Wind-solar-heat integrated system configuration optimization method considering toughness

Abstract

The invention discloses a wind-light-heat integrated system configuration optimization method considering toughness, and belongs to the technical field of comprehensive energy planning. Comprising the following steps: step 1, establishing a photovoltaic and wind power generation operation model according to a specific wind-solar-heat integrated system; step 2, selecting load loss depth, load loss time and load loss rate as indexes of system toughness evaluation, and obtaining a comprehensive index through weighting; step 3, establishing a safety model of the wind-light-heat integrated system with the absorption rate as an optimization target; step 4, using a non-dominated sorting genetic algorithm II to perform multi-objective optimization of the toughness and the consumption rate to obtain a Pareto boundary of a toughness index and a consumption rate index; and 5, taking the Pareto boundary obtained in the step 4 as a feasible solution, and obtaining an optimization result by adopting a TOPSIS analytic hierarchy process. According to the invention, the energy consumption rate and toughness of the system are balanced, the operation benefit of the integrated energy system is improved, and the construction of a novel power system is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy planning, and in particular to a configuration optimization method for a wind-solar-thermal integrated system taking resilience into consideration. Background Art

[0002] In recent years, due to the impact of climate change, the development of a clean and low-carbon energy system has become an increasingly severe challenge. Solar and wind energy resources are intermittent and unstable. The uncontrollable nature of photovoltaic and wind power places increasing demands on the grid's regulation capabilities, and even with the rapid growth of renewable energy installed capacity, safety and stability issues will arise. The integrated energy system is gradually forming a new energy supply system with electricity as the main body and other energy sources such as heat as auxiliary.

[0003] Energy system resilience refers to the ability of an energy system to adapt to internal and external disturbances and shocks and to resume normal operation in a timely manner. Research on the resilience of power systems mainly focuses on two key areas: one is the quantitative assessment of resilience, and the other is resilience enhancement strategies. In today's situation where the security of the power system is threatened by the access of a large number of distributed energy sources, the energy forms are more diverse, the network structure is more complex, and the integrated energy system is different from the traditional independently planned and operated distribution network and heat network. When resilience is enhanced, the corresponding new energy absorption rate will decrease. At this time, the system will be more affected by the volatility of new energy, and the safety and reliability will be greatly reduced. Therefore, it is very important to consider the balance between resilience and new energy absorption rate during configuration.

[0004] Chinese patents "CN116384142A Seismic resilience planning method for electric-gas-heat coupling system with multi-energy collaborative recovery", "CN117096943A Resilience optimization method for power system with high proportion of renewable energy based on underground energy storage", and "CN116542552A A low-carbon comprehensive evaluation index method for power grid resilience" are mainly aimed at improving resilience against disasters such as earthquakes, extreme ice and snow, and sudden power grid accidents. Chinese patent "CN117254495A A storage optimization configuration strategy for improving the comprehensive resilience index of distribution network" optimizes the configuration of system energy storage for the purpose of improving the resilience of the power system. In the selection of resilience indicators, "CN116384142A Seismic Resilience Planning Method for Electric-Gas-Heat Coupling System with Multi-Energy Collaborative Recovery" uses the performance loss of each sub-energy system as a resilience evaluation indicator. The Chinese patent "CN118297264A Safety Resilience Evaluation Method for Urban Gas Pipeline Network System Based on System Dynamic Simulation" selects indicators such as line overload rate, distribution transformer overload rate, voltage fluctuation, total harmonic current distortion rate, voltage deviation power supply reliability rate, and load power failure rate to represent resistance, resilience, and adaptability, respectively, and obtains a comprehensive resilience index by weighting. It can be seen that the purpose of the above inventions is relatively single, only to improve resilience or evaluate the resilience of the integrated energy system, without considering other safety perspectives such as comprehensive absorption rate and equipment power. At the same time, the resilience index is not used to optimize the installation of each subsystem of the entire energy system, and the planning is relatively limited. This makes the configuration and construction of large-scale integrated energy systems lack a reference for resilience, and its operational safety and reliability face challenges.

[0005] Therefore, a wind-solar-thermal integrated system configuration optimization method that takes resilience into consideration is needed to solve the problem that there is a lack of resilience reference in the configuration and construction of current large-scale integrated energy systems, and their operational safety and reliability face challenges. Summary of the invention

[0006] The purpose of the present invention is to propose a configuration optimization method for a wind-solar-thermal integrated system considering resilience, comprising:

[0007] Step 1: Establish photovoltaic and wind power generation operation models according to the specific wind-solar-thermal integration system;

[0008] Step 2: Select load loss depth, load loss time, and load loss rate as indicators for evaluating system resilience, and obtain a comprehensive indicator by weighting;

[0009] Step 3: Establish a safety model for the wind-solar-thermal integrated system with the absorption rate as the optimization target;

[0010] Step 4: Use the non-dominated sorting genetic algorithm II to perform multi-objective optimization of toughness and absorption rate, and obtain the Pareto frontier of the toughness index and absorption rate index;

[0011] Step 5: Take the Pareto frontier obtained in step 4 as the feasible solution and use the TOPSIS hierarchical analysis method to obtain the optimization result.

[0012] The wind power generation operation model in step 1 includes:

[0013] Output power of photovoltaic power station:

[0014] P PV =n MOD ·A MOD ·GHI·η PV ·η INV ·f PV

[0015] Where n MOD is the number of photovoltaic panels; A MOD is the effective area of ​​each photovoltaic panel, m 2 ; GHI is global horizontal irradiance, W / m 2 ; η PV is the photovoltaic panel efficiency related to nominal parameters and actual conditions; η INV is the efficiency of the inverter; f PV is the derating factor;

[0016] Wind power output power:

[0017]

[0018] Where P wt,R is the rated power of the wind turbine, W; v in 、v rate 、v out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively; v is the actual wind speed, m / s; a0, a1, a2, a3, a4 are the wind turbine power characteristic coefficients;

[0019] Output power of energy storage battery:

[0020]

[0021] Where P cha , P dis are charging and discharging power, W; Δt is the calculation time interval, s; k sd is the self-discharge rate; η ch , η dis are the charging efficiency and discharging efficiency respectively.

[0022] The calculation formula for the load loss depth in step 2 is as follows:

[0023]

[0024] Where Ppv,i , P wt,i , P pb,i , P ba,i are the output power of photovoltaic panels, wind turbines, solar thermal power generation and batteries at the i-th hour, MW; P load,i is the electricity demand in the ith hour, MW, and Δt is the time interval for calculation;

[0025] The calculation formula of load loss time is as follows:

[0026]

[0027] Among them, I is the indicator function

[0028]

[0029] The calculation formula of load loss rate is as follows:

[0030]

[0031] The calculation formula for the absorption rate in step 3 is as follows:

[0032]

[0033] Where, P Lrs The amount of renewable energy power consumed by the load, MW, P allrs is the total electricity generation from all renewable energy sources, MW.

[0034] The beneficial effects of the present invention are:

[0035] The present invention establishes a multi-objective optimization model for the resilience and new energy consumption rate of the integrated energy system. The resilience is taken into account when configuring the integrated energy system. The load loss depth, load loss time and load loss rate are used as resilience evaluation indicators. The three indicators are synthesized into a comprehensive resilience evaluation indicator using weighted coefficients. The configuration optimization of the integrated energy system when considering resilience and new energy consumption rate is obtained by multi-objective optimization, and the energy consumption rate and resilience of the system are balanced. The optimal configuration, resilience and new energy consumption rate of the energy system are obtained, which improves the operating efficiency of the integrated energy system and promotes the construction of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a flow chart of a configuration optimization method for a wind-solar-thermal integrated system taking toughness into consideration according to the present invention;

[0037] Figure 2 This is a schematic diagram of the wind-solar-thermal integration system;

[0038] Figure 3 Schematic diagram of the Pareto frontier obtained after optimization. DETAILED DESCRIPTION

[0039] The present invention proposes a configuration optimization method for a wind-solar-thermal integrated system taking resilience into consideration. The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0040] Figure 1 The flowchart of the wind-solar-thermal integrated system configuration optimization method considering toughness of the present invention is as follows:

[0041] Step 1: Establish photovoltaic and wind power generation operation models according to the specific system, including:

[0042] Step 101: Establish a solar tower power generation model. Figure 2 As shown in Figure 1, the system consists of a radially staggered heliostat field with a tower in the center and a receiver on top. Molten salt carrying solar thermal energy is stored in a heat tank and then pumped through the superheater, steam generator and preheater to heat feed water / steam. In this way, solar energy is converted into thermal energy carried by superheated steam, which is used to generate electricity through a steam turbine and generator.

[0043] First, calculate the efficiency of the heliostat in collecting solar energy:

[0044] η heli =ρ i ·f cosi ·f sbi ·f ati ·f inti

[0045] In the formula, ρ i 、f cosi 、f sbi 、f ati 、f inti They are the effective reflectivity considering pollution, cosine factor, shading and shielding factor, atmospheric attenuation factor and interception factor.

[0046] Next, calculate the energy reflected to the tower top receiver:

[0047]

[0048] Where N is the number of heliostats; A f is the effective area of ​​the heliostat field, m 2 ; DNI is direct normal irradiance, W / m 2 In the receiver, the transferred solar energy is absorbed by the molten salt as heat energy, and energy is lost in the heat transfer process. The heat energy absorbed by the molten salt is obtained through energy balance:

[0049] Q rec =Q reciver -Q loss,opt -Q loss,rad-Q loss,rad

[0050] In the formula, Q reciver , Q loss,opt , Q loss,rad , Q loss,rad They are respectively the energy reflected to the receiver on the top of the tower, light loss, radiation heat loss, and convection heat loss, and the unit is W.

[0051] Finally, during the heat transfer process between the receiver and the electric heater, the mass flow rate unloaded from the cold tank is adjusted to keep the heat exchanger outlet temperature constant. The mass flow rate during the energy exchange process is calculated based on the mass conservation principle:

[0052]

[0053] Where M h 、M c are the mass of molten salt reserved in the hot tank and the cold tank, kg; m load and m unload are respectively the loading and unloading mass flow rates, kg / s; m eh The mass flow rate of molten salt heated by the electric heater, kg / s; T h is the temperature of the molten salt in the hot tank, °C; T load and T eh is the molten salt temperature from the receiver and the electric heater, °C; c p is the specific heat of molten salt at constant pressure, J / kg / K.

[0054] Step 102: Establish photovoltaic and wind power generation operation models.

[0055] First is the output power of the photovoltaic power station:

[0056] P PV =n MOD ·A MOD ·GHI·η PV ·η INV ·f PV

[0057] Where n MOD is the number of photovoltaic panels; A MOD is the effective area of ​​each photovoltaic panel, m 2 ; GHI is global horizontal irradiance, W / m 2 ; η PV is the photovoltaic panel efficiency related to nominal parameters and actual conditions; η INV is the efficiency of the inverter; f PV is the derating factor; η PV Includes nominal operating battery temperature, nominal efficiency, and actual operating temperature.

[0058] The second is wind power output:

[0059]

[0060] Where P wt,R is the rated power of the wind turbine, W; v in 、v rate 、v out They are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine, which are 3, 12.5 and 25 m / s respectively.

[0061] Finally, the output power of the energy storage battery:

[0062]

[0063] Where P cha , P dis is the charging and discharging power, W; Δt is the calculation time interval, s; k sd is the self-discharge rate; η ch , η dis are the charging efficiency and discharging efficiency respectively.

[0064] Step 2: Consider whether the renewable energy integrated power generation system can independently meet the electricity demand, reflect the vulnerability of the system under specific circumstances, provide basic data for further resilience analysis, select load loss depth, load loss time, and load loss rate as indicators for system resilience evaluation, obtain comprehensive indicators through weighting, and construct a power system resilience assessment model.

[0065] Load loss depth is a measure of the extent to which the power system is unable to meet load demand within a specific period of time. It reflects the total amount of power shortage in the system during the assessment period. The calculation method is as follows:

[0066]

[0067] Where P pv,i , P wt,i , P pb,i , P ba,i are the output power of photovoltaic panels, wind turbines, solar thermal power generation and batteries at the i-th hour, MW; P load,i is the electricity demand in the ith hour, MW, and Δt is the calculation time interval.

[0068] The calculation method of load loss rate is as follows:

[0069]

[0070] Load loss time is usually used to measure the duration of the power system's inability to meet load demand within a certain period of time. The calculation formula is as follows

[0071]

[0072] I is the indicator function:

[0073]

[0074] Standardize the two indicators of load loss depth and load loss time

[0075]

[0076] Constructing a comprehensive index for resilience evaluation

[0077] R=(α1LOLD'+α2LOLP+α3LOLT')×100%

[0078] Among them, α1, α2, and α3 are weighting coefficients.

[0079] Step 3: Considering the new energy consumption rate of the integrated energy system, establish a safety model for the wind, solar and thermal energy system with the new energy consumption rate as the optimization target.

[0080] The new energy consumption rate refers to the ratio of the amount of electricity actually consumed (i.e. utilized) by the power grid system to the total amount of new energy power generation within a certain period of time. It is an important indicator to measure the effective utilization of new energy power and the safety and reliability of the comprehensive energy system. It is usually used to reflect the consumption of new energy power generation projects such as wind power and solar photovoltaic in the power system. The calculation method is as follows:

[0081]

[0082] Where, P Lrs The amount of renewable energy power consumed by the load, MW, P allrs is the total electricity generation from all renewable energy sources, MW.

[0083] Step 4: The non-dominated sorting genetic algorithm II (NSGA-II) was used to solve the multi-objective optimization problem of toughness and absorption rate, and the Pareto frontier of the toughness index and absorption rate index was obtained. The details are as follows:

[0084] First, the population is initialized, the cyclic algebra is set, and non-dominated sorting and crowding calculation are performed; then the population is selected, crossed over and mutated; after the parent and offspring populations are merged, non-dominated sorting and crowding calculation are performed again to generate a new population; finally, the selection, crossover, mutation and fusion of the population are repeated to obtain the Pareto optimization frontier.

[0085] Step 5: According to the Pareto frontier of step 4, the TOPSIS hierarchical analysis method is used to obtain the optimization result. First, the Pareto optimization frontier obtained in step 4 is used as the feasible solution; after standardization, the best and worst ideal solutions and Euclidean distance are calculated; and the optimal solution is obtained after the relative proximity of the solutions is sorted.

[0086] In this embodiment, two specific regions, Delingha and Lhasa, are selected for practice. The operation mode adopts the local consumption operation mode. The goal of this mode is to ensure the resilience and absorption rate of the system. The specific operation strategy is: photovoltaic and wind power generation are used first to meet the demand, followed by supplementary power provided by solar thermal power plants, battery discharge and electric heaters. When the photovoltaic and wind power generation exceeds the demand, the excess power is first stored in the battery. If the battery is full, the electric heater is used to convert the remaining power into thermal energy and store it in the thermal energy storage system to avoid energy waste. The main technical parameters of the initial system are as follows:

[0087] Table 1 Main technical parameters of the system

[0088]

[0089]

[0090] After the parameters are set, the configuration is optimized according to the above steps, and the Pareto frontier of the two regions is obtained as follows: Figure 3 The following table shows the configuration of typical points on the Pareto frontier under the consumption model.

[0091] Table 2 Configuration of typical points on the Pareto frontier under consumption model

[0092]

[0093] The energy configuration of Delingha is indicated by subscript 1, and the energy configuration of Lhasa is indicated by subscript 2. Point O is the optimal configuration obtained by combining TOPSIS hierarchical analysis method.

[0094] It can be seen that point A is the extreme case of low toughness and high absorption rate, point E is the extreme case of low absorption rate and high toughness, and point O is the optimal configuration that balances toughness and absorption rate.

[0095] The present invention makes up for the situation that most current systems do not take solar thermal energy into consideration, and the optimization configuration resilience of the integrated energy system after adding solar thermal energy is insufficiently considered, thereby improving the operating efficiency of the integrated energy system and providing a reference solution for promoting the construction of a new power system.

Claims

1. A configuration optimization method for wind-solar-thermal integrated system considering toughness, characterized in that: include: Step 1: Establish photovoltaic and wind power generation operation models according to the specific wind-solar-thermal integration system; Step 2: Select load loss depth, load loss time, and load loss rate as indicators for evaluating system resilience, and obtain a comprehensive indicator by weighting; Step 3: Establish a safety model for the wind-solar-thermal integrated system with the absorption rate as the optimization target; Step 4: Use the non-dominated sorting genetic algorithm II to perform multi-objective optimization of toughness and absorption rate, and obtain the Pareto frontier of the toughness index and absorption rate index; Step 5: Take the Pareto frontier obtained in step 4 as the feasible solution and use the TOPSIS hierarchical analysis method to obtain the optimization result.

2. The wind-solar-thermal integrated system configuration optimization method considering toughness according to claim 1 is characterized in that: The wind power generation operation model in step 1 includes: Output power of photovoltaic power station: P PV =n MOD ·A MOD ·GHI·h PV ·or INV ·f PV Where n MOD is the number of photovoltaic panels; A MOD is the effective area of ​​each photovoltaic panel, m 2 ; GHI is global horizontal irradiance, W / m 2 ; η PV is the photovoltaic panel efficiency related to nominal parameters and actual conditions; η INV is the efficiency of the inverter; f PV is the derating factor; Wind power output power: Where P wt,R is the rated power of the wind turbine, W; v in 、v rate 、v out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively; v is the actual wind speed, m / s; a0, a1, a2, a3, a4 are the wind turbine power characteristic coefficients; Output power of energy storage battery: Where P cha , P dis are charging and discharging power, W; Δt is the calculation time interval, s; k sd is the self-discharge rate; η ch , η dis are the charging efficiency and discharging efficiency respectively.

3. The wind-solar-thermal integrated system configuration optimization method considering toughness according to claim 1 is characterized in that: The calculation formula of the load loss depth in step 2 is as follows: Where P pv,i , P wt,i , P pb,i , P ba,i are the output power of photovoltaic panels, wind turbines, solar thermal power generation and batteries at the i-th hour, MW; P load,i is the electricity demand in the ith hour, MW, and Δt is the time interval for calculation; The calculation formula of load-loss time is as follows: Among them, I is the indicator function The calculation formula of load loss rate is as follows:

4. The wind-solar-thermal integrated system configuration optimization method considering toughness according to claim 1 is characterized in that: The calculation formula of the absorption rate in step 3 is as follows: Where, P Lrs The amount of renewable energy power consumed by the load, MW, P allrs is the total electricity generation from all renewable energy sources, MW.

Images