Smart distribution network operation optimization method, system, equipment and medium based on security domain

Abstract

The present invention relates to the technical field of distribution network dispatching and management, and provides a method, system, device and medium for optimizing the operation of a smart distribution network based on a security domain, the method comprising: determining a first dispatching model based on the demand response of a microgrid alliance and the security domain of the smart distribution network; determining a second dispatching model based on the remaining capacity arbitrage of a shared energy storage operator and the security domain of the smart distribution network; determining a target dispatching model based on the first dispatching model and the second dispatching model; and coordinating the dispatching of the microgrid alliance, the shared energy storage operator and the smart distribution network based on the solution of the target dispatching model. The technical solution disclosed in the present invention can effectively take into account the overall interests and the interests of various entities within the system, promote the efficient consumption of new energy, and has good engineering application value.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network dispatching and management, and in particular to a security domain-based intelligent distribution network operation optimization method, system, equipment and medium. Background Art

[0002] In the distribution network with multiple emerging entities, microgrids organically combine renewable energy and loads through effective operation and control strategies, reducing the impact of direct grid connection of renewable energy on the large grid. However, microgrids have the characteristics of autonomous operation and privacy protection. The traditional distribution network control center cannot directly control the equipment in the microgrid, which brings challenges to the optimization of system operation. At present, distributed energy storage applications are mainly used in microgrids. When multiple microgrids are connected to the same distribution network, the charging and discharging behaviors of multiple microgrid energy storage are disordered from the overall perspective, resulting in waste of energy storage resources. There is an electrical connection between shared energy storage and microgrid users and smart distribution networks. By utilizing the complementary characteristics of different microgrid loads, the fluctuation of new energy in microgrids can be smoothed according to user charging and discharging needs, grid time-of-use electricity prices, remaining energy storage capacity and other information, which can reduce the impact on smart distribution networks. The collaborative operation methods of multiple microgrids, shared energy storage and smart distribution networks have become a research hotspot.

[0003] Microgrids are an effective way to improve the consumption of distributed renewable energy. However, renewable energy generation within microgrids is still volatile. When multiple microgrids are connected to smart distribution networks, it is necessary to coordinate the timing power of the interconnection lines between smart distribution networks and microgrids to improve the overall power supply reliability and economic operation of the system. However, existing studies have failed to optimize the charging and discharging behaviors of multiple microgrid energy storage from a holistic perspective, and have not considered the formation of multiple microgrids into an alliance for cooperative operation, resulting in a waste of energy storage resources. When multiple microgrids jointly lease shared energy storage, the shared energy storage optimization scheduling models established by existing research institutes have different focuses, and only passively accept the charging and discharging power of users, without further exploring the benefit space of the remaining capacity of shared energy storage. When shared energy storage and energy storage users jointly participate in the joint scheduling of microgrid alliances and distribution networks, it is necessary to formulate the rental price of shared energy storage. With the continuous improvement of the power market, users can directly participate in power market transactions through point-to-point transactions. However, existing studies often ignore the impact of static security constraints of smart distribution networks on power interactions.

[0004] In summary, relevant scholars at home and abroad have not been able to solve the multilateral game problem involving multiple microgrids with shared energy storage in a large number of studies on shared energy storage. Summary of the invention

[0005] To solve the above problems, the present invention provides a method, system, device and medium for optimizing the operation of a smart distribution network based on a security domain, which can effectively balance the overall interests of the system and the interests of various internal entities, promote the efficient consumption of new energy, and have good engineering application value.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present disclosure provides a method for optimizing operation of a smart distribution network based on a security domain, comprising:

[0008] Determine the first dispatch model based on the demand response of the microgrid alliance and the security domain of the smart distribution network;

[0009] Determine the second dispatch model based on the surplus capacity arbitrage of the shared energy storage operator and the security domain of the smart distribution network;

[0010] Determining a target scheduling model based on the first scheduling model and the second scheduling model;

[0011] Based on the solution results of the target scheduling model, the microgrid alliance, shared energy storage operators and smart distribution networks are coordinated and scheduled.

[0012] Furthermore, based on the demand response of the microgrid alliance and the security domain of the smart distribution network, a first dispatching model is determined, including:

[0013] Based on the demand response of the microgrid alliance, a minimum cost optimization dispatch model for the operation of the microgrid alliance is established;

[0014] Based on the minimum cost optimization dispatching model and the security domain of the smart distribution network, the first dispatching model of the microgrid alliance and the smart distribution network is determined.

[0015] Furthermore, the constraints of the minimum cost optimization scheduling model include:

[0016] Multi-energy balance constraints, energy coupling equipment constraints, energy storage equipment constraints, and power purchase and sales interconnection line constraints.

[0017] Furthermore, the constraints of the first scheduling model include:

[0018] Ensure energy flow direction constraints and smart distribution network static safety domain constraints.

[0019] Furthermore, based on the surplus capacity arbitrage of the shared energy storage operator and the security domain of the smart distribution network, a second dispatching model is determined, including:

[0020] Based on the surplus capacity arbitrage of shared energy storage operators, a peak-valley arbitrage optimization scheduling model for shared energy storage operators is established;

[0021] Based on the peak-valley arbitrage optimization scheduling model and the safety domain of the smart distribution network, the second scheduling model of the shared energy storage operator and the smart distribution network is determined.

[0022] Furthermore, the constraints of the peak-valley arbitrage optimization scheduling model include:

[0023] Electricity sequential constraints and interconnection line capacity constraints.

[0024] Furthermore, the constraints of the second scheduling model include:

[0025] Static security domain constraints and power flow direction constraints of smart distribution networks.

[0026] Further, based on the first scheduling model and the second scheduling model, determining a target scheduling model includes:

[0027] Based on the first scheduling model, the second scheduling model is iteratively optimized to determine the target scheduling model of the microgrid alliance, shared energy storage operators and smart distribution network.

[0028] In a second aspect, the present disclosure further provides a security domain-based smart distribution network operation optimization system, comprising:

[0029] A first determination module is used to determine a first dispatching model based on the demand response of the microgrid alliance and the security domain of the smart distribution network;

[0030] A second determination module is used to determine a second dispatching model based on the remaining capacity arbitrage of the shared energy storage operator and the security domain of the smart distribution network;

[0031] A third determination module, configured to determine a target scheduling model based on the first scheduling model and the second scheduling model;

[0032] The scheduling module is used to coordinate the scheduling of microgrid alliances, shared energy storage operators and smart distribution networks based on the solution results of the target scheduling model.

[0033] Furthermore, the first determining module is further used for:

[0034] Based on the demand response of the microgrid alliance, a minimum cost optimization dispatch model for the operation of the microgrid alliance is established;

[0035] Based on the minimum cost optimization dispatching model and the security domain of the smart distribution network, the first dispatching model of the microgrid alliance and the smart distribution network is determined.

[0036] Furthermore, the constraints of the minimum cost optimization scheduling model include:

[0037] Multi-energy balance constraints, energy coupling equipment constraints, energy storage equipment constraints, and power purchase and sales interconnection line constraints.

[0038] Furthermore, the constraints of the first scheduling model include:

[0039] Ensure energy flow direction constraints and smart distribution network static safety domain constraints.

[0040] Furthermore, the second determining module is further used for:

[0041] Based on the surplus capacity arbitrage of shared energy storage operators, a peak-valley arbitrage optimization scheduling model for shared energy storage operators is established;

[0042] Based on the peak-valley arbitrage optimization scheduling model and the safety domain of the smart distribution network, the second scheduling model of the shared energy storage operator and the smart distribution network is determined.

[0043] Furthermore, the constraints of the peak-valley arbitrage optimization scheduling model include:

[0044] Electricity sequential constraints and interconnection line capacity constraints.

[0045] Furthermore, the constraints of the second scheduling model include:

[0046] Static security domain constraints and power flow direction constraints of smart distribution networks.

[0047] Furthermore, the third determining module is further used for:

[0048] Based on the first scheduling model, the second scheduling model is iteratively optimized to determine the target scheduling model of the microgrid alliance, shared energy storage operators and smart distribution network.

[0049] In a third aspect, the present disclosure further provides an electronic device, including: a processor and a memory;

[0050] The processor is coupled to the memory;

[0051] The processor is used to read and execute the program or instructions stored in the memory, so that the device executes the method of the first aspect.

[0052] In a fourth aspect, the present disclosure further provides a computer-readable storage medium storing a computer program, which implements the method of the first aspect when executed by a processor.

[0053] In summary, the technical solution provided by the present disclosure has at least the following technical effects or advantages:

[0054] The technical solution disclosed in this disclosure takes the new power system microgrid alliance, smart distribution network and shared energy storage collaborative optimization scheduling as the research object. When using this technical solution, you only need to input the easily accessible electricity price information, load data, renewable energy output data, dispatchable equipment parameters and network topology data of the local power system to carry out the optimization scheduling of the smart distribution network containing shared energy storage equipment. This disclosure establishes a three-party collaborative scheduling model of microgrid alliance, shared energy storage and smart distribution network respectively; then, considering the static safety constraints of the distribution network and the operating benefits of the microgrid alliance, a power interaction plan is formulated, and a fast solution method based on the security domain is proposed; finally, the remaining capacity of the shared energy storage is fully utilized, and the peak-valley price arbitrage is used to formulate an interactive power plan between the shared energy storage and the smart distribution network. This disclosure can effectively take into account the overall interests and the interests of various entities within the system, promote the efficient consumption of new energy, and has good engineering application value.

[0055] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A schematic diagram of a flow chart of a method for optimizing operation of a smart distribution network based on a security domain in an embodiment of the present invention;

[0058] Figure 2 A diagram showing the information and energy interaction relationship between the multilateral entities in an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a flow chart of another method for optimizing operation of a smart distribution network based on a security domain in an embodiment of the present invention;

[0060] Figure 4 It is a structural diagram of a security domain-based intelligent distribution network operation optimization system in an embodiment of the present invention;

[0061] Figure 5 The present invention is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] As more and more emerging entities join the distribution network, the intelligent control model of the smart distribution network becomes more and more complex and difficult to solve. The variables in the constraints of the operation and control model of the smart distribution network are implicitly associated through the power flow equation, resulting in high-dimensional and nonlinear optimization problems in the optimization process. Moreover, as more intelligent agents appear in the distribution system, the constraints and variables become more complex. The static safety domain of the distribution network, that is, the safety domain of the smart distribution network, refers to the area where the voltage, current, power and other parameters of the power grid are kept within a reasonable range during the steady-state operation of the power grid, and no abnormal conditions such as overload, short circuit, and voltage imbalance occur. It is determined based on the topological structure, electrical parameters, load distribution and other factors of the power grid. For a given network topology and root node voltage, the static safety domain of the distribution network is uniquely determined and does not change with changes in the operating state. The static safety domain method of the distribution network can linearly express the static safety constraints of the distribution network through the safety domain hyperplane expression, thereby achieving the purpose of quickly solving the optimization model.

[0064] Based on this, the technical solution disclosed in this invention takes the new power system microgrid alliance, smart distribution network and shared energy storage collaborative optimization dispatching as the research object. When using this technical solution, you only need to input the easily accessible electricity price information, load data, output data of renewable energy, dispatchable equipment parameters and network topology data of the local power system to carry out the optimization dispatching of the smart distribution network containing shared energy storage equipment. This invention respectively establishes a three-party collaborative dispatching model of microgrid alliance, shared energy storage and smart distribution network; then, considering the static safety constraints of the distribution network and the operating benefits of the microgrid alliance, a power interaction plan is formulated, and a fast solution method based on the security domain is proposed; finally, the remaining capacity of the shared energy storage is fully utilized, and the peak-valley price arbitrage is used to formulate an interactive power plan between the shared energy storage and the smart distribution network. This invention can effectively take into account the overall interests and the interests of various subjects within the system, promote the efficient consumption of new energy, and has good engineering application value.

[0065] Figure 1 The figure is a flow chart of a method for optimizing the operation of a smart distribution network based on a security domain in an embodiment of the present invention. As shown in the figure, the method includes:

[0066] S101. Determine a first dispatching model based on the demand response of the microgrid alliance and the security domain of the smart distribution network;

[0067] S102, determining a second dispatching model based on the remaining capacity arbitrage of the shared energy storage operator and the security domain of the smart distribution network;

[0068] S103, determining a target scheduling model based on the first scheduling model and the second scheduling model;

[0069] S104. Based on the solution results of the target scheduling model, the microgrid alliance, shared energy storage operators and smart distribution networks are coordinated and scheduled.

[0070] Figure 2 A diagram showing the information and energy interaction relationship between the multilateral entities in an embodiment of the present invention;

[0071] Figure 3 A schematic diagram of a flow chart of another method for optimizing operation of a smart distribution network based on a security domain in an embodiment of the present invention; Figure 2 , Figure 3 To further illustrate the technical solution of the present disclosure, the steps are as follows:

[0072] Step 1: Microgrid alliance users adjust load demand based on electricity price information;

[0073] User loads are divided into movable loads and immovable loads. Immovable loads include lighting equipment, air conditioners, refrigerators, televisions, and induction cookers, which are not involved in optimization scheduling. Movable loads include electric vehicles, washing machines, water heaters, and vacuum cleaners, which can be optimized and scheduled according to different time scales. By translating movable loads to make the net load curve smoother, on the one hand, the capacity of the energy storage system configuration and the number of charge and discharge times of the energy storage system can be reduced, and on the other hand, the power supply reliability of the power system can be improved and the power failure rate can be reduced.

[0074] Assume that the power system contains n For a microgrid connected to the node i For the microgrid, the optimization goal is to maximize the comprehensive benefits:

[0075] (1)

[0076] (2)

[0077] (3)

[0078] (4)

[0079] in, It means that the optimization goal is to maximize the comprehensive benefit. For microgrid users i The utility function obtained from consuming electricity is: For microgrid users i The loss of satisfaction caused when the electrical load deviates from the baseline load, and They are t Shike Micro Network Users i Electric load after demand response and baseline load, express t Moment User i The amount of electric load demand response, express t The time-of-use electricity price that the microgrid purchases from the distribution network. , , and Respectively i The demand response coefficient of each microgrid user is Representation variables The square of Representation variables The square of .

[0080] The demand response behavior of the Microgrid Alliance is subject to the following constraints:

[0081] (5)

[0082] (6)

[0083] in, express t Moment User i The amount of electric load demand response, and They are t Shike Micro Network Users i Electric load after demand response and baseline load, T represents the optimization cycle, m For the t The maximum proportion of the electric load in a time period can be adjusted. Equations (5) and (6) represent the intensity of demand response and the constraint of constant total load demand, respectively.

[0084] Step 2: The microgrid alliance formulates a dispatching plan with the goal of minimizing operating costs and reports it to the smart distribution network;

[0085] 1. The operating costs of the microgrid alliance;

[0086] The operating costs of the microgrid alliance mainly include the cost of electric energy exchange between the microgrid alliance and the smart distribution network, the fuel cost for the gas turbine, the service fee paid by the microgrid alliance for using the shared energy storage charging and discharging service, and the operating costs of the user-side energy storage in the microgrid, which can be calculated using the following formula:

[0087] (7)

[0088] in, F MGO represents the total operating cost of the microgrid alliance, F MtA The cost of electric energy exchange between the microgrid alliance and the smart distribution network, F Gas is the fuel cost of the gas turbine, F MtS Service fees paid for the use of shared energy storage charging and discharging services by the Microgrid Alliance, F es is the operating cost of user-side energy storage in the microgrid, represents the number of microgrids, represents the optimization cycle, express t The time-of-use electricity price of the microgrid selling electricity to the distribution network. represents the time-of-use electricity price that the microgrid purchases from the distribution network at time t, and Respectively t Moment Micro Network i The power purchased from and sold to the distribution network, Indicates the time interval, express t Moment i The electrical power of the gas turbine in each microgrid, , and They represent the unit price of natural gas, the calorific value of natural gas per unit volume, and the power generation efficiency of the gas turbine. and They represent the shared energy storage rental fee and the loss cost of the energy storage equipment in the microgrid caused by the unit charge / discharge volume, and Respectively t Moment i The power of each microgrid charging and discharging from the shared energy storage, and Respectively t Moment i The charging power and discharging power of the energy storage device in a microgrid.

[0089] 2. Operational constraints of the microgrid alliance;

[0090] The objective function of the Microgrid Alliance is to minimize operating costs, and the following constraints must be taken into account:

[0091] 1) Multi-energy balance constraints

[0092] (8)

[0093] in, express t Moment Micro Network i The power purchased from the distribution grid, express t Moment i The power discharged from the shared energy storage by each microgrid is represents the discharge power of the energy storage device in the i-th microgrid at time t, represents the electric power of the gas turbine in the i-th microgrid at time t, express t The power sold by microgrid i to the distribution network at time instant, represents the power of the i-th microgrid charging the shared energy storage at time t, Represents the charging power of the energy storage device in the i-th microgrid at time t and Respectively t Moment i The actual active power of photovoltaic and wind turbines in each microgrid, and Respectively t Moment i The input power of two types of energy conversion equipment, electric boilers and air conditioners, in a microgrid. express t The electric load after the microgrid user i’s demand response at time, and Respectively t Moment i Output power of electric boilers and air conditioners in each microgrid (expressed in kW), is the thermal power output of the gas turbine (expressed in kW), and Respectively t Moment i Thermal storage equipment in microgrid t The heat storage power and heat release power at the moment, Respectively t Moment i Microgrid internal cold storage equipment t The cold storage power and cooling power at all times, and Respectively t Moment i Micro-grid users t Heating and cooling loads at all times.

[0094] 2) Energy coupling equipment constraints, mainly including cogeneration gas turbines, electric boilers, air conditioners and other equipment.

[0095] There is a proportional relationship between the electrical power and thermal power of a cogeneration gas turbine:

[0096] (9)

[0097] in, is the thermal power output of the gas turbine (expressed in kW), is the electric power of the gas turbine in the i-th microgrid at time t, For power generation efficiency, is the heat loss rate, is the heating coefficient.

[0098] Electric boilers and air conditioners have output upper and lower limits and operating efficiency constraints:

[0099] (10)

[0100] (11)

[0101] in, , Respectively t Moment Micro Network i The operating status of the internal electric boiler and air conditioner, running is 1, and stopped is 0. , represent the energy conversion efficiency of electric boiler and air conditioner respectively; represents the input power of the electric boiler in the i-th microgrid at time t; represents the output power of the electric boiler in the i-th microgrid at time t; It is represented as the input power of the air conditioner in the i-th microgrid at time t; represents the output power of the air conditioner in the i-th microgrid at time t; and as well as and Respectively t Moment i The upper and lower limits of the input power of the two types of energy conversion equipment, electric boilers and air conditioners, within a microgrid.

[0102] 3) Energy storage device constraints;

[0103] The distribution network is connected to shared energy storage. Each microgrid may have electricity storage, cold storage and heat storage equipment, which are subject to sequential operation constraints. The charging and discharging power of the shared energy storage is the algebraic sum of the charging and discharging power of multiple integrated energy microgrid users:

[0104] (12)

[0105] in, and They represent the charging power and discharging power of the shared energy storage respectively; represents the power of the i-th microgrid charging the shared energy storage at time t; represents the power discharged from the shared energy storage by the i-th microgrid at time t; Indicates the number of nodes.

[0106] There is an upper limit on the interaction power between each integrated energy microgrid and the shared energy storage, and they cannot be charged and discharged at the same time:

[0107] (13)

[0108] in, represents the power discharged from the shared energy storage by the i-th microgrid at time t, represents the power of the i-th microgrid charging the shared energy storage at time t, express t Moment i The upper limit of the power that each microgrid can charge and discharge from the shared energy storage, and It is a microgrid i Charging and discharging flag variables between shared energy storage.

[0109] Shared energy storage sequential constraints:

[0110] (14)

[0111] in, represents the charging power of the shared energy storage, represents the discharge power of the shared energy storage, and They represent the efficiency of unidirectional charging and unidirectional discharging of shared energy storage, and Respectively represent the minimum and maximum remaining power of the shared energy storage, , and Represents shared storage in t The time, the initial time of the scheduling period and the remaining power at the end of the scheduling period.

[0112] There are similar sequential operation constraints for thermal energy storage, cold energy storage and electric energy storage on the user side of the integrated energy microgrid. Taking electric energy storage as an example:

[0113] (15)

[0114] in, represents the efficiency of the electric energy storage equipment in the microgrid, represents the charging power of the energy storage device in the i-th microgrid at time t, represents the discharge power of the energy storage device in the i-th microgrid at time t, Indicates the time interval, , and Microgrid i Internal energy storage t The remaining power, minimum remaining power and maximum remaining power at the moment. represents the upper limit of the charging and discharging power of the energy storage device in the i-th microgrid, and Microgrid i Internal energy storage t The charging and discharging flags at the moment, and Microgrid i The remaining power at the initial moment of internal energy storage and at the end of the optimization cycle.

[0115] 4) Constraints on the electricity purchase and sales connection lines;

[0116] After the smart distribution network verifies the power purchase and sale and adjusts the reactive power compensation equipment, it will issue the upper limit constraints of power purchase and sale to the downstream because it cannot meet the safety requirements:

[0117] (16)

[0118] in, represents the power purchased by microgrid i from the distribution network at time t, represents the power sold by microgrid i to the distribution network at time t, and They represent the t Moment Micro Network i The power upper limit of purchasing electricity from the distribution network and selling electricity to the distribution network can be infinite in the first round of iterative calculation.

[0119] When the distribution network cannot fully meet the power purchase and sales needs of the microgrid, it is possible to take measures to abandon wind and solar power:

[0120] (17)

[0121] in, represents the actual active power generated by the photovoltaic power generation in the i-th microgrid at time t, represents the actual active power of the wind turbine in the i-th microgrid at time t, and Microgrid i When there is no wind and solar abandonment in the internal photovoltaic and wind turbines tThe model established above is a linear model, which can be quickly solved using mature mathematical optimization methods, so as to obtain the power interaction scheme between the microgrid alliance and the smart distribution network and the charging and discharging scheme between the microgrid alliance and the shared energy storage, and report them to the smart distribution network and shared energy storage operators respectively.

[0122] Step 3: The smart distribution network determines the upper limit of power interaction and sends it to the microgrid alliance;

[0123] 1. Establish an optimization model of smart distribution network based on the security domain of smart distribution network;

[0124] Based on the principle of maximizing the trading solutions for microgrid alliances and shared energy storage, the safe operation optimization of smart distribution networks is carried out, and the safe operation of smart distribution networks is optimized at any time during the dispatch cycle. t ,have:

[0125] (18)

[0126] in, It means that the objective function is to minimize the sum of absolute value differences. M is the collection of all integrated energy microgrid grid-connected nodes, and Respectively t Moment Micro Network i The total power reported to the distribution network and the interactive power that meets the static safety constraints of the distribution network are calculated based on the power flow from the distribution network to the microgrid. i is positive, . for t The distribution network allows the nodes to be connected to the grid at any time i The power of interaction.

[0127] The following constraints exist:

[0128] 1) Ensure the direction of energy flow constraints:

[0129] (19)

[0130] in, represents the total power reported by microgrid i to the distribution network at time t, It represents the interactive power of microgrid i at time t that satisfies the static security constraints of the distribution network.

[0131] 2) Express the static security constraints of the distribution network in the form of a hyperplane;

[0132] Distribution network static security domain It is the intersection of the static voltage safety domain and the thermal stability safety domain, as shown in the following formula:

[0133] (20)

[0134] in, Represents a security domain set, and Distribution network nodes i Active power injection and reactive power injection, represents the reactive power injection of the reactive power automatic regulation device at the distribution network node i, represents the power vector injected by all nodes that make the distribution network meet the voltage and power flow constraints, and is the voltage upper limit safety region coefficient, and is the voltage lower limit safety region coefficient, and is the branch power flow safety region coefficient, N Represents the set of all nodes in the system. E For all branches of the distribution network A collection of S It is a collection of nodes connected to the distribution network equipped with SVG reactive power compensation devices. For S , M is the set of all nodes in the external distribution network. And

[0135] (twenty one)

[0136] for M The nodes in the collection, when and season ;when and season .when When t The power interaction demand at each moment is not i The upper limit of power interaction is issued, and the upper limit of power purchase and sale can still be set to infinity.

[0137] Step 4: Iterate and optimize between the microgrid alliance and the smart distribution network;

[0138] Repeat steps 2 to 3 above and perform iterative calculations until the interaction scheme between the smart distribution network and the microgrid alliance remains unchanged. k After iterations, t Time grid-connected nodes i The upper limit of electricity purchase is , and then k +1 The upper limit of power purchase expected to be issued by the smart distribution network after optimization by the microgrid alliance is ,but k+1 The actual upper limit of electricity purchase is .

[0139] Step 5: Shared energy storage operators further utilize surplus capacity for arbitrage;

[0140] The shared energy storage operator accepts the charging and discharging plan provided by the microgrid alliance, and formulates a power interaction plan between the shared energy storage and the smart distribution network based on the time-of-use electricity price information provided by the smart distribution network. The power interaction goals between the shared energy storage and the smart distribution network are:

[0141] (twenty two)

[0142] in, It means that the optimization goal is to maximize profit. represents the time-of-use electricity price sold by the microgrid to the distribution network at time t, represents the time-of-use electricity price of the microgrid purchasing electricity from the distribution network at time t, represents the optimization cycle, and Respectively t The power of energy storage selling electricity to the distribution network and purchasing electricity from the distribution network is shared at all times. for t Equipment maintenance cost per unit of charging / discharging power when sharing energy storage to arbitrage surplus capacity.

[0143] Shared energy storage uses the remaining capacity for further arbitrage, subject to power sequential constraints:

[0144] (twenty three)

[0145] in, represents the remaining power of the shared energy storage at time t+1, represents the remaining power of the shared energy storage at time t, represents the efficiency of one-way charging of shared energy storage, represents the power of the i-th microgrid charging the shared energy storage at time t, represents the power discharged from the shared energy storage by the i-th microgrid at time t, represents the efficiency of unidirectional discharge of shared energy storage, Indicates the time interval, represents the power purchased by the shared energy storage from the distribution network at time t, represents the power sold by the shared energy storage to the distribution network at time t, Indicates the minimum remaining power of shared energy storage, Indicates the maximum remaining power of the shared energy storage.

[0146] The interconnection line capacity constraints between shared energy storage and smart distribution networks need to meet:

[0147] (twenty four)

[0148] in, represents the power of the interconnection line between the shared energy storage and the smart distribution network at time t, and They represent the power purchased and sold by the shared energy storage to the smart distribution network at time t, The power limit of the interconnection line between the shared energy storage and the smart distribution network. The above model is a 0-1 mixed integer optimization model, which can be quickly solved using mature mathematical optimization methods. The shared storage operator will use the power interaction solution between itself and the smart distribution network. and Report to the distribution network control center.

[0149] Step 6: The smart distribution network determines the upper limit of power interaction and sends it to the shared energy storage operator;

[0150] At this point, the power injection and flow direction of the microgrid grid-connected nodes have been determined, and only the power of the shared energy storage node can be adjusted. for t The distribution network allows the upper limit of power interaction between the shared energy storage and the distribution network after taking into account the safety constraints at all times. The optimization goal of the distribution network is:

[0151] (25)

[0152] in, It means that the difference of interactive power in the distribution network is the smallest. It indicates the upper limit of power interaction between the shared energy storage and the distribution network without considering the safety constraints. It represents the upper limit of power interaction between shared energy storage and distribution network after taking security constraints into account.

[0153] The security domain constraints of the smart distribution network are adjusted as follows:

[0154] (26)

[0155] in, Represents a security domain set, and Distribution network nodes i Active power injection and reactive power injection, represents the reactive power injection of the reactive power automatic regulation device at the distribution network node i, is the set of all nodes in the distribution network except shared energy storage and SVG nodes, represents the power vector injected by all nodes that make the distribution network meet the voltage and power flow constraints, and is the voltage upper limit safety region coefficient, and is the voltage lower limit safety region coefficient, and is the branch power flow safety region coefficient, N Represents the set of all nodes in the system. E For all branches of the distribution network A collection of S It is a collection of distribution network connected nodes equipped with SVG reactive power compensation devices.

[0156] There are also constraints on the direction of power flow:

[0157] (27)

[0158] in, It represents the power of the interconnection line between the shared energy storage and the smart distribution network. represents the interconnection line power between the shared energy storage and the smart distribution network after taking into account the safety constraints. For the shared energy storage node, when and season ;when and season .when When t The power interaction demand at each moment, the upper limit of electricity purchase and sale can still be set to infinity.

[0159] Step 7: Iterative optimization between shared energy storage operators and smart distribution networks;

[0160] Based on the fixed microgrid interaction plan, repeat steps 5 and 6, and iterate until the interaction plan between the smart distribution network and the microgrid alliance does not change. k+ 1 iteration t The power limit range at the moment should be taken as k In iteration t The power limit range at the moment and k+1 The intersection of the power ranges that the smart distribution network is expected to deliver in the next iteration.

[0161] Step 8: Based on the above iteratively optimized solution, perform collaborative scheduling;

[0162] Based on the above iterative optimization scheme, the target dispatch model is obtained. Based on the solution of the target dispatch model, the microgrid alliance, shared energy storage operators and smart distribution networks are coordinated and dispatched. Ultimately, the overall interests of the system and the interests of various internal entities are taken into account, and the efficient consumption of new energy is promoted.

[0163] The variables in the formulas involved in the above embodiments of the present disclosure have the same meaning in the overall solution.

[0164] Figure 4 The present invention provides a schematic diagram of the structure of a security domain-based intelligent distribution network operation optimization system in an embodiment of the present invention.

[0165] like Figure 4 As shown, the system includes:

[0166] A first determination module is used to determine a first dispatching model based on the demand response of the microgrid alliance and the security domain of the smart distribution network;

[0167] A second determination module is used to determine a second dispatching model based on the remaining capacity arbitrage of the shared energy storage operator and the security domain of the smart distribution network;

[0168] A third determination module, configured to determine a target scheduling model based on the first scheduling model and the second scheduling model;

[0169] The scheduling module is used to coordinate the scheduling of microgrid alliances, shared energy storage operators and smart distribution networks based on the solution results of the target scheduling model.

[0170] It should be noted that, for the sake of convenience, Figure 4 For example, only the main modules of the security domain-based smart distribution network operation optimization system structure are shown. In actual applications, the system may also include modules or components not shown in the figure; the system is not limited to the above module structure, and can also be other module structures that implement the security domain-based smart distribution network operation optimization method of the above embodiment.

[0171] Figure 5 The present invention is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0172] like Figure 5 As shown, the electronic device includes: a processor and a memory;

[0173] Among them, the processor is used to read and execute the programs and instructions stored in the memory, so that the electronic device executes the smart distribution network operation optimization method based on the security domain of the above method embodiment.

[0174] It should be noted that, for the sake of convenience, Figure 5 Only the main components of the electronic device are shown. In practical applications, the electronic device may also include components or assemblies not shown in the figure.

[0175] The present invention also provides a computer-readable storage medium storing a program or instruction. When a computer reads and executes the program or instruction, the computer executes the smart distribution network operation optimization method based on the security domain of the above-mentioned method embodiment.

[0176] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the operation of a smart distribution network based on a security domain, characterized in that: include: Based on the demand response of the microgrid alliance, a minimum cost optimization scheduling model for the operation of the microgrid alliance is established, the minimum cost optimization scheduling model is solved, the interaction power between the microgrid alliance and the smart distribution network is obtained, and the interaction power is uploaded to the smart distribution network; Based on the interactive power between the microgrid alliance and the smart distribution network and the security domain of the smart distribution network, a first smart distribution network optimization model is established, the first smart distribution network optimization model is solved, an upper limit of the interactive power between the microgrid alliance and the smart distribution network is obtained, and the upper limit of the interactive power is issued to the microgrid alliance; Based on the interaction power between the microgrid alliance and the smart distribution network and the upper limit of the interaction power between the microgrid alliance and the smart distribution network, the minimum cost optimization scheduling model and the first smart distribution network optimization model are iteratively calculated to determine the first scheduling model of the microgrid alliance and the smart distribution network; Based on the surplus capacity arbitrage of the shared energy storage operator, a peak-valley arbitrage optimization scheduling model of the shared energy storage operator is established, the peak-valley arbitrage optimization scheduling model is solved, the interactive power between the shared energy storage operator and the smart distribution network is obtained, and the interactive power is uploaded to the smart distribution network; Based on the interactive power between the shared energy storage operator and the smart distribution network and the security domain of the smart distribution network, a second smart distribution network optimization model is established, the second smart distribution network optimization model is solved to obtain the interactive power upper limit between the shared energy storage operator and the smart distribution network, and the power upper limit is sent to the shared energy storage operator; Based on the interaction power between the shared energy storage operator and the smart distribution network and the upper limit of the interaction power between the shared energy storage operator and the smart distribution network, the peak-valley arbitrage optimization scheduling model and the second smart distribution network optimization model are iteratively calculated to determine the second scheduling model between the shared energy storage operator and the smart distribution network; On the basis of satisfying the first dispatch model, the second dispatch model is iteratively optimized to determine the target dispatch model of the microgrid alliance, the shared energy storage operator and the smart distribution network; Based on the solution of the target dispatch model, the microgrid alliance, shared energy storage operators and smart distribution networks are coordinated and dispatched; The minimum cost optimization scheduling model satisfies: in, F MGO represents the total operating cost of the microgrid alliance, F MtA The cost of electric energy exchange between the microgrid alliance and the smart distribution network, F Gas is the fuel cost of the gas turbine, F MtS Service fees paid for the use of shared energy storage charging and discharging services by the Microgrid Alliance, F es is the operating cost of user-side energy storage in the microgrid, represents the number of microgrids, represents the optimization cycle, express t The time-of-use electricity price of the microgrid selling electricity to the distribution network. represents the time-of-use electricity price that the microgrid purchases from the distribution network at time t, and Respectively t Moment Micro Network i The power purchased from and sold to the distribution network, Indicates the time interval, express t Moment i The electrical power of the gas turbine in each microgrid, , and They represent the unit price of natural gas, the calorific value of natural gas per unit volume, and the power generation efficiency of the gas turbine. and They represent the shared energy storage rental fee and the loss cost of the energy storage equipment in the microgrid caused by the unit charge / discharge volume, and Respectively t Moment i The power of each microgrid charging and discharging from the shared energy storage, and Respectively t Moment i The charging power and discharging power of the energy storage equipment in each microgrid; The first smart distribution network optimization model satisfies: in, It means that the objective function is to minimize the sum of absolute value differences. M is the collection of all integrated energy microgrid grid-connected nodes, To express t Moment Micro Network i The total power reported to the distribution network, for t The distribution network allows the nodes to be connected to the grid at any time i The power of interaction; The peak-valley arbitrage optimization scheduling model satisfies: in, It means that the optimization goal is to maximize profit. represents the time-of-use electricity price sold by the microgrid to the distribution network at time t, represents the time-of-use electricity price of the microgrid purchasing electricity from the distribution network at time t, represents the optimization cycle, and Respectively t The power of energy storage selling electricity to the distribution network and purchasing electricity from the distribution network is shared at all times. for t Equipment maintenance costs per unit of charging / discharging power when sharing energy storage to arbitrage surplus capacity; The second smart distribution network optimization model satisfies: in, It means that the difference of interactive power in the distribution network is the smallest. It indicates the upper limit of power interaction between the shared energy storage and the distribution network without considering the safety constraints. It represents the upper limit of power interaction between shared energy storage and distribution network after taking security constraints into account.

2. The method for optimizing the operation of a smart distribution network based on security domain according to claim 1, characterized in that: The constraints of the minimum cost optimization scheduling model include: Multi-energy balance constraints, energy coupling equipment constraints, energy storage equipment constraints, and power purchase and sales interconnection line constraints.

3. The method for optimizing the operation of a smart distribution network based on security domain according to claim 1, characterized in that: The constraints of the first smart distribution network optimization model include: Ensure energy flow direction constraints and smart distribution network static safety domain constraints.

4. The method for optimizing the operation of a smart distribution network based on security domain according to claim 1, characterized in that: The constraints of the peak-valley arbitrage optimization scheduling model include: Electricity sequential constraints and interconnection line capacity constraints.

5. The method for optimizing the operation of a smart distribution network based on security domain according to claim 1, characterized in that: The constraint conditions of the second smart distribution network optimization model include: Static security domain constraints and power flow direction constraints of smart distribution networks.

6. A security domain-based intelligent distribution network operation optimization system, characterized in that: include: The first determination module is used to establish a minimum cost optimization scheduling model for the operation of the microgrid alliance based on the demand response of the microgrid alliance, solve the minimum cost optimization scheduling model, obtain the interactive power between the microgrid alliance and the smart distribution network, and upload the interactive power to the smart distribution network; It is also used to establish a first smart distribution network optimization model based on the interactive power between the microgrid alliance and the smart distribution network and the security domain of the smart distribution network, solve the first smart distribution network optimization model, obtain the interactive power upper limit between the microgrid alliance and the smart distribution network, and send the interactive power upper limit to the microgrid alliance; It is also used to iteratively calculate the minimum cost optimization scheduling model and the first smart distribution network optimization model based on the interaction power between the microgrid alliance and the smart distribution network and the interaction power upper limit between the microgrid alliance and the smart distribution network, and determine the first scheduling model of the microgrid alliance and the smart distribution network; The second determination module is used to establish a peak-valley arbitrage optimization scheduling model of the shared energy storage operator based on the remaining capacity arbitrage of the shared energy storage operator, solve the peak-valley arbitrage optimization scheduling model, obtain the interactive power between the shared energy storage operator and the smart distribution network, and upload the interactive power to the smart distribution network; It is also used to establish a second smart distribution network optimization model based on the interactive power between the shared energy storage operator and the smart distribution network and the security domain of the smart distribution network, solve the second smart distribution network optimization model, obtain the interactive power upper limit between the shared energy storage operator and the smart distribution network, and send the power upper limit to the shared energy storage operator; It is also used to iteratively calculate the peak-valley arbitrage optimization scheduling model and the second smart distribution network optimization model based on the interactive power between the shared energy storage operator and the smart distribution network and the upper limit of the interactive power between the shared energy storage operator and the smart distribution network, and determine the second scheduling model of the shared energy storage operator and the smart energy distribution network; A third determination module is used to iteratively optimize the second scheduling model on the basis of satisfying the first scheduling model, and determine the target scheduling model of the microgrid alliance, the shared energy storage operator and the smart distribution network; The dispatching module is used to coordinate the dispatching of microgrid alliances, shared energy storage operators and smart distribution networks based on the solution results of the target dispatching model; The minimum cost optimization scheduling model satisfies: in, F MGO represents the total operating cost of the microgrid alliance, F MtA The cost of electric energy exchange between the microgrid alliance and the smart distribution network, F Gas is the fuel cost of the gas turbine, F MtS Service fees paid for the use of shared energy storage charging and discharging services by the Microgrid Alliance, F es is the operating cost of user-side energy storage in the microgrid, represents the number of microgrids, represents the optimization cycle, express t The time-of-use electricity price of the microgrid selling electricity to the distribution network. represents the time-of-use electricity price that the microgrid purchases from the distribution network at time t, and Respectively t Moment Micro Network i The power purchased from and sold to the distribution network, Indicates the time interval, express t Moment i The electrical power of the gas turbine in each microgrid, , and They represent the unit price of natural gas, the calorific value of natural gas per unit volume, and the power generation efficiency of the gas turbine. and They represent the shared energy storage rental fee and the loss cost of the energy storage equipment in the microgrid caused by the unit charge / discharge volume, and Respectively t Moment i The power of each microgrid charging and discharging from the shared energy storage, and Respectively t Moment i The charging power and discharging power of the energy storage equipment in each microgrid; The first smart distribution network optimization model satisfies: in, It means that the objective function is to minimize the sum of absolute value differences. M is the collection of all integrated energy microgrid grid-connected nodes, To express t Moment Micro Network i The total power reported to the distribution network, for t The distribution network allows the nodes to be connected to the grid at any time i The power of interaction; The peak-valley arbitrage optimization scheduling model satisfies: in, It means that the optimization goal is to maximize profit. represents the time-of-use electricity price sold by the microgrid to the distribution network at time t, represents the time-of-use electricity price of the microgrid purchasing electricity from the distribution network at time t, represents the optimization cycle, and Respectively t The power of energy storage selling electricity to the distribution network and purchasing electricity from the distribution network is shared at all times. for t Equipment maintenance costs per unit of charging / discharging power when sharing energy storage to arbitrage surplus capacity; The second smart distribution network optimization model satisfies: in, It means that the difference of interactive power in the distribution network is the smallest. It indicates the upper limit of power interaction between the shared energy storage and the distribution network without considering the safety constraints. It represents the upper limit of power interaction between shared energy storage and distribution network after taking security constraints into account.

7. The security domain-based intelligent distribution network operation optimization system according to claim 6 is characterized in that: The constraints of the minimum cost optimization scheduling model include: Multi-energy balance constraints, energy coupling equipment constraints, energy storage equipment constraints, and power purchase and sales interconnection line constraints.

8. The security domain-based intelligent distribution network operation optimization system according to claim 6 is characterized in that: The constraints of the first smart distribution network optimization model include: Ensure energy flow direction constraints and smart distribution network static safety domain constraints.

9. The security domain-based intelligent distribution network operation optimization system according to claim 6 is characterized in that: The constraints of the peak-valley arbitrage optimization scheduling model include: Electricity sequential constraints and interconnection line capacity constraints.

10. The security domain-based intelligent distribution network operation optimization system according to claim 6, characterized in that: The constraint conditions of the second smart distribution network optimization model include: Static security domain constraints and power flow direction constraints of smart distribution networks.

11. An electronic device, characterized in that: include: Processor and memory; The processor is coupled to the memory; The processor is used to read and execute the program or instruction stored in the memory, so that the device executes the method according to any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that: A computer program is stored, and when the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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