CN111585305A - Method suitable for multi-energy complementary linkage economic evaluation - Google Patents

Method suitable for multi-energy complementary linkage economic evaluation Download PDF

Info

Publication number
CN111585305A
CN111585305A CN202010538163.0A CN202010538163A CN111585305A CN 111585305 A CN111585305 A CN 111585305A CN 202010538163 A CN202010538163 A CN 202010538163A CN 111585305 A CN111585305 A CN 111585305A
Authority
CN
China
Prior art keywords
power
value
power generation
photovoltaic
particle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010538163.0A
Other languages
Chinese (zh)
Other versions
CN111585305B (en
Inventor
闫龙
张博文
何玉龙
孙云东
黄红军
冯建华
张恩杰
陆凌辉
李志斌
高海霞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Binhai Power Supply Co of State Grid Tianjin Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Binhai Power Supply Co of State Grid Tianjin Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, State Grid Tianjin Electric Power Co Ltd, Binhai Power Supply Co of State Grid Tianjin Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202010538163.0A priority Critical patent/CN111585305B/en
Publication of CN111585305A publication Critical patent/CN111585305A/en
Application granted granted Critical
Publication of CN111585305B publication Critical patent/CN111585305B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • 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
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a method suitable for multi-energy complementary linkage economy evaluation, which comprises the following steps: constructing an objective function about the economic evaluation of the distributed power supply; building a system model of the distributed power supply; initializing external factors, and inputting typical annual meteorological data; calling year load data and a hybrid microgrid scheduling strategy, and taking a charge and discharge model of a storage battery and pumped storage into account; calculating annual investment cost by adopting an objective function, and considering constraint conditions; and constructing a system evaluation system by utilizing a nonlinear particle swarm algorithm, and outputting an optimization result. The invention can greatly save the economic evaluation time of the multi-energy complementary linkage technology, increase the evaluation efficiency, improve the overall power generation efficiency of the micro-grid power generation system, reasonably configure the number of distributed power supplies, improve the power generation efficiency of the multi-energy complementary power generation system to the maximum extent under the condition of the same environment, temperature and illumination and reduce the power generation cost of the system.

Description

Method suitable for multi-energy complementary linkage economic evaluation
Technical Field
The invention relates to the field of economic optimization configuration of capacity of a distributed power supply complementary power generation system, in particular to a method suitable for multi-energy complementary linkage economy evaluation.
Background
In order to meet the user requirements of remote areas without electricity, an independent wind-solar-energy storage complementary system is taken as the main part, and the hot point of research on economic optimization configuration of the capacity of the multi-energy complementary linkage system is correspondingly focused on the independent multi-energy complementary linkage power generation system. For distributed energy such as wind energy and solar energy, strong uncertainty appears due to the restriction of multiple factors such as seasons, geography and climate, and the uncertainty causes huge economic loss no matter whether the distributed energy is incorporated into a power grid or independently generated.
In order to reduce loss and cost, economic evaluation research of the multi-energy complementary linkage technology is carried out. The multi-energy complementary linkage power generation technology plays an important role in improving power supply diversity, flexibility and reliability, but needs to be improved in the aspect of economic capacity optimization configuration.
Disclosure of Invention
The invention aims to make up for the defects of the prior art and provides the method suitable for the economic evaluation of the multi-energy complementary linkage technology.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method suitable for multi-energy complementary linkage economy evaluation comprises the following steps:
step S1, constructing an objective function about the economic evaluation of the distributed power supply, and evaluating the overall economic optimization level of the system by taking an economic objective, an environmental objective and the reliability of the equipment as final objectives;
step S2, building a system model of the distributed power supply, considering the constraint condition of the distributed power supply, and preparing for subsequent calculation;
s3, initializing external factors, inputting typical annual meteorological data based on a basic configuration optimal planning program, and outputting basic equipment configuration conditions as iteration original data;
step S4, calling year load data and a hybrid microgrid scheduling strategy, considering charge and discharge models of storage batteries and pumped storage, considering constraint conditions of equipment, and preparing for subsequent calculation;
step S5, calculating annual investment cost by adopting the objective function in the step S1, taking the calculation result as original reference data, and considering constraint conditions;
and S6, constructing a system evaluation system by using a nonlinear particle swarm algorithm, and outputting an optimization result through the nonlinear particle swarm algorithm by taking the objective function in the step S1, the typical annual meteorological data in the step S3 and the annual investment cost and constraint conditions in the step S5 as input based on the system evaluation system.
Further, the objective function in step S1 is:
Figure BDA0002537793540000021
in the formula (f)a、fβ、fχRespectively an economic target, an environmental target and a reliability target; x is a position variable and is established according to the power output power participating in power generation in a specific time period and the charging and discharging power of the energy storage device; f. ofa1、fa2、fa3Respectively the installation cost, the maintenance cost and the power failure compensation cost; n is a radical ofswi、Nsli、Nsdr、NsstThe number of the fan, the photovoltaic, the storage battery and the pumped storage energy are respectively; cswi、Csli、Csdr、CsstThe unit price of a fan, a photovoltaic, a storage battery and pumped storage is respectively; cswm、Cslm、Csdm、CssmThe maintenance costs of the fan, the photovoltaic, the storage battery and the water pumping and energy storage are respectively saved; pLCThe power is insufficient for the system electricity; chi is a power supply insufficiency compensation coefficient, and the load of residents is taken as 2.41 yuan/kW; n is the hour number of one year, and is more than or equal to 0 and less than or equal to 8760.
Further, the system model of the distributed power source in the step S2 includes a fan model and a photovoltaic cell model;
the fan model is as follows:
Vw＝Vwb+Vwg+Vwr+Vwn(2)
Figure BDA0002537793540000022
in the formula, VwRepresenting the actual speed of the fan; vwb、Vwg、Vwr、VwnRespectively the basic wind speed, the gust wind speed, the gradual change wind speed and the random wind speed; pswiRated power for the fan; vco、VciRespectively taking the cut-in wind speed and the cut-out wind speed of the fan, wherein the values are respectively 2.5m/s and 18 m/s;
the photovoltaic cell model is:
Figure BDA0002537793540000023
Figure BDA0002537793540000024
in the formula, Psli、PmaxThe output power and the maximum output power of the photovoltaic generator are respectively; gC、GSTCRespectively representing the illumination intensity and the standard illumination intensity; t iscIs the absolute temperature within the cell; t isiIs a temperature coefficient, Ti＝6.4×10-4(K-1)；TcoThe value is 25 ℃ for the quasi absolute temperature; t isaIs ambient temperature.
Further, the typical annual meteorological data in the step S3 includes load data, wind speed data, light intensity data, and temperature data.
Further, in the step S4,
the charge-discharge model of the storage battery is as follows:
Figure BDA0002537793540000031
C＝Id·td(7)
wherein E is the actual voltage; ei0Charging an open circuit voltage for the battery; r is the internal resistance of the battery; r0Actual internal resistance; q is the discharge capacity; k1、K2Is a constant; c represents the rated capacity of the storage battery, and the unit is A.h; i isdIs a charge-discharge current; t is tdIs the charging and discharging time;
virtualizing a pumped storage unit into a virtual motor, wherein a pumped storage energy charging and discharging model is as follows:
Figure BDA0002537793540000032
in the formula, Psst、QsstPower and heat dissipated by the virtual motor, respectively; n is the number of power points; thetat i.nThe variable is 0-1 and represents the power point n of the virtual motor at the time t; pi.n、Qi.nRespectively the power value and the water flow of the unit i at the power point n.
Further, the constraint conditions in step S5 include:
fan, photovoltaic output constraint conditions:
Figure BDA0002537793540000033
Figure BDA0002537793540000034
Figure BDA0002537793540000041
in the formula, Pswin、PslinRated power of a single fan and rated power of a photovoltaic are respectively set; pswi、Psli、Psdr、PsstRespectively representing the total power of wind power generation, photovoltaic power generation, pumped storage and storage battery power generation; xiRepresenting the load type; piIs the load power; m is the total number of the equipment; t is denoted as time t;
power supply number constraint of distributed power supply:
Figure BDA0002537793540000042
in the formula, Nwi、Nli、Nst、NdrThe number of wind power generation, photovoltaic power generation, storage batteries and pumped storage energy are respectively; n is a radical ofwi.min、Nli.min、Nst.min、Ndr.minMinimum quantities of wind power generation, photovoltaic power generation, storage batteries and water pumping and energy storage are respectively set to be 0; n is a radical ofwi.max、Nli.max、Nst.max、Ndr.maxThe maximum quantity of wind power generation, photovoltaic power generation, storage batteries and water pumping and energy storage is determined according to the capacity of the distributed power supply;
constraint conditions of the storage battery:
Figure BDA0002537793540000043
in the formula, Smin、Smax、Ssoc(t) represents the minimum, maximum, and run-time capacity of the battery, respectively; pch(t)、Pdch(t) respectively represents the charging and discharging power of the storage battery, the service life of the battery is reduced due to an excessively high charging and discharging rate, therefore, the upper limit of the charging power per hour cannot exceed SOC/5, and delta t is taken as 1 h; ebatRefers to the electromotive force of the battery;
the reliability constraint conditions of the multi-energy complementary power generation system are as follows:
POPS≤POPSset(14)
Figure BDA0002537793540000044
in the formula, POPS is the probability of power shortage of the load; POPS (Point of Care Package protection)setSetting the maximum power shortage probability for the system; t is the total simulation duration; psup(t)、PneeAnd (t) respectively representing the total power supply power of the power grid at the moment t when the power grid is connected and the required power of the load.
Further, the specific implementation steps of the nonlinear particle swarm algorithm in the step S6 are as follows:
step S61, setting inertia weight value, randomly generating acceleration weight coefficient, calculating the inertia weight coefficient once when the particle swarm is updated, and randomly generating acceleration weight coefficient r1，r2；
Step S62, setting the maximum value and the minimum value of the particle speed, and replacing the maximum value and the minimum value with a boundary value when exceeding the boundary;
step S63, updating the particle speed and position, judging whether the objective function meets the requirement, if so, selecting a nonlinear particle swarm algorithm to update the position of the particle, keeping the original speed of the particle unchanged, otherwise, updating the speed and the position of the particle by using a standard particle swarm algorithm, and limiting the maximum speed v of the particlemax；
Step S64, calling a judgment function to recalculate the objective function value, comparing and determining the individual and global extremum, comparing the individual and global extremum with the adaptation value of the optimal position which has been experienced, and if the current adaptation value is large, taking the current adaptation value as a new gbest; then, the adaptive value of each particle is compared with the optimal adaptive value of the population, the maximum value is taken as the optimal value gbest of the population, and a new objective function value is generated again;
step S65, judging whether the iteration number reaches the maximum iteration number M or whether the position distance of each particle is smaller than a certain threshold value, if not, performing step S64, continuing the iteration, and if a termination condition is met, outputting a gbest to obtain a final optimization result;
and step S66, outputting a seed value, a progression number, an optimal iteration number and an objective function value, and storing a particle displacement value and a velocity value.
The invention has the beneficial effects that:
1. the total investment cost of the multi-energy complementary power generation system is reduced, and the overall power generation efficiency of the power generation system is improved.
2. The number of distributed power supplies is reasonably configured, the power generation efficiency of the multi-energy complementary power generation system is improved to the maximum extent under the condition of the same environment, temperature and illumination, and the power generation cost of the system is reduced.
3. The time for the economic evaluation of the multi-energy complementary power generation system is greatly saved, and the evaluation efficiency is improved.
Drawings
In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
FIG. 1 is a schematic diagram of a multi-energy complementary power generation system;
FIG. 2a is an equivalent circuit diagram of a battery;
FIG. 2b is a schematic diagram of the operating point of the battery;
FIG. 3 is a standard particle swarm optimization flow chart;
FIG. 4 is a graph comparing inertial weights for a particle swarm algorithm;
FIG. 5 is a flow chart of a nonlinear particle swarm algorithm in the present invention;
FIG. 6a is a load graph;
FIG. 6b is a wind velocity profile;
FIG. 6c is a graph of light intensity;
FIG. 6d is a temperature profile;
fig. 7 is a comparison graph of total investment cost after two algorithms are optimized.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that embodiments of the invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in detail so as not to obscure the embodiments of the invention.
In the following description, a detailed structure will be presented for a thorough understanding of embodiments of the invention. It is apparent that the implementation of the embodiments of the present invention is not limited to the specific details familiar to those skilled in the art. The following detailed description of preferred embodiments of the invention, however, the invention is capable of other embodiments in addition to those detailed.
The invention is described in further detail below with reference to the accompanying drawings:
the distributed power supply in the multi-energy complementary power generation system mainly comprises a fan, a photovoltaic array, pumped storage, a storage battery and the like, as well as an inverter, a chopper, a rectifier and a controller thereof, and can be connected with a power distribution network through a public connection point to form a grid-connected mode and can also independently run to form an island mode. The invention assumes that the output of the combined microgrid power supply and the alternating current/direct current load thereof can be quickly tracked, various distributed power supplies realize plug and play, can stably run in a grid-connected mode and an island mode, and has a schematic structural diagram as shown in fig. 1.
In the multi-energy complementary power generation system, the distributed power sources are reasonably configured, the overall power generation efficiency of the micro-grid power generation system is improved, meanwhile, the economic efficiency, the reliability and the influence on the environment of the system can reach a satisfactory degree, the power generation efficiency of the multi-energy complementary power generation system is improved to the maximum extent, and the power generation cost of the system is reduced. Therefore, in the field of economic optimization configuration of the capacity of the distributed power supply complementary power generation system, the invention provides a system model of the distributed power supply and designs a method suitable for multi-energy complementary linkage economic performance evaluation according to the model. The method comprises the following specific steps:
step S1: and constructing an objective function for the economic evaluation of the distributed power supply, and evaluating the overall economic optimization level of the system by taking the economic objective, the environmental objective and the reliability of the equipment as final objectives.
The objective function is:
Figure BDA0002537793540000071
in the formula (f)α、fβ、fχRespectively an economic target, an environmental target and a reliability target; x is a position variable and is established according to the power output power participating in power generation in a specific time period and the charging and discharging power of the energy storage device; f. ofα1、fα2、fα3Respectively the installation cost, the maintenance cost and the power failure compensation cost; n is a radical ofswi、Nsli、Nsdr、NsstThe number of the fan, the photovoltaic, the storage battery and the pumped storage energy are respectively; cswi、Csli、Csdr、CsstThe unit price of a fan, a photovoltaic, a storage battery and pumped storage is respectively; cswm、Cslm、Csdm、CssmThe maintenance costs of the fan, the photovoltaic, the storage battery and the water pumping and energy storage are respectively saved; pLCThe power is insufficient for the system electricity; chi is a power supply insufficiency compensation coefficient, and the load of residents is taken as 2.41 yuan/kW in consideration; n is the hour number of one year, and is more than or equal to 0 and less than or equal to 8760.
Step S2: firstly, introducing a fan model and a photovoltaic cell model, building a system model of the distributed power supply, considering the constraint condition of the distributed power supply, and preparing for the subsequent calculation of a target function.
The fan model adopts a four-component method, namely the wind speed is divided into four components of basic wind, gust, gradual change wind and random wind, and the fan model is as follows:
Vw＝Vwb+Vwg+Vwr+Vwn(2)
Figure BDA0002537793540000072
in the formula, VwRepresenting the actual speed of the fan; vwb、Vwg、Vwr、VwnRespectively the basic wind speed, the gust wind speed, the gradual change wind speed and the random wind speed; pswiRated power for the fan; vco、VciThe cut-in wind speed and the cut-out wind speed of the fan are respectively 2.5m/s and 18 m/s.
The photovoltaic generator set is formed by connecting small module batteries in series or in parallel, and a photovoltaic array formed by integrating the small modules can transmit electric energy meeting the standard of a user. The photovoltaic cell model comprises formula (4) and formula (5), and formula (4) is photovoltaic generator output, to glass encapsulated solar cell panel, can be by formula (5) according to concrete ambient temperature approximate calculation battery operating temperature, specifically as follows:
Figure BDA0002537793540000081
Figure BDA0002537793540000082
in the formula, Psli、PmaxThe output power and the maximum output power of the photovoltaic generator are respectively; gC、GSTCRespectively, the intensity of light, the standard intensity of light, specifically, GSTCIs the light intensity under STC (1000W/m)2)；TcIs the absolute temperature within the cell; t isiIs a temperature coefficient, Ti＝6.4×10-4(K-1)；TcoThe value is 25 ℃ for the quasi absolute temperature; t isaIs the ambient temperature in units of ℃.
Step S3: initialising, i.e. losing, the extrinsic factorAnd inputting typical annual meteorological data, and outputting basic equipment configuration conditions as iteration original data through basic configuration optimal planning program operation. Typical annual meteorological data including load data, wind speed data, light intensity data, temperature data are input. Firstly, the regional load power consumption and the output characteristic curve of the wind driven generator are tested and used for analyzing the relationship between the regional power consumption and the building environment, and the results are shown in fig. 6a and 6 b. The output of the wind driven generator is connected with a variable resistance load after being rectified, and the P-V curve of the wind driven generator can be measured by adjusting the wind speed and the load resistance value. The average annual light intensity and temperature were collected and recorded, and the average values were used as output, and the output curves are shown in fig. 6c and 6d, respectively. By reasonably configuring the number of distributed power supplies of the system and combining local climate data analysis, the cost can be effectively reduced, and the power supply benefit is improved. Meanwhile, the maximum iteration number of the particle swarm is set to be M, the search space dimension is set to be D, and the maximum particle speed is set to be vmax。
Step S4: and calling year load data and a hybrid microgrid scheduling strategy, considering a charge and discharge model of a storage battery and a pumped storage, considering constraint conditions of equipment, and preparing for calculation of a subsequent objective function.
The storage battery can relieve the contradiction of renewable resource intermittent power generation in the running process of the power grid, and stores energy through a physical mode or a chemical mode. The storage battery used by the invention can be used as a standby power supply to stabilize electric energy fluctuation and improve the reliability of power supply of the multi-energy complementary micro-grid system. The equivalent circuit of the battery is shown in fig. 2 a.
The charge-discharge model of the storage battery consists of a formula (6) and a formula (7), and specifically comprises the following steps:
the internal part of the storage battery is regarded as a voltage source with small resistance, the actual voltage E of the storage battery linearly decreases along with the increase of the discharge capacity Q, the internal resistance r of the battery linearly increases along with the increase of the discharge capacity Q, and the relationship between the actual voltage and the internal resistance of the battery and the discharge capacity is as follows;
Figure BDA0002537793540000083
in the formula, E is actual electricityPressing; ei0Charging an open circuit voltage for the battery; r is the internal resistance of the battery; r0Actual internal resistance; q is the discharge capacity; k1、K2Is a constant.
The operating point of the battery is located at the intersection point P of the load line and the battery terminal voltage characteristic curve, as shown in fig. 2 b. The rated capacity of the battery can be expressed as:
C＝Id·td(7)
c represents the rated capacity of the storage battery, and the unit is A.h; i isdIs a charge-discharge current; t is tdThe charge and discharge time is shown.
The water pumping energy storage charging and discharging model is composed of a formula (8) as follows:
the pumped storage unit can be regarded as 2 working states of pumping and generating, and can not be randomly converted. These 2 operating states are virtualized into virtual generators and virtual motors, respectively. Wherein the virtual generator is similar to a normal machine set and will not be described in detail here. When the pumped storage unit is virtualized into a virtual motor, the pumped storage unit generally operates near an optimal power point, cannot be randomly adjusted and can only be positioned at a plurality of discontinuous power points. The active power and the reactive power (calorific value) consumed by the system are respectively as follows:
Figure BDA0002537793540000091
in the formula, Psst、QsstActive power and heating power consumed by the virtual motor respectively; n is the number of power points; thetat i.nThe variable is 0-1 and represents the power point n of the virtual motor at the time t; pi.n、Qi.nRespectively the power value and the water flow of the unit i at the power point n.
Step S5: the annual investment cost is calculated by adopting the objective function of the formula (1), the calculation result is used as original reference data, and constraint conditions of all modules (a photovoltaic module, a fan module and a storage battery module) are considered. Wherein each constraint is specifically as follows:
fan, photovoltaic output constraint conditions:
Figure BDA0002537793540000092
Figure BDA0002537793540000093
Figure BDA0002537793540000094
in the formula, Pswin、PslinRated power of a single fan and rated power of a photovoltaic are respectively set; pswi、Psli、Psdr、PsstRespectively representing the total power of wind power generation, photovoltaic power generation, pumped storage and storage battery power generation; xiRepresenting the load type; piIs the load power; m is the total number of the equipment; t is denoted as time t; the pumped storage power generation at a certain time meets the condition that the total load power is subtracted by the photovoltaic power generation and the fan power generation.
The generator sets of the distributed power supply, namely wind power generation, photovoltaic power generation, pumped storage and storage battery, need to meet the power supply number constraint as follows:
Figure BDA0002537793540000101
in the formula, Nwi、Nli、Nst、NdrThe number of wind power generation, photovoltaic power generation, storage batteries and pumped storage energy are respectively; n is a radical ofwi.min、Nli.min、Nst.min、Ndr.minMinimum quantities of wind power generation, photovoltaic power generation, storage batteries and water pumping and energy storage are respectively set to be 0; n is a radical ofwi.max、Nli.max、Nst.max、Ndr.maxThe maximum quantity of wind power generation, photovoltaic power generation, storage batteries and water pumping and energy storage is determined according to the capacity of the distributed power supply.
The state of charge and the charge-discharge power of the storage battery influence the operation safety and the service life of the storage battery, so the storage battery also meets three constraint conditions:
Figure BDA0002537793540000102
in the formula, Smin、Smax、Ssoc(t) represents the minimum, maximum, and run-time capacity of the battery, respectively; pch(t)、Pdch(t) respectively represents the charging and discharging power of the storage battery, the service life of the battery is reduced due to an excessively high charging and discharging rate, so that the upper limit of the charging power per hour cannot exceed SOC/5(SOC means State of charge, the conforming State of the battery reflects the residual capacity of the battery), and delta t is 1 h; ebatRefers to the electromotive force of the battery.
The constraint conditions of the reliability of the multi-energy complementary power generation system are as follows:
POPS≤POPSset(14)
Figure BDA0002537793540000103
in the formula, POPS is the probability of power shortage of the load; POPS (Point of Care Package protection)setSetting the maximum power shortage probability for the system; t is the total simulation duration; psup(t)、PneeAnd (t) respectively representing the total power supply power of the power grid at the moment t when the power grid is connected and the required power of the load.
Step S6: and (4) taking the objective function in the step S1, the typical meteorological data in the step S3, the investment cost in the step S5 and the constraint conditions of all modules as input, putting the modified nonlinear particle swarm algorithm into operation, and outputting a result value.
As shown in fig. 5, the specific implementation steps of the nonlinear particle swarm algorithm are as follows:
step S61, setting inertia weight value, randomly generating acceleration weight coefficient, calculating the inertia weight coefficient once when the particle swarm is updated, and randomly generating acceleration weight coefficient r1，r2；
Step S62, setting the maximum value and the minimum value of the particle speed, and replacing the maximum value and the minimum value with a boundary value when exceeding the boundary;
step S63, updating the particle speed and position, judging whether the objective function meets the requirement, if so, selecting a nonlinear particle swarm algorithmUpdating the position of the particle, keeping the original speed of the particle unchanged, otherwise updating the speed and the position of the particle by using a standard particle swarm algorithm, and limiting the maximum speed v of the particlemax；
Step S64, calling a judgment function to recalculate the objective function value, comparing and determining the individual and global extremum, comparing the individual and global extremum with the adaptation value of the optimal position which has been experienced, and if the current adaptation value is large, taking the current adaptation value as a new gbest; then, the adaptive value of each particle is compared with the optimal adaptive value of the group, the maximum is used as the optimal value of the group gbest, and a new objective function value is generated again (namely the number and configuration conditions of each device corresponding to the economic benefit, the link benefit and the device reliability);
step S65, judging whether the iteration number reaches the maximum iteration number M or whether the position distance of each particle is smaller than a certain threshold value, if not, performing step S64, continuing the iteration, and if a termination condition is met, outputting a gbest to obtain a final optimization result;
and step S66, outputting a seed value, a progression number, an optimal iteration number and an objective function value, and storing a particle displacement value and a velocity value.
When the intelligent algorithm is used for optimizing the combined optimization model and the economic evaluation of the multi-energy complementary power generation system, the scheduling strategy of the power generation system also needs to be considered. The scheduling strategy is as follows:
1) firstly, the photovoltaic array and the wind driven generator are put into use according to 80% of the maximum power generation amount under the climatic condition, the maximum power point of the power required by a user can be borne, and the wind power generation is prior to the photovoltaic power generation.
2) When wind power generation and photovoltaic power generation can not supply power, the storage battery pack can meet the requirement of maximum load power when independently supplying power, and can track the output change of photovoltaic and a fan to carry out charging and discharging.
3) In consideration of extreme conditions, when the wind power and the photovoltaic generator set break down and the residual electric energy of the storage battery is not enough to meet the load requirement. At the moment, the maximum electric power which can be reached by the energy storage battery pack of the energy-saving building can be borne and meets the requirement, and the charging standard is that the charging is carried out in the load valley period. Therefore, the power supply interruption condition which possibly occurs in a power supply system is perfected, the phenomenon that wind and light are abandoned is reduced, and the reliability and the practicability of the micro-grid are reflected.
4) When the four motors of the distributed power supply are in failure or the sum of the generated electric energy of the four motors of the distributed power supply cannot reach the load power utilization standard, the system is in load shortage. This load deficit affects the user experience. And judging the stability and reliability parameters of the system according to the amount of the shortage electric energy of the system.
The economic evaluation method used by the invention is a particle swarm algorithm, scientists find that individuals of the bird swarm and the fish swarm achieve a common target through information sharing and mutual cooperation through observation and research of the groups such as the bird swarm and the fish swarm, and the particle swarm algorithm is widely applied to the field of electric power due to the characteristics of high precision, fast convergence and the like. The velocity and position formula of the original particle swarm algorithm particle is as follows:
Figure BDA0002537793540000121
Figure BDA0002537793540000122
in the formula (I), the compound is shown in the specification,
Figure BDA0002537793540000123
is the velocity of the ith particle; w is an inertia factor, which is generally set to a constant of 1; c. C1、c2For the learning factor, it is generally set to a constant of 2; r is1、r2Random numbers between 0 and 1 to preserve the perturbation introduced by the diversity of the particles; p is a radical ofidFinding an individual optimal solution for the particle labeled i; p is a radical ofgdFinding a global optimal solution for the population;
Figure BDA0002537793540000124
d-dimensional position vector for kth iteration of ith particleAmount of the compound (A). The particle swarm algorithm flow is shown in fig. 3.
The nonlinear particle swarm algorithm is characterized in that the inertia weight in the algorithm is adjusted into a nonlinear self-optimizing inertia weight, and the nonlinear self-optimizing inertia weight is applied to the configuration and economic optimization of the multi-energy complementary power generation system, wherein the nonlinear inertia weight is shown in a formula (18), and the inertia weight is shown in a pair in figure 4.
Figure BDA0002537793540000125
In the formula: w represents the inertial weight, wmaxRepresenting the maximum inertial weight, wminThe minimum inertial weight is represented, k represents the current iteration number, and m represents the maximum iteration number.
The method comprises the following steps of applying a nonlinear particle swarm algorithm to carry out multisource optimal configuration on a certain complementary power supply system, wherein the power supply types are as follows: photovoltaic array, fan, storage battery. Assuming that 200 residents in a certain area use one hour as a basic step length of research simulation, the period is set to be 1 year, and typical year load data and annual wind speed, illumination intensity and climate and temperature conditions in the certain area are selected as input data of the system. In the algorithm, the initial population size of the particles is 100, the maximum iteration number is 100, c1＝c22.0. The parameters of the various distributed power sources are shown in table 1 in conjunction with the manufacturer data. Wherein the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine generator are respectively 2m/s, 4m/s and 6 m/s; the permitted depth of discharge of the accumulator is 80% and the rated capacity is 10 kWh. And comprehensively analyzing the multi-objective optimization functions of the micro-grid complementary power generation system, such as economy, reliability and the like, and combining the particle swarm optimization algorithm logic to achieve a final scheme. The final optimization results are shown in table 1, and the optimized curve of the distributed power source is shown in fig. 6.
TABLE 1 optimal configuration results for distributed power supplies
Figure BDA0002537793540000126
Figure BDA0002537793540000131
The distributed power supply optimization configuration result shows that under the condition of the same user load and power supply capacity, the number proportion of fans and photovoltaic sets used by the improved nonlinear particle swarm algorithm is increased compared with the basic particle swarm algorithm, and the proportion of storage batteries and pumped storage is reduced. Fig. 7 shows the comparison of the total investment costs after the two algorithms are optimized, and the total investment cost after the nonlinear particle swarm optimization is always smaller than that of the standard particle swarm optimization. The overall power generation efficiency of the micro-grid power generation system is improved through the optimized configuration of the nonlinear particle swarm algorithm, namely, the quantity of distributed power supplies is reasonably configured, the power generation efficiency of the system is improved to the maximum extent under the condition of the same environment, temperature and illumination, the power generation cost of the system is reduced, and the ideal expectation is met.
Secondly, as can be seen from fig. 7, when the iteration reaches about 40 th time, the difference between the two reaches the maximum, and the nonlinear particle swarm algorithm reaches the optimal result approximately 60 times earlier than the linear particle swarm algorithm, the time of economic evaluation can be greatly saved, and the evaluation efficiency is increased. The final result shows that the distributed power supply model and the corresponding nonlinear particle swarm algorithm provided by the method aiming at the multi-energy complementary linkage technology and the economic evaluation are better.
The invention provides a multi-energy complementary linkage technology economic evaluation optimization method, namely a nonlinear particle swarm algorithm, by taking multi-energy complementary linkage technology economic evaluation research as an entry point. The algorithm can greatly save the economic evaluation time of the multi-energy complementary linkage technology, increase the evaluation efficiency, improve the overall power generation efficiency of the micro-grid power generation system, reasonably configure the number of distributed power supplies, improve the power generation efficiency of the multi-energy complementary power generation system to the maximum extent under the same environment, temperature and illumination conditions, and reduce the power generation cost of the system.
In summary, the present invention is not limited to the above-mentioned embodiments, and those skilled in the art can propose other embodiments within the technical teaching of the present invention, but these embodiments are included in the scope of the present invention.

Claims (7)

1. A method suitable for multi-energy complementary linkage economy evaluation is characterized by comprising the following steps:
step S1, constructing an objective function about the economic evaluation of the distributed power supply, and evaluating the overall economic optimization level of the system by taking an economic objective, an environmental objective and the reliability of the equipment as final objectives;
step S2, building a system model of the distributed power supply, considering the constraint condition of the distributed power supply, and preparing for subsequent calculation;
s3, initializing external factors, inputting typical annual meteorological data based on a basic configuration optimal planning program, and outputting basic equipment configuration conditions as iteration original data;
step S4, calling year load data and a hybrid microgrid scheduling strategy, considering charge and discharge models of storage batteries and pumped storage, considering constraint conditions of equipment, and preparing for subsequent calculation;
step S5, calculating annual investment cost by adopting the objective function in the step S1, taking the calculation result as original reference data, and considering constraint conditions;
and S6, constructing a system evaluation system by using a nonlinear particle swarm algorithm, and outputting an optimization result through the nonlinear particle swarm algorithm by taking the objective function in the step S1, the typical annual meteorological data in the step S3 and the annual investment cost and constraint conditions in the step S5 as input based on the system evaluation system.
2. The method for the economics of the multi-energy complementary linkage according to claim 1, wherein the objective function in step S1 is:
Figure FDA0002537793530000011
in the formula (f)α、fβ、fcRespectively an economic target, an environmental target and a reliability target; x is a position variable according toThe output power of a power supply and the charge and discharge power of an energy storage device participating in power generation in a body time period are established; f. ofα1、fα2、fα3Respectively the installation cost, the maintenance cost and the power failure compensation cost; n is a radical ofswi、Nsli、Nsdr、NsstThe number of the fan, the photovoltaic, the storage battery and the pumped storage energy are respectively; cswi、Csli、Csdr、CsstThe unit price of a fan, a photovoltaic, a storage battery and pumped storage is respectively; cswm、Cslm、Csdm、CssmThe maintenance costs of the fan, the photovoltaic, the storage battery and the water pumping and energy storage are respectively saved; pLCThe power is insufficient for the system electricity; chi is a power supply insufficiency compensation coefficient, and the load of residents is taken as 2.41 yuan/kW; n is the hour number of one year, and is more than or equal to 0 and less than or equal to 8760.
3. The method for the economics of the multi-energy complementary linkage according to claim 2, wherein the system model of the distributed power source in step S2 comprises a fan model and a photovoltaic cell model;
the fan model is as follows:
Vw＝Vwb+Vwg+Vwr+Vwn(2)
Figure FDA0002537793530000021
in the formula, VwRepresenting the actual speed of the fan; vwb、Vwg、Vwr、VwnRespectively the basic wind speed, the gust wind speed, the gradual change wind speed and the random wind speed; pswiRated power for the fan; vco、VciRespectively taking the cut-in wind speed and the cut-out wind speed of the fan, wherein the values are respectively 2.5m/s and 18 m/s;
the photovoltaic cell model is:
Figure FDA0002537793530000022
Figure FDA0002537793530000023
in the formula, Psli、PmaxThe output power and the maximum output power of the photovoltaic generator are respectively; gC、GSTCRespectively representing the illumination intensity and the standard illumination intensity; t iscIs the absolute temperature within the cell; t isiIs a temperature coefficient, Ti＝6.4×10-4(K-1)；TcoThe value is 25 ℃ for the quasi absolute temperature; t isaIs ambient temperature.
4. The method for the multi-energy complementary linkage economic assessment according to claim 1, wherein the typical annual meteorological data in said step S3 includes load data, wind speed data, light intensity data, and temperature data.
5. The method for economic evaluation of multi-energy complementary linkage according to claim 3, wherein in step S4,
the charge-discharge model of the storage battery is as follows:
Figure FDA0002537793530000024
C＝Id·td(7)
wherein E is the actual voltage; ei0Charging an open circuit voltage for the battery; r is the internal resistance of the battery; r0Actual internal resistance; q is the discharge capacity; k1、K2Is a constant; c represents the rated capacity of the storage battery, and the unit is A.h; i isdIs a charge-discharge current; t is tdIs the charging and discharging time;
virtualizing a pumped storage unit into a virtual motor, wherein a pumped storage energy charging and discharging model is as follows:
Figure FDA0002537793530000031
in the formula, Psst、QsstPower and heat dissipated by the virtual motor, respectively; n is the number of power points; thetat i.nThe variable is 0-1 and represents the power point n of the virtual motor at the time t; pi.n、Qi.nRespectively the power value and the water flow of the unit i at the power point n.
6. The method for economic evaluation of multi-energy complementary linkage according to claim 5, wherein the constraint conditions in step S5 include:
fan, photovoltaic output constraint conditions:
Figure FDA0002537793530000032
Figure FDA0002537793530000033
Figure FDA0002537793530000034
in the formula, Pswin、PslinRated power of a single fan and rated power of a photovoltaic are respectively set; pswi、Psli、Psdr、PsstRespectively representing the total power of wind power generation, photovoltaic power generation, pumped storage and storage battery power generation; xiRepresenting the load type; piIs the load power; m is the total number of the equipment; t is denoted as time t;
power supply number constraint of distributed power supply:
Figure FDA0002537793530000035
in the formula, Nwi、Nli、Nst、NdrThe number of wind power generation, photovoltaic power generation, storage batteries and pumped storage energy are respectively; n is a radical ofwi.min、Nli.min、Nst.min、Ndr.minRespectively for wind power generationThe minimum quantity of photovoltaic power generation, storage batteries and water pumping energy storage is 0; n is a radical ofwi.max、Nli.max、Nst.max、Ndr.maxThe maximum quantity of wind power generation, photovoltaic power generation, storage batteries and water pumping and energy storage is determined according to the capacity of the distributed power supply;
constraint conditions of the storage battery:
Figure FDA0002537793530000041
in the formula, Smin、Smax、Ssoc(t) represents the minimum, maximum, and run-time capacity of the battery, respectively; pch(t)、Pdch(t) respectively represents the charging and discharging power of the storage battery, the service life of the battery is reduced due to an excessively high charging and discharging rate, therefore, the upper limit of the charging power per hour cannot exceed SOC/5, and delta t is taken as 1 h; ebatRefers to the electromotive force of the battery;
the reliability constraint conditions of the multi-energy complementary power generation system are as follows:
POPS≤POPSset(14)
Figure FDA0002537793530000042
in the formula, POPS is the probability of power shortage of the load; POPS (Point of Care Package protection)setSetting the maximum power shortage probability for the system; t is the total simulation duration; psup(t)、PneeAnd (t) respectively representing the total power supply power of the power grid at the moment t when the power grid is connected and the required power of the load.
7. The method for the economic evaluation of the multi-energy complementary linkage as claimed in claim 1, wherein the non-linear particle swarm algorithm in the step S6 is implemented by the following steps:
step S61, setting inertia weight value, randomly generating acceleration weight coefficient, calculating the inertia weight coefficient once when the particle swarm is updated, and randomly generating acceleration weight coefficient r1，r2；
Step S62, setting the maximum value and the minimum value of the particle speed, and replacing the maximum value and the minimum value with a boundary value when exceeding the boundary;
step S63, updating the particle speed and position, judging whether the objective function meets the requirement, if so, selecting a nonlinear particle swarm algorithm to update the position of the particle, keeping the original speed of the particle unchanged, otherwise, updating the speed and the position of the particle by using a standard particle swarm algorithm, and limiting the maximum speed v of the particlemax；
Step S64, calling a judgment function to recalculate the objective function value, comparing and determining the individual and global extremum, comparing the individual and global extremum with the adaptation value of the optimal position which has been experienced, and if the current adaptation value is large, taking the current adaptation value as a new gbest; then, the adaptive value of each particle is compared with the optimal adaptive value of the population, the maximum value is taken as the optimal value gbest of the population, and a new objective function value is generated again;
step S65, judging whether the iteration number reaches the maximum iteration number M or whether the position distance of each particle is smaller than a certain threshold value, if not, performing step S64, continuing the iteration, and if a termination condition is met, outputting a gbest to obtain a final optimization result;
and step S66, outputting a seed value, a progression number, an optimal iteration number and an objective function value, and storing a particle displacement value and a velocity value.
CN202010538163.0A 2020-06-12 2020-06-12 Method suitable for multi-energy complementary linkage economy evaluation Active CN111585305B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010538163.0A CN111585305B (en) 2020-06-12 2020-06-12 Method suitable for multi-energy complementary linkage economy evaluation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010538163.0A CN111585305B (en) 2020-06-12 2020-06-12 Method suitable for multi-energy complementary linkage economy evaluation

Publications (2)

Publication Number Publication Date
CN111585305A true CN111585305A (en) 2020-08-25
CN111585305B CN111585305B (en) 2023-08-11

Family

ID=72120167

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010538163.0A Active CN111585305B (en) 2020-06-12 2020-06-12 Method suitable for multi-energy complementary linkage economy evaluation

Country Status (1)

Country Link
CN (1) CN111585305B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113394807A (en) * 2021-06-23 2021-09-14 黄河勘测规划设计研究院有限公司 Method and device for optimizing installed ratio of clean energy complementary base

Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070142975A1 (en) * 2005-12-21 2007-06-21 Pegasus Technologies, Inc. Model based optimization of a single or multiple power generating units
CN105337315A (en) * 2015-10-21 2016-02-17 温州大学 Wind-light-storage battery supplementary independent micro power grid high dimension multi-target optimization configuration
CN106992549A (en) * 2017-05-22 2017-07-28 河南森源电气股份有限公司 The capacity configuration optimizing method and device of a kind of independent micro-grid system
CN107482638A (en) * 2017-07-21 2017-12-15 杭州电子科技大学 Supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method
CN107565610A (en) * 2017-08-17 2018-01-09 国网山东省电力公司电力科学研究院 A kind of NETWORK STRUCTURE PRESERVING POWER SYSTEM dispatching method containing wind, photoelectric source
CN107609693A (en) * 2017-08-31 2018-01-19 安徽大学 Micro-capacitance sensor Multipurpose Optimal Method based on Pareto archives particle cluster algorithms
CN107834601A (en) * 2017-11-17 2018-03-23 燕山大学 A kind of independent micro-grid system capacity configuration optimizing method for considering flexible load
CN107977803A (en) * 2017-12-30 2018-05-01 国网天津市电力公司电力科学研究院 A kind of micro-grid system economic evaluation methods for considering overall life cycle cost
CN108491992A (en) * 2018-02-05 2018-09-04 国网天津市电力公司滨海供电分公司 A kind of cooling heating and power generation system peak regulation containing photovoltaic and accumulation of energy is regulated and stored Optimal Operation Model
CN108711892A (en) * 2018-05-30 2018-10-26 南京工程学院 A kind of Optimization Scheduling of multi-energies hybrid power generating system
CN108734350A (en) * 2018-05-17 2018-11-02 燕山大学 A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor
CN108879793A (en) * 2018-07-12 2018-11-23 电子科技大学 A kind of off-grid type energy mix system optimization method of scene storage station complementation
CN109345019A (en) * 2018-10-10 2019-02-15 南京邮电大学 A kind of micro-capacitance sensor economic load dispatching optimisation strategy based on improvement particle swarm algorithm
CN109888806A (en) * 2019-02-28 2019-06-14 武汉大学 A kind of micro-capacitance sensor energy storage Optimal Configuration Method containing electric car
CN110112734A (en) * 2019-06-04 2019-08-09 华北电力大学 A kind of microgrid Optimization Scheduling based on heuristic rule
CN110659830A (en) * 2019-09-25 2020-01-07 国网天津市电力公司 Multi-energy micro-grid planning method for comprehensive energy system

Patent Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070142975A1 (en) * 2005-12-21 2007-06-21 Pegasus Technologies, Inc. Model based optimization of a single or multiple power generating units
CN105337315A (en) * 2015-10-21 2016-02-17 温州大学 Wind-light-storage battery supplementary independent micro power grid high dimension multi-target optimization configuration
CN106992549A (en) * 2017-05-22 2017-07-28 河南森源电气股份有限公司 The capacity configuration optimizing method and device of a kind of independent micro-grid system
CN107482638A (en) * 2017-07-21 2017-12-15 杭州电子科技大学 Supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method
CN107565610A (en) * 2017-08-17 2018-01-09 国网山东省电力公司电力科学研究院 A kind of NETWORK STRUCTURE PRESERVING POWER SYSTEM dispatching method containing wind, photoelectric source
CN107609693A (en) * 2017-08-31 2018-01-19 安徽大学 Micro-capacitance sensor Multipurpose Optimal Method based on Pareto archives particle cluster algorithms
CN107834601A (en) * 2017-11-17 2018-03-23 燕山大学 A kind of independent micro-grid system capacity configuration optimizing method for considering flexible load
CN107977803A (en) * 2017-12-30 2018-05-01 国网天津市电力公司电力科学研究院 A kind of micro-grid system economic evaluation methods for considering overall life cycle cost
CN108491992A (en) * 2018-02-05 2018-09-04 国网天津市电力公司滨海供电分公司 A kind of cooling heating and power generation system peak regulation containing photovoltaic and accumulation of energy is regulated and stored Optimal Operation Model
CN108734350A (en) * 2018-05-17 2018-11-02 燕山大学 A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor
CN108711892A (en) * 2018-05-30 2018-10-26 南京工程学院 A kind of Optimization Scheduling of multi-energies hybrid power generating system
CN108879793A (en) * 2018-07-12 2018-11-23 电子科技大学 A kind of off-grid type energy mix system optimization method of scene storage station complementation
CN109345019A (en) * 2018-10-10 2019-02-15 南京邮电大学 A kind of micro-capacitance sensor economic load dispatching optimisation strategy based on improvement particle swarm algorithm
CN109888806A (en) * 2019-02-28 2019-06-14 武汉大学 A kind of micro-capacitance sensor energy storage Optimal Configuration Method containing electric car
CN110112734A (en) * 2019-06-04 2019-08-09 华北电力大学 A kind of microgrid Optimization Scheduling based on heuristic rule
CN110659830A (en) * 2019-09-25 2020-01-07 国网天津市电力公司 Multi-energy micro-grid planning method for comprehensive energy system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
李祝红，赵灿明，闫龙，张信明: "智能电网中电力线通信网络负载均衡的机会路由协议", 《计算机应用》, pages 1 - 5 *
马荣荣，贾燕冰，张博文，陈浩，李玉博，谢栋，李晶晔: "多双馈风机变换器控制参数整定", 《中国科技论文》, pages 309 - 313 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113394807A (en) * 2021-06-23 2021-09-14 黄河勘测规划设计研究院有限公司 Method and device for optimizing installed ratio of clean energy complementary base

Also Published As

Publication number Publication date
CN111585305B (en) 2023-08-11

Similar Documents

Publication Publication Date Title
Wu et al. Optimal coordinate operation control for wind–photovoltaic–battery storage power-generation units
CN111445090B (en) Double-layer planning method for off-grid type comprehensive energy system
CN104319768B (en) A kind of micro-capacitance sensor is powered and method for supervising
Deng et al. System modeling and optimization of microgrid using genetic algorithm
Zhao et al. The optimal capacity configuration of an independent wind/PV hybrid power supply system based on improved PSO algorithm
CN103606913B (en) Distributed hybrid power system power source planning method
CN110288111A (en) A kind of method and system of family's electric energy management based on weather forecasting
CN112865075B (en) AC/DC hybrid micro-grid optimization method
Arani et al. A novel control method based on droop for cooperation of flywheel and battery energy storage systems in islanded microgrids
Kumaravel et al. Adapted multilayer feedforward ANN based power management control of solar photovoltaic and wind integrated power system
CN104037791A (en) Multi-agent technology based scenery storage power generation coordination control method
CN102983604A (en) Photovoltaic and fuel cell combined generating system
Aiswariya et al. Optimal microgrid battery scheduling using simulated annealing
CN110474348A (en) A kind of peak regulating method and device of power distribution network
Zhang et al. Optimized scheduling model for isolated microgrid of wind-photovoltaic-thermal-energy storage system with demand response
CN111585305B (en) Method suitable for multi-energy complementary linkage economy evaluation
jing Hu et al. Capacity optimization of wind/PV/storage power system based on simulated annealing-particle swarm optimization
CN115940284B (en) Operation control strategy of new energy hydrogen production system considering time-of-use electricity price
CN208386227U (en) Wind-light storage is provided multiple forms of energy to complement each other system
CN114914943A (en) Hydrogen energy storage optimization configuration method for green port shore power system
Lagouir et al. Optimal power flow management strategies of MicroGrid, using Petri Nets and fuzzy logic approaches
Kan et al. Optimal configuration of the hybrid energy storage system for reducing the amount of discarded photovoltaic
CN109119988B (en) Photovoltaic-battery microgrid energy scheduling management method based on dynamic wholesale market price
Liu et al. Optimal sizing of distributed generation based on chaotic free-search algorithm in an island microgrid
Vanitha et al. Control of microgrid with enhancement of wind power evacuation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant