Source and load coordinated planning method considering multi-energy complementation and direct current delivery

Abstract

The invention provides a source load coordinated planning method considering multi-energy complementation and direct current delivery, and relates to the technical field of power system planning. The method comprises the following steps: constructing a source-load coordination expansion planning model by taking the minimum total cost of a power system as a target; introducing the confidence set into the source-load coordination expansion planning model, and determining the constraint of the confidence set; establishing a two-stage source load coordination distribution robust planning model based on the source load coordination expansion planning model introducing the confidence set; and solving the source-load coordinated distribution robust planning model by adopting a column constraint uploading algorithm, and generating a planning scheme. The method can give full play to the dynamic regulation and control capability of the hydropower station and the pumped storage power station, and improves the multi-energy complementary benefit; and the power transmission flexibility can be improved by optimizing the power of a direct-current transmission line.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, in particular to a source-load coordinated planning method considering multi-energy complementarity and DC transmission. BACKGROUND

[0002] With the continuous growth of renewable energy installations represented by wind and solar energy, its role positioning will develop from supplementary energy to main power source. However, the intermittent and uncertain characteristics will have a great impact on power system expansion planning. How to efficiently utilize the complementarity between energies to reduce the waste of wind, light and other resources is a problem that needs to be solved. In some areas, wind, light and other new energies are abundant, and large-scale cascade hydropower station groups have been built. In recent years, pumped storage has been mainly used as a regulating power source in wind-pumped, light-pumped, wind-light-pumped and water-light-pumped systems. In the wind-pumped system, the addition of pumped storage can effectively alleviate the peak shaving pressure during the night when wind power is high, improve wind power consumption and reduce wind curtailment. In the light-pumped complementary system, centralized photovoltaic and pumped storage are combined, and the optimization goal is to minimize the system operation cost and maximize the photovoltaic consumption. The wind-light-pumped complementary system has stable power supply capacity and is used to solve the problem of large-scale new energy base transmission.

[0003] At present, research on water, wind, light and pumped storage complementarity is still in the exploratory stage, especially the research on long-distance transmission and consumption is insufficient, and the potential value of collaborative optimization of the power side and the load side has not been fully tapped. In the problem of renewable energy system expansion planning, mainly random optimization or robust optimization methods are used, which is difficult to ensure the economy and robustness of the system at the same time, especially the research on distributed robust optimization of water, wind, light and pumped storage power system is still blank. SUMMARY

[0004] To solve the above technical problems in the prior art, the present application aims to provide a source-load coordinated distributed robust expansion planning method considering water, wind, light, pumped storage multi-energy complementarity and wind and light output uncertainty, so as to fully exert the dynamic regulation and control capacity of cascade hydropower and pumped storage power stations and improve the multi-energy complementary benefit. At the same time, the power of DC transmission line is optimized, the limitation of traditional fixed power transmission mode is broken through, the transmission flexibility is improved, and the user end regulation and control architecture is constructed to promote the bidirectional dynamic matching of source and load.

[0005] Specifically, the present application provides a source-load coordinated planning method considering multi-energy complementarity and DC transmission, and the technical scheme is as follows:

[0006] Step S1: A source-load coordinated expansion planning model is constructed with the minimum total cost of the power system as the target;

[0007] Step S2: The confidence set is introduced into the source-load coordinated expansion planning model, and the constraints of the confidence set are determined;

[0008] Step S3: based on the source-load coordination extended planning model with confidence set, a two-stage source-load coordination distribution robust planning model is established;

[0009] Step S4: the source-load coordination distribution robust planning model is solved by using the algorithm listed in the constraint to generate a planning scheme.

[0010] Preferably, the source-load coordination extended planning model is constructed, specifically including:

[0011] The objective function is constructed ;

[0012] The constraint condition is constructed, including power equipment construction constraint, power system operation constraint, water and thermal power constraint, load reduction and wind and solar output adjustability constraint, load demand response and DC transmission constraint.

[0013] Preferably, the objective function is constructed , specifically including:

[0014] The formula is as follows:

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, is the power facility construction cost, is the operation cost of the thermal power unit, , and are the unit penalty costs of load reduction, wind power reduction and photovoltaic reduction respectively, , and are the load reduction, wind power reduction and photovoltaic reduction respectively, , , , and are the indexes of the thermal power unit, pumped storage power station, load demand response device, photovoltaic unit and wind power unit respectively, , and are the candidate construction scheme sets of the thermal power unit, pumped storage power station and load demand response device to be planned respectively, , and These are the construction costs for candidate equipment for thermal power units, pumped storage power stations, and load demand response, respectively. For years, for Annual market capitalization coefficient The discount rate; , and for The construction status of candidate equipment for annual thermal power units, pumped storage power stations and load demand response is 1 if the candidate equipment is in operation, and 0 otherwise. for Typical days of the year The planned number of days, For the fuel cost of thermal power units, This is the heat consumption curve of a thermal power unit. for Typical days of the year The output power of the thermal power unit, , , , and They are respectively Typical days of the year Load can be reduced, photovoltaic forecast, wind turbine forecast, photovoltaic output, and wind turbine output power.

[0021] Preferably, the constraints are constructed, including:

[0022] (1) Constraints on the construction of power equipment, the formula is as follows:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] In the formula, , and for Construction status of annual thermal power units, pumped storage power stations, and candidate equipment for load demand response. It is a collection of existing thermal power units; for The annual operating status of thermal power units is 1 if the thermal power unit is in operation, and 0 otherwise. This refers to the retirement age of thermal power units;

[0028] (2) Power system operating constraints, the formula is as follows:

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] where, , and are the indices of hydropower stations, DC sending nodes and buses, respectively, is the set of devices connected to bus , is the set of typical days of hydropower stations , is the set of typical days of load demand response devices , is the load power of planned load, is the set of typical days of DC sending nodes , and are the pumped storage power and the generated power of pumped storage power stations on typical days, is the set of typical days of transmission lines , is the power flow of transmission lines , is the reactance of transmission lines , is the sending-end bus of transmission lines , is the receiving-end bus of transmission lines , is the set of typical days of phase angles of sending-end buses, is the set of typical days of phase angles of sending-end buses, is the set of buses in the power system, is the maximum generated power of thermal power units and are the minimum and maximum phase angles of bus , For typical day busbar phase angle;

[0034] (3) Thermal power unit operation constraints, the formula is as follows:

[0035] ;

[0036] ;

[0037] ;

[0038] In the formula, is the maximum power generation of the thermal power unit , is the power generation of the thermal power unit typical day , is the power generation of the thermal power unit ,

[0039] (4) Load reduction and wind-solar power output adjustability constraints, the formula is as follows:

[0040] ;

[0041] ;

[0042] ;

[0043] In the formula, is the maximum power loss of the load demand response device typical day , is the load demand of the load demand response device typical day , is the predicted output value of the wind turbine typical day , is the predicted output value of the photovoltaic unit typical day ; typical day ;

[0044] (5) Hydropower station constraints, the formula is as follows:

[0045] ;

[0046] ; ​​

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] wherein, typical day of a year total power generation of the hydropower station, is the gravitational acceleration, is the conversion efficiency of the hydropower station and are the conversion flow and the conversion head of the hydropower station typical day of a year is the conversion head of the hydropower station of a year, is the initial head of the hydropower station is the head coefficient of the hydropower station, is the reservoir capacity of the hydropower station of a year, are the minimum and maximum values of the power generation of the hydropower station, is the ramping power limit of the hydropower station, total power generation of the hydropower station typical day of a year and are the minimum and maximum values of the conversion flow of the hydropower station, and are the minimum and maximum values of the reservoir capacity of the hydropower station, reservoir capacity of the hydropower station typical day of a year and ​​​​​​​​​​​​​​​​​​For the hydropower station during dispatch The initial and final values ​​of the storage capacity. and All are constants. for Typical days of the year hydroelectric power station Storage capacity, for Typical days of the year hydroelectric power station Natural runoff, For the upstream hydropower station To the hydroelectric power station Water flow time lag, for Typical days of the year Upstream hydropower station Inbound traffic;

[0054] (6) Constraints of pumped storage power stations, the formula is as follows:

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] In the formula, and for Typical days of the year Pumped storage power station The number of medium-duty pumped-storage units in both pumping and power generation modes. and for Typical days of the year Pumped storage power station The number of medium-duty pumped-storage units in both pumping and power generation modes. and for Typical days of the year Pumped storage power station The number of medium-duty pumped storage units that are in the start-up and shutdown states. and for Typical days of the year Pumped storage power station The number of medium-duty pumped storage units that are in the start-up and shutdown states. This refers to the number of times a single generating unit is allowed to start and stop within a scheduling cycle. Pumped storage power station The number of medium-duty pumped storage units. Represents an infinite positive number. and Pumped storage power station Minimum and maximum pumping power, Pumped storage power station exist Typical days of the year Pumping power, and Pumped storage power station Minimum and maximum power generation. for Typical days of the year Pumped storage power station Power generation capacity, and for Typical days of the year Pumped storage power station The capacity of the upper and lower reservoirs, and for Typical days of the year Pumped storage power station The capacity of the upper and lower reservoirs, and Pumped storage power station Power conversion factor between pumping and power generation;

[0065] (7) Load demand response, the formula is as follows:

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] In the formula, , for Typical days of the year Load demand response device The predicted electrical load value, and the demand-side electrical load output value after considering demand response. , and for Typical days of the year Load demand response device The electrical load values, interruptible electrical load values, and transferable electrical load values ​​involved in demand response. for Typical days of the year Load demand response device Maximum permissible electrical load for Typical days of the year Load demand response device The proportion of interruptible electrical loads, The maximum permissible interruptible electrical load. for Typical days of the year Load demand response device The proportion of transferable electrical load, for Typical days of the year Load demand response device The proportion of electrical load participating in demand response;

[0075] (8) DC power transmission constraint, the formula is as follows:

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] In the formula, for Typical days of the year node DC power transmission and For nodes Minimum and maximum values ​​of DC power transmission. for Typical days of the year node DC power transmission , For upward DC transmission ramp limits and downward DC transmission ramp limits, , express Typical days of the year node The DC power transmission capacity is in a state of downward adjustment or upward adjustment. , express Typical days of the year node The DC power transmission capacity is in a state of downward adjustment or upward adjustment. , for Annual Node Maximum number of times DC power transmission can be increased or decreased. for Annual Node The contracted amount of daily DC power transmission.

[0082] Preferably, step S2 specifically includes:

[0083] Step S21: Convert the source-load coordination extended planning model into a matrix, as shown in the following formula:

[0084] ;

[0085] In the formula, Represents 0-1 variables, This represents the variable being executed, i.e., the variable other than those between 0 and 1. and This is the correlation parameter matrix of the binary decision variables corresponding to the 0-1 variables. , , , and The combination of coefficient matrices for the running variables;

[0086] Step S22: Introduce the confidence set into the source-load coordinated extended programming model, as shown in the following formula:

[0087] ;

[0088] ;

[0089] In the formula, and The confidence intervals for the 1-norm and ∞-norm constraints are... To guide the scene, The total number of scenes, For a set of vectors, A positive real number vector For the scene initial probability value The corresponding probability vector, The maximum probability deviation corresponding to the 1-norm. The maximum probability deviation corresponding to the ∞-norm;

[0090] Step S23: Determine the vector set The required confidence level is calculated using the following formula:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] In the formula, The confidence level of the probability bias corresponding to the 1-norm constraint. The confidence level of the probability bias corresponding to the ∞-norm constraint. The number of typical scenarios, The amount of historical data;

[0096] Step S24: Use the composite norm The constraints are applied, as shown in the following formula:

[0097] ;

[0098] Step S25: Construct the equivalent absolute value constraints corresponding to the 1-norm and 0-norm constraints, as shown in the following formula:

[0099] ;

[0100] ;

[0101] In the formula, and Probability vector For probability vectors The positive and negative offset states; among them,

[0102] ;

[0103] ;

[0104] In the formula, and Positive offset state and negative offset state The logo.

[0105] Preferably, the source-load coordination distributed bar programming model specifically includes:

[0106] Phase 1: Minimize the total cost of the basic power system scenario;

[0107] The second stage: Based on historical data of wind and solar power output, construct probability distribution uncertainty sets with 1-norm and 0-norm constraints, and minimize the expected operating cost of the worst probability distribution;

[0108] The formula is as follows:

[0109] ;

[0110] ;

[0111] ;

[0112] In the formula, 0-1 variables The set, For runtime variables The set, , and For the combination of coefficient matrices of the running variables, For the scene The state variables under.

[0113] Preferably, step S4 specifically includes:

[0114] Step S41: Set the upper bound of the source-load coordination distributed bar programming model. lower bound value and number of iterations ;

[0115] Step S42: For the source-load coordination sub-bar programming model, use the column-constrained upload algorithm to solve the main problem, divide it into sub-problems, and set constraints for the main problem and sub-problems;

[0116] The main problem is to perform source-load coordination planning under a known worst-case probability distribution to obtain the optimal source-load planning scheme; the sub-problem is to divide the worst-case probability distribution of wind and solar power output.

[0117] Step S43: Solve the main problem to obtain the optimal source load planning scheme under the known worst probability distribution, and update the lower bound value. ;

[0118] Solve the subproblem to obtain the worst-case probability distribution of wind and solar power output, and update the upper bound value. ;

[0119] Step S44: Calculate the upper bound value and lower bound value The difference; if the difference is not less than the threshold Then update the iteration count. And add new runtime variables. and corresponding constraints;

[0120] Otherwise, output the iteration results to obtain the optimal source load planning scheme and the corresponding worst probability distribution.

[0121] Preferably,

[0122] The main question is:

[0123] ;

[0124] The constraints of the main problem are:

[0125] ;

[0126] ;

[0127] ;

[0128] In the formula, For operating costs, For the first The planning scheme obtained in the next iteration This is the iteration count value.

[0129] Preferably, the subproblem is:

[0130] ;

[0131] The constraints of the subproblems are:

[0132] ;

[0133] In the formula, For the first The planning scheme obtained in the next iteration.

[0134] Alternatively, the subproblem is:

[0135] ;

[0136] The constraints of the subproblems are:

[0137] ;

[0138] In the formula, For the first The planning scheme obtained in the next iteration.

[0139] Compared with existing technologies, the technical solution provided by this invention can fully utilize the dynamic regulation capabilities of hydropower stations and pumped storage power stations, and enhance the benefits of multi-energy complementarity; it can also improve power transmission flexibility by optimizing the power of DC transmission lines. Attached Figure Description

[0140] Figure 1 This is a schematic diagram of the topology of a power system that considers hydropower, wind power, solar power, energy storage, and DC power transmission in this invention.

[0141] Figure 2 This is a flowchart of the model solving process based on the constraint generation algorithm in this invention.

[0142] Figure 3 This is a predicted output curve for typical daily power load, wind power, and photovoltaic base scenarios in this invention.

[0143] Figure 4 This is a power output curve of hydropower, wind power and photovoltaic units in the fifth year of one embodiment of the present invention.

[0144] Figure 5 This refers to the pumping / power generation capacity of the pumped storage power station in the fifth year of one embodiment of the present invention.

[0145] Figure 6 This is a graph showing the net load and load transfer power curves before and after participating in demand response in one embodiment of the present invention.

[0146] Figure 7 This is a power curve diagram of DC power transmission and load transfer in one embodiment of the present invention.

[0147] Figure 8 This is a trend chart showing the relationship between the total number of DC adjustable cycles and the total cost in one embodiment of the present invention.

[0148] Figure 9 This is a graph showing the trend of total cost and penalty cost as a function of the number of historical scenarios in one embodiment of the present invention. Detailed Implementation

[0149] The technical solutions provided by the present invention will be further described in detail below with reference to the embodiments and accompanying drawings.

[0150] Example 1

[0151] This embodiment provides a source-load coordination planning method that considers multi-energy complementarity and DC transmission. Unlike traditional power system multi-energy complementarity which only considers energy complementarity characteristics, this method takes into account the uncertainty of new energy output, fully considers the dynamic regulation capabilities of cascade hydropower and pumped storage power stations, optimizes the power of DC transmission lines, breaks through the limitations of traditional constant power transmission mode, and improves the flexibility of power transmission.

[0152] Specifically, the method includes the following steps:

[0153] Step 1: With the objective of minimizing the total cost of the power system, establish a source-load coordination extended planning model that includes power equipment construction costs, unit operation costs, interruptible load compensation costs considering demand response, wind and solar curtailment loss of load compensation costs, and considers constraints on power equipment construction, power system operation, load reduction and wind / solar output adjustability, cascade hydropower station constraints, pumped storage power station constraints, load constraints considering demand response, and DC transmission constraints. The forms of electrical energy provided in the power system include hydropower, thermal power, wind power, solar power, and energy storage.

[0154] Step 2: Based on the above objective function constraints, the mathematical model is abstracted to obtain its matrix representation. The matrix representation of the deterministic source-load coordination extended planning mathematical model includes two parts: the objective function and the constraints. The objective is to minimize construction and operating costs. Operating costs mainly include the unit's coal-fired cost, compensation costs for interruptible loads, and active load reduction costs (load shedding, wind and solar curtailment penalties). The constraints mainly include three types: type 1 constraints are 0-1 variable constraints; type 2 constraints are operation-related constraints; and type 3 constraints are coupling constraints between type 1 and type 2 constraints.

[0155] Step 3: Considering the uncertainty of wind and solar power output, based on the generation and reduction method of multiple discrete scenarios, with the initial probability distribution as the center, the trajectory of the probability distribution is constrained by the confidence set defined by the 1-norm and ∞-norm, and the probability distribution of renewable energy scenarios under the most unfavorable conditions is determined. The uncertainty set is linearized by introducing 0-1 auxiliary variables.

[0156] Step 4: Based on the deterministic source-load coordination extended planning mathematical model established in Steps 1 and 2, and the two-stage source-load coordination sub-Browsing planning model considering the uncertainties of wind power and photovoltaic power established in Step 3, the first stage minimizes the total cost of the basic power system scenario; the second stage constructs the probability distribution uncertainty set of 1-norm and ∞-norm based on historical wind and solar power output data, and minimizes the expected operating cost under the worst scenario.

[0157] Step 5: Solve the model using the list constraint generation algorithm. The established source-load coordination two-stage sub-Bruker programming model is a robust optimization problem with a three-level structure of minimization-maximumization-minimization. The list constraint generation algorithm is used to transform the model into a mixed integer linear programming master problem and subproblems, until the optimization values ​​of the master problem and subproblems are less than a certain acceptable convergence accuracy.

[0158] Step 6: The model was solved using the mature optimization software Matlab Gurobi 12.0.1, and a case study was conducted using a clean energy transmission base in a certain region of Southwest my country to verify the effectiveness of the model.

[0159] Specifically, the deterministic source-load coordination extended programming model in step 1 includes the objective function. and constraints.

[0160] objective function The formula is as follows:

[0161] ;

[0162] ;

[0163] ;

[0164] ;

[0165] ;

[0166] In the formula, For the cost of power facility construction, For the operating costs of thermal power units, , and These are the unit penalty costs for load reduction, wind power reduction, and solar power reduction, respectively. , and These are respectively the load reduction, wind power reduction, and solar power reduction. , , , and These are indexes for thermal power units, pumped storage power stations, load demand response devices, photovoltaic units, and wind turbine units, respectively. , and These are collections of candidate construction schemes for planned thermal power units, hydropower stations, and load demand response equipment. , and These are the construction costs for candidate equipment for thermal power units, pumped storage power stations, and load demand response, respectively. For years, for Annual market capitalization coefficient The discount rate; , and for The construction status of candidate equipment for annual thermal power units, pumped storage power stations and load demand response is 1 if the candidate equipment is in operation, and 0 otherwise. for Typical days of the year The planned number of days, For the fuel cost of thermal power units, This is the heat consumption curve of a thermal power unit. for Typical days of the year The output power of the thermal power unit, , , , and They are respectively Typical days of the year Load can be reduced, photovoltaic forecast, wind turbine forecast, photovoltaic output, and wind turbine output power.

[0167] The constraints include constraints on the construction of power equipment, constraints on the operation of the power system, constraints on the operation of thermal power units, constraints on load reduction and the adjustability of wind and solar power output, constraints on hydropower stations, constraints on pumped storage power stations, constraints on load demand response and DC transmission.

[0168] (1) Constraints on the construction of power equipment, the formula is as follows:

[0169] ;

[0170] ;

[0171] ;

[0172] ;

[0173] In the formula, , and for Construction status of annual thermal power units, pumped storage power stations, and candidate equipment for load demand response. It is a collection of existing thermal power units; for The annual operating status of thermal power units is 1 if the thermal power unit is in operation, and 0 otherwise. This refers to the retirement age of thermal power units;

[0174] (2) Power system operating constraints, the formula is as follows:

[0175] ;

[0176] ;

[0177] ;

[0178] ;

[0179] In the formula, , and These are indices for hydropower stations, DC transmission nodes, and busbars, respectively. For the busbar A collection of connected devices for Typical days of the year hydroelectric power station 'output power' for Typical days of the year Load demand response device The load power, The load power of the planned load, for Typical days of the year DC transmission node power, and They are respectively Typical days of the year Pumped storage power station Pumped storage capacity and power generation capacity, for Typical days of the year transmission lines The trend For power transmission lines Reactance, For power transmission lines The sending-end busbar, For power transmission lines The receiving-end busbar, for Typical days of the year Phase angle of the sending busbar for Typical days of the year Phase angle of the sending busbar A collection of busbars in a power system. For thermal power units Maximum power generation capacity and busbar The minimum and maximum phase angles, for Typical days of the year busbar The phase angle;

[0180] (3) Operating constraints of thermal power units, the formula is as follows:

[0181] ;

[0182] ;

[0183] ;

[0184] In the formula, For thermal power units Maximum power generation capacity for Typical days of the year thermal power units Power generation capacity, The climbing power limit for thermal power units.

[0185] (4) Load reduction and wind and solar power output adjustability constraints, the formula is as follows:

[0186] ;

[0187] ;

[0188] ;

[0189] In the formula, for Typical days of the year Load demand response device The maximum power outage load ratio, for Typical days of the year Load demand response device The load demand, for Typical days of the year wind turbine The predicted output value, for Typical days of the year Photovoltaic units The predicted output value;

[0190] (5) Constraints of hydropower stations, the formula is as follows:

[0191] ;

[0192] ;

[0193] ;

[0194] ;

[0195] ;

[0196] ;

[0197] ;

[0198] ;

[0199] In the formula, for Typical days of the year hydroelectric power station Total power generation It is the acceleration due to gravity. For hydroelectric power station Conversion efficiency, and They are respectively Typical days of the year hydroelectric power station The conversion flow rate and conversion head, for Annual Hydropower Station The conversion head, For hydroelectric power station The initial head, For hydroelectric power station head coefficient, for Annual Hydropower Station Storage capacity, and For hydroelectric power station Minimum and maximum power generation For hydroelectric power station The ramp power limit, for Typical days of the year hydroelectric power station Total power generation and For hydroelectric power station Minimum and maximum values ​​of conversion traffic. and For hydroelectric power station Minimum and maximum storage capacity for Typical days of the year hydroelectric power station Storage capacity, and For the hydropower station during dispatch The initial and final values ​​of the storage capacity. and All are constants. for Typical days of the year hydroelectric power station Storage capacity, for Typical days of the year hydroelectric power station Natural runoff, For the upstream hydropower station To the hydroelectric power station Water flow time lag, for Typical days of the year Upstream hydropower station Inbound traffic;

[0200] (6) Constraints of pumped storage power stations, the formula is as follows:

[0201] ;

[0202] ;

[0203] ;

[0204] ;

[0205] ;

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] In the formula, and for Typical days of the year Pumped storage power station The number of medium-duty pumped-storage units in both pumping and power generation modes. and for Typical days of the year Pumped storage power station The number of medium-duty pumped-storage units in both pumping and power generation modes. and for Typical days of the year Pumped storage power station The number of medium-duty pumped storage units that are in the start-up and shutdown states. and for Typical days of the year Pumped storage power station The number of medium-duty pumped storage units that are in the start-up and shutdown states. This refers to the number of times a single generating unit is allowed to start and stop within a scheduling cycle. Pumped storage power station The number of medium-duty pumped storage units. It represents an infinitely large positive number. In practice, the largest possible positive number is chosen based on the needs. and Pumped storage power station Minimum and maximum pumping power, Pumped storage power station exist Typical days of the year Pumping power, and Pumped storage power station Minimum and maximum power generation. for Typical days of the year Pumped storage power station Power generation capacity, and for Typical days of the year Pumped storage power station The capacity of the upper and lower reservoirs, and for Typical days of the year Pumped storage power station The capacity of the upper and lower reservoirs, and Pumped storage power station Power conversion factor between pumping and power generation;

[0211] (7) Load demand response, the formula is as follows:

[0212] ;

[0213] ;

[0214] ;

[0215] ;

[0216] ;

[0217] ;

[0218] ;

[0219] ;

[0220] In the formula, , for Typical days of the year Load demand response device The predicted electrical load value, and the demand-side electrical load output value after considering demand response. , and for Typical days of the year Load demand response device The electrical load values, interruptible electrical load values, and transferable electrical load values ​​involved in demand response. for Typical days of the year Load demand response device Maximum permissible electrical load for Typical days of the year Load demand response device The proportion of interruptible electrical loads, The maximum permissible interruptible electrical load. for Typical days of the year Load demand response device The proportion of transferable electrical load, for Typical days of the year Load demand response device The proportion of electrical load participating in demand response;

[0221] (8) DC power transmission constraint, the formula is as follows:

[0222] ;

[0223] ;

[0224] ;

[0225] ;

[0226]

[0227] In the formula, for Typical days of the year node DC power transmission and For nodes Minimum and maximum values ​​of DC power transmission. for Typical days of the year node DC power transmission , For upward DC transmission ramp limits and downward DC transmission ramp limits, , express Typical days of the year node The DC power transmission capacity is in a state of downward adjustment or upward adjustment. , express Typical days of the year node The DC power transmission capacity is in a state of downward adjustment or upward adjustment. , for Annual Node Maximum number of times DC power transmission can be increased or decreased. for Annual Node The contracted amount of daily DC power transmission. Preferably, the matrix representation of the deterministic source-load coordination extended planning mathematical model mainly includes two parts: the objective function and the constraints. The objective function aims to minimize the construction cost and operating cost. The constraints are considered in three types: the first type of constraint is a 0-1 variable constraint; the second type of constraint is a constraint related to operation (constraint variables); and the third type of constraint is a coupling constraint between the first type of constraint and the second type of constraint. The formula is as follows:

[0228] ;

[0229] In the formula, Represents 0-1 variables, This represents the variable being executed, i.e., the variable other than those between 0 and 1. and This is the correlation parameter matrix of the binary decision variables corresponding to the 0-1 variables. , , , and This is a combination of coefficient matrices for the running variables.

[0230] Furthermore, considering the uncertainties of wind power and photovoltaic power, based on the deterministic source-load coordination extended planning mathematical model in steps 1 and 2, a matrix description considering the uncertainties of wind power and photovoltaic power in step 3 is established, as shown in the following formula:

[0231] ;

[0232] ;

[0233] In the formula, and The confidence intervals for the 1-norm and ∞-norm constraints are... To guide the scene, The total number of scenes, For a set of vectors, A positive real number vector For the scene initial probability value The corresponding probability vector, The maximum probability deviation corresponding to the 1-norm. The maximum probability deviation corresponding to the ∞-norm.

[0234] Vector set The formula for the required confidence level is as follows:

[0235] ;

[0236] ;

[0237] In the formula, The number of typical scenarios;

[0238] Let the confidence levels of the right-hand side of the above inequality be respectively , It can be converted into the following formula:

[0239] ;

[0240] ;

[0241] In the formula, The confidence level of the probability bias corresponding to the 1-norm constraint. The confidence level of the probability bias corresponding to the ∞-norm constraint. The amount of historical data.

[0242] To prevent one-sided or extreme cases caused by a single norm constraint, a comprehensive norm is adopted. The constraints are applied, as shown in the following formula:

[0243] .

[0244] When dealing with 1-norm constraints, variables are introduced. and Used to identify probability vectors respectively For probability vectors Positive offset state and negative offset state And it needs to satisfy the following constraints:

[0245] ;

[0246] In addition, the following constraints must be satisfied:

[0247] .

[0248] Therefore, the equivalent transformation of the 1-norm constraint and the 0-norm constraint absolute value constraint in the constraint formula is as follows:

[0249] ;

[0250] .

[0251] This embodiment uses a sub-Bluerg bar and, based on the deterministic source-load coordination extended programming mathematical model in steps 1 and 2, establishes a two-stage source-load coordination sub-Bluerg bar programming model that considers the uncertainties of wind power and photovoltaics.

[0252] The first phase aims to minimize the total cost of the basic power system scenario.

[0253] The second stage, based on historical wind and solar power output data, constructs an uncertainty set of probability distributions with 1-norm and ∞-norm constraints, minimizing the expected operating cost of the worst-case probability distribution.

[0254] The formula is as follows:

[0255] ;

[0256] ;

[0257] ;

[0258] In the formula, 0-1 variables The set, For runtime variables The set, , and The coefficients are the constituent coefficients of the coefficient matrix combination of the running variables. For uncertain scenarios State variables under the following conditions, including DC power transmission. Upward DC transmission ramp limit Downward DC transmission ramp limit DC power transmission status (downward adjustment status) , Adjust status ).

[0259] In this embodiment, step 5 is solved primarily using the mature optimization software Matlab Gurobi 12.0.1 combined with the constraint generation algorithm to solve the two-stage source-load coordination sub-Bruker programming model considering the uncertainties of wind power and photovoltaic power established in step 4. The specific solution process is as follows:

[0260] Set the upper bound of the source-load coordination distributed bar programming model. lower bound value and number of iterations For the source-load coordination sub-bar programming model, the list-constrained uploading algorithm is used to solve the main problem, divide it into subproblems, and set constraints for the main problem and subproblems. The main problem is to perform source-load coordination programming under a known worst-case probability distribution to obtain the optimal source-load planning scheme; the subproblem is to divide the worst-case probability distribution of wind and solar power output.

[0261] Solving the main problem yields the optimal source-load planning scheme under the known worst-case probability distribution, and the lower bound is updated. Solve the subproblems to obtain the worst-case probability distribution of wind and solar power output, and update the upper bound. Calculate the upper bound value. and lower bound value The difference; if the difference is not less than the threshold Then update the iteration count. And add new runtime variables. The corresponding constraints are determined; otherwise, the iteration results are output to obtain the optimal source load planning scheme and the corresponding worst probability distribution.

[0262] The main problem and its constraints are as follows:

[0263] ;

[0264] ;

[0265] ;

[0266] ;

[0267] In the formula, For the operating costs of the lower level, For the first The planning scheme obtained in the next iteration This is the iteration count value.

[0268] Based on the optimization results of the main problem The main problem is to find the worst-case probability distribution and update the lower bound value simultaneously. .

[0269] The formulas for the subproblems and their constraints are as follows:

[0270] ;

[0271] .

[0272] Since there is no coupling relationship between the power output scenario and the operational decision variables, and the power output scenario and the probability distribution uncertainty set are independent of each other.

[0273] Therefore, in the objective function of the subproblem, the minimization operator and the summation operator can be interchanged, as shown in the following formula:

[0274] ;

[0275] .

[0276] Example 2

[0277] The cascade hydro-wind-solar-storage power generation system considered in this embodiment consists of a cascade hydropower station, a wind power station, a photovoltaic power station, and a pumped storage power station. A schematic diagram of the topology is shown below. Figure 1 As shown. The pumped storage unit consists of a turbine, an electric generator, and a full-power converter, and does not share the reservoir with the cascade hydropower stations. The control center collects real-time data on the output power of wind and solar power, processes the operating data of the hydro-wind-solar-storage system, and sends control commands to the cascade hydropower stations, pumped storage stations, wind power stations, and solar power stations.

[0278] The proposed solution framework for the source-load coordination distributed bar extended programming model considering multi-energy complementarity and DC transmission in this embodiment is as follows: Figure 2 As shown, the constructed source-load coordination two-stage robust optimization model is a robust optimization problem with a minimization-maximization-minimization three-level structure. Using a list and constraint generation algorithm, the model is transformed into a mixed-integer linear programming master problem and subproblems, until the difference between the optimal values ​​of the master problem and the subproblems is less than a certain acceptable convergence accuracy, i.e., a threshold. Iteration complete.

[0279] This embodiment references the geographical distribution and output data of a clean energy transmission base in a certain region, specifically a hydropower, wind power, solar power, and energy storage facility. It modifies the commonly used standard IEEE 24-node system. The test system includes 26 traditional thermal power units, 38 transmission lines, and 17 power loads. Three wind farms are located at nodes 1, 2, and 22; two photovoltaic power plants are located at nodes 16 and 22; and three hydropower plants are located at nodes 1, 7, and 21, with two of these being cascade hydropower plants. Water inflow is considered based on the normal water season. DC transmission is located at node 22. Additionally, 18 candidate generator units and 19 candidate demand response services are considered. The study has a 5-year extension period. In the first year, the power loads for wind power, photovoltaic power, and electricity generation are 2850MW, 500MW, and 800MW, respectively, with annual growth rates of 3%, 8%, and 8%. The inter-regional DC transmission daytime contract power transmission volume is 5760MWh, with operating power limited to 100MW-500MW. The penalty prices for wind curtailment, solar curtailment, and load shedding are set at 50 yuan / MWh, 100 yuan / MWh, and 1000 yuan / MWh, respectively. The predicted output values ​​for typical daily electricity load, wind power, and solar power base scenarios are as follows: Figure 3 As shown.

[0280] Table 1 in the example lists the planning configuration results of schemes 1-5. The letters G, P, and D correspond to traditional thermal power units, pumped storage power stations, and demand response units, respectively. The subscripts indicate the construction sequence number and year of the candidate equipment (e.g., G1,2 corresponds to unit 1, which was put into construction in the second year). Table 2 lists the comparison of economic indicators for each scheme.

[0281] Table 1 Comparison of Planning Results for Schemes 1 to 5 .

[0282] Table 2 Cost Comparison of Options 1 to 5 .

[0283] In Option 1, seven traditional thermal power units with a total capacity of 569MW will be decommissioned. In the first year, after deducting the full power generation of hydropower, wind and solar power, and the output of existing units, the remaining load of the system will be 808MW. Based on the price-driven mechanism, candidate unit G1, with a maximum output of 176MW, will be constructed. In the second year, the remaining load will be 3820MW, and candidate unit G15, with a maximum output of 197MW, will be constructed. Similarly, candidate unit G3 will be constructed in the third year, and candidate units G6, G11, and G14 will be constructed in the fifth year to meet the load power demand.

[0284] To study the complementary characteristics of hydropower, wind power, and solar power, the output curves of hydropower, wind power, and photovoltaic units in Scheme 1 in the fifth year are as follows: Figure 4 As shown. From Figure 4As can be seen, without sunlight at night, photovoltaic power generation is concentrated between 9:00 AM and 5:00 PM, while wind power output is relatively high at night. Therefore, photovoltaic power plants and wind power generation have a certain degree of complementarity during the day. Furthermore, the inflow of water directly affects the output of cascade hydropower stations, and the inflow varies significantly between the wet and dry seasons. To further study the peak-shaving characteristics of hydropower stations, Figure 4 A comparison curve of hydropower station output during the wet and dry seasons was plotted. It can be seen that during the wet season, the inflow to hydropower stations is relatively abundant, resulting in a relatively stable and high output. The output curve shows a distinct concave characteristic when wind power output is high in the early morning and solar power output reaches its peak at noon, reducing wind and solar curtailment. During the dry season, the inflow to hydropower stations is reduced, and hydropower stations participate more in system peak shaving. From 17:00 to 22:00, solar power stations have no output at night, and load demand is high, allowing hydropower stations to maintain a high power output while significantly reducing the burden on thermal power units.

[0285] In Option 2, the construction strategy for the first year remains the same: constructing unit G1. In the second year, after deducting clean energy and the output of existing units, the system experiences a load deficit of 3760MW. Based on cost-benefit analysis, a traditional thermal power unit G14 with a maximum output of 197MW is selected. In the third year, pumped storage unit P1 is constructed to ensure power supply capacity. Compared to Option 1, which postpones unit G3 to the fourth year and simultaneously constructs unit G15, and prioritizes the construction of the economical unit G16 in the fifth year, avoiding the construction of high-cost units G6 and G11, Option 2 prioritizes pumped storage units, resulting in a total cost reduction of 9.5 million yuan compared to Option 1, thus improving the system's economic efficiency.

[0286] To further study the impact of pumped storage on the absorption of new energy sources, the pumping and power generation capacity of the pumped storage power station in the fifth year of Scheme 2 was obtained, such as... Figure 5 As shown in the diagram, during the period from 1:00 to 7:00, which is a low-load period, wind power output is relatively high. To reduce wind curtailment, pumped storage power stations pump water to store electrical energy. During the period from 9:00 to 10:00, the load increases rapidly, and wind power generation decreases. Although there is some photovoltaic output, it is insufficient to meet the increased load demand, so the pumped storage units generate electricity to meet the load demand. During the period from 11:00 to 14:00, photovoltaic power generation reaches its peak. To reduce solar curtailment, the pumped storage power stations start the pumped units to store electrical energy. During the period from 17:00 to 22:00, which is a peak load period, the pumped storage units assist the thermal power units in peak shaving, releasing the stored electrical energy to meet the load demand.

[0287] Option 3, building upon Option 2, introduces a load demand response (RTR) device plan, reducing the total cost by 5.93 million yuan through source-load coordination and interactive planning. Considering the load demand response service, the RRT device allows adjustable loads to actively participate in optimizing supply and demand matching. In the first year, unit G1 and RRT device D3 are commissioned, meaning one node load can be transferred through demand response. In the second year, RRT device D15 is commissioned, postponing the originally planned unit G14 to the fourth year, with device D10 being commissioned simultaneously. In the third year, unit G3 and device D17 are commissioned, postponing the originally planned pumped storage unit P1 to the fifth year. In the fifth year, unit G11 and device D13 are commissioned, reducing the commissioning of units G15 and G16 and lowering costs.

[0288] To further investigate the peak-valley regulation of load-side demand response and its impact on power planning decisions, the net load and load power transfer curves before and after participating in demand response are shown below. Figure 6 As shown in the figure, during the peak load period in the fifth year, approximately 130MW of load could be shifted to the off-peak period. Therefore, it is evident that the net load curve after demand response shows an increase during the off-peak period and a decrease during the peak period. The implementation of demand response devices smooths the load curve, making the power system more flexible and avoiding the construction of expensive generating units, thus demonstrating excellent economic efficiency.

[0289] Based on Scheme 3, Scheme 4 considers the optimization of inter-regional power transmission dispatch. Compared with Scheme 3, it reduces the construction of unit G11, resulting in a total cost reduction of 8.35 million yuan. The results show that optimizing DC power transmission can significantly improve the economic efficiency of the system and effectively tap the potential for synergistic optimization between DC transmission and sending-end power generation equipment. While maintaining a daily transmission volume of 5760 MWh, precise control of DC power transmission was achieved by comprehensively considering power source characteristics, load demand, and hydropower surplus. The DC power transmission and load transfer curves before and after optimization are shown below. Figure 7 As shown in Scheme 4, the DC power transmission capacity is adjusted twice a day. From 1:00 to 11:00, the DC power transmission capacity remains at a lower level of 260MW. From 12:00 to 15:00, due to higher photovoltaic output and lower load demand, the DC power transmission capacity can be stably maintained at the highest level of 500MW. From 16:00 to 24:00, the DC power transmission capacity remains consistently and stably at a lower level of 100MW. Compared to the fixed DC power transmission in Scheme 3, the optimized DC power transmission significantly reduces load transfer power, indicating that the optimized DC power transmission effectively reduces reliance on load transfer. Especially during peak electricity consumption periods, by optimizing the frequency of DC power transmission adjustments, it better adapts to the real-time needs of the grid, providing more power support and reducing the need to balance supply and demand through load transfer.

[0290] Furthermore, to study the impact of the number of DC adjustment cycles on system planning, the number of DC adjustment cycles was optimized based on Scheme 4. Figure 8 The relationship between the number of DC adjustment cycles and the total cost is demonstrated. The results show that increasing the number of DC adjustment cycles leads to a downward trend in the total system cost curve. In the initial stage, the total cost decreases significantly; however, after the number of adjustments reaches a threshold, the total cost gradually converges. Simulation results show that when the total number of adjustments is set to 8, the total system cost is minimized. A reasonable adjustment frequency setting can improve the flexibility of the generation side to some extent; however, excessive adjustment will adversely affect the stable operation of the load side. Specifically, excessive adjustment will accelerate the aging process of power electronic devices, thereby increasing the risk of system failure. Therefore, in actual operation, it is necessary to strictly limit the number of adjustments to the DC power transmission and select an appropriate number of DC adjustment cycles.

[0291] In Scheme 5, the planned construction of equipment has changed to better adapt to the fluctuations caused by the uncertainties of wind and solar power. Compared with Scheme 4, the construction strategy for units G1, G3, and G14 has been advanced by one year, with the addition of units G6 and G15 in the fifth year. The number of pumped storage stations and load demand response devices remains unchanged. Due to the addition of two candidate units, the construction cost has increased by RMB 9.57 million. By introducing distributed robust optimization and adjusting the operation plan of the power generation equipment, the operating cost has been significantly reduced. However, due to the uncertainty factors considered, the total cost has increased by RMB 6.67 million compared with Scheme 4, sacrificing some economic efficiency to ensure the safe operation of the power system and the effective consumption of new energy sources such as wind and solar power. To verify the scientificity and effectiveness of the proposed distributed robust optimization method, tests were conducted by adjusting the values ​​of the confidence level and the number of sample scenarios. The corresponding results are shown in Table 3 and... Figure 9 As shown.

[0292] Table 3. Penalty costs of the DRO method at different confidence levels. .

[0293] As shown in Table 3, the allowable deviation range of the distributed robust method increases with the increase of the confidence level, allowing it to cover more uncertainty probability distributions. This makes it more effective in finding extreme cases, but also leads to an increase in penalty costs.

[0294] Depend on Figure 9 It is evident that as the scale of historical data increases, the allowable range of deviation decreases, the uncertainty set of the probability distribution becomes closer to the actual distribution, the total planning cost and penalty cost decrease, and the conservatism of the decision-making decreases. If the scale of historical data is too small, the solution process becomes overly conservative, and the penalty cost is too high. If the scale of historical data is too large, extreme cases will be ignored, reducing the model's adaptability to uncertainty and increasing the computational burden. Therefore, a suitable trade-off should be found by comprehensively considering the robustness and conservatism of the model.

[0295] Scheme 5 was solved by using stochastic optimization, robust optimization and the sub-robust optimization method proposed in this invention, and the planning results are shown in Table 4.

[0296] Table 4 Calculation results obtained by different optimization methods .

[0297] Analysis of the planning results obtained by the three methods reveals differences in both construction schemes and operating costs, with the total cost of decomposed robust optimization falling between that of stochastic optimization and robust optimization. Decomposed robust optimization demonstrates better balance and economy, while also offering greater flexibility. To examine the out-of-sample performance of the results from the three methods, Monte Carlo simulations were used to generate scenario sets of 500, 1000, and 2000 scenarios for out-of-sample robustness testing. For each method, under a fixed planning strategy, the corresponding average expected penalty cost and robustness ratio were calculated using out-of-sample scenarios, and the results are shown in Table 5.

[0298] Table 5. Out-of-sample performance of the optimization method .

[0299] In Table 5, MC represents the average penalty cost (107 yuan); RR represents the robustness ratio, which is calculated as (1 - number of penalty cost scenarios / total number of scenarios) * 100%.

[0300] As shown in Table 5, the average penalty cost decreased as the number of scenes increased from 500 to 2000. Random optimization had the highest average penalty cost, robust optimization had the lowest, and the average penalty cost of split-blob optimization fell in between. Meanwhile, the robustness ratio increased for all three methods. The robustness of all three methods improved with the increase in the number of scenes, but the overall robustness of random optimization remained relatively low. Robust optimization exhibited the best robustness, while split-blob optimization's robustness fell in between. In summary, the split-blob optimization method proposed in this embodiment demonstrates a good balance between average penalty cost and robustness ratio, and its overall performance further improves with the increase in the number of scenes.

[0301] This embodiment addresses the absorption problem caused by large-scale wind and solar power integration by proposing a source-load coordination planning model that considers the complementarity of multiple energy sources, including hydropower, wind power, solar power, hydropower storage, and DC power transmission. By rationally configuring the coordinated operation of pumped storage power stations and cascade hydropower stations, flexible regulation is achieved. Simultaneously, an optimization model for load-side demand response and DC power transmission is established, effectively reducing the long-term planning cost of the system. Considering the volatility of wind and solar power output, a comprehensive probability distribution uncertainty set is constructed, leading to the establishment of a two-stage source-load coordination sub-Bruker planning model based on multiple discrete scenarios, which is then solved using a list-constraint generation algorithm.

[0302] The following conclusions can be drawn:

[0303] (1) By configuring pumped storage power stations and cascade hydropower to work together to smooth out the fluctuations in wind and solar power output, not only is the peak-shaving capacity of the power system enhanced, but also the optimal allocation of water, wind, solar and storage resources is achieved, the level of renewable energy consumption is improved, and the planning cost of the system is reduced.

[0304] (2) The configuration of load demand response devices enables load resources to participate in the dynamic regulation of the power grid, effectively reducing the demand for expansion of conventional units and optimizing the overall investment cost of the system. At the same time, by optimizing the DC power transmission, the flexible adjustment margin of the transmission channel is released, achieving power balance support and significantly improving the level of new energy consumption and economic benefits.

[0305] (3) The distributed robust optimization method is used to deal with the uncertainty of wind and solar power generation, which is closer to the actual operation. This method can make full use of the historical data of wind and solar power generation for planning decisions. The resulting source-load planning scheme has the advantages of both stochastic optimization and robust optimization, and achieves a good balance between economy and safety.

[0306] As can be seen from the above embodiments and accompanying drawings, the technical solution provided by the present invention can fully utilize the dynamic regulation capabilities of hydropower stations and pumped storage power stations, and enhance the benefits of multi-energy complementarity; it can also improve the flexibility of power transmission by optimizing the power of DC transmission lines.

[0307] Furthermore, a two-stage source-load coordination robust planning model based on multiple discrete scenarios is proposed. In the first stage, the total system cost is optimized based on the basic scenario. In the second stage, based on historical wind and solar power output data, a probability distribution uncertainty set of 1-norm constraints and ∞-norm constraints is constructed to minimize the expected operating cost under the worst scenario. By making full use of historical wind and solar power generation data for planning decisions, the resulting source-load planning scheme has the advantages of both stochastic optimization and robust optimization, achieving a good balance between economy and safety.

Claims

1. A source-load coordination planning method considering multi-energy complementarity and DC power transmission, characterized in that, Includes the following steps: A source-load coordinated extended planning model is constructed with the goal of minimizing the total cost of the power system. The confidence set is introduced into the source-load coordination extended planning model, and the constraints of the confidence set are determined. Based on the source-load coordination extended planning model with confidence set, a two-stage source-load coordination sub-Brow bar planning model is established. The constrained upload algorithm is used to solve the source load coordination sub-Bruker planning model and generate a planning scheme.

2. The source-load coordination planning method considering multi-energy complementarity and DC power transmission as described in claim 1, characterized in that, The construction of the source-load coordination expansion planning model specifically includes: Construct the objective function ; The constraints are constructed, including constraints on the construction of power equipment, constraints on the operation of the power system, constraints on the operation of thermal power units, constraints on load reduction and the adjustability of wind and solar power output, constraints on hydropower stations, constraints on pumped storage power stations, constraints on load demand response and DC transmission.

3. The source-load coordination planning method considering multi-energy complementarity and DC power transmission as described in claim 2, characterized in that, The construction objective function Specifically, it includes: The formula is as follows: ： ； ； ； ； In the formula, For the cost of power facility construction, For the operating costs of thermal power units, , and These are the unit penalty costs for load reduction, wind power reduction, and solar power reduction, respectively. , and These are respectively the load reduction, wind power reduction, and solar power reduction. , , , and These are indexes for thermal power units, pumped storage power stations, load demand response devices, photovoltaic units, and wind turbine units, respectively. , and These are collections of candidate construction schemes for planned thermal power units, hydropower stations, and load demand response equipment. , and These are the construction costs for candidate equipment for thermal power units, pumped storage power stations, and load demand response, respectively. For years, for Annual market capitalization coefficient The discount rate; , and for The construction status of candidate equipment for annual thermal power units, pumped storage power stations and load demand response is 1 if the candidate equipment is in operation, and 0 otherwise. for Typical days of the year The planned number of days, For the fuel cost of thermal power units, This is the heat consumption curve of a thermal power unit. for Typical days of the year The output power of the thermal power unit, , , , and They are respectively Typical days of the year Load can be reduced, photovoltaic forecast, wind turbine forecast, photovoltaic output, and wind turbine output power.

4. The source-load coordination planning method considering multi-energy complementarity and DC transmission as described in claim 3, characterized in that, The construction constraints specifically include: (1) Constraints on the construction of power equipment, the formula is as follows: ； ； ； ； In the formula, , and for Construction status of thermal power units, pumped storage power stations, and candidate equipment for load demand response in recent years. It is a collection of existing thermal power units; for The annual operating status of thermal power units is 1 if the thermal power unit is in operation, and 0 otherwise. This refers to the retirement age of thermal power units; (2) Power system operating constraints, the formula is as follows: ； ； ； ； In the formula, , and These are indices for hydropower stations, DC transmission nodes, and busbars, respectively. For the busbar A collection of connected devices for Typical days of the year hydroelectric power station 'output power' for Typical days of the year Load demand response device The load power, The load power of the planned load, for Typical days of the year DC transmission node power, and They are respectively Typical days of the year Pumped storage power station Pumped storage capacity and power generation capacity, for Typical days of the year transmission lines The trend For power transmission lines Reactance, For power transmission lines The sending-end busbar, For power transmission lines The receiving-end busbar, for Typical days of the year Phase angle of the sending busbar for Typical days of the year Phase angle of the sending busbar A collection of busbars in a power system. For thermal power units Maximum power generation capacity and busbar The minimum and maximum phase angles, for Typical days of the year busbar The phase angle; (3) Operating constraints of thermal power units, the formula is as follows: ； ； ； In the formula, For thermal power units Maximum power generation capacity for Typical days of the year thermal power units Power generation capacity, The climbing power limit for thermal power units. (4) Load reduction and wind and solar power output adjustability constraints, the formula is as follows: ； ； ； In the formula, for Typical days of the year Load demand response device The maximum power outage load ratio, for Typical days of the year Load demand response device The load demand, for Typical days of the year wind turbine The predicted output value, for Typical days of the year Photovoltaic units The predicted output value; (5) Constraints of hydropower stations, the formula is as follows: ； ； ； ； ； ； ； ； In the formula, It is the acceleration due to gravity. For hydroelectric power station Conversion efficiency, and They are respectively Typical days of the year hydroelectric power station The conversion flow rate and conversion head, for Annual Hydropower Station The conversion head, For hydroelectric power station The initial head, For hydroelectric power station head coefficient, for Annual Hydropower Station Storage capacity, and For hydroelectric power station Minimum and maximum power generation For hydroelectric power station The ramp power limit, for Typical days of the year hydroelectric power station Total power generation and For hydroelectric power station Minimum and maximum values ​​of conversion traffic. and For hydroelectric power station Minimum and maximum storage capacity for Typical days of the year hydroelectric power station Storage capacity, and For the hydropower station during dispatch The initial and final values ​​of the storage capacity. and All are constants. for Typical days of the year hydroelectric power station Storage capacity, for Typical days of the year hydroelectric power station Natural runoff, For hydroelectric power station Water flow time lag, for Typical days of the year Upstream hydropower station Inbound traffic; (6) Constraints of pumped storage power stations, the formula is as follows: ； ； ； ； ； ； ； ； ； In the formula, and for Typical days of the year Pumped storage power station The number of medium-duty pumped-storage units in both pumping and power generation modes. and for Typical days of the year Pumped storage power station The number of medium-duty pumped-storage units in both pumping and power generation modes. and for Typical days of the year Pumped storage power station The number of medium-duty pumped storage units that are in the start-up and shutdown states. and for Typical days of the year Pumped storage power station The number of medium-duty pumped storage units that are in the start-up and shutdown states. This refers to the number of times a single generating unit is allowed to start and stop within a scheduling cycle. Pumped storage power station The number of medium-duty pumped storage units; and for Typical days of the year Pumped storage power station The working status, if Then pumped storage power station If it is in pumping mode, Then pumped storage power station It is in power generation mode; for Typical days of the year Pumped storage power station The switch state, if Then pumped storage power station It is in working condition; otherwise, it is in off condition. Represents an infinite positive number. and Pumped storage power station Minimum and maximum pumping power, Pumped storage power station exist Typical days of the year Pumping power, and Pumped storage power station Minimum and maximum power generation. for Typical days of the year Pumped storage power station Power generation capacity, and for Typical days of the year Pumped storage power station The capacity of the upper and lower reservoirs, and for Typical days of the year Pumped storage power station The capacity of the upper and lower reservoirs, and Pumped storage power station Power conversion factor between pumping and power generation; (7) Load demand response, the formula is as follows: ； ； ； ； ； ； ； ； In the formula, , for Typical days of the year Load demand response device The predicted electrical load value, and the demand-side electrical load output value after considering demand response. , and for Typical days of the year Load demand response device The electrical load values, interruptible electrical load values, and transferable electrical load values ​​involved in demand response. for Typical days of the year Load demand response device Maximum permissible electrical load for Typical days of the year Load demand response device The proportion of interruptible electrical loads, The maximum permissible interruptible electrical load. for Typical days of the year Load demand response device The proportion of transferable electrical load, for Typical days of the year Load demand response device The proportion of electrical load participating in demand response; (8) DC power transmission constraint, the formula is as follows: ； ； ； ； ； In the formula, for Typical days of the year node DC power transmission and For nodes Minimum and maximum values ​​of DC power transmission. for Typical days of the year node DC power transmission , For upward DC transmission ramp limits and downward DC transmission ramp limits, , express Typical days of the year node The DC power transmission capacity is in a state of downward adjustment or upward adjustment. , express Typical days of the year node The DC power transmission capacity is in a state of downward adjustment or upward adjustment. , for Annual Node Maximum number of times DC power transmission can be increased or decreased. for Annual Node Contracted daily DC power transmission volume.

5. The source-load coordination planning method considering multi-energy complementarity and DC power transmission as described in claim 4, characterized in that, The process of introducing the confidence set into the source-load coordination extended planning model and determining the constraints of the confidence set specifically includes: The source-load coordination extended planning model is converted into a matrix, as shown in the following formula: ； In the formula, Represents 0-1 variables, This represents the variable being executed, i.e., the variable other than those between 0 and 1. and This is the correlation parameter matrix of the binary decision variables corresponding to the 0-1 variables. , , , and The combination of coefficient matrices for the running variables; It is the transpose symbol; The confidence set is introduced into the source-load coordination extended programming model, as shown in the following formula: ； ； In the formula, and The confidence intervals for the 1-norm and ∞-norm constraints are... To guide the scene, The total number of scenes, For a set of vectors, A positive real number vector For the scene initial probability value The corresponding probability vector, The maximum probability deviation corresponding to the 1-norm. The maximum probability deviation corresponding to the ∞-norm; Determine the set of vectors The required confidence level is calculated using the following formula: ； ； ； ； In the formula, The confidence level of the probability bias corresponding to the 1-norm constraint. The confidence level of the probability bias corresponding to the ∞-norm constraint. The number of typical scenarios, The amount of historical data; Using the comprehensive norm The constraints are applied, as shown in the following formula: ； The equivalent absolute value constraints corresponding to the 1-norm and 0-norm constraints are constructed as follows: ； ； In the formula, and Probability vector For probability vectors The positive and negative offset states; among them, ； ； In the formula, and Positive offset state and negative offset state The logo.

6. The source-load coordination planning method considering multi-energy complementarity and DC power transmission as described in claim 5, characterized in that, The source-load coordination distributed bar programming model specifically includes: Phase 1: Minimize the total cost of the basic power system scenario; The second stage: Based on historical data of wind and solar power output, construct probability distribution uncertainty sets with 1-norm and 0-norm constraints, and minimize the expected operating cost of the worst probability distribution; The formula is as follows: ； ； ； In the formula, 0-1 variables The set, For runtime variables The set, , and For the combination of coefficient matrices of the running variables, For the scene The state variables under.

7. The source-load coordination planning method considering multi-energy complementarity and DC transmission as described in claim 6, characterized in that, The method of using the constrained upload algorithm to solve the source-load coordination distributed bar programming model and generate a planning scheme specifically includes: Set the upper bound of the source-load coordination distributed bar programming model. lower bound value and number of iterations ; For the source-load coordination sub-Bruker programming model, the column-constrained upload algorithm is used to solve the main problem, divide it into sub-problems, and set constraints for the main problem and sub-problems; The main problem is to perform source-load coordination planning under a known worst-case probability distribution to obtain the optimal source-load planning scheme; the sub-problem is to divide the worst-case probability distribution of wind and solar power output. Solving the main problem yields the optimal source-load planning scheme under the known worst-case probability distribution, and the lower bound is updated. ; Solve the subproblem to obtain the worst-case probability distribution of wind and solar power output, and update the upper bound value. ; Calculate the upper bound value and lower bound value The difference; if the difference is not less than the threshold Then update the iteration count. And add new runtime variables. and corresponding constraints; Otherwise, output the iteration results to obtain the optimal source load planning scheme and the corresponding worst probability distribution.

8. The source-load coordination planning method considering multi-energy complementarity and DC transmission as described in claim 7, characterized in that, The main problem is: ； The constraints of the main problem are: ； ； ； In the formula, For operating costs, For the first The planning scheme obtained in the next iteration This is the iteration count value.

9. The source-load coordination planning method considering multi-energy complementarity and DC power transmission as described in claim 7, characterized in that, The sub-problem is: ； The constraints of the subproblems are: ； In the formula, For the first The planning scheme obtained in the next iteration.

10. The source-load coordination planning method considering multi-energy complementarity and DC transmission as described in claim 7, characterized in that, The sub-problem is: ； The constraints of the subproblems are: ； In the formula, For the first The planning scheme obtained in the next iteration.

Images