CN111224422A - Reliability-based micro-grid distributed power supply configuration method and system - Google Patents
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Abstract
The invention discloses a reliability-based micro-grid distributed power supply configuration method and system. The method comprises the following steps: acquiring the load condition and meteorological condition of a set area; respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load condition and the meteorological condition; determining the power supply reliability by adopting a Monte Carlo method; establishing a power supply configuration model based on power supply reliability by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function; and obtaining the optimal wind-solar energy storage capacity configuration by adopting a particle swarm algorithm according to the power supply configuration model. By adopting the method or the system, the optimal configuration of the cost of the micro-grid power supply can be realized on the premise of reliability.
Description
Technical Field
The invention relates to the field of micro-grid distributed power supply configuration, in particular to a micro-grid distributed power supply configuration method and system based on reliability.
Background
In recent years, the development of small-capacity generators, power electronic equipment and battery energy storage technology makes it possible to realize an energy supply pattern of mainly clean energy and secondarily fossil energy. The micro-grid is a better solution for accessing the distributed power supply to the power distribution network, and can be regarded as a controllable unit relative to the power distribution network, so that the effective utilization rate of new energy can be improved, and the new energy can be flexibly and intelligently controlled. The micro-grid consists of various DGs, energy storage, loads, a current converter and a control and protection device, and can be used as an autonomous system to perform self control, protection and management, so that the free switching between grid-connected operation and island operation is realized. Compared with the direct grid connection of a single or a plurality of DGs, the grid connection power supply in the form of a micro-grid is more reliable, the spare capacity of the system can be effectively reduced, the stability of the system is improved, and good social benefits and economic benefits are generated.
The single machine cost of the distributed power supply is high, the output power difference of various distributed power supplies is large, and the capacities are different. If the capacity configuration is too large, the investment and operation maintenance cost of the micro-grid are increased, and energy waste is caused; if the capacity configuration is too small, the generated power is difficult to meet the load requirement, and the power supply reliability is reduced.
Disclosure of Invention
The invention aims to provide a reliability-based microgrid distributed power supply configuration method and system, which can realize the optimal configuration of microgrid power supply cost under the condition of ensuring reliability and power constraint.
In order to achieve the purpose, the invention provides the following scheme:
a reliability-based microgrid distributed power supply configuration method comprises the following steps:
acquiring the load condition and meteorological condition of a set area;
respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load condition and the meteorological condition;
determining the power supply reliability by adopting a Monte Carlo method;
establishing a power supply configuration model based on power supply reliability by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function;
and obtaining the optimal wind-solar energy storage capacity configuration by adopting a particle swarm algorithm according to the power supply configuration model.
Optionally, the electricity purchase cost is acquired.
Optionally, respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load condition and the meteorological condition, specifically including:
establishing a photovoltaic power generation cost model C according to the load condition and the meteorological condition2＝Pg·b+C'2(ii) a Wherein, C2For photovoltaic power generation costs, PgB is photovoltaic construction cost unit price for photovoltaic power generation capacity, C2' is the total operating cost of the photovoltaic power generation system;
establishing a fan power generation cost model according to the load condition and the meteorological condition
Wherein, CI is investment construction cost of the wind power plant, and CI is Pf·a，PfFor the capacity of the wind farm, a is the unit price of wind power construction, CMkFor maintenance costs of the k year of the operational period, CMkL is the maintenance cost rate of the equipment, and S is the wind power equipment and the installation operation fee;
establishing an energy storage cost model C according to the load condition3＝r1·Q+C'3(ii) a Wherein, C3For the cost of the energy storage device, r1Is energy storage unit cost, Q is capacity of energy storage device, C'3Is a fixed operating cost for the energy storage device.
Optionally, the determining the power supply reliability by using the monte carlo method specifically includes:
acquiring the fault rate and the repair rate of each element in the microgrid;
calculating the normal working time and the fault time of each element according to the fault rate and the repair rate;
establishing a normal working time model and a fault time model of each element according to the normal working time and the fault time;
simulating the working condition and the fault condition of each element by adopting a Monte Carlo method according to the normal working time model and the fault time model to obtain the average power failure time;
determining the average power supply availability according to the average power failure time;
and taking the average power supply availability as an evaluation standard of power supply reliability.
Optionally, the establishing a power configuration model based on power supply reliability with the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model, and the electricity purchase cost as an objective function specifically includes:
establishing a first power supply configuration model by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function and taking fan capacity constraint, photovoltaic capacity constraint, power constraint and power supply reliability constraint as first constraint conditions;
the objective function is:
Pffor wind power generation capacity, Q is an energy storage configuration, PgFor photovoltaic capacity, PdPower called from the large grid for the ith moment, C1、C2、C3、C4Respectively, the fan cost, the photovoltaic cost, the energy storage cost and the electricity purchasing cost;
energy storage configuration; q is 2Pl；
Wherein Q is the energy storage capacity, PlIs an important load capacity;
the first constraint is:
Pfmaxfor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, Pf(t)、Pg(t)、PL(t) fan output, photovoltaic output and load power at time t, Pd(t) is the amount of electricity taken from the grid, ASAI is the average power supply availability, an indicator for measuring power supply reliability, N is the total number of users, UiThe power failure time for each user, a, is the lower limit of the reliability requirement.
Optionally, the establishing a power configuration model based on power supply reliability with the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model, and the electricity purchase cost as an objective function specifically includes:
establishing a second power supply configuration model by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function and taking fan capacity constraint, photovoltaic capacity constraint, energy storage electric quantity state constraint, energy storage power constraint, power balance constraint and power supply reliability constraint as second constraint conditions;
the objective function is:
Pffor wind power generation capacity, Q is an energy storage configuration, PgFor photovoltaic capacity, PdPower called from the large grid for the ith moment, C1、C2、C3、C4Respectively, the fan cost, the photovoltaic cost, the energy storage cost and the electricity purchasing cost;
energy storage configuration;
wherein,
for the maximum capacity of the energy storage system in the whole sample data control period,
controlling the minimum capacity of the energy storage system in the whole sample data control period;
the second constraint is:
wherein, PfmaxFor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, QmaxFor maximum capacity limitation of energy storage, Pf(t)、Pg(t)、Pc(t)、PL(t) fan output, photovoltaic output, stored energy power and load power at time t, Pd(t) is the amount of power drawn from the grid, β is the rate of change of the peak-to-valley difference, PavIs the average value of the comprehensive load, and is the difference between the maximum value and the minimum value of the comprehensive load in one day; the comprehensive load refers to the difference between the load demand in the micro-grid and the distributed generation and transmission power.
Optionally, the method further includes:
applying a formula to the second power supply configuration model
Correcting to obtain a corrected second power supply configuration model;
wherein, W is the capacity of the energy storage system under the ideal condition, A is a safety coefficient, K is a temperature correction coefficient, η is the power conversion efficiency of the energy storage system, and DOD is the depth of discharge.
A reliability-based microgrid distributed power supply configuration system comprising:
the acquisition module is used for acquiring the load condition and the meteorological condition of a set area;
the cost model establishing module is used for respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load condition and the meteorological condition;
the power supply reliability determining module is used for determining the power supply reliability by adopting a Monte Carlo method;
the power supply configuration model establishing module is used for establishing a power supply configuration model based on power supply reliability by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function;
and the optimal wind and light storage capacity configuration module is used for obtaining optimal wind and light storage capacity configuration by adopting a particle swarm algorithm according to the power supply configuration model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a reliability-based microgrid distributed power supply configuration method, which is characterized in that reliability constraint is utilized to carry out optimal configuration on a distributed power supply, simulation is carried out by utilizing a Monte Carlo method when power supply reliability is calculated, the operation condition of each component and the whole working condition in the operating period of a microgrid can be simulated more practically, and the time of fault occurrence and whether the fault affects the power supply of an internal load of the microgrid or not are judged. Compared with the analytic method for calculating the power supply reliability, the simulation method is simpler and more efficient, and is suitable for calculating the reliability under a complex grid structure. The method is used for calculating and solving based on the particle swarm algorithm, the result accuracy is high, the convergence of the calculation process is fast, the configuration result can be solved fast, and the calculation process is simple and fast.
In addition, the method of the invention provides a method for calculating the distributed power supply and the energy storage capacity under two energy storage strategies according to different energy storage functions. The energy storage device has the advantages that the power supply can be configured according to the functions of the energy storage under different functions, and when the energy storage is used as standby, the energy storage capacity is configured according to the important load, so that the power supply reliability of the important load is ensured; when the stored energy participates in peak clipping and valley filling, the load is subjected to peak clipping and valley filling control according to an upper limit and lower limit constraint control method, and the fluctuation of the difference between the power supply and the load after the distributed power supply is connected is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a flow chart of a reliability-based method for configuring a distributed power supply of a microgrid according to an embodiment of the present invention;
FIG. 2 is a power characteristic curve diagram of a wind turbine generator according to an embodiment of the present invention;
FIG. 3 is a sequence diagram of the states of the elements according to the embodiment of the present invention;
FIG. 4 is a schematic diagram of an upper and lower bound constraint control principle according to an embodiment of the present invention;
fig. 5 is a block diagram of a reliability-based distributed power supply configuration system of a microgrid according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a reliability-based method for configuring a distributed power supply of a microgrid according to an embodiment of the present invention. As shown in fig. 1, a reliability-based microgrid distributed power supply configuration method includes:
step 101: acquiring the load condition and meteorological condition of a set area; and obtaining related wind speed, temperature and illumination data through meteorological conditions.
Step 102: according to the load condition and meteorological conditions, respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model, and specifically comprising:
establishing a photovoltaic power generation cost model C according to the load condition and the meteorological condition2＝Pg·b+C'2(ii) a Wherein, C2For photovoltaic power generation costs, PgIs photovoltaic power generation capacity, b is photovoltaic construction cost unit price, C'2The total operation cost of the photovoltaic power generation system is obtained;
establishing a fan power generation cost model according to the load condition and the meteorological condition
Wherein, CI is investment construction cost of the wind power plant, and CI is Pf·a，PfFor capacity of wind farm, a is wind power construction order, CMkFor maintenance costs of the k year of the operational period, CMkL is the maintenance cost rate of the equipment, and S is the wind power equipment and the installation operation fee;
according to the load condition, establishing an energy storage cost model C3＝r1·Q+C'3(ii) a Wherein, C3For the cost of the energy storage device, r1Is energy storage unit cost, Q is capacity of energy storage device, C'3Is a fixed operating cost for the energy storage device.
A photovoltaic power generation model, a fan power generation model and an energy storage model can be established according to the load condition and the meteorological condition. These models are used to build constraints and the cost models are used to build an objective function.
The photovoltaic power generation model is established as follows:
after the double-shaft tracking device and the maximum power point tracking technology are used, the output of the solar cell module is only determined by the solar radiation intensity and the temperature. Through the comparison of the general situation and the standard test conditions, a photovoltaic power generation model is obtained:
in the formula, PPVAnd PSTCActual output power and rated power, G, of the solar cell moduleTAs actual solar radiation intensity, GSTCThe intensity of solar radiation under standard test conditions is 1000W/m2， αPThe power temperature coefficient of the solar cell module is-0.35%/DEG C, TCIs the actual temperature, T, of the solar cell modulerThe cell temperature was taken at 25 ℃ for standard test conditions.
TC＝Ta+(Tr-20)GT/800
In the formula, TaIs ambient temperature.
The annual operating cost of the photovoltaic power generation system is 2% of the investment cost, and the total photovoltaic power generation cost is as follows:
C2＝Pg·b+C'2
in the formula, C2For photovoltaic power generation costs, PgB is photovoltaic construction cost unit price with unit of yuan/kW, C 'for photovoltaic power generation capacity'2The total operation cost of the photovoltaic power generation system is saved.
The establishment process of the fan power generation model is as follows:
where v is the wind speed at the hub height of the wind turbine, v beingciFor cutting into the wind speed, vRRated wind speed, vcoFor cutting out the wind speed, PRThe rated power of the wind turbine generator is obtained. FIG. 2 is a power characteristic curve diagram of a wind turbine generator according to an embodiment of the present invention. The wind turbine generator power characteristic curve corresponding to the equation is shown in fig. 2.
Considering the difference between the wind speeds of different types of fan hubs and the actually measured ground surface wind speed, the ground surface wind speed is reduced according to a wind speed distribution model, and the relationship between the wind speed and the height is as follows:
V＝V0·(H/H0)n
wherein V represents the wind speed at height H, and V0Represents a height of H0And n is a wind shear index, which depends on atmospheric stability and surface roughness. (H)0Generally 10m, and n generally 0.125-0.5).
The wind power generation cost mainly comprises investment construction cost and maintenance cost, and the investment construction cost of the wind power plant is as follows:
CI＝Pf·a
in the formula, CI is investment construction cost of wind power plant, PfAnd a is the capacity of the wind power plant, and a is the wind power construction unit with unit of yuan/kW.
The maintenance cost is counted and extracted from the construction period of the wind power plant, the counting and extracting rate is changed in a staged mode between 0.5% and 2.5%, and the counting and extracting rate is generally increased by 0.5% every 5 years. Calculating according to the comprehensive unit price of the wind power operation fee, wherein the maintenance cost estimation model during operation is as follows:
CMk＝L×S
in the formula, CMkAnd L is the maintenance cost of the equipment, S is the wind power equipment and the installation operation fee, and S is CI. l is the wind farm life.
Thus, the cost of generating electricity in a wind farm is:
the energy storage model is established as follows:
because the problem of optimal configuration of a Battery Energy Storage System (BESS) is researched, the internal circuit process of an energy storage unit does not need to be considered, and therefore the BESS can be subjected to mathematical modeling from the aspects of residual electric quantity, charge and discharge power and the like. The power recurrence relation is as follows.
And (3) charging process:
SOC(t)＝(1-δ)SOC(t-1)+PcΔtηc/Ec
and (3) discharging:
in the formula, SOC (t) is the residual electric quantity of the energy storage system at the end of the tth time period; SOC (t-1) is the residual capacity at the end of the t-1 th period; delta is the self-discharge rate of the energy storage system, and the unit is%/h; pc、Pdcharging and discharging power of the energy storage system in kW and eta respectivelyc、ηdRespectively representing the charging efficiency and the discharging efficiency of the energy storage system in unit percent; eCRated capacity of the energy storage system, unit kW.h.
The cost of energy storage is:
C3＝r1·Q+C3'
in the formula, C3For the cost of the energy storage device, r1Is energy storage unit cost, Q is capacity of energy storage device, C'3Is a fixed operating cost for the energy storage device.
Step 103: determining the power supply reliability by adopting a Monte Carlo method, which specifically comprises the following steps:
acquiring the fault rate and the repair rate of each element in the microgrid;
calculating the normal working time and the fault time of each element according to the fault rate and the repair rate;
establishing a normal working time model and a fault time model of each element according to the normal working time and the fault time;
simulating the working condition and the fault condition of each element by adopting a Monte Carlo method according to the normal working time model and the fault time model to obtain the average power failure time;
determining the average power supply availability according to the average power failure time;
and taking the average power supply availability as an evaluation standard of power supply reliability.
The Monte Carlo method (Monte Carlo method), also called statistical simulation method, is a very important numerical calculation method which is proposed in the middle of the Twenty century and the forty-year generation and guided by the probability statistical theory due to the development of scientific technology and the invention of electronic computers. The monte carlo method is a method that implements sampling from a known probability distribution by constructing or describing a probabilistic process. The basic idea of the monte carlo method is to estimate the probability of a random event with the frequency of the occurrence of the event or obtain some digital features of the random variable as the solution of the problem by some "experimental" method when the problem to be solved is the probability of the occurrence of the random event or the expected value of the random variable. The sequential monte carlo simulation method, also called sequential monte carlo simulation method, simulates the historical process of system operation and then analyzes the extracted system state, and simulates the state alternation process of 'operation-repair-operation-repair' of elements according to the time sequence to obtain the operation repair state duration of each element, and a sequential system state sequence can be obtained by combining the operation and repair processes of each element, as shown in fig. 3. FIG. 3 is a sequence diagram of the states of the elements according to the embodiment of the present invention.
Normal working time T of elementTTFAnd time of failure TTTRRespectively as follows:
TTTF＝-(1/λ)·lnu1
TTTR＝-(1/μ)·lnu2
in the formula, λ and μ are failure rate and repair rate of the element, respectively; mu.s1And mu2Is a random number in the interval (0,1) which is subject to uniform distribution. The failure rate and repair time of each element are shown in table 1.
TABLE 1 failure Rate and repair time of the respective elements
Calculating the normal working time T of each element on the basis of the failure rate and the repair rate of all elements in the known systemTTFAnd time of failure TTTRThen, mathematical models of the normal working time and the failure time of each element are established. And simulating the working condition and the fault condition of each component by a sequential Monte Carlo algorithm, and judging by adopting an N-2 criterion. After two elements in the system are lost, the system isolates the fault through the segmentation and the isolation switch to realize load transfer and recover normal power supply, and then the N-2 criterion is met. Otherwise, the average power supply availability ratio is used as the evaluation standard of the power supply reliability.
Average power supply availability: the average ratio of the number of hours of power supplied to the user per year to the total number of hours of power supplied required by the user is recorded as ASAI.
In the formula, the number of annual calendar hours is 8760 hours.
The specific steps of calculating the reliability by the Monte Carlo method comprise:
1) and reading in system original data. Firstly, analyzing a network topological structure, marking the positions and the connection modes of elements: the serial numbers, the failure rates, the repair time, the voltage grades, the power and other parameters of the equipment such as the bus, the feeder line, the transformer, the breaker, the disconnecting switch, the photovoltaic, the fan, the energy storage load and the like are written in corresponding preparation files.
2) Setting simulation years, initializing simulation time, fault parameters, element states and the like. The flag for searching to the element is initialized, the simulation timer is 0, and the initial statistical variable is 0.
3) The normal operating time (TTF) and the time to failure (TTR) of all the elements are sampled and arranged in sequence to form a running state duration time sequence of each element in the simulated total time. A sequence of time-series changes of the distributed power supply and the load is generated simultaneously. Element i:
where λ and μ are failure rate and repair rate of the element, μ, respectively1And mu2Random numbers which are evenly distributed in the interval of (0, 1).
4) And (4) simulating by using a sequential Monte Carlo algorithm to find the load influenced by the fault. By analyzing the operating mode transition, the loads are classified into those which cannot be restored and those which can be restored by the transition and those which are not affected.
5) For loads that cannot be recovered by transshipment,the duration of the system fault is the outage time of the load, order
Wherein T isTTRkThe subsystem fault duration.
6) And judging whether the current simulation time is greater than the preset time, if so, executing the next step, and otherwise, turning to the step 3).
7) And calculating the reliability of the system according to the reliability indexes of the load points.
Step 104: the method comprises the following steps of establishing a power supply configuration model by taking the minimum cost of a photovoltaic power generation cost model, a fan power generation cost model, an energy storage cost model and electricity purchasing cost as a target function based on power supply reliability, and specifically comprises the following steps:
establishing a first power supply configuration model by taking a minimum cost of a photovoltaic power generation cost model, a fan power generation cost model, an energy storage cost model and a power purchase cost as an objective function and taking fan capacity constraint, photovoltaic capacity constraint, power constraint and power supply reliability constraint as first constraint conditions;
the objective function is:
Pffor wind power generation capacity, Q is an energy storage configuration, PgFor photovoltaic capacity, PdPower called from the large grid for the ith moment, C1、C2、C3、C4Respectively, the fan cost, the photovoltaic cost, the energy storage cost and the electricity purchasing cost;
energy storage configuration; q is 2Pl；
Wherein Q is the energy storage capacity, PlIs an important load capacity; the important loads comprise a primary power supply load, a secondary power supply load and a part of a tertiary power supply load, and mainly depend on the power supply requirement of the region.
The first constraint is:
Pfmaxfor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, Pf(t)、Pg(t)、PL(t) fan output, photovoltaic output and load power at time t, Pd(t) is the amount of electricity taken from the grid, ASAI is the average power supply availability, an indicator for measuring power supply reliability, N is the total number of users, UiThe power failure time for each user, a, is the lower limit of the reliability requirement.
As another preferred scheme, a power supply configuration model is established based on power supply reliability by taking a minimum cost of a photovoltaic power generation cost model, a fan power generation cost model, an energy storage cost model and a power purchase cost as an objective function, and specifically comprises the following steps:
establishing a second power supply configuration model by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchase cost as an objective function and taking fan capacity constraint, photovoltaic capacity constraint, energy storage electric quantity state constraint, energy storage power constraint, power balance constraint and power supply reliability constraint as second constraint conditions;
the objective function is:
Pffor wind power generation capacity, Q is an energy storage configuration, PgFor photovoltaic capacity, PdPower called from the large grid for the ith moment, C1、C2、C3、C4Respectively, the fan cost, the photovoltaic cost, the energy storage cost and the electricity purchasing cost;
energy storage configuration;
wherein,
for the whole sampleThe maximum capacity of the energy storage system during the data control period,
controlling the minimum capacity of the energy storage system in the whole sample data control period;
the second constraint is:
wherein, PfmaxFor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, QmaxFor maximum capacity limitation of energy storage, Pf(t)、Pg(t)、Pc(t)、PL(t) fan output, photovoltaic output, stored energy power and load power at time t, Pd(t) is the amount of power drawn from the grid, β is the rate of change of the peak-to-valley difference, PavIs the average value of the comprehensive load, and is the difference between the maximum value and the minimum value of the comprehensive load in one day; the comprehensive load refers to the difference between the load demand in the micro-grid and the distributed generation and transmission power.
The invention utilizes the Monte Carlo method to carry out simulation calculation when calculating the reliability. When the energy is used as a standby power supply, the energy storage system is not put into operation at ordinary times, only can be put into operation for maintaining the normal operation of important loads when a fault occurs in a power grid, and can maintain the power supply of the important loads for two hours; when the energy storage function is peak clipping and valley filling, the energy storage function participates in daily power regulation, so that a first power supply configuration model and a second power supply configuration model appear, and the specific modeling process is as follows:
when the stored energy is used as backup:
assuming that the required wind power generation capacity is PfEnergy storage configuration is Q and photovoltaic capacity is PgThe power called from the large power grid at the ith moment is Pd，C1、C2、C3、C4Respectively, the cost of the fan, the cost of the photovoltaic, the cost of energy storage and the cost of electricity purchaseCost, the objective function is as follows.
The constraints include fan capacity constraints, photovoltaic capacity constraints, power constraints, and power reliability constraints.
In the formula, PfmaxFor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, Pf(t)、Pg(t)、 PLAnd (t) the fan output, the photovoltaic output and the load power at the moment t. PdAnd (t) the electric quantity taken from the power grid. ASAI is the average power supply availability, which is an index for measuring the power supply reliability, N is the total number of users, UiIs the power outage time per user, and a is the lower limit of the reliability requirement.
The stored energy is used as a standby power supply and does not participate in power regulation at ordinary times, and the stored energy is used as a standby power supply for supplying power for two hours to the important load only when the power grid fails and cannot supply power to the important load. When the energy storage device is used for standby, the power regulation does not need to be frequently participated, so that the energy storage device does not need to be replaced in the power generation period of the wind power plant and the photovoltaic power station. Assume an important load capacity of PlIf the energy storage capacity is Q ═ 2Pl。
When the stored energy participates in peak clipping and valley filling:
assuming that the required wind power generation capacity is PfEnergy storage configuration is Q and photovoltaic capacity is PgThe power called from the large power grid at the ith moment is Pd，C1、C2、C3、C4Respectively, the fan cost, the photovoltaic cost, the energy storage cost and the electricity purchasing cost, and then the objective function is as follows.
The constraint conditions comprise fan capacity constraint, photovoltaic capacity constraint, energy storage electric quantity state constraint, energy storage power constraint, power balance constraint and power supply reliability constraint.
In the formula, PfmaxFor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, QmaxFor maximum capacity limitation of energy storage, Pf(t)、Pg(t)、Pc(t)、PLAnd (t) the fan output, the photovoltaic output, the energy storage power and the load power at the moment t. Pd(t) is the amount of power drawn from the grid β is the rate of change of the peak-to-valley difference, PavΔ P is the difference between the maximum and minimum values of the combined load during the day, which is the average value of the combined load. The comprehensive load refers to the difference between the load demand in the micro-grid and the distributed generation and transmission power.
The strategy for peak clipping and valley filling control is as follows: and subtracting the load from the distributed power output to obtain the comprehensive load power, and comparing and judging the comprehensive load power with the upper and lower power limit constraints. When the load demand is too high, the energy storage system is required to discharge to supplement the power difference; when the comprehensive load is in a valley, the energy storage device is required to be put into operation and is in a charging state so as to increase the comprehensive load requirement; otherwise, the energy storage device exits operation.
And performing peak clipping and valley filling control on the load by adopting an upper and lower limit constraint control method. And (3) upper and lower limit constraint control, namely setting upper and lower limits of a load demand curve, and calculating the charge and discharge power of the energy storage system in each time period according to the power difference between the load demand curve and the upper and lower limits. Fig. 4 is a schematic diagram of an upper and lower limit constraint control principle according to an embodiment of the present invention. In FIG. 4, P is the integrated load, PupUpper limit of peak fluctuation, PlowAs can be seen from fig. 4, after the energy storage device is added to the system, when the integrated load power is lower than the valley power lower limit, the energy storage device is controlled to be properly charged, as shown by the cross-hatched portion in fig. 4; when the integrated load power is higher than the upper limit of the peak power, controlling the energy storage device to discharge properly, as shown in the figure4 are shown with vertical hatching. The comprehensive load after peak clipping and valley filling control is within the upper and lower limit constraint range, namely the peak clipping and valley filling function is realized. The load is subjected to peak clipping and valley filling control, and P is determinedup、PlowThe size of (2). Pup、PlowIs determined by the following formula:
Pup＝(1+0.5β)Pav
Plow＝(1-0.5β)Pav
the specific calculation steps of the maximum capacity required by the energy storage system are as follows:
1) calculating ideal charging and discharging power values of the energy storage device at each time interval
Pb(k)＝P(k)-Pref(k)
In the formula, Pb(k) The ideal value of the charging (discharging) electric power of the energy storage system in the kth control period is greater than 0, and the energy storage system is discharged; p (k) is the actual comprehensive load size of the kth control period; pref(k) The load power ideal value after the peak clipping and valley filling control or the power fluctuation suppression control is taken as the expected integrated load power value.
2) And accumulating the charge and discharge electric quantity of the energy storage system in the control period according to the determined actual output power of the energy storage system. Assuming that the residual energy of the energy storage system at the end of the kth control period is represented by E (k), then
In the formula, TcTo smooth out the control period, E0Is the initial energy of the energy storage system.
3) Calculating the maximum and minimum energy difference values of the energy storage system in each control period, and determining the capacity W of the energy storage system to be configured to meet the energy requirement of the stabilizing process according to the following formula:
in the formula,
respectively representing the maximum capacity and the minimum capacity of the energy storage system in the whole sample data control period.
4) In consideration of the limitations of Depth of discharge (DOD) temperature and actual operating efficiency of the energy storage system, the capacity that the energy storage system should have may be modified by the following equation:
in the formula, W is the capacity of the energy storage system under an ideal condition, A is a safety coefficient (generally taking a value of 1.1-1.4), K is a temperature correction coefficient (generally taking a value of 1 above 0 ℃ and 1.1 above-10 ℃ and 1.2 below-10 ℃), η is the power conversion efficiency (taking 0.8) of the energy storage system, and DOD is the discharge depth (generally taking 80%).
5) If the charging and discharging period is considered to be one day, obviously, the configuration of the energy storage configuration is different every day in one year, the energy storage capacity of each day in one year is calculated once in consideration of the cost problem, and then the average value is taken as the energy storage capacity configuration.
Step 105: and obtaining the optimal wind-solar energy storage capacity configuration by adopting a particle swarm algorithm according to the power supply configuration model.
A Particle Swarm Optimization (PSO) algorithm is derived from behavior research on bird swarm predation, the PSO algorithm firstly initializes the positions of particles in a particle swarm, the position of each particle represents a solution, in the particle iteration process, the position and the speed of the PSO algorithm are changed by tracking the optimal solution of the particle in the iteration process and the optimal solution in the particle swarm iteration process, and when the iteration is ended, the global optimal solution is output.
The invention has the following advantages:
1. the method disclosed by the invention carries out distributed power supply optimization configuration by combining reliability constraint, and carries out simulation by using a Monte Carlo method when calculating the power supply reliability. Compared with the analytic method for calculating the power supply reliability, the simulation method is simpler and more efficient, and is suitable for calculating the reliability under a complex grid structure.
2. The method of the invention provides a method for calculating the distributed power supply and the energy storage capacity under two energy storage strategies according to different energy storage functions. The energy storage device has the advantages that the power supply can be configured according to the functions of the energy storage under different functions, and when the energy storage is used as standby, the energy storage capacity is configured according to the important load, so that the power supply reliability of the important load is ensured; when the stored energy participates in peak clipping and valley filling, the load is subjected to peak clipping and valley filling control according to an upper limit and lower limit constraint control method, and the fluctuation of the difference between the power supply and the load after the distributed power supply is connected is reduced.
3. The method is used for calculating and solving based on the particle swarm algorithm, the accuracy of the solving result is high, the convergence of the calculating process is fast, the configuration result can be solved fast, and the calculating process is simple and fast.
Fig. 5 is a block diagram of a reliability-based distributed power supply configuration system of a microgrid according to an embodiment of the present invention. As shown in fig. 5, a reliability-based microgrid distributed power supply configuration system comprises:
an obtaining module 201, configured to obtain a load condition and a weather condition of a set area;
the cost model establishing module 202 is used for respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load condition and the meteorological condition;
the power supply reliability determining module 203 is used for determining the power supply reliability by adopting a Monte Carlo method;
a power configuration model establishing module 204, configured to establish a power configuration model based on power supply reliability with a minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model, and the electricity purchase cost as a target function;
and the optimal wind and light storage capacity configuration module 205 is configured to obtain optimal wind and light storage capacity configuration by adopting a particle swarm algorithm according to the power supply configuration model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A reliability-based microgrid distributed power supply configuration method is characterized by comprising the following steps:
acquiring the load condition and meteorological condition of a set area;
respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load condition and the meteorological condition;
determining the power supply reliability by adopting a Monte Carlo method;
establishing a power supply configuration model based on power supply reliability by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function;
and obtaining the optimal wind-solar energy storage capacity configuration by adopting a particle swarm algorithm according to the power supply configuration model.
2. The reliability-based microgrid distributed power supply configuration method of claim 1, further comprising:
and acquiring the electricity purchasing cost.
3. The reliability-based microgrid distributed power supply configuration method according to claim 1, characterized in that the building of a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load conditions and the meteorological conditions respectively specifically includes:
establishing a photovoltaic power generation cost model C according to the load condition and the meteorological condition2＝Pg·b+C'2(ii) a Wherein, C2For photovoltaic power generation costs, PgIs photovoltaic power generation capacity, b is photovoltaic construction cost unit price, C'2The total operation cost of the photovoltaic power generation system is obtained;
establishing a fan power generation cost model according to the load condition and the meteorological condition
Wherein, CI is investment construction cost of the wind power plant, and CI is Pf·a，PfFor the capacity of the wind farm, a is the unit price of wind power construction, CMkFor maintenance costs of the k year of the operational period, CMkL is the maintenance cost rate of the equipment, and S is the wind power equipment and the installation operation fee;
establishing an energy storage cost model C according to the load condition3＝r1·Q+C'3(ii) a Wherein, C3For the cost of the energy storage device, r1Is energy storage unit cost, Q is capacity of energy storage device, C'3Is a fixed operating cost for the energy storage device.
4. The reliability-based microgrid distributed power supply configuration method of claim 1, characterized in that the determining of power supply reliability by using the monte carlo method specifically includes:
acquiring the fault rate and the repair rate of each element in the microgrid;
calculating the normal working time and the fault time of each element according to the fault rate and the repair rate;
establishing a normal working time model and a fault time model of each element according to the normal working time and the fault time;
simulating the working condition and the fault condition of each element by adopting a Monte Carlo method according to the normal working time model and the fault time model to obtain the average power failure time;
determining the average power supply availability according to the average power failure time;
and taking the average power supply availability as an evaluation standard of power supply reliability.
5. The reliability-based microgrid distributed power supply configuration method of claim 1, wherein the establishing of the power supply configuration model based on the power supply reliability with the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchase cost as an objective function specifically comprises:
establishing a first power supply configuration model by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function and taking fan capacity constraint, photovoltaic capacity constraint, power constraint and power supply reliability constraint as first constraint conditions;
the objective function is:
Pffor wind power generation capacity, Q is an energy storage configuration, PgFor photovoltaic capacity, PdPower drawn from the large grid for the ith moment, C1、C2、C3、C4Respectively, the fan cost, the photovoltaic cost, the energy storage cost and the electricity purchasing cost;
energy storage configuration; q is 2Pl；
Wherein Q is the energy storage capacity, PlIs an important load capacity;
the first constraint is:
Pfmaxfor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, Pf(t)、Pg(t)、PL(t) fan output, photovoltaic output and load power at time t, Pd(t) is the amount of electricity taken from the grid, ASAI is the average power supply availability, an indicator for measuring power supply reliability, N is the total number of users, UiIs the power outage time per user, and a is the lower limit of the reliability requirement.
6. The reliability-based microgrid distributed power supply configuration method of claim 1, wherein the establishing of the power supply configuration model based on the power supply reliability with the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchase cost as an objective function specifically comprises:
establishing a second power supply configuration model by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function and taking fan capacity constraint, photovoltaic capacity constraint, energy storage electric quantity state constraint, energy storage power constraint, power balance constraint and power supply reliability constraint as second constraint conditions;
the objective function is:
Pffor wind power generation capacity, Q is an energy storage configuration, PgFor photovoltaic capacity, PdPower drawn from the large grid for the ith moment, C1、C2、C3、C4Respectively, the fan cost, the photovoltaic cost, the energy storage cost and the electricity purchasing cost;
energy storage configuration;
wherein,
for the maximum capacity of the energy storage system in the whole sample data control period,
controlling the minimum capacity of the energy storage system in the whole sample data control period;
the second constraint is:
wherein, PfmaxFor maximum capacity limitation of the fan, PgmaxFor photovoltaic maximum capacity limitation, QmaxFor maximum capacity limitation of energy storage, Pf(t)、Pg(t)、Pc(t)、PL(t) fan output, photovoltaic output, stored energy power and load power at time t, Pd(t) is the amount of power drawn from the grid, β is the rate of change of the peak-to-valley difference, PavIs the average value of the comprehensive load, and is the difference between the maximum value and the minimum value of the comprehensive load in one day; the comprehensive load refers to the difference between the load demand in the micro-grid and the distributed generation and transmission power.
7. The reliability-based microgrid distributed power supply configuration method of claim 6, further comprising:
applying a formula to the second power supply configuration model
Correcting to obtain a corrected second power supply configuration model;
wherein, W is the capacity of the energy storage system under the ideal condition, A is a safety coefficient, K is a temperature correction coefficient, η is the power conversion efficiency of the energy storage system, and DOD is the depth of discharge.
8. A reliability-based microgrid distributed power supply configuration system, comprising:
the acquisition module is used for acquiring the load condition and the meteorological condition of a set area;
the cost model establishing module is used for respectively establishing a photovoltaic power generation cost model, a fan power generation cost model and an energy storage cost model according to the load condition and the meteorological condition;
the power supply reliability determining module is used for determining the power supply reliability by adopting a Monte Carlo method;
the power supply configuration model establishing module is used for establishing a power supply configuration model based on power supply reliability by taking the minimum cost of the photovoltaic power generation cost model, the fan power generation cost model, the energy storage cost model and the electricity purchasing cost as an objective function;
and the optimal wind and light storage capacity configuration module is used for obtaining optimal wind and light storage capacity configuration by adopting a particle swarm algorithm according to the power supply configuration model.
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