Power supply precise planning method under extreme weather photovoltaic output limited condition

Abstract

The invention discloses a power supply accurate planning method under the condition of limited photovoltaic output in extreme weather, relates to the technical field of power grid planning, and aims to provide a power supply accurate planning method under the condition of limited photovoltaic output in extreme weather through an extreme scene with limited photovoltaic output in extreme weather by taking a year as a unit by fully utilizing the existing controllable adjustment means on the basis of ensuring long-period supply and demand balance. A finer and more realistic model is constructed, the annual actual operation condition is fully reflected, the accuracy of the power supply planning result is improved, the annual value cost and the annual operation cost are obviously reduced, the annual value power supply investment cost proportion is reduced by more than 15%, and the power supply planning capacity is reduced by considering the comprehensive demand response. Power supply and demand balance under the condition that photovoltaic output is limited in extreme weather is met, economical efficiency and operation reliability of power grid planning are improved, and the method is suitable for large-scale application and popularization in a power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid planning, in particular to a power source accurate planning method under the condition of extreme weather photovoltaic output limitation, which is applied to a new type of power system with high proportion of distributed photovoltaic access. BACKGROUND

[0002] Extreme weather, such as sandstorm, rainstorm, snowstorm, persistent high temperature or cold wave, etc., will cause the output limitation of new energy, hydropower and other power sources, and serious imbalance of power supply and demand, thereby affecting the reliability of power supply. However, the current power source planning research only considers the typical day and continuous multi-day no light extreme weather scene, although it can meet the power supply and demand balance under the extreme weather scene, it lacks to consider the typical scene considering the extreme weather photovoltaic output limitation in units of years, and cannot reflect the actual operation situation throughout the year, thereby affecting the accuracy of power grid planning, and in addition, the comprehensive demand response is also lacked in the planning process, resulting in the increase of power source investment cost. SUMMARY

[0003] In order to solve the above technical problems, the present application provides a power source accurate planning method under the condition of extreme weather photovoltaic output limitation, which improves the accuracy of power source planning results and reduces the power system power source investment cost.

[0004] In order to achieve the above purpose, the technical scheme adopted by the present application is: A power source accurate planning method under the condition of extreme weather photovoltaic output limitation, which is realized by the following steps: Step S1: input power grid parameters and historical data, i.e. line parameters, load and photovoltaic output data in recent years, installed capacity of existing thermal power, hydropower, photovoltaic, wind power, etc., upper and lower limit values of reducible load and transferable load, and parameters of to-be-selected line type; Step S2: define extreme weather events, when the photovoltaic output in a typical year is less than times of the normal value of historical photovoltaic output at the same period, it is considered to enter the low output period, and when the continuous low output duration exceeds ( ), it is considered that an extreme weather event of continuous low output has occurred; Step S3: obtain extreme scenarios, based on the photovoltaic power generation historical output data in recent years in a certain regional power grid, obtain the historical data of the year with the longest duration of photovoltaic low output extreme weather event as the extreme scenario; Step S4: Establish a power planning mathematical model that takes into account the comprehensive demand-side response under extreme scenarios. The goal is to minimize the sum of power planning cost, line modification and upgrade cost, and system operation cost under extreme scenarios. By combining power flow equation constraints, branch constraints, unit output upper and lower limit constraints, unit ramp rate constraints, unit minimum start-up and shutdown time constraints, demand response constraints, and candidate line model parameters, a mixed integer nonlinear programming model is obtained. Step S5: Initialize particle swarm optimization algorithm parameters; Step S6: Solve for the power supply capacity using the particle swarm optimization algorithm; Step S7: Determine if there is a branch with excessive power. If there is, proceed to step S8; otherwise, proceed to end step S9. Step S8: Upgrade the over-limit line model, upgrade all over-limit branches to the next model, and proceed to step S6; Step S9: Output the planning results. Output the sum of the power planning cost, line modification and upgrade cost, and system operation cost that minimizes the objective function, the planned power supply capacity, and the number and model of the modified line.

[0005] Step S6 includes the following steps: Step S6.1: Calculate the current particle planning cost; Step S6.2: Update the local and global optimal solutions; Step S6.3: Update particle positions; Step S6.4 determines whether the number of iterations has been reached. If not, proceed to step S6.1; otherwise, proceed to step S7.

[0006] In step S4, the power flow equation can be either the DC power flow equation or the Newtonian power flow equation.

[0007] In step S4, the branch constraint conditions can be power inequality constraints or current inequality constraints.

[0008] Step S6 is solved using a genetic algorithm.

[0009] The power supply precision planning method of this invention, while ensuring long-term supply and demand balance, makes full use of existing controllable adjustment means. By constructing a more refined and realistic model based on extreme scenarios of limited photovoltaic output under extreme weather conditions on an annual (8760-hour) basis, it fully reflects the actual operating conditions throughout the year, improves the accuracy of power supply planning results, significantly reduces equivalent annual costs and annual operating costs, reduces equivalent annual power supply investment costs by more than 15%, meets the supply and demand balance under extreme weather conditions of limited photovoltaic output, improves the economy and operational reliability of power grid planning, and is suitable for large-scale application in power systems. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a power grid system circuit diagram in an embodiment of the present invention; Figure 3 This is a load curve diagram in an embodiment of the present invention; Figure 4 This is a photovoltaic power generation curve diagram in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments: like Figure 1 As shown, the method for precise power planning under extreme weather conditions with limited photovoltaic output is achieved through the following steps: Step S1: Input grid parameters and historical data, namely line parameters, load and photovoltaic output data in recent years, existing installed capacity of thermal power, hydropower, photovoltaic, wind power, etc., upper and lower limits of load that can be reduced and transferred, and parameters of the line model to be selected.

[0012] Step S2: Define extreme weather events, when photovoltaics... Time output Lower than the historical normal value for photovoltaic power output during the same period of ( When the output is increased by ) times, it is considered to enter a low-output period. When the duration of continuous low output exceeds 100%, it is considered to enter a low-output period. If this occurs, it is considered that a series of extreme weather events with low power output has taken place.

[0013] The criterion for extreme weather events with sustained low photovoltaic power output is defined as shown in equation (1): (1) In the formula, This marks the start of low photovoltaic power output. The moment when photovoltaic power output is low and terminated.

[0014] Step S3: Based on the historical output data of photovoltaic power generation in a certain regional power grid in recent years, according to formula (1), obtain the historical data of the year with the longest duration of extreme weather events with low photovoltaic output as the extreme scenario.

[0015] Step S4: Establish a mathematical model for power planning that takes into account the comprehensive demand-side response under extreme scenarios. The goal is to minimize the sum of power planning costs, line modification and upgrade costs, and system operating costs under extreme scenarios, as shown in Equation (2): (2) In the formula, The optimal total cost for the system; This refers to the annual investment cost of system power supplies, etc. This refers to the annual investment cost for line renovation and upgrades; This is the system's annual operating cost; It is the discount rate; It refers to the planned service life of the power supply; This is the unit investment cost of thermal power units, in RMB 10,000 / MW; This indicates that the thermal power unit is at the grid node. The planned increase in installed capacity; It is the set of all nodes in the system; This indicates the unit investment cost of renovating transmission lines, expressed in yuan / km; To extend the length of the power transmission line; A collection of power transmission lines to be renovated; and These are the unit operating cost and start-up cost of thermal power units, respectively. It is a power grid node During the period of the thermal power unit ; output power; It is a power grid node During the period of the thermal power unit The start / stop status is indicated by 0 for shutdown and 1 for startup. It is the compensation cost per unit of load that can be reduced; It is a node Load can be reduced during certain periods. The amount of reduction; It is the compensation cost per unit of transferable load; It is a node Transferable load during time period The amount of transfer.

[0016] The power flow equations are constrained, and the DC power flow equations are as follows (3): (3) In the formula, For nodes At any moment Injected power; , , , They are nodes Thermal power units, hydropower plants, wind farms, and photovoltaic power stations are constantly contribution; For nodes At any moment The load power; The injection volume for transferable loads; It is the number of branches; For the line susceptance; For nodes At any moment The phase angle.

[0017] The branch inequality constraint condition adopts the power inequality constraint as shown in equation (4): (4) In the formula, for Time Node Flow to Node The active power; Branch roads The upper and lower limits of the active power that can flow through.

[0018] The upper and lower limits of the unit output are constrained by the following formula (5): (5) In the formula, , They are nodes The minimum and maximum output power of the thermal power unit; , They are nodes The minimum and maximum output power of the newly added thermal power units; , They are nodes The minimum and maximum output power of the hydroelectric generator unit.

[0019] ④ Unit ramp rate constraint conditions (6) In the formula, For nodes Adjusting the ramp rate of thermal power units; For nodes Adjust the uphill / downhill climbing rate of the hydroelectric generator unit.

[0020] The minimum start-up and shutdown time constraints for the unit are shown in equation (7) below: (7) In the formula, They are nodes The initial start-up and shutdown times of the unit; Represents a node Minimum start / stop time for the unit.

[0021] Demand response constraints: The integrated demand response of this invention includes reduceable load and transferable load. By regulating some or all of the reduceable load and transferable load, the power imbalance under extreme weather conditions can be reduced, the planned power supply capacity can be reduced, and the investment cost can be reduced. The reduceable load and transferable load must satisfy equation (8): (8) In the formula, They are nodes exist Maximum load shedding value at any given time; They are nodes Load at The maximum value of injection and transfer at any given time.

[0022] The parameters of the candidate line models are determined according to the parameters of the line models to be improved in Table 1 below.

[0023] Table 1. Line Model Parameter Table.

[0024] Equations (2)-(8) are power configuration models that are mixed integer nonlinear programming models. In order to obtain the global optimal solution, the particle swarm optimization algorithm is used to solve them in this invention.

[0025] Step S5: Initialize particle swarm optimization (PSO) parameters. Set the population size. Particles are vectors ; velocity vector Initial value of local optimal solution and the initial value of the global optimal solution The local optimal solution is a vector. The global optimal solution particle is a vector. Number of iterations Global and local learning factors Inertia weighting coefficient Number of iterations .

[0026] Step S6: Solve for the power supply capacity using the particle swarm optimization algorithm.

[0027] As a preferred approach, step S6 includes the following steps: Step S6.1: Calculate the current particle planning cost, without considering the branch power limit constraint (4), and according to equations (2), (3), (5)-(8), calculate the current particle planning cost. Calculate the planning cost; Step S6.2: Obtain local and global optimal solutions, and obtain the local optimal solution. and the global optimal solution That is, for ,like ,make , ;exist Select the minimum value less than Assign it to At the same time, obtain ; Step S6.3: Update particle position. Update the particle position according to equation (9), that is, update the position of the particle according to equation (9). have (9) Step S6.4: Determine if the iteration count has been reached, let ,like If yes, proceed to step S6.1; otherwise, proceed to step S7.

[0028] Step S7: Determine if there is a branch with excessive power. If there is, proceed to step S8; otherwise, proceed to end step S9. Step S8: Upgrade the over-limit line model. According to Table 1, upgrade all over-limit branches to the next model, and then proceed to step S6. Step S9: Output the planning results. Output the sum of the power planning cost, line modification and upgrade cost, and system operation cost that minimizes the objective function, the planned power supply capacity, and the number and model of the modified line.

[0029] As a preferred approach, in step S4, the power flow equation can be either the DC power flow equation or the Newtonian power flow equation.

[0030] As a preferred approach, in step S4, the branch constraint condition can be a power inequality constraint or a current inequality constraint.

[0031] As a preferred approach, step S6 uses a genetic algorithm to solve the problem.

[0032] The following example, using historical power grid data from a certain region, verifies the superiority and beneficial effects of this invention. The specific power line is as follows: Figure 2 As shown.

[0033] Step S1: Input the power transmission network parameters.

[0034] Table 2 shows the branch parameters: Table 3 shows the generator set data.

[0035] The start-up and shutdown cost for thermal power units is 700 yuan / MW / time, and the unit operating cost for thermal power units is 150 yuan / MWh; the unit operating cost for hydropower units is 70 yuan / MWh, the load transfer cost is 50 yuan / MWh, the load reduction cost is 300 yuan / MWh, and the service life of the generator units is 30 years.

[0036] Step S2: Given , The time was identified as a continuous low-output scenario.

[0037] Step S3: Use historical data from 2024, where the extreme event duration is 240 hours, as the extreme scenario. The load curve and photovoltaic output curve for this region are shown below. Figure 3 and Figure 4 As shown.

[0038] Step S4: Establish a mathematical model for power planning, with a maximum transferable load of 15MW and a maximum shelvable load of 5MW.

[0039] Step S5: The particle swarm optimization (PSO) algorithm parameters are as follows: initial population size 100, number of iterations 40, inertia weight coefficient 0.5, global and local learning factors 0.2 and 0.2 respectively, velocity vector... Local optimal solution Global optimal solution .

[0040] Steps S6-S9: A new power plant is built at node 6. The calculation results of the particle swarm optimization algorithm are as follows: Table 4 compares the planning costs of photovoltaic power generation with limited output.

[0041] As shown in Table 4, the annual cost of the present invention is significantly reduced, by RMB 5.146 million compared with the power planning method that only considers typical days and extreme scenarios, and by RMB 5.247 million compared with the power planning method that does not consider demand-side response. The results show that the power planning method proposed in this invention can effectively reduce the annual cost.

[0042] The annual operating cost of this invention is 308,275,400 yuan, while the annual operating cost of the method considering only typical days and extreme scenarios is 308,356,100 yuan. The numerical results show that the method of this invention uses 8760 hours as the time scale, constructs a more refined and realistic model, more accurately reflects the actual operating conditions throughout the year, and significantly reduces the annual operating cost, which is superior to the power planning method that uses 24 hours as the time scale of a typical day.

[0043] The annual power investment cost of this invention is 23.14 million yuan, which is significantly lower than the 27.48 million yuan of the power planning method that only considers typical days and extreme scenarios and the 28.93 million yuan of the power planning method that does not consider demand-side response. The reduction rates of annual power investment cost are 15.8% and 20%, respectively. This is because the power planning capacity of this invention is 80MW, which is lower than the planning capacities of the latter two, 95MW and 100MW. These numerical results show that the power planning method of this invention, which takes demand-side response into account, can reduce the power planning capacity, reduce power investment cost, improve the economy and operational reliability of grid planning, and has more advantages.

[0044] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for precise power supply planning under extreme weather conditions that limit photovoltaic output, characterized in that, This can be achieved through the following steps: Step S1: Input grid parameters and historical data, namely line parameters, load and photovoltaic output data in recent years, existing installed capacity of thermal power, hydropower, photovoltaic, wind power, etc., upper and lower limits of load that can be reduced and load that can be transferred, and parameters of the line model to be selected; Step S2: Define extreme weather events, which are those in which photovoltaic power output in a typical year is lower than the historical normal value for photovoltaic power output during the same period. When the output is doubled, it is considered to enter a low-output period. When the duration of continuous low output exceeds [a certain value], it is considered to enter a low-output period. ( If the output is low, it is considered that a series of extreme weather events with low power output has occurred. Step S3: Obtain extreme scenarios. Based on the historical output data of photovoltaic power generation in a certain region's power grid in recent years, obtain the historical data of the year with the longest duration of extreme weather events with low photovoltaic output as the extreme scenarios. Step S4: Establish a power planning mathematical model that takes into account the comprehensive demand-side response under extreme scenarios. The goal is to minimize the sum of power planning cost, line modification and upgrade cost, and system operation cost under extreme scenarios. By combining power flow equation constraints, branch constraints, unit output upper and lower limit constraints, unit ramp rate constraints, unit minimum start-up and shutdown time constraints, demand response constraints, and candidate line model parameters, a mixed integer nonlinear programming model is obtained. Step S5: Initialize particle swarm optimization algorithm parameters; Step S6: Solve for the power supply capacity using the particle swarm optimization algorithm; Step S7: Determine if there is a branch with excessive power. If there is, proceed to step S8; otherwise, proceed to end step S9. Step S8: Upgrade the over-limit line model, upgrade all over-limit branches to the next model, and proceed to step S6; Step S9: Output the planning results. Output the sum of the power planning cost, line modification and upgrade cost, and system operation cost that minimizes the objective function, the planned power supply capacity, and the number and model of the modified line.

2. The method for precise power supply planning under extreme weather conditions with limited photovoltaic output, as described in claim 1, is characterized in that, Step S6 includes the following steps: Step S6.1: Calculate the current particle planning cost; Step S6.2: Update the local and global optimal solutions; Step S6.3: Update particle positions; Step S6.4: Determine if the number of iterations has been reached. If not, proceed to step S6.

1. Otherwise, proceed to step S7.

3. The method for precise power supply planning under extreme weather conditions with limited photovoltaic output, as described in claim 1, is characterized in that... In step S4, the power flow equation can be either the DC power flow equation or the Newtonian power flow equation.

4. The energy storage planning method under extreme weather photovoltaic output limitation conditions according to claim 1, characterized in that, In step S4, the branch constraint conditions can be power inequality constraints or current inequality constraints.

5. The method for precise power supply planning under extreme weather conditions with limited photovoltaic output, as described in claim 1, is characterized in that... Step S6 is solved using a genetic algorithm.

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