CN108667054B - Energy storage planning method and device - Google Patents
Energy storage planning method and device Download PDFInfo
- Publication number
- CN108667054B CN108667054B CN201710190530.0A CN201710190530A CN108667054B CN 108667054 B CN108667054 B CN 108667054B CN 201710190530 A CN201710190530 A CN 201710190530A CN 108667054 B CN108667054 B CN 108667054B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- power
- storage system
- objective function
- grid
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 208
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 238000009499 grossing Methods 0.000 claims description 35
- 238000001914 filtration Methods 0.000 claims description 27
- 238000004422 calculation algorithm Methods 0.000 claims description 19
- 230000002068 genetic effect Effects 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 9
- 238000007599 discharging Methods 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 125000004432 carbon atom Chemical group C* 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000006870 function Effects 0.000 description 71
- 238000010586 diagram Methods 0.000 description 12
- 238000004590 computer program Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 5
- 230000006872 improvement Effects 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- 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
-
- H02J3/382—
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to an energy storage planning method and device, wherein the method comprises the following steps: establishing a multi-target planning objective function and constraint conditions of the energy storage system; determining an optimal solution of a multi-target planning objective function of the energy storage system; performing energy storage planning on the new energy power station according to the optimal solution; according to the technical scheme provided by the invention, the planning scheme of the energy storage system is optimized in a multi-objective optimization mode, so that the efficient and stable operation of a power grid system is ensured.
Description
Technical Field
The invention relates to the technical field of new energy storage control, in particular to an energy storage planning method and device.
Background
With the rapid development of social economy, the shortage of fossil energy and the problem of environmental pollution become more severe, and renewable energy sources such as photovoltaic energy, wind power and the like become hot spots for the research of broad scholars due to the advantages of cleanness and environmental protection, and have wide research and application prospects. Renewable energy will gradually become an important electric energy resource in China, and the transition from supplementary energy to alternative energy fields is about to be completed.
However, renewable energy represented by wind power, photovoltaic and the like has remarkable characteristics of randomness, intermittence and fluctuation, and brings great challenges to stable, safe and efficient operation of a power grid during large-scale grid connection. At present, when a new energy power station energy storage planning scheme is formulated, most of the new energy power station energy storage planning schemes only take limitation of grid-connected power fluctuation or other factors as a single optimization target, wherein a multi-target function considering a plurality of influence factors is established in part of technologies, however, a restriction relation exists among a plurality of target values, and a global optimal solution is difficult to obtain.
Disclosure of Invention
The invention provides an energy storage planning method and device, and aims to optimize a planning scheme of an energy storage system in a multi-objective optimization mode so as to ensure efficient and stable operation of a power grid system.
The purpose of the invention is realized by adopting the following technical scheme:
in a method of energy storage planning, the improvement comprising:
respectively establishing a multi-target planning objective function and constraint conditions of the energy storage system;
determining an optimal solution of the multi-objective planning function;
and performing energy storage planning on the power station according to the optimal solution.
Preferably, the objective function includes: a first objective function of energy storage and a second objective function of energy storage;
the energy storage first objective function is the minimum value of the fluctuation amplitude of grid-connected power;
the energy storage second objective function takes the maximum value of the economic index of the energy storage battery;
the constraints include constraint data of the energy storage system.
Further, the energy storage first objective function:
in the above formula, PC(k) For grid-connected power at time k, λdA unit penalty factor for exceeding the power fluctuation range, N is the total number of sampling moments,
wherein, PClimitIs a grid-connected power change rate limit value;
further, the energy storage second objective function:
in the above formula, PmaxFor maximum power of energy storage system, lambdaPCost factor for the maximum power of the energy storage system, EmaxFor maximum capacity of the energy storage system, λECost factor for maximum capacity of energy storage system, EcycleFor controlling the quantity of circulating electric power in the time period, λmvA cost factor for the circulating electric quantity in the control period, wherein:
in the above formula, the first and second carbon atoms are,
PS(k) and the energy storage power at the moment k, and delta T is a control time interval.
Further, the energy storage system constraint data includes: the method comprises the following steps of carrying out grid-connected power constraint, low-pass filtering smoothing time constant constraint, and charge-discharge power constraint on an energy storage system of a renewable energy power station;
establishing the grid-connected power constraint of the renewable energy power station according to the following formula:
Pc(k)≤min{Pcref,Pcline},k＝1,...,N
in the above formula, PC(k) For grid-connected power at time k, PcrefFor renewable energy power station grid-connected power upper limit, PclineThe maximum transmission power of the grid-connected line is obtained, and N is the total number of sampling moments;
a low-pass filter smoothing time constant constraint is established as follows:
τmin≤τ≤τmax
in the above formula, τ is the smoothing time constant of low-pass filtering, τminSmoothing the lower limit of the time constant for low-pass filtering, τmaxSmoothing the time constant upper limit for the low pass filter;
establishing a state of charge constraint for the energy storage system as follows:
SOCmin≤SOC(k)≤SOCmax,k＝1,...,N
in the above formula, SOC (k) is the state of charge of the energy storage system at time k, SOCminThe lower limit of the state of charge, SOC, of the energy storage system at the moment kmaxThe upper limit of the state of charge of the energy storage system at the moment k;
establishing a charge-discharge power constraint according to the following formula:
in the above formula, PS(k) For storing power at time k, PRFor rating the energy storage system, ERFor rating the capacity of the energy storage system, PROAnd delta T is the control time interval for the maximum output power of the energy storage system at the current moment.
Further, the determining of the optimal solution of the multi-objective planning objective function of the energy storage system is as follows:
introducing a low-pass filtering smoothing time constant tau and energy storage power P at k momentS(k) And the optimal solution of the multi-target planning objective function of the energy storage system is determined by utilizing a multi-target optimization algorithm as a variable of the multi-target planning objective function of the energy storage system.
Further, the multi-objective optimization algorithm comprises:
a second generation fast non-dominated sorting genetic algorithm is utilized.
Further, the planning of energy storage of the new energy power station according to the optimal solution includes:
determining grid-connected power according to the low-pass filtering smoothing time constant tau in the optimal solution;
and planning the charging and discharging power of the energy storage system and the grid-connected power of the renewable energy source in real time according to the energy storage power and the grid-connected power in the optimal solution.
Further, the grid-connected power P at the time kC(k)：
In the above formula, τ is the smoothing time constant of the low-pass filter in the optimal solution, Δ T is the control time interval, PPV(k) And outputting power for the renewable energy power station at the moment k.
In an energy storage planning apparatus, the improvement comprising:
the building module is used for respectively building a multi-target planning objective function and constraint conditions of the energy storage system;
the determining module is used for determining the optimal solution of the multi-target planning function;
and the planning module is used for carrying out energy storage planning on the power station according to the optimal solution.
Preferably, in the building module, the objective function includes: a first objective function of energy storage and a second objective function of energy storage;
the energy storage first objective function is the minimum value of the fluctuation amplitude of grid-connected power;
the energy storage second objective function takes the maximum value of the economic index of the energy storage battery;
the constraints include constraint data of the energy storage system.
Preferably, the determining module includes:
a first determination unit for introducing a low-pass filtering smoothing time constant tau and a storage power P at the moment kS(k) As a variable of a multi-objective planning objective function of the energy storage system, and is determined by using a multi-objective optimization algorithmAnd determining the optimal solution of the multi-target planning objective function of the energy storage system.
Preferably, the planning module includes:
the second determining unit is used for determining grid-connected power according to the low-pass filtering smoothing time constant tau in the optimal solution;
and the planning unit is used for planning the charging and discharging power of the energy storage system and the grid-connected power of the renewable energy source in real time according to the energy storage power in the optimal solution and the grid-connected power.
The invention has the beneficial effects that:
according to the technical scheme provided by the invention, energy storage planning objective functions are respectively established according to the minimum fluctuation amplitude of grid-connected power and the maximum economic index of the energy storage battery, and a series of Pareto optimal solution sets are obtained by simultaneously carrying out non-dominated optimization on the two indexes of grid-connected power fluctuation and the economic index of the energy storage battery in a multi-objective optimization mode, so that the multi-objective optimization energy storage planning scheme is obtained.
Drawings
FIG. 1 is a flow chart of a method of energy storage planning of the present invention;
FIG. 2 is a schematic diagram of an application scenario structure in an embodiment of the present invention;
FIG. 3 is a flow chart of a second generation non-dominated sorting genetic algorithm in an embodiment of the invention;
fig. 4 is a schematic structural diagram of an energy storage planning apparatus according to the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The energy storage planning method provided by the invention optimizes the planning scheme of the energy storage system in a multi-objective optimization mode, thereby ensuring the efficient and stable operation of a power grid system, as shown in fig. 1, the method comprises the following steps:
101. respectively establishing a multi-target planning objective function and constraint conditions of the energy storage system;
wherein the objective function comprises: a first objective function of energy storage and a second objective function of energy storage;
the energy storage first target function takes the minimum fluctuation amplitude of grid-connected power;
the energy storage second objective function takes the maximum value of the economic index of the energy storage battery;
the constraints include constraint data of the energy storage system.
102. Determining an optimal solution of the multi-objective planning function;
103. and performing energy storage planning on the power station according to the optimal solution.
For example, in an application scenario provided in an embodiment of the present invention, a combined force diagram of a renewable energy power station and an energy storage system is shown in fig. 2, and the combined force diagram is composed of a power generation system of the renewable energy power station, an energy storage system, two converters, a grid-connected transformer, and a central control unit, where the renewable energy power station and the energy storage system are respectively connected with an ac bus through the converters, and the ac bus is incorporated into a power grid through the transformer. The grid-connected power is the difference between the generated power of the renewable energy power station and the charging power of the energy storage system. The central processing unit receives output power data of a renewable energy power station prediction model, power station grid-connected scheduling data and constraint data of an energy storage system, the data are brought into two objective functions, the two objective functions are simultaneously optimized through a second-generation non-dominated sorting genetic algorithm, a series of Pareto optimal solutions are obtained, and then an optimal planning scheme of the energy storage system is obtained.
Specifically, in the embodiment provided by the present invention, the energy storage planning objective function is respectively established with the target of the minimum fluctuation amplitude of the grid-connected power and the maximum economic index of the energy storage battery, and the step 101 includes:
establishing an energy storage first objective function by taking the minimum fluctuation amplitude of grid-connected power as a target;
establishing an energy storage second objective function by taking the maximum economic index of the energy storage battery as an objective;
and establishing constraint conditions of the multi-target planning objective function of the energy storage system by using the constraint data of the energy storage system.
The method for establishing the energy storage first objective function by taking the minimum fluctuation amplitude of the grid-connected power as a target comprises the following steps of:
establishing the energy storage first objective function according to the following formula:
in the above formula, PC(k) For grid-connected power at time k, λdA unit penalty factor for exceeding the power fluctuation range, N is the total number of sampling moments,
wherein, PClimitIs a grid-connected power change rate limit value;
the method for establishing the energy storage second objective function by taking the maximum economic index of the energy storage battery as the target comprises the following steps:
establishing the energy storage second objective function according to the following formula:
in the above formula, PmaxFor maximum power of energy storage system, lambdaPCost factor for the maximum power of the energy storage system, EmaxFor maximum capacity of the energy storage system, λECost factor for maximum capacity of energy storage system, EcycleFor controlling the quantity of circulating electric power in the time period, λmvA cost factor for the circulating electric quantity in the control period, wherein:
in the above formula, the first and second carbon atoms are,
PS(k) and the energy storage power at the moment k, and delta T is a control time interval.
The method for establishing the constraint condition of the multi-target planning objective function of the energy storage system by using the constraint data of the energy storage system comprises the following steps:
the energy storage system constraint data comprises: the method comprises the following steps of (1) grid-connected power of a renewable energy power station, a low-pass filtering smoothing time constant, and a charge state and charge-discharge power of an energy storage system;
establishing the renewable energy power station grid-connected power constraint of the constraint condition of the multi-objective planning objective function of the energy storage system according to the following formula:
Pc(k)≤min{Pcref,Pcline},k＝1,...,N
in the above formula, PC(k) For grid-connected power at time k, PcrefFor renewable energy power station grid-connected power upper limit, PclineThe maximum transmission power of the grid-connected line is obtained, and N is the total number of sampling moments;
establishing a low-pass filtering smoothing time constant constraint of a constraint condition of a multi-target planning objective function of the energy storage system according to the following formula:
τmin≤τ≤τmax
in the above formula, τ is the smoothing time constant of low-pass filtering, τminSmoothing the lower limit of the time constant for low-pass filtering, τmaxSmoothing the time constant upper limit for the low pass filter;
establishing the state of charge constraint of the energy storage system according to the constraint conditions of the multi-target planning objective function of the energy storage system as follows:
SOCmin≤SOC(k)≤SOCmax,k＝1,...,N
in the above formula, SOC (k) is the state of charge of the energy storage system at time k, SOCminThe lower limit of the state of charge, SOC, of the energy storage system at the moment kmaxThe upper limit of the state of charge of the energy storage system at the moment k;
establishing charge and discharge power constraint of constraint conditions of a multi-target planning objective function of the energy storage system according to the following formula:
in the above formula, PS(k) For storing power at time k, PRFor rating the energy storage system, ERFor rating the capacity of the energy storage system, PROAnd delta T is the control time interval for the maximum output power of the energy storage system at the current moment.
In the embodiment provided by the invention, through a multi-objective optimization mode, two indexes of grid-connected power fluctuation and energy storage battery economy are simultaneously subjected to non-dominated optimization, and the step 102 includes:
introducing a low-pass filtering smoothing time constant tau and energy storage power P at k momentS(k) And the optimal solution of the multi-target planning objective function of the energy storage system is determined by utilizing a multi-target optimization algorithm as a variable of the multi-target planning objective function of the energy storage system.
In the optimal embodiment provided by the invention, as shown in fig. 3, a second-generation fast non-dominated sorting genetic algorithm is used to determine an optimal solution of a multi-objective planning objective function of the energy storage system.
The second generation of rapid non-dominated sorting genetic algorithm is one of evolutionary multi-objective algorithms with better effect for solving the problem of multi-objective optimization in the prior art, and effectively solves the defects of complex calculation, lack of elite strategy and need to consider to specify a sharing radius of the original non-dominated sorting genetic method, so that the algorithm is adopted to optimize a multi-objective function model, for example: solving two objective functions according to a second-generation rapid non-dominated sorting genetic algorithm, wherein the algorithm adopts real number coding, a selection mechanism adopts a championship selection method, and cross operation adopts analog binary crossAn operator adopts a polynomial mutation operator in mutation operation, and low-pass filtering smoothing time constant tau and k moment energy storage power P are introduced based on constraint data of the grid-connected systemS(k) Two variables, the problem to be solved is represented as:
(minf1,maxf2)＝f(τ,PS(k)),k＝1,2,...,N
and solving the multi-target problem by using a second-generation rapid non-dominated sorting genetic algorithm to obtain a series of Pareto optimal solutions, thereby providing important reference for a decision maker in energy storage planning of the power grid system.
Finally, determining a planning scheme of the energy storage system by using the optimal solution of the objective function, wherein the step 103 comprises the following steps:
determining grid-connected power according to the low-pass filtering smoothing time constant tau in the optimal solution;
and planning the charging and discharging power of the energy storage system and the grid-connected power of the renewable energy source in real time according to the energy storage power and the grid-connected power in the optimal solution.
Wherein, the grid-connected power P at the time of k is shown as the following formulaC(k)：
In the above formula, Δ T is the control time interval, PPV(k) And outputting power for the renewable energy power station at the moment k.
The present invention also provides an energy storage planning apparatus, as shown in fig. 4, the apparatus includes:
the building module is used for respectively building a multi-target planning objective function and constraint conditions of the energy storage system;
the determining module is used for determining the optimal solution of the multi-target planning function;
and the planning module is used for carrying out energy storage planning on the power station according to the optimal solution.
Wherein the objective function comprises: a first objective function of energy storage and a second objective function of energy storage;
the energy storage first target function takes the minimum fluctuation amplitude of grid-connected power;
the energy storage second objective function takes the maximum value of the economic index of the energy storage battery;
the constraints include constraint data of the energy storage system.
Establishing the energy storage first objective function according to the following formula:
in the above formula, PC(k) For grid-connected power at time k, λdA unit penalty factor for exceeding the power fluctuation range, N is the total number of sampling moments,
wherein, PClimitIs a grid-connected power change rate limit value;
establishing the energy storage second objective function according to the following formula:
in the above formula, PmaxFor maximum power of energy storage system, lambdaPCost factor for the maximum power of the energy storage system, EmaxFor maximum capacity of the energy storage system, λECost factor for maximum capacity of energy storage system, EcycleFor controlling the quantity of circulating electric power in the time period, λmvA cost factor for the circulating electric quantity in the control period, wherein:
in the above formula, the first and second carbon atoms are,
PS(k) and the energy storage power at the moment k, and delta T is a control time interval.
The energy storage system constraint data comprises: the method comprises the following steps of (1) grid-connected power of a renewable energy power station, a low-pass filtering smoothing time constant, and a charge state and charge-discharge power of an energy storage system;
establishing the renewable energy power station grid-connected power constraint of the constraint condition of the multi-objective planning objective function of the energy storage system according to the following formula:
Pc(k)≤min{Pcref,Pcline},k＝1,...,N
in the above formula, PC(k) For grid-connected power at time k, PcrefFor renewable energy power station grid-connected power upper limit, PclineThe maximum transmission power of the grid-connected line is obtained, and N is the total number of sampling moments;
establishing a low-pass filtering smoothing time constant constraint of a constraint condition of a multi-target planning objective function of the energy storage system according to the following formula:
τmin≤τ≤τmax
in the above formula, τ is the smoothing time constant of low-pass filtering, τminSmoothing the lower limit of the time constant for low-pass filtering, τmaxSmoothing the time constant upper limit for the low pass filter;
establishing the state of charge constraint of the energy storage system according to the constraint conditions of the multi-target planning objective function of the energy storage system as follows:
SOCmin≤SOC(k)≤SOCmax,k＝1,...,N
in the above formula, SOC (k) is the state of charge of the energy storage system at time k, SOCminThe lower limit of the state of charge, SOC, of the energy storage system at the moment kmaxThe upper limit of the state of charge of the energy storage system at the moment k;
establishing charge and discharge power constraint of constraint conditions of a multi-target planning objective function of the energy storage system according to the following formula:
in the above formula, PS(k) For storing power at time k, PRFor rating the energy storage system, ERFor rating the capacity of the energy storage system, PROAnd delta T is the control time interval for the maximum output power of the energy storage system at the current moment.
The determining module includes:
a first determination unit for introducing a low-pass filtering smoothing time constant tau and a storage power P at the moment kS(k) And the optimal solution of the multi-target planning objective function of the energy storage system is determined by utilizing a multi-target optimization algorithm as a variable of the multi-target planning objective function of the energy storage system.
And determining the optimal solution of the multi-target planning objective function of the energy storage system by utilizing a second-generation rapid non-dominated sorting genetic algorithm.
The planning module comprises:
the second determining unit is used for determining grid-connected power according to the low-pass filtering smoothing time constant tau in the optimal solution;
and the planning unit is used for planning the charging and discharging power of the energy storage system and the grid-connected power of the renewable energy source in real time according to the energy storage power in the optimal solution and the grid-connected power.
Wherein, the grid-connected power P at the time of k is shown as the following formulaC(k)：
In the above formula, Δ T is the control time interval, PPV(k) And outputting power for the renewable energy power station at the moment k.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. An energy storage planning method, characterized by:
respectively establishing a multi-target planning objective function and constraint conditions of the energy storage system;
determining an optimal solution of the multi-target planning objective function;
performing energy storage planning on the renewable energy power station according to the optimal solution;
the objective function includes: a first objective function of energy storage and a second objective function of energy storage;
the energy storage first objective function is the minimum value of the fluctuation amplitude of grid-connected power;
the energy storage second objective function takes the maximum value of the economic index of the energy storage battery;
the constraint condition comprises constraint data of the energy storage system;
the energy storage first objective function:
in the above formula, PC(k) For grid-connected power at time k, λdA unit penalty factor for exceeding the power fluctuation range, N is the total number of sampling moments,
wherein, PClimitIs a grid-connected power change rate limit value;
the energy storage second objective function:
in the above formula, PmaxFor maximum power of energy storage system, lambdaPCost factor for the maximum power of the energy storage system, EmaxFor maximum capacity of the energy storage system, λECost factor for maximum capacity of energy storage system, EcycleFor controlling the quantity of circulating electric power in the time period, λmvA cost factor for the circulating electric quantity in the control period, wherein:
in the above formula, the first and second carbon atoms are,
PS(k) the energy storage power at the moment k and the delta T are control time intervals.
2. The method of claim 1,
the energy storage system constraint data comprises: the method comprises the following steps of carrying out grid-connected power constraint, low-pass filtering smoothing time constant constraint, and charge-discharge power constraint on an energy storage system of a renewable energy power station;
establishing the grid-connected power constraint of the renewable energy power station according to the following formula:
PC(k)≤min{Pcref,Pcline},k＝1,...,N
in the above formula, PC(k) For grid-connected power at time k, PcrefFor renewable energy power station grid-connected power upper limit, PclineThe maximum transmission power of the grid-connected line is obtained, and N is the total number of sampling moments;
a low-pass filter smoothing time constant constraint is established as follows:
τmin≤τ≤τmax
in the above formula, τ is the smoothing time constant of low-pass filtering, τminSmoothing the lower limit of the time constant for low-pass filtering, τmaxSmoothing the time constant upper limit for the low pass filter;
establishing a state of charge constraint for the energy storage system as follows:
SOCmin≤SOC(k)≤SOCmax,k＝1,...,N
in the above formula, SOC (k) is the state of charge of the energy storage system at time k, SOCminThe lower limit of the state of charge, SOC, of the energy storage system at the moment kmaxThe upper limit of the state of charge of the energy storage system at the moment k;
establishing a charge-discharge power constraint according to the following formula:
in the above formula, PS(k) For storing power at time k, PRFor rating the energy storage system, ERFor rating the capacity of the energy storage system, PROThe maximum output power of the energy storage system at the current moment, and the delta T is a control time interval.
3. The method of any one of claims 1-2, wherein determining the optimal solution for the multi-objective planning objective function of the energy storage system is:
introducing a low-pass filtering smoothing time constant tau and energy storage power P at k momentS(k) And the optimal solution of the multi-target planning objective function of the energy storage system is determined by utilizing a multi-target optimization algorithm as a variable of the multi-target planning objective function of the energy storage system.
4. The method of claim 3, wherein the multi-objective optimization algorithm comprises: second generation fast non-dominated sorting genetic algorithms.
5. The method of claim 3, wherein said planning the energy storage of the renewable energy power plant according to the optimal solution comprises:
determining grid-connected power according to the low-pass filtering smoothing time constant tau in the optimal solution;
and planning the charging and discharging power of the energy storage system and the grid-connected power of the renewable energy source in real time according to the energy storage power and the grid-connected power in the optimal solution.
6. The method according to claim 5, characterized in that the time k is the grid-connected power PC(k)：
In the above formula, τ is the smoothing time constant of the low-pass filter in the optimal solution, Δ T is the control time interval, PPV(k) And outputting power for the renewable energy power station at the moment k.
7. An energy storage planning apparatus, the apparatus comprising:
the building module is used for respectively building a multi-target planning objective function and constraint conditions of the energy storage system;
the determining module is used for determining the optimal solution of the multi-target planning objective function;
the planning module is used for planning the energy storage of the renewable energy power station according to the optimal solution;
in the building module, the objective function includes: a first objective function of energy storage and a second objective function of energy storage;
the energy storage first objective function is the minimum value of the fluctuation amplitude of grid-connected power;
the energy storage second objective function takes the maximum value of the economic index of the energy storage battery;
the constraint condition comprises constraint data of the energy storage system;
the energy storage first objective function:
in the above formula, PC(k) For grid-connected power at time k, λdA unit penalty factor for exceeding the power fluctuation range, N is the total number of sampling moments,
wherein, PClimitIs a grid-connected power change rate limit value;
the energy storage second objective function:
in the above formula, PmaxFor maximum power of energy storage system, lambdaPCost factor for the maximum power of the energy storage system, EmaxFor maximum capacity of the energy storage system, λECost factor for maximum capacity of energy storage system, EcycleFor controlling the quantity of circulating electric power in the time period, λmvA cost factor for the circulating electric quantity in the control period, wherein:
in the above formula, the first and second carbon atoms are,
PS(k) the energy storage power at the moment k and the delta T are control time intervals.
8. The apparatus of claim 7, wherein the determining module comprises:
a first determination unit for introducing a low-pass filtering smoothing time constant tau and a storage power P at the moment kS(k) And the optimal solution of the multi-target planning objective function of the energy storage system is determined by utilizing a multi-target optimization algorithm as a variable of the multi-target planning objective function of the energy storage system.
9. The apparatus of claim 7, wherein the planning module comprises:
the second determining unit is used for determining grid-connected power according to the low-pass filtering smoothing time constant tau in the optimal solution;
and the planning unit is used for planning the charging and discharging power of the energy storage system and the grid-connected power of the renewable energy source in real time according to the energy storage power in the optimal solution and the grid-connected power.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710190530.0A CN108667054B (en) | 2017-03-28 | 2017-03-28 | Energy storage planning method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710190530.0A CN108667054B (en) | 2017-03-28 | 2017-03-28 | Energy storage planning method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108667054A CN108667054A (en) | 2018-10-16 |
| CN108667054B true CN108667054B (en) | 2021-09-03 |
Family
ID=63785718
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710190530.0A Active CN108667054B (en) | 2017-03-28 | 2017-03-28 | Energy storage planning method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108667054B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109787259B (en) * | 2019-01-23 | 2020-10-27 | 西安交通大学 | Multi-type energy storage joint planning method based on new energy random fluctuation |
| CN109962485B (en) * | 2019-03-08 | 2021-07-16 | 台州宏远电力设计院有限公司 | Source network charge-friendly interaction-oriented composite energy storage device site selection and volume fixing method |
| CN113507134B (en) * | 2021-06-24 | 2022-04-05 | 东北电力大学 | Optimal planning method for planning target year new energy power supply installed capacity |
| CN115864429A (en) * | 2022-08-31 | 2023-03-28 | 湖北工业大学 | Multi-objective optimization AGC method for wind and fire storage cooperation under double-carbon target |
| CN116415799B (en) * | 2023-06-06 | 2023-09-12 | 南方电网调峰调频发电有限公司储能科研院 | Energy storage scale and structure planning method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104701871A (en) * | 2015-02-13 | 2015-06-10 | 国家电网公司 | Wind, light and water-containing multi-source complementary micro-grid hybrid energy storage capacity optimal proportion method |
| CN106408131A (en) * | 2016-09-30 | 2017-02-15 | 安徽工程大学 | Photovoltaic microgrid multi-target scheduling method based on demand-side management |
-
2017
- 2017-03-28 CN CN201710190530.0A patent/CN108667054B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104701871A (en) * | 2015-02-13 | 2015-06-10 | 国家电网公司 | Wind, light and water-containing multi-source complementary micro-grid hybrid energy storage capacity optimal proportion method |
| CN106408131A (en) * | 2016-09-30 | 2017-02-15 | 安徽工程大学 | Photovoltaic microgrid multi-target scheduling method based on demand-side management |
Non-Patent Citations (2)
| Title |
|---|
| Study on Energy storage system smoothing wind power fluctuations;Li Jianlin等;《2010 International Conference on Power System Technology》;20101028;第1-4页 * |
| 平抑风电场功率波动的储能容量选取方法;李文斌等;《华东电力》;20120331;第40卷(第3期);第439-443页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108667054A (en) | 2018-10-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108667054B (en) | Energy storage planning method and device | |
| CN108471143A (en) | Micro-grid multi-energy method for optimizing scheduling based on positive and negative feedback particle cluster algorithm | |
| CN112583017B (en) | Hybrid micro-grid energy distribution method and system considering energy storage operation constraint | |
| CN111509781B (en) | Distributed power supply coordination optimization control method and system | |
| CN113541166B (en) | Distributed energy storage optimal configuration method, system, terminal and storage medium | |
| CN112784484A (en) | Multi-objective optimization method and optimization system for regional comprehensive energy system | |
| CN105896575B (en) | Hundred megawatt energy storage power control method and system based on self-adaptive dynamic programming | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| CN113708366A (en) | Power distribution network optimal scheduling method and system considering new energy and electric automobile | |
| CN107528344A (en) | A kind of light storage integrated generating device is incorporated into the power networks control method and system | |
| CN114243791A (en) | Multi-objective optimization configuration method, system and storage medium for wind-solar-hydrogen storage system | |
| CN111628558A (en) | System and method for optimizing energy management and capacity configuration of hybrid energy storage system | |
| CN114154790A (en) | Industrial park light storage capacity configuration method based on demand management and flexible load | |
| CN116307087A (en) | Micro-grid system energy storage optimal configuration method considering charging and discharging of electric automobile | |
| CN112134307A (en) | Electric energy storage/heat energy storage capacity joint optimization method and system of multi-energy complementary system | |
| CN114493143A (en) | Virtual power plant multi-objective optimization scheduling system and method for grid-connected micro-grid | |
| CN114386323A (en) | Power distribution network reactive power optimization method containing distributed power supply based on improved butterfly algorithm | |
| CN111478335B (en) | Power distribution network load flow calculation method and system considering distributed photovoltaic | |
| CN115940284B (en) | Operation control strategy of new energy hydrogen production system considering time-of-use electricity price | |
| CN109119988B (en) | Photovoltaic-battery microgrid energy scheduling management method based on dynamic wholesale market price | |
| CN114285093B (en) | Source network charge storage interactive scheduling method and system | |
| Sun et al. | Optimal operation strategy of wind-hydrogen integrated energy system based on NSGA-II algorithm | |
| CN107482658B (en) | Micro-grid energy storage economic operation control method and device | |
| CN115425650A (en) | Power supply station microgrid configuration method, device, equipment and medium | |
| CN115018140A (en) | Active power distribution network light storage multi-target planning method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |