CN108155672B - Micro-grid real-time optimization scheduling method and system - Google Patents
Micro-grid real-time optimization scheduling method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The embodiment of the invention provides a method and a system for optimizing and scheduling a micro-grid in real time, which optimize a real-time optimization problem in two stages, wherein a first-stage model unifies power purchasing power and output power of an adjustable micro-power source into supplementary power supply power, the charging and discharging power and the supplementary power supply power of an energy storage unit are determined by taking the minimum equivalent power supply cost as a target, and a second-stage model determines an optimal scheduling scheme for balancing the supplementary power supply power between the power purchasing power and the output power of the adjustable micro-power source by taking the minimum actual power supply cost as a target, so that the optimization process of the micro-grid real-time optimization and scheduling is fast and reliable in convergence.
Description
Technical Field
The embodiment of the invention relates to the technical field of micro-grid operation control, in particular to a micro-grid real-time optimization scheduling method and system.
Background
In recent years, with the increasing exhaustion of fossil energy and the increasing awareness of environmental protection, distributed power generation technologies represented by micro gas turbines, photovoltaic cells, and wind power generation have attracted much attention. The micro-grid is a new technology developed on the basis, and becomes a very effective way for ensuring the safe operation of the power grid. The Distributed power Generation (DG) function can be fully exerted, and meanwhile, the power quality and the power supply reliability can be improved, so that the Distributed power Generation (DG) is widely applied.
The micro-grid system has small inertia and poor bearing capacity on load power fluctuation; the existence of the energy storage unit and the difference of the off-grid and on-grid operation modes increase the diversity and the complexity of the energy transmission path and the direction inside the microgrid. Therefore, the safe and economic operation of the micro-grid must be supported by a perfect energy optimization scheduling system so as to reasonably control each power generation unit and each energy storage unit and achieve the purpose of improving the quality of electric energy. Energy scheduling on a microgrid can be divided into two major categories, day-ahead (short-term) energy scheduling and real-time (ultra-short-term) energy scheduling, on a time scale. The day-ahead energy scheduling of the micro-grid is to make a start-up and shut-down plan of each controllable micro-power supply and an integral output plan of each power generation unit on the basis of day-ahead load prediction, and the real-time energy scheduling of the micro-grid is to balance the output coefficient of each power supply under the condition of meeting the energy balance and the output limit constraint of each power supply. The real-time energy scheduling of the microgrid plays a crucial role in the safe and efficient operation of the microgrid, so that a microgrid real-time optimization scheduling method is urgently needed.
Disclosure of Invention
Embodiments of the present invention provide a method and system for optimizing and scheduling a microgrid in real time, which overcome the above problems or at least partially solve the above problems.
On one hand, the embodiment of the invention provides a micro-grid real-time optimization scheduling method, which comprises the following steps:
s1, solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid in a preset real-time scheduling period; the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid;
s2, solving a second preset optimization model of the microgrid to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model comprises that the sum of the first electric power and the second electric power is equal to the supplementary power supply;
and S3, performing real-time optimized scheduling on the microgrid according to the charging and discharging power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power.
Further, the expression of the objective function of the first preset optimization model is as follows:
minCEQ＝min{cbuy,tPbuy,t+cBAT,tPBAT-SGRcGRsell,tPGRsell,t}
wherein, CEQThe equivalent power supply cost of the micro-grid is represented by t, which is a preset real-time scheduling period; c. Cbuy,tFor a supplementary power cost factor, P, within tbuy,tSupplying power for the supplement in t; c. CBAT,tIs the charge-discharge guide coefficient, P, of the energy storage unit within tBATThe charging and discharging power of the energy storage unit is obtained; sGRThe operation mode of the microgrid within t is S when the microgrid isolated island operatesGRIs 0, S is during the grid-connected operation of the micro-gridGRIs 1, cGRsell,tIs the actual electricity selling price in t, PGRsell,tSold electric power within t.
Further, step S1 specifically includes:
s11, acquiring an empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid according to the running state of the microgrid;
and S12, solving a first preset optimization model of the microgrid according to the empirical formula to obtain the charge and discharge power of the energy storage unit in the microgrid, the sold electric power of the microgrid and the supplementary power supply power of the microgrid.
Further, step S11 specifically includes:
when the operation state of the microgrid is grid-connected operation and the renewable energy in the microgrid is insufficient in power generation, an empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a first empirical formula:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe charge state in the preset real-time scheduling period predicted for the day-ahead plan, alpha, can supplement a proportionality coefficient between power supply cost and electricity selling price when the power generation of the renewable energy source is insufficient, alpha is more than 1 and
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, an objective function of the first preset optimization model is converted into a first objective function:
minCEQ1＝min{cbuy,tPbuy,t+cBAT,tPBAT}。
further, step S11 specifically includes:
when the operation state of the microgrid is grid-connected operation and renewable energy sources in the microgrid generate surplus power, the empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a second empirical formula, and the second empirical formula is as follows:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe charging state in the preset real-time scheduling period predicted for the day-ahead plan, lambda can supplement a proportionality coefficient between power supply cost and electricity selling price when the power generation of the renewable energy sources is redundant, lambda is more than 1
Correspondingly, when a first preset optimization model of the microgrid is solved according to the empirical formula, an objective function of the first preset optimization model is converted into a second objective function:
minCEQ2＝min{-SGRcGRsell,tPGRsell,t+cBAT,tPBAT}。
further, step S11 specifically includes:
when the operation state of the microgrid is island operation and the renewable energy in the microgrid is insufficient in power generation, the empirical formula between the charge and discharge guidance coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a third empirical formula:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe state of charge within the preset real-time scheduling period predicted for the day-ahead plan;
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, the objective function of the first preset optimization model is converted into a third objective function:
minCEQ3＝min{cbuy,tPbuy,t+cBAT,tPBAT}。
further, step S11 specifically includes:
when the operation state of the microgrid is island operation and renewable energy sources in the microgrid generate surplus power, the empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a fourth empirical formula, and the fourth empirical formula is as follows:
cBAT,t＝βcbuy,t
wherein beta is more than 0 and less than 1;
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, the objective function of the first preset optimization model is converted into a fourth objective function:
minCEQ4＝min{cBAT,tPBAT}。
in another aspect, an embodiment of the present invention provides a microgrid real-time optimization scheduling system, where the system includes:
the first optimization module is used for solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid within a preset real-time scheduling period; the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid;
the second optimization module is used for solving a second preset optimization model of the microgrid to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model comprises that the sum of the first electric power and the second electric power is equal to the supplementary power supply;
and the optimization scheduling module is used for performing real-time optimization scheduling on the microgrid according to the charging and discharging power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power.
A third aspect of embodiments of the invention provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above method.
A fourth aspect of the invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the above method.
According to the method and the system for optimizing and scheduling the micro-grid in real time provided by the embodiment of the invention, the real-time optimization problem is optimized in two stages, the power purchasing power and the output power of the adjustable micro-power source to the power grid are unified into the supplementary power supply power by the first stage model, the charging and discharging power and the supplementary power supply power of the energy storage unit are determined by taking the minimum equivalent power supply cost as a target, the optimal scheduling scheme of balancing the supplementary power supply power between the power purchasing power and the output of the adjustable micro-power source is determined by the second stage model by taking the minimum actual power supply cost as a target, and the quick optimization process and the reliable convergence of the real-time optimization and scheduling of the micro-grid are.
Drawings
Fig. 1 is a flowchart of a method for optimizing and scheduling a microgrid in real time according to an embodiment of the present invention;
fig. 2 is a block diagram of a real-time optimization scheduling system for a microgrid according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. 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.
Fig. 1 is a flowchart of a method for optimizing and scheduling a microgrid in real time according to an embodiment of the present invention, where as shown in fig. 1, the method includes: s1, solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid in a preset real-time scheduling period; the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid; s2, solving a second preset optimization model of the microgrid to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model comprises that the sum of the first electric power and the second electric power is equal to the supplementary power supply; and S3, performing real-time optimized scheduling on the microgrid according to the charging and discharging power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power.
Further, the expression of the objective function of the first preset optimization model is as follows:
minCEQ＝min{cbuy,tPbuy,t+cBAT,tPBAT-SGRcGRsell,tPGRsell,t}
wherein, CEQThe equivalent power supply cost of the micro-grid is represented by t, which is a preset real-time scheduling period; c. Cbuy,tFor a supplementary power cost factor, P, within tbuy,tSupplying power for the supplement in t; c. CBAT,tIs the charge-discharge guide coefficient, P, of the energy storage unit within tBATThe charging and discharging power of the energy storage unit is obtained; sGRThe operation mode of the microgrid within t is S when the microgrid isolated island operatesGRIs 0, S is during the grid-connected operation of the micro-gridGRIs 1, cGRsell,tIs the actual electricity selling price in t, PGRsell,tSold electric power within t.
In step S1, the size of the preset real-time scheduling period may be selected according to actual situations. The supplementary power supply is the insufficient power of renewable energy sources smaller than loads of the micro-grid in the micro-grid, the supplementary power supply is mainly provided by two aspects, the first aspect is purchased from an external power grid, the second aspect is provided by an adjustable micro-power source in the micro-grid, and the electric power provided by the two aspects is unified into supplementary electric power without distinguishing. The equivalent power supply cost is different from the actual power supply cost, and the equivalent power supply cost refers to the total cost after all income and expense in the integrated system. And the actual power supply cost only includes the cost of purchasing power from the external grid in the system and the power supply cost of the adjustable micro power supply. And performing first-stage optimization on the microgrid by using a first optimization model, namely, in order to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling power of the microgrid and the supplementary power supply power of the microgrid in a preset real-time scheduling period.
In step S2, the ratio of the first electric power and the second electric power in the supplementary electric power is optimized so that the actual electric power supply cost is minimized.
Specifically, the embodiment of the invention is divided into two stages for real-time optimization, and a linear model based on the energy storage system charge-discharge guide coefficient and having the minimum equivalent power supply cost, a linear model based on the minimum actual power supply cost, a first preset optimization model and a second optimization model are respectively established. In the first-stage model, the insufficient power of renewable energy sources smaller than the load of the power grid is collectively called as supplementary electric power, and the power supply is not distinguished from the power purchase from the power grid or the power supply by virtue of an adjustable micro power source; and the second stage model determines the optimal distribution scheme between the output of the adjustable micro power source and the power purchasing power of the power grid by taking the minimum actual power supply cost as a target.
Firstly, a first-stage optimization is carried out, and on the premise of meeting the output limit value constraint of each variable and the power balance constraint of the whole power grid, the optimal control scheme of the charging and discharging power, the electricity selling power and the supplementary power supply power of the energy storage unit of the micro-grid in the preset real-time scheduling period is solved, so that the equivalent power supply cost is minimized.
The control variables for this problem are:
K＝[PBAT,PGRsell,t,Pbuy,t]
the objective function is:
minCEQ＝min{cbuy,tPbuy,t+cBAT,tPBAT-SGRcGRsell,tPGRsell,t}
the constraint conditions include:
(1) power balance constraint
Pbuy,t-SGRPGRsell,t+PBAT＝PLOAD,t-PUC,t
In the formula PLOAD,tTo presetA predicted value of a load in a scheduling period; pUC,tAnd predicting the power generated by the renewable energy source in the preset scheduling period.
(2) Supplemental power supply and power sales constraints
0≤PGRsell,t≤PGRsellmax,t
0≤Pbuy,t≤Pbuymax,t
In the formula PGRsellmax,tAnd Pbuymax,tThe power upper limit of the power sold by the micro-grid to the upper-level power grid and the power upper limit of the supplementary power supply are respectively preset scheduling periods. The upper limit value of the supplementary power supply is the sum of the power purchasing of the power grid and the total power output by the adjustable micro power supply in a grid-connected mode, and the sum of the power purchasing of the power grid and the total power output of the adjustable micro power supply in an island mode. The power purchase and sale of the micro-grid to the upper-level power grid are mutually exclusive constraints, namely:
PGRsell,tPbuy,t＝0
although the mutual exclusion constraint for two control variables is non-linear, such problems can be mathematically solved by simple enumeration plus linear programming solution methods, and can therefore be considered as quasi-linear programming problems.
(3) Energy storage unit charge and discharge power constraint
-PminBT(ΔSocmin_t)≤PBAT,t≤PmaxBT(ΔSocmax_t)
In the formula PminBT(ΔSocmin_t) And PmaxBT(ΔSocmax_t) Respectively, the change quantity delta Soc of the state of charge in the real-time schedulingtThe upper and lower limits define maximum limits for discharge and charge powers.
(4) Renewable energy power output constraint s
0≤PUC,t≤PmaxUC,t
In the formula PmaxUC,tAnd predicting the total output power of the renewable energy sources for the preset scheduling period.
And then carrying out second-stage optimization to obtain the ratio of the first electric power to the second electric power in the supplementary power supply.
The objective function is:
in the formula: cfactRepresenting the actual power supply cost of a preset real-time scheduling period; c. CiThe power generation cost of the ith adjustable micro power supply; c. CGRbuy,tThe actual electricity purchase price of the preset dispatching cycle is obtained; pGRbuy,tActual electricity purchasing power; pi,tThe output power of the ith adjustable micro power supply in a preset scheduling period; si,tThe operation/outage state of the ith adjustable micro power supply in a preset scheduling period established by a day-ahead plan is represented by 1, and the outage state is represented by 0; and N is the total number of the adjustable micro power supplies in the micro power grid.
The constraint conditions include
(1) Supplementing supply power and constraints
The sum of the preset scheduling period power grid and the adjustable micro power supply power output must meet the supplementary power supply power optimized by the first step of the equivalent power supply cost, namely:
(2) adjustable micro-power supply output power constraint
The output power of the micro power supply with the adjustable preset scheduling period is jointly determined by the output variation range and the output capacity of the micro power supply, the lower limit of the power output is the smaller value of the output variation range and the output capacity of the micro power supply, and the higher value of the output upper limit and the output lower limit is the larger value of the output variation range, namely:
max{PCmin,i,Pi,0-rdiΔt}≤Pi,t≤min{PCmax,i,Pi,0+ruiΔt}
in the formula: pCmin,i、PCmax,iThe minimum output power and the maximum output power allowed by the ith adjustable micro power source are respectively; pi,0The current output power of the ith adjustable micro power supply; r isdi、ruiRespectively the maximum output reduction rate and the maximum output increase rate of the ith adjustable micro power supply; Δ t was 5 minutes.
(3) Power constraint of micro-grid from power grid
0≤PGRbuy,t≤PGRbuymax
In the formula: pGRbuymaxAnd (4) actually purchasing the maximum power of the power from the power grid for the micro-grid.
According to the method for optimizing and scheduling the micro-grid in real time, provided by the embodiment of the invention, the real-time optimization problem is optimized in two stages, the power purchasing power and the output power of the adjustable micro-power source to the power grid are unified into the supplementary power supply power by the first-stage model, the charging and discharging power and the supplementary power supply power of the energy storage unit are determined by taking the minimum equivalent power supply cost as a target, the optimal scheduling scheme of balancing the supplementary power supply power between the power purchasing power and the output power of the adjustable micro-power source is determined by the second-stage model by taking the minimum actual power supply cost as a target, and the quick optimization process and the reliable convergence of the real-time optimization and scheduling of the micro.
Based on the above embodiment, step S1 specifically includes: and S11, acquiring an empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid according to the running state of the microgrid. And S12, solving a first preset optimization model of the microgrid according to the empirical formula to obtain the charge and discharge power of the energy storage unit in the microgrid, the sold electric power of the microgrid and the supplementary power supply power of the microgrid.
Further, step S11 specifically includes:
when the operation state of the microgrid is grid-connected operation and the renewable energy in the microgrid is insufficient in power generation, an empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a first empirical formula:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe state of charge, α, within the preset real-time scheduling period predicted for the day ahead planWhen the power generation of the renewable energy source is insufficient, the proportionality coefficient between the supplementary power supply cost and the electricity selling price is larger than 1 and
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, an objective function of the first preset optimization model is converted into a first objective function:
minCEQ1＝min{cbuy,tPbuy,t+cBAT,tPBAT}。
further, step S11 specifically includes:
when the operation state of the microgrid is grid-connected operation and renewable energy sources in the microgrid generate surplus power, the empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a second empirical formula, and the second empirical formula is as follows:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe charging state in the preset real-time scheduling period predicted for the day-ahead plan, lambda can supplement a proportionality coefficient between power supply cost and electricity selling price when the power generation of the renewable energy sources is redundant, lambda is more than 1
Correspondingly, when a first preset optimization model of the microgrid is solved according to the empirical formula, an objective function of the first preset optimization model is converted into a second objective function:
minCEQ2＝min{-SGRcGRsell,tPGRsell,t+cBAT,tPBAT}。
further, step S11 specifically includes:
when the operation state of the microgrid is island operation and the renewable energy in the microgrid is insufficient in power generation, the empirical formula between the charge and discharge guidance coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a third empirical formula:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe state of charge within the preset real-time scheduling period predicted for the day-ahead plan;
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, the objective function of the first preset optimization model is converted into a third objective function:
minCEQ3＝min{cbuy,tPbuy,t+cBAT,tPBAT}。
further, step S11 specifically includes:
when the operation state of the microgrid is island operation and renewable energy sources in the microgrid generate surplus power, the empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a fourth empirical formula, and the fourth empirical formula is as follows:
cBAT,t＝βcbuy,t
wherein beta is more than 0 and less than 1;
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, the objective function of the first preset optimization model is converted into a fourth objective function:
minCEQ4＝min{cBAT,tPBAT}。
fig. 2 is a block diagram of a real-time optimization scheduling system for a microgrid according to an embodiment of the present invention, and as shown in fig. 2, the system includes: a first optimization module 1, a second optimization module 2 and an optimization scheduling module 3. Wherein:
the first optimization module 1 is used for solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid within a preset real-time scheduling period; and the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid. The second optimization module 2 is configured to solve a second preset optimization model of the microgrid, so as to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model includes that a sum of the first electric power and the second electric power is equal to the supplementary power supply. The optimization scheduling module 3 is configured to perform real-time optimization scheduling on the microgrid according to the charge and discharge power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power.
According to the microgrid real-time optimization scheduling system provided by the embodiment of the invention, a real-time optimization problem is optimized in two stages, the first stage model unifies the power purchasing power and the output power of an adjustable microgrid into the supplementary power supply power, the charging and discharging power and the supplementary power supply power of an energy storage unit are determined with the aim of minimum equivalent power supply cost, the second stage model determines the optimal scheduling scheme of balancing the supplementary power supply power between the power purchasing power and the output power of the adjustable microgrid by taking the minimum actual power supply cost as the aim, and the optimization process of the microgrid real-time optimization scheduling is rapid and reliable in convergence.
The method and system of the embodiments of the present invention are further described below by way of examples, and it is to be understood that the embodiments of the present invention are not limited thereto. And simulating different modes of the micro-grid in different time periods, and comparing real-time scheduling experiments of energy storage.
(1) Peak period working process in grid-connected mode
When the system operates in the peak period, four states are listed, which are respectively:
a. the equivalent net load at the scheduling moment is less than zero, and the current charge state of the energy storage unit is higher than the optimal charge state planned in the day ahead, namely Z is [010 ];
b. the equivalent net load is less than zero at the scheduling moment, and the current state of charge of the energy storage unit is lower than the optimal state of charge given by the plan before the day, namely Z is [011 ];
c. the equivalent net load is larger than zero at the scheduling moment, and the current charge state of the energy storage unit is higher than the optimal charge state given by the plan before the day, namely Z is [000 ];
d. and when the equivalent net load at the scheduling moment is larger than zero, the current state of charge of the energy storage unit is lower than the optimal state of charge planned in the day ahead, namely Z is [001 ].
The specific operation data of the system is shown in table 1, calculation is performed according to a real-time optimization scheduling model, and the operation result is shown in table 2.
TABLE 1
TABLE 2
The optimization result shows that: in the peak period, when the equivalent net load is less than zero, the adjustable micro power supply does not generate electricity, and the residual electricity preferentially meets the day-ahead plan requirement of the energy storage unit. When the equivalent net load is larger than zero, the adjustable micro power supply selectively outputs power, and if the charge state of the energy storage unit is higher than the optimal charge plan, the energy storage unit discharges; below the day-ahead schedule no longer charges. The optimization result conforms to the peak time interval real-time scheduling strategy.
(2) Working process of normal period under grid-connected mode
When the system operates in the normal period, four states are also listed, and the brief description thereof is similar to the peak period and is not repeated herein. The specific operation data of the system is shown in table 3, the calculation is carried out according to the real-time optimization scheduling model, and the operation result is shown in table 4.
TABLE 3
| a | b | c | d | |
| Photovoltaic power (kW) | 28.59 | 30.22 | 32.33 | 0 |
| Wind power generation power (kW) | 3.16 | 3.58 | 3.28 | 1.93 |
| Total generated power (kW) | 31.75 | 33.8 | 35.61 | 1.93 |
| Load (kW) | 23.33 | 22.98 | 35.96 | 32.71 |
| Equivalent net load (kW) | -8.42 | -10.82 | 0.35 | 30.78 |
| Current State of Charge Soc0 (%) | 93 | 74 | 66 | 77 |
| Optimum Charge State Socb (%) | 91 | 75 | 65 | 78 |
TABLE 4
The optimization result shows that: in the normal period, the adjustable micro power supply does not generate electricity, and when the equivalent net load is less than zero, the surplus electricity preferentially meets the day-ahead plan requirement of the energy storage unit; when the equivalent net load is larger than zero, discharging for load supply if the state of charge of the energy storage unit is higher than the optimal charge plan; and purchasing power and charging when the charging is lower than the optimal charging plan. The optimization result accords with the real-time scheduling strategy of the ordinary period.
(3) Off-peak time under grid-connected mode
The same four states are enumerated when the system is operating in the valley period. The specific operation data of the system is shown in table 5, the calculation is carried out according to the real-time optimization scheduling model, and the calculation result is shown in table 6.
TABLE 5
TABLE 6
The optimization result shows that: in the valley period, the adjustable micro power supply does not generate electricity, and when the equivalent net load is less than zero, the surplus electricity preferentially meets the day-ahead plan requirement of the energy storage unit. When the equivalent net load is larger than zero, discharging for load supply if the state of charge of the energy storage unit is higher than the optimal charge plan; and purchasing power and charging when the charging is lower than the optimal charging plan. The optimization result accords with a real-time scheduling strategy of the valley period.
(4) Island mode
When the system runs in an island state, four states are listed respectively as follows:
a. the equivalent net load at the moment is less than zero, and the current charge state of the energy storage unit is equal to the highest charge state;
b. the equivalent net load at the moment is less than zero, and the current charge state of the energy storage unit is lower than the highest charge state;
c. the equivalent net load at the moment is larger than zero, and the current charge state of the energy storage unit is higher than the optimal charge state given by the plan;
d. the equivalent net load at the moment is larger than zero, and the current state of charge of the energy storage unit is lower than the optimal state of charge given by the plan.
The specific operation data of the system is shown in a table 7, calculation is carried out according to a real-time optimization scheduling model, and the operation result is shown in a table 8.
TABLE 7
TABLE 8
| a | b | c | d | |
| Photovoltaic power (kW) | 20.95 | 34.23 | 16.28 | 12.55 |
| Wind power generation power (kW) | 10.67 | 5.2 | 3.35 | 10.29 |
| Accumulator charging power (kW) | 0 | 15 | 0 | 1.04 |
| Accumulator discharge power (kW) | 0 | 0 | 13.1 | 0 |
| Post-dispatch state of charge (%) | 95 | 93 | 82 | 75 |
| Mini gas turbine (kW) | 0 | 5.6 | 0 | 6.83 |
| Fuel cell (kW) | 0 | 0 | 0 | 3.41 |
| Removal of load power (kW) | 0 | 0 | 0 | 0 |
When the system is operated in an isolated island mode, if the equivalent net load is smaller than zero, the energy storage unit is charged by redundant electric energy, and if the energy storage unit still has surplus energy after the equivalent net load is charged to the upper limit, the renewable energy output is reduced (the photovoltaic output is preferably reduced by setting the scheme). If the equivalent net load is larger than zero, the current charge state of the energy storage unit is higher than the optimal charge state, discharging is carried out, and the insufficient part is supplemented by the adjustable micro power supply; if the charge state is lower than the optimal charge state, the adjustable micro power supply generates power for load supply, and the energy storage unit is charged under the condition of surplus power. And the optimization result accords with a real-time scheduling strategy in a system island mode.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided by the above method embodiments, for example, the method includes: solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid within a preset real-time scheduling period; the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid; solving a second preset optimization model of the microgrid to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model comprises that the sum of the first electric power and the second electric power is equal to the supplementary power supply; and carrying out real-time optimization scheduling on the microgrid according to the charging and discharging power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power.
Embodiments of the present invention provide a non-transitory computer-readable storage medium, which stores computer instructions, where the computer instructions cause the computer to perform the methods provided by the above method embodiments, for example, the methods include: solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid within a preset real-time scheduling period; the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid; solving a second preset optimization model of the microgrid to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model comprises that the sum of the first electric power and the second electric power is equal to the supplementary power supply; and carrying out real-time optimization scheduling on the microgrid according to the charging and discharging power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A microgrid real-time optimization scheduling method is characterized by comprising the following steps:
s1, solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid in a preset real-time scheduling period; the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid;
s2, solving a second preset optimization model of the microgrid to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model comprises that the sum of the first electric power and the second electric power is equal to the supplementary power supply;
s3, performing real-time optimized scheduling on the microgrid according to the charging and discharging power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power;
wherein the objective function of the second preset optimization model is as follows:
in the formula: cfactRepresenting the actual power supply cost of a preset real-time scheduling period; c. CiThe power generation cost of the ith adjustable micro power supply; c. CGRbuy,tThe actual electricity purchase price of the preset dispatching cycle is obtained; pGRbuy,tActual electricity purchasing power; pi,tThe output power of the ith adjustable micro power supply in a preset scheduling period; si,tThe operation/outage state of the ith adjustable micro power supply in a preset scheduling period established by a day-ahead plan is represented by 1, and the outage state is represented by 0; n is the total number of the adjustable micro power sources in the micro power grid; the expression of the objective function of the first preset optimization model is as follows:
minCEQ＝min{cbuy,tPbuy,t+cBAT,tPBAT-SGRcGRsell,tPGRsell,t}
wherein, CEQThe equivalent power supply cost of the micro-grid is represented by t, which is a preset real-time scheduling period; c. Cbuy,tFor a supplementary power cost factor, P, within tbuy,tSupplying power for the supplement in t; c. CBAT,tIs the charge-discharge guide coefficient, P, of the energy storage unit within tBATThe charging and discharging power of the energy storage unit is obtained; sGRThe operation mode of the microgrid within t is S when the microgrid isolated island operatesGRIs 0, S is during the grid-connected operation of the micro-gridGRIs 1, cGRsell,tIs the actual electricity selling price in t, PGRsell,tSold electric power within t.
2. The method according to claim 1, wherein step S1 specifically includes:
s11, acquiring an empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid according to the running state of the microgrid;
and S12, solving a first preset optimization model of the microgrid according to the empirical formula to obtain the charge and discharge power of the energy storage unit in the microgrid, the sold electric power of the microgrid and the supplementary power supply power of the microgrid.
3. The method according to claim 2, wherein step S11 specifically includes:
when the operation state of the microgrid is grid-connected operation and the renewable energy in the microgrid is insufficient in power generation, an empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a first empirical formula:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe charge state in the preset real-time scheduling period predicted for the day-ahead plan, alpha, can supplement a proportionality coefficient between power supply cost and electricity selling price when the power generation of the renewable energy source is insufficient, alpha is more than 1 and
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, an objective function of the first preset optimization model is converted into a first objective function:
minCEQ1＝min{cbuy,tPbuy,t+cBAT,tPBAT}。
4. the method according to claim 2, wherein step S11 specifically includes:
when the operation state of the microgrid is grid-connected operation and renewable energy sources in the microgrid generate surplus power, the empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a second empirical formula, and the second empirical formula is as follows:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe charging state in the preset real-time scheduling period predicted for the day-ahead plan, lambda can supplement a proportionality coefficient between power supply cost and electricity selling price when the power generation of the renewable energy sources is redundant, lambda is more than 1
Correspondingly, when a first preset optimization model of the microgrid is solved according to the empirical formula, an objective function of the first preset optimization model is converted into a second objective function:
minCEQ2＝min{-SGRcGRsell,tPGRsell,t+cBAT,tPBAT}。
5. the method according to claim 2, wherein step S11 specifically includes:
when the operation state of the microgrid is island operation and the renewable energy in the microgrid is insufficient in power generation, the empirical formula between the charge and discharge guidance coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a third empirical formula:
wherein, Soc0For the state of charge, Soc, within the preset real-time scheduling periodbThe state of charge within the preset real-time scheduling period predicted for the day-ahead plan;
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, the objective function of the first preset optimization model is converted into a third objective function:
minCEQ3＝min{cbuy,tPbuy,t+cBAT,tPBAT}。
6. the method according to claim 2, wherein step S11 specifically includes:
when the operation state of the microgrid is island operation and renewable energy sources in the microgrid generate surplus power, the empirical formula between the charge and discharge guide coefficient and the supplementary power supply cost coefficient of the energy storage unit in the microgrid is a fourth empirical formula, and the fourth empirical formula is as follows:
cBAT,t＝βcbuy,t
wherein beta is more than 0 and less than 1;
correspondingly, when the first preset optimization model of the microgrid is solved according to the empirical formula, the objective function of the first preset optimization model is converted into a fourth objective function:
minCEQ4＝min{cBAT,tPBAT}。
7. a microgrid real-time optimization scheduling system, the system comprising:
the first optimization module is used for solving a first preset optimization model of the microgrid to obtain the charging and discharging power of an energy storage unit in the microgrid, the selling electric power of the microgrid and the supplementary power supply power of the microgrid within a preset real-time scheduling period; the objective function of the first preset optimization model is the minimum equivalent power supply cost of the microgrid;
the second optimization module is used for solving a second preset optimization model of the microgrid to obtain first electric power purchased by the microgrid and second electric power provided by an adjustable microgrid in the preset real-time scheduling period; wherein an objective function of the second preset optimization model is that an actual power supply cost of the microgrid is minimum, and a constraint condition of the second preset optimization model comprises that the sum of the first electric power and the second electric power is equal to the supplementary power supply;
the optimization scheduling module is used for performing real-time optimization scheduling on the microgrid according to the charging and discharging power of the energy storage unit, the sold electric power of the microgrid, the first electric power and the second electric power;
wherein the expression of the objective function of the first preset optimization model is as follows:
minCEQ＝min{cbuy,tPbuy,t+cBAT,tPBAT-SGRcGRsell,tPGRsell,t}
wherein, CEQThe equivalent power supply cost of the micro-grid is represented by t, which is a preset real-time scheduling period; c. Cbuy,tFor a supplementary power cost factor, P, within tbuy,tSupplying power for the supplement in t; c. CBAT,tIs the charge-discharge guide coefficient, P, of the energy storage unit within tBATThe charging and discharging power of the energy storage unit is obtained; sGRThe operation mode of the microgrid within t is S when the microgrid isolated island operatesGRIs 0, S is during the grid-connected operation of the micro-gridGRIs 1, cGRsell,tIs the actual electricity selling price in t, PGRsell,tSold electric power within t;
the objective function of the second preset optimization model is as follows:
in the formula: cfactRepresenting the actual power supply cost of a preset real-time scheduling period; c. CiThe power generation cost of the ith adjustable micro power supply; c. CGRbuy,tThe actual electricity purchase price of the preset dispatching cycle is obtained; pGRbuy,tActual electricity purchasing power; pi,tThe output power of the ith adjustable micro power supply in a preset scheduling period; si,tThe operation/outage state of the ith adjustable micro power supply in a preset scheduling period established by a day-ahead plan is represented by 1, and the outage state is represented by 0; n is adjustable micro-power supply in micro-gridAnd (4) total number.
8. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 6.
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