CN112541609A - Wind-light-heat and water energy storage combined renewable energy power generation system capacity optimization model - Google Patents

Wind-light-heat and water energy storage combined renewable energy power generation system capacity optimization model Download PDF

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CN112541609A
CN112541609A CN202010643655.6A CN202010643655A CN112541609A CN 112541609 A CN112541609 A CN 112541609A CN 202010643655 A CN202010643655 A CN 202010643655A CN 112541609 A CN112541609 A CN 112541609A
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wind
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power generation
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郭苏
阿依努尔·库尔班
何意
裴焕金
郑堃
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Hohai University HHU
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    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
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    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
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    • Y02E10/76—Power conversion electric or electronic aspects
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The capacity optimization model of the wind-light-heat and water energy storage combined renewable energy power generation system is established by aiming at a wind-light-water mixed renewable energy system containing light-heat (heat storage) and pumped storage, a scheduling strategy is formulated, and the optimization target of minimizing the leveling cost and the load power shortage rate is provided. The capacity optimization result of the wind power-photovoltaic-photothermal-pumped storage hybrid power generation system is compared with a system without an energy storage system, a system with a pumped storage power station for independent energy storage and a system with a photothermal power station for independent energy storage, and the result shows that the hybrid renewable energy power generation system with two energy storage regulation modes of photothermal and pumped storage has better economical efficiency and power supply reliability.

Description

Wind-light-heat and water energy storage combined renewable energy power generation system capacity optimization model
Technical Field
The invention belongs to the field of renewable energy sources, and particularly relates to a wind-light-heat and water energy storage combined renewable energy source power generation system capacity optimization model.
Background
Research on the conventional hybrid renewable energy Power generation system mainly focuses on Wind Turbine (WT) -Photovoltaic (PV) Power generation systems, Photovoltaic-photo-thermal (CSP) Power generation systems, Wind Turbine-Photovoltaic-hydroelectric systems, Wind Turbine-Photovoltaic-energy storage (storage battery) hybrid Power generation systems, and the like. The energy storage system is an important component of a hybrid renewable energy power generation system, and is an effective means for improving the energy utilization rate and stabilizing power fluctuation. Natural resources such as solar energy and wind energy are greatly influenced by weather and seasons, and a large-capacity energy storage device needs to be equipped. Annette et al[12]The energy storage mode of renewable energy is researched, and four main energy storage modes are introduced and compared. The pumped storage investment risk is small, the power generation cost is low, and the technology is mature; the storage battery has high response speed and high energy density, but is expensive; the heat storage system has low cost and large capacity, but the conversion efficiency from heat to electricity is inevitable; the fuel cell has the highest energy density, but the capital cost is also the highest, and the fuel cell is difficult to scale. When the hybrid power generation system is provided with the energy storage system, the optimal energy storage mode can be selected according to the requirements and considering factors such as cost, efficiency and the like. The current research situation of capacity optimization of the hybrid renewable energy power generation system is discussed below according to different energy storage modes.
The storage battery has high energy storage response speed and high energy density, is not limited by natural conditions, and is widely applied to aspects of multi-energy complementary power generation, power grid operation assistance and the like. Orhan Ekran et al introduced a response surface analysis method for capacity optimization of a wind power-photovoltaic combined power generation system with a storage battery as an energy storage unit. The royal bud and the like provide a wind-storage combined power generation system, and the combined power generation system adopts a sodium-sulfur battery with high response speed as an energy storage unit, so that the power fluctuation of a power grid is stabilized, and the economical efficiency of the system is improved. However, the national grid company releases that new storage battery energy storage equipment is forbidden to be built on the grid side so as to avoid excessive pursuit of reliability. In addition, research has shown that batteries have lower energy storage economics than thermal storage systems.
The heat storage system has low price, is easy to construct in large scale, and also has the functions of peak clipping and valley filling. Therefore, the method is very suitable for power generation by renewable energy sources. A CSP-PV system with a large-capacity and low-cost heat storage system is provided, the low-cost heat storage system is used for replacing an expensive storage battery, and the heat storage system is used for storing redundant energy of a photovoltaic power station. Yong Yang et al introduce an electric heater in a wind power-photo-thermal combined power generation system to solve the problem of wind abandonment. The electric heater can convert redundant wind energy into heat energy to be stored in a heat storage system of the photo-thermal power station, and the heat energy is used for generating electricity when needed, so that energy loss can be reduced, and power supply reliability can be improved. The optimized scheduling of the combined power generation system is a mixed integer linear programming problem, and the profit maximization can be realized. Adriana et al studied CSP-PV electric fields with battery energy storage systems for power regulation with fused salt heat storage systems of tower-type photothermal power stations and battery energy storage systems of photovoltaic power stations. On a photovoltaic scale, the thermal storage system was parametrically analyzed with respect to hours and battery size and the performance of the power plant was studied in terms of capacity factor and leveling cost.
The pumped storage power generation has low cost and mature technology, and is an ideal energy storage mode. Wu Wanlu establishes an optimization model by taking the minimum cost of the life cycle as a target and the water balance and the power utilization reliability of a water storage as constraint conditions, and solves the model by using an improved genetic algorithm, and the result shows that the wind, light and water combined power generation system can ensure higher power supply reliability and stability when running independently; and when the grid-connected operation is carried out, the economic cost can be reduced and the power supply reliability can be improved. The capacity of a water-light-wind complementary power generation system under the condition of certain wind abandon and light abandon is optimized by means of the Hainan red-crowned plum, and the like, the minimum wind abandon and light abandon quantity of the system, the maximum total scale of wind and light and the like are taken as objective functions, constraint conditions such as system channel capacity limit, certain wind abandon light rate, hydropower station output constraint and the like are combined, an optimization model is established by means of C # language, the model can effectively solve the problem of photovoltaic and wind power absorption, and the wind abandon light rate is reduced.
The optimization algorithms can be classified into neural network algorithms, group intelligence algorithms, and common data processing algorithms. The swarm intelligence algorithm comprises a plurality of algorithms such as a Genetic Algorithm (GA), a particle swarm algorithm (PSO), an ant colony Algorithm (ACO), a simulated annealing algorithm (SA), a crowd search algorithm (SOA) and the like. Liu Yan Ping, etc[19]The method combines the charge and discharge constraints of the storage battery, the wind-solar complementary characteristic constraints and the power supply number constraints to provide a multi-objective function with the minimum total system cost and the minimum LPSP and SPSP as targets, an improved genetic algorithm is used for solving the multi-objective optimization problem, and the defect of low convergence speed of the conventional genetic algorithm is overcome by improving the fitness function of the genetic algorithm. Old day and the like[20]And (4) a multi-target function is provided by considering the lowest cost of the system and LPSP, and an improved multi-target compound differential evolution algorithm is used for solving. The algorithm introduces the concepts of crowdedness and pareto ordering in a composite differential evolution algorithm, and has multi-target optimizing capability. Yang Guohua, etc[21]By optimizing the asymmetric acceleration factor, the particle swarm algorithm is improved, Matlab simulation is carried out, and the results of the example simulation show that the method accelerates the convergence speed and optimizes the working state, but the problems of easy falling into local optimization, low search precision and the like still exist. Mao Tan and the like provide a mixed integer linear programming problem of a load scheduling model for self-power generation and considering carbon emission and time-of-use electricity price for obtaining the best economic benefit and environmental benefit. A model with carbon trading cost and power supply cost as optimization targets is established.
According to the analysis, the pumped storage is the most mature and economic energy storage form at present, and the heat storage is a potential low-cost energy storage form. The photo-thermal power station with the heat storage system and the pumped storage power station are jointly responsible for peak load regulation and valley filling of the wind power-photovoltaic combined power generation system, so that the stability of the system can be effectively improved, and the problems that pumped storage is limited by regions and the price of a novel energy storage form is higher can be solved. Therefore, a new wind-photovoltaic-photo-thermal-pumped hydro storage (WT-PV-CSP-PHS) combined power generation mode is proposed, and the capacity optimization result of the hybrid power generation system is obtained by using an improved particle swarm algorithm with the aim of minimizing the standardized cost and the load power shortage as the optimization target. The structure of a wind power-photovoltaic-photo-thermal-pumped storage hybrid power generation system is introduced in the section 2, and a mathematical model of the wind power-photovoltaic-photo-thermal-pumped storage hybrid power generation system is established; establishing a capacity optimization model with the aim of minimizing the leveling cost and the load power shortage rate; step 4, a scheduling strategy of the hybrid power generation system is formulated; sections 5 and 6 are analysis and conclusions, respectively.
Disclosure of Invention
The invention aims to solve the technical problem of providing a capacity optimization model of a wind-solar-heat and water energy storage combined renewable energy power generation system aiming at the defects of the background technology.
The invention adopts the following technical scheme for solving the technical problems:
wind-light heat and water energy storage combined renewable energy power generation system capacity optimization model comprises:
step 1, constructing an objective function
The optimization goal of the cogeneration system herein is to minimize the leveling cost LCOE and the load power shortage LPSP; wherein the content of the first and second substances,
Figure RE-GDA0002896700560000041
in the formula: IC (integrated circuit)w，ICpv，ICcspAnd ICphsInitial costs, AC, for wind, photovoltaic, photothermal and pumped storage power stations, respectivelyw，ACpv，ACcspAnd ACphsAnnual operation and maintenance costs of wind power, photovoltaic, photo-thermal and pumped storage power stations, respectively, Ew，EpvAnd E, andcspannual energy production of wind, photovoltaic and photothermal power stations, dw，dpvAnd dcspThe annual degradation rates of wind power, photovoltaic and photo-thermal power stations are respectively, i is the discount rate, and N is the service life;
Figure RE-GDA0002896700560000042
in the formula: pl(t) is the power, kW, required by the load at time t; pw(t)，Ppv(t)，Pcsp(t) and Pphs(t) wind power, photovoltaic, photo-thermal and pumped storage power station output power, kW, at time t respectively; m is the number of moments when the load power demand is not met;
in summary, the optimization objective function herein is:
Figure RE-GDA0002896700560000043
step 2, adding constraint conditions
The constraint conditions comprise wind power and photovoltaic climbing constraint conditions, heat storage system constraint conditions, power generation module constraint conditions, pumped storage system constraint conditions and energy waste rates; the energy waste rate is used as an index of wind and light abandonment, and the energy waste rate represents the proportion of electric quantity wasted by the system to electric quantity required by all loads;
step 3, optimization algorithm
Obtaining an optimal solution by adopting a weight coefficient method, and selecting the optimal solution by adopting the weight coefficient method according to the comprehensive index I;
Figure RE-GDA0002896700560000051
in the formula: n is the number of optimization objectives; w is aiIs a weight coefficient; f. ofi(x) The corresponding objective function value.
Wind power and photovoltaic climbing constraints are as follows:
Figure RE-GDA0002896700560000052
Figure RE-GDA0002896700560000053
where T is the time interval of the hill climbing restriction indicator, as used hereinTaking for 1 min; pw，rated，Ppv，ratedRespectively representing wind power and photovoltaic rated power; gamma raywTaking 5% as the wind power climbing index; gamma ray pv10 percent is taken as the photovoltaic climbing index.
Constraint conditions of the heat storage system:
Figure RE-GDA0002896700560000054
Figure RE-GDA0002896700560000055
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0002896700560000056
the maximum value and the minimum value of the heat storage capacity of the heat storage system, kJ, respectively;
Figure RE-GDA0002896700560000057
Figure RE-GDA0002896700560000058
the maximum and minimum values of the output power of the heat storage system, kW, are respectively.
Constraint conditions of the power generation module:
receive maximum power when PB is in operation
Figure RE-GDA0002896700560000059
And minimum power
Figure RE-GDA00028967005600000510
The limit of (2); when the power converter is in a stop state, the output power is 0, and the constraint conditions are as follows:
Figure RE-GDA00028967005600000511
(4) pumped storage system constraints
1) The water storage capacity of the upper reservoir is aboutBinding condition[7]：
Figure RE-GDA0002896700560000061
In the formula:
Figure RE-GDA0002896700560000062
the minimum and maximum water storage amount of the upper reservoir are respectively set; vURIs the upper reservoir capacity.
The constraint conditions of the output power and the pumping power of the pumping energy storage system are as follows:
0＜Pgen(t)＜PE (3.10)
0≤Ppump(t)≤PE (3.11)
in the formula: pgen(t)，PpumpAnd (t) respectively representing the output power and the pumping power of the pumped storage system.
The energy waste rate is used as an index for abandoning wind and light, and represents the electric quantity P wasted by the systemSPOccupies all load required electric quantity PlThe expression is as follows:
Figure RE-GDA0002896700560000063
where δ is an energy waste rate reference value.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the invention provides a WT-PV-CSP-PHS combined power generation system aiming at instability of wind power generation and photovoltaic power generation. The combined power generation system fully utilizes the adjusting characteristics of the heat storage systems of the pumped storage power station and the photo-thermal power station, absorbs redundant electric quantity and reduces energy waste, thereby reducing the LCOE and LPSP of the system. Research results show that the LPSP of the WT-PV-CSP-PHS system is 9.95 percent, which is reduced by 10.8 percent compared with the WT-PV-PHS system; LCOE is 120.98$/MWh, 28.57% less than WT-PV-CSP system. The WT-PV-CSP-PHS system has better economic benefit and power supply reliability. In addition, the effect of thermal storage capacity of the thermal storage system on the system is also investigated herein. The results show that as the duration of heat-up increases, the system's LCOE decreases and then increases, while the LPSP decreases.
Drawings
FIG. 1 is a schematic diagram of the overall structure of a combined power generation system model according to the present embodiment;
FIG. 2 is a schematic structural diagram of a simplified flow chart of the system operation in the present embodiment;
FIG. 3 is a graph illustrating typical output curves of the wind field in this embodiment;
FIG. 4 is a diagram illustrating a typical output curve of a photovoltaic power plant in this embodiment;
FIG. 5 is a graph illustrating an exemplary output curve of the opto-thermal power plant in this embodiment;
FIG. 6 is a graph illustrating the monthly average output curve of the present embodiment;
FIG. 7 is a schematic diagram of the present embodiment illustrating sunrise power;
FIG. 8 is a schematic diagram of a Pareto optimal solution set in the present embodiment;
fig. 9 is a schematic diagram of a curve of the grid power of the combined power generation system in the embodiment;
fig. 10 is a graph illustrating a daily power consumption curve in the present embodiment;
fig. 11 is a graph illustrating a daily power consumption curve in the present embodiment;
FIG. 12 is a schematic representation of the effect of heat storage capacity on LCOE and LPSP in this example.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
as shown in fig. 1, the combined power generation system according to the present invention has a specific structure in which a wind farm and a photovoltaic power plant are connected to a power grid through an inverter and a transformer. The solar-thermal power station heat collection field is connected with the heat storage system, the heat storage system is respectively connected with the power generation module and the electric heater, and the output end of the power generation module is connected with the power grid. One end of the electric heater is connected with the power grid, and the other end of the electric heater is connected with the heat storage system. The pumped storage power station is connected with an upper reservoir and a lower reservoir through pressure pipelines and is finally connected with a power grid.
The working principle of the combined power generation system is as follows:
the electricity generated by wind power and photovoltaic is boosted and connected to the grid through a transformer, redundant electric energy drives a water pump of a pumped storage power station to pump water from a lower reservoir to an upper reservoir for storage, and the stored water of the upper reservoir is reused for power generation and is transmitted to a power grid when needed. And if the pumped storage power station cannot absorb the redundant electric energy, the residual electric energy is used for heating the heat transfer working medium in the cold tank through the electric heater and storing the heat transfer working medium in the hot tank. The heat collected by the heat collection field of the photo-thermal power station is also stored in the hot tank, when other power supplies cannot meet rated load, the heat transfer working medium in the hot tank heats water to generate high-temperature and high-pressure steam to drive the steam turbine unit to operate so as to generate power, and the heat transfer working medium is stored in the cold tank after the temperature is reduced.
The invention relates to a wind-light-heat and water energy storage combined renewable energy power generation system capacity optimization model based on a WT-PV-CSP-PHS combined power generation system model. The method comprises the following specific steps:
in the combined power generation system, wind power generation and photovoltaic power generation are used for meeting the load requirement of a power system, and a photo-thermal power station and a pumped storage power station play a role in regulation, namely absorbing redundant electric quantity when the supply is larger than the demand; when the supply is less than the demand, the insufficient electric quantity is supplemented. In addition, the photo-thermal power station has a reactive compensation function, and the pumped storage power station has frequency modulation and phase modulation functions.
Compared with a photo-thermal power station, the pumped storage power station is low in construction cost and high in energy conversion efficiency, and therefore redundant electric quantity of wind power and photovoltaic in the model is preferentially stored in the pumped storage power station. Because the construction of the pumped storage power station is limited by geographical positions and natural resources to a great extent, the capacity of the pumped storage power station is limited by the text to be not too large, the photo-thermal power station and the pumped storage power station share the adjusting task, and electric quantity which cannot be absorbed by the pumped storage power station is converted into heat energy through the electric heater and stored in the heat storage system.
The mathematical model of the combined power generation system is as follows:
the combined power generation system consists of a wind power subsystem, a photovoltaic subsystem, a photo-thermal subsystem, a pumped storage subsystem and an electric heater. The following are mathematical models of the various subsystems:
calculating the output power of the fan according to the rated power and the wind speed parameter of the fan:
Figure RE-GDA0002896700560000081
Figure RE-GDA0002896700560000082
in the formula: pwindThe output power of the fan is kW; peRated power of the fan, kW; v. ofiCutting in wind speed for the fan, m/s; v. ofoCutting out the wind speed m/s for the fan; v. ofeThe rated wind speed of the fan is m/s; v is real-time wind speed, m/s;
the photovoltaic power generation output is mainly related to factors such as solar radiation intensity, surface temperature of a photovoltaic cell, ambient temperature and the like, and a photovoltaic power generation simplified model is as follows:
Ppv＝PsMn(1+β(Tn-Ts))/Ms (2.3)
Ts＝Th+30Mn/Ms (2.4)
in the formula: ppvOutputting power for the photovoltaic cell, kW; psIs the maximum output power under standard test conditions, kW; msThe intensity of the sun light under the standard test condition is 1000W/m2；MnIs the intensity of sunlight irradiation, W/m2(ii) a Beta is a temperature coefficient which is generally-0.47%/K; t issThe surface temperature of the photovoltaic cell under standard test conditions is 25 ℃; t isnSurface temperature of the photovoltaic cell, DEG C; t ishAmbient temperature, deg.C;
the output power of the photothermal power station is calculated by adopting tower-type performance evaluation model software developed by a certain project in China according to the Chinese environment. Wherein, the mirror field takes the optical efficiency and the distribution of the projection light spots into consideration in detail; the heat absorber takes convection heat loss and radiant heat loss into consideration, and adopts a cross flow type of two paths; and each model establishes a mathematical model according to an equilibrium equation of energy conservation, momentum conservation and the like.
The heat storage system uses a cold and hot tank storage mode of a molten salt working medium, and the state model of the heat storage system is as follows:
Figure RE-GDA0002896700560000091
Figure RE-GDA0002896700560000092
in the formula:
Figure RE-GDA0002896700560000093
the heat storage states after the operation of the heat storage system and the current heat storage state are kW/h respectively; epsilontesThe self-heat release rate is generally 0.01;
Figure RE-GDA0002896700560000094
respectively thermal storage power and heat release power, kW; etates.c，ηtes.dRespectively, the heat storage efficiency and the heat release efficiency.
The conversion relation of the potential energy from the electric energy of the pumped storage power station to the water is as follows:
Figure RE-GDA0002896700560000095
ηpump＝ηp*ηm*ηpipe (2.8)
in the formula: wi(t) water pumping quantity of the water pump m in t time period3(ii) a Rho is the density of water, 1000kg/m3(ii) a g is a gravity coefficient of 9.81m/s2(ii) a h is the average head, m; etapumpTo the efficiency of the power generation; pp(t) the electric quantity for pumping water in the time period t, kWh; etap，ηm，ηpipeIs pump, motor and piping efficiency.
Conversion relationship of potential energy of water to electric energy:
Figure RE-GDA0002896700560000101
ηgen＝ηtr*ηgr*ηpipe (2.10)
in the formula: pg(t) the output electric quantity of the water turbine at the time t, kWh; wo(t) the amount of water used for power generation in the period of t, m3；ηtr，ηgr，ηpipeRespectively hydraulic turbine, generator and pipeline efficiency.
The model was verified as follows:
the output of wind power plants, photovoltaic power plants and photothermal power plants is tested and verified. Data for a 49.5MW wind farm and a 50MW photovoltaic power plant in a certain region of Pakistan were used for calculations. And comparing the calculation results of wind power and photovoltaic output with a document [14], wherein the comparison result is shown in a table 2.1. The output of the 50MW photothermal power station was compared with the calculated results of the SAM software, and the comparison results are shown in table 2.1.
The comparison results show that the difference between the calculated value and the reference value is within a reasonable range, which indicates that the model is feasible.
TABLE 2.1 comparison of the forces exerted by the models
Figure RE-GDA0002896700560000102
The capacity optimization model of the wind-light-heat and water energy storage combined renewable energy power generation system comprises the following steps: step 1, constructing an objective function
The optimization goals of the cogeneration systems herein are to minimize levelled Cost of Energy, LCOE, and Loss of load, LPSP.
Wherein the content of the first and second substances,
Figure RE-GDA0002896700560000111
in the formula: IC (integrated circuit)w，ICpv，ICcspAnd ICphsRespectively wind power, photovoltaic and photo-thermalAnd initial cost of pumped storage power plants, ACw，ACpv，ACcspAnd ACphsAnnual operation and maintenance costs of wind power, photovoltaic, photo-thermal and pumped storage power stations, respectively, Ew，EpvAnd E, andcspannual energy production of wind, photovoltaic and photothermal power stations, dw，dpvAnd dcspThe annual degradation rates of wind power, photovoltaic and photothermal power stations are respectively, i is the discount rate, and N is the service life.
In the WT-PV-CSP-PHS combined power generation system model proposed herein, wind power generation and photovoltaic power generation are responsible for meeting the system load requirements. When wind power and photovoltaic output power are smaller than rated power and the pumped storage power station and the heat storage system cannot generate electricity, system load cannot be met. The load power shortage rate represents the proportion of the electric quantity which cannot meet the load power demand of the system in one year to the electric quantity required by all the loads in one year, and the expression is shown as follows[19]：
Figure RE-GDA0002896700560000112
In the formula: pl(t) is the power, kW, required by the load at time t; pw(t)，Ppv(t)，Pcsp(t) and Pphs(t) wind power, photovoltaic, photo-thermal and pumped storage power station output power, kW, at time t respectively; m is the number of times when the load power demand is not met.
In summary, the optimization objective function herein is:
Figure RE-GDA0002896700560000113
step 2, adding constraint conditions
(1) The climbing restraint can effectively restrain the influence of climbing events on the frequency deviation of the power grid. Wind power and photovoltaic climbing constraint is shown as the following formula[27，28]：
Figure RE-GDA0002896700560000121
Figure RE-GDA0002896700560000122
In the formula, T is the time interval of the climbing restriction index, and is taken as 1 min; pw，rated，Ppv，ratedRespectively representing wind power and photovoltaic rated power; gamma raywTaking 5% as the wind power climbing index; gamma ray pv10 percent is taken as the photovoltaic climbing index.
(2) Constraint conditions of the heat storage system:
Figure RE-GDA0002896700560000123
Figure RE-GDA0002896700560000124
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0002896700560000125
the maximum value and the minimum value of the heat storage capacity of the heat storage system, kJ, respectively;
Figure RE-GDA0002896700560000126
Figure RE-GDA0002896700560000127
the maximum and minimum values of the output power of the heat storage system, kW, are respectively.
(3) Power Block (PB) constraints
Receive maximum power when PB is in operation
Figure RE-GDA0002896700560000128
And minimum power
Figure RE-GDA0002896700560000129
The limit of (2); when in a stop state, outputPower is 0 with the constraint of[16]：
Figure RE-GDA00028967005600001210
(4) Pumped storage system constraints
1) The water storage capacity constraint conditions of the upper reservoir are as follows:
Figure RE-GDA00028967005600001211
in the formula:
Figure RE-GDA00028967005600001212
the minimum and maximum water storage amount of the upper reservoir are respectively set; vURIs the upper reservoir capacity.
2) The constraint conditions of the output power and the pumping power of the pumped storage system are[26]：
0≤Pgen(t)≤PE (3.10)
0≤Ppump(t)≤PE (3.11)
In the formula: pgen(t)，PpumpAnd (t) respectively representing the output power and the pumping power of the pumped storage system.
(5) Wind and light rejection restraint
The energy waste rate (SPSP) is used as an index of wind and light abandonment, and represents the electric quantity P wasted by the systemSPOccupies all load required electric quantity PlThe expression is shown below[19]：
Figure RE-GDA0002896700560000131
Where δ is an energy waste rate reference value.
Step 3, optimization algorithm
The method establishes a multi-objective function with LCOE and LPSP minimum as optimization targets. Compared with other algorithms, the improved Particle Swarm Optimization (PSO) algorithm has stronger searching capability and is beneficial to obtaining the optimal solution in the multi-objective meaning. The particle group in the traditional PSO algorithm is quickly converged to one point along with the optimal individual, and if the traditional PSO algorithm is directly used in the multi-objective optimization problem, the local convergence can be easily caused[29]. Therefore, the selected PSO algorithm is evaluated by using the optimal solution[14，30-32]The selection of the global extremum and the individual extremum of the particle is improved.
The solution result of the multi-objective optimization problem is a group of Pareto optimal solution sets, and a better solution is selected as an optimal solution according to the actual situation. The optimal solution is obtained by adopting a weight coefficient method, and the optimal solution is selected by adopting the weight coefficient method according to the comprehensive index I[29]。
Figure RE-GDA0002896700560000132
In the formula: n is the number of optimization objectives; w is aiIs a weight coefficient; f. ofi(x) The corresponding objective function value.
The scheduling strategy of the combined power generation system is that wind power and photovoltaic bear base load, and a photo-thermal power station and a pumped storage power station which are provided with a heat storage system are responsible for peak regulation and frequency modulation. Because the pumped storage power station has high energy conversion efficiency and low cost, the pumped storage power station is preferentially used for peak clipping and valley filling. A simplified flow chart of the operation of the system is shown in figure 2.
In fig. 2: pw (i), pv (i), pl (i), pr (i), plp (i) respectively represent wind power output power, photovoltaic output power, system network electricity quantity, real-time load data and system electricity abandoning quantity at the moment i, and dp (i) represents the difference value between the system power supply quantity and the system load quantity. m represents the energy storage capacity of the heat storage system for one hour, ms represents the current vacant capacity of the heat storage system, ms _ max represents the maximum heat storage capacity of the heat storage system, and the system time satisfies that ms is less than or equal to ms _ max. n represents the energy storage capacity of the pumped storage power station for one hour, nS represents the current free capacity of the pumped storage power station, nS _ max represents the maximum energy storage capacity of the pumped storage power station, and the system time satisfies nS not more than nS _ max. The specific process of system operation is as follows:
(1) when dp (i) > 0 and dp (i) — n/ηpWhen the power generation capacity is more than 0, the wind power generation capacity and the photovoltaic power generation capacity are larger than the rated power load of the system, and the redundant electric quantity cannot be completely consumed by the pumped storage power station. At the moment, the heat storage system is required to absorb heat and store energy; if the heat storage system cannot absorb the residual electricity completely, the residual electricity is discarded.
(2) When dp (i) > 0 and dp (i) — n/ηpWhen the power generation capacity is less than 0, the power generation capacity of wind power and photovoltaic is larger than the rated load, and redundant electric quantity can be completely absorbed by the pumped storage power station. At the moment, the pumped storage power station absorbs redundant electric quantity, and the heat storage system only needs to store the heat converted by the heat collection field.
(3) When dp (i) < 0 and dp (i) — n · ηgAnd when the power generation capacity is more than 0, the wind power generation capacity and the photovoltaic power generation capacity are smaller than the rated load, and the pumped storage power station cannot completely supplement the rated load. At this time, the heat storage system is required to generate heat and power.
(4) When dp (i) < 0 and dp (i) — n · ηgWhen the load is less than 0, the wind power and photovoltaic power generation is smaller than the real-time load, and the pumped storage power station can supplement the rated load. At the moment, the pumped storage power station consumes water to generate electricity.
Analysis of specific cases:
5.1 basic parameters
The wind and light resource data of a certain region of Pakistan in one year are adopted in the method. Due to the reduction coefficient, the wind power and photovoltaic output can not reach the rated value. The unit megawatt output curve of the wind power plant in the area is shown in figure 3. The main parameters of the wind turbine are shown in the table 5.1:
TABLE 5.1 wind turbine Main parameters
Figure RE-GDA0002896700560000141
Figure RE-GDA0002896700560000151
The photovoltaic output curve is shown in fig. 4, and the main parameters of the photovoltaic panel are shown in table 5.2:
TABLE 5.2 photovoltaic Power station Main parameters
Figure RE-GDA0002896700560000152
The unit megawatt output curve of the photothermal power station is shown in fig. 5, and the main parameters of the photothermal power station are shown in table 5.3:
TABLE 5.3 photothermal Power station and Heat storage System principal parameters[15]
Figure RE-GDA0002896700560000161
The WT-PV-CSP-PHS combined operation system studied here performs capacity optimization based on the output characteristics of power generation, photovoltaic, and photo-thermal power stations. The monthly average output curves and representative solar output for a unit megawatt wind, photovoltaic, and thermal power station are shown in fig. 6 and 7:
as can be seen from FIGS. 6 and 7, the output power of the wind power plant in different seasons of the area is very uneven, and the wind-solar complementary characteristic is not very good.
TABLE 5.4 pumped storage Power station Primary parameters[26]
Figure RE-GDA0002896700560000162
Figure RE-GDA0002896700560000171
5.2 optimization results
The optimization calculation is carried out by using an improved particle swarm optimization algorithm. In the algorithm, the inertia weight w is 0.5, and a learning factor c1＝1.6， c22.0, the search space dimension D is 4, the maximum number of iterations M is 50, and the initialization particle number N is 1500. The Pareto optimal solution set obtained after the algorithm is run is shown in fig. 8:
to meet load power demand, the final LPSP is specified herein to be less than 10%. The optimal solution is selected in the Pareto frontier by the weight coefficient method, as shown by point B in fig. 8. The accurate value of the point B cannot be obtained due to the limitation of the capacity of each power supply single machine and the geographical position of the pumped storage power station, so that the final optimization result is determined near the point B, and the specific value is as follows: LCOE is 120.98$/MWh, LPSP is 0.0995 (9.95%), installed capacities of corresponding subsystems are wind power 195MW, photovoltaic 299MW, photo-thermal 12MW, pumped storage 130MW, and photo-thermal power station heat storage system capacity 10 h. The rated electrical load of the system is 100 MW.
The annual grid power curve of the cogeneration system is shown in fig. 9. It can be seen that the days for which the grid power does not meet the load requirement in months 1 to 4 and 10 to 12 are more, the load power shortage rates are respectively 11.19% and 19.68%, and the load power shortage rate in months 5 to 9 is only 3.19%. This is because the output power of wind, photovoltaic and photothermal power stations in the months of 5 to 9 is significantly greater than in other times.
5.3 comparative analysis of optimization results
(1) Comparison between WT-PV system, WT-PV-PHS system, and WT-PV-CSP-PHS system
In WT-PV systems, the system cannot meet customer load demands when wind and solar resources are in short supply. And when the electricity generated by the wind power plant and the photovoltaic power plant is greater than the rated load, wind and light are abandoned. The pumped storage power station in the WT-PV-PHS system can absorb redundant electric quantity and generate electricity when the generated electric quantity is insufficient. As can be seen from Table 5.5, when the installed capacities of wind power and photovoltaic are the same, the LCOE of the WT-PV-PHS system is reduced by $ 9.32/MWh compared with that of the WT-PV system, and the LPSP is reduced from 27.76% to 11.02%. The method shows that the economy and the reliability of the system are improved to a certain extent after the pumped storage power station is added.
TABLE 5.5 comparison between WT-PV System and WT-PV-PHS System
Figure RE-GDA0002896700560000181
However, the LPSP of the WT-PV-PHS system is still greater than 10%. The maximum capacity of pumped storage power stations cannot be exceeded, and therefore cannot be exceeded, limited by geographical locationThe power is fully regulated. Although the storage battery has high energy storage efficiency, the storage battery has short service life, and researches show that the cost of the storage battery is higher than that of a heat storage system[14，15]. The WT-PV-CSP-PHS system with the photo-thermal power station and the pumped storage power station as the adjusting power supply well makes up the defects. The photo-thermal power station can generate electricity, and the heat storage system has the functions of peak load regulation and valley load filling. As shown in Table 5.6, although the LCOE of the WT-PV-CSP-PHS system increased by 5.3% compared to the WT-PV-PHS system, its LPSP decreased by 10.8%. It can be seen that the WT-PV-CSP-PHS system can better suppress power fluctuations, and has better power supply reliability than the WT-PV-PHS system.
TABLE 5.6 comparison between WT-PV-CSP-PHS System and WT-PV-PHS System
Figure RE-GDA0002896700560000182
Representative daily net power curves of the WT-PV system, WT-PV-PHS system, and WT-PV-CSP-PHS system are shown in fig. 10(a) and (b). In FIG. 10(a), the output power of the 1:00-9:00 and 22:00-24:00WT-PV system is less than the rated load and 9:00-22:00 is greater than the rated load. In the WT-PV-PHS system, a pumped storage power station starts to pump water for storage at 9:00 and discharges water for power generation at 22:00-24:00 and 1:00-4:00, so that only 4:00-9:00 cannot meet the load requirement. The WT-PV-CSP-PHS system comprises a photo-thermal power station with a heat storage system besides the pumped storage power station, electricity which cannot be absorbed by the pumped storage power station is converted into heat energy through an electric heater and stored in the heat storage system, and the heat energy is converted into electric energy through a steam turbine set when the pumped storage power station cannot continue to generate electricity. Therefore, the WT-PV-CSP-PHS system can satisfy the load requirement in most cases, and less likely to fail to satisfy the requirement as shown in fig. 10 (b).
(2) Comparison between WT-PV-CSP-PHS System and WT-PV-CSP System
There is no direct connection between each power supply in the conventional wind power, photovoltaic and photo-thermal power generation system. In the WT-PV-CSP system, redundant electric quantity generated by the wind power plant and the photovoltaic power station can be absorbed by the heat storage system of the photo-thermal power station. However, the photo-thermal power station has a high construction cost, and if the installed capacity is too large, the leveling cost of the system is rapidly increased.
TABLE 5.7 comparison between WT-PV-CSP-PHS System and WT-PV-CSP System
Figure RE-GDA0002896700560000191
Table 5.7 shows a comparison between the WT-PV-CSP-PHS system and the WT-PV-CSP system. Under the same LPSP condition, the capacity of the WT-PV-CSP system photothermal power station is increased from 12MW to 87MW, and the LCOE is increased from 120.98$/MWh to 155.54 $/MWh. It is clear that the WT-PV-CSP-PHS system is more economical. In fig. 11, the capacity of each system photothermal power station is 12 MW. It can be seen that the WT-PV-CSP system supplies power in an amount of 1:00-5:00 and 22:00-24:00 larger than that of the conventional power generation system, but still does not satisfy the load requirement, and the WT-PV-CSP-PHS system supplies power in an amount of 24 hours, which reaches the rated load. Therefore, the WT-PV-CSP-PHS system has better power supply reliability.
5.4 Effect of Heat storage Capacity on System
The heat storage system has good peak load regulation function, and the influence of the heat storage capacity on the system LCOE and LPSP is shown in fig. 12. It can be seen that when the heat storage time length is increased from 1h to 5h, the LCOE of the system is reduced from 121.16$/MWh to 120.74 $/MWh; the LCOE of the system increases thereafter as the duration of the heat-up increases. The LPSP is reduced along with the increase of the heat storage time length, and is reduced from 10.77% to 9.80%. When the heat storage time is 5 hours, LCOE is the lowest, and the system has the best economic benefit, but the LPSP of the system is not small enough in the case. When the heat storage time is longer than 9h, the LPSP is less than 10 percent. Therefore, the heat storage capacity of the heat storage system is 10h in comprehensive consideration.
Summary of the invention
Aiming at the instability of wind power generation and photovoltaic power generation, the WT-PV-CSP-PHS combined power generation system is provided. The combined power generation system fully utilizes the adjusting characteristics of the heat storage systems of the pumped storage power station and the photo-thermal power station, absorbs redundant electric quantity and reduces energy waste, thereby reducing the LCOE and LPSP of the system. Research results show that the LPSP of the WT-PV-CSP-PHS system is 9.95 percent, which is reduced by 10.8 percent compared with the WT-PV-PHS system; LCOE is 120.98$/MWh, 28.57% less than WT-PV-CSP system. The WT-PV-CSP-PHS system has better economic benefit and power supply reliability. In addition, the effect of thermal storage capacity of the thermal storage system on the system is also investigated herein. The results show that as the duration of heat-up increases, the system's LCOE decreases and then increases, while the LPSP decreases.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention. While the embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (6)

1. Wind-light heat and water energy storage are united renewable energy power generation system capacity optimization model, its characterized in that: the method comprises the following steps:
step 1, constructing an objective function
The optimization goal of the cogeneration system herein is to minimize the leveling cost LCOE and the load power shortage LPSP;
wherein the content of the first and second substances,
Figure FDA0002570801930000011
in the formula: IC (integrated circuit)w，ICpv，ICcspAnd ICphsRespectively wind power generation,Initial cost of photovoltaic, photothermal and pumped storage power stations, ACw，ACpv，ACcspAnd ACphsAnnual operation and maintenance costs of wind power, photovoltaic, photo-thermal and pumped storage power stations, respectively, Ew，EpvAnd E, andcspannual energy production of wind, photovoltaic and photothermal power stations, dw，dpvAnd dcspThe annual degradation rates of wind power, photovoltaic and photo-thermal power stations are respectively, i is the discount rate, and N is the service life;
Figure FDA0002570801930000012
in the formula: pl(t) is the power, kW, required by the load at time t; pw(t)，Ppv(t)，Pcsp(t) and Pphs(t) wind power, photovoltaic, photo-thermal and pumped storage power station output power, kW, at time t respectively; m is the number of moments when the load power demand is not met;
in summary, the optimization objective function herein is:
Figure FDA0002570801930000013
step 2, adding constraint conditions
The constraint conditions comprise wind power and photovoltaic climbing constraint conditions, heat storage system constraint conditions, power generation module constraint conditions, pumped storage system constraint conditions and energy waste rates; the energy waste rate is used as an index of wind and light abandonment, and the energy waste rate represents the proportion of electric quantity wasted by the system to electric quantity required by all loads;
step 3, optimization algorithm
Obtaining an optimal solution by adopting a weight coefficient method, and selecting the optimal solution by adopting the weight coefficient method according to the comprehensive index I;
Figure FDA0002570801930000014
in the formula: n is the number of optimization objectives; w is aiIs a weight coefficient; f. ofi(x) The corresponding objective function value.
2. The capacity optimization model of the wind, light, heat and water energy storage combined renewable energy power generation system according to claim 1, wherein: wind power and photovoltaic climbing constraints are as follows:
Figure FDA0002570801930000015
Figure FDA0002570801930000016
in the formula, T is the time interval of the climbing restriction index, and is taken as 1 min; pw，rated，Ppv，ratedRespectively representing wind power and photovoltaic rated power; gamma raywTaking 5% as the wind power climbing index; gamma raypv10 percent is taken as the photovoltaic climbing index.
3. The capacity optimization model of the wind, light, heat and water energy storage combined renewable energy power generation system according to claim 1, wherein: constraint conditions of the heat storage system:
Figure FDA0002570801930000017
Figure FDA0002570801930000018
in the formula (I), the compound is shown in the specification,
Figure FDA0002570801930000021
the maximum value and the minimum value of the heat storage capacity of the heat storage system, kJ, respectively;
Figure FDA0002570801930000022
the maximum and minimum values of the output power of the heat storage system, kW, are respectively.
4. The capacity optimization model of the wind, light, heat and water energy storage combined renewable energy power generation system according to claim 1, wherein: constraint conditions of the power generation module:
receive maximum power when PB is in operation
Figure FDA0002570801930000023
And minimum power
Figure FDA0002570801930000024
The limit of (2); when the power converter is in a stop state, the output power is 0, and the constraint conditions are as follows:
Figure FDA0002570801930000025
(4) pumped storage system constraints
1) Upper reservoir water storage capacity constraint condition[7]：
Figure FDA0002570801930000026
In the formula:
Figure FDA0002570801930000027
the minimum and maximum water storage amount of the upper reservoir are respectively set; vURIs the upper reservoir capacity.
5. The capacity optimization model of the wind, light, heat and water energy storage combined renewable energy power generation system according to claim 1, wherein: the constraint conditions of the output power and the pumping power of the pumping energy storage system are as follows:
0≤Pgen(t)≤PE (3.10)
0≤Ppump(t)≤PE (3.11)
in the formula: pgen(t)，PpumpAnd (t) respectively representing the output power and the pumping power of the pumped storage system.
6. The capacity optimization model of the wind, light, heat and water energy storage combined renewable energy power generation system according to claim 1, wherein: the energy waste rate is used as an index for abandoning wind and light, and represents the electric quantity P wasted by the systemSPOccupies all load required electric quantity PlThe expression is as follows:
Figure FDA0002570801930000028
where δ is an energy waste rate reference value.
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