Robust optimization scheduling method, system and equipment for cooperative distribution of main network and distribution network, and medium

Abstract

The invention relates to a main and distribution network collaborative distribution robust optimization scheduling method, system, equipment and medium, and the method comprises the steps: building a power transmission and distribution network coordinated operation model based on the operation characteristics of a thermal power generating unit and the operation characteristics of flexible resources by considering the electric energy interaction characteristics of a transmission and distribution network under the grid connection of high-proportion renewable energy sources; generating a plurality of photovoltaic output scenes based on data driving, and establishing a main and distribution network collaborative optimization operation model considering the plurality of photovoltaic output scenes according to the plurality of photovoltaic output scenes and the main and distribution network collaborative operation model; establishing a photovoltaic output scene probability set by adopting a probability-based distribution robust optimization method, and converting the main and distribution network collaborative optimization operation model into a probability-based distribution robust optimization model according to the photovoltaic output scene probability set; and based on a column sum constraint generation algorithm, solving the converted main and distribution network scheduling model considering the photovoltaic output uncertainty to obtain a main and distribution network collaborative distribution robust optimization scheduling result. The method can be widely applied to the technical field of power system dispatching.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method, system, device and medium for optimizing dispatching based on probabilistic coordinated distribution of main and distribution networks. Background Art

[0002] In order to solve the increasingly serious energy shortage and climate change problems in the world, connecting renewable energy sources (RES) to the power system is an effective solution to alleviate the energy crisis and enhance environmental protection. With the rapid development and widespread integration of distributed renewable energy sources (such as solar and wind power), the operating characteristics of the power system have undergone profound changes. For example, the grid connection of distributed renewable energy can reduce the dependence of the distribution network on centralized power supply, and sometimes the distribution network can even supply power to the transmission network; the uncertainty of the output of distributed renewable energy has brought great challenges to the safe operation of the power system, and even caused local power imbalance.

[0003] In the traditional power system, the main distribution network manages the high-voltage transmission network and the low-voltage distribution network respectively. There is relatively independent scheduling and operation between the two, which cannot fully utilize the power generation resources, resulting in resource waste and additional costs. In the context of the construction of new power systems, the coordinated operation of the transmission system and the distribution system is an effective strategy to achieve the optimal allocation of power generation resources in multi-level power grids, which can improve the absorption of distributed renewable energy. However, due to the timing and volatility of renewable energy output, the coordinated scheduling between the main distribution network faces huge challenges of high uncertainty.

[0004] The overly conservative dispatching and operation scheme of the traditional main and distribution network coordination method in the context of the new power system will cause unnecessary cost losses and is not conducive to the grid connection and consumption of renewable energy. Therefore, there is an urgent need for a flexible dispatching and optimization method for the main and distribution network under the grid connection of a high proportion of distributed renewable energy to improve the power supply reliability, economy and sustainability of the power system and improve energy utilization efficiency. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide a method, system, equipment and medium for coordinated distributed robust optimization scheduling of the main and distribution networks based on probability. While considering the minimization of the total operating cost of the transmission and distribution system, the impact of photovoltaic output uncertainty on the unit output and flexibility resource operation is taken into account, and the probability-based distributed robust optimization method is used to achieve optimal scheduling.

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

[0007] In a first aspect, the present invention provides a main distribution network coordinated distribution and optimization scheduling method, comprising the following steps:

[0008] Based on the operating characteristics of thermal power units and flexible resources, and considering the interactive characteristics of power transmission and distribution networks under the high proportion of renewable energy connected to the grid, a coordinated operation model of the main and distribution networks is established;

[0009] Generate multiple photovoltaic output scenarios based on data drive, and establish a main distribution network collaborative optimization operation model considering multiple photovoltaic output scenarios according to multiple photovoltaic output scenarios and main distribution network collaborative operation models;

[0010] A probability-based distributed blue-rod optimization method is used to establish a probability set of photovoltaic output scenarios. According to the probability set of photovoltaic output scenarios, the main distribution network collaborative optimization operation model is transformed into a probability-based distributed blue-rod optimization model.

[0011] Based on the column and constraint generation algorithm, the probability-based distributed optimization model is solved to obtain the coordinated distributed optimization scheduling result of the main and distribution network.

[0012] Furthermore, based on the operating characteristics of thermal power units and flexible resources, the main distribution network coordinated operation model is established by considering the electric energy interaction characteristics of the transmission and distribution network under the high proportion of renewable energy grid connection, including:

[0013] Establish a transmission network model and determine its operating constraints based on the operating characteristics of thermal power units;

[0014] Establish a distribution network model and determine its operating constraints based on the operating characteristics of flexibility resources;

[0015] Based on the established transmission network model and its constraints, a compact main and distribution network coordinated operation model is established.

[0016] Furthermore, the transmission network model is:

[0017]

[0018] Among them, FC represents the operation cost of the transmission network; T represents the dispatch period; N represents the set of thermal power units in the transmission network; represents the active output of the nth generator set at the tth time interval; and They represent the start and stop state variables of the generator set at the tth time interval and the t-1th time interval respectively; It represents the power generation cost of thermal power units per unit capacity; It represents the start-up and shutdown cost coefficient of thermal power units;

[0019] The operation constraints of the transmission network model include unit constraints, power balance constraints and line constraints;

[0020] The unit constraint is expressed as:

[0021]

[0022] The power balance constraint is expressed as:

[0023]

[0024] The line constraints are expressed as:

[0025]

[0026] in, and Respectively represent the upper and lower limits of the generator set output power; They represent the up and down ramp coefficients of thermal power units respectively, and Δt represents the dispatching period; and Respectively represent the shutdown time and startup time of the thermal power unit; T i on / off Indicates the shortest cooling time; represents the conventional transmission system load, Represents the load connected through the substation tie line, N ADG represents a collection of distribution systems connected to the transmission network; represents the transfer distribution coefficient between the active current of line l and bus b, represents the capacity limit of the transmission line transfer;

[0027] The distribution network model is:

[0028]

[0029] Among them, FV represents the operating cost of the distribution network; Indicates the photovoltaic active power output, Indicates the abandoned optical power. and They represent the photovoltaic operation cost and the penalty cost coefficient for abandoned photovoltaic power, respectively;

[0030] The operation constraints of the distribution network model include energy storage operation constraints, distribution network power balance constraints, distribution network line capacity constraints, photovoltaic power constraints and substation power transmission constraints;

[0031] Among them, the energy storage operation constraint is expressed as:

[0032]

[0033] Among them, P t ESC and P t ESD Respectively represent the charging and discharging power; and Represents a binary variable, which limits the existence of only one operating state at a time; Indicates the energy storage charging and discharging power limit; Represents the energy storage capacity at each time period, η ESC and η ESD They represent the energy storage charging and discharging efficiency, and Respectively represent the upper and lower limits of energy storage capacity; Indicates the initial capacity of energy storage;

[0034] The power balance constraint of the distribution network is expressed as:

[0035]

[0036] The distribution network line capacity constraint is expressed as:

[0037]

[0038] The photovoltaic power constraint is expressed as:

[0039]

[0040] in, Indicates the load of the distribution network; Indicates the transmission power of the upper power grid; Represents photovoltaic output power; P t ESC and P t ESD Respectively represent the energy storage charging and discharging power; represents the power injection transfer distribution factor; Indicates the substation transmission power; Indicates photovoltaic output; Indicates the upper limit of the distribution network line capacity, Indicates the load of the power distribution system; represents the maximum photovoltaic output, Indicates actual photovoltaic output; is the abandoned power;

[0041] The substation power transfer constraint is expressed as:

[0042]

[0043] in, and Respectively represent the power purchase and sale variables of the substation in each dispatching period; P t Sub+ and P t Sub-They represent the amount of electricity purchased and sold by the substation in each period of the distribution network; and P l Sub They represent the capacity limits of power purchase and sale of substations, P t Sub Indicates the amount of electricity purchased and sold by the substation in each period.

[0044] Furthermore, the main distribution network coordinated operation model is expressed as:

[0045]

[0046] Where FC represents the transmission network operation cost; FV k represents the operating cost of the kth distribution network; N TDG Represents a collection of distribution networks.

[0047] Furthermore, the main distribution network collaborative optimization operation model considering multiple photovoltaic output scenarios is:

[0048]

[0049] Among them, P s is the probability associated with the sth scenario, N s is the total number of scenes, C, D, E, F, G, H are the corresponding coefficient matrices, and a, b, c, d, e, f, g are the corresponding coefficient vectors; Ψ S It is a set of PV output probability scenarios.

[0050] Furthermore, a probability-based distributed blue-rod optimization method is used to establish a set of photovoltaic output probability scenarios, and the main distribution network collaborative optimization operation model is transformed into a probability-based distributed blue-rod optimization model, including:

[0051] According to the clustering of historical data, typical scenarios can be obtained, and the photovoltaic output probability scenario set Ψ can be calculated. S ;

[0052] Among them, the photovoltaic output probability scenario set Ψ S The calculation formula is:

[0053]

[0054] Where N s represents the number of PV output probability scenarios studied; P0 represents the initial probability obtained by clustering; P s represents the true probability distribution; θ1 and θ ∞ are the coefficients of 1-norm and ∞-norm respectively;

[0055] Determine whether the distribution of the obtained photovoltaic output probability scenario set meets the preset conditions and determine the coefficients θ1 and θ ∞; Among them, the distribution of the photovoltaic output probability scenario set should satisfy:

[0056]

[0057] Where P(·) is the probability operator and K is the number of real scenarios;

[0058] Let the right side of the above formula be abbreviated as α1 and α ∞ , then θ1 and θ ∞ It is expressed as:

[0059]

[0060] According to the determined coefficients θ1 and θ ∞ , the main distribution network collaborative optimization operation model is transformed into an easy-to-solve probability-based distributed robust optimization model.

[0061] Furthermore, the column-based and constraint-based generation algorithm solves the probability-based distributed blue optimization model to obtain the main distribution network coordinated distributed blue optimization scheduling results, including:

[0062] According to the column and constraint generation algorithm, the probability-based distributed robust optimization model is decomposed into a main problem and sub-problems; the main problem is expressed as:

[0063]

[0064] Where η is the transfer parameter, which is used to transfer the sub-problem state to the main problem;

[0065] The sub-problem is expressed as:

[0066]

[0067] The main problem and sub-problems are solved iteratively to obtain the main and distribution network coordinated distribution optimization scheduling result.

[0068] In a second aspect, the present invention provides a main distribution network coordinated distribution and optimization scheduling system, comprising:

[0069] The module for establishing the coordinated operation model of the main and distribution networks is used to establish the coordinated operation model of the main and distribution networks based on the operating characteristics of thermal power units and the operating characteristics of flexible resources, taking into account the interactive characteristics of power transmission and distribution networks under the high proportion of renewable energy grid connection;

[0070] A data-driven multi-scenario generation module is used to generate a variety of photovoltaic output scenarios based on data drive, and establish a main distribution network collaborative optimization operation model considering multiple photovoltaic output scenarios based on a variety of photovoltaic output scenarios and main distribution network collaborative operation models;

[0071] The probability-based distributed blue-rod optimization model building module is used to establish a probability set of photovoltaic output scenarios using a probability-based distributed blue-rod optimization method. According to the probability set of photovoltaic output scenarios, the main distribution network collaborative optimization operation model is converted into a probability-based distributed blue-rod optimization model;

[0072] The solving module based on the column and constraint generation algorithm is used to solve the probability-based distributed blue stick optimization model based on the column and constraint generation algorithm to obtain the main distribution network coordinated distributed blue optimization scheduling result.

[0073] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods when executing the computer program.

[0074] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the steps of the method when executed by a processor.

[0075] The present invention adopts the above technical solution, which has the following advantages:

[0076] 1. Based on the operating characteristics of thermal power units and the operating characteristics of flexibility resources, the present invention establishes a main distribution network coordinated operation model, generates a variety of photovoltaic output scenarios based on data drive, and establishes a probability-based distributed robust optimization model. While considering the minimization of the total operating cost of the transmission and distribution system, the influence of photovoltaic output uncertainty on the unit output and flexibility resource operation is taken into account, and the probability-based distributed robust optimization method is adopted to achieve optimal scheduling.

[0077] 2. The present invention can realize the efficient coordinated operation of the transmission and distribution systems under the consideration of uncertainty. The proposed method can strike a balance between conservatism and optimism. In view of the operating characteristics of the power system with a high proportion of renewable energy, a transmission and distribution network coordinated optimization operation model is proposed to improve resource utilization efficiency.

[0078] 3. In view of the influence of uncertainty on photovoltaic output, the present invention proposes a probability-based distributed robust optimization method to improve economic benefits while ensuring safe operation.

[0079] 4. The present invention uses an improved column constraint generation algorithm (C&CG) to solve the main distribution network robust optimization scheduling problem under the worst case. The algorithm has good convergence and calculation speed.

[0080] Therefore, the present invention can be widely applied in the technical field of power system dispatching and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings:

[0082] Figure 1 This is a flow chart of the main and distribution network coordinated distribution robust optimization scheduling method based on probabilistic scheduling proposed by the present invention;

[0083] Figure 2 It is a topological diagram of the main distribution network in the present invention;

[0084] Figure 3 The output and energy supply and demand diagram of thermal power units in the transmission network of the present invention;

[0085] Figure 4 It is the energy interaction and energy storage operation diagram of the distribution network in the present invention;

[0086] Figure 5 This is a schematic diagram of the main and distribution network coordinated distribution and blue stick optimization scheduling system module based on probabilistic optimization proposed by the present invention;

[0087] Figure 6 Schematic diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0089] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0090] In some embodiments of the present invention, a main distribution network coordinated distributed rod optimization scheduling method is provided, which takes into account the minimization of the total operating cost of the transmission and distribution system, takes into account the impact of photovoltaic output uncertainty on the unit output and flexibility resource operation, and adopts a probability-based distributed rod optimization method to achieve optimal scheduling.

[0091] Correspondingly, in some other embodiments of the present invention, a main distribution network collaborative distribution and robust optimization scheduling system, equipment and storage medium are provided.

[0092] Example 1

[0093] like Figure 1 As shown, this embodiment provides a main distribution network coordinated distribution and optimization scheduling method, which includes the following steps:

[0094] S1. Based on the operating characteristics of thermal power units and flexible resources, and considering the electric energy interaction characteristics of the transmission and distribution network under the high proportion of renewable energy grid connection, a main distribution network coordinated operation model is established.

[0095] Specifically, the steps include:

[0096] S11. Establish a transmission network model and determine its operating constraints based on the operating characteristics of the thermal power units.

[0097] In this embodiment, the transmission network model is established with the goal of minimizing the operating cost, as follows:

[0098]

[0099] Among them, FC represents the operation cost of the transmission network; T represents the dispatch period; N represents the set of thermal power units in the transmission network; represents the active output of the nth generator set at the tth time interval; and They represent the start and stop state variables of the nth generator set at the tth time interval and the t-1th time interval respectively; It represents the power generation cost of thermal power units per unit capacity; represents the start-up and shutdown cost coefficient of thermal power units; It can be regarded as the cost of power generation. It can be regarded as power generation output. and It satisfies a quadratic relationship and can be approximated by piecewise linearization.

[0100] The operation constraints of the transmission system mainly include unit constraints and line constraints, as follows:

[0101]

[0102] Among them, equations (2) to (4) are unit constraints. In equation (2), and They represent the upper and lower limits of the generator set output power respectively. Formula (3) limits the ramp rate of the unit. and They represent the up and down ramp coefficients of the thermal power unit, and Δt represents the scheduling period. Formula (4) ensures that the cooling interval of the unit is not less than the shortest cooling time T i on / off , and They respectively represent the shutdown time and startup time of the thermal power unit.

[0103]

[0104] Since only active power balance is studied, the current model of the transmission and distribution network adopts the DC power flow model. Formula (5) is the power balance constraint, where: represents the conventional transmission system load, Represents the load connected through the substation tie line, N ADG represents the set of distribution systems connected to the transmission network. Equation (6) is the flow limit of the transmission line, represents the transfer distribution coefficient between the active current of line l and bus b, represents the capacity limit of the transmission line transfer, represents the load demand of the transmission network, represents the transmission load demand of the substation, Represents a transmission network connection node.

[0105] Step S12: Establish a distribution network model and determine its operating constraints based on the operating characteristics of the flexibility resources.

[0106] The distribution network uses flexible resources such as energy storage (ESS) to promote the consumption of renewable energy while ensuring safety, while minimizing operating costs, as follows:

[0107]

[0108] Among them, FV represents the operating cost of the distribution network; Indicates the photovoltaic active power output, Indicates the abandoned optical power. and They represent the photovoltaic operation cost and the penalty cost coefficient for abandoned photovoltaic power generation; T represents the scheduling period, Indicates the node where the PV system is connected to the distribution network.

[0109] The distribution system operation constraints considered in the coordinated dispatch model include:

[0110]

[0111]

[0112] Among them, formulas (8)-(12) are the energy storage operation constraints, Pt ESC and P t ESD Respectively represent the charging and discharging power; y t ESC and Represents a binary variable, which limits the existence of only one operating state at a time; Indicates the energy storage charging and discharging power limit; Represents the energy storage capacity in each period; η ESC and η ESD Respectively represent the energy storage charging and discharging efficiency; and Respectively represent the upper and lower limits of energy storage capacity; Indicates the initial capacity of energy storage; represents the capacity at the end of the energy storage scheduling cycle. Formula (10) represents the relationship between energy storage capacity and power, and formula (11) represents the energy storage capacity limit. Formula (12) ensures the same scheduling flexibility in each time period.

[0113]

[0114] Formula (13) represents the power balance of the distribution network of the distribution system. Similar to the transmission network, P t Sub Indicates the transmission power of the substation in each period; Indicates the actual photovoltaic output power; P t ESC and P t ESD Respectively represent the energy storage charging and discharging power; Indicates the load of the distribution network; Represents the distribution network connection node; P t Sub represents the transmission power of the upper power grid. Formula (14) is the capacity constraint of the distribution network line. represents the power injection transfer distribution factor; Indicates the substation transmission power; Indicates photovoltaic output; Indicates the load of the power distribution system; Represents the upper limit of the distribution network line capacity. Formula (15) is the photovoltaic power constraint. Due to the limitations of line capacity and other factors, photovoltaic power cannot be fully consumed every time. Indicates the maximum photovoltaic output; Indicates the abandoned power; Indicates the node where the PV system is connected to the distribution network.

[0115] The access of a high proportion of distributed photovoltaics makes the distribution network no longer a single receiving network, and sometimes it will send power back to the transmission network. The power transmission constraints of the substation are as follows:

[0116]

[0117] Equations (16)-(18) represent the substation power transmission constraints, and They represent the power purchase and sale 01 variables of the substation in each dispatching period. Similar to energy storage, the interaction status of the substation (i.e., the power purchase and sale status of the distribution network and the transmission network) needs to be determined in advance at each moment; P t Sub+ and P t Sub- They represent the amount of electricity purchased and sold by the substation in each period of the distribution network. In the same period, only one of the two variables is positive, and the other variable is 0. and P l Sub They represent the capacity limits of power purchase and sale of substations, P t Sub Indicates the amount of electricity purchased and sold by the substation in each period, which is P t Sub+ and P t Sub- The difference.

[0118] Step S13: Based on the established transmission network model and its constraints, a compact main and distribution network coordinated operation model is established.

[0119] In this embodiment, the main distribution network coordinated operation is to minimize the total operating cost and improve the photovoltaic utilization efficiency. Its compact form is as follows:

[0120]

[0121] Among them, formula (19) can be transformed into a linear programming model, FV k represents the operating cost of the kth distribution network; N TDG Represents a collection of distribution networks.

[0122] S2. Generate a variety of photovoltaic output scenarios based on data-driven, and establish a main distribution network collaborative optimization operation model that considers multiple photovoltaic output scenarios based on a variety of photovoltaic output scenarios and the main distribution network collaborative operation model, so as to reduce the additional adjustment cost caused by the photovoltaic output prediction error.

[0123] In order to reduce the additional adjustment cost caused by the photovoltaic output prediction error, this embodiment generates multiple photovoltaic output scenarios based on data drive and optimizes the coordinated operation plan. Considering the uncertainty, the main distribution network coordinated operation model can be divided into two stages. The first stage is to formulate the binary decision variable state in advance (such as ), and secondly, the need to coordinate power generation resources and flexibility resources to produce power in various photovoltaic power generation scenarios (such as ) to ensure the balance of energy supply and demand in each photovoltaic power generation scenario.

[0124] Formula (19) can be converted into the following form (the subscript t is omitted here for ease of expression) to obtain the main distribution network coordinated optimization operation model considering multiple photovoltaic output scenarios:

[0125]

[0126] Among them, P s is the probability associated with the sth scenario, N s is the total number of scenarios, C, D, E, F, G, H are the corresponding coefficient matrices, a, b, c, d, e, f, g are the corresponding coefficient vectors, x represents the first-stage decision variables, including the start-stop variables of thermal power units, the start-stop variables of energy storage equipment, and the power transmission state variables of substations; y s Represents the energy dispatch variables of the second stage, including the output variables of thermal power units, energy storage charging and discharging variables, and substation power transmission variables.

[0127] S3. A probability-based distributed blue-rod optimization method is used to establish a probability set of photovoltaic output scenarios. According to the probability set of photovoltaic output scenarios, the main and distribution network collaborative optimization operation model is transformed into a probability-based distributed blue-rod optimization model, that is, a main and distribution network scheduling model that takes into account the uncertainty of photovoltaic output.

[0128] Different from the random optimization method with certain probability, the probability-based distributed robust optimization method takes into account that the true probability distribution is unavailable. The distributed robust optimization method aims to find the worst case of the probability distribution. Therefore, the probability set of PV output scenario Ψ S The details are as follows:

[0129]

[0130] Where N s Represents the number of PV output probability scenarios studied. Typical scenarios can be obtained by clustering historical data, P0 is the initial probability obtained by clustering, and P s is the true probability distribution. The uncertainty set is based on the 1-norm and ∞-norm restrictions, θ1 and θ ∞ is the corresponding coefficient.

[0131] The probability scenario distribution should meet the following conditions:

[0132]

[0133] Where P(·) is the probability operator and K is the number of real scenarios. Let the right side of formula (22) be abbreviated as α1 and α ∞ , then θ1 and θ ∞ It can be expressed as:

[0134]

[0135] According to the method of obtaining the probability distribution of photovoltaic output described in formulas (21)-(23), the probability-based distributed robust optimization model can be converted into a form that is easy to solve.

[0136] S4. Based on the column and constraint generation algorithm, the probability-based distributed optimization model is solved to obtain the main and distribution network coordinated distributed optimization scheduling results.

[0137] For formula (20), according to the column and constraint generation (C&CG) algorithm, the model can be decomposed into a main problem and sub-problems and solved iteratively. The objective function of the main problem is the start-stop cost, and the binary decision variables will be pre-formulated and passed to the sub-problems.

[0138]

[0139] Where η is the transfer parameter, which is used to transfer the sub-problem state to the main problem.

[0140] The main problem determines the decision variables and passes them to the subproblems, which aim to optimize the running cost in each deterministic case, as follows:

[0141]

[0142] In formulas (25) and (26), the sub-problems in each collaborative optimization scenario are independent of each other and can be solved by decoupling. Based on the internal optimization results, the external worst-case probability distribution can be obtained:

[0143]

[0144] In the formula, h s is the optimal operating cost in each case, W is the cost under the worst case probability distribution, Represents the optimal scheduling cost in each scenario.

[0145] The subproblem aims to find the worst-case probability distribution under real-time operation And feedback to the main problem. At the same time, it also provides an upper limit for formula (20):

[0146]

[0147] The main problem updates the decision variables according to (28) and iterates until the upper and lower bounds are the same and convergence is achieved.

[0148] Example 2

[0149] In order to verify the effectiveness of the main distribution network coordinated distribution rod optimization model proposed in the present invention, this embodiment takes a six-bus system (TG) with two active distribution networks (ADG) as an example for further introduction. Figure 2 As shown. The transmission network is connected to two thermal power generating units (G1 and G2, with installed capacities of 220 MW and 150 MW respectively). Each distribution network is connected to an energy storage device and multiple distributed photovoltaic power generation devices (the total photovoltaic power generation capacity of TDG1 is 37 MW and the other is 55 MW). The basic data parameters are shown in Tables 1 and 2.

[0150] As for the uncertainty set of PV output, the error is set to 20% of the PV predicted output, and 1000 PV output scenarios are randomly generated. Based on K-means clustering, 5 typical scenarios are finally generated, with confidence levels set to α1 = 0.7, α ∞ =0.9.

[0151] All programs were programmed using MATLAB, installed on a desktop computer with 16GB of memory, and the solver was Gurobi9.1.

[0152] Table 1 Basic operation and economic parameters of thermal power unit output in the present invention

[0153]

[0154]

[0155] *h i It is the operating cost coefficient of the thermal power unit output.

[0156] Table 2 Basic operating parameters of energy storage in the present invention

[0157]

[0158] Attached Figure 3 and attached Figure 4 A typical scenario of coordinated operation of transmission and distribution networks is demonstrated.

[0159] As attached Figure 3 As shown in Figure 1, when the photovoltaic power generation output is sufficient, the thermal power generation unit adjusts the output and uses more economical photovoltaic resources, while at 19:00-23:00, G1 and G2 coordinate with each other to reduce operating costs. Figure 4The coordinated operation of ADG1 and ADG2 in terms of power flow reverse transmission and energy storage operation is shown in Figure 1. The distribution network feeds back excess photovoltaic output to the transmission network at 9-16 o'clock. Due to the difference in the acquisition of energy storage resources and the scale of the grid, it can be clearly seen that ADG2 has a one-hour deviation in the overall operation trend compared with ADG1. Limited by the capacity of the transmission line, ADG2 stores energy at 15 o'clock instead of releasing energy to improve the overall efficiency of the transmission and distribution network.

[0160] In order to verify the effectiveness of the proposed distributed robust optimization method, this paper analyzes three methods: the proposed model (DRO), the robust optimization model (RO) and the non-cooperative model (NCO). The network costs under different methods are shown in Table 3.

[0161] Table 3 Comparison of different optimization operation methods in the present invention

[0162]

[0163] Compared with the two traditional methods, the proposed method can reduce the total cost by 4.62% and 1.41% respectively. At the same time, the proposed main distribution network distributed blue rod coordinated optimization method can significantly reduce the photovoltaic abandonment rate from 3.18% to 0.97%, which is particularly important in the context of the increasing speed of renewable energy access to the grid.

[0164] The algorithm converged within 9.8 seconds after three iterations, which proved that the proposed method has excellent convergence and computational speed.

[0165] Example 3

[0166] The above-mentioned embodiment 1 provides a main distribution network collaborative distribution robust optimization scheduling method. Correspondingly, this embodiment provides a main distribution network collaborative distribution robust optimization scheduling system. The system provided in this embodiment can implement the main distribution network collaborative distribution robust scheduling method of embodiment 1, and the system can be implemented by software, hardware, or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the process described in this embodiment is relatively simple, and the relevant parts can refer to the partial description of embodiment 1. The embodiment of the system provided in this embodiment is only schematic.

[0167] like Figure 5 As shown, the main distribution network coordinated distribution and optimization scheduling system provided in this embodiment includes:

[0168] The module for establishing the coordinated operation model of the main and distribution networks is used to establish the coordinated operation model of the main and distribution networks based on the operating characteristics of thermal power units and the operating characteristics of flexible resources, taking into account the interactive characteristics of power transmission and distribution networks under the high proportion of renewable energy grid connection;

[0169] A data-driven multi-scenario generation module is used to generate multiple photovoltaic output scenarios based on data drive. According to multiple photovoltaic output scenarios and main distribution network coordinated operation models, a main distribution network coordinated optimization operation model considering multiple photovoltaic output scenarios is established to reduce the additional adjustment cost caused by photovoltaic output prediction errors;

[0170] The probability-based distributed blue-rod optimization model building module is used to establish a probability set of photovoltaic output scenarios using a probability-based distributed blue-rod optimization method. According to the probability set of photovoltaic output scenarios, the main distribution network collaborative optimization operation model is converted into a probability-based distributed blue-rod optimization model, that is, a main distribution network scheduling model that takes into account the uncertainty of photovoltaic output;

[0171] The solving module based on the column and constraint generation algorithm is used to solve the probability-based distributed blue stick optimization model based on the column and constraint generation algorithm to obtain the main distribution network coordinated distributed blue optimization scheduling result.

[0172] Example 4

[0173] This embodiment provides a processing device corresponding to the main distribution network collaborative distribution blue stick optimization scheduling method provided in this embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0174] like Figure 6 As shown, the processing device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The memory stores a computer program that can be run on the processor, and the processor executes the main distribution network coordinated distribution optimization scheduling method provided in this embodiment 1 when running the computer program.

[0175] Preferably, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0176] Preferably, the processor may be a central processing unit (CPU), a digital signal processor (DSP) or other general-purpose processors of various types, which are not limited here.

[0177] Example 5

[0178] The main distribution network coordinated distributed robust optimization scheduling method of this embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the main distribution network coordinated distributed robust optimization scheduling method described in this embodiment 1 are loaded.

[0179] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0180] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0184] 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. Any modification or equivalent replacement that does 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. A main distribution network coordinated distribution and optimization scheduling method, characterized in that: The following steps are involved: Based on the operating characteristics of thermal power units and flexible resources, and considering the interactive characteristics of power transmission and distribution networks under the high proportion of renewable energy connected to the grid, a coordinated operation model of the main and distribution networks is established; Generate multiple photovoltaic output scenarios based on data drive, and establish a main distribution network collaborative optimization operation model considering multiple photovoltaic output scenarios according to multiple photovoltaic output scenarios and main distribution network collaborative operation models; A probability-based distributed blue-rod optimization method is used to establish a probability set of photovoltaic output scenarios. According to the probability set of photovoltaic output scenarios, the main distribution network collaborative optimization operation model is transformed into a probability-based distributed blue-rod optimization model. Based on the column and constraint generation algorithm, the probability-based distributed optimization model is solved to obtain the coordinated distributed optimization scheduling result of the main and distribution network.

2. A main distribution network coordinated distribution and robust optimization scheduling method as claimed in claim 1, characterized in that: Based on the operating characteristics of thermal power units and flexible resources, the main distribution network coordinated operation model is established by considering the interactive characteristics of power transmission and distribution networks under the high proportion of renewable energy grid connection, including: Establish a transmission network model and determine its operating constraints based on the operating characteristics of thermal power units; Establish a distribution network model and determine its operating constraints based on the operating characteristics of flexibility resources; Based on the established transmission network model and its constraints, a compact main and distribution network coordinated operation model is established.

3. A main distribution network coordinated distribution and robust optimization scheduling method as claimed in claim 2, characterized in that: The transmission network model is: Among them, FC represents the operation cost of the transmission network; T represents the dispatch period; N represents the set of thermal power units in the transmission network; represents the active output of the nth generator set at the tth time interval; and They represent the start and stop state variables of the generator set at the tth time interval and the t-1th time interval respectively; It represents the power generation cost of thermal power units per unit capacity; It represents the start-up and shutdown cost coefficient of thermal power units; The operation constraints of the transmission network model include unit constraints, power balance constraints and line constraints; The unit constraint is expressed as: The power balance constraint is expressed as: The line constraints are expressed as: in, and P n G Respectively represent the upper and lower limits of the generator set output power; They represent the up and down ramp coefficients of thermal power units respectively, and Δt represents the dispatching period; and Respectively represent the shutdown time and startup time of the thermal power unit; T i on / off Indicates the shortest cooling time; represents the conventional transmission system load, Represents the load connected through the substation tie line, N ADG represents a collection of distribution systems connected to the transmission network; represents the transfer distribution coefficient between the active current of line l and bus b, represents the capacity limit of the transmission line transfer; The distribution network model is: Among them, FV represents the operating cost of the distribution network; Indicates the photovoltaic active power output, Indicates the abandoned optical power. and They represent the photovoltaic operation cost and the penalty cost coefficient for abandoned photovoltaic power, respectively; The operation constraints of the distribution network model include energy storage operation constraints, distribution network power balance constraints, distribution network line capacity constraints, photovoltaic power constraints and substation power transmission constraints; Among them, the energy storage operation constraint is expressed as: Among them, P t ESC and P t ESD Respectively represent the charging and discharging power; and Represents a binary variable, which limits the existence of only one operating state at a time; Indicates the energy storage charging and discharging power limit; Represents the energy storage capacity at each time period, η ESC and η ESD They represent the energy storage charging and discharging efficiency, and Respectively represent the upper and lower limits of energy storage capacity; Indicates the initial capacity of energy storage; The power balance constraint of the distribution network is expressed as: The distribution network line capacity constraint is expressed as: The photovoltaic power constraint is expressed as: in, Indicates the load of the distribution network; Indicates the transmission power of the upper power grid; Represents photovoltaic output power; P t ESC and P t ESD Respectively represent the energy storage charging and discharging power; represents the power injection transfer distribution factor; Indicates the substation transmission power; Indicates photovoltaic output; Indicates the upper limit of the distribution network line capacity, Indicates the load of the power distribution system; represents the maximum photovoltaic output, Indicates actual photovoltaic output; is the abandoned power; The substation power transfer constraint is expressed as: in, and Respectively represent the power purchase and sale variables of substations in each dispatching period; P t Sub+ and P t Sub- They represent the amount of electricity purchased and sold by the substation in each period of the distribution network; and P l Sub They represent the capacity limit of power purchase and sale of substation, P t Sub Indicates the amount of electricity purchased and sold by the substation in each period.

4. A main distribution network coordinated distribution and robust optimization scheduling method as claimed in claim 3, characterized in that: The main distribution network coordinated operation model is expressed as: Where FC represents the transmission network operation cost; FV k represents the operating cost of the kth distribution network; N TDG Represents a collection of distribution networks.

5. A main distribution network coordinated distribution and robust optimization scheduling method as claimed in claim 1, characterized in that: The main distribution network coordinated optimization operation model considering multiple photovoltaic output scenarios is: Among them, P s is the probability associated with the sth scenario, N s is the total number of scenes, C, D, E, F, G, H are the corresponding coefficient matrices, and a, b, c, d, e, f, g are the corresponding coefficient vectors; Ψ S It is a set of PV output probability scenarios.

6. A main distribution network coordinated distribution and robust optimization scheduling method as claimed in claim 1, characterized in that: The probability-based distributed blue-rod optimization method is used to establish a set of photovoltaic output probability scenarios, and the main distribution network collaborative optimization operation model is transformed into a probability-based distributed blue-rod optimization model, including: According to the clustering of historical data, typical scenarios can be obtained, and the photovoltaic output probability scenario set Ψ can be calculated. S ; Among them, the photovoltaic output probability scenario set Ψ S The calculation formula is: Where N s represents the number of PV output probability scenarios studied; P0 represents the initial probability obtained by clustering; P s represents the true probability distribution; θ1 and θ ∞ are the coefficients of 1-norm and ∞-norm respectively; Determine whether the distribution of the obtained photovoltaic output probability scenario set meets the preset conditions and determine the coefficients θ1 and θ ∞ ; Among them, the distribution of the photovoltaic output probability scenario set should satisfy: Where P(·) is the probability operator and K is the number of real scenarios; Let the right side of the above formula be abbreviated as α1 and α ∞ , then θ1 and θ ∞ It is expressed as: According to the determined coefficients θ1 and θ ∞ , the main distribution network collaborative optimization operation model is transformed into an easy-to-solve probability-based distributed robust optimization model.

7. A main distribution network coordinated distribution and robust optimization scheduling method as claimed in claim 1, characterized in that: The column-based and constraint-generating algorithm solves the probability-based distributed blue optimization model to obtain the main distribution network coordinated distributed blue optimization dispatching results, including: According to the column and constraint generation algorithm, the probability-based distributed robust optimization model is decomposed into a main problem and sub-problems; The main problem is expressed as: Where η is the transfer parameter, which is used to transfer the sub-problem state to the main problem; The sub-problem is expressed as: The main problem and sub-problems are solved iteratively to obtain the main and distribution network coordinated distribution optimization scheduling result.

8. A main distribution network coordinated distribution and optimization dispatching system, characterized in that: include: The module for establishing the coordinated operation model of the main and distribution networks is used to establish the coordinated operation model of the main and distribution networks based on the operating characteristics of thermal power units and the operating characteristics of flexible resources, taking into account the interactive characteristics of power transmission and distribution networks under the high proportion of renewable energy grid connection; A data-driven multi-scenario generation module is used to generate a variety of photovoltaic output scenarios based on data drive, and establish a main distribution network collaborative optimization operation model considering multiple photovoltaic output scenarios based on a variety of photovoltaic output scenarios and main distribution network collaborative operation models; The probability-based distributed blue-rod optimization model building module is used to establish a probability set of photovoltaic output scenarios using a probability-based distributed blue-rod optimization method. According to the probability set of photovoltaic output scenarios, the main distribution network collaborative optimization operation model is converted into a probability-based distributed blue-rod optimization model; The solving module based on the column and constraint generation algorithm is used to solve the probability-based distributed blue stick optimization model based on the column and constraint generation algorithm to obtain the main distribution network coordinated distributed blue optimization scheduling result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the probabilistic main and distribution network collaborative distribution optimization scheduling method described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of any one of items 1-7 of the probabilistic main and distribution network collaborative optimization scheduling method are implemented.

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