CN112086975A - Optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation - Google Patents
Optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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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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Abstract
The invention provides an optimized scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation, which not only can take the characteristic parameters of the energy storage units into consideration, but also can effectively reduce the frequency modulation cost and meet the power requirement of frequency modulation. The method comprises the steps of designing a relation function of single cycle cost and discharge depth according to characteristic parameters of different energy storage units, solving the cost required by unit discharge capacity, minimizing the product of the discharge capacity and the unit cost of all the energy storage units in a control period, and further reducing the cost of energy storage participation in frequency modulation on the premise of considering different parameters of different systems. The inertial coefficient is optimized by adding the chaos algorithm to the traditional particle swarm algorithm to obtain the MDDCIWPSO algorithm, which is beneficial to the algorithm to jump out of the local optimal solution and improve the control accuracy.
Description
Technical Field
The invention belongs to the field of auxiliary frequency modulation of energy storage systems, and particularly relates to an optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation.
Background
With the continuous development of the current science and technology, in recent years, a large amount of renewable energy sources such as wind power and the like are connected to a power grid, and the output of the renewable energy sources often has strong volatility and randomness, so that much pressure is brought to the frequency modulation control of a power grid system. Here, the traditional Automatic Generation Control (AGC) mainly adjusts the frequency of the power grid through a unit, and mainly has the problems of slow adjustment speed, low precision and the like, so that a novel optimization control strategy needs to be researched to meet the requirement of power grid frequency modulation.
In the power system, the energy storage is a power supply which can flexibly respond and bidirectionally transfer energy and is not limited by time scale, and the charging and discharging period of the energy storage participating in the frequency modulation of the system is from second level to minute level, so that the requirements of the system on rapidity and precision can be met. However, because the general energy storage capacity is small, the AGC control needs to be completed by matching with the unit. The energy storage units in the power grid are numerous, power needs to be effectively distributed in the energy storage units, different characteristics of various energy storage systems need to be considered, and the energy storage systems are maintained to operate in a healthy working state.
Because the cost of the energy storage system participating in frequency modulation is high, most of the current research mainly focuses on maintaining the state of charge (SOC) of the energy storage system in a healthy state and reducing the cost of the whole energy storage system participating in frequency modulation in terms of power distribution. However, when the multiple energy storage units participate in frequency modulation, different battery characteristics of the multiple energy storage units need to be considered, such as: the method has different cycle life curves, different investment costs, different power limits, different capacity limits, different ramp rates, different charge and discharge efficiencies and the like, and few researches can take the cycle life curves into consideration completely. Therefore, on the basis of considering various characteristic parameters, the optimization research on the power distribution strategy among the energy storage units is of great significance.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation in order to solve the problem that the energy storage units have different characteristic parameters when an auxiliary unit carries out AGC frequency modulation and consider a comprehensive power distribution scheme.
The purpose of the invention can be realized by the following technical scheme:
an optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation comprises the following steps:
s1, acquiring cycle life curves of the energy storage systems;
s2 shows a general functional form N as a · DodbObtaining specific characteristic parameters a and b;
s3, synthesizing investment cost C of each energy storage unitt＝Crated·EratedLife loss cost function of different energy storage units
S4, determining the F and Dod relation of each energy storage unit, and reversely determining the discharge depth of each energy storage system according to the cost when Dod is 1;
s5, calculating the cost required by each unit SOC when each energy storage unit discharges
S6, in unit period, the product sum of the variation of SOC and unit cost is minimum, and at time k, an evaluation objective function is established
S7, establishing SOC consistency evaluation function
S8, further optimizing the SOC consistency effect, correcting the energy storage units with different capacities, and establishing a capacity correction function
S9, determining constraint conditions;
s10, establishing an objective function and a constraint condition formula
S11, obtaining the power quantity P to be distributed in the unit control periodb；
S12, if PbIf P is greater than or equal to 0, adopting the target function under the discharge conditionbIf the value is less than 0, adopting a target function under the charging condition;
s13, adopting a particle swarm algorithm for increasing multidimensional inertia weight attenuation chaos to control;
s14, obtaining the power distribution quantity of each energy storage unit;
s15, the next control cycle is entered.
Further, in S2, N is the number of cycles, Dod is the depth of discharge, a and b are characteristic parameters, and if a and b are not given directly, a curve needs to be fitted to obtain a specific value thereof.
Further, F in S3 represents a cost amount, CtRepresents the total investment cost of the energy storage battery, CratedInvestment cost per unit volume, EratedIs the battery capacity.
Further, F in S5i,1％Is the equivalent cost per 1% of discharge, FminIs the minimum cost, the charging process is the reverse of the minimum cost, and the principle is the same.
Further, A in S6cIs a cost evaluation function, Fi,1％,kIs the equivalent cost of the ith cell at time k, EiIs the capacity of the ith battery, n is the number of batteries participating in frequency modulation, Pi,kOutput power at time k, ηi,dFor this reason, Δ t is the control period for the discharge efficiency of BESS.
Further, A in S7sFor the evaluation function of consistency, SOCi,kThe state of charge at time ith cell k,
is a battery at the time of kCluster average state of charge.
Further, P in S10i minIs the minimum value of the charge-discharge power of the ith battery, Pi maxIs the maximum value of the charge-discharge power of the ith battery,
is the minimum value of the charge-discharge ramp rate of the ith battery,
is the maximum value of the charge-discharge climbing rate of the ith battery, Pi,k-1Is the power of the ith battery at the time k-1, SOCminIs the lower limit of the SOC of the battery, SOCmaxIs the upper limit of the SOC of the battery, SOCi,k-1The state of charge at the moment of dropping i cells k-1.
Further, P in S12bNot less than 0 and PbThe form of the < 0 objective function is the same, only at AcAre reciprocal in design.
The invention has the beneficial effects that:
1. the invention not only can take the characteristic parameters of the energy storage unit into consideration, but also can effectively reduce the frequency modulation cost and meet the power requirement of frequency modulation;
2. the optimization control strategy of the invention combines different characteristics of a plurality of energy storage units, including cycle life curves of different energy storage systems, different investment costs, different capacities, different power limits, different slope climbing rate limits and different charging and discharging efficiencies, and can achieve the effects of reducing the cost of the whole energy storage participating in frequency modulation and effectively controlling the SOC by establishing a function and a limit condition thereof which take the low cost and the consistent SOC area in unit time of the whole energy storage cluster participating in frequency modulation as a target, and then optimizing a common particle swarm algorithm.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic overall flow diagram of an embodiment of the present invention;
FIG. 2 is a graph illustrating the relationship between the cost of single charge and discharge and the depth of discharge obtained by a set of 5 energy storage units according to the present invention;
FIG. 3 is a SOC diagram under power distribution control under the conventional SOC-based proportional and SOC-optimized control under a Matlab/Simulink platform;
FIG. 4 is a SOC diagram under the control strategy of the present invention under the Matlab/Simulink platform.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.
As shown in fig. 1, an optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation includes the following steps:
s1, acquiring a cycle life curve of each energy storage system;
s2 has a general functional form of N ═ a · DodbWherein N is cycle number, Dod is depth of discharge, a and b are characteristic parameters, if a and b are not given directly, a curve needs to be fitted to obtain a specific numerical value;
s3, acquiring investment unit cost and configuration capacity of different energy storage units, and acquiring the total investment cost of the energy storage unit as follows: ct＝Crated·EratedAnd expressing the life loss cost function of different energy storage units
Wherein F represents a cost amount, CtRepresents the total investment cost of the energy storage battery, CratedInvestment cost per unit volume, EratedIs the battery capacity;
s4, taking a system composed of a group of 5 energy storage units as an example according to the relationship between F and Dod of each energy storage unit, as shown in fig. 2, determining the minimum F value among all energy storage units when the depth of discharge is maximum (i.e. Dod is 100%), and determining the corresponding depth of discharge Dod of each energy storage system under the condition of the minimum F value according to the relationship in the opposite directioni,sAt this time, the cost required for discharging different energy storage units to the respective determined depths is the same, and the minimum cost acceptable in the battery pack is the same;
s5, under the condition that the required cost is the same, calculating the required cost of each unit SOC when each energy storage unit discharges, wherein the required cost can be expressed as
Wherein Fi,1％Is the equivalent cost per 1% of discharge, FminThe minimum cost is obtained, the charging process is opposite to the minimum cost, and the principle is the same;
s6, in the process of power distribution, the product sum of the variation of SOC and unit cost is minimum in unit period, and at the time of k, the evaluation objective function is established as
Wherein A iscIs a cost evaluation function, Fi,1％,kIs the equivalent cost of the ith cell at time k, EiIs the capacity of the ith battery, n is the number of batteries participating in frequency modulation, Pi,kOutput power at time k, ηi,dFor this reason, the discharge efficiency of BESS is controlled by Δ tA manufacturing period;
s7, considering control of ensuring the SOC consistency of the energy storage unit cluster, and establishing an evaluation function of a consistency part as
Wherein A issFor the evaluation function of consistency, SOCi,kThe state of charge at time ith cell k,
the average state of charge of the battery cluster at the moment k;
s8, because the capacity proportion also affects the final consistency result, because the system is more inclined to mobilize the energy storage system with large capacity under the same condition, and the long-term accumulation can cause the system with small capacity to be difficult to be invoked, in order to further optimize the SOC consistency effect, certain correction needs to be carried out on the energy storage units with different capacities, and the correction function of the part is
Wherein A isfIs a capacity correction function;
s9, because different energy storage systems have different power limit values and slope climbing rate limits, the power limit values and the slope climbing rate limits need to be considered separately and are set as limit conditions under a target function;
s10, establishing and obtaining a final objective function and a constraint condition formula:
wherein P isi minIs the minimum value of the charge-discharge power of the ith battery, Pi maxIs the maximum value of the charge-discharge power of the ith battery,
is the minimum value of the charge-discharge ramp rate of the ith battery,
is the maximum value of the charge-discharge climbing rate of the ith battery, Pi,k-1Is the power of the ith battery at the time k-1, SOCminIs the lower limit of the SOC of the battery, SOCmaxThe upper limit of the SOC of the battery is set to 0.2 and 0.8, SOCi,k-1The state of charge at the moment of dropping i batteries k-1;
s11, obtaining the power quantity P to be distributed in the unit control periodb；
S12, if PbIf P is greater than or equal to 0, adopting the target function under the discharge conditionbIf < 0, the objective functions under the charging condition are adopted, the forms of the objective functions are the same, and only in AcAre reciprocal in design;
s13, because the power to be distributed has fluctuation in a large range, a particle swarm algorithm (MDDCIWPSO) which adds Multi-Dimensional inertial Weight attenuation chaos is adopted, and the inertial Weight is updated in order to prevent the particle position from falling into a local optimal solution each time. Solving the above formula by using an optimized particle swarm algorithm, selecting Logistic mapping, wherein the random quantity obtained by mapping is shown in FIG. 3;
s14, obtaining the power distribution quantity of each energy storage unit;
s15, the next control cycle is entered.
The two schemes are compared and verified by using MATLAB/Simulink simulation software, wherein the embodiment 1 is an experiment according to energy storage capacity ratio and SOC optimal control, and the embodiment 2 is a control experiment of the invention. The resulting simulation diagrams are shown in fig. 3 and 4. The cost of the batteries for the two above examples is shown in the table below.
From the above table, the two control schemes are both good for the consistency control of the SOC control, but the cost of the two control schemes is different by calculating through a rain flow counting method. Under the control condition of the invention, the requirement of the SOC consistency of the energy storage cluster is met, and the cost of the energy storage system participating in frequency modulation is effectively reduced.
In the description of the present specification, the description of an optimized scheduling method for coordinating participation of multiple energy storage units in secondary frequency modulation with reference to the terms "one embodiment", "an example", "a specific example" and the like means that a specific feature, structure, material or characteristic described in connection with the embodiment or the example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (8)
1. An optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation is characterized by comprising the following steps:
s1, acquiring cycle life curves of the energy storage systems;
s2 shows a general functional form N as a · DodbObtaining specific characteristic parameters a and b;
s3, synthesizing investment cost C of each energy storage unitt＝Crated·EratedLife loss cost function of different energy storage units
S4, determining the F and Dod relation of each energy storage unit, and reversely determining the discharge depth of each energy storage system according to the cost when Dod is 1;
s5, calculating the cost required by each unit SOC when each energy storage unit discharges
S6, in unit period, the product sum of the variation of SOC and unit cost is minimum, and at time k, an evaluation objective function is established
S7, establishing SOC consistency evaluation function
S8, further optimizing the SOC consistency effect, correcting the energy storage units with different capacities, and establishing a capacity correction function
S9, determining constraint conditions;
s10, establishing an objective function and a constraint condition formula
S11, obtaining the power quantity P to be distributed in the unit control periodb；
S12, if PbIf P is greater than or equal to 0, adopting the target function under the discharge conditionbIf the value is less than 0, adopting a target function under the charging condition;
s13, adopting a particle swarm algorithm for increasing multidimensional inertia weight attenuation chaos to control;
s14, obtaining the power distribution quantity of each energy storage unit;
s15, the next control cycle is entered.
2. The method according to claim 1, wherein N in S2 is cycle number, Dod is depth of discharge, and a and b are characteristic parameters, and if a and b are not given directly, a curve needs to be fitted to obtain a specific value.
3. The optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulation according to claim 1, wherein F in S3 represents a cost amount, CtRepresents the total investment cost of the energy storage battery, CratedInvestment cost per unit volume, EratedIs the battery capacity.
4. The method according to claim 1, wherein F in S5 is the scheduling optimization method for coordinating multiple energy storage units to participate in secondary frequency modulationi,1％Is the equivalent cost per 1% of discharge, FminIs the minimum cost, the charging process is the reverse of the minimum cost, and the principle is the same.
5. The method according to claim 1, wherein in step S6, A is the optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulationcIs a cost evaluation function, Fi,1％,kIs the equivalent cost of the ith cell at time k, EiIs the capacity of the ith battery, n is the number of batteries participating in frequency modulation, Pi,kOutput power at time k, ηi,dFor this reason, Δ t is the control period for the discharge efficiency of BESS.
6. The method according to claim 1, wherein in step S7, A is the optimal scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulationsFor the evaluation function of consistency, SOCi,kThe state of charge at time ith cell k,
the average state of charge of the battery cluster at time k.
7. The method according to claim 1, wherein P in S10 is P in an optimized scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulationi minIs the minimum value of the charge-discharge power of the ith battery, Pi maxIs the maximum value of the charge-discharge power of the ith battery,
is the minimum value of the charge-discharge ramp rate of the ith battery,
is the maximum value of the charge-discharge climbing rate of the ith battery, Pi,k-1Is the power of the ith battery at the time k-1, SOCminIs the lower limit of the SOC of the battery, SOCmaxIs the upper limit of the SOC of the battery, SOCi,k-1The state of charge at the moment of dropping i cells k-1.
8. The method according to claim 1, wherein P in S12 is P in an optimized scheduling method for coordinating multiple energy storage units to participate in secondary frequency modulationbNot less than 0 and PbThe form of the < 0 objective function is the same, only at AcAre reciprocal in design.
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