CN117254491A - Time domain rolling optimization method and system for wind-light-hydrogen storage micro-grid system - Google Patents
Time domain rolling optimization method and system for wind-light-hydrogen storage micro-grid system Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 58
- 229910052744 lithium Inorganic materials 0.000 claims description 58
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of 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
- H02J15/00—Systems for storing electric energy
- H02J15/008—Systems for storing electric energy using hydrogen as energy vector
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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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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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
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—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 provides a time domain rolling optimization method and a time domain rolling optimization system for a wind-light-hydrogen storage micro-grid system, and relates to the field of optimal scheduling of wind-light-hydrogen storage micro-grid systems. The method comprises the following steps: basic data and topological structure information of the wind-solar-hydrogen energy storage micro-grid system are obtained, BESS and HESS charge-discharge states are judged, constraint conditions in the optimal scheduling process are determined, and a multi-objective function is constructed; acquiring a scheduling instruction of the wind-light-hydrogen energy storage micro-grid system, constructing a state space model corresponding to the wind-light-hydrogen energy storage micro-grid system based on a model predictive control theory, constructing a time domain rolling optimization scheduling model of running state information of each subsystem in the wind-light-hydrogen energy storage micro-grid system at any moment, and formulating an intra-day micro-grid energy optimization scheduling strategy to correct the current state of each subsystem in the wind-light-hydrogen energy storage micro-grid system, forming a wind power, photovoltaic and energy storage system control output sequence and determining a final intra-day optimization scheduling result. The invention weakens the influence of uncertainty factors in the micro-grid to a great extent.
Description
Technical Field
The invention relates to the field of optimal scheduling of wind-light-hydrogen storage micro-grid systems, in particular to a time domain rolling optimization method and a time domain rolling optimization system of a wind-light-hydrogen storage micro-grid system.
Background
Because of the excessive consumption and non-renewable nature of traditional fossil energy, the establishment of an advanced integrated energy supply system with the aim of safety, reliability, economy, high efficiency, cleanliness and environmental protection has become a development target for the co-pursuit all over the world. As a key link in the comprehensive energy system, the energy storage system can play a role in smoothing load and stabilizing uncertainty of output of renewable energy sources; meanwhile, when the system is in an island independent operation state, the energy storage equipment is a key factor for determining whether the load can be continuously supplied. Therefore, it is necessary to study the impact of multiple energy storage devices on integrated energy systems.
The hydrogen energy storage technology is taken as a novel and green energy storage technology, and is one of important solving paths for realizing deep decarburization in the industry, traffic and building industries. The electric hydrogen production can generate a large amount of clean hydrogen, has incomparable flexible regulation capability and energy storage advantages, can effectively counteract the adverse effect of wind power solar random fluctuation, and can make great contribution to the flexibility and the safety of an electric power system. The electric hydrogen production system can play the characteristics of large installed capacity and wide adjustment range, can improve the flexibility of the electric power system and promote the new energy consumption of the electric network. The lithium battery energy storage device is oriented to a power grid frequency modulation scene, can play the characteristics of high response speed, high adjustment precision and strong climbing capacity, and can alleviate short-term power unbalance of the system through flexibility, improve the quick adjustment capacity of the system and ensure the stable and safe operation of the power system. Therefore, the energy management problem of the micro-grid system containing high-proportion new energy is solved by complementing the advantages of lithium battery energy storage and hydrogen energy storage, which is helpful for improving the comprehensive energy utilization rate and the economic benefit of the energy Internet.
The hydrogen energy storage system consists of an electrolytic cell hydrogen production unit, a hydrogen storage tank unit and a hydrogen fuel cell power generation unit, the three units are decoupled in power and flexible in charging and discharging, the hydrogen energy storage system is an excellent green energy storage system, and the lithium battery energy storage system is a mature energy storage form in current application and has the advantages of high start-stop speed, good economy and the like.
Considering the coordinated interaction of the battery energy storage system (Battery Energy Storage System, BESS) and the hydrogen energy storage system (Hydrogen Energy Storage System, HESS) and the strong uncertainty of the power load demand, the power distribution and control strategy of the BESS and the HESS and the key thereof under the power grid dispatching instruction directly affect the economy and stability of the operation of the micro-grid, the service life of the energy storage system and the new energy utilization rate in the micro-grid system.
The multi-element energy storage combined operation optimization is developed based on the layout and the economical efficiency modeling of the energy storage system, meanwhile, the planning and the economical efficiency of the energy storage system are influenced by the strategy of the combined operation mode, the combined operation strategy optimization is a coupling problem of various angular forces, the contradiction between the maximum energy consumption of new energy and the most economical operation of the energy storage, and the contradiction between the current energy consumption and the optimal output choice considering the future demands, so that the optimization process of the multi-element energy storage system combined operation strategy is difficult. Technical indexes such as SOHR, charge-discharge efficiency and the like in the HESS system have the characteristics of time variation and nonlinearity, various constraint conditions are involved in the operation process, and real-time accurate control of new energy output, HESS and BESS dispatching in the micro-grid is difficult.
Disclosure of Invention
The invention aims to provide a time domain rolling optimization method and a time domain rolling optimization system for a wind-solar-hydrogen storage micro-grid system, which are used for weakening the influence of uncertainty factors in the micro-grid and ensuring the rationality of a short-time rolling scheduling plan of the system and the stability of the system operation.
In order to achieve the above object, the present invention provides the following solutions:
a time domain rolling optimization method of a wind-light-hydrogen storage micro-grid system comprises the following steps:
basic data and topological structure information of the wind-solar-hydrogen energy storage micro-grid system are obtained; the basic data comprise wind power, photovoltaic day-ahead predictive schedulable power information, power grid load demand predictive information, rated power, HESS and BESS rated power and rated operation parameters, SOC and SOHR states and a power grid time-of-use electricity price curve; the topological structure information comprises a system connection mode and a power supply bus mode;
based on the topological structure information, judging the charge and discharge states of the BESS and the HESS according to the basic data, and determining constraint conditions in the optimal scheduling process; the constraint conditions comprise system power balance constraint, wind power photovoltaic power generation capacity constraint, wind power photovoltaic day-ahead predicted schedulable output power interval constraint, energy storage safe operation interval and operation state constraint and day-ahead day-in scheduling curve residual constraint;
Constructing a multi-objective function based on the constraint condition; the multi-objective function comprises a daily plan tracking function, a maximum new energy utilization function, a function which plays a constraint role in optimizing the operation characteristics of each unit of the wind-solar-hydrogen storage in the scheduling process according to the demand expansion objective function and the constraint condition of corresponding coupling, and a maximum economic benefit function;
acquiring a scheduling instruction of a wind-light-hydrogen energy storage micro-grid system, and constructing a state space model corresponding to the wind-light-hydrogen energy storage micro-grid system based on a model predictive control theory;
constructing a time domain rolling optimization scheduling model of the running state information of each subsystem in the wind, light and hydrogen energy storage micro-grid system at any moment according to the state space model, the multi-objective function and the constraint condition;
solving the time domain rolling optimization scheduling model, and formulating an intra-day micro-grid energy optimization scheduling strategy;
correcting the current state of each subsystem in the wind-solar-hydrogen energy storage micro-grid system according to the micro-grid energy optimization scheduling strategy in the day, sampling the real-time system state, updating the ultra-short-term predicted power value, forming a wind power, photovoltaic and energy storage system control output sequence, and determining the final day optimization scheduling result.
Optionally, the system power balance constraint is:
wherein P is load (k) Power is required for the load; n (N) PV Is the photovoltaic quantity;the ith photovoltaic output power at the kth moment; i is a photovoltaic serial number; n (N) Wind The wind power quantity; j is a wind power sequence number; />The power output of the jth wind power at the kth moment; n (N) BESS The energy storage quantity of lithium batteries is calculated; l is the lithium battery energy storage serial number; />The charging and discharging power of the first lithium battery energy storage at the kth moment; n (N) HESS The hydrogen storage machine quantity; m is a hydrogen energy storage serial number; />Charging and discharging power for the mth hydrogen energy storage at the kth moment; p (P) grid (k) Exchanging power for the microgrid connection line at the kth time.
Optionally, the wind power photovoltaic power generation capability constraint is:
wherein P is PV (k) The photovoltaic output power; p (P) Wind (k) The wind power output power is the wind power output power.
Optionally, the wind power photovoltaic day-ahead predicted schedulable output power interval constraint is:
wherein,is the minimum photovoltaic output power; />Is the maximum photovoltaic output power; />The minimum wind power output power is obtained; />The maximum wind power output power is obtained; />The energy storage charging and discharging power of the minimum lithium battery is; p (P) BESS (k) Charging and discharging power for lithium battery energy storage; />The maximum lithium battery energy storage charge and discharge power; />The minimum hydrogen energy storage charge and discharge power; p (P) HESS (k) Charging and discharging power for hydrogen energy storage; / >The maximum hydrogen energy storage charge and discharge power;exchanging power for the minimum micro-grid tie line; p (P) grid (k) Exchanging power for the microgrid tie lines; />Exchanging power for the maximum microgrid connection.
Optionally, the energy storage safe operation interval and the operation state constraint comprise an energy storage system operation state constraint and an energy storage system safe operation interval;
the energy storage system operation state constraint is as follows:wherein SOC is BESS (k) The charge state of the lithium battery energy storage system is as follows; SOHR HESS (k) The hydrogen pressure state of the hydrogen energy storage system is set; />The method is a state-of-charge limit value of a lithium battery energy storage system; />The hydrogen pressure state limiting value is the hydrogen pressure state limiting value of the hydrogen energy storage system;
the safe operation interval of the energy storage system is thatWherein (1)>The state of charge of the lithium battery energy storage system is the minimum; />The state of charge of the maximum lithium battery energy storage system; />The hydrogen pressure state of the minimum hydrogen energy storage system is set; />Is at most +.>
Optionally, the intra-day scheduling curve residual constraint is:
wherein,the sum of squares of residual errors of the day-ahead optimization curve corresponding to the photovoltaic power generation optimization curve at the kth momentA lower limit of square root value; />The method comprises the steps that a residual error square sum root mean square value of a day-ahead optimization curve corresponding to a photovoltaic power generation optimization curve is calculated; />The upper limit of the sum of squares of residual errors and root mean square values of the day-ahead optimization curve corresponding to the photovoltaic power generation optimization curve at the kth moment; / >The lower limit of the square sum of residual errors and root mean square value of the day-ahead optimization curve corresponding to the wind power generation optimization curve at the kth moment; />The method comprises the steps of optimizing a residual error square sum root mean square value of a curve corresponding to a wind power generation optimization curve;the upper limit of the sum of squares of residual errors and root mean square value of the day-ahead optimization curve corresponding to the kth moment wind power generation optimization curve is +.>The lower limit of the square sum of residual errors of the day-ahead optimization curve and the root mean square value corresponding to the lithium battery energy storage output power at the kth moment is set; />Optimizing the residual square sum root mean square value of the curve for the day-ahead corresponding to the lithium battery energy storage output power;the upper limit of the residual square sum root mean square value of the day-ahead optimization curve corresponding to the lithium battery energy storage output power at the kth moment is set; />The lower limit of the square sum of residual errors of the day-ahead optimization curve and the root mean square value corresponding to the lithium battery energy storage SOC change curve at the kth moment; />The method comprises the steps of optimizing a residual square sum root mean square value of a curve corresponding to a lithium battery energy storage SOC change curve; />The upper limit of the square sum of residual errors and root mean square value of a day-ahead optimization curve corresponding to the lithium battery energy storage SOC change curve at the kth moment; />The lower limit of the square sum of residual errors of the optimized curve and the root mean square value of the current day corresponding to the hydrogen energy storage output power at the kth moment; />Optimizing the residual square sum root mean square value of the curve for the day-ahead corresponding to the hydrogen storage output power; / >The upper limit of the sum of squares of residual errors and root mean square values of the day-ahead optimization curves corresponding to the hydrogen energy storage output power at the kth moment; />The lower limit of the square sum of the residual errors of the daily optimization curve and the root mean square value corresponding to the hydrogen storage SOHR change curve at the kth moment; />Optimizing the residual square sum root mean square value of the curve for the future corresponding to the hydrogen storage SOHR change curve; />The upper limit of the square sum of the residual errors of the daily optimization curve corresponding to the hydrogen storage SOHR change curve at the kth moment is set; RSS (really simple syndication) Pgrid_min (k) The lower limit of the square sum of residual errors of the optimized curves before the day corresponding to the microgrid interconnecting line power at the kth moment is obtained; RSS (really simple syndication) Pgrid (k) Optimizing the residual square sum root mean square value of the curve for the day time corresponding to the micro-grid interconnection line power; RSS (really simple syndication) Pgrid_max (k) And optimizing the upper limit of the residual square sum root mean square value of the curve for the day before corresponding to the microgrid interconnection line power at the k moment.
Optionally, the multiple objective function J is:
wherein J is n N=1, 2,3 for the nth objective function; p (P) Wind_ab (k) The air quantity is discarded at the kth moment; p (P) PV_ab (k) Discarding the light quantity at the kth moment; c (C) grid (k) The energy interaction cost of the micro power grid and the large power grid at the moment k is; c (C) BESS (k) The cost of the battery is reduced at the moment k; c (C) HESS (k) The cost of the hydrogen storage system at time k is reduced.
Optionally, the state space model is:
Wherein x (k+Δt) is the state of the state vector at the time k+Δt; p (P) PV (k+Δt) is the power state value of the photovoltaic system at the time k+Δt; p (P) Wind (k+deltat) is the power state value of the wind power system at the moment k+deltat; p (P) BESS (k+delta t) is a power state value of the lithium battery energy storage system at the moment k+delta t; p (P) HESS (k+Δt) is the power state value of the hydrogen energy storage system at the time k+Δt; SOC (State of Charge) BESS (k+delta t) is the SOC state value of the lithium battery energy storage system at the moment k+delta t; SOHR HESS (k+Δt) is the SOHR state value of the hydrogen storage system at time k+Δt; p (P) grid (k+Δt) exchanging power status values for the grid tie; Δt is the rolling optimization single stepping time length; q (Q) BESS The capacity of the lithium battery energy storage system is the total assembly machine capacity; sigma (sigma) B The lithium battery is used for storing energy and self-discharging power; q (Q) HESS The capacity of the hydrogen energy storage system is the total assembly machine; sigma (sigma) H Self-discharge power for hydrogen energy storage; ΔP PV (k) Force increment can be scheduled for the photovoltaic system; ΔP Wind (k) The output increment can be scheduled for the wind power system; ΔP BESS (k) The power increment can be scheduled for the lithium battery energy storage system; ΔP HESS (k) For hydrogen storage systemsThe output increment can be scheduled; ΔP load (k) To meet the power change increment of the demand; y (k) is a system output variable and is a vector composed of the output of each subsystem, the SOC and the SOHR.
Optionally, solving the time domain rolling optimization scheduling model, and formulating an intra-day micro-grid energy optimization scheduling strategy specifically includes:
Using the formulaSolving the time domain rolling optimization scheduling model, and formulating an intra-day micro-grid energy optimization scheduling strategy; wherein J is mix A fusion objective function for synthesizing the n objective functions; j (J) r Vector composed of the optimized subsystem output values under the r-th objective function; />Vector reference values formed by the output values of the subsystems optimized under the r-th objective function.
A time domain rolling optimization system of a wind-solar-hydrogen storage micro-grid system, comprising:
the parameter acquisition module is used for acquiring basic data and topological structure information of the wind-light-hydrogen energy storage micro-grid system; the basic data comprise wind power, photovoltaic day-ahead predictive schedulable power information, power grid load demand predictive information, rated power, HESS and BESS rated power and rated operation parameters, SOC and SOHR states and a power grid time-of-use electricity price curve; the topological structure information comprises a system connection mode and a power supply bus mode;
the constraint condition determining module is used for judging the charge and discharge states of the BESS and the HESS according to the basic data based on the topological structure information and determining constraint conditions in the optimal scheduling process; the constraint conditions comprise system power balance constraint, wind power photovoltaic power generation capacity constraint, wind power photovoltaic day-ahead predicted schedulable output power interval constraint, energy storage safe operation interval and operation state constraint and day-ahead day-in scheduling curve residual constraint;
The multi-objective function construction module is used for constructing a multi-objective function based on the constraint condition; the multi-objective function comprises a daily plan tracking function, a maximum new energy utilization function, a function which plays a constraint role in optimizing the operation characteristics of each unit of the wind-solar-hydrogen storage in the scheduling process according to the demand expansion objective function and the constraint condition of corresponding coupling, and a maximum economic benefit function;
the state space model construction module is used for acquiring a scheduling instruction of the wind-light-hydrogen energy storage micro-grid system and constructing a state space model corresponding to the wind-light-hydrogen energy storage micro-grid system based on a model prediction control theory;
the time domain rolling optimization scheduling model construction module is used for constructing a time domain rolling optimization scheduling model of the running state information of each subsystem in the wind-solar-hydrogen energy storage micro-grid system at any moment according to the state space model, the multi-objective function and the constraint condition;
the intra-day micro-grid energy optimization scheduling strategy formulation module is used for solving the time domain rolling optimization scheduling model and formulating an intra-day micro-grid energy optimization scheduling strategy;
and the final daily optimization scheduling result determining module is used for correcting the current state of each subsystem in the wind-solar-hydrogen energy storage micro-grid system according to the daily micro-grid energy optimization scheduling strategy, sampling the real-time system state, updating the ultra-short-term predicted power value, forming a wind power, photovoltaic and energy storage system control output sequence, and determining the final daily optimization scheduling result.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a time domain rolling optimization method and a time domain rolling optimization system for a wind-solar-hydrogen storage micro-grid system, which fully consider uncertainty of wind-solar photovoltaic power generation and time-space translation characteristics of an energy storage system, combine differentiated demand response with a model predictive control method to carry out optimization solution on a time domain rolling optimization scheduling model constructed by multiple objective functions, combine feedback correction of time domain rolling and real-time state of the system, weaken influence of uncertainty factors in the micro-grid to a great extent, and ensure rationality of a short-time rolling scheduling plan of the system and stability of system operation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a time domain rolling optimization method of a wind-solar-hydrogen storage micro-grid system provided by the invention;
Fig. 2 is a diagram illustrating topological structure information of the wind-light-hydrogen energy storage micro-grid system provided by the invention;
FIG. 3 is a graph of the indication of different regions of the BESS and HESS systems, SOCs and SOHR provided by the present invention;
fig. 4 is a flow chart of model predictive control provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a time domain rolling optimization method and system for a wind-solar-hydrogen storage micro-grid system, which greatly weaken the influence of uncertainty factors in the micro-grid and ensure the rationality of a short-time rolling scheduling plan of the system and the stability of the system operation.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The micro-grid system comprises renewable energy power generation equipment such as wind and light and energy storage equipment, wherein the energy storage equipment mainly refers to a lithium battery energy storage and hydrogen energy storage system.
The model predictive control idea is a feedback control strategy, by acquiring system measurement data in real time, solving the open loop optimization problem of the system on line, obtaining a group of future control instructions, then only adopting the control value at the moment, repeating the optimization process when the next rolling period arrives, and sending the feedback correction effect. The method can consider various constraint conditions and perform optimal scheduling on a plurality of objective functions, and is very suitable for optimal scheduling of new energy and energy storage equipment containing high proportion of fluctuation in a micro-grid system.
Aiming at the problem of multi-objective optimization in a micro-grid system, the time domain rolling optimization scheduling method based on model predictive control is provided with the following specific steps:
considering the error problem of the daily forecast information of the micro-grid, the situation that the output of each unit deviates more from the planned value can be generated in the daily scheduling process, so that p steps are forecast forward each time in the daily time domain rolling optimization process, the daily time is segmented, the delta t time is taken as a period, the estimated output value is obtained in the forecast period p delta t, and then the model is optimized and solved by the objective functions of daily plan tracking, daily energy balance, maximum new energy utilization, highest economic benefit and the like, and the obtained current moment control value in the output adjustment quantity control sequence of each subsystem in the micro-grid system is output, and the next scheduling period is waited for to come to repeat the steps.
As shown in fig. 1, the invention provides a time domain rolling optimization method of a wind-solar-hydrogen storage micro-grid system, which comprises the following steps:
step 101: basic data and topological structure information of the wind-solar-hydrogen energy storage micro-grid system are obtained; the basic data comprise wind power, photovoltaic day-ahead predictive schedulable power information, power grid load demand predictive information, rated power, HESS and BESS rated power and rated operation parameters, SOC and SOHR states and a power grid time-of-use electricity price curve; the topology information comprises a system connection mode and a power supply bus mode. Fig. 2 is a diagram illustrating topological structure information of the wind-light-hydrogen energy storage micro-grid system, as shown in fig. 2.
Step 102: based on the topological structure information, judging the charge and discharge states of the BESS and the HESS according to the basic data, and determining constraint conditions in the optimal scheduling process; the constraint conditions comprise system power balance constraint, wind power photovoltaic power generation capacity constraint, wind power photovoltaic day-ahead predictive schedulable output power interval constraint, energy storage safe operation interval and operation state constraint and day-ahead day-in scheduling curve residual constraint.
In practical application, acquiring future prediction information of a wind-solar-hydrogen storage micro-grid system, judging BESS and HESS charging and discharging states according to future prediction wind power and photovoltaic data, and determining constraint conditions in an optimal scheduling process, wherein the constraint conditions comprise common basic constraints such as system power balance constraint, wind power photovoltaic power generation capacity constraint, wind-solar photovoltaic future prediction schedulable output power interval constraint, energy storage safe operation interval and operation state constraint, micro-grid tie line transmission power constraint and the like, and the corresponding pertinence constraint is adopted based on different objective functions, and the method specifically comprises the following steps:
System power balance constraint:
wind-photovoltaic power generation capacity constraint:
wind photovoltaic day-ahead predictive schedulable power constraint, energy storage system optimal operation interval constraint and tie line exchange power upper and lower limit constraint are shown as follows:
energy storage system operating state constraints:
energy storage system safe operation interval:
taking the daily schedule tracking as an example, adding corresponding constraint conditions as daily intra-day scheduling curve residual constraint is as follows:
step 103: constructing a multi-objective function based on the constraint condition; the multi-objective function comprises a daily plan tracking function, a maximum new energy utilization function, a function which plays a constraint role in optimizing the operation characteristics of each unit of the wind-solar-hydrogen storage in the scheduling process according to the demand expansion objective function and the constraint condition of corresponding coupling, and a maximum economic benefit function.
Step 104: and acquiring a scheduling instruction of the wind-light-hydrogen energy storage micro-grid system, and constructing a state space model corresponding to the wind-light-hydrogen energy storage micro-grid system based on a model predictive control theory.
In practical application, a micro-grid system dispatching instruction is acquired, the issuing basis of the power grid dispatching instruction is the change of the unbalance amount of the micro-grid, and the energy unbalance relation of the wind, light and hydrogen storage micro-grid system at each moment is analyzed by the following process:
Δp is the amount of power imbalance that requires compensation by the large grid and the energy storage system.
If Δp >0, then the energy storage device is required to discharge or deliver power from the large grid to the micro grid to compensate for the power imbalance.
If Δp <0, then the energy storage device is required to charge or deliver power to the large grid to compensate for the power imbalance.
If Δp=0, neither the energy storage device nor the large power grid supplies power to the load, but electric energy conversion can be performed between the energy storage devices and between the large power grid and the energy storage device, so that the state of charge of the energy storage device is within the optimal working range, that is, in the BESS operation process, on the premise of meeting the scheduling instruction, the state of charge SOC in the charging and discharging process is preferentially considered to be controlled to be within the optimal operation interval S min ～S max The hydrogen storage is the same, and on the premise of meeting the scheduling instruction, the SOHR is used for preferentially controlling the hydrogen production/power generation processThe hydrogen storage quantity SOHR is at S low ～S high As shown in fig. 3.
In practical application, based on a model predictive control theory, a corresponding state space model is built in a wind-solar-hydrogen storage micro-grid system, multi-objective optimization solution is carried out by using multi-objective functions, a micro-grid intra-day time domain rolling optimization scheduling strategy is built, and the model in the optimization scheduling method is characterized by comprising the following input and output information:
According to a power balance equation and an energy storage SOC iterative equation of each period of the micro-grid, a vector formed by the schedulable wind photovoltaic output power, the charge and discharge power of HESS and BESS, the energy storage SOC and hydrogen energy storage SOHR values and the micro-grid interconnecting line exchange power is used as a state vector:
x(k)＝[P PV (k),P Wind (k),P BESS (k),P HESS (k),SOC BESS (k),SOHR HESS (k),P grid (k)] T 。
vector u (k) = [ delta P ] consisting of output increments of schedulable wind and photovoltaic units and energy storage systems PV (k),ΔP Wind (k),ΔP BESS (k),ΔP HESS (k)] T As a control variable.
Vector d (k) = [ delta P ] formed by ultra-short-term predicted power increment of wind power, photovoltaic and microgrid load demands PV (k),ΔP Wind (k),ΔP load (k)] T As a perturbation input in the time domain rolling optimization process.
The system output vector is a vector y (k) = [ P ] formed by the link exchange power between the micro-grid and the large power grid and the energy storage system SOC and SOHR grid (k)，SOC BESS (k)，SOHR HESS (k)] T 。
Establishing a corresponding state space model:
wherein x is%k+Δt) is the state of the state vector at the time k+Δt; p (P) PV (k+Δt) is the power state value of the photovoltaic system at the time k+Δt; p (P) Wind (k+deltat) is the power state value of the wind power system at the moment k+deltat; p (P) BESS (k+delta t) is a power state value of the lithium battery energy storage system at the moment k+delta t; p (P) HESS (k+Δt) is the power state value of the hydrogen energy storage system at the time k+Δt; SOC (State of Charge) BESS (k+delta t) is the SOC state value of the lithium battery energy storage system at the moment k+delta t; SOHR HESS (k+Δt) is the SOHR state value of the hydrogen storage system at time k+Δt; p (P) grid (k+Δt) exchanging power status values for the grid tie; Δt is the rolling optimization single stepping time length; q (Q) BESS The capacity of the lithium battery energy storage system is the total assembly machine capacity; sigma (sigma) B The lithium battery is used for storing energy and self-discharging power; q (Q) HESS The capacity of the hydrogen energy storage system is the total assembly machine; sigma (sigma) H Self-discharge power for hydrogen energy storage; ΔP PV (k) Force increment can be scheduled for the photovoltaic system; ΔP Wind (k) The output increment can be scheduled for the wind power system; ΔP BESS (k) The power increment can be scheduled for the lithium battery energy storage system; ΔP HESS (k) A force increment can be scheduled for the hydrogen energy storage system; ΔP load (k) To meet the power change increment of the demand; y (k) is a system output variable and is a vector composed of the output of each subsystem, the SOC and the SOHR.
Further, the multi-objective function includes: j (J) 1 : day-ahead plan tracking, J 2 : maximum new energy utilization, J … …: according to the requirement expansion objective function and the constraint conditions of corresponding coupling, the operation characteristics of each unit of the wind-solar-hydrogen storage in the optimal scheduling process are constrained; j (J) n : the economic benefit is the largest.
The multi-objective function is specifically represented by the following formula:
objective function J 1 The residual calculation formula in (a) is as follows:
P BESS_opt (k)、P Wind (k) Respectively representing an intra-day optimized output value and a pre-day optimized output value of the lithium battery energy storage system; SOC (State of Charge) BESS_opt (k)、SOC BESS (k) Respectively representing an intra-day optimized state of charge change curve and a pre-day optimized state of charge change curve of the lithium battery energy storage system; p (P) HESS_opt (k)、P HESS (k) Respectively representing an intra-day optimized output value and a pre-day optimized output value of the hydrogen energy storage system; SOHR HESS_opt (k)、SOHR HESS (k) Respectively representing a daily optimized hydrogen storage state change curve and a daily optimized hydrogen storage state change curve of the hydrogen energy storage system; p (P) PV_opt (k)、P PV (k) Respectively representing an intra-day optimized output curve and a pre-day optimized output curve of photovoltaic power generation; p (P) Wind_opt (k)、P Wind (k) Respectively representing an optimized output curve in the day of wind power generation and an optimized output curve before the day.
Objective function J 2 Middle P Wind_ab (k)、P PV_ab (k) Respectively representing the amount of abandoned wind and abandoned light at the kth moment;
objective function J n The medium economy calculation formula:
C grid (k) Representing energy interaction cost of micro-grid and large grid at time (k), C Hgrid 9k) The hydrogen production/power generation cost of the hydrogen energy storage system at the moment k is represented, the electricity purchasing hydrogen production cost is represented positively, the hydrogen power generation cost is represented negatively, and C Bgrid (k) The energy storage battery and charging and discharging costs at the moment k are represented, the positive value represents the charging energy storage cost, and the negative value represents the electricity selling cost for the power grid; c (C) BESS (k) Represents the Battery (BESS) break cost at time k, C HESS (k) Representing the cost of hydrogen storage system (HESS) break at time k; p (P) grid (k) Representing the power of a power purchasing and selling tie line when the micro-grid interacts with a large grid at the moment k, and P price (k) Representing the power interactive electricity price of the micro-grid and the large grid at the moment k; k (K) dep1 For the depreciation coefficient, Q of the lithium battery energy storage system BESS (k) Represents the residual capacity of the lithium battery energy storage system at the moment k, Q BESS Representing the total capacity of the lithium battery energy storage system; k (K) dep2 Depreciation coefficient, Q of hydrogen energy storage system HESS (k) Represents the residual capacity of the hydrogen energy storage system at the moment k, Q HESS Representing the total capacity of the hydrogen storage system; p (P) grid (k)、P Wind (k)、P PV (k)、P load (k)、P BESS (k)、P HESS (k) And the power grid interaction power, the wind power supply power, the photovoltaic supply power, the load demand power, the battery energy storage and the hydrogen energy storage supply power at the moment k are respectively shown.
Step 105: and constructing a time domain rolling optimization scheduling model of the running state information of each subsystem in the wind, light and hydrogen energy storage micro-grid system at any moment according to the state space model, the multi-objective function and the constraint condition.
Step 106: solving the time domain rolling optimization scheduling model, and formulating an intra-day micro-grid energy optimization scheduling strategy.
Step 107: correcting the current state of each subsystem in the wind-solar-hydrogen energy storage micro-grid system according to the micro-grid energy optimization scheduling strategy in the day, sampling the real-time system state, updating the ultra-short-term predicted power value, forming a wind power, photovoltaic and energy storage system control output sequence, and determining the final day optimization scheduling result.
In practical application, optimization solution is carried out based on the time domain rolling optimization scheduling model obtained in the prior art, and an intra-day micro-grid energy optimization scheduling strategy is established.
Rolling optimization is performed through model predictive control to obtain a power generation plan (P) of wind power, photovoltaic power, lithium power energy storage and hydrogen energy storage in a future P delta t period PV (k+pΔt) P Wind (k+pΔt) … P HESS (k+pΔt))。
And executing the generated power plan corresponding to the control quantity of each unit of the wind, light and hydrogen storage at the k moment, and calculating to obtain the corresponding state information at the k+1 moment.
Let k=k+1 and repeat the above scroll optimization step.
Because the rolling optimization is executed each time, after a group of future control instructions are obtained through optimization calculation, the MPC algorithm only uses the control value at the moment to restrain the deviation of the control on the target state caused by the mismatching of the model and the external interference; the real state of the system may not be the same as the estimated value of the state, so that the current state of each subsystem in the micro-grid system needs to be corrected at any time, the real-time system state is sampled, and the ultra-short-term predicted power value is updated, thereby achieving the purpose of correction.
After the n objective functions are optimized and solved, the scheduling values of the control quantities obtained in the previous step are combined to form a matrix form to form new reference values, and finally comprehensive optimization of n targets is achieved.
Wherein J is mix A fusion objective function for synthesizing the n objective functions; j (J) r Vector composed of the optimized subsystem output values under the r-th objective function; Vector reference values formed by the output values of the subsystems optimized under the r-th objective function.
And solving to obtain a wind power, photovoltaic and energy storage system control output sequence in the final system through the objective function, so as to form a final optimal scheduling result.
The invention adopts a model predictive control mode to solve the ultra-short-term optimal scheduling problem of a time-varying nonlinear system. The basic idea of model predictive control is to solve a finite time domain open loop optimization problem on line according to the obtained current measurement information at each sampling moment by using a feedback control strategy, and act the first element of the obtained control sequence on the controlled object. At the next sampling instant, the above process is repeated: the optimization problem is refreshed and solved again with new measurements. Solving the open-loop optimization problem online to obtain an open-loop optimization sequence is the main difference between model predictive control and traditional control methods, because the latter usually solves a feedback control law offline and acts the resulting feedback control law on the system all the time, as shown in fig. 4.
The invention obtains system basic data and topology structure information; determining a system state space model; determining a system operation constraint condition; by using a model predictive control idea, an intra-day micro-grid energy management optimization strategy is established by taking n requirements of a day-ahead plan, maximum new energy utilization, maximum economic benefit and the like of the output tracking of each unit as an objective function; carrying out optimization solution according to a multi-objective intra-day micro-grid energy management optimization strategy; and carrying out matrix combination on the processing power control quantity sequences of each unit obtained by optimizing and solving the n targets to form a new optimizing target reference value, and forming the latest deviation minimization optimizing calculation to obtain the final output power optimizing and dispatching result of each unit of the micro-grid system. According to the invention, the problem of energy management of the micro-grid system containing high-proportion new energy is solved by utilizing the multi-element energy storage system, and the multi-objective daily optimization scheduling strategy based on model predictive control is constructed, so that the comprehensive energy utilization rate is improved, the economic benefit of the energy Internet is improved, and the method has important reference value for realizing efficient and stable operation of the micro-grid.
Example two
In order to execute the corresponding method of the embodiment to realize the corresponding functions and technical effects, a time domain rolling optimization system of the wind-solar-hydrogen storage micro-grid system is provided below.
A time domain rolling optimization system of a wind-solar-hydrogen storage micro-grid system, comprising:
the parameter acquisition module is used for acquiring basic data and topological structure information of the wind-light-hydrogen energy storage micro-grid system; the basic data comprise wind power, photovoltaic day-ahead predictive schedulable power information, power grid load demand predictive information, rated power, HESS and BESS rated power and rated operation parameters, SOC and SOHR states and a power grid time-of-use electricity price curve; the topology information comprises a system connection mode and a power supply bus mode.
The constraint condition determining module is used for judging the charge and discharge states of the BESS and the HESS according to the basic data based on the topological structure information and determining constraint conditions in the optimal scheduling process; the constraint conditions comprise system power balance constraint, wind power photovoltaic power generation capacity constraint, wind power photovoltaic day-ahead predictive schedulable output power interval constraint, energy storage safe operation interval and operation state constraint and day-ahead day-in scheduling curve residual constraint.
The multi-objective function construction module is used for constructing a multi-objective function based on the constraint condition; the multi-objective function comprises a daily plan tracking function, a maximum new energy utilization function, a function which plays a constraint role in optimizing the operation characteristics of each unit of the wind-solar-hydrogen storage in the scheduling process according to the demand expansion objective function and the constraint condition of corresponding coupling, and a maximum economic benefit function.
The state space model construction module is used for acquiring a scheduling instruction of the wind-light-hydrogen energy storage micro-grid system and constructing a state space model corresponding to the wind-light-hydrogen energy storage micro-grid system based on a model prediction control theory.
The time domain rolling optimization scheduling model construction module is used for constructing a time domain rolling optimization scheduling model of the running state information of each subsystem in the wind-solar-hydrogen energy storage micro-grid system at any moment according to the state space model, the multi-objective function and the constraint condition.
And the daily micro-grid energy optimization scheduling strategy formulation module is used for solving the time domain rolling optimization scheduling model and formulating a daily micro-grid energy optimization scheduling strategy.
And the final daily optimization scheduling result determining module is used for correcting the current state of each subsystem in the wind-solar-hydrogen energy storage micro-grid system according to the daily micro-grid energy optimization scheduling strategy, sampling the real-time system state, updating the ultra-short-term predicted power value, forming a wind power, photovoltaic and energy storage system control output sequence, and determining the final daily optimization scheduling result.
Aiming at the operation characteristics of wind, light and hydrogen storage units in a micro-grid system and the scheduling requirement of a power grid, the invention considers the differentiated price requirement response of different time periods of the power grid, takes maximized new energy consumption, maximum system benefit, daily schedule tracking and the like as objective functions, and adopts a multi-objective model prediction rolling optimization method considering the cluster difference of the energy storage system to realize the optimal distribution of the output of each distributed energy source so as to meet the scheduling requirement of the power grid. The method specifically comprises the following steps: under the constraint of power balance, SOHR, SOC value of lithium battery energy storage system, upper and lower limits of wind-electricity photovoltaic output and other conditions, a power grid dispatching instruction is used as a system input, the charge and discharge states of each distributed power supply in the wind-light-hydrogen storage micro-grid system are determined according to the power grid demand, n optimization targets such as daily planned tracking, maximum new energy consumption, maximum system income and the like are used for predicting wind power, photovoltaic, hydrogen energy storage and lithium battery energy storage in a rolling prediction space respectively, an optimized output sequence of each part in a rolling window area is obtained, a predicted value at the first moment is taken as a power value input to the micro-grid at the next moment of the wind power, photovoltaic, hydrogen energy storage and lithium battery energy storage system, only a correction plan in a later time period is output at the current moment, the optimization process is repeated when the next rolling period arrives, and finally the output of each part of the system obtained by optimization solution under n objective functions is comprehensively evaluated, and the final dispatching result of the wind-light-hydrogen storage micro-grid system is obtained and output. According to the method, the uncertainty of wind-driven photovoltaic power generation and the space-time translation characteristic of the energy storage system are fully considered, the differentiated demand response and the model predictive control method are combined to carry out optimization solving on the multi-objective function, and the influence of uncertainty factors in the micro-grid is greatly weakened by combining time domain rolling and feedback correction of the real-time state of the system, so that the rationality of a system short-time rolling scheduling plan and the stability of system operation are ensured, theoretical basis can be provided for large-scale demonstration of the hydrogen energy storage system, and the coupling development of the hydrogen energy and the energy storage field is accelerated.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. The time domain rolling optimization method of the wind-solar-hydrogen storage micro-grid system is characterized by comprising the following steps of:
basic data and topological structure information of the wind-solar-hydrogen energy storage micro-grid system are obtained; the basic data comprise wind power, photovoltaic day-ahead predictive schedulable power information, power grid load demand predictive information, rated power, HESS and BESS rated power and rated operation parameters, SOC and SOHR states and a power grid time-of-use electricity price curve; the topological structure information comprises a system connection mode and a power supply bus mode;
Based on the topological structure information, judging the charge and discharge states of the BESS and the HESS according to the basic data, and determining constraint conditions in the optimal scheduling process; the constraint conditions comprise system power balance constraint, wind power photovoltaic power generation capacity constraint, wind power photovoltaic day-ahead predicted schedulable output power interval constraint, energy storage safe operation interval and operation state constraint and day-ahead day-in scheduling curve residual constraint;
constructing a multi-objective function based on the constraint condition; the multi-objective function comprises a daily plan tracking function, a maximum new energy utilization function, a function which plays a constraint role in optimizing the operation characteristics of each unit of the wind-solar-hydrogen storage in the scheduling process according to the demand expansion objective function and the constraint condition of corresponding coupling, and a maximum economic benefit function;
acquiring a scheduling instruction of a wind-light-hydrogen energy storage micro-grid system, and constructing a state space model corresponding to the wind-light-hydrogen energy storage micro-grid system based on a model predictive control theory;
constructing a time domain rolling optimization scheduling model of the running state information of each subsystem in the wind, light and hydrogen energy storage micro-grid system at any moment according to the state space model, the multi-objective function and the constraint condition;
Solving the time domain rolling optimization scheduling model, and formulating an intra-day micro-grid energy optimization scheduling strategy;
correcting the current state of each subsystem in the wind-solar-hydrogen energy storage micro-grid system according to the micro-grid energy optimization scheduling strategy in the day, sampling the real-time system state, updating the ultra-short-term predicted power value, forming a wind power, photovoltaic and energy storage system control output sequence, and determining the final day optimization scheduling result.
2. The method for optimizing time domain rolling of a wind-solar-hydrogen storage micro-grid system according to claim 1, wherein the system power balance constraint is as follows:
wherein P is load (k) Power is required for the load; n (N) PV Is the photovoltaic quantity;the ith photovoltaic output power at the kth moment; i is a photovoltaic serial number; n (N) Wind The wind power quantity; j is a wind power sequence number; />The power output of the jth wind power at the kth moment; n (N) BESS The energy storage quantity of lithium batteries is calculated; l is the lithium battery energy storage serial number; />The charging and discharging power of the first lithium battery energy storage at the kth moment; n (N) HESS The hydrogen storage machine quantity; m is a hydrogen energy storage serial number; />Charging and discharging power for the mth hydrogen energy storage at the kth moment; p (P) grid (k) Exchanging power for the microgrid connection line at the kth time.
3. The method for optimizing time domain rolling of the wind, light and hydrogen storage micro-grid system according to claim 2, wherein the constraint of the wind power photovoltaic power generation capacity is as follows:
Wherein P is PV (k) The photovoltaic output power; p (P) Wind (k) The wind power output power is the wind power output power.
4. The method for optimizing time domain rolling of the wind-solar-hydrogen storage micro-grid system according to claim 3, wherein the wind power photovoltaic pre-day predicted schedulable output power interval constraint is as follows:
wherein,is the minimum photovoltaic output power; />Is the maximum photovoltaic output power; />The minimum wind power output power is obtained; />The maximum wind power output power is obtained; />The energy storage charging and discharging power of the minimum lithium battery is; p (P) BESS (k) Charging and discharging power for lithium battery energy storage; />The maximum lithium battery energy storage charge and discharge power; />The minimum hydrogen energy storage charge and discharge power; p (P) HESS (k) Charging and discharging power for hydrogen energy storage; />The maximum hydrogen energy storage charge and discharge power;exchanging power for the minimum micro-grid tie line; p (P) grid (k) Exchanging power for the microgrid tie lines; />Exchanging power for the maximum microgrid connection.
5. The method for optimizing time domain rolling of the wind, solar and hydrogen storage micro-grid system according to claim 4, wherein the energy storage safe operation interval and the operation state constraint comprise an energy storage system operation state constraint and an energy storage system safe operation interval;
the energy storage system operation state constraint is as follows:wherein SOC is BESS (k) The charge state of the lithium battery energy storage system is as follows; SOHR HESS (k) The hydrogen pressure state of the hydrogen energy storage system is set; />The method is a state-of-charge limit value of a lithium battery energy storage system; />The hydrogen pressure state limiting value is the hydrogen pressure state limiting value of the hydrogen energy storage system;
the safe operation interval of the energy storage system is thatWherein (1)>The state of charge of the lithium battery energy storage system is the minimum; />The state of charge of the maximum lithium battery energy storage system; />The hydrogen pressure state of the minimum hydrogen energy storage system is set; />Is at most +.>
6. The method for optimizing time domain rolling of the wind-solar-hydrogen storage micro-grid system according to claim 5, wherein the intra-day scheduling curve residual constraint is as follows:
wherein,the lower limit of the sum of squares of residual errors and root mean square values of the day-ahead optimization curve corresponding to the photovoltaic power generation optimization curve at the kth moment; />The method comprises the steps that a residual error square sum root mean square value of a day-ahead optimization curve corresponding to a photovoltaic power generation optimization curve is calculated;the upper limit of the sum of squares of residual errors and root mean square values of the day-ahead optimization curve corresponding to the photovoltaic power generation optimization curve at the kth moment; />The lower limit of the square sum of residual errors and root mean square value of the day-ahead optimization curve corresponding to the wind power generation optimization curve at the kth moment; />The method comprises the steps of optimizing a residual error square sum root mean square value of a curve corresponding to a wind power generation optimization curve;the upper limit of the sum of squares of residual errors and root mean square value of the day-ahead optimization curve corresponding to the kth moment wind power generation optimization curve is +. >The lower limit of the square sum of residual errors of the day-ahead optimization curve and the root mean square value corresponding to the lithium battery energy storage output power at the kth moment is set; />Optimizing the residual square sum root mean square value of the curve for the day-ahead corresponding to the lithium battery energy storage output power;the upper limit of the residual square sum root mean square value of the day-ahead optimization curve corresponding to the lithium battery energy storage output power at the kth moment is set; />The lower limit of the square sum of residual errors of the day-ahead optimization curve and the root mean square value corresponding to the lithium battery energy storage SOC change curve at the kth moment; />The method comprises the steps of optimizing a residual square sum root mean square value of a curve corresponding to a lithium battery energy storage SOC change curve; />Is the firstThe upper limit of the square sum root mean square value of the residual error of the day-ahead optimization curve corresponding to the lithium battery energy storage SOC change curve at the moment k; />The lower limit of the square sum of residual errors of the optimized curve and the root mean square value of the current day corresponding to the hydrogen energy storage output power at the kth moment; />Optimizing the residual square sum root mean square value of the curve for the day-ahead corresponding to the hydrogen storage output power; />The upper limit of the sum of squares of residual errors and root mean square values of the day-ahead optimization curves corresponding to the hydrogen energy storage output power at the kth moment; />The lower limit of the square sum of the residual errors of the daily optimization curve and the root mean square value corresponding to the hydrogen storage SOHR change curve at the kth moment; />Optimizing the residual square sum root mean square value of the curve for the future corresponding to the hydrogen storage SOHR change curve; / >The upper limit of the square sum of the residual errors of the daily optimization curve corresponding to the hydrogen storage SOHR change curve at the kth moment is set; RSS (really simple syndication) Pgrid_min (k) The lower limit of the square sum of residual errors of the optimized curves before the day corresponding to the microgrid interconnecting line power at the kth moment is obtained; RSS (really simple syndication) Pgrid (k) Optimizing the residual square sum root mean square value of the curve for the day time corresponding to the micro-grid interconnection line power; RSS (really simple syndication) Pgrid_max (k) And optimizing the upper limit of the residual square sum root mean square value of the curve for the day before corresponding to the microgrid interconnection line power at the k moment.
7. The method for optimizing time domain rolling of the wind-solar-hydrogen storage micro-grid system according to claim 6, wherein the multi-objective function J is:
wherein J is n N=1, 2,3 for the nth objective function; p (P) Wind_ab (k) The air quantity is discarded at the kth moment; p (P) PV_ab (k) Discarding the light quantity at the kth moment; c (C) grid (k) The energy interaction cost of the micro power grid and the large power grid at the moment k is; c (C) BESS (k) The cost of the battery is reduced at the moment k; c (C) HESS (k) The cost of the hydrogen storage system at time k is reduced.
8. The method for optimizing time domain rolling of a wind-solar-hydrogen storage micro-grid system according to claim 7, wherein the state space model is:
wherein x (k+Δt) is the state of the state vector at the time k+Δt; p (P) PV (k+Δt) is the power state value of the photovoltaic system at the time k+Δt; p (P) Wind (k+deltat) is the power state value of the wind power system at the moment k+deltat; p (P) BESS (k+delta t) is a power state value of the lithium battery energy storage system at the moment k+delta t; p (P) HESS (k+Δt) is the power state value of the hydrogen energy storage system at the time k+Δt; SOC (State of Charge) BESS (k+delta t) is the SOC state value of the lithium battery energy storage system at the moment k+delta t; SOHR HESS (k+Δt) is the SOHR state value of the hydrogen storage system at time k+Δt; p (P) grid (k+Δt) exchanging power status values for the grid tie; Δt is the rolling optimization single stepping time length; q (Q) BESS The capacity of the lithium battery energy storage system is the total assembly machine capacity; sigma (sigma) B The lithium battery is used for storing energy and self-discharging power; q (Q) HESS The capacity of the hydrogen energy storage system is the total assembly machine; sigma (sigma) H Self-discharge power for hydrogen energy storage; ΔP PV (k) Force increment can be scheduled for the photovoltaic system; ΔP Wind (k) The output increment can be scheduled for the wind power system; ΔP BESS (k) The power increment can be scheduled for the lithium battery energy storage system; ΔP HESS (k) A force increment can be scheduled for the hydrogen energy storage system; ΔP load (k) To meet the power change increment of the demand; y (k) is a system output variable and is a vector composed of the output of each subsystem, the SOC and the SOHR.
9. The time domain rolling optimization method of the wind, light and hydrogen storage micro-grid system according to claim 8, wherein solving the time domain rolling optimization scheduling model and formulating an intra-day micro-grid energy optimization scheduling strategy specifically comprises:
Using the formulaSolving the time domain rolling optimization scheduling model, and formulating an intra-day micro-grid energy optimization scheduling strategy; wherein J is mix A fusion objective function for synthesizing the n objective functions; j (J) r Vector composed of the optimized subsystem output values under the r-th objective function; />Vector reference values formed by the output values of the subsystems optimized under the r-th objective function.
10. The utility model provides a scene hydrogen stores up micro-grid system time domain roll optimizing system which characterized in that includes:
the parameter acquisition module is used for acquiring basic data and topological structure information of the wind-light-hydrogen energy storage micro-grid system; the basic data comprise wind power, photovoltaic day-ahead predictive schedulable power information, power grid load demand predictive information, rated power, HESS and BESS rated power and rated operation parameters, SOC and SOHR states and a power grid time-of-use electricity price curve; the topological structure information comprises a system connection mode and a power supply bus mode;
the constraint condition determining module is used for judging the charge and discharge states of the BESS and the HESS according to the basic data based on the topological structure information and determining constraint conditions in the optimal scheduling process; the constraint conditions comprise system power balance constraint, wind power photovoltaic power generation capacity constraint, wind power photovoltaic day-ahead predicted schedulable output power interval constraint, energy storage safe operation interval and operation state constraint and day-ahead day-in scheduling curve residual constraint;
The multi-objective function construction module is used for constructing a multi-objective function based on the constraint condition; the multi-objective function comprises a daily plan tracking function, a maximum new energy utilization function, a function which plays a constraint role in optimizing the operation characteristics of each unit of the wind-solar-hydrogen storage in the scheduling process according to the demand expansion objective function and the constraint condition of corresponding coupling, and a maximum economic benefit function;
the state space model construction module is used for acquiring a scheduling instruction of the wind-light-hydrogen energy storage micro-grid system and constructing a state space model corresponding to the wind-light-hydrogen energy storage micro-grid system based on a model prediction control theory;
the time domain rolling optimization scheduling model construction module is used for constructing a time domain rolling optimization scheduling model of the running state information of each subsystem in the wind-solar-hydrogen energy storage micro-grid system at any moment according to the state space model, the multi-objective function and the constraint condition;
the intra-day micro-grid energy optimization scheduling strategy formulation module is used for solving the time domain rolling optimization scheduling model and formulating an intra-day micro-grid energy optimization scheduling strategy;
and the final daily optimization scheduling result determining module is used for correcting the current state of each subsystem in the wind-solar-hydrogen energy storage micro-grid system according to the daily micro-grid energy optimization scheduling strategy, sampling the real-time system state, updating the ultra-short-term predicted power value, forming a wind power, photovoltaic and energy storage system control output sequence, and determining the final daily optimization scheduling result.
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