CN111585305B - Method suitable for multi-energy complementary linkage economy evaluation - Google Patents
Method suitable for multi-energy complementary linkage economy evaluation Download PDFInfo
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- 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
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- 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
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- 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]
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- 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
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- 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
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- 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 evaluating multi-energy complementary linkage economy, which comprises the following steps: constructing an objective function for distributed power supply economy evaluation; constructing a system model of the distributed power supply; initializing external factors and inputting model year weather data; the annual load data and the mixed micro-grid dispatching strategy are called, and charge and discharge models of the storage battery and the pumped storage are considered; calculating annual investment cost by adopting an objective function, and considering constraint conditions; and constructing a system evaluation system by using a nonlinear particle swarm algorithm, and outputting an optimization result. The invention can greatly save the time of economic evaluation of the multi-energy complementary linkage technology, increase the evaluation efficiency, not only can improve the overall power generation efficiency of the micro-grid power generation system and reasonably allocate the quantity of distributed power sources, but also can furthest improve the power generation efficiency of the multi-energy complementary power generation system and reduce the power generation cost of the system under the same environment, temperature and illumination.
Description
Technical Field
The invention relates to the field of capacity economy optimization configuration 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 demands of remote non-electricity areas, the independent wind-solar energy storage complementary system is taken as a main material, and the hot spot for the research on the capacity economic optimization configuration of the multi-energy complementary linkage system is correspondingly concentrated on the independent multi-energy complementary linkage power generation system. For distributed energy sources such as wind energy, solar energy and the like, the system has strong uncertainty due to the restriction of various factors such as seasons, geographies, climates and the like, and the uncertainty can cause huge economic loss whether the system is integrated into a power grid or independently generates power.
In order to reduce loss and cost, economic evaluation research on a multi-energy complementary linkage technology is generated. The multi-energy complementary linkage power generation technology plays an important role in improving power supply diversity, flexibility and reliability, but has to be improved in the aspect of capacity economy optimization configuration.
Disclosure of Invention
The invention aims to make up the defects of the prior art and provides a method suitable for evaluating the economic performance of the multi-energy complementary linkage, which can greatly save the time of the economic evaluation of the multi-energy complementary linkage technology and increase the evaluation efficiency.
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:
s1, constructing an objective function for evaluating the economical efficiency of a distributed power supply, taking an economic target, an environmental target and the reliability of equipment as final targets, and evaluating the overall economic optimization level of a system;
s2, constructing a system model of the distributed power supply, and taking constraint conditions of the distributed power supply into consideration to prepare for subsequent calculation;
step S3, initializing external factors, inputting model year weather data based on a basic configuration optimal planning program, and outputting basic equipment configuration conditions as iteration original data;
s4, calling annual load data and a hybrid micro-grid scheduling strategy, considering charge and discharge models of a storage battery and pumped storage, and taking constraint conditions of equipment into consideration to prepare 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 taking constraint conditions into consideration;
and S6, constructing a system evaluation system by using a nonlinear particle swarm algorithm, and outputting an optimization result by using the nonlinear particle swarm algorithm based on the system evaluation system by taking the objective function in the step S1, the model annual meteorological data in the step S3 and the annual investment cost and constraint conditions in the step S5 as inputs.
Further, the objective function in the step S1 is:
wherein f a 、f β 、f χ Respectively an economic target, an environmental target and a reliability target; x is a position variable, and the output power of a power supply and the charge and discharge power of an energy storage device which participate in power generation are established according to a specific time period; f (f) a1 、f a2 、f a3 The installation cost, the maintenance cost and the power failure compensation cost are respectively; n (N) swi 、N sli 、N sdr 、N sst The number of fans, photovoltaics, storage batteries and pumped storage batteries is respectively; c (C) swi 、C sli 、C sdr 、C sst The unit price of the blower, the photovoltaic, the storage battery and the pumped storage respectively; c (C) swm 、C slm 、C sdm 、C ssm The maintenance cost of the blower, the photovoltaic, the storage battery and the pumped storage respectively; p (P) LC The system is insufficient in electric quantity; chi is a power supply deficiency compensation coefficient, and is taken as 2.41 yuan/kW in consideration of resident load; n is the number of hours of one year, and N is more than or equal to 0 and less than or equal to 8760.
Further, the system model of the distributed power supply in the step S2 includes a fan model and a photovoltaic cell model;
the fan model is as follows:
V w ＝V wb +V wg +V wr +V wn (2)
wherein V is w Representing the actual speed of the fan; v (V) wb 、V wg 、V wr 、V wn The wind speed is respectively the basic wind speed, the gust wind speed, the gradual change wind speed and the random wind speed; p (P) swi Rated power of the fan; v (V) ci 、V co The cut-in wind speed and the cut-out wind speed are respectively 2.5m/s and 18m/s;
the photovoltaic cell model is:
wherein P is sli 、P max The output power and the maximum output power of the photovoltaic generator are respectively; g C 、G STC Respectively the illumination intensity and the standard illumination intensity; t (T) c Absolute temperature within the battery; t (T) i Is the temperature coefficient, T i ＝6.4×10-4(K -1 )；T co The absolute temperature is the standard, and the value is 25 ℃; t (T) a Is ambient temperature.
Further, the model year weather data in the step S3 includes load data, wind speed data, illumination intensity data, and temperature data.
Further, in the step S4,
the charge and discharge model of the storage battery is as follows:
C＝I d ·t d (7)
wherein E is the actual voltage; e (E) i0 Charging an open circuit voltage for the battery; r is the internal resistance of the battery; r is R 0 Is the actual internal resistance; q is the discharge capacity; k (K) 1 、K 2 Is a constant; c represents the rated capacity of the storage battery, and the unit is A.h; i d Is a charge-discharge current; t is t d Is the charge and discharge time;
virtual motor is formed by the pumped storage unit, and the charge and discharge model of the pumped storage unit is as follows:
wherein P is sst 、Q sst The power and the heating value consumed by the virtual motor are respectively; n is the number of power points; v t i.n A variable of 0-1, which represents a power point n of the virtual motor at a time t; p (P) i.n 、Q i.n The power value and the water flow of the unit i at the power point n are respectively.
Further, the constraint condition in the step S5 includes:
fan, photovoltaic output constraint condition:
wherein P is swin 、P slin Rated power of a single fan and rated power of photovoltaic are respectively; p (P) swi 、P sli 、P sdr 、P sst Respectively representing the total power of wind power generation, photovoltaic power generation, pumped storage and storage battery power generation; x is X i Representing load types; p (P) i Is the load power; m is the total number of the devices; t is denoted as time t;
power number constraint for distributed power supplies:
wherein N is wi 、N li 、N st 、N dr The quantity of wind power generation, photovoltaic power generation, storage batteries and pumped storage respectively; n (N) wi.min 、N li.min 、N st.min 、N dr.min The minimum numbers of wind power generation, photovoltaic power generation, storage battery and pumped storage are respectively 0; n (N) wi.max 、N li.max 、N st.max 、N dr.max The maximum number of wind power generation, photovoltaic power generation, storage battery and pumped storage is determined according to the capacity of the distributed power supply;
battery constraint conditions:
wherein S is min 、S max 、S soc (t) represents the minimum, maximum, and run-time capacities of the battery, respectively; p (P) ch (t)、P dch (t) respectively represents the charge and discharge power of the storage battery, and too high a charge and discharge rate will beThe service life of the battery is reduced, so that the upper limit of the charging power per hour cannot exceed SOC/5, and Deltat is taken as 1h; e (E) bat Refers to the electromotive force of the battery;
reliability constraint conditions of the multi-energy complementary power generation system:
POPS≤POPS set (14)
wherein POPS is load power failure probability; POPS (Power over protection System) set Maximum power loss probability set for the system.
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 inertia weight coefficient once every time particle swarm is updated, and randomly generating acceleration weight coefficient r 1 ，r 2 ；
Step S62, setting a maximum value and a minimum value of the particle speed, and replacing the maximum value and the minimum value by boundary values when the particle speed exceeds the boundary;
step S63, updating the particle speed and position, judging whether the objective function meets the requirements, if yes, updating the particle position by using a nonlinear particle swarm algorithm, keeping the original particle speed unchanged, otherwise, updating the particle speed and position by using a standard particle swarm algorithm, and limiting the maximum particle speed v max ；
Step S64, a judging function is called to recalculate the objective function value, the individual extremum and the global extremum are compared and determined, the individual extremum and the adaptation value of the once-traversed optimal position are compared, and if the current adaptation value is large, the current adaptation value is used as a new gbest; then comparing the adaptive value of each particle with the group optimal adaptive value, and regenerating a new objective function value by taking the maximum adaptive value as the group optimal value gbest;
step S65, judging whether the iteration number reaches the maximum iteration number M or the position distance of each particle is smaller than a certain threshold value, if not, performing step S64, continuing iteration, and if the termination condition is met, outputting gbest to obtain a final optimization result;
and step S66, outputting a seed value, an advance degree, an optimal iteration number and an objective function value, and storing a particle displacement value and a velocity value.
The beneficial effects of the invention are as follows:
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 same environment, temperature and illumination, and the power generation cost of the system is reduced.
3. The time for 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 a diagram of an equivalent circuit of a battery;
FIG. 2b is a schematic diagram of a battery operating point;
FIG. 3 is a flow chart for standard particle swarm optimization;
FIG. 4 is a graph of inertial weight versus particle swarm algorithm;
FIG. 5 is a flow chart of a nonlinear particle swarm algorithm according to the present invention;
FIG. 6a is a load graph;
FIG. 6b is a graph of wind speed;
FIG. 6c is a graph of illumination intensity;
FIG. 6d is a temperature profile;
FIG. 7 is a graph comparing total investment costs after optimization of two algorithms.
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 details. In other instances, well-known features have not been described in detail in order to avoid obscuring the embodiments of the invention.
In the following description, a detailed structure will be presented for a thorough understanding of embodiments of the present invention. It will be apparent that embodiments of the invention may be practiced without limitation to the specific details that are set forth by those skilled in the art. Preferred embodiments of the present invention are described in detail below, however, the present invention may have other embodiments in addition to these detailed descriptions.
The invention is described in further detail below with reference to the accompanying drawings:
the distributed power supply in the multifunctional complementary power generation system mainly comprises a fan, a photovoltaic array, a pumped storage battery, a storage battery and the like, an inverter, a chopper, a rectifier and a controller, and can be connected with a power distribution network through a public connection point to form a grid-connected mode or independently run to form an island mode. The invention presumes that the output of the combined micro-grid power supply and the alternating current and direct current load thereof can be tracked quickly, various distributed power supplies realize plug and play and can stably operate in a grid-connected mode and an island mode, and the structural schematic diagram is shown in figure 1.
In the multi-energy complementary power generation system, the invention aims to improve the overall power generation efficiency of the micro-grid power generation system by reasonably configuring a distributed power supply, and simultaneously, the economical efficiency, the reliability and the influence on the environment of the system can reach satisfactory degree, thereby improving the power generation efficiency of the multi-energy complementary power generation system to the maximum extent and reducing the power generation cost of the system. Therefore, in the field of capacity economy optimization configuration of a distributed power supply complementary power generation system, a system model of the distributed power supply is provided, and a method suitable for multi-energy complementary linkage economy evaluation is designed according to the model. The method comprises the following specific steps:
step S1: an objective function for distributed power supply economy evaluation is constructed, and the overall economy optimization level of the system is evaluated with an economic objective and an environmental objective and the reliability of equipment as final objectives.
The objective function is:
wherein f a 、f β 、f χ Respectively an economic target, an environmental target and a reliability target; x is a position variable, and the output power of a power supply and the charge and discharge power of an energy storage device which participate in power generation are established according to a specific time period; f (f) a1 、f a2 、f a3 The installation cost, the maintenance cost and the power failure compensation cost are respectively; n (N) swi 、N sli 、N sdr 、N sst The number of fans, photovoltaics, storage batteries and pumped storage batteries is respectively; c (C) swi 、C sli 、C sdr 、C sst The unit price of the blower, the photovoltaic, the storage battery and the pumped storage respectively; c (C) swm 、C slm 、C sdm 、C ssm The maintenance cost of the blower, the photovoltaic, the storage battery and the pumped storage respectively; p (P) LC The system is insufficient in electric quantity; the χ is a compensation coefficient of insufficient power supply, and the invention considers the load of residents and takes the load as 2.41 yuan/kW; n is the number of hours of one year, and N is more than or equal to 0 and less than or equal to 8760.
Step S2: firstly, a fan model and a photovoltaic cell model are introduced, a system model of the distributed power supply is built, constraint conditions of the distributed power supply are considered, and preparation is made for calculation of a subsequent objective function.
The fan model adopts a four-component method, namely, the wind speed is divided into four components of basic wind, gust wind, gradual change wind and random wind, and the fan model is as follows:
V w ＝V wb +V wg +V wr +V wn (2)
wherein V is w Representing the actual speed of the fan; v (V) wb 、V wg 、V wr 、V wn Respectively the basic wind speed, the gust wind speed, the gradual change wind speed and the random wind speedSpeed is high; p (P) swi Rated power of the fan; v (V) ci 、V co The cut-in wind speed and the cut-out wind speed are respectively 2.5m/s and 18m/s.
The photovoltaic generator set is formed by connecting small module batteries in series or in parallel, and the photovoltaic array formed by integrating the small modules can transmit electric energy reaching the user standard. The photovoltaic cell model consists of a formula (4) and a formula (5), wherein the formula (4) is the output power of a photovoltaic generator, and for a glass-packaged solar panel, the working temperature of the battery can be approximately calculated according to the specific environment temperature by the formula (5), and the specific steps are as follows:
wherein P is sli 、P max The output power and the maximum output power of the photovoltaic generator are respectively; g C 、G STC Respectively the illumination intensity and the standard illumination intensity, in particular G STC For the illumination intensity under STC (1000W/m) 2 )；T c Absolute temperature within the battery; t (T) i Is the temperature coefficient, T i ＝6.4×10-4(K -1 )；T co The absolute temperature is the standard, and the value is 25 ℃; t (T) a Is the ambient temperature in degrees celsius.
Step S3: and initializing external factors, namely inputting model year weather data, and calculating and outputting basic equipment configuration conditions as iteration original data through a basic configuration optimal planning program. And inputting model year weather data, including load data, wind speed data, illumination intensity data and temperature data. Firstly, testing the regional load electricity consumption and the output characteristic curve of the wind driven generator, and analyzing the relation between the regional electricity consumption and the building environment, wherein the results are shown in fig. 6a and 6 b. The output of the wind driven generator is rectified and then connected with a variable resistance load, and the P-V curve of the wind driven generator can be measured by adjusting the wind speed and the load resistance value. Average illumination intensity of the record year is collectedThe degree and temperature, with the average value as the output, 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 can be improved. Setting the maximum iteration number of the particle swarm as M, the dimension of the search space as D and the maximum speed of the particles as v max 。
Step S4: and calling annual load data and a hybrid micro-grid scheduling strategy, taking charge and discharge models of the storage battery and the pumped storage into account, and taking constraint conditions of equipment into consideration to prepare for calculation of a subsequent objective function.
The storage battery can relieve intermittent power generation contradiction of renewable resources in the running process of the power grid, and stores energy in a physical mode or a chemical mode. The storage battery used by the invention can be used as a standby power supply to stabilize the power fluctuation and improve the reliability of the 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 is specifically as follows:
regarding the interior of the storage battery as a voltage source with small resistance, wherein the actual voltage E linearly decreases along with the increase of the discharge quantity Q, the internal resistance r of the battery linearly increases along with the increase of the discharge quantity Q, and the relationship between the actual voltage and the internal resistance of the battery and the discharge quantity is as follows;
wherein E is the actual voltage; e (E) i0 Charging an open circuit voltage for the battery; r is the internal resistance of the battery; r is R 0 Is the actual internal resistance; q is the discharge capacity; k (K) 1 、K 2 Is constant.
The operating point of the battery is located at the intersection point P of the load line and the battery terminal voltage characteristic, as shown in fig. 2 b. The rated capacity of the battery can be expressed as:
C＝I d ·t d (7)
c represents the rated capacity of the storage battery, and the unit is A.h; i d Is a charge-discharge current; t is t d Is charged and dischargedAnd (5) electric time.
The charge-discharge model of the pumped storage consists of a formula (8), and the concrete steps are as follows:
the pumped storage unit can be regarded as 2 working states of pumping and generating, and can not be converted at will. These 2 operating states are respectively virtual as virtual generators and virtual motors. Wherein the virtual generator is similar to a conventional unit and will not be described in detail herein. When the pumped storage unit is virtual as a virtual motor, the motor generally operates near an optimal power point, cannot be adjusted at will, and can only be positioned at a plurality of intermittent power points. The active power and reactive power (heating value) consumed by the device are respectively:
wherein P is sst 、Q sst Active power and heating value consumed by the virtual motor respectively; n is the number of power points; v t i.n A variable of 0-1, which represents a power point n of the virtual motor at a time t; p (P) i.n 、Q i.n The power value and the water flow of the unit i at the power point n are respectively.
Step S5: and (3) calculating annual investment cost by adopting an objective function of the formula (1), taking a calculation result as original reference data, and taking constraint conditions of each module (photovoltaic module, fan module and storage battery module) into consideration. Wherein each constraint condition is specifically as follows:
fan, photovoltaic output constraint condition:
wherein P is swin 、P slin Rated power of a single fan and rated power of photovoltaic are respectively; p (P) swi 、P sli 、P sdr 、P sst Respectively representing the total power of wind power generation, photovoltaic power generation, pumped storage and storage battery power generation; x is X i Representing load types; p (P) i Is the load power; m is the total number of the devices; t is denoted as time t; at a certain moment, the pumped storage power generation meets the total load power, and the photovoltaic and the fan power generation are reduced.
The distributed power supplies, namely wind power generation, photovoltaic power generation, pumped storage and storage batteries, are required to meet the constraint of the number of power supplies:
wherein N is wi 、N li 、N st 、N dr The quantity of wind power generation, photovoltaic power generation, storage batteries and pumped storage respectively; n (N) wi.min 、N li.min 、N st.min 、N dr.min The minimum numbers of wind power generation, photovoltaic power generation, storage battery and pumped storage are respectively 0; n (N) wi.max 、N li.max 、N st.max 、N dr.max The maximum number of wind power generation, photovoltaic power generation, storage battery and pumped storage is determined according to the capacity of the distributed power supply.
The state of charge and the charge and discharge power of the storage battery affect the operation safety and the service life of the storage battery, so the storage battery also meets three constraint conditions:
wherein S is min 、S max 、S soc (t) represents the minimum, maximum, and run-time capacities of the battery, respectively; p (P) ch (t)、P dch (t) represents the charge/discharge power of the secondary battery respectively, and an excessively high charge/discharge rate will reduce the service life of the battery, so that the upper limit of the charge power per hour cannot exceed SOC/5 (SOC refers to State of charge, the coincidence State of the battery, reflects electricity)Residual capacity of the pool), Δt is taken as 1h; e (E) bat Refers to the electromotive force of the battery.
The reliability constraint conditions of the multi-energy complementary power generation system are as follows:
POPS≤POPS set (14)
wherein POPS is load power failure probability; POPS (Power over protection System) set Maximum power loss probability set for the system.
Step S6: and taking the objective function in the step S1, the model year weather data in the step S3, the investment cost in the step S5 and the constraint conditions of all the modules as inputs, putting the inputs into the improved nonlinear particle swarm algorithm for operation, and outputting a result value.
As shown in fig. 5, the specific implementation steps of the nonlinear particle swarm algorithm are:
step S61, setting inertia weight value, randomly generating acceleration weight coefficient, calculating inertia weight coefficient once every time particle swarm is updated, and randomly generating acceleration weight coefficient r 1 ，r 2 ；
Step S62, setting a maximum value and a minimum value of the particle speed, and replacing the maximum value and the minimum value by boundary values when the particle speed exceeds the boundary;
step S63, updating the particle speed and position, judging whether the objective function meets the requirements, if yes, updating the particle position by using a nonlinear particle swarm algorithm, keeping the original particle speed unchanged, otherwise, updating the particle speed and position by using a standard particle swarm algorithm, and limiting the maximum particle speed v max ；
Step S64, a judging function is called to recalculate the objective function value, the individual extremum and the global extremum are compared and determined, the individual extremum and the adaptation value of the once-traversed optimal position are compared, and if the current adaptation value is large, the current adaptation value is used as a new gbest; then comparing the adaptive value of each particle with the group optimal adaptive value, and regenerating a new objective function value (namely the number and configuration conditions of each device corresponding to the economic benefit, link benefit and device reliability) by taking the maximum adaptive value as the group optimal value gbest;
step S65, judging whether the iteration number reaches the maximum iteration number M or the position distance of each particle is smaller than a certain threshold value, if not, performing step S64, continuing iteration, and if the termination condition is met, outputting gbest to obtain a final optimization result;
and step S66, outputting a seed value, an advance degree, an optimal iteration number and an objective function value, and storing a particle displacement value and a velocity value.
And when the intelligent algorithm is used for optimizing the combination optimization model and the economic evaluation of the multi-energy complementary power generation system, the scheduling strategy of the power generation system is also required to be considered. The scheduling policy is as follows:
1) The photovoltaic array and the wind driven generator are put into use according to 80% of the maximum power generation under the climate condition, the maximum power point of the electric energy required by a user can be born, and the wind power generation is superior to the photovoltaic power generation.
2) When wind power generation and photovoltaic power generation cannot supply power, the storage battery pack can meet the maximum load power requirement when independently supplying power, and can track the output change of the photovoltaic and the fan to charge and discharge, and the charging standard is that no matter what type of unit is currently used for supplying power to a power grid, only electric energy remains, and the electric energy is fed into the storage battery.
3) Considering extreme cases, when the wind power and photovoltaic generator set fails and the residual energy of the storage battery is insufficient to meet the load demand. At this time, the maximum electric power which can be achieved by the energy storage battery pack of the energy-saving building can meet the requirement, and the charging standard is to charge in the load valley period. Therefore, the power supply interruption condition possibly occurring in the power supply system is perfected, the wind and light discarding phenomenon is reduced, and the reliability and the practicability of the micro-grid are embodied.
4) When all four motors of the distributed power supply fail or the sum of the generated electric energy cannot reach the load electricity utilization standard, the system is subjected to load shortage. Such load deficit affects the user experience. And judging the stability and reliability parameters of the system according to the amount of the lack of electric energy of the system.
The economic evaluation method used by the invention is a particle swarm algorithm, and scientists find that individuals and other particles share and cooperate with each other through information through observation and research on the swarm such as the shoal and the shoal so as to achieve a common target, and the particle swarm algorithm is widely applied in the electric power field due to the characteristics of high precision, rapid convergence and the like. The velocity and position formula of the original particle swarm algorithm particles is as follows:
in the method, in the process of the invention,is the speed of the ith particle; w is an inertia factor, which is generally set to a constant 1; c 1 、c 2 As a learning factor, it is generally set to a constant 2; r is (r) 1 、r 2 Random numbers between 0 and 1 to maintain the disturbance introduced by the diversity of the particles; p is p id An individual best solution sought for the particles of index i; p is p gd A global optimal solution searched for the population; />The D-dimensional position vector for the kth iteration of the ith particle. The particle swarm algorithm flow is shown in FIG. 3.
The nonlinear particle swarm algorithm is to adjust the inertia weight in the algorithm into nonlinear self-optimizing inertia weight, and apply the nonlinear self-optimizing inertia weight to the configuration and economic optimization of the multi-energy complementary power generation system, wherein the specific formula of the nonlinear inertia weight is shown in formula (18), and the inertia weight pair is shown in fig. 4.
Wherein: w represents inertial weight, w max Represents the maximum inertial weight, w min Representing the minimum inertial weight, k representing the current iteration number, and m representing the maximum iteration number.
Applying nonlinear particle swarm algorithm to a complementary power supply systemThe system carries out multisource optimal configuration, and the power supply types are as follows: photovoltaic array, fan, storage battery. Assuming that residents 200 in a certain area take one hour as a basic step length of research simulation, the period is set to be 1 year, model year load data and the annual wind speed, illumination intensity and climate temperature conditions in the certain area are selected as input data of the system. In the algorithm, the initial population size of particles is 100, the maximum iteration number is 100, and c 1 ＝c 2 =2.0. The parameters of the various distributed power sources are shown in table 1 in combination with the manufacturer data. The wind speeds of the wind turbine generator are 2m/s, 4m/s and 6m/s respectively; the allowable discharge depth of the battery was 80% and the rated capacity was 10kWh. And comprehensively analyzing multi-objective optimization functions such as economy, reliability and the like of the micro-grid complementary power generation system, and combining particle swarm optimization algorithm logic to obtain a final scheme. The final optimization results are shown in table 1, and the curves after distributed power supply optimization are shown in fig. 6.
Table 1 distributed Power optimization configuration results
The distributed power supply optimal configuration result shows that under the condition of the same user load and power supply capacity, the number proportion of fans and photovoltaic units used by the improved nonlinear particle swarm algorithm is increased compared with that of the basic particle swarm algorithm, and the proportion of storage batteries and pumped storage is reduced. Fig. 7 shows a comparison of the total investment costs of the two algorithms after optimization, and the total investment costs of the nonlinear particle swarm algorithm after optimization are always smaller than the total investment costs of the standard particle swarm algorithm. The method is characterized in that the overall power generation efficiency of the micro-grid power generation system is improved through the optimal configuration of the nonlinear particle swarm algorithm, namely, the number of distributed power sources is reasonably configured, the power generation efficiency of the system is improved to the greatest extent under the same environment, temperature and illumination conditions, the power generation cost of the system is reduced, and the method meets ideal expectations.
Secondly, as can be seen from fig. 7, when the iteration reaches about 40 th time, the difference between the two is maximum, and the nonlinear particle swarm algorithm is advanced about 60 times to reach the optimal result compared with the linear particle swarm algorithm, so that the time of economic evaluation can be greatly saved, and the evaluation efficiency can be increased. The final result shows that the model of the distributed power supply and the corresponding nonlinear particle swarm algorithm are provided for the multi-energy complementary linkage technology and the economic evaluation.
The invention provides a multi-energy complementary linkage technology economic evaluation optimization method-nonlinear particle swarm algorithm by taking multi-energy complementary linkage technology economic evaluation research as an entry point. The algorithm can greatly save the time of economic evaluation of the multi-energy complementary linkage technology, increase the evaluation efficiency, and secondly, not only can improve the overall power generation efficiency of the micro-grid power generation system and reasonably allocate the quantity of distributed power sources, but also can furthest improve the power generation efficiency of the multi-energy complementary power generation system and reduce the power generation cost of the system under the same environment, temperature and illumination.
In view of the foregoing, the present invention is not limited to the above-described embodiments, and those skilled in the art may devise other embodiments that fall within the spirit and scope of the invention.
Claims (6)
1. The method suitable for evaluating the multi-energy complementary linkage economy is characterized by comprising the following steps of:
s1, constructing an objective function for evaluating the economical efficiency of a distributed power supply, taking an economic target, an environmental target and the reliability of equipment as final targets, and evaluating the overall economic optimization level of a system;
s2, constructing a system model of the distributed power supply, and taking constraint conditions of the distributed power supply into consideration to prepare for subsequent calculation;
step S3, initializing external factors, inputting model year weather data based on a basic configuration optimal planning program, and outputting basic equipment configuration conditions as iteration original data;
s4, calling annual load data and a hybrid micro-grid scheduling strategy, considering charge and discharge models of a storage battery and pumped storage, and taking constraint conditions of equipment into consideration to prepare 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 taking constraint conditions into consideration;
s6, constructing a system evaluation system by using a nonlinear particle swarm algorithm, and outputting an optimization result by using the nonlinear particle swarm algorithm based on the system evaluation system by taking the objective function in the step S1, the model annual meteorological data in the step S3 and the annual investment cost and constraint conditions in the step S5 as inputs;
the objective function in the step S1 is as follows:
wherein f a 、f β 、f χ Respectively an economic target, an environmental target and a reliability target; x is a position variable, and the output power of a power supply and the charge and discharge power of an energy storage device which participate in power generation are established according to a specific time period; f (f) a1 、f a2 、f a3 The installation cost, the maintenance cost and the power failure compensation cost are respectively; n (N) swi 、N sli 、N sdr 、N sst The number of fans, photovoltaics, storage batteries and pumped storage batteries is respectively; c (C) swi 、C sli 、C sdr 、C sst The unit price of the blower, the photovoltaic, the storage battery and the pumped storage respectively; c (C) swm 、C slm 、C sdm 、C ssm The maintenance cost of the blower, the photovoltaic, the storage battery and the pumped storage respectively; p (P) LC The system is insufficient in electric quantity; chi is a power supply deficiency compensation coefficient, and is taken as 2.41 yuan/kW in consideration of resident load; n is the number of hours of one year, and N is more than or equal to 0 and less than or equal to 8760.
2. The method for evaluating the economic efficiency of the multi-energy complementary linkage according to claim 1, wherein the system model of the distributed power supply in the step S2 comprises a fan model and a photovoltaic cell model;
the fan model is as follows:
V w ＝V wb +V wg +V wr +V wn (2)
wherein V is w Representing the actual speed of the fan; v (V) wb 、V wg 、V wr 、V wn The wind speed is respectively the basic wind speed, the gust wind speed, the gradual change wind speed and the random wind speed; p (P) swi Rated power of the fan; v (V) ci 、V co The cut-in wind speed and the cut-out wind speed are respectively 2.5m/s and 18m/s;
the photovoltaic cell model is:
wherein P is sli 、P max The output power and the maximum output power of the photovoltaic generator are respectively; g C 、G STC Respectively the illumination intensity and the standard illumination intensity; t (T) c Absolute temperature within the battery; t (T) i Is the temperature coefficient, T i ＝6.4×10-4(K -1 )；T co The absolute temperature is the standard, and the value is 25 ℃; t (T) a Is ambient temperature.
3. The method for evaluating the economic efficiency of multi-energy complementary linkage according to claim 1, wherein the model year weather data in the step S3 includes load data, wind speed data, illumination intensity data and temperature data.
4. The method for evaluating the economy of multi-energy complementary linkage according to claim 2, wherein in the step S4,
the charge and discharge model of the storage battery is as follows:
C＝I d ·t d (7)
wherein E is the actual voltage; e (E) i0 Charging an open circuit voltage for the battery; r is the internal resistance of the battery; r is R 0 Is the actual internal resistance; q is the discharge capacity; k (K) 1 、K 2 Is a constant; c represents the rated capacity of the storage battery, and the unit is A.h; i d Is a charge-discharge current; t is t d Is the charge and discharge time;
virtual motor is formed by the pumped storage unit, and the charge and discharge model of the pumped storage unit is as follows:
wherein P is sst 、Q sst The power and the heating value consumed by the virtual motor are respectively; n is the number of power points; v t i.n A variable of 0-1, which represents a power point n of the virtual motor at a time t; p (P) i.n 、Q i.n The power value and the water flow of the unit i at the power point n are respectively.
5. The method for evaluating the economy of multi-energy complementary linkage according to claim 4, wherein the constraint condition in step S5 includes:
fan, photovoltaic output constraint condition:
wherein P is swin 、P slin Rated power of a single fan and rated power of photovoltaic are respectively; p (P) swi 、P sli 、P sdr 、P sst Respectively representing the total power of wind power generation, photovoltaic power generation, pumped storage and storage battery power generation; x is X i Representing load types; p (P) i Is the load power; m is the total number of the devices; t is denoted as time t;
power number constraint for distributed power supplies:
wherein N is wi 、N li 、N st 、N dr The quantity of wind power generation, photovoltaic power generation, storage batteries and pumped storage respectively; n (N) wi.min 、N li.min 、N st.min 、N dr.min The minimum numbers of wind power generation, photovoltaic power generation, storage battery and pumped storage are respectively 0; n (N) wi.max 、N li.max 、N st.max 、N dr.max The maximum number of wind power generation, photovoltaic power generation, storage battery and pumped storage is determined according to the capacity of the distributed power supply;
battery constraint conditions:
wherein S is min 、S max 、S soc (t) represents the minimum, maximum, and run-time capacities of the battery, respectively; p (P) ch (t)、P dch (t) represents the charge power and the discharge power of the storage battery respectively, and too high charge-discharge power will reduce the service life of the batteryTherefore, the upper limit of charge-discharge power per hour cannot exceed SOC/5, Δt is taken as 1h; e (E) bat Refers to the electromotive force of the battery;
reliability constraint conditions of the multi-energy complementary power generation system:
POPS≤POPS set (14)
wherein POPS is load power failure probability; POPS (Power over protection System) set Maximum power loss probability set for the system.
6. The method for evaluating the economic efficiency of the multi-energy complementary linkage according to claim 1, wherein 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 inertia weight coefficient once every time particle swarm is updated, and randomly generating acceleration weight coefficient r 1 ，r 2 ；
Step S62, setting a maximum value and a minimum value of the particle speed, and replacing the maximum value and the minimum value by boundary values when the particle speed exceeds the boundary;
step S63, updating the particle speed and position, judging whether the objective function meets the requirements, if yes, updating the particle position by using a nonlinear particle swarm algorithm, keeping the original particle speed unchanged, otherwise, updating the particle speed and position by using a standard particle swarm algorithm, and limiting the maximum particle speed v max ；
Step S64, a judging function is called to recalculate the objective function value, the individual extremum and the global extremum are compared and determined, the current adaptive value is compared with the adaptive value of the once-traversed optimal position, and if the current adaptive value is large, the current adaptive value is used as a new gbest; then comparing the adaptive value of each particle with the group optimal adaptive value, and regenerating a new objective function value by taking the maximum adaptive value as the group optimal value gbest;
step S65, judging whether the iteration number reaches the maximum iteration number M or the position distance of each particle is smaller than a certain threshold value, if not, performing step S64, continuing iteration, and if the termination condition is met, outputting gbest to obtain a final optimization result;
and step S66, outputting a seed value, an advance degree, an optimal iteration number and an objective function value, and storing a particle displacement value and a velocity value.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7599750B2 (en) * | 2005-12-21 | 2009-10-06 | Pegasus Technologies, Inc. | Model based sequential optimization of a single or multiple power generating units |
-
2020
- 2020-06-12 CN CN202010538163.0A patent/CN111585305B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 (1)
| Title |
|---|
| 多双馈风机变换器控制参数整定;马荣荣，贾燕冰，张博文，陈浩，李玉博，谢栋，李晶晔;《中国科技论文》;309-313 * |
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