CN116799835A - Layered cooperative control method and system for energy storage clusters and storage medium - Google Patents
Layered cooperative control method and system for energy storage clusters and storage medium 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
- H02J3/28—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
- 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
Abstract
The application discloses a layered cooperative control method and system of an energy storage cluster and a storage medium, and belongs to the field of electrical engineering. Comprising the following steps: the power command for recovering the frequency deviation is acquired to carry out bottom cooperative control through compensating and correcting the power reference value; establishing an aggregation model of the multiple energy storage units, and estimating average statistical parameters and sum statistical parameters in the aggregation model by using a distributed state observer and a sum state observer respectively; based on the statistical parameters of the aggregation model, an optimization model for minimizing the incremental power generation cost of the distributed energy storage and generator is constructed; and taking the model optimization result as a power correction value of the generator in the system. According to the application, frequency adjustment and accurate output distribution are realized for the multi-energy-storage aggregation system, meanwhile, an aggregation model of the multi-energy-storage units is provided, and an optimization model is established, so that the running cost of the system is reduced, and the difficult problem of high complex variable dimension of the optimization problem faced by a large number of distributed energy-storage access systems is solved.
Description
Technical Field
The application belongs to the field of electrical engineering, and in particular relates to a layered cooperative control method and system of an energy storage cluster and a storage medium.
Background
As the number of distributed power sources (DER) increases, research into micro-grids has attracted attention. The micro-grid is an autonomous system with specific boundaries including load, energy storage, distributed power sources and the like, and can operate in a grid-connected mode or a grid-isolated mode. The stability of the micro-grid in isolated grid operation is greatly reduced without the support of a large grid, and the influence of the volatility and the randomness of intermittent renewable energy sources on the stable operation of the system is not negligible. Energy storage systems are considered as efficient means of maintaining power balance and regulating frequency/voltage in micro-grids, and the rise of distributed energy storage has increased significantly in recent years to the number of small capacity energy storage devices. However, after a large number of small-capacity Energy Storage Systems (ESS) are connected to the micro-grid running independently, the calculation and communication costs of the system energy management are significantly increased, and the management of the micro-grid is a challenge.
To address this difficulty, the prior art introduces ESS control and aggregation methods. ESS control distributes power among a large number of ESS, and an ESS aggregation method obtains an aggregated energy storage model operating state from a single energy storage cell operating state. The energy storage aggregator can obtain the whole information of the energy storage units without monitoring all the energy storage systems, thereby reducing the burden of the control and communication systems. The aggregation thought is widely applied to optimal scheduling of ESS, electric vehicles and constant temperature control loads, the aggregation model parameters are often obtained through centralized communication, adjustment of real-time operation conditions is not considered, and real-time performance and accuracy are not guaranteed.
While in most cases the use of an abusive energy storage system for power compensation is not an economical option, for example, the cost of power generation from a battery is far higher than a Synchronous Generator (SG) in view of energy capacity and its expensive replacement cost. Therefore, in order to consider the economy of the system, the hierarchical control result is adopted to realize the cooperative control and the economy distribution of each energy storage in the micro-grid. In the primary control layer, the ESS uses a decentralized droop controller or virtual synchronous generator controller for power balancing and power distribution. In the secondary control layer, the ESS combines with a consistency algorithm to compensate for frequency/voltage deviation caused by droop control in the primary control layer and realize adaptive power sharing. The energy management strategy at the tertiary control layer redistributes load power between the ESS and other sources to reduce operating costs. However, in the prior art, only one of reliable or economic operation of the micro-grid is concerned, but cooperative and reliable operation and economic operation of the micro-grid are not considered, and most of hierarchical control technologies also adopt centralized control measures, so that the limitations of single-point failure, poor flexibility, privacy exposure and the like exist.
Disclosure of Invention
Aiming at the blank of the prior art, the application provides a layered cooperative control method, a layered cooperative control system and a storage medium of an energy storage cluster, wherein the layered cooperative control method comprises a local sagging controller, a plurality of cooperative control methods among ESS and an energy management strategy of an alternating current power grid, and a perfect layered cooperative control solution is provided for the reliable and economic operation of the independent alternating current power grid.
The technical scheme for solving the technical problems is as follows: a layered cooperative control method of an energy storage cluster comprises the following steps:
s1, compensating and correcting a sagging controller power reference command value of each energy storage unit in the energy storage cluster according to the operation condition of each energy storage unit, and obtaining a power command value for recovering frequency deviation, wherein the command value is used for controlling the frequency of the distributed energy storage regulating system and realizing output distribution.
S2, an aggregation model of the multiple energy storage units is established, and average type statistical parameters and sum type statistical parameters in the aggregation model of the energy storage units are estimated respectively.
S3, based on the average statistical parameter, the sum statistical parameter and the power instruction value of the energy storage units in the energy storage cluster, an optimization model for minimizing the incremental power generation cost of the distributed energy storage and the generator is constructed, and constraint conditions of the optimization model are constructed.
S4, solving the optimization model to obtain an optimization result, and taking the optimization result as a power correction value of the miniature gas turbine generator in the energy storage cluster.
Further, the step S1 includes:
the energy storage unit responds to the power reference instruction value obtained by the local frequency through the sagging relation between the active power and the frequency, and introduces the power compensation deviation instruction obtained by calculation of the energy capacity of the energy storage unit, the current SoC (State of Charge), the output power, the power State variable and other operating condition variables;
according to the power compensation deviation command, obtaining a power command which enables the energy storage unit to distribute the active output according to the proportionThe instruction definition is as follows:
δ i ＝E bi f SoCi (SoC bi ,P bi )x bpi
wherein r is pi 、ω i 、δ i Respectively the droop coefficient, angular frequency and power compensation deviation instruction, omega of the ith energy storage unit ref For reference angular frequency of electric power system E bi 、x bpi Energy capacity, power state variables, f of the ith energy storage unit, respectively SoCi As a piecewise function, soC bi For the state of charge SoC value, P of the ith energy storage cell bi The output power of the ith energy storage cluster; f (f) SoCi (SoC bi ,P bi ) Obtained by the following expression:
wherein, soC bi SoC for the state of charge SoC value of the i-th energy storage unit bmaxi SoC (System on a chip) bmini Upper and lower limits of SoC value, P, for state of charge allowed by the ith energy storage unit bi The output power of the ith energy storage cluster;
state variable x bpi Obtained by the following expression:
wherein g ω For frequency modulation factor, a ij Is the firstAlternating current weight between i energy storage units and j energy storage units, omega ref 、ω i For the reference frequency and the actual frequency of the energy storage unit i, x bpj Is the power state variable of the jth energy storage unit,is the differential value of the power state variable of the ith energy storage cell.
The application has the further beneficial effects that: under the control instruction of the application, the power instruction value of the energy storage unit is in direct proportion to the current SoC when discharging, the larger the SoC is, the larger the power instruction value is, and the corresponding energy storage unit outputs more power. When the energy storage unit discharges, the power instructions are opposite, the larger the SoC is, the smaller the corresponding power instruction is, and the corresponding energy storage unit outputs less power. Therefore, the system can recover the control frequency of the system and realize reasonable power distribution in the multiple energy storage units.
Further, the step S2 includes:
s2.1. parameters in the aggregate model of the plurality of energy storage units include state of charge SoC bci Rated energy capacity E bci Output power P bci Upper power limit P bcmini Lower power limit P bcmaxi And is characterized by the following calculation:
wherein, the liquid crystal display device comprises a liquid crystal display device,n b representing the number of energy storage units.
S2.2 for average statistical parameter x ai The expression is:
for sum-type statistical parameter x si The expression is:
x si ＝∫g oi ε i dt
wherein u is i B is the input state of the ith energy storage cell ij Is the communication weight between the energy storage unit i and the energy storage unit j. Epsilon i G is the auxiliary state variable of the ith energy storage unit oi For the energy storage unit connected with the tertiary control, g is the proportionality coefficient o More than 0, the rest energy storage units have g o ＝0；
Input state u in the expression of average type parameter and sum type statistical parameter i The method comprises the steps of replacing parameters such as charge states, rated energy capacity, output power, upper and lower power limits and the like of energy storage units in an aggregation model of a plurality of energy storage units, and obtaining real-time estimated values of average type statistical parameters and real-time estimated values of sum type statistical parameters according to the replaced expression of the average type parameters and sum type statistical parameters; the real-time estimated value of the average statistical parameter and the real-time estimated value of the sum total statistical parameter comprise the real-time estimated values of the total rated energy capacity, the total output power and the upper and lower limits of the total power of the aggregation model.
The application has the further beneficial effects that: the aggregation model can greatly reduce the calculation order of the model and lighten the burden of system communication and calculation. For the sum type statistical parameters, the sum type statistical parameter observer (namely the corresponding expression) provided by the application can realize the estimation of the sum type statistical parameters in a cooperative mode without knowing the quantity of the energy storage units, so that the flexibility of the state observer is greatly improved.
Further, the optimization model includes a power generation incremental cost of the generator, a power generation incremental cost of the energy storage unit, and an offset cost of the SoC of the energy storage cluster, and the S3 includes:
s3.1 incremental cost of generator generation J sgi The method comprises the following steps:
wherein P is sgbi For the base line power of the ith micro gas turbine generator, ΔP sgi ＝P sgi -P sgbi A is the offset of the actual output power of the ith miniature gas turbine generator and the baseline power sgi 、b sgi The power generation cost coefficients of the i-th power generator are respectively obtained.
Incremental cost of power generation J for energy storage unit bc1i The method comprises the following steps:
J bc1i ＝C bi f bi (P bci ,SoC i )
wherein C is bi F is the replacement cost of the energy storage unit capacity bi The function is the capacity degradation function of the ith miniature gas turbine generator, and the capacity degradation function is related to the energy storage type.
Offset cost J of SoC of energy storage cluster bc2i The method comprises the following steps:
wherein g s And (5) offsetting the weight coefficient for the SoC. The aim of the optimized operation of the system is the generation marginal cost J of the miniature gas turbine generator sgi Incremental cost J of power generation of energy storage cluster bc1i SoC offset cost J of energy storage cluster bc2i Is the sum of:
s3.2, constraint conditions in the solving process are as follows:
for the energy storage cluster, the dynamic response speed of the energy storage cluster unit is high, the response time is far less than the three-time control time, the power climbing constraint of the energy storage unit is not considered, and the power capacity constraint of the energy storage unit is expressed as:
P bcmini ≤P bci (k)≤P bcmaxi
wherein P is bcmaxi 、P bcmini Maximum output power and minimum output power of the ith aggregation energy storage cluster respectively
And outputting power. The SoC constraint of the energy storage unit is expressed as:
SoC min ≤SoC bc,i (k)≤SoC max
wherein, soC max 、SoC min The maximum SoC and the minimum SoC of the ith aggregation energy storage cluster are respectively.
For the constraint of a micro gas turbine, the micro gas turbine generator output power offset is approximately:
P sgmini -P sgbi ≤ΔP sgi (k)≤P sgmaxi -P sgbi
wherein P is sgbi 、P sgmini 、P sgmaxi The base line power, the minimum output power and the maximum output power of the ith generator are respectively. Meanwhile, the miniature gas turbine generator is limited by the self dynamic response speed, and has the power climbing constraint:
|ΔP sgi (k-1)-ΔP sgi (k)|≤P sgrampi
wherein P is sgrampi At [ t ] for the generator set k- ,t k ) Maximum power change value allowed in the time period.
The distributed energy storage moment maintains the power balance of the system, and the output power of the distributed energy storage and the output power of the miniature gas turbine generator meet the following conditions:
wherein n is sg 、n bc Representing the number of micro gas turbines and the number of distributed energy storage clusters.
Further, the step S4 includes: a distributed optimization algorithm based on nerve dynamics is introduced to optimize the power command value delta P of the miniature wheel generator sgi And enabling the target J of the system optimization operation to be minimum, and solving the optimization model.
The application has the further beneficial effects that: and constructing an optimization model for minimizing incremental power generation cost of the distributed energy storage and miniature gas turbine generator in a third-stage control layer through the distributed energy storage aggregation model, and solving through a distributed algorithm based on nerve dynamics, so that an optimal output instruction of the generator meeting operation constraint is obtained, power redistribution is realized, and the operation cost of the system is reduced.
The application also provides a layered cooperative control system of the energy storage cluster, which comprises the following steps:
the power command acquisition module is used for compensating and correcting the sagging controller power reference command value of the energy storage unit according to the operation condition of each energy storage unit of the energy storage cluster to acquire a power command value for recovering the frequency deviation;
the statistical parameter estimation module is used for establishing an aggregation model of the multiple energy storage units and respectively estimating average statistical parameters and sum statistical parameters in the aggregation model of the multiple energy storage units;
the optimization model construction module is used for constructing an optimization model for minimizing the incremental power generation cost of the distributed energy storage and the generator based on the average statistical parameter, the sum statistical parameter and the power instruction value of the energy storage units in the energy storage cluster, and constructing constraint conditions of the optimization model;
and the optimization model solving module is used for solving the optimization model to obtain an optimization result and taking the optimization result as a power correction value of the generator in the energy storage cluster.
The application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method described above.
In general, compared with the prior art, the technical scheme of the application provides a layered cooperative control method for the realization of the multi-energy storage aggregation system, realizes the frequency adjustment and accurate output distribution of the distributed energy storage system, simultaneously provides a large number of aggregation models of distributed energy storage and estimates the parameters of the distributed energy storage aggregation model, establishes an optimization model for the system, reduces the running cost of the system, and solves the problem of high complicated variable dimension of the optimization problem faced by a large number of distributed energy storage access systems.
Drawings
FIG. 1 is an IEEE Standard 33 node system for use with the simulation example provided by the present application;
FIG. 2 is a schematic flow chart of a hierarchical coordinated control method of an energy storage cluster provided by the application;
FIG. 3 is a schematic diagram of a distributed frequency controller according to the present application;
FIG. 4 is a graph showing power curves of various power sources and loads under a distributed cooperative frequency control method according to the present application;
FIG. 5 shows the variation of the frequency (a) and the voltage (b) of each node in the simulation system according to the present application under the distributed cooperative control method;
FIG. 6 is a graph of the power state variable of each energy storage unit according to the present application;
FIG. 7 is a graph of the power curves of the various power supplies under the third level control layer of the present application.
FIG. 8 is a single iteration process of the present application under a distributed optimization algorithm with (a) error and (b) state variables.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. In addition, the technical features of the embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
Examples
The present example verifies the proposed method in an IEEE standard 33 node system shown in fig. 1, and builds the system in Matlab/Simulink and simulates. The circuit breaker connected with the PCC is set to be in an open state in the simulation process, and the system is an independently operated alternating current power grid. The 33-node system comprises 6 distributed energy storage units, 2 synchronous generators and 1 wind driven generator. Wherein the capacity of each single energy storage unit is 100kW/kWh, the rated capacity of the generator is 2.5MW and 0.6MW, and the rated capacity of the wind driven generator is 2.2MW. The wind driven generator adopts MPPT control measures to maximize new energy consumption, the energy storage unit adopts the distributed control and the distributed cooperative control provided by the application to adjust respective active output, and meanwhile, reactive output is not provided, and the generator outputs according to a plan and sends out a power command in real time to adjust the output. All distributed energy storage units in the system belong to the same energy storage cluster, data exchange can be carried out between adjacent energy storage units and synchronous generators through communication links, data exchange can also be carried out between the generators, and the broken line in fig. 1 represents the communication link, so that the distributed solving of the global optimization problem is realized. The control interval of the distributed algorithm is taken to be 10s, and the communication frequency is 100Hz. The distributed frequency controller and the distributed state observer need faster control interval and communication frequency to realize better dynamic performance, the control interval adopted in the control simulation is 10ms, and the communication frequency is 1kHz.
It should be noted that the energy storage units involved in the system belong to the same category, i.e. lead-acid batteries, and the same cost parameters are chosen. Multiple aggregate energy storage models can be built to improve the control effect of the layered control approach when energy storage units belonging to different classes are present.
As shown in fig. 2, the hierarchical cooperative control method of the energy storage cluster includes the following steps:
s1, according to the power control quantity of each energy storage unit, a power instruction for recovering the frequency deviation is obtained through compensation and correction of the power reference value.
S2, in order to acquire the real-time state of the energy storage unit, providing parameters for the optimization model in the S3, establishing an aggregation model of a plurality of energy storage units, and respectively estimating average type statistical parameters and sum type statistical parameters in the aggregation model of the energy storage units.
S3, based on the average statistical parameter, the sum statistical parameter and the power instruction value of the energy storage units in the energy storage cluster, an optimization model for minimizing the incremental power generation cost of the distributed energy storage and the miniature gas turbine generator is constructed, and constraint conditions of the optimization model are constructed.
S4, solving an optimization model, and taking an optimization result as a power correction value of the miniature gas turbine generator in the system.
As shown in fig. 3, the power command of each energy storage unit in step S1 is determined by the current SoC, output power, state variable, current frequency value, etc. of the energy storage unit. The energy storage unit responds to the local frequency to obtain a power reference instruction value through the sagging relation between the active power and the frequency; introducing a power compensation bias command obtained by calculating the energy capacity, the current SoC, the output power and the power state variable of the energy storage unit;
according to the power compensation deviation command, a power command value which enables the energy storage unit to distribute the active output according to the proportion is obtainedThe instruction definition is as follows:
δ i ＝E bi f SoCi (SoC bi ,P bi )x bpi
wherein r is pi 、ω i 、δ i Respectively the droop coefficient, angular frequency and power compensation deviation instruction, omega of the ith energy storage unit ref For reference angular frequency of electric power system E bi 、x bpi Energy capacity, power state variables, f of the ith energy storage unit, respectively SoCi As a piecewise function, soC bi SoC for the i-th energy storage unitValue, P bi The output power of the ith energy storage cluster; f (f) SoCi (SoC bi ,P bi ) Obtained by the following expression:
wherein, soC bi SoC for the state of charge SoC value of the i-th energy storage unit bmaxi SoC (System on a chip) bmini Upper and lower limits of SoC value, P, for state of charge allowed by the ith energy storage unit bi The output power of the ith energy storage cluster;
state variable x bpi Obtained by the following expression:
wherein g ω For frequency modulation factor, a ij For the alternating current weight between the ith energy storage unit and the jth energy storage unit, omega ref 、ω i For the reference frequency and the actual frequency of the energy storage unit i, x bpj Is the power state variable of the jth energy storage unit,is the differential value of the power state variable of the ith energy storage cell.
The step S2 comprises the following steps:
s2.1. parameters in the aggregate model of the plurality of energy storage units include state of charge SoC bci Rated energy capacity E bci Output power P bci Upper power limit P bcmini Lower power limit P bcmaxi And is represented by the following formula:
wherein n is b Representing the number of energy storage units, soC bi 、E bi 、P bi 、P bmaxi 、P bmini The charge state value, the energy capacity, the output power and the upper and lower power limits of the ith energy storage unit are respectively;
s2.2 for average parameter x ai The expression is:
wherein u is i B is the input state of the ith energy storage cell ij For the communication weight between the energy storage units i and j, x ai And x aj Average parameters of the energy storage units i and j respectively;
for sum-type statistical parameter x si The expression is:
x si ＝∫g oi ε i dt
wherein ε i 、ε j G is the auxiliary state variable of the ith energy storage unit and the j th energy storage unit oi For the energy storage unit connected with the tertiary control, g is the proportionality coefficient oi More than 0, the rest energy storage units have g oi ＝0；
Input state u in the expression of average type parameter and sum type statistical parameter i Energy storage unit in aggregation model replaced by multiple energy storage unitsParameters such as the state of charge of the element, rated energy capacity, output power, upper and lower power limits and the like, and obtaining a real-time estimated value of the average type statistical parameter and a real-time estimated value of the sum type statistical parameter according to the expression of the replaced average type parameter and the sum type statistical parameter; the real-time estimated value of the average statistical parameter and the real-time estimated value of the sum total statistical parameter comprise the real-time estimated values of the total rated energy capacity, the total output power and the upper and lower limits of the total power of the aggregation model.
S3, the optimization model comprises the incremental cost of power generation of the generator, the incremental cost of power generation of the energy storage unit and the offset cost of the SoC of the energy storage cluster:
s3.1 incremental cost of generator generation J sgi The method comprises the following steps:
wherein P is sgbi A is the baseline power of the ith miniature gas turbine generator, a sgi 、b sgi The power generation cost coefficients of the ith power generator are respectively k is the iteration step number and delta P sgi ＝P sgi -P sgbi The offset between the actual output power and the baseline power of the ith miniature gas turbine generator;
incremental cost of power generation J for energy storage unit bc1i The method comprises the following steps:
J bc1i ＝C bi f bi (P bci ,SoC i )
wherein C is bi F is the replacement cost of the energy storage unit capacity bi The function is a capacity degradation function of the ith generator, and the capacity degradation function is related to the energy storage type;
offset cost J of SoC of energy storage cluster bc2i The method comprises the following steps:
wherein g s Offset weight coefficients for the SoC; optimized operation of systemIs aimed at the generation marginal cost J of the generator sgi Incremental cost J of power generation of energy storage cluster bc1i SoC offset cost J of energy storage cluster bc2i Is the sum of:
wherein n is sg N is the number of miniature gas turbine generators in the system bc The number of distributed energy storage clusters in the system;
s3.2, when the generator operates, the constraint of the output power offset of the generator is as follows:
P sgmini -P sgbi ≤ΔP sgi (k)≤P sgmaxi -P sgbi
wherein P is sgbi 、P sgmini 、P sgmaxi The base line power, the minimum output power and the maximum output power of the ith generator are respectively; meanwhile, the generator is limited by the self dynamic response speed, and has the power climbing constraint:
|ΔP sgi (k-1)-ΔP sgi (k)|≤P sgrampi
wherein P is sgrampi At [ t ] for the generator set k- ,t k ) A maximum power variation value allowed in the time period;
the power capacity constraint of the energy storage cluster unit is expressed as:
P bcmini ≤P bci (k)≤P bcmaxi
wherein P is bcmaxi 、P bcmini Maximum output power and minimum output power of the ith aggregation energy storage cluster respectively;
the SoC constraint of the energy storage unit is expressed as:
SoC min ≤SoC bc,i (k)≤SoC max
wherein, soC max 、SoC min The maximum SoC and the minimum SoC of the ith aggregation energy storage cluster are respectively;
output power P of distributed energy storage bci (k) Output power delta P of miniature gas turbine generator sgi (k) The following are satisfied:
wherein n is sg 、n bc Representing the number of micro gas turbines and the number of distributed energy storage clusters.
The step S4 comprises the following steps: a distributed optimization algorithm based on nerve dynamics is introduced to optimize the power command value delta P of the miniature wheel generator sgi And enabling the target J of the system optimization operation to be minimum, and solving the optimization model.
The embodiment also provides a layered cooperative control system of an energy storage cluster, which comprises:
the power command acquisition module is used for compensating and correcting the sagging controller power reference command value of the energy storage units according to the operation condition of each energy storage unit in the energy storage cluster to acquire a power command value for recovering the frequency deviation;
the statistical parameter estimation module is used for establishing an aggregation model of the multiple energy storage units and respectively estimating average statistical parameters and sum statistical parameters in the aggregation model of the multiple energy storage units;
the optimization model construction module is used for constructing an optimization model for minimizing the incremental power generation cost of the distributed energy storage and the generator based on the average statistical parameter, the sum statistical parameter and the power instruction value of the energy storage units in the energy storage cluster, and constructing constraint conditions of the optimization model;
and the optimization model solving module is used for solving the optimization model to obtain an optimization result and taking the optimization result as a power correction value of the generator in the energy storage cluster.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.
The parameters of the controller and the observer adopted in the simulation are shown in table 1, the generator operates according to preset power in the simulation process, the 12-node load power requirement is increased by 0.1Mw when t=8s, and the third-stage control layer is not started due to the slow dynamic performance of the third-stage control layer so as not to interfere the output result.
Table 1 distributed controller and observer parameters
Fig. 4 is a power curve of each power supply and load in the ac power grid during the simulation time, for the synchronous generator, the output power is kept constant, because the threshold value of the synchronous generator in the distributed cooperative control layer is larger and the third-stage control layer does not participate in the adjustment, the energy storage units perform real-time power compensation when the output power of the wind power generator fluctuates and the load power demand increases, and the output power of each energy storage unit is distributed according to the scaling factor, when the energy storage unit 1 reaches the power limit at about t=12s, the output of the energy storage unit is maintained at the upper limit value, but the consistency of the power state is not affected, and the distributed frequency controller can still effectively adjust the output proportion of other energy storage units.
As shown in FIG. 5, due to the regulation effect of the energy storage unit, the frequency and voltage waveform of the whole system are kept stable in the whole simulation process, the energy storage unit can rapidly respond to power fluctuation caused by wind power generators and load fluctuation, and the frequency of the system is also kept at about 50 Hz.
As shown in fig. 6, the power state variable variation of each energy storage unit is not affected by the consistency in load fluctuation.
The cost and constraint parameters shown in table 2 are adopted to verify the system optimization operation strategy of the distributed algorithm provided by the application. The planned output of the two synchronous generators in the simulation remained unchanged, namely 1.272Mw and 0.6926Mw. The load is a constant value 3.559Mw, and fig. 7 shows the output power of each power supply.
Table 2 distributed energy storage and generator parameters
As can be seen from fig. 7, in the first half stage of the continuous rising phase of the output power of the wind power generator, the energy storage unit starts to absorb power to compensate the power imbalance of the system. Because the power generation cost of the energy storage unit is far greater than that of the synchronous generator, the generator starts to reduce the output force of the generator to replace the energy storage unit under the action of the optimized operation strategy. The two generator sets do not gradually readjust the output power of the energy storage unit to the vicinity of zero value until the wind power becomes gentle due to the smaller power climbing rate. In the latter half stage, the fluctuation of wind power is small, the system maintains the power balance of the system by means of the energy storage quick response and the subsequent replacement of the generators, the output power of the energy storage unit fluctuates near zero, and the power is redistributed between the two generators. The generator 1 cuts out more output in the first half stage due to the larger power climbing rate, and the generator 2 has cost advantage though the power response is slow, so that the generator 2 replaces the output of the generator 1 from the second half stage, and the economic cost of the system is reduced.
The present example analyzes the impact of distributed optimization algorithms and aggregation operations on the iterative process. Fig. 8 shows a single iterative optimization process of the distributed optimization algorithm corresponding to t=40s, and it can be found from the iteration curve of (a) in fig. 8 that the distributed optimization algorithm controlled by three times in the present application can achieve convergence in a smaller number of iterations. The deviation curve of the optimal variable and the theoretical optimal solution in fig. 8 (b) may indicate that the distributed optimization algorithm may gradually converge to the optimal solution of the global optimization problem as the number of iterations increases.
The system operation data of t=40s is used as the initial input of the distributed optimization algorithm, and table 3 lists the space dimension of the optimization variables and the iteration time required by algorithm convergence for a single algorithm iteration process under different scale distributed energy storage. It can be seen that as the number of distributed energy storage units increases, the dimension of the variable space increases accordingly. Thus, a large variable space dimension means that the communication link is required to afford a huge exchange of data per unit time. Meanwhile, the convergence time required for the same convergence accuracy increases as the number of energy storage units increases. The communication quality of the system will be affected by a large communication burden, while the control interval for optimizing the operation strategy will have to be increased to ensure a reliable convergence of the algorithm. By carrying out distributed cooperative control on distributed energy storage and constructing a real-time aggregation model, the variable space dimension and the iteration time required by convergence in the optimization model can be greatly reduced, and the influence of the increase of the number of the distributed energy storage units can be avoided.
Table 3 comparison of iterative process performance of different-Scale distributed energy storage single algorithm
It will be appreciated by those skilled in the art that 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 scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The layered cooperative control method of the energy storage cluster is characterized by comprising the following steps of:
s1, compensating and correcting a sagging controller power reference command value of each energy storage unit according to the operation condition of each energy storage unit in an energy storage cluster, and obtaining a power command value for recovering frequency deviation;
s2, establishing an aggregation model of the multiple energy storage units, and respectively estimating average type statistical parameters and sum type statistical parameters in the aggregation model of the multiple energy storage units;
s3, constructing an optimization model for minimizing the incremental power generation cost of the distributed energy storage and the generator based on the average statistical parameter, the sum statistical parameter and the power instruction value of the energy storage units in the energy storage cluster, and constructing constraint conditions of the optimization model;
s4, solving the optimization model to obtain an optimization result, and taking the optimization result as a power correction value of the generator in the energy storage cluster.
2. The hierarchical cooperative control method of an energy storage cluster according to claim 1, wherein S1 includes:
the energy storage unit responds to the local frequency to obtain a power reference instruction value through the sagging relation between the active power and the frequency; introducing a power compensation bias command obtained by calculating the energy capacity, the current SoC, the output power and the power state variable of the energy storage unit;
according to the power compensation deviation command, a power command value which enables the energy storage unit to distribute the active output according to the proportion is obtainedThe instruction definition is as follows:
δ i ＝E bi f SoCi (SoC bi ,P bi )x bpi
wherein r is pi 、ω i 、δ i Respectively the droop coefficient, angular frequency and power compensation deviation instruction, omega of the ith energy storage unit ref For reference angular frequency of electric power system E bi 、x bpi Energy capacity, power state variables, f of the ith energy storage unit, respectively SoCi As a piecewise function, soC bi For the state of charge SoC value, P of the ith energy storage cell bi The output power of the ith energy storage cluster; f (f) SoCi (SoC bi ,P bi ) Obtained by the following expression:
wherein, soC bi SoC for the state of charge SoC value of the i-th energy storage unit bmaxi SoC (System on a chip) bmini Upper and lower limits of SoC value, P, for state of charge allowed by the ith energy storage unit bi The output power of the ith energy storage cluster;
state variable x bpi Obtained by the following expression:
wherein g ω For frequency modulation factor, a ij For the alternating current weight between the ith energy storage unit and the jth energy storage unit, omega ref 、ω i For the reference frequency and the actual frequency of the energy storage unit i, x bpj Is the power state variable of the jth energy storage unit,is the differential value of the power state variable of the ith energy storage cell.
3. The hierarchical cooperative control method of an energy storage cluster according to claim 1, wherein the step S2 includes:
s2.1. parameters in the aggregate model of the plurality of energy storage units include state of charge SoC bci Rated energy capacity E bci Output power P bci Upper power limit P bcmini Lower power limit P bcmaxi And is represented by the following formula:
wherein n is b Representing the number of energy storage units, soC bi 、E bi 、P bi 、P bmaxi 、P bmini The charge state value, the energy capacity, the output power and the upper and lower power limits of the ith energy storage unit are respectively;
s2.2 for average parameter x ai The expression is:
wherein u is i B is the input state of the ith energy storage cell ij For the communication weight between the energy storage units i and j, x ai And x aj Average parameters of the energy storage units i and j respectively;
for sum-type statistical parameter x si The expression is:
x si ＝∫g oi ε i dt
wherein ε i 、ε j For the ith, j th energy storageAuxiliary state variables g of the cell oi For the energy storage unit connected with the tertiary control, g is the proportionality coefficient oi More than 0, the rest energy storage units have g oi ＝0；
Input state u in the expression of average type parameter and sum type statistical parameter i The method comprises the steps of replacing parameters such as charge states, rated energy capacity, output power, upper and lower power limits and the like of energy storage units in an aggregation model of a plurality of energy storage units, and obtaining real-time estimated values of average type statistical parameters and real-time estimated values of sum type statistical parameters according to the replaced expression of the average type parameters and sum type statistical parameters; the real-time estimated value of the average statistical parameter and the real-time estimated value of the sum total statistical parameter comprise the real-time estimated values of the total rated energy capacity, the total output power and the upper and lower limits of the total power of the aggregation model.
4. The hierarchical cooperative control method of an energy storage cluster according to claim 1, wherein the optimization model includes a power generation incremental cost of a generator, a power generation incremental cost of an energy storage unit, and an offset cost of an SoC of the energy storage cluster in S3:
s3.1 incremental cost of generator generation J sgi The method comprises the following steps:
wherein P is sgbi A is the baseline power of the ith miniature gas turbine generator, a sgi 、b sgi The power generation cost coefficients of the ith power generator are respectively k is the iteration step number and delta P sgi ＝P sgi -P sgbi The offset between the actual output power and the baseline power of the ith miniature gas turbine generator;
incremental cost of power generation J for energy storage unit bc1i The method comprises the following steps:
J bc1i ＝C bi f bi (P bci ,SoC i )
wherein C is bi Replacement of energy storage unit capacityCost f bi The function is a capacity degradation function of the ith generator, and the capacity degradation function is related to the energy storage type;
offset cost J of SoC of energy storage cluster bc2i The method comprises the following steps:
wherein g s Offset weight coefficients for the SoC; the aim of the optimized operation of the system is the generation marginal cost J of the generator sgi Incremental cost J of power generation of energy storage cluster bc1i SoC offset cost J of energy storage cluster bc2i Is the sum of:
wherein n is sg N is the number of miniature gas turbine generators in the system bc The number of distributed energy storage clusters in the system;
s3.2, when the generator operates, the constraint of the output power offset of the generator is as follows:
P sgmini -P sgbi ≤ΔP sgi (k)≤P sgmaxi -P sgbi
wherein P is sgbi 、P sgmini 、P sgmaxi The base line power, the minimum output power and the maximum output power of the ith generator are respectively; meanwhile, the generator is limited by the self dynamic response speed, and has the power climbing constraint:
|ΔP sgi (k-1)-ΔP sgi (k)|≤P sgrampi
wherein P is sgrampi At [ t ] for the generator set k- ,t k ) A maximum power variation value allowed in the time period;
the power capacity constraint of the energy storage cluster unit is expressed as:
P bcmini ≤P bci (k)≤P bcmaxi
wherein P is bcmaxi 、P bcmini Maximum output power and minimum output power of the ith aggregation energy storage cluster respectively;
the SoC constraint of the energy storage unit is expressed as:
SoC min ≤SoC bc,i (k)≤SoC max
wherein, soC max 、SoC min The maximum SoC and the minimum SoC of the ith aggregation energy storage cluster are respectively;
output power P of distributed energy storage bci (k) Output power delta P of miniature gas turbine generator sgi (k) The following are satisfied:
wherein n is sg 、n bc Representing the number of micro gas turbines and the number of distributed energy storage clusters.
5. The hierarchical cooperative control method of an energy storage cluster according to claim 4, wherein S4 includes: a distributed optimization algorithm based on nerve dynamics is introduced to optimize the power command value delta P of the miniature wheel generator sgi And enabling the target J of the system optimization operation to be minimum, and solving the optimization model.
6. A hierarchical cooperative control system of an energy storage cluster, comprising:
the power command acquisition module is used for compensating and correcting the sagging controller power reference command value of the energy storage units according to the operation condition of each energy storage unit in the energy storage cluster to acquire a power command value for recovering the frequency deviation;
the statistical parameter estimation module is used for establishing an aggregation model of the multiple energy storage units and respectively estimating average statistical parameters and sum statistical parameters in the aggregation model of the multiple energy storage units;
the optimization model construction module is used for constructing an optimization model for minimizing the incremental power generation cost of the distributed energy storage and the generator based on the average statistical parameter, the sum statistical parameter and the power instruction value of the energy storage units in the energy storage cluster, and constructing constraint conditions of the optimization model;
and the optimization model solving module is used for solving the optimization model to obtain an optimization result and taking the optimization result as a power correction value of the generator in the energy storage cluster.
7. The hierarchical cooperative control system of an energy storage cluster according to claim 6, wherein the power instruction obtaining module obtains a power reference instruction value obtained by responding to a local frequency by an energy storage unit through a droop relation between active power and frequency, and introduces a power compensation correction instruction obtained by calculating energy capacity, current SoC, output power and power state variables of the energy storage unit; according to the power compensation deviation command, a power command value which enables the energy storage unit to distribute the active output according to the proportion is obtainedThe instruction definition is as follows:
δ i ＝E bi f SoCi (SoC i ,P bi )x bpi
wherein r is pi 、ω i 、δ i Droop coefficient, angular frequency, power compensation offset command, omega of the ith energy storage unit respectively ref For reference angular frequency of electric power system E bi 、x bpi Energy capacity, power state variables, f of the ith energy storage unit, respectively SoCi As a piecewise function, soC bi For the state of charge SoC value, P of the ith energy storage cell bi The output power of the ith energy storage cluster; f (f) SoCi (SoC bi ,P bi ) Obtained by the following expression:
wherein, soC i SoC for the state of charge SoC value of the i-th energy storage unit maxi SoC (System on a chip) mini Upper and lower limits of SoC value, P, for state of charge allowed by the ith energy storage unit bi The output power of the ith energy storage cluster;
state variable x bpi Obtained by the following expression:
wherein g ω For frequency modulation factor, a ij For the alternating current weight between the ith energy storage unit and the jth energy storage unit, omega ref 、ω i For the reference frequency and the actual frequency of the energy storage unit i, x bpj Is the power state variable of the jth energy storage unit,is the differential value of the power state variable of the ith energy storage cell.
8. The hierarchical cooperative control system of an energy storage cluster according to claim 6, wherein, in the statistical parameter estimation module:
the parameters in the aggregate model of the plurality of energy storage units include state of charge SoC bci Rated energy capacity E bci Output power P bci Upper power limit P bcmini Lower power limit P bcmaxi And is represented by the following formula:
wherein n is b Representing the number of energy storage units, soC bi 、E bi 、P bi 、P bmaxi 、P bmini The charge state value, the energy capacity, the output power and the upper and lower power limits of the ith energy storage unit are respectively;
for average parameter x ai The expression is:
wherein u is i B is the input state of the ith energy storage cell ij For the communication weight, x, between the energy storage unit i and the energy storage unit j ai And x aj Average parameters of the energy storage units i and j respectively;
for sum-type statistical parameter x si The expression is:
x si ＝∫g oi ε i dt
wherein ε i G is the auxiliary state variable of the ith energy storage unit oi For the energy storage unit connected with the tertiary control, g is the proportionality coefficient o More than 0, the rest energy storage units have g o =0; input state u in the expression of average type parameter and sum type statistical parameter i Polymerization mould replaced by a plurality of energy storage unitsParameters such as the charge state, rated energy capacity, output power, upper and lower power limits and the like of the energy storage unit in the model, and according to the expression of the average model parameter and the sum model statistical parameter after replacement, obtaining a real-time estimated value of the average model statistical parameter and a real-time estimated value of the sum model statistical parameter; the real-time estimated value of the average statistical parameter and the real-time estimated value of the sum total statistical parameter comprise the real-time estimated values of the total rated energy capacity, the total output power and the upper and lower limits of the total power of the aggregation model.
9. The hierarchical cooperative control system of an energy storage cluster according to claim 6, wherein, in the optimization model building module:
incremental cost of generator J sgi The method comprises the following steps:
wherein P is sgbi A is the baseline power of the ith miniature gas turbine generator, a sgi 、b sgi The power generation cost coefficients of the ith power generator are respectively k is the iteration step number and delta P sgi ＝P sgi -P sgbi The offset of the actual output power and the baseline power of the ith miniature gas turbine generator;
incremental cost of power generation J for energy storage unit bc1i The method comprises the following steps:
J bc1i ＝C bi f bi (P bci ,SoC i )
wherein C is bi F is the replacement cost of the energy storage unit capacity bi The function is a capacity degradation function of the ith generator, and the capacity degradation function is related to the energy storage type;
offset cost J of SoC of energy storage cluster bc2i The method comprises the following steps:
wherein g s Offset weight coefficients for the SoC; the aim of the optimized operation of the system is the generation marginal cost J of the generator sgi Incremental cost J of power generation of energy storage cluster bc1i SoC offset cost J of energy storage cluster bc2i Is the sum of:
wherein n is sg N is the number of miniature gas turbine generators in the system bc The number of distributed energy storage clusters in the system;
for generator operation, the constraints on the generator output power offset are:
P sgmini -P sgbi ≤ΔP sgi (k)≤P sgmaxi -P sgbi
wherein P is sgbi 、P sgmini 、P sgmaxi The base line power, the minimum output power and the maximum output power of the ith generator are respectively; meanwhile, the generator is limited by the self dynamic response speed, and has the power climbing constraint:
|ΔP sgi (k-1)-ΔP sgi (k)|≤P sgrampi
wherein P is sgrampi At [ t ] for the generator set k- ,t k ) A maximum power variation value allowed in the time period;
the power capacity constraint of the energy storage cluster unit is expressed as:
P bcmini ≤P bci (k)≤P bcmaxi
wherein P is bcmaxi 、P bcmini Maximum output power and minimum output power of the ith aggregation energy storage cluster respectively;
the SoC constraint of the energy storage unit is expressed as:
SoC min ≤SoC bc,i (k)≤SoC max
wherein, soC max 、SoC min The maximum SoC and the minimum SoC of the ith aggregation energy storage cluster are respectively;
maintaining system power balance at distributed energy storage moment, and outputting power P of distributed energy storage bci (k) Output power delta P of miniature gas turbine generator sgi (k) The following are satisfied:
wherein n is sg 、n bc Representing the number of micro gas turbines and the number of distributed energy storage clusters.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.
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