the following relationship:

[0240]

[0241] Where: is the upper limit of transmission power of the i-th microgrid tie line, is the sum of the power transmitted from other microgrids to the i-th microgrid, is the sum of the power transmitted from the i-th microgrid to other microgrids;

[0242] 3) The direction constraint of power interaction between microgrids satisfies the following relationship:

[0243]

[0244] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period y;

[0245] 4) The power generation constraint of the gas turbine satisfies the following relationship:

[0246]

[0247] In the formula, are the upper and lower limits of the power generation of the gas turbine in the i-th microgrid respectively;

[0248] 5) Power balance constraint, satisfying the following relationship:

[0249]

[0250] 6) Energy storage charging and discharging constraints satisfy the following relationship:

[0251]

[0252] In the formula, is the upper limit of energy storage charging and discharging power of the i-th microgrid in period t;

[0253] 7) Wind and solar power abandonment constraints satisfy the following relationship:

[0254]

[0255] The wind and solar power abandonment rate is limited to between 0 and 1. In actual operation, power companies and energy management departments can set the upper limit or target value of the wind and solar power abandonment rate based on policy objectives and technical means. Through scheduling, the wind and solar power abandonment rate can be controlled within a reasonable range to maximize the use of renewable energy.

[0256] Step 4: The sub-dispatching model of each microgrid sends the net load, power interaction direction and wind and solar power abandonment penalty cost of each microgrid to the slave dispatching model of the active distribution network according to the operation data of each microgrid; the slave dispatching model allocates the cost savings of all microgrids to each microgrid according to the ratio of the sum of the interaction power of each microgrid in the dispatching period to the sum of the interaction power of all microgrids in the dispatching period;

[0257] In order to maintain a long-term stable cooperative alliance relationship, it is necessary to formulate a reasonable benefit distribution mechanism to maintain the balance of interests among microgrids. The power mutual assistance and benefit distribution mechanism among microgrids should satisfy the following relationship: the overall operating cost of the microgrid alliance is less than the sum of the costs of microgrids operating individually; under the microgrid alliance, after the implementation of the benefit distribution mechanism, the operating cost of each microgrid is less than the cost of microgrid independent operation.

[0258] The present invention proposes to distribute the cost savings of all microgrids to each microgrid according to the ratio of the sum of the interactive power of each microgrid in the scheduling period to the sum of the interactive power of all microgrids in the scheduling period, satisfying the following relationship:

[0259]

[0260] Where, ΔF MGi is the revenue of the i-th microgrid, is the cost saving of the allocated microgrid alliance, C CO,MGi , C NOCO,MGi are the cost of the i-th microgrid when it cooperates with other microgrids and the cost of non-cooperative operation, respectively. To save costs for the microgrid alliance, MGi is the cost saving allocation coefficient corresponding to the i-th microgrid, is the interactive power of the i-th microgrid in period t, is the sum of the interactive power of the i-th microgrid in the scheduling period, is the sum of the interactive powers of all microgrids within the dispatch period;

[0261] The cost savings generated by the cooperation are distributed according to the proportion of power interaction between microgrids. After the distribution by this method, the microgrid can reduce the operating cost. The cost savings are distributed according to the proportion of power interaction between microgrids, which reflects the actual contribution of each microgrid to the alliance. The cost savings distributed according to the size of the contribution can fully reflect the rationality and fairness of the distribution, and the calculation efficiency of this method is high.

[0262] In the existing technology, the benefit distribution mechanism is often not reasonable, resulting in insufficient cooperation enthusiasm among microgrids. The new benefit distribution mechanism determines the contribution of each microgrid to the alliance according to the power interaction ratio between microgrids, and reasonably distributes the benefits generated by cooperation based on these contributions. This mechanism improves the efficiency and rationality of benefit distribution, encourages each microgrid to actively participate in cooperation, and thus enhances the overall stability and economy of the system.

[0263] Step 5, the slave dispatching model and the main dispatching model interaction module is used to send the sum of the net loads of all microgrids from the slave dispatching model to the main dispatching model, and the main dispatching model outputs the customized electricity price; the slave dispatching model outputs a response plan to the customized electricity price.

[0264] Step 6, establishing an interactive relationship between the customized electricity price output by the main dispatch model and the response scheme to the customized electricity price output by the dispatch model based on a fixed point mapping method; including:

[0265] Step 6.1, the customized electricity price output by the main dispatch model is mapped to the electricity consumption response output by the dispatch model, satisfying the following relationship:

[0266]

[0267] In the formula, is the customized electricity price set of DSO in period t, is the set of MGCO’s response plans to the customized electricity price in period t, D A () is the mapping function, is the mapping of the customized electricity price set of DSO in period t;

[0268] Step 6.2, the power consumption response output from the dispatch model is mapped to the updated customized electricity price output by the main dispatch model, satisfying the following relationship:

[0269]

[0270] In the formula, is the updated customized electricity price set of DSO in period t, is the set of MGCO’s response plans to the customized electricity price in period t, D B () is the mapping function, is a mapping of the set of response plans of MGCO to the customized electricity price in period t;

[0271] according to Figure 1The interactive relationship between DSO and MGCO described above, DSO affects MGCO's power consumption response strategy through pricing strategy, and MGCO's power consumption response strategy will also react to the electricity price strategy. Therefore, DSO and MGCO influence and restrict each other until the interests of both parties are balanced. The present invention proposes a method based on fixed point mapping to establish the interactive relationship between the master scheduling model and the slave scheduling model, and realizes the distributed iterative calculation of customized electricity prices based on fixed point mapping.

[0272] Taking the electricity price of period t customized by DSO as the fixed point, the mapping function D A () and mapping function D B () is a pair of mapping functions of fixed points, satisfying the following relationship:

[0273]

[0274] In the embodiment, the power consumption response is defined as a bounded closed interval. When the mapping from the customized electricity price to the power consumption response is continuous, and the mapping from the power consumption response to the customized electricity price is also continuous, the master-slave game can reach an equilibrium state.

[0275] Step 7, based on the master-slave game model, solve the equilibrium solution of the master scheduling model and the slave scheduling model with an interactive relationship as an active distribution network scheduling method, the equilibrium solution includes: the customized electricity price of the distribution system operator and the response plan of the microgrid alliance to the customized electricity price.

[0276] The master-slave game model is represented by the following set:

[0277] S={H;ρ DSO ; δ MGCO ; C DSO ; C MGCO}

[0278] Among them, DSO and MGCO constitute the set of game participants H, and the decision variable of DSO is the set of electricity prices customized by DSO for period t satisfy The decision variables of MGCO are the set of electricity consumption responses of MGCO in period t: satisfy DSO and MGCO have their own objective functions C DSO , C MGCO .

[0279] The DSO and MGCO master-slave game models are large-scale mixed integer programming problems, which have the characteristics of many layers and many integer variables. If the KKT (Karushkuhn-Tucker) transformation or the duality principle is used, it is more complicated and difficult to operate to transform the multi-layer model into a single-layer mixed integer linear programming (MILP) problem. In this regard, the present invention adopts the QPSO algorithm nested Cplex solver to solve, which effectively reduces the complexity of model solution.

[0280] The master-slave game model is represented by the following set:

[0281] S={H;ρ DSO ; δ MGCO ; C DSO ; C MGCO}

[0282] Among them, the distribution system operator and the microgrid alliance constitute the set of game participants H, and the decision variable of the distribution system operator is the set of customized electricity prices of the distribution system operator in period t: satisfy The decision variables of the microgrid alliance are the set of response plans of the microgrid alliance to the customized electricity price in period t: satisfy Distribution system operators and microgrid alliances have their own objective functions C DSO , C MGCO , are the electricity purchase and sales prices of the distribution system operator in period t, respectively. DSO is the comprehensive operating cost of the active distribution network, C MGCO Responsive operating costs for microgrid alliances.

[0283] In the method proposed in the present invention, the upper-layer DSO is the leader of the master-slave game, and formulates a time-of-use electricity price according to its net load curve, and proposes an electricity price constraint model to perform interval constraints on the electricity price, so as to encourage MGCO to participate in the peak-shaving dispatch of DSO. The peak electricity price encourages MGCO to sell more electricity and purchase less electricity to promote the peak shaving of ADN, and the valley electricity price encourages MGCO to purchase more electricity and sell less electricity to promote the valley filling of ADN, and realizes the minimum operation cost of DSO; the lower-layer MGCO is a follower, and formulates an operation strategy in response to the time-of-use electricity price. Within the MGCO, the power mutual assistance and wind and solar abandonment rates between microgrids are comprehensively considered to maximize the absorption of new energy and reduce the power backfeed to the superior power grid. A cost allocation strategy based on the power interaction ratio between microgrids is proposed to reduce the operation cost of MGCO.

[0284] Existing technologies often lack effective pricing strategies and optimization mechanisms when dealing with multi-agent games. The above-mentioned active distribution network game dispatching framework forms a more complex market structure by introducing the interaction of multiple subjects (such as microgrid operators MGO, microgrid alliance MGCO and distribution system operators DSO). This structure not only makes the status between different subjects clearer, but also solves the pricing and optimization problems in multi-agent games through clear master-slave relationships and cooperation mechanisms. This structure can effectively improve the coordination between the various subjects, prompt them to form a reasonable competition and cooperation relationship in the market, and ultimately achieve the optimal allocation of resources.

[0285] Traditional dispatch models often fail to fully consider the impact of electricity price fluctuations on operation strategies. This technology strengthens the optimization goal of DSO in peak-load dispatching by taking time-of-use electricity prices as decision variables. This method effectively guides the operation strategy of MGCO, making it more responsive to changes in electricity prices. This mechanism not only improves the stability of the power grid, but also provides a guarantee for the rationality of electricity prices, which helps to form a healthier market competition environment.

[0286] In terms of solution efficiency, traditional models often face the problem of nested iterations in the solution process, resulting in low efficiency. The distributed iterative algorithm proposed above solves the master-slave game model by combining QPSO with the Cplex commercial solver, effectively avoiding nested iterations. This method significantly improves the solution efficiency and makes it possible to quickly obtain feasible scheduling solutions in a complex power market environment.

[0287] Use Figure 3 The improved IEEE33-node active distribution network is shown. Nodes 7, 24, and 29 are connected to three MGs. Node 1 is the common connection point between the ADN and the upper power grid, and nodes 11, 16, and 21 are connected to two photovoltaic units and one wind turbine, respectively.

[0288] Example 1, using the method proposed by the present invention: DSO formulates time-of-use electricity prices and adopts a master-slave game scheduling strategy, MGCO responds to the time-of-use electricity prices and formulates a scheduling strategy, and microgrids perform power mutual assistance and distribute cooperative surplus.

[0289] Example 2: DSO sets time-of-use electricity prices and adopts a master-slave game scheduling strategy. MGCO responds to time-of-use electricity prices and formulates a scheduling strategy, but there is no power mutual assistance between microgrids.

[0290] Example 3: DSO adopts a fixed electricity purchase and sale price, MGCO formulates a dispatch strategy based on the fixed electricity price, and microgrids cooperate with each other and distribute cooperative surplus.

[0291] The operation cost of multiple agents and the peak-shaving and valley-filling effect of ADN under different examples are analyzed. Table 1 shows the iterative process of the DSO and MGCO multi-agent game. After 15 iterations, the operation costs of the two game agents almost do not change, and the game equilibrium is reached.

[0292] Table 1 Iterative process of multi-agent game

[0293] Iterations DSO cost / yuan MGCO cost / yuan 1 33255 9741 5 32315 10281 10 32004 10667 15 31948 10959 20 31943 10963

[0294] Table 2 shows the time-of-use electricity purchase and sale prices set by DSO when the game is in equilibrium.

[0295] Table 2DSO time-of-use electricity purchase and sale prices

[0296]

[0297] As shown in Tables 3 and 4, examples 1 and 2 are compared and analyzed. The MGCO in example 1 takes into account the power mutual assistance between microgrids, reduces the power purchase and sale between the microgrid and the distribution network, and makes the MGCO cost 10,963 yuan, which is less than 11,613 yuan in example 2; the cost of DSO in the two examples does not change much, and the peak regulation effect of ADN is roughly the same.

[0298] Table 3 Cost of each subject in different examples

[0299] Calculation example DSO cost / yuan MGCO cost / yuan 1 31943 10963 2 31355 11613 3 34061 7417

[0300] Table 4 ADN peak regulation effect of different examples

[0301] Calculation example <![CDATA[ADN Payload Mean Square Error / kW 2 > ADN net load peak-to-valley difference rate / % 1 7.97×104 35.97％ 2 7.96×104 35.95％ 3 2.05×105 45.79％

[0302] Comparison and analysis between Example 1 and Example 3. The DSO in Example 1 implements time-of-use electricity price peak-shaving dispatch, while the DSO in Example 3 implements fixed electricity price dispatch. The peak-shaving and valley-filling effect of ADN in Example 1 is significantly better than that in Example 3. Its net load mean square error is reduced by 1.25×105kW2, and the peak-to-valley difference rate is reduced by 9.82%, indicating that the implementation of the time-of-use electricity price mechanism by DSO can effectively smooth its net load fluctuations and optimize the load curve.

[0303] The output results of each ADN device are as follows: Figure 4 As shown. Comparing Example 1 with Example 2, Example 1 takes into account the power interaction between microgrids, reduces the power purchase and sale between microgrids and distribution networks, and reduces the impact of microgrids on distribution networks; Comparing Example 1 with Example 3, Example 1 adopts a time-of-use electricity price mechanism, which greatly mobilizes the enthusiasm of MGCO to participate in the dispatch of distribution networks, and the power purchased by the distribution network from microgrids during peak hours increases, and the power sold to microgrids decreases, which promotes peak shaving and valley filling of distribution networks.

[0304] In order to verify that power interaction between microgrids can improve node voltage quality, the voltages of the connection nodes between each microgrid and the distribution network in Example 1 and Example 2 are compared. The results are as follows: Figure 5 As shown. Figure 5 It can be seen that during the 11-19 period, photovoltaic power generation is sufficient, and microgrid 1 provides electricity to microgrid 3, which reduces the voltage value of microgrid 1 nodes and avoids the risk of voltage exceeding the upper limit. At the same time, it increases the voltage value of microgrid 3 nodes and avoids the risk of voltage exceeding the lower limit. During the 1-8 period and 22-24 period, wind power is sufficient, and microgrid 3 provides electricity to microgrid 1, which reduces the voltage value of microgrid 3 nodes and avoids the risk of voltage exceeding the upper limit. At the same time, it increases the voltage value of microgrid 1 nodes and avoids the risk of voltage exceeding the lower limit.

[0305] As the leader of dispatching, the distribution network bears the network loss generated during the dispatching process. When there is no interaction between microgrids, the microgrid will increase the interaction with the distribution network, and the power reverse transmission generated by the microgrid will cause certain network losses. Comparing Example 1 and Example 2, the network loss generated by the dispatching is calculated as follows: Figure 6 As shown in the figure. After considering the power interaction between microgrids, due to the close distance and low voltage level between microgrids, the interaction between microgrids will reduce the network loss value to a certain extent. The interaction between microgrids can also reduce the amount of electricity purchased from the upper power grid during the peak period of the distribution network, thereby reducing the network loss value and improving the economic efficiency of operation.

[0306] Figure 7 , Figure 8 and Fig. 9 The optimized output diagrams of each device in microgrids 1, 2, and 3 under example 1 and example 2 are shown respectively. The energy scheduling results are analyzed by taking microgrid 1 as an example.

[0307] In the case of example 1, during the valley period of the distribution network (periods 1-7), the photovoltaic unit has no output during the night period 1-6, and power balance is achieved through energy storage discharge and receiving excess new energy from other microgrids. During the period 7, power balance is achieved through photovoltaic power generation and energy storage discharge, and electricity is sold to the distribution network for profit. The gas turbine power generation cost is high during this period, so it is not put into operation. During the normal period of the distribution network (periods 8-10, 15-19, and 23-24), during the periods 8-10 and 15-19 when the photovoltaic output is relatively sufficient, the load demand is mainly met by photovoltaic power generation. During this period, the gas turbine power generation cost is high, and only a small amount of power is invested, and the excess electricity is first charged to the energy storage and transmitted to other microgrids, and then sold to the distribution network for profit. There is no photovoltaic output during the period 23-24, and energy storage discharge and receiving power from other microgrids are prioritized, and then the gas turbine output is used to achieve power balance. During the peak hours of the distribution network (11-14 and 20-22), photovoltaic power generation is sufficient and the cost of gas turbine power generation is low during the 11-14 period. Photovoltaic power generation and gas turbine power generation jointly meet the load demand, and the excess electricity is first charged to the energy storage and transmitted to other microgrids, and then sold to the distribution network for profit. During the 20-22 period, photovoltaic power generation is insufficient, and energy storage discharge is prioritized to meet part of the load demand. The insufficient power is compensated by gas turbine power generation and power purchase from the distribution network. Compared with Example 2, since Example 1 considers the power mutual assistance between microgrids, the power purchased from the distribution network is reduced during the 23-24 period and the 1-6 period, and the power sold to the distribution network is reduced during the 11-18 period. The power interaction between microgrids increases, which promotes the local consumption of new energy, reduces the electricity cost of microgrids, and reduces dependence on the distribution network. Fig.10 This is the power interaction diagram between microgrids in Example 1.

[0308] Distributing the cost savings generated by cooperation according to the proportion of power interaction between microgrids can reflect the rationality and fairness of the distribution. Table 5 shows the cost savings comparison of various microgrid alliance forms, and Table 6 shows the cost comparison before and after the microgrid alliance. The total operating cost of the three MGs after the alliance is 10,963 yuan, which is less than the sum of the costs of the three MGs operating independently, which is 11,669 yuan. Moreover, the costs of the three MGs after distributing the benefits of cooperation are all less than the costs of their independent operation, which meets the conditions of microgrid alliance and benefit distribution.

[0309] Table 5 Cost savings of various alliance forms

[0310] Serial number Alliance Form Cost saving / yuan 1(Example 2) {MG1} 0 2(Example 2) {MG2} 0 3(Example 2) {MG3} 0 4 {MG1,MG2} 41 5 {MG1,MG3} 614 6 {MG2,MG3} 132 7(Example 1) {MG1,MG2,MG3} 706

[0311] Table 6 Comparison of costs before and after the microgrid alliance

[0312] Microgrid Before the alliance / yuan Alliance Post / Yuan Cost saving / yuan Cost saving allocation factor / % MG1 1857 1509 348 49.29％ MG2 4053 4004 49 6.94％ MG3 5759 5450 309 43.77％ total 11669 10963 706 100％

[0313] Combination Fig.10The power mutual assistance relationship between microgrids shows that: MG1's new energy is photovoltaic, which does not generate electricity in the early morning and at night, while MG3's new energy is wind power, which generates more electricity at night and less electricity during the day. MG1 and MG3 have wind-solar complementarity, so the power mutual assistance between MG1 and MG3 is more frequent, while MG2's new energy is photovoltaic and wind power, which has strong wind-solar complementarity, and has less power mutual assistance with MG1 and 3. Therefore, when the benefits are distributed according to the proportion of power interaction between microgrids, MG1 and MG3 have more cost savings, while MG2 has less cost savings.

[0314] The present invention also proposes an active distribution network dispatching system taking into account power interaction between microgrids, comprising:

[0315] The sub-dispatching model establishment module is used to obtain the operating data of each microgrid to determine the net load of each microgrid, and establish the constraints of the power interaction direction between each microgrid according to the net load of each microgrid; taking the minimum penalty cost of wind and solar abandonment of each microgrid as the objective function, and using the objective function and the constraints of the power interaction direction between each microgrid, establish the sub-dispatching model of each microgrid;

[0316] The main dispatch model establishment module is used to build the main dispatch model of the active distribution network with the minimum comprehensive operation cost of the active distribution network as the objective function, and the system stability constraints, customized electricity price range constraints and customized electricity price constraints;

[0317] A module for establishing a slave dispatch model is used to construct a slave dispatch model of the active distribution network with the minimum response operation cost of the microgrid alliance as the objective function and the operation constraints of the microgrid alliance;

[0318] The slave dispatch model and sub-dispatching model interaction module is used for the sub-dispatching model of each microgrid. According to the operation data of each microgrid, the net load, power interaction direction and wind and solar power abandonment penalty cost of each microgrid are sent to the slave dispatch model; the slave dispatch model distributes the cost savings of the microgrid alliance to each microgrid according to the ratio of the sum of the interaction power of each microgrid to the sum of the interaction power of all microgrids within the dispatch period;

[0319] The module for interacting with the slave dispatching model and the master dispatching model is used to send the sum of the net loads of all microgrids from the slave dispatching model to the master dispatching model, and the master dispatching model outputs a customized electricity price; the slave dispatching model outputs a response plan to the customized electricity price; an interactive relationship between the customized electricity price output by the master dispatching model and the response plan to the customized electricity price output by the slave dispatching model is established based on a fixed point mapping method; based on a master-slave game model, an equilibrium solution of the master dispatching model and the slave dispatching model having an interactive relationship is solved, and the equilibrium solution includes: the customized electricity price of the distribution system operator and the response plan of the microgrid alliance to the customized electricity price.

[0320] In the sub-dispatching model establishment module, the operating data of each microgrid includes: wind power generation power, photovoltaic power generation power, energy storage charging power, energy storage discharge power, wind power abandonment rate and photovoltaic power abandonment rate; the net load of each microgrid in each time period satisfies the following relationship:

[0321]

[0322] In the formula, is the net load of the i-th microgrid in period t, are the load power, wind power generation power, photovoltaic power generation power, energy storage charging power and energy storage discharging power of the i-th microgrid in period t, are the wind power abandonment rate and solar power abandonment rate of the i-th microgrid in period t, respectively;

[0323] The constraints on the power interaction direction between microgrids are expressed as follows:

[0324]

[0325] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period y, N is the number of microgrids;

[0326] The objective function of the sub-dispatching model of the i-th microgrid satisfies the following relationship:

[0327]

[0328] In the formula, is the penalty cost of wind and solar power abandonment in the i-th microgrid, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

[0329] In the main dispatch model establishment module, the comprehensive operation cost of the active distribution network satisfies the following relationship:

[0330]

[0331] In the formula, C DSO is the comprehensive operating cost of the active distribution network, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of electricity purchased by the distribution system operator from the upper grid. Penalty costs for voltage over-limit and branch power over-limit;

[0332] The transaction cost between the distribution system operator and the microgrid alliance satisfies the following relationship:

[0333]

[0334] In the formula, are the purchase and sale electricity prices for period t set by the distribution system operator, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively, N is the number of microgrids, and T is the total number of time periods in the dispatch cycle;

[0335] The cost of electricity purchased by the distribution system operator from the upper-level power grid satisfies the following relationship:

[0336]

[0337] In the formula, is the power purchased by the distribution system operator from the upper grid during period t, λ sell The electricity price sold by the upper power grid;

[0338] The penalty costs for voltage over-limit and branch power over-limit satisfy the following relationship:

[0339]

[0340] In the formula, β U , β L are the voltage over-limit penalty coefficient and the branch power over-limit penalty coefficient, U k is the voltage at node k, are the upper and lower limits of the voltage at node k, respectively, l is the power of branch l, is the upper power limit of branch l, L is the number of branches;

[0341] System stability constraints include: power flow constraints, branch power constraints, and node voltage constraints;

[0342] Customize the electricity price range constraint to satisfy the following relationship:

[0343]

[0344]

[0345] In the formula, are the electricity purchase and sales prices of the distribution system operator in period t, respectively, buy,p , buy,f , buy,vare the electricity purchase prices set by distribution system operators during peak, flat and valley periods, respectively, sell,p , sell,f , sell,v are the electricity prices customized by distribution system operators during peak, flat and valley periods, respectively. p , T f , T v They are peak, flat and valley time periods respectively;

[0346] The constraints for customized electricity prices for electricity sales are as follows:

[0347]

[0348] In the formula, The upper limit of the average value of the electricity price set by the distribution system operator during the dispatch period.

[0349] From the dispatch model building module, the response operation cost of the microgrid alliance satisfies the following relationship:

[0350]

[0351] In the formula, C MGCO The responsive operating costs of the microgrid alliance, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of gas turbine power generation participating in the microgrid alliance is is the energy storage usage cost of participating in the response in the microgrid alliance, Penalty costs for wind and solar curtailment in microgrid alliances;

[0352] The power generation cost of gas turbines participating in the microgrid alliance satisfies the following relationship:

[0353]

[0354] In the formula, is the power generation of the gas turbine participating in the response in the i-th microgrid, a MT,i 、b MT,i 、c MT,i are the cost coefficients of gas turbine power generation participating in the response in the i-th microgrid, P is the total number of pollutant types, is the unit emission cost of the p-th type of pollutant, is the emission of the pth type of pollutant corresponding to the power generation of the gas turbine participating in the response in the i-th microgrid, N is the number of microgrids, and T is the total number of time periods in the scheduling cycle;

[0355] The energy storage usage cost participating in the response in the microgrid alliance satisfies the following relationship:

[0356]

[0357] In the formula, γ ES is the charging and discharging cost per unit power of energy storage, are respectively the charging power and discharging power of the energy storage participating in the response of the i-th microgrid in period t;

[0358] The penalty cost for wind and solar power abandonment in the microgrid alliance satisfies the following relationship:

[0359]

[0360] In the formula, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t respectively;

[0361] The operating constraints of the microgrid alliance include:

[0362] 1) The power consumption of the microgrid must satisfy the following relationship:

[0363]

[0364] In the formula, are the upper limit of power purchase and power sale of the i-th microgrid, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively;

[0365] 2) The power purchased and sold by the microgrid and the power mutual assistance between microgrids are transmitted through the microgrid interconnection line. The transmission power constraint of the microgrid interconnection line satisfies the following relationship:

[0366]

[0367] Where: is the upper limit of transmission power of the i-th microgrid tie line, is the sum of the power transmitted from other microgrids to the i-th microgrid, is the sum of the power transmitted from the i-th microgrid to other microgrids;

[0368] 3) The direction constraint of power interaction between microgrids satisfies the following relationship:

[0369]

[0370] In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t;

[0371] 4) The power generation constraint of the gas turbine satisfies the following relationship:

[0372]

[0373] In the formula, are the upper and lower limits of the power generation of the gas turbine in the i-th microgrid respectively;

[0374] 5) Power balance constraint, satisfying the following relationship:

[0375]

[0376] In the formula, is the net load of the i-th microgrid in period t;

[0377] 6) Energy storage charging and discharging constraints satisfy the following relationship:

[0378]

[0379] In the formula, is the upper limit of energy storage charging and discharging power of the i-th microgrid in period t;

[0380] 7) Wind and solar power abandonment constraints satisfy the following relationship:

[0381]

[0382] In the formula, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, respectively.

[0383] 25. The active distribution network dispatching system considering power interaction between microgrids according to claim 14, characterized in that:

[0384] From the interaction module between the dispatch model and the sub-dispatching model, the cost savings of all microgrids are allocated to each microgrid, satisfying the following relationship:

[0385]

[0386] Where, ΔF MGi is the revenue of the i-th microgrid, is the cost saving of the allocated microgrid alliance, C CO,MGi , C NOCO,MGi are the cost of the i-th microgrid when it cooperates with other microgrids and the cost of non-cooperative operation, respectively. To save costs for the microgrid alliance, MGi is the cost saving allocation coefficient corresponding to the i-th microgrid, is the interactive power of the i-th microgrid in period t, is the sum of the interactive power of the i-th microgrid in the scheduling period, is the sum of the interactive powers of all microgrids in the scheduling period, N is the number of microgrids, and T is the total number of time periods in the scheduling period.

[0387] In the interaction module between the slave scheduling model and the master scheduling model, the interaction relationship between the master scheduling model and the slave scheduling model is established based on the fixed point mapping method; including:

[0388] The customized electricity price output by the main dispatch model is mapped to the electricity consumption response output by the dispatch model, satisfying the following relationship:

[0389]

[0390] In the formula, is the customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D A () is the mapping function, is the mapping of the set of customized electricity prices for the distribution system operator in period t;

[0391] The power consumption response output from the dispatch model is mapped to the updated customized electricity price output by the main dispatch model, satisfying the following relationship:

[0392]

[0393] In the formula, is the updated customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D B () is the mapping function, It is a mapping of the set of response plans of the microgrid alliance to the customized electricity price in period t.

[0394] The master-slave game model is represented by the following set:

[0395] S={H;ρ DSO ; δ MGCO ; C DSO ; C MGCO}

[0396] Among them, the distribution system operator and the microgrid alliance constitute the set of game participants H, and the decision variable of the distribution system operator is the set of customized electricity prices of the distribution system operator in period t: satisfy The decision variables of the microgrid alliance are the set of response plans of the microgrid alliance to the customized electricity price in period t: satisfy Distribution system operators and microgrid alliances have their own objective functions C DSO , C MGCO , are the electricity purchase and sales prices of the distribution system operator in period t, respectively. DSO is the comprehensive operating cost of the active distribution network, C MGCO Responsive operating costs for microgrid alliances.

[0397] Existing simulation systems often fail to effectively integrate the coordinated dispatching of multiple microgrid groups and distribution networks. The improved IEEE33 node simulation system, by connecting to multiple microgrid groups, verifies the effectiveness of the dispatching strategy and the coordination of various entities. This simulation system not only promotes the local consumption of new energy in microgrids, but also reduces the overall operating costs, providing strong technical support for practical applications.

[0398] Through scientific scheduling strategies, this framework not only enhances the flexibility of multi-microgrid power interaction, but also promotes the efficient use of new energy. First, in the design of scheduling strategies, a new power interaction model between microgrids is proposed based on the operating characteristics of microgrids. This model judges the operating status of microgrids based on their surplus / shortage power, and uses it as a constraint to formulate power interaction strategies, in order to reduce operating costs and improve the utilization rate of new energy while meeting power demand. This design enables microgrids to adjust their operating strategies more flexibly when facing fluctuating loads and power generation capacity. Secondly, a master-slave game model of DSO and MGCO is constructed. DSO adopts a time-of-use electricity price mechanism to guide MGCO to optimize its operating strategy during the game process, thereby reducing the overall operating cost. The core of this strategy is to promote the coordination between DSO and MGCO through a reasonable pricing mechanism to maximize the overall benefit. In order to effectively solve the above game model, a distributed iterative algorithm based on fixed point mapping is proposed. The algorithm combines QPSO with Cplex to effectively solve the game equilibrium solution. This innovative method significantly improves the solution efficiency and makes the strategy adjustment in the multi-agent game more rapid and effective. Finally, a simulation system based on IEEE33 nodes was established to verify the proposed dispatching strategy. The experimental results show that the strategy performs well in coordinating the operation of various entities, significantly improves the absorption capacity of new energy, and effectively reduces the comprehensive operation cost, demonstrating the practical application value of the dispatching strategy and providing an important reference for the future development of smart grids.

[0399] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0400] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0401] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0402] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0403] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An active distribution network scheduling method taking into account power interaction between microgrids, the scheduling framework of the active distribution network includes a distribution system operator and a microgrid alliance, the microgrid alliance includes multiple microgrids, characterized in that: include: Obtain the operating data of each microgrid to determine the net load of each microgrid, and establish the constraint conditions of the power interaction direction between the microgrids according to the net load of each microgrid; Taking the minimization of the penalty cost of wind and solar power abandonment of each microgrid as the objective function, and using the objective function and the constraints of the power interaction direction between microgrids, a sub-dispatching model of each microgrid is established; Taking the minimum comprehensive operation cost of the active distribution network as the objective function, combined with the system stability constraints, customized electricity price range constraints and customized electricity price constraints, the main dispatching model of the active distribution network is constructed; Taking the minimization of the response operation cost of the microgrid alliance as the objective function and the operation constraints of the microgrid alliance, a slave dispatch model of the active distribution network is constructed; The sub-dispatching model of each microgrid sends the net load, power interaction direction and wind and solar power abandonment penalty cost of each microgrid to the sub-dispatching model according to the operation data of each microgrid; The cost savings of the microgrid alliance are allocated to each microgrid according to the ratio of the sum of the interaction power of each microgrid to the sum of the interaction power of all microgrids in the scheduling period from the scheduling model; The sum of the net loads of all microgrids is sent from the dispatch model to the main dispatch model, and the main dispatch model outputs a customized electricity price; Outputting a response plan to the customized electricity price from the dispatch model; The interactive relationship between the customized electricity price output by the main dispatch model and the response scheme to the customized electricity price output by the dispatch model is established based on the fixed point mapping method; Based on the master-slave game model, an equilibrium solution of a master dispatching model and a slave dispatching model having an interactive relationship is solved, and the equilibrium solution includes: a customized electricity price of a distribution system operator and a response plan of a microgrid alliance to the customized electricity price.

2. The active distribution network scheduling method considering power interaction between microgrids according to claim 1 is characterized in that: The operating data of each microgrid includes: wind power generation power, photovoltaic power generation power, energy storage charging power, energy storage discharge power, wind power abandonment rate and photovoltaic power abandonment rate; the net load of each microgrid in each time period satisfies the following relationship: In the formula, is the net load of the i-th microgrid in period t, are the load power, wind power generation power, photovoltaic power generation power, energy storage charging power and energy storage discharging power of the i-th microgrid in period t, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, respectively.

3. The active distribution network scheduling method considering power interaction between microgrids according to claim 2 is characterized in that: The constraints on the power interaction direction between microgrids are expressed as follows: In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t, and N is the number of microgrids.

4. The active distribution network scheduling method considering power interaction between microgrids according to claim 3 is characterized in that: The objective function of the sub-dispatching model of the i-th microgrid satisfies the following relationship: In the formula, is the penalty cost of wind and solar power abandonment in the i-th microgrid, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

5. The active distribution network scheduling method considering power interaction between microgrids according to claim 1 is characterized in that: The comprehensive operating cost of the active distribution network satisfies the following relationship: In the formula, C DSO is the comprehensive operating cost of the active distribution network, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of electricity purchased by the distribution system operator from the upper grid. Penalty costs for voltage exceeding limit and branch power exceeding limit.

6. The active distribution network scheduling method considering power interaction between microgrids according to claim 1 is characterized in that: The transaction cost between the distribution system operator and the microgrid alliance satisfies the following relationship: In the formula, are the purchase and sale electricity prices for period t set by the distribution system operator, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively, N is the number of microgrids, and T is the total number of time periods in the dispatch cycle; The cost of electricity purchased by the distribution system operator from the upper-level power grid satisfies the following relationship: In the formula, is the power purchased by the distribution system operator from the upper grid during period t, λ sell The electricity price sold by the upper power grid; The penalty costs for voltage over-limit and branch power over-limit satisfy the following relationship: In the formula, β U , β L are the voltage over-limit penalty coefficient and the branch power over-limit penalty coefficient, U k is the voltage at node k, are the upper and lower limits of the voltage at node k, respectively, l is the power of branch l, is the upper power limit of branch l, and L is the number of branches.

7. The active distribution network scheduling method considering power interaction between microgrids according to claim 6 is characterized in that: System stability constraints include: power flow constraints, branch power constraints, and node voltage constraints; Customize the electricity price range constraint to satisfy the following relationship: In the formula, are the electricity purchase and sales prices of the distribution system operator in period t, respectively, buy,p , buy,f , buy,v are the electricity purchase prices set by distribution system operators during peak, flat and valley periods, respectively, sell,p , sell,f , sell,v are the electricity prices customized by distribution system operators during peak, flat and valley periods, respectively. p , T f , T v They are peak, flat and valley time periods respectively; The constraints for customized electricity prices for electricity sales are as follows: In the formula, The upper limit of the average value of the electricity price set by the distribution system operator during the dispatch period.

8. The active distribution network scheduling method considering power interaction between microgrids according to claim 1 is characterized in that: The response operation cost of the microgrid alliance satisfies the following relationship: In the formula, C MGCO The responsive operating costs of the microgrid alliance, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of gas turbine power generation participating in the microgrid alliance is is the energy storage usage cost of participating in the response in the microgrid alliance, Penalty costs for wind and solar curtailment in microgrid alliances.

9. The active distribution network scheduling method considering power interaction between microgrids according to claim 8, characterized in that: The power generation cost of gas turbines participating in the microgrid alliance satisfies the following relationship: In the formula, is the power generation of the gas turbine participating in the response in the i-th microgrid, a MT,i 、b MT,i 、c MT,i are the cost coefficients of gas turbine power generation participating in the response in the i-th microgrid, P is the total number of pollutant types, is the unit emission cost of the p-th type of pollutant, is the emission of the pth type of pollutant corresponding to the power generation of the gas turbine participating in the response in the i-th microgrid, N is the number of microgrids, and T is the total number of time periods in the scheduling cycle; The energy storage usage cost participating in the response in the microgrid alliance satisfies the following relationship: In the formula, γ ES is the charging and discharging cost per unit power of energy storage, are respectively the charging power and discharging power of the energy storage participating in the response of the i-th microgrid in period t; The penalty cost for wind and solar power abandonment in the microgrid alliance satisfies the following relationship: In the formula, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period i, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

10. The active distribution network scheduling method considering power interaction between microgrids according to claim 9, characterized in that: The operating constraints of the microgrid alliance include: 1) The power consumption of the microgrid must satisfy the following relationship: In the formula, are the upper limit of power purchase and power sale of the i-th microgrid, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively; 2) The power purchased and sold by the microgrid and the power mutual assistance between microgrids are transmitted through the microgrid interconnection line. The transmission power constraint of the microgrid interconnection line satisfies the following relationship: Where: is the upper limit of transmission power of the i-th microgrid tie line, is the sum of the power transmitted from other microgrids to the i-th microgrid, is the sum of the power transmitted from the i-th microgrid to other microgrids; 3) The direction constraint of power interaction between microgrids satisfies the following relationship: In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t; 4) The power generation constraint of the gas turbine satisfies the following relationship: In the formula, are the upper and lower limits of the power generation of the gas turbine in the i-th microgrid respectively; 5) Power balance constraint, satisfying the following relationship: In the formula, is the net load of the i-th microgrid in period t; 6) Energy storage charging and discharging constraints satisfy the following relationship: In the formula, is the upper limit of energy storage charging and discharging power of the i-th microgrid in period t; 7) Wind and solar power abandonment constraints satisfy the following relationship: In the formula, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, respectively.

11. The active distribution network scheduling method considering power interaction between microgrids according to claim 1, characterized in that: Allocate the cost savings of all microgrids to each microgrid to satisfy the following relationship: Where, ΔF MGi is the revenue of the i-th microgrid, is the cost saving of the allocated microgrid alliance, C CO,MGi , C NOCO,MGi are the cost of the i-th microgrid when it cooperates with other microgrids and the cost of non-cooperative operation, respectively. To save costs for the microgrid alliance, MGi is the cost-saving allocation coefficient corresponding to the i-th microgrid, is the interactive power of the i-th microgrid in period t, is the sum of the interactive power of the i-th microgrid in the scheduling period, is the sum of the interactive powers of all microgrids in the scheduling period, N is the number of microgrids, and T is the total number of time periods in the scheduling period.

12. The active distribution network scheduling method considering power interaction between microgrids according to claim 1, characterized in that: The interactive relationship between the master scheduling model and the slave scheduling model is established based on the fixed point mapping method; including: The customized electricity price output by the main dispatch model is mapped to the electricity consumption response output by the dispatch model, satisfying the following relationship: In the formula, is the customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D A () is the mapping function, is the mapping of the set of customized electricity prices for the distribution system operator in period t; The power consumption response output from the dispatch model is mapped to the updated customized electricity price output by the main dispatch model, satisfying the following relationship: In the formula, is the updated customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D B () is the mapping function, It is a mapping of the set of response plans of the microgrid alliance to the customized electricity price in period t.

13. The active distribution network dispatching method considering power interaction between microgrids according to claim 12, characterized in that: The master-slave game model is represented by the following set: S={H;ρ DSO ;d MGCO ;C DSO ;C MGCO } Among them, the distribution system operator and the microgrid alliance constitute the set of game participants H, and the decision variable of the distribution system operator is the set of customized electricity prices of the distribution system operator in period t: satisfy The decision variables of the microgrid alliance are the set of response plans of the microgrid alliance to the customized electricity price in period t: satisfy Distribution system operators and microgrid alliances have their own objective functions C DSO , C MGCO , are the electricity purchase and sales prices of the distribution system operator in period t, respectively. DSO is the comprehensive operating cost of the active distribution network, C MGCO Responsive operating costs for microgrid alliances.

14. An active distribution network dispatching system taking into account power interaction between microgrids, characterized in that: include: The sub-dispatching model establishment module is used to obtain the operating data of each microgrid to determine the net load of each microgrid, and establish the constraint conditions of the power interaction direction between the microgrids according to the net load of each microgrid; Taking the minimization of the penalty cost of wind and solar power abandonment of each microgrid as the objective function, and using the objective function and the constraints of the power interaction direction between microgrids, a sub-dispatching model of each microgrid is established; The main dispatch model establishment module is used to build the main dispatch model of the active distribution network with the minimum comprehensive operation cost of the active distribution network as the objective function, and the system stability constraints, customized electricity price range constraints and customized electricity price constraints; A module for establishing a slave dispatch model is used to construct a slave dispatch model of the active distribution network with the minimum response operation cost of the microgrid alliance as the objective function and the operation constraints of the microgrid alliance; The slave dispatch model and sub-dispatching model interaction module is used for the sub-dispatching model of each microgrid. According to the operation data of each microgrid, the net load, power interaction direction and wind and solar power abandonment penalty cost of each microgrid are sent to the slave dispatch model; The cost savings of the microgrid alliance are allocated to each microgrid according to the ratio of the sum of the interaction power of each microgrid to the sum of the interaction power of all microgrids in the scheduling period from the scheduling model; The slave dispatch model and the master dispatch model interaction module is used to send the sum of the net loads of all microgrids from the slave dispatch model to the master dispatch model, and the master dispatch model outputs a customized electricity price; Outputting a response plan to the customized electricity price from the dispatch model; The interactive relationship between the customized electricity price output by the main dispatch model and the response scheme to the customized electricity price output by the dispatch model is established based on the fixed point mapping method; Based on the master-slave game model, an equilibrium solution of a master dispatching model and a slave dispatching model having an interactive relationship is solved, and the equilibrium solution includes: a customized electricity price of a distribution system operator and a response plan of a microgrid alliance to the customized electricity price.

15. The active distribution network dispatching system considering power interaction between microgrids according to claim 14, characterized in that: In the sub-dispatching model establishment module, the operating data of each microgrid includes: wind power generation power, photovoltaic power generation power, energy storage charging power, energy storage discharge power, wind abandonment rate and solar abandonment rate; the net load of each microgrid in each time period satisfies the following relationship:

16. In the formula, is the net load of the i-th microgrid in period t, are the load power, wind power generation power, photovoltaic power generation power, energy storage charging power and energy storage discharging power of the i-th microgrid in period t, are the wind power abandonment rate and solar power abandonment rate of the i-th microgrid in period t, respectively; The constraints on the power interaction direction between microgrids are expressed as follows: In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t, N is the number of microgrids; The objective function of the sub-dispatching model of the i-th microgrid satisfies the following relationship: In the formula, is the penalty cost of wind and solar power abandonment in the i-th microgrid, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are respectively the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t.

17. The active distribution network dispatching system considering power interaction between microgrids according to claim 14, characterized in that: In the main dispatch model establishment module, the comprehensive operation cost of the active distribution network satisfies the following relationship: In the formula, C DSO is the comprehensive operating cost of the active distribution network, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of electricity purchased by the distribution system operator from the upper grid. Penalty costs for voltage over-limit and branch power over-limit; The transaction cost between the distribution system operator and the microgrid alliance satisfies the following relationship: In the formula, are the purchase and sale electricity prices for period t set by the distribution system operator, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively, N is the number of microgrids, and T is the total number of time periods in the dispatch cycle; The cost of electricity purchased by the distribution system operator from the upper-level power grid satisfies the following relationship: In the formula, is the power purchased by the distribution system operator from the upper grid during period t, λ sell The electricity price sold by the upper power grid; The penalty costs for voltage over-limit and branch power over-limit satisfy the following relationship: In the formula, β U , β L are the voltage over-limit penalty coefficient and the branch power over-limit penalty coefficient, U K is the voltage at node k, are the upper and lower limits of the voltage at node k, respectively, l is the power of branch l, is the upper power limit of branch l, L is the number of branches; System stability constraints include: power flow constraints, branch power constraints, and node voltage constraints; Customize the electricity price range constraint to satisfy the following relationship: In the formula, are the electricity purchase and sales prices of the distribution system operator in period t, respectively, buy,p , buy,f , buy,v are the electricity purchase prices set by distribution system operators during peak, flat and valley periods, respectively, sell,p , sell,f , sell,v are the electricity prices customized by distribution system operators during peak, flat and valley periods, respectively. p 、T f 、T v They are peak, flat and valley time periods respectively; The constraints for customized electricity prices for electricity sales are as follows: In the formula, The upper limit of the average value of the electricity price set by the distribution system operator during the dispatch period.

18. The active distribution network dispatching system considering power interaction between microgrids according to claim 14, characterized in that: From the dispatch model building module, the response operation cost of the microgrid alliance satisfies the following relationship: In the formula, C MGCO The responsive operating costs of the microgrid alliance, is the transaction cost between the distribution system operator and the microgrid alliance, The cost of gas turbine power generation participating in the microgrid alliance is is the energy storage usage cost of participating in the response in the microgrid alliance, Penalty costs for wind and solar curtailment in microgrid alliances; The power generation cost of gas turbines participating in the microgrid alliance satisfies the following relationship: In the formula, is the power generation of the gas turbine participating in the response in the microgrid, a MT,i 、b MT,i 、c MT,i are the cost coefficients of gas turbine power generation participating in the response in the i-th microgrid, P is the total number of pollutant types, is the unit emission cost of the p-th type of pollutant, is the emission of the pth type of pollutant corresponding to the power generation of the gas turbine participating in the response in the i-th microgrid, N is the number of microgrids, and T is the total number of time periods in the scheduling cycle; The energy storage usage cost participating in the response in the microgrid alliance satisfies the following relationship: In the formula, γ ES is the charging and discharging cost per unit power of energy storage, are respectively the charging power and discharging power of the energy storage participating in the response of the i-th microgrid in period t; The penalty cost for wind and solar power abandonment in the microgrid alliance satisfies the following relationship: In the formula, ρ CUT,WT , CUT,PV are the penalty coefficients for wind and solar abandonment, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, are the wind power generation power and photovoltaic power generation power of the i-th microgrid in period t respectively; The operating constraints of the microgrid alliance include: 1) The power consumption of the microgrid must satisfy the following relationship: In the formula, are the upper limit of power purchase and power sale of the i-th microgrid, are the purchased and sold power of the i-th microgrid and the distribution system operator, respectively; 2) The power purchased and sold by the microgrid and the power mutual assistance between microgrids are transmitted through the microgrid interconnection line. The transmission power constraint of the microgrid interconnection line satisfies the following relationship: Where: is the upper limit of transmission power of the i-th microgrid tie line, is the sum of the power transmitted from other microgrids to the i-th microgrid, is the sum of the power transmitted from the i-th microgrid to other microgrids; 3) The direction constraint of power interaction between microgrids satisfies the following relationship: In the formula, is the power transmitted from the jth microgrid to the ith microgrid and from the ith microgrid to the jth microgrid in period t; 4) The power generation constraint of the gas turbine satisfies the following relationship: In the formula, are the upper and lower limits of the power generation of the gas turbine in the i-th microgrid respectively; 5) Power balance constraint, satisfying the following relationship: In the formula, is the net load of the i-th microgrid in period t; 6) Energy storage charging and discharging constraints satisfy the following relationship: In the formula, is the upper limit of energy storage charging and discharging power of the i-th microgrid in period t; 7) Wind and solar power abandonment constraints satisfy the following relationship: In the formula, are the wind curtailment rate and solar curtailment rate of the i-th microgrid in period t, respectively.

19. The active distribution network dispatching system considering power interaction between microgrids according to claim 14, characterized in that: From the interaction module between the dispatch model and the sub-dispatching model, the cost savings of all microgrids are allocated to each microgrid, satisfying the following relationship: Where, ΔF MGi is the revenue of the i-th microgrid, is the cost saving of the allocated microgrid alliance, C CO,MGi , C NOCO,MGi are the cost of the i-th microgrid when it cooperates with other microgrids and the cost of non-cooperative operation, respectively. To save costs for the microgrid alliance, MGi is the cost-saving allocation coefficient corresponding to the i-th microgrid, is the interactive power of the i-th microgrid in period t, is the sum of the interactive power of the i-th microgrid in the scheduling period, is the sum of the interactive powers of all microgrids in the scheduling period, N is the number of microgrids, and T is the total number of time periods in the scheduling period.

20. The active distribution network dispatching system considering power interaction between microgrids according to claim 14, characterized in that: In the interaction module between the slave scheduling model and the master scheduling model, the interaction relationship between the master scheduling model and the slave scheduling model is established based on the fixed point mapping method; including: The customized electricity price output by the main dispatch model is mapped to the electricity consumption response output by the dispatch model, satisfying the following relationship: In the formula, is the customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D A () is the mapping function, is the mapping of the set of customized electricity prices for the distribution system operator in period t; The power consumption response output from the dispatch model is mapped to the updated customized electricity price output by the main dispatch model, satisfying the following relationship: In the formula, is the updated customized electricity price set of the distribution system operator in period t, is the set of response plans of the microgrid alliance to the customized electricity price in period t, D B () is the mapping function, It is a mapping of the set of response plans of the microgrid alliance to the customized electricity price in period t.

21. The active distribution network dispatching system taking into account power interaction between microgrids according to claim 20, characterized in that: The master-slave game model is represented by the following set: S={H;ρ DSO ;d MGCO ;C DSO ;C MGCO } Among them, the distribution system operator and the microgrid alliance constitute the set of game participants H, and the decision variable of the distribution system operator is the set of customized electricity prices of the distribution system operator in period t: satisfy The decision variables of the microgrid alliance are the set of response plans of the microgrid alliance to the customized electricity price in period t: satisfy Distribution system operators and microgrid alliances have their own objective functions C DSO , C MGCO , are the electricity purchase and sales prices of the distribution system operator in period t, respectively. DSO is the comprehensive operating cost of the active distribution network, C MGCO Responsive operating costs for microgrid alliances.

22. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 13.

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

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