CN111614087B - Energy storage double-layer optimization configuration method participating in power grid peak shaving - Google Patents
Energy storage double-layer optimization configuration method participating in power grid peak shaving Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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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
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H02J3/381—Dispersed generators
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- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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Abstract
The invention relates to an energy storage double-layer optimization configuration method participating in power grid peak shaving, which is characterized in that an outer layer model is used as an optimization configuration model, energy storage peak shaving economic indexes and system technical indexes are used as multi-target optimization configuration functions, an energy storage system optimal configuration scheme taking economic efficiency and technical efficiency into consideration is obtained, and in addition, because various price mechanisms of energy storage participating in peak shaving are not linked at present, only the direct economic efficiency indexes of an energy storage system are considered in the economic efficiency index model, and the generated social benefits are ignored; the calculation of each index of the outer layer model depends on the output parameters of the inner layer model; the inner layer model is an optimized scheduling model, in order to more fully utilize the peak regulation capacity of the system and reduce the generation of abandoned wind, the model comprehensively considers the peak regulation effect of the energy storage system and the deep peak regulation effect of the thermal power unit, and optimizes the energy storage and the output of the thermal power unit by taking the minimum total peak regulation cost of the system as a target. The peak regulation cost of the system can be reduced, the generation of abandoned wind is reduced, and the peak regulation pressure of the power grid is favorably improved.
Description
Technical Field
The invention relates to the field of deep peak shaving of an energy storage auxiliary thermal power generating unit, in particular to an energy storage double-layer optimal configuration method participating in peak shaving of a power grid.
Background
The new energy development trend is faster and faster, but a new problem is brought to the stable operation of a power grid, and taking wind power as an example, the peak-valley difference of the power grid is improved by the reverse peak regulation characteristic of the wind power, so that the difficulty is brought to the stable operation of the power grid. In order to relieve the contradiction, a deep peak regulation means of the thermal power generating unit needs to be developed, but the operation cost of the unit is increased by deep peak regulation, and how to balance the relation between the economy and the peak regulation performance is a key factor for determining the operation of the unit. The energy storage technology has high response speed, the power structure can be optimized, the system peak regulation capacity is increased, the energy storage auxiliary thermal power generating unit participates in the power grid peak regulation, the power grid peak regulation pressure can be improved, and the generation of abandoned wind in the wind power high-permeability area is reduced. However, the high cost of the energy storage technology is one of the key factors influencing the development of the energy storage technology, and therefore, the current research focus is on how to determine a proper energy storage system configuration scheme so as to ensure the economy and have a good peak regulation effect.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a scientific and reasonable energy storage double-layer optimization configuration method which is high in applicability, can reduce the peak shaving cost of a system, reduces the generation of abandoned wind and is beneficial to improving the peak shaving pressure of a power grid and participates in the peak shaving of the power grid
The technical scheme adopted for realizing the aim of the invention is that an energy storage double-layer optimization configuration method participating in power grid peak shaving is characterized by comprising the following contents:
1) Constructing a two-layer optimal configuration model structure
(1) Establishing an energy storage system configuration scheme alternative set in the outer layer model;
(2) selecting a certain energy storage system configuration alternative scheme as a constraint condition, according to input system load and wind power data, in an inner layer model, adopting the deep peak regulation effect of an energy storage system and a thermal power unit, considering the operation cost of the energy storage system in the peak regulation process, the operation coal consumption cost of the thermal power unit, the deep peak regulation additional cost of the thermal power unit, the deep compensation income of the thermal power unit and the risk cost factors of the system, optimizing the peak regulation economy of the system by taking the minimum total peak regulation cost of the system as a target, and obtaining the charging and discharging power of the energy storage system, the output power of the thermal power unit and the wind power acceptance under the energy storage system configuration alternative scheme;
(3) calculating various cost benefits of the energy storage system, the output standard deviation of the thermal power generating unit and newly increased wind power admission according to the charge and discharge power of the energy storage system, the output standard deviation of the thermal power generating unit and the wind power admission output by the inner layer model, taking the net benefit of the energy storage system in the whole life cycle as an economic index, taking the output standard deviation improvement of the thermal power generating unit and the newly increased wind power admission as technical indexes to form a multi-objective optimization configuration function, calculating a multi-objective optimization function value under the alternative configuration scheme, and selecting an optimal value as an optimization configuration result of the energy storage system;
2) Optimizing a model objective function
(a) Outer model objective function
The outer layer model optimizes the configuration of the energy storage system according to the maximum target of the net gain of the energy storage system in the whole life cycle, the improvement of the standard deviation of the output of the thermal power generating unit after the energy storage system is added and the acceptance of newly added wind power, and each sub-objective function is as follows:
in the formula: I.C. A ALL For the net gain of the energy storage system in the whole life cycle, SD is the average value of the improvement quantity of the standard deviation of the output of the thermal power generating unit, E wind The wind power acceptance is the average value of the newly added wind power acceptance; i is BZ,d Profit for energy storage on day d, I BP,d Compensation for stored energy gain on day d, C BY,d The energy storage running cost on day d, C BI For energy storage investment cost, SD GS,d The improvement amount of the standard deviation of the output of the thermal power generating unit on the d day, E windnew,d The wind power admission amount is newly increased on the day D, and D is the full life cycle of the energy storage system;
(b) Inner layer model objective function
The inner layer model considers the operation cost of the energy storage system, the coal consumption cost of the thermal power generating unit, the additional cost of deep regulation of the unit, the compensation income and the risk cost of the system, the lowest peak regulation total cost of the system is taken as a target, the charge and discharge power of the energy storage system, the output of the thermal power generating unit and the wind power receiving capacity which are optimized under each energy storage configuration scheme are obtained by the following objective functions:
in the formula: c G,i For a unit deep peak shaving grading energy consumption cost function, I G,i A gain function is compensated for the unit depth peak regulation grading,
the maximum output value of the unit;
the lowest force output value for the conventional peak regulation of the unit,
the lowest peak output value of the unit without oil injection depth regulation,
the lowest peak load value is regulated for the oil feeding depth of the unit;
3) Optimization model solving method
(a) Outer layer model solving method
In the outer-layer multi-objective optimization configuration model, firstly, setting an energy storage system capacity alternative set [ delta E,2 delta E, say, M delta E ] and an energy storage system power alternative set [ delta P,2 delta P, say, N delta P ] by taking delta E and delta P as capacity and power configuration basic units of an energy storage system, thereby forming M multiplied by N alternative schemes, calculating multi-objective function values under each scheme by an iterative method, and selecting a configuration scheme corresponding to an optimal value as an energy storage system configuration result;
(b) Inner layer model solving method
Solving by adopting a CPLEX solver in matlab in the peak regulation optimization scheduling model of the inner-layer energy storage auxiliary thermal power generating unit, so as to obtain energy storage charging and discharging power, thermal power generating unit output and wind power receiving capacity under different configuration alternative values of the energy storage system;
4) Constraint conditions
(a) Energy storage system operation constraints
The energy storage system needs to satisfy the charging and discharging power limit constraint and the charging state limit constraint,
in the formula: p C,t Charging power, P, for the energy storage system D,t Discharging power, P, for energy storage systems B Rated power, eta, of the energy storage system C For charging efficiency of energy storage systems, E SOC,t To the state of charge of the energy storage system, E B Rated capacity for the energy storage system, E SOC,min Is the lower limit value of the state of charge of the energy storage system, E SOC,max Is the upper limit value of the state of charge of the energy storage system, E SOC,start The state of charge of the energy storage system at the initial moment; e SOC,end The state of charge of the energy storage system at the end moment;
(b) Thermal power unit operation constraints
The thermal power generating unit needs to meet the power upper and lower limit constraint, the climbing rate constraint and the start-stop constraint of the thermal power generating unit,
in the formula:
is the minimum value of the output of the ith thermal power generating unit,
the maximum value of the output of the ith thermal power generating unit,
the maximum downward climbing amount of the ith thermal power generating unit,
maximum upward climbing amount v of the ith thermal power generating unit i,t The operation state of the thermal power generating unit is set; t is on,i Minimum continuous operation time, T, of the ith thermal power generating unit off,i The minimum continuous shutdown time of the ith thermal power generating unit is set; t is on,i,t For the duration of time, T, of the ith unit in time period T off,i,t The continuous shutdown time of the ith unit in the time period t.
The invention relates to an energy storage double-layer optimization configuration method participating in power grid peak shaving, wherein an outer layer model is taken as an optimization configuration model, and an energy storage peak shaving economic index and a system technical index are taken as a multi-objective optimization configuration function to obtain an energy storage system optimal configuration scheme taking economy and technology into consideration; the calculation of each index of the outer layer model depends on the output parameter of the inner layer model; the inner layer model is an optimized scheduling model, in order to more fully utilize the peak regulation capacity of the system and reduce the generation of abandoned wind, the model comprehensively considers the peak regulation effect of the energy storage system and the deep peak regulation effect of the thermal power unit, and optimizes the energy storage and the output of the thermal power unit by taking the minimum total peak regulation cost of the system as a target. The method can reduce the peak shaving cost of the system, reduce the generation of abandoned wind and contribute to improving the peak shaving pressure of the power grid. Has the advantages of scientific and reasonable structure, strong applicability, good effect and the like.
Drawings
FIG. 1 is a model structure diagram of an energy storage double-layer optimization configuration method participating in power grid peak shaving;
FIG. 2 is a flow chart of an outer configuration model solution;
FIG. 3 is a schematic diagram of wind power and load data for seven days;
FIG. 4 is a schematic diagram of the charge and discharge power and state of charge of the energy storage system;
FIG. 5 is a cumulative graph of output power and wind power acceptance power of each unit.
Detailed Description
The energy storage double-layer optimal configuration method participating in power grid peak shaving according to the present invention is further described with the accompanying drawings and embodiments.
Referring to fig. 1 and fig. 2, the energy storage double-layer optimization configuration method participating in power grid peak shaving provided by the invention comprises the following steps:
1) Construction of a two-layer optimal configuration model structure
(1) Establishing an energy storage system configuration scheme alternative set in the outer layer model;
(2) selecting a certain energy storage system configuration alternative scheme as a constraint condition, according to input system load and wind power data, in an inner layer model, adopting the deep peak regulation effect of an energy storage system and a thermal power unit, considering the operation cost of the energy storage system in the peak regulation process, the operation coal consumption cost of the thermal power unit, the deep peak regulation additional cost of the thermal power unit, the deep compensation income of the thermal power unit and the risk cost factors of the system, optimizing the peak regulation economy of the system by taking the minimum total peak regulation cost of the system as a target, and obtaining the charging and discharging power of the energy storage system, the output power of the thermal power unit and the wind power acceptance under the energy storage system configuration alternative scheme;
(3) calculating various cost benefits of the energy storage system, the output standard deviation of the thermal power generating unit and newly increased wind power admission according to the charge and discharge power of the energy storage system, the output standard deviation of the thermal power generating unit and the wind power admission output by the inner layer model, taking the net benefit of the energy storage system in the whole life cycle as an economic index, taking the output standard deviation improvement of the thermal power generating unit and the newly increased wind power admission as technical indexes to form multi-objective optimization configuration and functions, calculating a multi-objective optimization function value under the alternative scheme of the configuration, and selecting an optimal value as an optimization configuration result of the energy storage system;
2) Optimizing a model objective function
(a) Outer model objective function
The outer layer model optimizes the configuration of the energy storage system by taking the maximum of the net income of the energy storage system in the whole life cycle, the output standard deviation improvement of the thermal power generating unit after the energy storage system is added and the newly added wind power acceptance as targets, and the sub-target functions are as follows:
in the formula: i is ALL SD is the average value of the improvement of the standard deviation of the output of the thermal power generating unit for the net gain in the whole life cycle of the energy storage system, E wind The wind power acceptance is the average value of the newly increased wind power acceptance; i is BZ,d Profit for energy storage on day d, I BP,d Compensation for stored energy gain on day d, C BY,d For stored energy operation on day d, C BI For energy storage investment cost, SD GS,d The improvement amount of the standard deviation of the output of the thermal power generating unit on the d day, E windnew,d The wind power admission amount is newly increased on the day D, and D is the full life cycle of the energy storage system;
(b) Inner layer model objective function
The inner layer model considers the operation cost of the energy storage system, the coal consumption cost of the thermal power generating unit, the additional cost of deep regulation of the unit, the compensation income and the risk cost of the system, the lowest peak regulation total cost of the system is taken as a target, the charge and discharge power of the energy storage system, the output of the thermal power generating unit and the wind power receiving capacity which are optimized under each energy storage configuration scheme are obtained by the following objective functions:
in the formula: c G,i For a unit deep peak shaving grading energy consumption cost function, I G,i A gain function is compensated for the unit depth peak regulation grading,
the maximum output value of the unit;
the lowest force output value for the conventional peak regulation of the unit,
the lowest peak load value is adjusted for the depth without oil feeding of the unit,
the lowest peak load value is regulated for the oil feeding depth of the unit;
3) Optimization model solving method
(a) Outer layer model solving method
In the outer-layer multi-objective optimization configuration model, firstly, setting an energy storage system capacity alternative set [ delta E,2 delta E, say, M delta E ] and an energy storage system power alternative set [ delta P,2 delta P, say, N delta P ] by taking delta E and delta P as capacity and power configuration basic units of an energy storage system, thereby forming M multiplied by N alternative schemes, calculating multi-objective function values under each scheme by an iterative method, and selecting a configuration scheme corresponding to an optimal value as an energy storage system configuration result;
(b) Inner layer model solving method
Solving by adopting a CPLEX solver in matlab in the peak regulation optimization scheduling model of the inner-layer energy storage auxiliary thermal power generating unit, so as to obtain energy storage charging and discharging power, thermal power generating unit output and wind power receiving capacity under different configuration alternative values of the energy storage system;
4) Constraint conditions
(a) Energy storage system operation constraints
The energy storage system needs to satisfy the charging and discharging power limit constraint and the charging state limit constraint,
in the formula: p C,t Charging power, P, for the energy storage system D,t Discharging power, P, for energy storage systems B Rated power, eta, of the energy storage system C Efficiency of charging the energy storage system, η D For the discharge efficiency of the energy storage system, E SOC,t To the state of charge of the energy storage system, E B Rated capacity for energy storage systems, E SOC,min Is the lower limit value of the state of charge of the energy storage system, E SOC,max Is the upper limit value of the state of charge of the energy storage system, E SOC,start The initial moment is the charge state of the energy storage system; e SOC,end The state of charge of the energy storage system at the end moment;
(b) Thermal power generating unit operation constraints
The thermal power generating unit needs to meet the power upper and lower limit constraint, the climbing rate constraint and the start-stop constraint of the thermal power generating unit,
in the formula:
is the minimum value of the output of the ith thermal power generating unit,
the maximum value of the output of the ith thermal power generating unit,
the maximum downward climbing amount of the ith thermal power generating unit,
the maximum upward climbing amount v of the ith thermal power generating unit i,t The operation state of the thermal power generating unit is set; t is on,i Minimum continuous operation time, T, of the ith thermal power generating unit off,i The minimum continuous shutdown time of the ith thermal power generating unit is obtained; t is on,i,t For the duration of time, T, of the ith unit in time period T off,i,t The continuous shutdown time of the ith unit in the time period t.
The examples employ a lithium iron phosphate battery configured at 200MW/800MWh and a total installed capacity of the thermal power generating unit of 3200MW, with the parameters of each thermal power generating unit shown in table 1.
TABLE 1 thermal power generating unit parameters
The wind power data and the load power data of the local power grid for seven days are shown in fig. 3.
The charging and discharging power, the energy storage charge state, the output of each thermal power generating unit and the wind power receiving capacity of the energy storage system after the inner layer model solving method are shown in the following figures 4 and 5.
As can be seen from fig. 4 and 5, the energy storage system reasonably charges and discharges, so that the output curve of the thermal power generating unit in the load low valley and peak periods is smoother, the daily peak valley difference adjustment amount of the thermal power generating unit is reduced, and the charge state of the energy storage system is always kept within the limit range.
In order to carry out comparative analysis on the inner layer model solving method, three different peak regulation scheduling schemes are respectively set.
The first scheme is as follows: conventional peak regulation of a thermal power generating unit;
scheme II: deeply regulating the peak of the thermal power generating unit;
and a third scheme is as follows: and (4) deeply regulating the peak of the energy storage and fire adding motor set.
The optimization results of each protocol are shown in table 2 below.
TABLE 2 comparison table of peak regulation effect and economy of different schemes
When the thermal power generating unit carries out deep peak regulation, the wind abandoning power of the first comparison scheme is reduced by 3639.9MWh, and the amplitude reduction is 36.59%. However, the peak-to-valley difference adjustment amount of the unit is increased due to the fact that the output value of the unit is low in the load valley period due to deep peak regulation.
When the energy storage assisted thermal power generating unit deep peak regulation scheduling is adopted, the abandoned wind power is obviously improved, compared with the scheme that the MWh is reduced by 6257.2, the reduction amplitude is 62.90%, compared with the scheme that the MWh is reduced by 2617.3, the reduction amplitude is 41.50%. In addition, due to the peak clipping and valley filling functions of the energy storage system, the peak-valley difference of the system is equivalently reduced, and the unit peak-valley difference regulating quantity is relieved. As can be seen from Table 2, the peak-to-valley difference improvement of the third embodiment was 90.08MW compared to the first embodiment, and the peak-to-valley difference improvement of the second embodiment was 207.28MW.
In addition, from the perspective of system peak regulation economy, the deep peak regulation of the thermal power generating unit generates extra high loss cost and high oil injection cost, so that the operation cost of the unit is increased, the risk penalty cost of the system is reduced, and the total peak regulation cost of the scheme II is reduced by 151.61 ten thousand yuan compared with the scheme I.
After the energy storage system is added, the deep peak regulation pressure of the unit is relieved, the output of the unit in a load peak period is reduced, the running cost of the unit is reduced, meanwhile, the risk punishment cost of the system is greatly reduced, and the total peak regulation cost of the system in the scheme III is reduced by 195.86 ten thousand yuan compared with that in the scheme II and is reduced by 347.47 ten thousand yuan compared with that in the scheme I.
Through the analysis, the deep peak regulation of the energy storage auxiliary thermal power generating unit has certain advantages in the aspects of system peak regulation effect and system peak regulation economy.
And analyzing the outer layer optimal configuration scheme by an effective inner layer model solving method. The most effective way to make the energy storage system reach the economic balance point is found by analysis to provide corresponding auxiliary service compensation. However, the above analysis only reaches the economic balance point from unilateral price change, and the economy of the energy storage system will be greatly improved along with the improvement of the policy and the reduction of the cost of the energy storage system in the future.
The terms, diagrams, tables and the like in the embodiments of the present invention are used for further description, are not exhaustive, and do not limit the scope of the claims, and those skilled in the art can conceive of other substantially equivalent alternatives without inventive step in light of the teachings of the embodiments of the present invention, which are within the scope of the present invention.
Claims (1)
1. An energy storage double-layer optimization configuration method participating in power grid peak shaving is characterized by comprising the following contents:
1) Construction of a two-layer optimal configuration model structure
(1) Establishing an energy storage system configuration scheme alternative set in the outer layer model;
(2) selecting a certain energy storage system configuration alternative scheme as a constraint condition, according to input system load and wind power data, in an inner layer model, adopting the deep peak regulation effect of an energy storage system and a thermal power unit, considering the operation cost of the energy storage system in the peak regulation process, the operation coal consumption cost of the thermal power unit, the deep peak regulation additional cost of the thermal power unit, the deep compensation income of the thermal power unit and the risk cost factors of the system, optimizing the peak regulation economy of the system by taking the minimum total peak regulation cost of the system as a target, and obtaining the charging and discharging power of the energy storage system, the output power of the thermal power unit and the wind power acceptance under the energy storage system configuration alternative scheme;
(3) calculating various cost benefits of the energy storage system, the standard deviation of the output of the thermal power unit and the newly increased wind power acceptance according to the charging and discharging power of the energy storage system, the output of the thermal power unit and the wind power acceptance output of the inner layer model, taking the net benefit of the energy storage system in the whole life cycle as an economic index, taking the improved quantity of the standard deviation of the output of the thermal power unit and the newly increased wind power acceptance as technical indexes to form a multi-objective optimization configuration function, calculating a multi-objective optimization function value under the alternative configuration scheme, and selecting an optimal value as an optimization configuration result of the energy storage system;
2) Optimizing a model objective function
(a) Outer model objective function
The outer layer model optimizes the configuration of the energy storage system according to the maximum target of the net gain of the energy storage system in the whole life cycle, the improvement of the standard deviation of the output of the thermal power generating unit after the energy storage system is added and the acceptance of newly added wind power, and each sub-objective function is as follows:
in the formula: I.C. A ALL SD is the average value of the improvement of the standard deviation of the output of the thermal power generating unit for the net gain in the whole life cycle of the energy storage system, E wind The wind power acceptance is the average value of the newly added wind power acceptance; i is BZ,d Profit for the stored energy on day d, I BP,d Compensation for energy storage gain on day d, C BY,d The energy storage running cost on day d, C BI For energy storage investment cost, SD GS,d The improvement amount of the standard deviation of the output of the thermal power generating unit on the d day, E windnew,d The wind power acceptance is newly increased on the day D, and D is the life cycle of the energy storage system;
(b) Inner layer model objective function
The inner layer model considers the operation cost of the energy storage system, the coal consumption cost of the thermal power unit, additional cost of deep power regulation of the unit, compensation income and risk cost of the system, the lowest peak regulation total cost of the system is taken as a target, the charge-discharge power of the energy storage system, the output of the thermal power unit and the wind power receiving capacity which are optimized under each energy storage configuration scheme are obtained, and the target functions are as follows:
in the formula: c G,i For a unit deep peak shaving grading energy consumption cost function, I G,i A gain function is compensated for the unit depth peak regulation grading,
the maximum value of the unit output is obtained;
the lowest output value for the conventional peak regulation of the unit,
the lowest peak load value is adjusted for the depth without oil feeding of the unit,
the lowest peak load value is regulated for the oil feeding depth of the unit;
3) Optimization model solving method
(a) Outer layer model solving method
In the outer-layer multi-objective optimization configuration model, firstly, setting an energy storage system capacity alternative set [ delta E,2 delta E, say, M delta E ] and an energy storage system power alternative set [ delta P,2 delta P, say, N delta P ] by taking delta E and delta P as capacity and power configuration basic units of an energy storage system, thereby forming M multiplied by N alternative schemes, calculating multi-objective function values under each scheme by an iterative method, and selecting a configuration scheme corresponding to an optimal value as an energy storage system configuration result;
(b) Inner layer model solving method
Solving by adopting a CPLEX solver in matlab in the internal-layer energy storage assisting thermal power generating unit peak regulation optimizing scheduling model, so as to obtain energy storage charging and discharging power, thermal power generating unit output and wind power receiving capacity under different configuration alternative values of the energy storage system;
4) Constraint conditions
(a) Energy storage system operating constraints
The energy storage system needs to satisfy the charging and discharging power limit constraint and the charging state limit constraint,
in the formula: p is C,t Charging power for energy storage systems, P D,t Discharging power, P, for energy storage systems B Rated power, eta, of the energy storage system C For charging efficiency of energy storage systems, E SOC,t To the state of charge of the energy storage system, E B Rated capacity for energy storage systems, E SOC,min Is the lower limit of the state of charge of the energy storage system, E SOC,max Is the upper limit value of the state of charge of the energy storage system, E SOC,start The initial moment is the charge state of the energy storage system; e SOC,end The energy storage system charge state at the end moment;
(b) Thermal power generating unit operation constraints
The thermal power generating unit needs to meet the power upper and lower limit constraint, the climbing rate constraint and the start-stop constraint of the thermal power generating unit,
in the formula:
the minimum value of the output of the ith thermal power generating unit,
the maximum value of the output of the ith thermal power generating unit,
the maximum downward climbing amount of the ith thermal power generating unit,
the maximum upward climbing amount v of the ith thermal power generating unit i,t The operation state of the thermal power generating unit is set; t is on,i Minimum continuous operation time, T, of the ith thermal power generating unit off,i Is the ithMinimum continuous downtime of the thermal power generating unit; t is on,i,t For the duration of time, T, of the ith unit in time period T off,i,t The continuous shutdown time of the ith unit in the time period t.
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| CN113036750A (en) * | 2020-12-17 | 2021-06-25 | 国网青海省电力公司清洁能源发展研究院 | Power grid peak regulation resource coordination optimization method containing energy storage power station |
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| CN114301089B (en) * | 2021-12-17 | 2023-11-03 | 国核电力规划设计研究院有限公司 | Energy storage capacity configuration optimization method for wind-solar combined power generation system |
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| CN114938011B (en) * | 2022-07-07 | 2022-10-14 | 中国长江三峡集团有限公司 | Wind-solar-fire-storage system combined operation method considering energy storage optimization configuration |
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