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CN112186808A - Microgrid energy optimization scheduling method - Google Patents

Microgrid energy optimization scheduling method Download PDF

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CN112186808A
CN112186808A CN202010839581.3A CN202010839581A CN112186808A CN 112186808 A CN112186808 A CN 112186808A CN 202010839581 A CN202010839581 A CN 202010839581A CN 112186808 A CN112186808 A CN 112186808A
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power
optimization scheduling
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陈忠华
俞容江
胡晨刚
徐强
杨先进
高振宇
向新宇
周晋雅
黄帅
李于宝
胡倩
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Hangzhou Electric Power Design Institute Co ltd
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    • H—ELECTRICITY
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Abstract

A microgrid energy optimization scheduling method, the method comprising the steps of: constructing a microgrid optimal scheduling system; performing MPC (multi-control packet radio) optimization scheduling on the microgrid optimization scheduling system; acquiring constraint conditions of the microgrid optimal scheduling system; and solving the microgrid optimal scheduling system meeting the constraint condition. According to the microgrid energy optimization scheduling method, a basic structure, a scheduling framework, a basic theory and a method of a microgrid system with uncertainty are considered, a basic model and a basic strategy of microgrid system application are established, a random planning theory and a scene analysis method based on microgrid application are provided, the problem of uncertainty caused by microgrid random energy and equipment is solved, and empirical estimation and analysis are carried out on the sample capacity of a target scene.

Description

Microgrid energy optimization scheduling method
Technical Field
The invention belongs to the technical field of micro-grids, and particularly relates to a micro-grid energy optimization scheduling method.
Background
Clean and low carbon become a development trend of global energy transformation, an energy revolution characterized by deep integration of new energy and information technology is pushing the human society to enter a brand new energy system, at present, a power system still has a series of problems of how to consume the new energy, uncertain disturbance, source and load imbalance and the like, and as of 2018, the installed capacity of solar energy and wind energy in China reaches 3.6 hundred million KW and accounts for 19% of the installed capacity, but the generated energy only accounts for 7.8% of the total amount, and the problems of wind abandoning and light abandoning are still serious. The proposal of the concept of Energy internet becomes a key for a good solution and Energy transformation of the above problems, and microgrid is regarded as an "organic cell" constructed by future Energy Internet (EIS) as an advanced stage of distributed power generation under the concept architecture of "cell-organization" Energy internet.
The microgrid can be an independent controllable system which only contains electric energy and can realize local energy supply and demand balance, or can be a multi-energy microgrid which contains various energy sources such as cold/heat/electricity/gas and the like, and a plurality of microgrids can form an active power distribution network with complete functions, so that building the microgrid is a preferable and precedent scheme for building the EIS. Different from the traditional power grid, the optimized scheduling of the microgrid is influenced by uncertain factors such as a distributed power supply and an energy storage system, but the scheduling strategy of the microgrid under the uncertain environment is lacked at present.
Disclosure of Invention
In order to solve the above problems, the present invention provides a microgrid energy optimization scheduling method, including:
constructing a microgrid optimal scheduling system;
performing MPC (multi-control packet radio) optimization scheduling on the microgrid optimization scheduling system;
acquiring constraint conditions of the microgrid optimal scheduling system;
and solving the microgrid optimal scheduling system meeting the constraint condition.
Preferably, the microgrid optimization scheduling system includes: the fan, the functional relation expression between the predicted output power and the wind speed of the wind power is as follows:
Figure BDA0002640952430000021
wherein, PwindIs the predicted output power of the fan, upsilonfAs wind speed prediction value, PrIs rated power of a wind driven generator in the fan, upsilonr、υciAnd upsiloncoThe wind power generation system comprises a wind power generator, a wind speed controller and a wind speed controller.
Preferably, the microgrid optimization scheduling system includes: the expression of the output electric power and the output thermal power of the gas turbine is as follows:
Figure BDA0002640952430000022
Figure BDA0002640952430000023
Figure BDA0002640952430000024
wherein, PGT(t) and QGT(t) electric power output and thermal power output, eta, respectively, of a gas turbine of the gas turbine at time ttAnd ηeCoefficient of generating and heating efficiency, V, respectivelyGTConsumption of natural gas for gas turbines, LHVgasIs the low calorific value of natural gas, kGTIs the thermoelectric efficiency ratio of the gas turbine.
Preferably, the microgrid optimization scheduling system includes: the SOC expression of the storage battery is as follows:
Figure BDA0002640952430000025
where SOC (t) is the state of charge of the battery at time t, Pbat(t) represents the charge-discharge power, Pbat>0 represents charging, Pbat<0 denotes discharge,. DELTA.Cbat(t | t-1) represents the charge and discharge variation amount.
Preferably, the constraint condition includes: a stochastic programming constraint, the stochastic programming constraint being formulated as:
Figure BDA0002640952430000031
wherein, PBat-minAnd PBat-maxRespectively representing the minimum and maximum values of the energy stored in the battery, SOCminAnd SOCmaxRepresenting the minimum and maximum values of the state of charge of the battery,
Figure BDA0002640952430000032
representing the probability of occurrence of meeting the upper and lower battery power limits and the SOC constraints, and 1-alpha representing the confidence level of occurrence of the event.
Preferably, the constraint condition includes: a power balance constraint condition, wherein the power balance constraint formula is as follows:
Figure BDA0002640952430000033
wherein,
Figure BDA0002640952430000034
representing the predicted wind energy output at the t + i moment which is earlier than the t moment by i steps under the scene s,
Figure BDA0002640952430000035
representing the predicted t + i user load demand at time t ahead by i steps under scenario s,
Figure BDA0002640952430000036
represents the charging and discharging power at the moment t + i under the scene s
Figure BDA0002640952430000037
Which indicates the state of charge of the storage battery,
Figure BDA0002640952430000038
time, indicates battery discharge state, and NS indicates the total number of scenarios that consider the optimization process.
Preferably, the constraint condition includes: energy storage constraint, wherein the energy storage constraint formula is as follows:
Figure BDA0002640952430000039
Figure BDA00026409524300000310
Figure BDA00026409524300000311
wherein C represents the nominal value of the rated capacity of the storage battery, SOC0Indicating the current SOC value, SOC of the batteryminAnd SOCmaxRespectively representing the minimum and maximum values of the SOC of the batteryValue, PBat-minAnd PBat-maxRespectively representing a minimum power value and a maximum power value of the storage battery.
Preferably, the constraint condition includes: the climbing capacity constraint formula is as follows:
RDGT≤PGT(t+i+1|t)-PGT(t+i|t)≤RUGT,(i＝0,…,T-1)，
wherein, PGT(t + i +1| t) and PGT(t + i | t) represents the output value, RD, of the generator set at the adjacent momentGTAnd RUGTRespectively representing descending and ascending climbing rates.
Preferably, the constraint condition includes: the output of the unit is defined by the following constraint formula:
0≤PGT(t+i|t)≤PGT-max；
wherein, PGT-maxAnd the maximum power of the unit output is represented.
Preferably, the constraint condition includes: the power purchasing power constraint formula is as follows:
0≤PGrid(t+i|t)≤PGrid-max；
wherein, PGrid-maxRepresenting the maximum power purchased from a large power grid.
According to the microgrid energy optimization scheduling method, a basic structure, a scheduling framework, a basic theory and a method of a microgrid system with uncertainty are considered, a basic model and a basic strategy of microgrid system application are established, a random planning theory and a scene analysis method based on microgrid application are provided, the problem of uncertainty caused by microgrid random energy and equipment is solved, and empirical estimation and analysis are carried out on the sample capacity of a target scene.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art 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 for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic diagram of a microgrid optimization scheduling system in the microgrid energy optimization scheduling method provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1, in an embodiment of the present application, the present invention provides a microgrid energy optimization scheduling method, including the steps of:
s1: constructing a microgrid optimal scheduling system;
s2: performing MPC (multi-control packet radio) optimization scheduling on the microgrid optimization scheduling system;
s3: acquiring constraint conditions of the microgrid optimal scheduling system;
s4: and solving the microgrid optimal scheduling system meeting the constraint condition.
In the embodiment of the application, the renewable energy power generation is exemplified by a wind power generation mathematical model, the wind power generation is that a hub is driven to rotate by utilizing torque generated by wind acting on a fan impeller, then the hub drives an asynchronous generator rotor to rotate through mechanical transmission equipment such as a gear box and the like to generate electric energy, and the process of converting the mechanical energy into the electric energy is realized. The functional relationship between the predicted output power and the wind speed of a typical wind turbine can be approximately described as shown in equation 2.1:
Figure BDA0002640952430000051
in the formula 2.1, PwindPredicting output power and wind speed upsilon for a typical wind generating setf，PrIs rated power of the wind driven generator, upsilonr、υciAnd upsiloncoThe wind generating set comprises a wind generating set and a wind speed controller.
Wind energy has strong randomness, the statistical characteristics of the wind energy can be described through the probability distribution of the wind speed, the probability distribution of the general wind speed is mostly in partial normal distribution, a plurality of models for simulating and counting the randomness of the wind speed of the wind power plant are provided, and the models are generally used for fitting the wind speed distribution and comprise lognormal distribution, Ray Leigh distribution, Weibull distribution and the like. Research shows that the double-parameter Weibull distribution form is simple, the distribution of the actual wind speed can be well fitted, the two-parameter Weibull distribution form is more general than exponential distribution, and the two-parameter Weibull distribution form is often used in a statistical reliability theory; according to the change of the parameter change of the double-parameter Weibull distributed wind power along with the change of the height rule in the near stratum, after the Weibull distributed parameter of the wind speed frequency at a certain height is known, the wind speed frequency distributed parameter at any height can be obtained according to the change rule, and the change rule of the wind energy can be described better compared with other distributions.
The two-parameter Weibull distribution is a unimodal function family, and is used as a model for reflecting the probability distribution of wind speed data, and the probability distribution density function is shown as the formula (2.2):
Figure BDA0002640952430000061
in the formula, vw,iIs the wind speed, kw,iIs a Weibull shape parameter, cw,iFor the distribution parameter, the shape parameter k of the Weibull distribution is knownw,iAnd a scale parameter cw,iA time series of wind speeds can be fitted.
The cumulative probability distribution of the Weibull distribution is shown as formula 2.3:
Figure BDA0002640952430000062
the expected value and variance of the distribution of the two parameters weibull distribution are shown in equations 2.4 and 2.5, respectively:
Figure BDA0002640952430000063
Figure BDA0002640952430000064
where (·) is a Gamma function, E (v)w,i) For the mathematical expectation operator, a smaller variance indicates better stability of wind speed and less randomness of wind power generation.
The Gas Turbine (GT) is an internal combustion type power machine which takes continuously flowing Gas as a working medium to drive an impeller to rotate at a high speed and then converts the energy of fuel into useful work, the main components of the internal combustion type power machine comprise a compressor, a generator, a combustion chamber, a heat regenerator, a Turbine, a control device and the like, the working principle is that external air enters the combustion chamber after being compressed by the compressor, enters the combustion chamber with natural Gas and the like in proportion to be combusted in the combustion chamber to generate high-temperature and high-pressure Gas to push the impeller to rotate, then the high-temperature Gas generates electric energy through the generator, and meanwhile, the high-temperature Gas can also be recycled through. The micro-grid power supply device has the advantages of small size, simple structure, less pollutant discharge, low maintenance cost and high energy conversion rate, and is convenient to move and install and high in power supply reliability, so that the micro-grid power supply device is widely applied to micro-grids. The output power of the gas turbine is mainly divided into electric power and thermal power, and the general mathematical model thereof has the following main expression:
Figure BDA0002640952430000071
Figure BDA0002640952430000072
Figure BDA0002640952430000073
in the formula, PGT(t) and QGT(t) electric power output and thermal power output, eta, of the gas turbine at time t, respectivelytAnd ηeCoefficient of generating and heating efficiency, V, respectivelyGTConsumption of natural gas for gas turbines, LHVgasIs the low calorific value of natural gas, kGTIs the thermoelectric efficiency ratio of the gas turbine.
In the embodiment of the present application, the relationship between the power generation efficiency and the active power output of the gas turbine can be approximately fitted as shown in the following formula 2.9:
Figure BDA0002640952430000074
key influencing factors affecting the operation of an energy storage battery include battery capacity (C)bat) State of charge (SOC), charge/discharge amount, charge/discharge power, depth of discharge, and the like.
In the embodiment of the present application, the battery capacity CbatThe nominal capacity of the storage battery is represented, and the maximum amount of electricity that can be released to the outside after the battery is fully charged under a certain charging and discharging condition is generally represented in the form of ampere hour (Ah) or kilowatt hour (kWh).
In the embodiment of the present application, the State of Charge (SOC) of the battery represents the ratio of the remaining capacity of the battery to the rated capacity thereof, as shown in equation (2.10):
Figure BDA0002640952430000081
in the formula, Qbat_sumIndicating the remaining capacity of the battery.
In the embodiment of the present application, the state of charge SOC of the battery satisfies the following equation:
Figure BDA0002640952430000082
in the formula, SOC (t) is the state of charge of the battery at time t, Pbat(t) represents a charge-discharge power (KW), Pbat>0 represents charging, Pbat<0 denotes discharge,. DELTA.Cbat(t | t-1) represents the charge and discharge variation amount.
In the embodiment of the application, in order to ensure the service life of the storage battery, the state of charge (SOC) of the storage battery meets the upper and lower limit constraints:
SOCmin≤SOC(t)≤SOCmax (2.12)
in the formula, SOCminAt minimum state of charge, the battery is discharged to SOCminDischarge will not continue; SOCmaxAt maximum state of charge, when the battery is charged to SOCmaxThe charging action will be stopped.
In the embodiment of the present application, the Ah metric is the most commonly used SOC estimation method, and can be expressed as:
Figure BDA0002640952430000083
therein, SOC0The charge-discharge initial state is represented, I is the charge-discharge current, and η is the charge efficiency, which is determined by the battery performance and the external environment.
In the embodiment of the application, for any time t, the charging and discharging of the battery cannot exceed the maximum charging and discharging power, and the following constraint conditions are met:
-Pbatt_max≤Pbatt(t)≤Pbatt_max (2.14)
in the embodiment of the present application, the Depth of Discharge (DOD) of the storage battery represents the percentage of the Discharge amount of the storage battery to the rated capacity, and the DOD is shown as the formula (4.5):
DOD＝1-SOC(t) (2.15)
for a certain type of battery, the depth of discharge represents the working state of the battery, the smaller the DOD is, the larger the state of charge is, the less the energy released by the battery is, and the more the residual energy is, and on the contrary, the more the discharge is, the DOD is kept in a reasonable range, which is beneficial to the maintenance of the service life of the battery.
The time-of-use electricity price is an electricity price system which is commonly applied in recent years in China, the electricity price per day is divided into different price intervals according to the load requirements of users, electricity charges are collected according to the average marginal cost of system operation in each time interval, the system can stimulate the users to reasonably allocate electricity utilization time, the situation of electricity utilization shortage can be effectively relieved, and the users can utilize electricity in a peak-off mode. Under the background of a smart power grid, a power department guides a user to adjust charging and discharging behaviors by formulating peak-valley time-of-use electricity prices, and particularly under the participation of the V2G technology of an electric automobile, the charge and discharge of the electric automobile realize cross-space-time transfer, and the peak clipping and valley filling effects are better realized. Besides the peak-valley time-of-use electricity price, the method also has a scheme of real-time electricity price, which is actually one of the time-of-use electricity prices and can reflect the electricity price fluctuation in an hour or even a shorter time. The basic model of peak-to-valley time-of-use electricity prices is:
Figure BDA0002640952430000091
wherein, cp、cfAnd cvRespectively peak time interval electricity price, normal time interval electricity price and valley time interval electricity price, T1、T2And T2Respectively peak-valley-level time periods.
In the embodiment of the present application, for an end user of electric energy, when the market cost of power supply is high or the stability of the system is impacted, the end user receives the price guidance of the power company to adjust the power consumption mode, that is, Demand Response (DR), so as to optimize the power market resources. Time of use price (TOU) guides users to transfer part of peak load to the valley Time through the difference of electricity prices in different Time periods, which becomes a main measure of demand response in the power market. The peak electricity price is based on the time-of-use electricity price, a related price system is preset by an electric power company, when an emergency situation possibly occurring, such as sudden high power grid load, is predicted, a user can be informed in advance, and compensation is carried out by using the high electricity price, so that the problem of insufficient power supply caused by over-high load can be avoided, the risk of system operation is avoided, and a time-of-use electricity price response strategy is also an important component of demand side response.
In the embodiment of the application, the scenario refers to the description of all possible future development situations of things, and includes both quantitative and qualitative descriptions of basic features of various situations and descriptions of the possibility of occurrence of various situations. The scene analysis method is based on the theoretical basis of probability correlation, uncertainty information encountered by a research object is described in a scene mode, each established scene corresponds to a certain occurrence probability, and the key point of the research of the method is how to use limited scenes to fully reflect the characteristics and uncertainty information of an original system, which is also the primary problem to be solved by random planning. For a set of discrete probability distributions
Figure BDA0002640952430000101
Can approximately describe the continuous probability distribution function F (x)1,x2,…xN) The process of discretizing the continuous probability distribution is the process of scene generation, and the process of sampling the probability distribution model of the random variable to obtain the massive scene set is the process of scene generation.
In the embodiment of the present application, the important point of the scene analysis is to study generation of a large number of scenes and reduction of a target scene set, and for a typical stochastic optimization process containing random variables, the expectation model can be expressed as the following convex optimization expectation model:
Figure BDA0002640952430000102
where P represents a probability measure of ω in Ω space,
Figure BDA0002640952430000103
is a set of decision variables, known as a set of non-null convex closures, Ω is a set of closures in real-domain space, EPRepresenting a mathematical expectation with respect to P, and the function value of f (ω, x) represents a possible consequence of the objective function with respect to the decision variable. To solve the mathematical optimization problem of equation 2.31, the academics design various approximate models and varied specific structures for their models to a specific problem, and do not describe here.
In the embodiment of the present application, for an original probability measure P, the original probability measure P itself is composed of a plurality of discrete scenes, or corresponds to some random optimization problems in production practice, or is a continuous variable or a continuous random vector satisfying a certain probability distribution, and the difficulty is that an optimal solution or an expected value of a target cannot be directly obtained through a probability distribution function P, so that discretization processing needs to be performed on a continuous and analytic probability measure P, the probability measure P is approximately represented by a limited huge number of samples, the limited number of samples that are approximately represented by the probability measure are so-called scenes, and the problem that a large number of samples that are approximately represented by the probability measure P are obtained through the continuously analytic probability distribution function is also a scene generation process. However, in the calculation process, a large number of samples are generated, the number is excessively large, and great difficulty is brought to the calculation complexity and the calculation amount, so that reduction processing needs to be performed on a large number of samples, that is, probability measures Q which only contain a small number of probability measures Q capable of representing the original probability distribution attributes are used to approximately represent the probability measures P, and the process of obtaining the optimal simplified scene set Q is the process of scene reduction.
In the embodiment of the present application, the method for generating a scene generally adopts a method of Latin Hypercube Sampling (LHS) or monte Carlo (Mente Carlo), and the method is applied to an original scene set P { ω ″1,ω2,…ωNAnd the clipped target scene set
Figure BDA0002640952430000111
Satisfying M < N, { p1,p2,…pNAnd q1,q2,…qMThe probabilities are the probability weights of the scenes corresponding to the probabilities respectively, and the Kantorovich functional can be expressed as shown in an expression 2.32:
Figure BDA0002640952430000112
therein, functional
Figure BDA0002640952430000113
Representing the probability measure distance between the clipped target scene set Q and the original scene set P,
Figure BDA0002640952430000114
represents the optimal value of a linear transport problem, i.e. the objective function of scene cuts.
In the embodiment of the present application, a heuristic scene Reduction algorithm is generally adopted for an objective function of scene Reduction, an objective is to enable a reduced scene set to sufficiently approach an original scene set from the perspective of probability measure, common Reduction technologies include Fast Forward Reduction (Fast Forward Reduction) and synchronous Backward Reduction (Simultaneous Backward Reduction), and specific scene Reduction steps are described in detail in section three.
After the target scene is generated, it is also a more critical step to judge the accuracy, which is not only related to the sampling values, but also related to the correlation between the sampling values of each random variable. Generally, the accuracy is higher when the correlation is smaller, and the objective of the Cholesky decomposition method is to reduce the correlation between scenes. Constructing an approximately orthogonal permutation matrix H according to the generated correlation coefficient matrix among different scenesMNReordering the sampling matrix XMNTo reduce its correlation and to keep the value of each element unchanged.
In the embodiment of the present application, the main steps of solving the microgrid optimal scheduling system satisfying the constraint condition are as follows
Step 1: randomly generating an M N matrix HMNMatrix HMNIs randomly arranged by integers 1,2, …, N, whose element values represent the matrix XMNThe position of the k-th row vector arrangement;
step 2: calculating a permutation matrix HMNCorrelation coefficient ρ between rowsL，ρLIs a positive definite matrix which can be decomposed into a nonsingular lower triangular matrix D, rhoi,jAnd ρLThe expression is shown in equations 2.33 and 2.34:
Figure BDA0002640952430000121
ρL＝DDT (2.34)
wherein cov (-) is a covariance function, HiNAnd HjNIs a matrix LKNRow i, j of (1).
And step 3: computing an MxN order matrix QMN：
QMN＝D-1HMN (2.35)
And 4, step 4: matrix HMNAccording to the matrix QMNThe position sizes of the corresponding elements are sequenced, and then an approximately orthogonal array matrix is constructed;
and 5: matrix XMNIn the first step, the elements in each row are rearranged, and the arrangement method is according to the updated arrangement matrix HMNThe position indicated by the corresponding element in (a);
in Cholesky decomposition, the matrix QMNAre uncorrelated between the rows of (a), thus the matrix HKNAnd XMNAccording to the matrix QMNThe correlation of each row is correspondingly weakened after the corresponding row is reordered.
According to the microgrid energy optimization scheduling method, a basic structure, a scheduling framework, a basic theory and a method of a microgrid system with uncertainty are considered, a basic model and a basic strategy of microgrid system application are established, a random planning theory and a scene analysis method based on microgrid application are provided, the problem of uncertainty caused by microgrid random energy and equipment is solved, and empirical estimation and analysis are carried out on the sample capacity of a target scene.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.

Claims (10)

1. A microgrid energy optimization scheduling method is characterized by comprising the following steps:
constructing a microgrid optimal scheduling system;
performing MPC (multi-control packet radio) optimization scheduling on the microgrid optimization scheduling system;
acquiring constraint conditions of the microgrid optimal scheduling system;
and solving the microgrid optimal scheduling system meeting the constraint condition.
2. The microgrid energy optimization scheduling method of claim 1, wherein the microgrid optimization scheduling system comprises: the fan, the functional relation expression between the predicted output power and the wind speed of the wind power is as follows:
Figure FDA0002640952420000011
wherein, PwindIs the predicted output power of the fan, upsilonfAs wind speed prediction value, PrIs rated power of a wind driven generator in the fan, upsilonr、υciAnd upsiloncoRespectively rated wind corresponding to the wind-driven generatorSpeed, cut-in wind speed, and cut-out wind speed.
3. The microgrid energy optimization scheduling method of claim 1, wherein the microgrid optimization scheduling system comprises: the expression of the output electric power and the output thermal power of the gas turbine is as follows:
Figure FDA0002640952420000012
Figure FDA0002640952420000013
Figure FDA0002640952420000014
wherein, PGT(t) and QGT(t) electric power output and thermal power output, eta, respectively, of a gas turbine of the gas turbine at time ttAnd ηeCoefficient of generating and heating efficiency, V, respectivelyGTConsumption of natural gas for gas turbines, LHVgasIs the low calorific value of natural gas, kGTIs the thermoelectric efficiency ratio of the gas turbine.
4. The microgrid energy optimization scheduling method of claim 1, wherein the microgrid optimization scheduling system comprises: the SOC expression of the storage battery is as follows:
Figure FDA0002640952420000021
where SOC (t) is the state of charge of the battery at time t, Pbat(t) represents the charge-discharge power, Pbat>0 represents charging, Pbat<0 denotes discharge,. DELTA.Cbat(t | t-1) represents a change in charge and dischargeAmount of the compound (A).
5. The microgrid energy optimization scheduling method of claim 1, wherein the constraint condition comprises: a stochastic programming constraint, the stochastic programming constraint being formulated as:
Figure FDA0002640952420000022
wherein, PBat-minAnd PBat-maxRespectively representing the minimum and maximum values of the energy stored in the battery, SOCminAnd SOCmaxRepresenting the minimum and maximum values of the state of charge of the battery,
Figure FDA0002640952420000023
representing the probability of occurrence of meeting the upper and lower battery power limits and the SOC constraints, and 1-alpha representing the confidence level of occurrence of the event.
6. The microgrid energy optimization scheduling method of claim 1, wherein the constraint condition comprises: a power balance constraint condition, wherein the power balance constraint formula is as follows:
Figure FDA0002640952420000024
wherein,
Figure FDA0002640952420000025
representing the predicted wind energy output at the t + i moment which is earlier than the t moment by i steps under the scene s,
Figure FDA0002640952420000026
representing the predicted t + i user load demand at time t ahead by i steps under scenario s,
Figure FDA0002640952420000027
indicating presenceCharging and discharging power at t + i moment under scene s
Figure FDA0002640952420000028
Which indicates the state of charge of the storage battery,
Figure FDA0002640952420000029
time, indicates battery discharge state, and NS indicates the total number of scenarios that consider the optimization process.
7. The microgrid energy optimization scheduling method of claim 1, wherein the constraint condition comprises: energy storage constraint, wherein the energy storage constraint formula is as follows:
Figure FDA0002640952420000031
Figure FDA0002640952420000032
Figure FDA0002640952420000033
wherein C represents the nominal value of the rated capacity of the storage battery, SOC0Indicating the current SOC value, SOC of the batteryminAnd SOCmaxRespectively representing the minimum and maximum values of the battery SOC, PBat-minAnd PBat-maxRespectively representing a minimum power value and a maximum power value of the storage battery.
8. The microgrid energy optimization scheduling method of claim 5, wherein the constraint condition comprises: the climbing capacity constraint formula is as follows:
RDGT≤PGT(t+i+1|t)-PGT(t+i|t)≤RUGT,(i＝0,…,T-1)，
wherein, PGT(t + i +1| t) and PGT(t + i | t) represents the output value, RD, of the generator set at the adjacent momentGTAnd RUGTRespectively representing descending and ascending climbing rates.
9. The microgrid energy optimization scheduling method of claim 1, wherein the constraint condition comprises: the output of the unit is defined by the following constraint formula:
0≤PGT(t+i|t)≤PGT-max；
wherein, PGT-maxAnd the maximum power of the unit output is represented.
10. The microgrid energy optimization scheduling method of claim 1, wherein the constraint condition comprises: the power purchasing power constraint formula is as follows:
0≤PGrid(t+i|t)≤PGrid-max；
wherein, PGrid-maxRepresenting the maximum power purchased from a large power grid.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112886577A (en) * 2021-02-01 2021-06-01 中国电建集团华东勘测设计研究院有限公司 Building group microgrid energy optimization scheduling method
CN112949976A (en) * 2021-01-14 2021-06-11 台州宏远电力设计院有限公司 Optimal scheduling method for microgrid energy in commercial park

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112949976A (en) * 2021-01-14 2021-06-11 台州宏远电力设计院有限公司 Optimal scheduling method for microgrid energy in commercial park
CN112886577A (en) * 2021-02-01 2021-06-01 中国电建集团华东勘测设计研究院有限公司 Building group microgrid energy optimization scheduling method

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