CN112531756A - Distributed control method, system and equipment for electric quantity balance of energy storage system - Google Patents

Distributed control method, system and equipment for electric quantity balance of energy storage system Download PDF

Info

Publication number
CN112531756A
CN112531756A CN202011371673.XA CN202011371673A CN112531756A CN 112531756 A CN112531756 A CN 112531756A CN 202011371673 A CN202011371673 A CN 202011371673A CN 112531756 A CN112531756 A CN 112531756A
Authority
CN
China
Prior art keywords
power
energy storage
storage system
control
distributed
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.)
Withdrawn
Application number
CN202011371673.XA
Other languages
Chinese (zh)
Inventor
赵俊华
赵焕
潘梓彬
梁高琪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chinese University of Hong Kong CUHK
Original Assignee
Chinese University of Hong Kong CUHK
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Chinese University of Hong Kong CUHK filed Critical Chinese University of Hong Kong CUHK
Priority to CN202011371673.XA priority Critical patent/CN112531756A/en
Publication of CN112531756A publication Critical patent/CN112531756A/en
Withdrawn legal-status Critical Current

Links

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
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention provides a distributed control method and system, equipment and storage medium for electric quantity balance of an energy storage system, wherein the energy storage system comprises an upper layer control frame and a lower layer control frame, and the distributed control method comprises the following steps: acquiring the charge state level of an energy storage system, the supply and demand balance among intermittent distributed power generation and the time-varying load demand; obtaining an upper-layer charge and discharge power reference value of the energy storage system based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation; and based on the lower layer control framework, according to a discrete robustness control algorithm, enabling the power value of the lower layer energy storage system to be the same as the reference value of the upper layer charging and discharging power. The method only needs local and adjacent information to update the control law, can effectively distribute the calculation burden among a plurality of local controllers, and estimates and compensates the uncertainty and the disturbance in the system by using a disturbance estimation technology which does not need to know an uncertainty boundary in advance in order to improve the robustness and the control precision of the system.

Description

Distributed control method, system and equipment for electric quantity balance of energy storage system
Technical Field
The invention belongs to the technical field of energy storage of power systems, and particularly relates to a distributed control method, a distributed control system, distributed control equipment and a storage medium for electric quantity balance of an energy storage system.
Background
Today, the high penetration level and ever increasing power generation capacity of renewable energy power generation pose many challenges to existing power grids. Such as power quality and system stability due to intermittent renewable power generation and time varying load requirements. Integration of energy storage devices has become a promising option to improve system flexibility, stability and reliability.
Since the controllability of BESS (Battery Energy Storage Systems) is limited by the Energy Storage capacity, a reasonable control strategy is required to balance the Charge-discharge rate and the SOC (State of Charge). Extensive research has been conducted on module level SOC balancing. In addition to cell level SOC balancing, package level SOC balancing is also essential in the power distribution grid for two main reasons. First, the packet level SOC balancing operation of the BESS may provide safety redundancy at a higher system level, protecting against overcharge/overdischarge. Second, the balancing of the SOC of the BESS maximizes the power capacity of the entire BESS and the ability to stabilize the grid frequency voltage. However, if there is no packet level SOC balancing, one or more battery packet SOCs may reach a high or low limit and then will be forced offline to trigger the protection device. As a result, the power capacity of the entire BESS may be reduced, thereby reducing the performance of the system.
The centralized control strategy is designed to broadcast multiple BESS coordinated charge/discharge signals to distributed SOC controllers. However, centralized control strategies require fast and reliable communication networks to collect global information and rely on the performance of powerful controllers to process large amounts of data, which may result in a single point of failure, thereby increasing reliability. On the other hand, decentralized control strategies, such as methods based on droop control, do not rely on inter-cell communication, and due to insufficient information, it is difficult to achieve good coordination of distributed BESS. The distributed control strategy overcomes the defects of a centralized control strategy and a decentralized control strategy, and is suitable for BESS coordination control of the smart grid.
Recent researchers in this field have conducted a great deal of research into the coordinated control of BESS. Some scholars mainly aim at the traditional power sharing strategy, and realize equal power sharing proportion among BESS units according to the rated capacity of the BESS units. This situation may reduce the overall utilization efficiency of the available BESS units. This is because the best SOC BESS unit needs to provide more power to the normal load, and when the SOC of the BESS unit falls below a threshold during peak loads, the BESS unit needs to be shut down to ensure a balance of SOC utilization. Therefore, the charge/discharge rates of the different BESS should also be adjusted according to their SOC. A direct current micro-grid SOC coordination cooperative control strategy based on multiple intelligent agents is provided. The predecessor proposed a consistency-based distributed control strategy for SOC regulation to avoid premature depletion or saturation of the BESS unit under different operating conditions. However, low level control capable of tracking the desired charge-discharge power output has not been investigated.
Morstyn et al propose a micro grid island operation SOC balancing method based on drop control. In grid-connected microgrids, both frequency and voltage are controlled by the main grid, so methods based on droop control may not be applicable. Some scholars propose various distributed SOC balance control methods based on a vector control method to provide safety redundancy and protect the BESS unit from overcharge or overdischarge. Vector control originates from the control of motor drive systems and is currently widely used to control three-phase inverters to convert dc power to ac power by converting the three-phase inverter to a d-q reference frame. However, the dynamic characteristics of the pll may cause stability problems for the inverter, especially in weak gates, and the response of the pll may also affect the control performance.
Computer implementation of the control algorithm provides significant advantages and convenience, and therefore, a control strategy is necessarily applied to a sampled data system. However, discrete time systems that directly implement continuous time control algorithms may present several problems, such as sample/hold effects, discrete errors, and even instability.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the problem of the prior art is solved by providing a system which can balance supply and demand between intermittent distributed power sources and time-varying load demands while balancing SOC level of BESS.
In a first aspect, an embodiment of the present application provides a distributed control method for power balance of an energy storage system, where the energy storage system includes an upper control framework and a lower control framework, and the method includes:
acquiring the charge state level of an energy storage system, the supply and demand balance among intermittent distributed power generation and the time variation of load demand;
obtaining an upper-layer charge and discharge power reference value of the energy storage system based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation;
and based on a lower layer control framework, according to a discrete robustness control algorithm, enabling the power value of the lower layer energy storage system to be the same as the reference value of the upper layer charge-discharge power.
In a second aspect, an embodiment of the present application provides a distributed control system for energy storage system power balance, where the system includes:
an acquisition module: the system is used for acquiring the charge state level of the energy storage system, the supply and demand balance among intermittent distributed generation and the time-varying load demand;
an upper layer module: the upper-layer charge and discharge power reference value of the energy storage system is obtained based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation;
a lower layer module: and the method is used for enabling the power value of the lower energy storage system to be the same as the reference value of the upper charging and discharging power based on the lower control framework according to the discrete robustness control algorithm.
In a third aspect, an embodiment of the present application further provides a distributed control apparatus for energy storage system power balance, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements each step in the distributed control method for energy storage system power balance according to the first aspect.
In a fourth aspect, the present application further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the distributed control method for energy storage system power balance according to the first aspect.
The invention provides a distributed control method for electric quantity balance of an energy storage system, wherein the energy storage system comprises an upper-layer control framework and a lower-layer control framework, and the method comprises the following steps: acquiring the charge state level of an energy storage system, the supply and demand balance among intermittent distributed power generation and the time-varying load demand; obtaining an upper-layer charge and discharge power reference value of the energy storage system based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation; and based on a lower layer control framework, according to a discrete robustness control algorithm, enabling the power value of the lower layer energy storage system to be the same as the reference value of the upper layer charging and discharging power. The method only needs local and adjacent information to update the control law, can effectively distribute the calculation burden among a plurality of local controllers, and estimates and compensates the uncertainty and the disturbance in the system by using a disturbance estimation technology which does not need to know an uncertainty boundary in advance in order to improve the robustness and the control precision of the system.
Drawings
The detailed structure of the invention is described in detail below with reference to the accompanying drawings
Fig. 1 is a schematic flow chart of a distributed control method for energy storage system power balance according to the present invention;
FIG. 2 is an updated BESS active power output reference for the distributed control method for energy storage system power balance of the present invention;
FIG. 3 is a diagram illustrating a supply and demand balance condition of the energy storage system according to the distributed control method for balancing the electric quantity of the energy storage system of the present invention;
FIG. 4 is an inverter frequency dynamic response of BESS # 2 unit for a distributed control method of energy storage system power balance of the present invention;
FIG. 5 is a BESS # 2 unit inverter voltage amplitude dynamic response for a distributed control method for energy storage system power balance in accordance with the present invention;
FIG. 6 is a BESS No. 2 unit reactive output of the distributed control method for energy storage system power balance of the present invention;
FIG. 7 is a graph of inverter and grid voltage distributions under low voltage fault conditions for a distributed control method for energy storage system power balance in accordance with the present invention
FIG. 8 is a single line diagram of a 39 bus test system for a distributed control method of energy storage system power balancing of the present invention;
fig. 9 is a schematic diagram of program modules of the distributed control method for energy storage system power balance according to the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all 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.
Fig. 1 is a flowchart illustrating a distributed control method for energy storage system power balance in an embodiment of the present application, where the energy storage system includes an upper control framework and a lower control framework, and the method includes:
step 101, acquiring the charge state level of the energy storage system, the supply and demand balance among intermittent distributed power generation and the time variation of load demand.
The method includes the steps of obtaining a State of Charge level of an Energy Storage system, balance between supply and demand and time variation of load demand among intermittent distributed power generation, wherein the Energy Storage system is BESS (Battery Energy Storage Systems), the State of Charge level is SOC (State of Charge), and the SOC is generally defined as a ratio of a current capacity to a nominal capacity, which indicates a maximum amount of electricity that can be stored in a Battery.
And 102, obtaining an upper-layer charging and discharging power reference value of the energy storage system based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation.
According to the obtained charge state level, the upper-layer control framework in the method obtains a formula among the supply and demand balance and the load demand time variation among the intermittent distributed power generation, and obtains a power reference value of upper-layer charging and discharging of the energy storage system according to the formula.
And 103, enabling the power value of the lower energy storage system to be the same as the reference value of the upper charging and discharging power based on the lower control framework according to a discrete robustness control algorithm.
The embodiment of the application provides a distributed control method for electric quantity balance of an energy storage system, wherein the energy storage system comprises an upper-layer control framework and a lower-layer control framework, and the method comprises the following steps: acquiring the charge state level of an energy storage system, the supply and demand balance among intermittent distributed power generation and the time-varying load demand; obtaining an upper-layer charge and discharge power reference value of the energy storage system based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation; and based on a lower layer control framework, according to a discrete robustness control algorithm, enabling the power value of the lower layer energy storage system to be the same as the reference value of the upper layer charging and discharging power. The method only needs local and adjacent information to update the control law, can effectively distribute the calculation burden among a plurality of local controllers, and estimates and compensates the uncertainty and the disturbance in the system by using a disturbance estimation technology which does not need to know an uncertainty boundary in advance in order to improve the robustness and the control precision of the system.
Further, in this embodiment, the upper control framework and the lower control framework both adopt a distributed SOC equalization control strategy, and the formula of the distributed SOC equalization control strategy is as follows:
Figure BDA0002806933730000051
Pi[h+1]＝xi[h+1]ECiFi(SOCi)/ρi
Figure BDA0002806933730000061
Figure BDA0002806933730000062
wherein epsilonEIn order to supply and demand the mismatch coefficient,
Figure BDA0002806933730000063
for local estimation of supply-demand mismatch, PEiFor the latest supply-demand mismatch, h is the communication update interval of ULC, i, j are all buses, dijAs communication coefficient, ECiIs the standard capacity of the ith BESS, piFor the charging/discharging efficiency of the ith BESS, PiInput power, x, for the ith BESSiA variable in an equilibrium state, FiN is the number of connections, where n is equal to n, as a function of definitionj。
Define the BESS SOC balance state variables as:
Figure BDA0002806933730000064
Figure BDA0002806933730000065
wherein ECiIs the standard capacity of the ith BESSfIs a small coefficient, FiIs a function of the definition.
To analyze the convergence of the proposed distributed ULC, the first three of the above equations are represented in the form of the following matrices:
x[h+1]＝D·x[h]-εEPE[h] (3)
P[h+1]＝ηBx[h+1]
PE[h+1]＝D(PE[h]+P[h+1]-P[h]) (4)
wherein
ηB＝diag{ηBi} (5)
Can be further written as:
Figure BDA0002806933730000071
wherein InIs an identity matrix.
Coefficient of mismatch when supply and demandEWhen sufficiently small, the eigenvalues of M can be approximated as:
|λI2n-M|≈|λIn-D|2 (7)
d has eigenvalues within 1 and the eigenvector associated with the largest eigenvalue is
Figure BDA0002806933730000072
This is because:
Figure BDA0002806933730000073
when h is close to h8When the system is approaching
Figure BDA0002806933730000074
Thus, PE[h8]Down to 0, i.e. the supply-demand balance is met, according to the following formula x[h8]Converge to a common value.
Figure BDA0002806933730000075
Thus, the BESS with the higher SOC discharges faster (charges slower), and eventually the SOC of all BESS will be synchronized.
Further, in the present embodiment, based on the upper control framework, according to the state of charge level, supply and demand balance between intermittent distributed power generation, and load demand time variation, it is possible to obtain:
Figure BDA0002806933730000076
wherein N isG，NLAnd NBRepresenting all generators, loads and energy storage systems, respectively, i is the bus, where each BESS unit is reduced to one dc voltage source, one Voltage Source Inverter (VSI), one RL power line in series. If a communication channel exists between bus i and bus j, then bus j is considered to be at NiIs marked as j belongs to Ni。
The ith BESS can only receive information from neighbors, the communication coefficient dijIs defined as:
Figure BDA0002806933730000081
wherein n isiAnd njThe number of connections to buses i and j, respectively.
Defining matrix D ═ Dij]According to the formula (10), it can be easily observed that the sum of the columns and rows of D is 1.
Let the charge/discharge efficiency of the ith BESS be:
Figure BDA0002806933730000082
wherein 0<ξci,ξdi<1 is a coefficient caused by internal loss.
Injection (extraction) into BESS (P)i) And in the ith BESS (P)Bi) The relationship between the energy stored (consumed) is:
PBi＝ρiPi (12)
further, in this embodiment, the inequality constraint conditions for processing the maximum charge and discharge power of the BESS by the projection operation are as follows:
Figure BDA0002806933730000083
wherein, Pi maxThe maximum charge/discharge power.
Further, in this embodiment, the power flow models adopted by the discrete robustness control algorithm respectively include:
Figure BDA0002806933730000084
Figure BDA0002806933730000085
wherein, deltaiIs the angular difference between inverter i and bus i, EiAnd ViThe voltage components of the inverter and the bus are respectively, Q is reactive power, V is voltage, and theta is a phase angle. The above two equations calculate the ith BESS pass impedance as Zi∠θiAnd the power line of i injects the active power P of the bus iiAnd reactive power Qi。
Specifically, the first derivative is obtained from the above equation, and the trend dynamics can be expressed as:
Figure BDA0002806933730000091
Figure BDA0002806933730000092
wherein deltaiAnd EiThe real power output and the reactive power output of the inverter are mainly determined. Due to change of environmentFor example, the line impedance may be disturbed due to inductance change caused by magnetic saturation caused by large current and resistance change caused by high temperature, which needs to be considered. Due to the inductance of the output LC filter or line impedance, the power line impedance is largely inductance dominated, which means θ ≈ 90, while the angular difference between inverter and bus is typically very small, which means δ i0. Let theta ≈ 90 and deltaiAnd substituting the value of 0 into the formula to obtain:
Figure BDA0002806933730000093
Figure BDA0002806933730000094
the difference between the equations (13) and (14), (15), (16) can be represented by Δ PiAnd Δ QiIs represented by the lumped uncertainty term of (a). Therefore, the equations (13) and (14) are rewritten as:
Figure BDA0002806933730000095
Figure BDA0002806933730000096
Figure BDA0002806933730000097
Figure BDA0002806933730000098
where ξ piAnd xi QiIntroduced to account for perturbations in power line parameters.
In particular, the lumped uncertainties in equations (19) and (20) may be assumed to be small and bounded. In many recent articles, the power flow dynamics of (13) and (14) are simplified to linear equations.
Further, in this embodiment, the discrete robustness control algorithm adopts a zero-order hold sampling method:
Figure BDA0002806933730000101
Figure BDA0002806933730000102
where Δ t is a sampling period and k is a natural number.
In particular, wherein uPi(k)＝[δi(k+1)-δi(k)]/Δt,uQi(k)＝[Ei(k+1)-Ei(k)]/Δt
For simplicity, we only discuss active power flow control, and the same method can also obtain similar passive power flow control results.
ηPi(k)＝ΔPi(k)-ΔPi(k-1) (21)
|ηPi(k)|≤ηPi≤εΔt (22)
Wherein eta isPiIs etaPi(k) Is the boundary coefficient associated with the sampling time interval.
Defining a Discrete Sliding Mode Surface (DSMS) as:
σPi(k)＝(1-τΔt)σPi(k-1)-εΔtsign(σPi(k-1))+ηpi(k-1) (23)
where τ is a convergence rate parameter satisfying 0<1- τ Δ t < 1.
The uncertainty term is usually estimated at a conservative upper bound and is difficult to obtain in practical applications.
Further, in this embodiment, based on a lower layer control framework and according to a discrete robustness control algorithm, making a power value of a lower layer energy storage system the same as the upper layer charge-discharge power reference value further includes estimating uncertainty and disturbance by using a disturbance estimation technique, and introducing a saturation function to alleviate the buffeting problem, where the saturation function is:
Figure BDA0002806933730000103
where ρ is a small positive gain.
Specifically, the uncertainty estimated by the perturbation estimation method is as follows:
Figure BDA0002806933730000104
when σ Pi(k) At around 0, the sign (-) function of the third term of equation (24) oscillates between- ε Δ t and ε Δ t, causing a buffeting problem, thereby introducing the saturation function described above.
The discrete control algorithm in equation (24) is rewritten as:
Figure BDA0002806933730000111
similarly, the reactive control ratio is designed to be:
Figure BDA0002806933730000112
specifically, in this embodiment, the test stand system consists of five vehicles with five DG, five BESS and five loads. The grid-connected microgrid is taken as a research object, reactive power sharing of the BESS is not considered, and reactive power reference of the BESS is set to be zero in simulation. The communication links between the five BESS are bi-directional.
A. Constant distributed power generation and load
In the first test, the distributed generators and loads were kept constant and the local grid power imbalance for the respective bus bars was [ -5,65,40,0,35] kW. The initial SOC of the five BESSs was [0.40,0.45,0.38,0.42,0.44 ]. Each BESS unit communicates with its neighboring BESS units, updating the active power reference every 4 milliseconds. The discrete time interval for power tracking control is 20 milliseconds and the SOC level is updated every 2 minutes because it changes much more slowly.
As can be seen from fig. 2 and 3, the proposed upper-level control framework strategy enables supply-demand mismatch to be shared and balanced among bayesian enterprises, with updates to the information state increasing until saturation. The information state value is small, which shows that the SOC difference of each Bayesian system is large. At about 40min the information state stops increasing and the SOC difference between the betes is almost zero. Wherein, power imbalance is the unbalanced power, and Frequency is the Frequency.
To better demonstrate the dynamic performance of LLC, only the simulation results of the first 6 minutes of the 2 nd BESS are shown in fig. 4, when the power output is updated at 0,120 and 240s, the inverter frequency has only a small spike and settles rapidly. The voltage of the inverter is kept around 1p.u, as shown in fig. 5. Fig. 6 is a graph of the reactive power output of a pass unit No. 2 and its reference curve. Wherein, voltage is voltage, reactive power is reactive power, and reactive power reference is reactive power reference.
B. Time-varying distributed power generation and load demand
As the generation and load demand of DG is usually constantly changing, the supply-demand imbalance is time consuming. In order to verify the effectiveness of the method, the dynamic performance of the distributed control strategy under the condition of variable supply and demand imbalance is researched.
The information state of the BESS may be updated based on a local estimate of the total supply-demand imbalance. And when the information state is more than or less than zero, controlling BESS to be in a charging/discharging mode. When the SOC of all BESS's are controlled to the same level, to compensate for the varying net supply-demand balance between DG and load demand. Simulation results show that the proposed distributed BESS SOC balance strategy can fully exert the capacity of each BESS under the condition of time-varying supply and demand imbalance.
C. Robustness to grid interference
The distributed control strategy proposed by this case study has performance under two types of disturbances, namely low voltage ride through fault and grid frequency variation.
Firstly, the simulation power grid runs at a nominal frequency of 60Hz, a nominal voltage of 110Vrms, an active power output reference value of 20kW and a reactive power output reference value of 0 kW. At t 10s, the grid voltage increases by 9% and returns to the nominal value at t 15 s. The grid voltage then drops by 18% when t is 15s, and returns to the nominal value when t is 25s, and the low-voltage fault state is assumed. After t 10s, the reactive power output of BESS converges to the reference value within 1s although it has several peaks. The inverter voltage E can track the amplitude variation of the grid voltage as shown in fig. 7. Although active power and inverter frequency experience multiple spikes during grid voltage changes, they decay rapidly. The active power of these spikes and the frequency of the inverter are caused by coupling effects. Wherein, inverter voltage is inverter voltage, and grid voltage is grid voltage.
Secondly, we studied the grid frequency disturbance. The grid frequency increases by 0.15Hz at t-10 s and returns to 60Hz at t-15 s. Then, at t 20s, the grid frequency drops by 0.08Hz, and at t 25s, the grid frequency returns to the normal 60 Hz. When the frequency of the power grid changes, the active power can follow the change of the reference power (within 5%), and the proposed discrete robustness control algorithm provides a small error within the asymptotic convergence range. Determined by the disturbance term (the grid frequency changes at this time). This is also the reason that the 10-15s error is slightly larger than the 15-25s error. During the change of the grid frequency, the reactive power and the voltage E remain substantially unchanged.
Thus, the BESS SOC level equalization distributed robust control strategy presented herein may provide reliable power output under frequency and grid voltage disturbances.
D. Extensibility
In the present case, a 39 bus system is shown in fig. 8, which has 10 conventional generators, 19 load lines of 34, 15 Regenerative Generators (RG) and 15 BESS to demonstrate scalability.
The BESS charge/discharge power references are controlled to compensate for time-varying supply-demand imbalances between renewable power generation and load demand, while regulating the SOC of all BESSs to the same level. Simulation results show that the distributed method has good application prospect in large-scale system application.
Further, an embodiment of the present application further provides a distributed control device 200 for electric quantity balance of an energy storage system, referring to fig. 9, where fig. 9 is a schematic diagram of a device module for distributed control of electric quantity balance of an energy storage system in an embodiment of the present application, and in this embodiment, the distributed control method for electric quantity balance of an energy storage system includes:
the acquisition module 201: the system is used for acquiring the charge state level of the energy storage system, the supply and demand balance among intermittent distributed generation and the time-varying load demand;
the upper layer module 202: the upper-layer charge and discharge power reference value of the energy storage system is obtained based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed generation and the load demand time variation;
the lower module 203: and the method is used for enabling the power value of the lower-layer energy storage system to be the same as the reference value of the upper-layer charging and discharging power based on the lower-layer control framework according to the discrete robustness control algorithm.
The distributed control equipment 200 for electric quantity balance of the energy storage system provided by the embodiment of the application can realize that: acquiring the charge state level of an energy storage system, the supply and demand balance among intermittent distributed power generation and the time variation of load demand; obtaining an upper-layer charge and discharge power reference value of the energy storage system based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation; and based on the lower layer control frame, according to a discrete robustness control algorithm, enabling the power value of the lower layer energy storage system to be the same as the reference value of the upper layer charge-discharge power. The method only needs local and adjacent information to update the control law, can effectively distribute the calculation burden among a plurality of local controllers, in order to improve the robustness and the control precision of the system, the uncertainty and the disturbance in the system are estimated and compensated by using a disturbance estimation technology which does not need to know an uncertainty boundary in advance, the supply and demand balance is maintained by adopting a consensus-based control algorithm while the SOC level of the BESS is balanced, the average SOC of all the BESS does not need to be known, in addition, the method is different from a continuous time system model which is used as the BESS for most of the existing work learning control, a discrete and stable control algorithm is provided to ensure the realization of the computer control, and the continuous control law can be applied to a discrete sampling system to avoid the caused problem.
Further, the present application also provides a distributed control apparatus for energy storage system power balance, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps in the above-mentioned distributed control method for energy storage system power balance.
Further, the present application also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the distributed control method for energy storage system power balance as described above.
Each functional module in the embodiments of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a form of hardware or a form of a software functional module. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, because some steps can be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the above description, for a person skilled in the art, according to the idea of the embodiment of the present application, there are variations in the specific implementation and application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A distributed control method for power balance of an energy storage system, wherein the energy storage system comprises an upper control framework and a lower control framework, and the method comprises the following steps:
acquiring the charge state level of an energy storage system, the supply and demand balance among intermittent distributed power generation and the time-varying load demand;
obtaining an upper-layer charge and discharge power reference value of the energy storage system based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed power generation and the load demand time variation;
and based on a lower layer control framework, according to a discrete robustness control algorithm, enabling the power value of the lower layer energy storage system to be the same as the reference value of the upper layer charging and discharging power.
2. The method of claim 1, wherein the upper control framework and the lower control framework both employ a distributed SOC balancing control strategy, and the formula of the distributed SOC balancing control strategy is as follows:
Figure FDA0002806933720000011
Pi[h+1]＝xi[h+1]ECiFi(SOCi)/ρi
Figure FDA0002806933720000012
Figure FDA0002806933720000013
wherein epsilonEIn order to supply and demand the mismatch coefficient,
Figure FDA0002806933720000014
for local estimation of supply-demand mismatch, PEiFor the latest supply-demand mismatch, h is the communication update interval of ULC, i, j are all buses, dijAs communication coefficient, ECiIs the standard capacity of the ith BESS, piFor the charging/discharging efficiency of the ith BESS, PiInput power, x, for the ith BESSiA variable in an equilibrium state, FiFor a defined function, n is the number of connections.
3. The method of claim 2, wherein the upper-level based control framework is available from state-of-charge levels, supply-demand balancing between intermittent distributed power generation, and load demand time-varying:
Figure FDA0002806933720000015
wherein N isG，NLAnd NBRespectively representing all power generationMachine, load and energy storage system, i being bus, PGiActive power of the i-th generator, PLiThe power consumed for the ith load.
4. The method of claim 3, wherein the inequality constraint for processing the maximum charge-discharge power of the BESS using the projection operation is:
Figure FDA0002806933720000021
wherein the content of the first and second substances,
Figure FDA0002806933720000022
the maximum charge/discharge power.
5. The method of claim 4, wherein the discrete robustness control algorithm employs a power flow model comprising:
Figure FDA0002806933720000023
Figure FDA0002806933720000024
wherein, deltaiIs the angular difference between inverter i and bus i, EiAnd ViRespectively inverter and bus voltage component, QiTo reactive power, PiFor active power, V is voltage and θ is phase angle.
6. The method of claim 5, wherein the discrete robustness control algorithm employs a zero-order hold sampling method:
Figure FDA0002806933720000025
Figure FDA0002806933720000026
wherein t is a sampling period, k is a natural number,
uPi(k)＝[δi(k+1)-δi(k)]/Δt,uQi(k)＝[Ei(k+1)-Ei(k)]/Δt。
7. the method of claim 6, wherein the lower layer-based control framework equating the power value of the lower layer energy storage system with the upper layer charge-discharge power reference value according to a discrete robustness control algorithm further comprises estimating uncertainty and disturbance using a disturbance estimation technique and introducing a saturation function to mitigate buffeting, wherein the saturation function is:
Figure FDA0002806933720000027
where ρ is a small positive gain.
8. A distributed control system for energy storage system power balancing, the system comprising:
an acquisition module: the system is used for acquiring the charge state level of the energy storage system, the supply and demand balance among intermittent distributed generation and the time-varying load demand;
an upper layer module: the upper-layer charge and discharge power reference value of the energy storage system is obtained based on the upper-layer control framework according to the charge state level, the supply and demand balance among the intermittent distributed generation and the load demand time variation;
a lower layer module: and the method is used for enabling the power value of the lower energy storage system to be the same as the reference value of the upper charging and discharging power based on the lower control framework according to the discrete robustness control algorithm.
9. A distributed control apparatus for energy storage system power balance, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements each step in the distributed control method for energy storage system power balance according to any one of claims 1 to 7.
10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the distributed control method for energy storage system power balancing according to any one of claims 1 to 7.
CN202011371673.XA 2020-11-30 2020-11-30 Distributed control method, system and equipment for electric quantity balance of energy storage system Withdrawn CN112531756A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011371673.XA CN112531756A (en) 2020-11-30 2020-11-30 Distributed control method, system and equipment for electric quantity balance of energy storage system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011371673.XA CN112531756A (en) 2020-11-30 2020-11-30 Distributed control method, system and equipment for electric quantity balance of energy storage system

Publications (1)

Publication Number Publication Date
CN112531756A true CN112531756A (en) 2021-03-19

Family

ID=74995300

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011371673.XA Withdrawn CN112531756A (en) 2020-11-30 2020-11-30 Distributed control method, system and equipment for electric quantity balance of energy storage system

Country Status (1)

Country Link
CN (1) CN112531756A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113346574A (en) * 2021-05-19 2021-09-03 山东大学 Energy storage battery cooperative control method and system
CN113346547A (en) * 2021-06-28 2021-09-03 重庆大学 Wind power plant energy storage power supply consistency control method based on sliding mode control

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106849164A (en) * 2017-02-17 2017-06-13 中南大学 A kind of isolated island micro-capacitance sensor unifies SoC balance control methods
CN111371112A (en) * 2020-04-15 2020-07-03 苏州科技大学 Distributed finite time control method for island microgrid heterogeneous battery energy storage system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106849164A (en) * 2017-02-17 2017-06-13 中南大学 A kind of isolated island micro-capacitance sensor unifies SoC balance control methods
CN111371112A (en) * 2020-04-15 2020-07-03 苏州科技大学 Distributed finite time control method for island microgrid heterogeneous battery energy storage system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YINLIANG XU 等: "Distributed Robust Control Strategy of Grid-Connected Inverters for Energy Storage Systems’ State-of-Charge Balancing", 《IEEE TRANSACTIONS ON SMART GRID》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113346574A (en) * 2021-05-19 2021-09-03 山东大学 Energy storage battery cooperative control method and system
CN113346547A (en) * 2021-06-28 2021-09-03 重庆大学 Wind power plant energy storage power supply consistency control method based on sliding mode control
CN113346547B (en) * 2021-06-28 2023-11-10 重庆大学 Wind farm energy storage power supply consistency control method based on sliding mode control

Similar Documents

Publication Publication Date Title
Lopez-Garcia et al. Artificial neural networks in microgrids: A review
Zhao et al. Cooperative optimal control of battery energy storage system under wind uncertainties in a microgrid
CN106877398B (en) Micro-power source distributed coordination control method based on multiple intelligent agents
CN108363306B (en) Micro-grid distributed controller parameter determination method based on linear quadratic optimization
Xu et al. Distributed robust control strategy of grid-connected inverters for energy storage systems’ state-of-charge balancing
Sutikno et al. A review of recent advances on hybrid energy storage system for solar photovoltaics power generation
CN104578045B (en) Intelligent power distribution method of independent direct-current microgrid
Bayat et al. A unified framework for participation of responsive end-user devices in voltage and frequency control of the smart grid
Han et al. Coordinated power control with virtual inertia for fuel cell-based DC microgrids cluster
Rocabert et al. Intelligent control agent for transient to an island grid
Sepehrzad et al. Islanded micro-grid frequency control based on the optimal-intelligent lyapunov algorithm considering power dynamic and communication uncertainties
CN112531756A (en) Distributed control method, system and equipment for electric quantity balance of energy storage system
Nguyen et al. Cluster-based predictive PCC voltage control of large-scale offshore wind farm
Jmii et al. Centralized VSM control of an AC meshed microgrid for ancillary services provision
Pandi et al. Adaptive coordinated feeder flow control in distribution system with the support of distributed energy resources
Sahoo et al. Scaled Conjugate-Artificial Neural Network-Based novel framework for enhancing the power quality of Grid-Tied Microgrid systems
Darabian et al. Stability improvement of large-scale power systems including offshore wind farms and MTDC grid aiming at compensation of time delay in sending robust damping signals
Ashourian et al. Controlling and modeling power-electronic interface DERs in islanding mode operation micro grid
Bhaduri et al. Supervisory controller for power management of microgrid using hybrid technique
Qin et al. Tertiary control based on non-parametric model prediction for dc microgrid cluster
Wu et al. Small signal security region of droop coefficients in autonomous microgrids
Chaitanya et al. Optimized Power Management Control Scheme for Transportation System Electrified with High Voltage DC Microgrid
Hua et al. Stochastic optimal control scheme for operation cost management in energy Internet
Khaledian et al. A new power sharing control method for stability enhancement of islanding microgrids
Lin et al. Distributed generator with virtual inertia using intelligent controller for grid-connected microgrid

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
WW01 Invention patent application withdrawn after publication

Application publication date: 20210319

WW01 Invention patent application withdrawn after publication