A power distribution network planning method, device, equipment and storage medium

Abstract

The application discloses a power distribution network planning method, device and equipment and a storage medium. The method comprises the following steps: acquiring at least one planning data in renewable energy characteristic data, power distribution network topology and element parameters, load time sequence data and economic parameters; then inputting the planning data into an initial model to construct a target model comprising a target function, system constraints and polyhedral uncertainty set constraints; then obtaining an adaptive uncertainty set based on the polyhedral uncertainty set constraints to accurately adapt to long-term and short-term output uncertainty of the renewable energy; and finally inputting the adaptive uncertainty set into the target model to obtain an optimal site selection and fixed capacity scheme of the power distribution network. The application can effectively depict the uncertainty characteristics of the renewable energy, improve the scientificity and economy of the planning scheme, guarantee the safe and stable operation of the power distribution network and improve the renewable energy consumption capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, and particularly relates to a power distribution network planning method, device and equipment and a storage medium. BACKGROUND

[0002] With the acceleration of global low-carbon transformation, renewable energy such as wind energy and solar energy is widely connected to the power distribution network, becoming an important support for the low-carbon construction and economic operation of urban power grids. However, the output of renewable energy has significant intermittency and uncertainty, which brings severe challenges to the safe and stable, economic and efficient operation of the power distribution network, and the scientific siting and sizing of the energy storage system becomes the key to solving the problem.

[0003] The current energy storage planning method of the power distribution network mainly includes a scenario-based stochastic planning and a traditional robust optimization method. The stochastic planning needs to construct a large number of discrete scenarios to simulate uncertainty, is prone to dimension disaster, and has insufficient accuracy in describing short-term fluctuation characteristics; the traditional robust optimization equivalently models different time scale uncertainties, resulting in overly conservative planning results and increasing investment costs.

[0004] Therefore, how to efficiently develop an economically feasible and safe and reliable power distribution network energy storage siting and sizing scheme is a problem to be solved at present. SUMMARY

[0005] Therefore, the power distribution network planning method, device and equipment and storage medium provided by the embodiments of the present application can effectively describe the uncertainty characteristics of renewable energy, improve the scientificity and economy of the planning scheme, ensure the safe and stable operation of the power distribution network, and improve the renewable energy consumption capacity. The power distribution network planning method, device and equipment and storage medium provided by the embodiments of the present application are implemented as follows:

[0006] The power distribution network planning method provided by the embodiments of the present application comprises the following steps:

[0007] Obtaining planning data, wherein the planning data comprises at least one of renewable energy characteristic data, power distribution network topology and element parameters, load time series data and economic parameters;

[0008] Inputting the planning data into an initial model to obtain a target model, wherein the target model comprises a target function, system constraints and polyhedral uncertainty set constraints;

[0009] Obtaining an adaptive uncertainty set according to the polyhedral uncertainty set constraints;

[0010] Inputting the adaptive uncertainty set into the target model to obtain an optimal siting and sizing scheme of the power distribution network.

[0011] In some embodiments, the planning data is input into an initial model to obtain a target model, comprising:

[0012] a target function is constructed according to the economic parameters, the target function comprising investment cost and operation cost of the power distribution network;

[0013] system constraints are constructed according to the power distribution network topology and element parameters and the load time series data, the system constraints comprising installation quantity, investment constraints, transmission safety and equipment operation state;

[0014] a polyhedral uncertainty set constraint is constructed according to the renewable energy characteristic data, the polyhedral uncertainty set constraint being used to represent output fluctuation range of the renewable energy in different time periods;

[0015] a target model is obtained according to the target function, the system constraints and the polyhedral uncertainty set constraint.

[0016] In some embodiments, the adaptive uncertainty set is obtained according to the polyhedral uncertainty set constraint, comprising:

[0017] the renewable energy output possible range is divided into a plurality of small uncertainty sets according to the polyhedral uncertainty set constraint, wherein each small uncertainty set is used to represent short-term output fluctuation characteristics of the renewable energy, and all small uncertainty sets comprise long-term output uncertainty of the renewable energy;

[0018] the adaptive uncertainty set is obtained by minimizing total size of all small uncertainty sets and maximum size of a single small uncertainty set.

[0019] In some embodiments, the adaptive uncertainty set is input into the target model to obtain the optimal site selection and capacity determination scheme of the power distribution network, comprising:

[0020] the adaptive uncertainty set is input into the target model to obtain a plurality of renewable energy parameters, the plurality of renewable energy parameters corresponding to specific output values and fluctuation ranges of the renewable energy in a plurality of scenarios in the adaptive uncertainty set;

[0021] the plurality of renewable energy parameters are verified based on the system constraints, and in the case that any renewable energy parameter does not satisfy the system constraints, the current scenario is adjusted until all renewable energy parameters satisfy the system constraints;

[0022] the plurality of renewable energy parameters are optimized according to the target function to obtain optimal values, the optimal values being the optimal site selection and capacity determination scheme output of the power distribution network.

[0023] In some embodiments, the constructing a polyhedral uncertainty set constraint according to the renewable energy characteristic data comprises:

[0024] obtaining an expected output value of the renewable energy in each time period based on long and short term output history data in the renewable energy characteristic data;

[0025] obtaining a maximum deviation range of an actual output value relative to the expected output value in each time period;

[0026] obtaining a possible fluctuation interval of the renewable energy output in each time period according to the expected output value and the corresponding maximum deviation range of each time period, and obtaining the polyhedral uncertainty set constraint according to the possible fluctuation interval.

[0027] In some embodiments, the inputting the planning data into an initial model to obtain a target model comprises:

[0028] inputting the economic parameters into the initial model to obtain an objective function of the target model according to a formula;

[0029]

[0030] wherein, is an investment cost of the power distribution network, is an operation cost of the power distribution network, is a weight coefficient of the investment cost and the operation cost, is a set of investment variables, is a set of operation variables, d is an uncertain variable, is an uncertainty set.

[0031] In some embodiments, the operation cost comprises a fuel cost, a start-stop cost and an interaction cost with an external power grid.

[0032] The embodiments of the present application provide a power distribution network planning device, comprising:

[0033] an obtaining module, configured to obtain planning data, wherein the planning data comprises at least one of renewable energy characteristic data, power distribution network topology and element parameters, load time sequence data and economic parameters;

[0034] an inputting module, configured to input the planning data into an initial model to obtain a target model, wherein the target model comprises an objective function, system constraints and a polyhedral uncertainty set constraint;

[0035] a determining module, configured to obtain an adaptive uncertainty set according to the polyhedral uncertainty set constraint;

[0036] The determination module is further configured to input the adaptive uncertainty set into the target model to obtain an optimal site selection and capacity determination scheme of the power distribution network.

[0037] The computer device provided by the embodiment of the present application comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements the method provided by the embodiment of the present application when executing the program.

[0038] The computer readable storage medium provided by the embodiment of the present application stores a computer program, and the computer program is executed by a processor to implement the method provided by the embodiment of the present application.

[0039] The power distribution network planning method, device, equipment and storage medium provided by the embodiment of the present application, by acquiring at least one planning data of renewable energy characteristic data, power distribution network topology and element parameters, load time sequence data and economic parameters, then inputting the planning data into an initial model to construct a target model comprising a target function, system constraints and polyhedral uncertainty set constraints, then obtaining an adaptive uncertainty set based on the polyhedral uncertainty set constraints to accurately adapt to long-term and short-term renewable energy output uncertainty, and finally inputting the adaptive uncertainty set into the target model to obtain an optimal site selection and capacity determination scheme of the power distribution network. In this way, the uncertainty characteristics of renewable energy can be effectively described, the scientificity and economy of the planning scheme are improved, the safe and stable operation of the power distribution network is ensured, the renewable energy consumption capacity is improved, and the technical problems proposed in the background art are solved. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Figure 1 An implementation flowchart of the power distribution network planning method provided by the embodiment of the present application is shown in the figure.

[0042] Figure 2 An implementation flowchart of the target model provided by the embodiment of the present application is shown in the figure.

[0043] Figure 3 A structure diagram of the power distribution network planning device provided by the embodiment of the present application is shown in the figure.

[0044] Figure 4 A schematic diagram of the uncertainty set modeling method provided by the embodiment of the present application is shown in the figure.

[0045] Figure 5 A structural schematic diagram of a node system provided by an embodiment of the present application is shown in FIG. 1.

[0046] Figure 6 A schematic diagram of energy storage installed capacity provided by an embodiment of the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of, rather than all of, the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort fall within the protection scope of the present application.

[0048] The following descriptions are made to some technologies related to the embodiments of the present application to help understanding, which should be considered as merely exemplary. Therefore, a person of ordinary skill in the art should recognize that various changes and modifications can be made to the described embodiments without departing from the scope and spirit of the present application. Also, for the sake of clarity and conciseness, the descriptions of some well-known functions and structures are omitted in the following description.

[0049] Figure 1 FIG. 1 is a schematic diagram of an implementation process of a power distribution network planning method provided by an embodiment of the present application, including steps 101 to 104. In the steps, Figure 1 The shown steps can be executed in parallel or in reverse. Figure 1 The shown steps can be executed in parallel or in reverse.

[0050] Step 101, obtaining planning data.

[0051] In the embodiments of the present application, the long-term and short-term output history data of wind power and photovoltaic power in the planning area are collected, including measured hourly output values (such as hourly output data of 24 time periods in a day) and output change trend data in different seasons (such as average output differences in spring, summer, autumn and winter).

[0052] Based on the IEEE33 node system, the number of nodes (a total of 33 nodes) of the system, impedance parameters (including resistance and reactance) of branch lines between nodes, maximum apparent power transmission capacity of each branch line (i.e. the maximum power upper limit allowed to be transmitted by the line), and voltage operating range of each node bus are obtained.

[0053] The load demand data of each node in the IEEE33 node system at different time periods are collected, including active load demand and reactive load demand, covering load fluctuations in weekdays, holidays and different seasons (such as higher air conditioning load in summer and higher heating load in winter).

[0054] Various economic cost parameters related to power distribution network planning are obtained, including capacity cost coefficient of energy storage system (i.e. investment cost per unit of energy storage capacity), power cost coefficient of energy storage system (i.e. investment cost per unit of energy storage power), fuel cost coefficient of thermal power unit (i.e. fuel consumption cost per unit of power generation), start-stop cost coefficient of thermal power unit (i.e. cost of starting or stopping operation of thermal power unit once), interaction cost coefficient of power distribution network and external main grid (i.e. cost of power exchange per unit with main grid), investment discount rate, planning period, and total investment cost upper limit of power distribution network energy storage planning.

[0055] In step 102, the planning data is input into the initial model to obtain the target model.

[0056] In the embodiments of the present application, based on the obtained economic parameters, a target function with the minimum of the full-cycle annualized comprehensive cost as the core is constructed. The target function includes two parts of cost: one is the annualized investment cost, mainly the planning investment cost of the energy storage system, which needs to convert the total investment cost into the equivalent cost per year (i.e. annualized cost) according to the discount rate and the planning period; the other is the annualized operation cost, mainly including the power generation cost of the thermal power unit, the interaction cost of the power distribution network and the external main grid, and the start-stop cost of the thermal power unit.

[0057] The specific formula of the target function is as follows:

[0058] Annualized investment cost:

[0059] (1)

[0060] wherein, is the function representation of the annualized investment cost, is the matrix form of the function independent variable, is the annualized interest rate, is the investment period, and are the unit energy cost and unit power cost of the energy storage, respectively, and are the rated capacity and rated power of the planning energy storage, respectively.

[0061] Operation cost:

[0062] (2)

[0063] wherein, is the annualized operation cost, y is the matrix form of operation related variables, is the start-stop cost, is the fuel cost, is the electricity trading cost, represents the on-off state of the unit, 0 represents off, and 1 represents on. is the power generation of the unit, represents the exchange power of the gateway (substation).

[0064] The economic parameters are input into the initial model, and the objective function of the target model is obtained according to formula (3).

[0065] (3)

[0066] wherein, is the investment cost of the distribution network, is the operation cost of the distribution network, is the weight coefficient of the investment cost and the operation cost, is the investment variable set, is the operation variable set, and d is an uncertain variable, is the uncertain set.

[0067] Based on the obtained distribution network topology and element parameters and load time series data, system constraints for ensuring safe and economic operation of the distribution network are constructed, including three types of constraints: 1) investment constraints, including the upper limit of the number of energy storage systems installed at each node (i.e., the maximum number of energy storage devices allowed to be installed at a single node), and the upper limit of the total investment cost of the entire distribution network energy storage planning (i.e., the total investment cost cannot exceed the preset investment upper limit); 2) power flow safety constraints, including line power flow constraints (to ensure that the transmission power of each line does not exceed its maximum apparent power transmission capacity) and node voltage constraints (to ensure that the bus voltage of each node is within the preset voltage operating range); 3) device operation state constraints, including energy storage system operation constraints (to ensure that the state of charge of the energy storage system is within a reasonable range, and the charging and discharging power does not exceed its maximum charging and discharging power limit), thermal power unit operation constraints (to ensure that the power generation of the thermal power unit is between its minimum output and maximum output, and the change rate of power generation does not exceed its maximum climbing rate limit), and renewable energy curtailment constraints (to ensure that the curtailed power of renewable energy does not exceed its actual output, avoiding excessive waste of renewable energy generation).

[0068] Among them, the system investment constraint mainly includes the cost budget constraint and the installation upper limit constraint of the energy storage device.

[0069]

[0070]

[0071]

[0072]

[0073] (4)

[0074] wherein in formula (4), denotes the planning scheme of energy storage, which is an integer variable. is the upper limit of energy storage planning for node j . and are the rated capacity and rated power of the planned energy storage, respectively, and are the rated capacity and rated power of the unit energy storage, respectively, is the total planning upper limit of energy storage, is the upper limit of investment budget, is the total bus (node) set.

[0075] The system operation constraints include the power flow constraints to ensure the safety of line transmission, various boundary constraints to ensure the safe and economic operation of equipment, and the operation constraints of thermal power units.

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] (5)

[0083] wherein, are the active and reactive power on branch in period , respectively, are the active and reactive net injection power on bus in period , respectively, are the active and reactive transmission power on bus in period , respectively, are the active and reactive load demand on bus in period , respectively, respectively, the time period voltage on bus thermal power, wind power, photovoltaic power and renewable energy curtailment power, generation power of the reactive power compensation device, respectively, the time period energy storage level on bus , charging and discharging power of the energy storage, respectively, the time period voltage squared value on bus , and respectively, the resistance and reactance of line l . branch line from bus to bus , respectively, the set of branch lines to and from bus , is a set of time periods, is a set of branch lines, is a set of nodes other than slack nodes.

[0084] (6)

[0085] wherein, respectively, the time period voltage squared value on bus , is its lower and upper limit.

[0086] (7)

[0087] wherein, respectively, the time period active and reactive power on branch , is the upper limit of apparent power on branch .

[0088] (8)

[0089] wherein, respectively, the time period active and reactive transmission power on bus , is the upper and lower limit of active exchange power, is the upper and lower limit of reactive exchange power.

[0090]

[0091] (9)

[0092] where, is the time period the energy storage level on the bus, represents the charge-discharge power of the energy storage, are the minimum and maximum state of charge allowed by the planning energy storage, respectively, and are related to the planning scheme is the maximum discharge power, x is the maximum charge power.

[0093] (10)

[0094] The above is the renewable energy curtailment constraint, i.e., the sum of the output power of wind power and photovoltaic power at least reaches the renewable energy reduction requirement. Wherein are the wind power, photovoltaic output power and renewable energy reduction power on the bus

[0095] (11)

[0096] where, is the generation power of the reactive power compensation device, are the upper and lower bounds of the reactive power generation power.

[0097]

[0098] (12)

[0099]

[0100] where, is the time period the start-stop decision variable of the thermal generating unit on the bus, is the generation power of the unit, are the upper and lower bounds of the active power generation power, are the upper and lower bounds of the climbing rate of the thermal generating unit on the bus

[0101] ​​​​​​Based on the obtained renewable energy characteristic data, a polyhedral uncertainty set constraint is constructed for characterizing the fluctuation range of renewable energy output in different time periods. The construction logic of the constraint is as follows: first, according to the historical output data of the renewable energy, the expected value of the output of the renewable energy in each time period is calculated (that is, the average value of the historical output in the time period); then the maximum deviation range of the actual output value relative to the expected value in each time period is counted; finally, the possible fluctuation interval of the output of the renewable energy in the time period is defined with the expected value of the output in the time period as the center and the corresponding maximum deviation range as the boundary, and the fluctuation intervals of all time periods together constitute the polyhedral uncertainty set constraint for describing the uncertainty range of the output of the renewable energy.

[0102] In step 103, an adaptive uncertainty set is obtained according to the polyhedral uncertainty set constraint.

[0103] In the embodiment of the present application, based on the limited coverage theorem, the possible range of the output of the renewable energy defined by the polyhedral uncertainty set constraint is divided into several small uncertainty sets. Among them, each small uncertainty set is specially used to characterize the output fluctuation characteristics of the renewable energy in a short period; and the overall set of all small uncertainty sets is used to cover the output uncertainty of the renewable energy in a long period (that is, the output characteristics of the six small uncertainty sets correspond to spring, summer, autumn, winter and two transition seasons respectively, which collectively cover the long-term output change of the whole year).

[0104] The polyhedral uncertainty set can be used to describe the possible range of the uncertain variable, so as to cover the load

[0105] (13)

[0106] Among them, is the uncertainty set, represents the matrix form of the uncertain variable in the uncertainty set, N represents the number (dimension) of the uncertain variable, is the expected value of the uncertain variable, is the estimated deviation.

[0107] In order to ensure that all possible renewable energy output scenarios that may occur in the planning period can be covered by the small uncertainty set, the corresponding constraint condition is set. For any output scenario existing in the historical data and the output fluctuation of any time period in the scenario, at least one small uncertainty set can be found, so that the output value and the fluctuation range of the scenario are completely contained in the small uncertainty set; at the same time, by introducing auxiliary parameters and 0-1 variables, the above coverage logic is converted into a calculable constraint condition to ensure the integrity of the coverage.

[0108] To balance the coverage accuracy of small uncertainty sets and the operation cost of distribution network, the size of small uncertainty sets is optimized. Firstly, the total size of all small uncertainty sets (i.e., the sum of the output range covered by all small uncertainty sets is minimized) is minimized to reduce unnecessary coverage range. Secondly, the maximum size of a single small uncertainty set (i.e., among all small uncertainty sets, the size of the small uncertainty set with the largest coverage range is minimized) is minimized. The minimization of the maximum size of a single small uncertainty set is based on the principle of the wooden bucket effect. The flexible reserve capacity required by the distribution network to cope with uncertainty is determined by the maximum output fluctuation, so reducing the size of the largest small uncertainty set can effectively reduce the operation cost of the distribution network. Through the above optimization, the adaptive uncertainty set that can accurately adapt to the long-term and short-term uncertainty characteristics of renewable energy is finally obtained.

[0109]

[0110] s.t.

[0111] (14)

[0112]

[0113] wherein, represents the lower bound and the upper bound of the proposed uncertainty set, represents the lower bound and the upper bound of the uncertainty set in the historical data s , and are used to describe the historical data set and the adaptive uncertainty set, respectively, is a 0-1 variable used to describe the coverage relationship of uncertain factors. is a parameter used to adjust the weight of the two terms in the objective function. M is a large enough number, which is a linearization auxiliary parameter.

[0114] Step 104, input the adaptive uncertainty set into the target model to obtain the optimal site and capacity scheme of the distribution network.

[0115] In the embodiments of the present application, according to the robust optimization theory, all possible scenarios in the adaptive uncertainty set can be represented by the vertex scenarios thereof (i.e., only the vertex corresponding to the output scenario of each small uncertainty set needs to be considered, i.e., all scenarios in the small uncertainty set can be covered). Therefore, first, the vertex scenarios of each small uncertainty set are extracted, and each vertex scenario corresponds to a specific set of renewable energy output values and fluctuation range.

[0116] All the extracted vertex scenarios are substituted into the target model, and based on system constraints, the running variables (including the charge and discharge power of the energy storage system, the output of the thermal power unit, and the interactive power of the distribution network and the main power grid) in each scenario are checked for feasibility. If the running variables in a vertex scenario do not meet the system constraints (such as the line transmission power exceeding the maximum capacity or the node voltage exceeding the allowed range), the planning scheme is adjusted, the vertex scenarios are reextracted, and the checking is performed again until all the vertex scenarios meet the system constraints. For example Figure 4 As shown in Figure 4 A schematic diagram of an uncertain set modeling method provided in an embodiment of the present application.

[0117] On the basis that all the vertex scenarios meet the system constraints, the target model is solved with the minimum annualized comprehensive cost in the whole planning cycle as the target. In the solving process, the planning variables to be determined are the installation node of the energy storage system, the energy storage capacity of each installation node, and the energy storage power of each installation node.

[0118] According to the optimization solving result, the optimal installation node of the energy storage system in the distribution network, the energy storage capacity of each installation node, and the energy storage power are determined to form a final optimal site selection and capacity determination scheme of the distribution network.

[0119] The embodiments of the present application break through the contradiction between the uncertainty description and the planning economy of the existing methods by the progressive modeling of the polyhedral uncertain set to the adaptive uncertain set, realizing the accurate adaptation to the long-term and short-term uncertainty characteristics of the renewable energy, and breaking through the contradiction between the uncertainty description and the planning economy of the existing methods. By integrating the renewable energy characteristics, the distribution network topology, the load, and the multi-dimensional planning data, a complete target model including the target function, the system constraints, and the polyhedral uncertain set constraints is constructed, and then combined with the scene coverage capability of the adaptive uncertain set, it is ensured that the planning scheme can cope with the short-term fluctuations and long-term changes of the renewable energy in the whole cycle, and simulation verification shows that there is no infeasible scenario, and the safe and stable operation of the distribution network is ensured.

[0120] On the basis of the above Figure 1 As shown in the above, the present application further provides an implementation process schematic diagram of obtaining a target model, as shown in Figure 2 The implementation process schematic diagram of obtaining a target model includes steps 201 to 204.

[0121] In step 201, a target function is constructed according to economic parameters.

[0122] In the embodiments of the present application, the target function aims to balance the investment and operation economy of the distribution network in the whole planning cycle to ensure the minimum total annualized cost.

[0123] The investment cost is mainly the planning investment cost of the energy storage system, and needs to be annualized and converted in combination with the energy storage capacity cost coefficient, the energy storage power cost coefficient, the discount rate and the planning period in the economic parameters. Specifically, the total investment cost of the energy storage system is calculated first, and then the total investment cost is converted into an annual investment cost equivalent to each year according to the discount rate of 5% and the planning period of 20 years through the capital time value formula.

[0124] The operation cost is the persistent cost generated in the annual operation process of the distribution network, which is calculated in combination with the fuel cost coefficient of the thermal power unit, the start-stop cost coefficient of the thermal power unit and the grid interaction cost coefficient in the economic parameters, and specifically includes three parts: 1. the fuel cost of the thermal power unit; 2. the start-stop cost of the thermal power unit; and 3. the grid interaction cost.

[0125] The annualized investment cost and the annualized operation cost are added to form a complete objective function, that is, the objective function = annualized investment cost + annualized operation cost, and the optimization direction is to minimize the value of the objective function, so as to ensure the optimal comprehensive cost of the distribution network in the whole planning period.

[0126] In step 202, system constraints are constructed according to the distribution network topology and element parameters and the load time sequence data.

[0127] In the embodiment of the present application, the installation quantity constraint is used to limit the installation scale of energy storage at a single node, so as to avoid resource waste caused by excessive concentration of energy storage at local nodes.

[0128] The investment constraint is used to control the total investment scale of the distribution network energy storage planning, which includes two core limitations: 1. the total investment cost upper limit constraint, that is, the sum of the investment costs of all node energy storages cannot exceed the total investment cost upper limit; and 2. the capacity-power correlation constraint, that is, the energy storage capacity and the energy storage power of a single node need to be matched.

[0129] The transmission safety constraint is constructed based on the distribution network topology and element parameters, and is the core constraint for ensuring the physical safety of the distribution network, which specifically includes: 1. line flow constraint, which ensures that the transmission power (including active power and reactive power) of each branch line does not exceed its maximum apparent power capacity, so as to avoid line overload and burnout; 2. node voltage constraint, which ensures that the bus voltage of each node is maintained within the default range of 0.95 per unit (lower limit)-1.05 per unit (upper limit), so as to avoid low voltage leading to equipment failure or high voltage damaging equipment; and 3. power balance constraint, which ensures the balance between active power and reactive power supply and demand at each node, so as to avoid power imbalance leading to frequency fluctuation.

[0130] The device operation state constraint is used to regulate the operation boundary of various devices in the power distribution network, and to ensure that the devices operate within a safe range. Specifically, the device operation state constraint includes: (1) a storage system operation constraint, which limits the state of charge (i.e., the proportion of remaining power of the storage battery) of the storage system within a reasonable range (e.g., 20%-80% to avoid damage to the battery due to overcharging or overdischarging), and limits the charging and discharging power of the storage system to be less than the maximum charging and discharging power; (2) a thermal power unit operation constraint, which limits the output of the thermal power unit within a range of minimum output-maximum output, and limits the change rate (i.e., the climb rate) of the output to be less than a maximum allowed value; and (3) a renewable energy curtailment constraint, which limits the curtailed power of the renewable energy.

[0131] In step 203, a polyhedral uncertainty set constraint is constructed based on the renewable energy characteristic data.

[0132] In the embodiments of the present application, based on the long-term and short-term output historical data of the renewable energy, the output is grouped and counted according to time periods-seasons: one day is divided into 24 time periods, and one year is divided into four seasons of spring, summer, autumn, and winter. The average output in each season-time period combination is calculated, which is the expected output value of the renewable energy in the time period (e.g., the expected output value of the photovoltaic power at 12 o'clock in summer is 80 kW, and the expected output value of the photovoltaic power at 12 o'clock in winter is 50 kW), which is used to reflect the average level of the output in the time period.

[0133] For each season-time period combination, the deviation between the actual output value and the expected value in the historical output data is analyzed, and the maximum absolute value of the deviation is taken as the maximum deviation range of the time period, which is used to reflect the fluctuation amplitude of the output in the time period.

[0134] Taking the expected output value of each time period as the center and the corresponding maximum deviation range as the upper and lower boundaries, the possible fluctuation interval of the renewable energy output in the time period is defined (e.g., the fluctuation interval of the photovoltaic power at 12 o'clock in summer is 80 kW-20 kW to 80 kW+20 kW, i.e., 60-100 kW; the fluctuation interval of the photovoltaic power at 12 o'clock in winter is 50 kW-10 kW to 50 kW+10 kW, i.e., 40-60 kW).

[0135] The output fluctuation intervals of all season-time period combinations are integrated to form a polyhedral uncertainty set constraint. The polyhedral uncertainty set constraint is used to clearly define the possible value range of the renewable energy output in any time period within the planning period and the time period correlation, without missing the actual possible output scenarios or including extreme scenarios beyond the historical fluctuation rules.

[0136] In step 204, a target model is obtained based on the target function, the system constraints, and the polyhedral uncertainty set constraint.

[0137] In the embodiments of the present application, the target function is taken as the optimization core, the system constraints are taken as the safety boundary conditions of the power distribution network operation, and the polyhedral uncertainty set constraints are taken as the uncertainty boundary conditions of the renewable energy output. The three together constitute the complete framework of the target model. That is, the model needs to realize the optimization of the target function (minimum cost) under the premise of meeting the system constraints (safety boundary) and the polyhedral uncertainty set constraints (uncertainty boundary).

[0138] The energy storage capacity and power parameters in the target function need to be matched with the installation quantity constraints and investment constraints in the system constraints. The renewable energy output range in the polyhedral uncertainty set constraints needs to be matched with the power balance constraints in the system constraints.

[0139] After integration and consistency check, the initial model is converted into a complete target model containing optimization target-safety constraints-uncertainty constraints.

[0140] The embodiments of the present application construct the target function of annualized investment cost + operation cost based on economic parameters, convert the one-time energy storage investment into annualized cost through the discount rate, avoid the deviation of short-term investment and long-term operation cost accounting, avoid the over-concentration of energy storage or investment overspending through installation quantity and investment constraints, and ensure reasonable allocation of resources; the transmission safety constraints (line flow, voltage range) prevent line overload and voltage out-of-limit, and protect the physical safety of the power distribution network; the equipment operation state constraints (energy storage state of charge, thermal power output range) standardize the equipment operation boundary, prolong the equipment life and avoid fault risk, and form a full-dimensional safety protection covering investment-transmission-equipment.

[0141] In some embodiments, the adaptive uncertainty set is obtained according to the polyhedral uncertainty set constraints, including: dividing the possible range of renewable energy output into a plurality of small uncertainty sets according to the polyhedral uncertainty set constraints, wherein each small uncertainty set is used to represent the short-term output fluctuation characteristics of the renewable energy, and all small uncertainty sets include all uncertainties of the long-term output of the renewable energy.

[0142] Specifically, the renewable energy output has a double-time scale characteristic of short-term fluctuation and long-term change: the short-term fluctuation represents the output fluctuation within a day, and the long-term change represents the output trend difference at the seasonal level. Although the polyhedral uncertainty set constraints can cover the entire planning period output range, they cannot distinguish the time scale difference, so it is necessary to realize short-term accurate description and long-term complete coverage by dividing small uncertainty sets.

[0143] The possible range of renewable energy output defined by the polyhedral uncertainty set constraints in the entire planning period is divided into six small uncertainty sets.

[0144] Each small uncertainty set focuses on short-term power fluctuation within a day, for example, one small uncertainty set covers the range of photovoltaic power fluctuation within a typical summer day, containing the possible values of different time periods (e.g. high power from 8 am to 6 pm, zero power at night) within the day, accurately depicting the characteristics of hourly fluctuations.

[0145] The 6 small uncertainty sets correspond to typical spring days, typical summer days, typical autumn days, typical winter days, typical transition season 1 days, and typical transition season 2 days, respectively. Through the union of all small uncertainty sets, the long-term power uncertainty of renewable energy sources within the entire planning period is fully covered. Whether it is the difference in power mean values of different seasons or extreme power scenarios across seasons (such as sudden increase in wind power due to winter cold wave or sudden decrease in photovoltaic power due to summer continuous rain), at least one small uncertainty set can cover it.

[0146] The setting of constraints ensures that the divided small uncertainty sets cover all possible scenarios without omission. For the obtained long-term and short-term power historical data of renewable energy sources, each historical scenario must be fully contained in at least one small uncertainty set. This is achieved through auxiliary large parameters and scenario-small set matching rules. Finally, through simulation verification, historical scenarios are covered by the 6 small uncertainty sets without omission.

[0147] If the above constraints are met, there must be a certain For each , it satisfies , that is, any scenario and fluctuation must belong to the established uncertainty set.

[0148] Proof: If , the above constraints are equivalent to:

[0149]

[0150] (15)

[0151] This means that the scenario set considering short-term fluctuations is enveloped in the proposed uncertainty set , that is .

[0152] where represent the lower and upper bounds of the proposed uncertainty set, represent the lower and upper bounds of the uncertainty set in the historical data s , and are used to describe the historical data set and the adaptive uncertainty set, respectively.

[0153] Further, by minimizing the total size of all small uncertainty sets and the maximum size of a single small uncertainty set, the adaptive uncertainty set is obtained.

[0154] Specifically, the reserve capacity demand (such as reserve thermal power output and energy storage reserve power) of the power distribution network for the renewable energy uncertainty is determined by the maximum output fluctuation range. That is, the larger the size of a single small uncertainty set, the more intense the output fluctuation it covers, the higher the required reserve capacity, and the higher the system operation cost. At the same time, the total size of the small uncertainty sets is too large, which easily leads to coverage redundancy, increases the model calculation amount and planning conservatism. Therefore, the total size of all small uncertainty sets and the maximum size of a single small uncertainty set need to be minimized at the same time.

[0155] Avoiding coverage redundancy and improving calculation efficiency The total size of the small uncertainty sets refers to the sum of the output range covered by all small sets (such as the sum of the output interval widths covered by the six small sets). During optimization, the overlapping output range needs to be removed. For example, there is some overlap between the output range of the spring season and the output range of the transition season 1. By adjusting the boundaries of the small sets and reducing the overlap area, the total size is minimized.

[0156] Reducing operation cost and balancing fluctuation response capacity The maximum size of a single small uncertainty set refers to the size of the small set with the widest output fluctuation range in the six small sets. During optimization, the boundaries of each small set are adjusted (such as appropriately reducing the upper limit of the extreme output of the summer small set and increasing the lower limit of the extreme output of the winter small set) to balance the sizes of the six small sets.

[0157] Final formation of the adaptive uncertainty set After the division of the small uncertainty sets and the size optimization, the six small uncertainty sets obtained together constitute the adaptive uncertainty set. This set can accurately depict the short-term output fluctuation through a single small set, and can completely cover the seasonal long-term output uncertainty through the union of all small sets, while balancing the coverage completeness and cost economy through size optimization.

[0158] By dividing the small uncertainty sets, the present application focuses on the short-term output fluctuation of a single small uncertainty set, and the union of all small sets covers the seasonal long-term uncertainty, solving the problem of insufficient accuracy caused by the inability of existing methods to distinguish time scales, making the uncertainty modeling more in line with the actual output law of renewable energy. Through the optimization of minimizing the total size of all small uncertainty sets + minimizing the maximum size of a single small uncertainty set, on the one hand, the redundant output range of the repeated coverage is removed, and the model calculation amount is reduced.

[0159] In some embodiments, the adaptive uncertainty set is input into a target model to obtain an optimal site and capacity scheme of the power distribution network, including: inputting the adaptive uncertainty set into the target model to obtain a plurality of renewable energy parameters, the plurality of renewable energy parameters corresponding to specific output values and fluctuation ranges of renewable energy in a plurality of scenarios in the adaptive uncertainty set.

[0160] Specifically, the adaptive uncertainty set is composed of multiple small uncertainty sets, and the vertex scenario of each small uncertainty set can fully represent all possible output scenarios in the small set. Therefore, the renewable energy parameters need to be extracted from the vertex scenarios of each small uncertainty set, which not only guarantees the completeness of coverage, but also reduces the computational complexity.

[0161] For the six small uncertainty sets (corresponding to the typical days of spring, summer, autumn, winter and two transition seasons), four vertex scenarios are taken for each small uncertainty set, and 24 vertex scenarios are extracted corresponding to the renewable energy parameters.

[0162] The core content of each parameter includes the specific output value and the fluctuation range. The specific output value is the actual output of the renewable energy at a certain time period (such as 80 kilowatts) under the vertex scenario (such as 12 o'clock on a typical summer day); the fluctuation range is the upper and lower limits of the output of the 24 time periods in a day (such as the fluctuation range of photovoltaic output on a typical summer day is 0-100 kilowatts), which fully reflects the short-term fluctuation characteristics of the scenario.

[0163] In order to simplify the expression, the planning model in S1 is uniformly represented in the following compact form:

[0164] (16)

[0165] where, x is the vector form of the planning layer variable, y is the vector form of the running layer variable, d is the vector form of the uncertain variable, is the uncertainty set. are parameter matrices (constants), is a parameter for adjusting the weight of the two planning objectives.

[0166] According to the set operation rules and robust optimization theory, if all the vertices in the uncertainty set can be ensured to have a feasible solution, the feasibility of all possible scenarios in the uncertainty set can be represented by the convex combination of the limited vertices. The specific solution model is as follows:

[0167] (17)

[0168] where, rep represents the index of the scenario used to estimate the cost, represents a set of running variables introduced for the n th uncertainty set, which is used to simulate its running state and estimate the running cost, represents a set of running variables introduced for the n th vertex of the v th uncertainty set, is the feasible space of the planning variable. is the vertex set of the uncertainty set, represent the first n vertex scenario of the first uncertain set v , is the state vector related to the vertex scenario. The lower and upper bounds of the state vector are used to constrain the state vector. Each parameter is marked with the number of the small uncertain set to which it belongs, ensuring that subsequent verification and optimization can be traced back to the uncertainty of a specific time scale.

[0169] Further, based on the system constraints, the multiple renewable energy parameters are checked, and in the case where any renewable energy parameter does not meet the system constraints, the current scenario is adjusted until all renewable energy parameters meet the system constraints.

[0170] Further, based on the system constraints, the multiple renewable energy parameters are checked, and in the case where any renewable energy parameter does not meet the system constraints, the current scenario is adjusted until all renewable energy parameters meet the system constraints.

[0171] Specifically, it is checked whether the transmission power of all branch lines in the scenario exceeds the maximum apparent power capacity, and whether the bus voltage of all nodes is within the range of 0.95-1.05 per unit value, to avoid line overload or voltage out-of-limit.

[0172] It is checked whether the state of charge of the energy storage system in the scenario is within the reasonable interval of 20%-80%, whether the output of the thermal power unit is within the minimum-maximum output range, and whether the renewable energy curtailment power does not exceed the actual output.

[0173] It is checked whether the energy storage investment cost calculated based on the parameters in the scenario exceeds the total investment upper limit, to ensure that the planning scheme is economically feasible.

[0174] If a scenario corresponding to a renewable energy parameter does not meet any of the above constraints, the boundary of the small uncertain set is adjusted.

[0175] It is determined whether the output of a certain period in the scenario is too high or too low, resulting in constraint violation.

[0176] The boundary of the small uncertain set corresponding to the scenario is appropriately adjusted, and the vertex scenario parameters of the small set are re-extracted.

[0177] The adjusted parameters are again substituted into the system constraint verification until all parameters corresponding to the scenario meet the constraints.

[0178] According to the robust optimization theory, for the scenarios in the uncertain set , there is a convex combination of a group of vertices that satisfies the following formula:

[0179] (18)

[0180] where represents the matrix form of the uncertain quantity, is the uncertain set a set of vertices, v a vertex index, a set of convex combination coefficients.

[0181] This means that according to the calculation result of the proposed robust optimization model, the coping decision of the uncertain scenario can be constructed as follows:

[0182] (19)

[0183] wherein, denotes a matrix form of the operation variable, is a set of feasible decisions for the vertex v, is a set of vertices of the uncertain set a set of vertices, v a vertex index, a set of convex combination coefficients.

[0184] Further, according to the objective function, a plurality of renewable energy parameters are optimized to obtain optimal values, and the optimal values are optimal site selection and capacity selection scheme outputs of the power distribution network.

[0185] Specifically, the variables are divided into planning layer variables and operation layer variables. The planning layer variables are to be solved energy storage sites; the operation layer variables are the thermal power output, the grid interaction power, and the energy storage charging and discharging power that change with the scenario.

[0186] Under the premise that all scenarios corresponding to the renewable energy parameters satisfy the system constraints, the planning layer variables are iteratively adjusted by the algorithm to minimize the objective function. For example, if the installation of energy storage at a node can significantly reduce the fuel cost and grid interaction cost of multiple scenarios, the algorithm will preferentially select the node as the optimal installation point and calculate the optimal energy storage capacity.

[0187] The planning layer variables obtained by optimization are substituted into all scenarios again to ensure that the constraints are still satisfied and the cost is optimal under extreme scenarios (such as sudden increase of wind power in winter), avoiding local optimal problems.

[0188] Determine the 5th, 12th, 18th, and 25th nodes in the IEEE33 node system as the optimal energy storage installation nodes. These nodes are key nodes with large renewable energy output fluctuations and high load demands, and the installation of energy storage can maximize the fluctuation suppression and cost reduction. As shown in Figure 5 Figure 5 is a structural schematic diagram of a node system provided by an embodiment of the application.

[0189] ​​The embodiments of the present application verify all renewable energy parameters one by one through the cycle of system constraint verification-scenario adjustment, and timely adjust the scenario boundary if there are problems such as line overrun, voltage overrun, and device operation overrun, until all parameters meet the constraints. A multi-stage robust optimization algorithm is adopted to optimize the parameters with the minimum cost as the target under the premise of meeting the constraints. The finally determined energy storage installation nodes are all key nodes with large renewable energy fluctuation and high load demand, and the energy storage capacity distribution is accurately matched with the node uncertainty response demand, which can maximize the fluctuation suppression and avoid investment waste, so as to maximize the peak clipping and valley filling-fluctuation suppression benefit of the energy storage system. Figure 6 Figure 6 A schematic diagram of energy storage installed capacity provided by the embodiments of the present application.

[0190] In some embodiments, a polyhedral uncertainty set constraint is constructed according to renewable energy characteristic data, including: obtaining the expected value of renewable energy output in each period based on the long-term and short-term output history data in the renewable energy characteristic data.

[0191] Specifically, the range and dimension of the historical data are clear, and the historical data used to calculate the expected value need to cover a long period and a short time step: the long period is nearly 3 years (to ensure that the complete seasonal variation characteristics are included), and the short time step is 1 hour (to ensure that the short-term fluctuation rules within a day are captured), and the data types include hourly measured output values of wind power and photovoltaic power. At the same time, the data need to be divided by season-period dimension, wherein the season is divided into spring, summer, autumn, and winter four seasons, and the period is divided into 24 hours a day, forming 96 data groups of 4 seasons x 24 periods, to ensure that the expected value can reflect the output difference of the same period in different seasons.

[0192] Summer 12 o'clock group: including photovoltaic output data of all 12 o'clock in summer for 3 years (a total of 3x92≈276 data points), and the average value is calculated as 80 kilowatts, that is, the expected value of photovoltaic output at 12 o'clock in summer is 80 kilowatts.

[0193] Winter 12 o'clock group: including photovoltaic output data of all 12 o'clock in winter for 3 years (a total of 3x90≈270 data points), and the average value is calculated as 50 kilowatts, that is, the expected value of photovoltaic output at 12 o'clock in winter is 50 kilowatts.

[0194] Other periods (such as spring 6 o'clock, autumn 18 o'clock, etc.) are calculated in this way, and finally the expected output value corresponding to 96 season-periods is obtained, forming the center reference of the polyhedral uncertainty set constraint.

[0195] Further, the maximum deviation range of the actual output value relative to the expected output value in each period is obtained.

[0196] ​Specifically, the patent document explicitly covers most actual scenarios, and the maximum deviation range needs to be determined based on a 95% confidence interval. That is, among the historical data of each season-period group, the deviation of the actual output value from the expected value is calculated, and the 5% extreme data (such as abnormal wind power caused by typhoon, abnormal photovoltaic output caused by heavy rain) is removed. Take the maximum absolute value of the deviation of the remaining 95% data as the maximum deviation range of this period. This principle not only avoids excessive conservatism caused by extreme abnormal data, but also ensures that it covers most normal operation scenarios, which meets the goal of accurately depicting uncertainty in the patent document.

[0197] The expected value at 12 o'clock in summer is 80 kW, and 95% of the actual output values in the historical data are distributed between 60-100 kW. The deviation range of the actual output from the expected value is -20 kW to +20 kW, so the maximum deviation range of this period is ±20 kW.

[0198] The expected value at 12 o'clock in winter is 50 kW, and 95% of the actual output values in the historical data are distributed between 40-60 kW. The deviation range of the actual output from the expected value is -10 kW to +10 kW, so the maximum deviation range of this period is ±10 kW.

[0199] The expected value at 6 o'clock in spring is 10 kW, and 95% of the actual output values in the historical data are distributed between 5-15 kW. The maximum deviation range is ±5 kW.

[0200] Complete the maximum deviation range statistics of all 96 season-periods in the above manner to form the boundary basis of the polyhedral uncertainty set constraint.

[0201] Further, according to the expected output value of each period and the corresponding maximum deviation range, the possible fluctuation interval of the renewable energy output in each period is obtained, and the polyhedral uncertainty set constraint is obtained according to the possible fluctuation interval.

[0202] Specifically, the definition of the fluctuation interval of a single period is centered on the expected output value of each season-period, and the corresponding maximum deviation range is the upper and lower boundary, which defines the possible fluctuation interval of the renewable energy output in this period. That is, the fluctuation interval = expected value - maximum deviation to expected value + maximum deviation.

[0203] The fluctuation interval at 12 o'clock in summer is: 80 kW - 20 kW to 80 kW + 20 kW, i.e. 60-100 kW.

[0204] The fluctuation interval at 12 o'clock in winter is: 50 kW - 10 kW to 50 kW + 10 kW, i.e. 40-60 kW.

[0205] The fluctuation interval at 6 o'clock in spring is: 10 kW - 5 kW to 10 kW + 5 kW, i.e. 5-15 kW.

[0206] The integration of the polyhedral uncertainty set constraint integrates the output fluctuation intervals of all 96 seasonal-time periods to form a complete polyhedral uncertainty set constraint. The core feature of the constraint is to contain the output fluctuation range of all seasons and all time periods in the planning period, ensuring that both long-term (seasonal) and short-term (intraday) uncertainties are described; the constraint can be directly connected to the target model, providing an initial uncertainty boundary for subsequent generation of adaptive uncertainty sets.

[0207] The embodiment of the present application determines the deviation range through the 95% confidence interval, and eliminates 5% extreme abnormal data, which avoids the problem that the deviation range is too large and the planning is too conservative due to extreme values, and ensures that 95% of normal operation scenarios are covered, so that the deviation range is more suitable for the actual needs of the distribution network, and solves the problem that the deviation range setting of the existing method is random, resulting in loose or tight constraints. Based on the time period fluctuation interval defined by the expected value + maximum deviation range, the reasonable output range of the renewable energy of each time period is accurately reflected, and the integrated polyhedral uncertainty set constraint ensures that the uncertainty processing logic of the entire planning process is coherent and accurate.

[0208] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps can be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is only one of the many execution orders, and does not represent the only execution order. When the device or client product is executed in practice, it can be executed in sequence or in parallel (for example, in a parallel processor or multi-thread processing environment) according to the method order shown in the embodiments or the drawings.

[0209] As shown in Figure 3 The embodiment of the present application also provides a power distribution network planning device 300. The device comprises:

[0210] The acquisition module 301 is configured to acquire planning data, the planning data comprising at least one of renewable energy characteristic data, power distribution network topology and element parameters, load time series data, and economic parameters.

[0211] The input module 302 is configured to input the planning data into an initial model to obtain a target model, the target model comprising a target function, system constraints, and a polyhedral uncertainty set constraint.

[0212] The determination module 303 is configured to obtain an adaptive uncertainty set according to the polyhedral uncertainty set constraint.

[0213] The determination module 303 is further configured to input the adaptive uncertainty set into the target model to obtain an optimal site and capacity scheme of the power distribution network.

[0214] In some embodiments, the determining module 303 is further configured to construct a target function according to the economic parameter, the target function comprising an investment cost and an operation cost of the power distribution network.

[0215] The determining module 303 is further configured to construct system constraints according to the power distribution network topology and element parameters and the load time series data, the system constraints comprising installation quantity, investment constraints, transmission safety and equipment operation state.

[0216] The determining module 303 is further configured to construct a polyhedral uncertainty set constraint according to the renewable energy feature data, the polyhedral uncertainty set constraint being used to represent an output fluctuation range of the renewable energy in different time periods.

[0217] The determining module 303 is further configured to obtain a target model according to the target function, the system constraints and the polyhedral uncertainty set constraint.

[0218] In some embodiments, the input module 302 is further configured to divide a possible range of the renewable energy output into a plurality of small uncertainty sets according to the polyhedral uncertainty set constraint, wherein each small uncertainty set is used to represent a short-term output fluctuation feature of the renewable energy, and all the small uncertainty sets comprise all long-term output uncertainties of the renewable energy.

[0219] The determining module 303 is further configured to obtain an adaptive uncertainty set by minimizing a total size of all the small uncertainty sets and a maximum size of a single small uncertainty set.

[0220] In some embodiments, the determining module 303 is further configured to input the adaptive uncertainty set into the target model to obtain a plurality of renewable energy parameters, the plurality of renewable energy parameters corresponding to specific output values and fluctuation ranges of the renewable energy in a plurality of scenarios in the adaptive uncertainty set.

[0221] The determining module 303 is further configured to verify the plurality of renewable energy parameters based on the system constraints, and adjust a current scenario until all the renewable energy parameters satisfy the system constraints, in a case that any renewable energy parameter does not satisfy the system constraints.

[0222] The determining module 303 is further configured to optimize the plurality of renewable energy parameters according to the target function to obtain an optimal value, the optimal value being an optimal site selection and capacity determination scheme output of the power distribution network.

[0223] In some embodiments, the input module 302 is further configured to obtain an expected output value of the renewable energy in each time period based on long-term and short-term output historical data in the renewable energy feature data.

[0224] The obtaining module 301 is further configured to obtain a maximum deviation range of an actual output value relative to the expected output value in each time period.

[0225] The determining module 303 is further configured to obtain a possible fluctuation interval of the renewable energy output in each time period according to the output expectation value and the corresponding maximum deviation range of each time period, and obtain a polyhedral uncertainty set constraint according to the possible fluctuation interval.

[0226] In some embodiments, the determining module 303 is further configured to input the economic parameter into the initial model, and obtain the objective function of the target model according to the formula.

[0227]

[0228] wherein, is an investment cost of the power distribution network, is an operation cost of the power distribution network, is a weight coefficient of the investment cost and the operation cost, is a set of investment variables, is a set of operation variables, and d is an uncertain variable, is an uncertainty set.

[0229] Some of the modules in the apparatus described in the present application can be described in the general context of computer-executable instructions, such as program modules, which are executed by computers. Generally, program modules include routines, programs, objects, components, data structures, classes, and the like, which perform particular tasks or implement particular abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including storage devices.

[0230] The apparatus or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above apparatus is described as various modules with functions. In the implementation of the embodiments of the present application, the functions of the modules can be implemented in one or more software and / or hardware. Of course, the modules implementing certain functions can also be implemented by a combination of multiple sub-modules or sub-units.

[0231] The methods, apparatuses or modules described in the present application can be implemented in a computer readable program code in any appropriate manner, for example, the controller can take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (for example, software or firmware) executable by the (micro)processor, logic gates, switches, application specific integrated circuits (ASIC), programmable logic controllers and embedded microcontrollers, examples of the controller include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in pure computer readable program code, the same function can be achieved by logically programming the method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both a software module for implementing the method and a structure within the hardware component.

[0232] The embodiments of the present application also provide a device, which comprises: a processor; a memory for storing processor executable instructions; and the processor implements the method as described in the embodiments of the present application when executing the executable instructions.

[0233] The embodiments of the present application also provide a non-volatile computer readable storage medium, which stores a computer program or instructions, and when the computer program or instructions are executed, the method as described in the embodiments of the present application is implemented.

[0234] In addition, the functional modules in the various embodiments of the present application can be integrated in one processing module, or each module can exist independently, or two or more modules can be integrated in one module.

[0235] The storage medium described above includes but is not limited to random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD) or memory card. The memory can be used to store computer program instructions.

[0236] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and the necessary hardware. Based on such an understanding, the technical solutions of the present application can be embodied in the form of a software product or can be embodied in the form of data migration in the implementation process. The computer software product can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the method described in each embodiment or some parts of the embodiments of the present application.

[0237] The various embodiments in the specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment mainly describes the difference from other embodiments. The whole or part of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, mobile communication terminals, multi-processor systems, microprocessor-based systems, programmable electronic devices, network PCs, small computers, large computers, distributed computing environments including any of the above systems or devices, etc.

[0238] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present application.

Claims

1. A method of planning a power distribution network, characterized by, The method comprises the following steps: acquiring planning data, wherein the planning data comprises at least one of renewable energy characteristic data, power distribution network topology and element parameters, load time series data and economic parameters; inputting the planning data into an initial model to obtain a target model, wherein the target model comprises a target function, system constraints and polyhedral uncertainty set constraints; obtaining an adaptive uncertainty set according to the polyhedral uncertainty set constraints; inputting the adaptive uncertainty set into the target model to obtain an optimal site selection and capacity determination scheme of the power distribution network; the step of obtaining the adaptive uncertainty set according to the polyhedral uncertainty set constraints comprises the following steps: dividing a possible range of renewable energy output into a plurality of small uncertainty sets according to the polyhedral uncertainty set constraints, wherein each small uncertainty set is used to represent short-term output fluctuation characteristics of the renewable energy, and all the small uncertainty sets comprise all long-term output uncertainties of the renewable energy; obtaining the adaptive uncertainty set by minimizing the total size of all the small uncertainty sets and the maximum size of a single small uncertainty set; a formula of the polyhedral uncertainty set is as follows: wherein, is an uncertain set, denotes a matrix form of uncertain variables in the uncertain set, N denotes the number of uncertain variables, is an expected value of the uncertain variables, is an estimation bias.

2. The method of claim 1, wherein, the step of inputting the planning data into the initial model to obtain the target model comprises the following steps: constructing a target function according to the economic parameters, wherein the target function comprises investment cost and operation cost of the power distribution network; constructing system constraints according to the power distribution network topology and element parameters and the load time series data, wherein the system constraints comprise installation quantity, investment constraints, transmission safety and equipment operation state; constructing polyhedral uncertainty set constraints according to the renewable energy characteristic data, wherein the polyhedral uncertainty set constraints are used to represent output fluctuation ranges of the renewable energy in different time periods; obtaining the target model according to the target function, the system constraints and the polyhedral uncertainty set constraints.

3. The method of claim 1, wherein, the step of inputting the adaptive uncertainty set into the target model to obtain the optimal site selection and capacity determination scheme of the power distribution network comprises the following steps: inputting the adaptive uncertainty set into the target model to obtain a plurality of renewable energy parameters, wherein the plurality of renewable energy parameters correspond to specific output values and fluctuation ranges of the renewable energy in a plurality of scenarios in the adaptive uncertainty set; verifying the plurality of renewable energy parameters based on the system constraints, and adjusting the current scenario until all the renewable energy parameters satisfy the system constraints, in the case that any renewable energy parameter does not satisfy the system constraints; optimizing the plurality of renewable energy parameters according to the target function to obtain optimal values, wherein the optimal values are the optimal site selection and capacity determination scheme output of the power distribution network.

4. The method of claim 2, wherein, the step of constructing the polyhedral uncertainty set constraints according to the renewable energy characteristic data comprises the following steps: obtaining output expectation values of the renewable energy in each time period based on long-term and short-term output historical data in the renewable energy characteristic data; obtaining a maximum deviation range of an actual output value relative to the output expectation value in each time period; obtaining possible fluctuation intervals of the renewable energy output in each time period according to the output expectation value and the corresponding maximum deviation range in each time period, and obtaining the polyhedral uncertainty set constraints according to the possible fluctuation intervals.

5. The method of claim 2, wherein, the step of constructing the target function according to the economic parameters comprises the following steps: The economic parameters are input into the initial model, and a target function of a target model is obtained according to a formula; wherein, is the investment cost of the distribution grid, is the operating cost of the distribution grid, is the weight coefficient of the investment cost and the operating cost, is the investment variable set, is the operating variable set, d is the uncertain variable, is the uncertain set.

6. The method of claim 2, wherein, The operation cost includes fuel cost, start-stop cost and interaction cost with an external power grid.

7. A power distribution network planning apparatus characterized by comprising: Comprise: An acquisition module is configured to acquire planning data, the planning data including at least one of renewable energy characteristic data, power distribution network topology and element parameters, load time series data and economic parameters; An input module is configured to input the planning data into an initial model to obtain a target model, the target model including a target function, system constraints and polyhedral uncertainty set constraints; A determination module is configured to obtain an adaptive uncertainty set according to the polyhedral uncertainty set constraints; The determination module is further configured to input the adaptive uncertainty set into the target model to obtain an optimal site selection and capacity determination scheme of the power distribution network; The determination module is further configured to obtain an adaptive uncertainty set according to the polyhedral uncertainty set constraints, wherein: The possible range of renewable energy output is divided into a plurality of small uncertainty sets according to the polyhedral uncertainty set constraints, wherein each small uncertainty set is used to represent the short-term output fluctuation characteristics of the renewable energy, and all small uncertainty sets include all uncertainties of the long-term output of the renewable energy; The adaptive uncertainty set is obtained by minimizing the total size of all small uncertainty sets and the maximum size of a single small uncertainty set; The formula of the polyhedral uncertainty set is: wherein, is an uncertain set, represents a matrix form of uncertain variables in the uncertain set, N represents the number of uncertain variables, is an expected value of the uncertain variable, is an estimation bias.

8. A computer device comprising a memory and a processor, the memory storing a computer program capable of running on the processor, characterized in that, The processor executes the program to realize the steps of the method in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the method in any one of claims 1 to 6. The computer program is executed by the processor to realize the method in any one of claims 1 to 6.

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