Method of Controlling of Battery Energy Storage System of Power System with High Dynamic Loads

Abstract

A method of controlling battery energy storage comprises a renewable active power source (P.sub.R), a battery energy storage system (BESS), at least one static load (P.sub.Load) and at least one high dynamic load P.sub.H.LOAD. Active power demand of the system in the point of common coupling is at quasi-constant level. The method further includes gathering constant active power limit from the external grid (P.sub.GRID) and historical data of active power profiles of: predicting the following active power profiles for day+1, calculating required active power of the battery energy storage system, setting daily peak of state of charge and minimum state of charge of the battery energy storage system, and verifying whether daily peak of state of charge and minimum state of charge ensure that instantaneous values of state of charge through the entire day of the battery energy storage system is within range of 20% to 80%.

Claims

1. A method of controlling of battery energy storage system of power system with high dynamic loads, wherein said power system comprises at least one renewable active power source, a battery energy storage system (BESS), at least one static load and at least one high dynamic load, wherein said system is connected in a single point of common coupling to the external power grid, and the method comprises: predicting the power generation capacity of at least one renewable active power source and predicting loads in said system based on historical data for optimizing power supply and lifetime of the battery energy storage system; active power demand of the system in the point of common coupling being at quasi-constant level; controlling the battery energy storage system by: gathering (10) constant active power limit from the external grid (P.sub.GRID) and historical data of active power profiles of: P.sub.R(day)—active power from renewable active power sources during 24 h period, P.sub.H.LOAD(day)—active power demand from high dynamic loads during 24 h period, P.sub.Load(day)—active power demand from static load during 24 h period, predicting the following active power profiles for day+1: P.sub.R(day+1)—active power from renewable active power sources during next 24 h period, P.sub.H.LOAD(day+1)—active power demand from high dynamic loads during next 24 h period, P.sub.Load(day+1)—active power demand from static load during next 24 h period, calculating (30) required active power of the battery energy storage system (BESS) according to following formula:
P.sub.BESS=P.sub.R(day+1)+P.sub.GRID−P.sub.LOAD(day+1)−P.sub.H.LOAD(day+1) wherein: P.sub.BESS—required active power of the battery energy storage system P.sub.R(day+1)—predicted active power from renewable active power sources, P.sub.GRID—constant active power limit from the external grid, P.sub.LOAD(day+1)—predicted active power demand from high dynamic loads, P.sub.H.LOAD(day+1)—predicted active power demand from high dynamic loads, setting daily peak of state of charge (SoC) and minimum state of charge (SoC) of the battery energy storage system (BESS) corresponding to capacity of the battery energy storage system that ensures required active power of the battery energy storage system (BESS) calculated in the previous step, verifying if set daily peak of state of charge (SoC) and minimum state of charge (SoC) ensure that instantaneous values of state of charge (Soc) through the entire day of the battery energy storage system (BESS) is within range of 20% to 80%, having the battery energy storage system (BESS) rating, and profiles of P.sub.LOAD(day+1), P.sub.H.LOAD(day+1) P.sub.R(day+1), when yes, the daily peak of state of charge (SoC) and minimum state of charge (SoC) of the battery energy storage system (BESS) is properly set, when no, the daily peak of state of charge (SoC) and minimum state of charge (SoC) must be raised or lowered and then the calculation of required active power of the battery energy storage system (BESS) and subsequent steps must be repeated.

2. The method according to claim 1, wherein a depth of discharge (DoD) of the battery energy storage system (BESS) corresponds to the calculated required active power of the battery energy storage system (BESS).

3. The method according to claim 1, wherein an average state of charge (SoC) of the battery energy storage system (BESS) is in the range of 20% to 60%.

4. The method according to claim 1, wherein battery energy storage system (BESS) charging and discharging sessions are predicted when predicting the active power profiles for day+1.

5. The method according to claim 1, wherein the battery energy storage system (BESS) is charged with surplus energy generated by renewable power sources when is a lack of load.

6. The method according to claim 4, wherein the battery energy storage system (BESS) supports operation of at least one high dynamic load when price of the energy during a day is high and charge itself to store energy when price of the energy during a day is low.

7. The method according to claim 1, wherein the at least one high dynamic load is an electric vehicle charger.

8. The method according to claim 1, wherein the steps of the method being performed by means of a processing employing artificial intelligence and/or machine learning techniques and/or at least one trained algorithm.

9. The method according to claim 1, wherein when microgrid is built or is under development machine learning and historical data concerning: battery energy storage system (BESS) charging and discharging profiles, active power from renewable active power sources during 24 h day period, active power demand from high dynamic loads, active power demand from static load during 24 h day period, are used and/or previously measured, to determine capacity of microgrid components.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

[0059] FIG. 1 is a schematic of a power grid system with possible directions of active power flow in accordance with the disclosure.

[0060] FIG. 2 is a flowchart for a method in accordance with the disclosure.

[0061] FIG. 3 is a chart with 24-h active power flow, and battery energy storage system state of charge, where battery is optimally charged, in accordance with the disclosure.

[0062] FIG. 4 is a chart with battery energy storage system work in accordance with the disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[0063] The present disclosure describes a method of controlling of battery energy storage system. A first embodiment is described hereinbelow. A power system comprises a microgrid which is isolated and connected in single point of common coupling PCC with an external power grid. The microgrid includes two renewable active power sources P.sub.R, namely photovoltaic panels PV and wind turbines WF. Power generation from renewable active power sources is dependent on rapidly changing environmental conditions, namely Sun's irradiance for photovoltaic panels PV, and wind speed for wind turbines WF. Therefore, they are characterized as hard to predict high dynamic sources.

[0064] In the microgrid there are also a battery energy storage system BESS, static loads and high dynamic loads. Static loads are understood as typical loads form users of microgrid, like loads in a household. These static loads are quite predictable and are well recognized in the state of the art. Whereas high dynamic loads are characterized by intermittent operation. On the one-day time scale, encompassing 24 h, the operating time of a device in relation to the idle time is relatively short. Electric vehicle chargers EVC are very good example of high dynamic loads, but such high dynamic loads might be also generated by the heating, ventilation, and air conditioning devices or lighting. Electric vehicle chargers EVC in case of commercial vehicles operates with peak power close to even 350 kW for relatively short period of a time. For example, Porsche Taycan or Audi eTron GT is charging from minimum to 80% with peak power up to 270 kW, and it takes around 23 minutes. However, high power equal to 270 kW is used only for approximately 10 min. Charging from 80% to 100% takes another hour and power is lower than 50 kW during this time.

[0065] The power system is equipped with electrical appliances, which are responsible for power conversion DC to AC and AC to DC depending on purpose, steering devices and/or electrical safety devices. The power system is also equipped with a control unit CU which is connected with all components of the power system. The control unit CU is configured to execute all steps of the method of controlling of battery energy storage system BESS.

[0066] In the power system it is assumed that energy stored in battery energy storage system BESS is sufficient to cover excessive active power demand from any dynamic loads like electric vehicle chargers EVC. Thus, the following equations that lead to this can be pointed out for an instantaneous power equality condition:

P.sub.PV+P.sub.WF−P.sub.LOAD−P.sub.EVC±P.sub.BESS=P.sub.GRID

This equation could also be presented as:

P.sub.R−P.sub.LOAD−P.sub.H.LOAD±P.sub.BESS=P.sub.GRID

For an energy equality condition:

E.sub.PV+E.sub.WF+E.sub.GRID−E.sub.LOAD−E.sub.EVC=0

E.sub.PV+E.sub.WF+E.sub.GRID=ΔE.sub.BESS+

E.sub.LOAD+E.sub.EVC=ΔE.sub.BESS−

ΔE.sub.BESS+=ΔE.sub.BESS−

This equation could also be presented as:

E.sub.R+E.sub.GRID−E.sub.LOAD−E.sub.H.LOAD=0

E.sub.R+E.sub.GRID=ΔE.sub.BESS+

E.sub.LOAD+E.sub.H.LOAD=ΔE.sub.BESS−

ΔE.sub.BESS+=ΔE.sub.BESS−

Where:

[0067] P.sub.GRID—active power from external power grid
P.sub.R—active power from renewable active power sources
P.sub.PV—active power from photovoltaic power plant
P.sub.WF—active power from wind turbine
P.sub.BESS—active power charged to and discharged from BESS.
P.sub.EVC—active power demand from electric vehicle charger EVC
P.sub.LOAD—active power demand from static load
P.sub.H.LOAD—active power demand from high dynamic load

[0068] In the microgrid of the power system active power demand of the system in single point of common coupling power has been established at quasi-constant level and following steps has been used for controlling of the battery energy storage system BESS of the power system: [0069] gathering 10 constant active power limit from the external grid P.sub.GRID and historical data of active power profiles of: [0070] P.sub.R(day)—active power from renewable active power sources during 24 h period [0071] P.sub.H.LOAD(day)—active power demand from high dynamic loads during 24 h period [0072] P.sub.Load(day)—active power demand from static load during 24 h period [0073] predicting 20 the following active power profiles for day+1: [0074] P.sub.R(day+1)—active power from renewable active power sources during next 24 h period [0075] P.sub.H.LOAD(day+1)—active power demand from high dynamic loads during next 24 h period [0076] P.sub.Load(day+1)—active power demand from static load during next 24 h period [0077] calculating 30 required active power of the battery energy storage system BESS according to following formula:

P.sub.BESS=P.sub.R(day+1)+P.sub.GRID−P.sub.LOAD(day+1)−P.sub.H.LOAD(day+1) [0078] wherein: [0079] P.sub.BESS—required active power of the battery energy storage system. [0080] P.sub.R(day+1)—predicted active power from renewable active power sources. [0081] P.sub.GRID—active power limit from the external grid [0082] P.sub.LOAD(day+1)—predicted active power demand from high dynamic loads. [0083] P.sub.H.LOAD(day+1)—predicted active power demand from high dynamic loads. [0084] setting 40 daily peak of state of charge SoC and minimum state of charge SoC of the battery energy storage system BESS corresponding to capacity of the battery energy storage system BESS that ensures required active power of the battery energy storage system BESS calculated in the previous step, [0085] verifying 50 if set daily peak of state of charge SoC and minimum state of charge SoC ensures that instantaneous value of state of charge SoC through the entire day of the battery energy storage system BESS is within range of 20% to 80%, having the battery energy storage system BESS rating, and profiles of P.sub.LOAD(day+1), P.sub.H.LOAD(day+1), P.sub.R(day+1), [0086] If yes, the daily peak of state of charge SoC and minimum state of charge SoC of the battery energy storage system BESS is properly set, [0087] If no, the daily peak of state of charge SoC and minimum state of charge SoC must be raised or lowered and then the calculation of required active power of the battery energy storage system BESS and subsequent steps must be repeated.

[0088] Following state of charge SoC of the battery energy storage system BESS has been established for the subsequent days taking into account battery depth of discharge DoD. The schedule for the battery energy storage system BESS daily peak of state of charge SoC and minimum state of charge SoC for consecutive days could look as follows:

TABLE-US-00001 Required active power of the BESS - as % of DoD Daily peak SoC Minimum SoC Day (1) 60% 80% 20% Day (2) 50% 75% 25% Day (3) 40% 70% 30% . . . Day (n)* 20% 50% 30% Day (n + 1) 55% 80% 25% *n - integer consecutive number

[0089] Therefore, daily peak of state of charge SoC and minimum state of charge SoC in the battery energy storage system BESS are set according to the method. Minimum state of charge SoC might occur as an initial state of charge SoC of the battery energy storage system BESS at the beginning of 24 h period. Minimum state of charge SoC might be also at the end of 24 h period or when power demand from electric vehicle charger EVC has been stopped. The daily peak of state of charge SoC of the battery energy storage system BESS it the maximum level of state of charge SoC during 24 h period, it might occur for example exactly just before a usage of high dynamic load is predicted.

[0090] Set levels of daily peak of state of charge SoC and minimum state of charge SoC are also verified 50 in order to ensure that instantaneous value of state of charge SoC through the entire day of the battery energy storage system BESS is within range of 20% to 80%. It is necessary to avoid unfavorable circumstances which are detrimental for battery life span, for example when because of extraordinary occurrence minimum state of charge SoC of the battery energy storage system BESS will occur during continuous use of electric vehicle charger EVC. In such case the battery energy storage system BESS performance rapidly deteriorates. So verification 50 allows to exclude these circumstances.

[0091] What is more, provided method has been used for controlling microgrid as described above. 24 h power flow chart has been drawn up (FIG. 3). It presents active power profiles from renewable active power sources P.sub.R, active power demand from electric vehicle charger EVC, and finally active power delivered by battery energy storage system BESS. Active power demand from electric vehicle charger EVC exceeds available active power from the renewable active power sources P.sub.R, hence battery energy storage system BESS must cover missing demand. In this case: [0092] battery energy storage system BESS is charged during the day up to 80% state of charge; [0093] this level of state of charge allows to fully cover electric vehicle charger EVC active power demand; [0094] at the end of the day, remaining battery energy storage system BESS state of charge is still at 20%; [0095] and from the lifetime perspective, optimal state of charge for battery energy storage system BESS is between 20% and 80% and this state of charge is maintained.

[0096] Further illustrating charts might be created. During a full day, the battery energy storage system BESS works only with part of available power in order to achieve sate of charge between 20% and 80% (FIG. 4).

[0097] Preferably, the average state of charge SoC of the battery energy storage system BESS during whole period 24 h is in the range of 20% to 60%.

[0098] It is also preferred when the battery energy storage system BESS is charged with surplus energy generated by renewable power sources when is a lack of load.

[0099] In method of controlling the battery energy storage system BESS historical data has been used. Prediction of energy flow has been used as shown in energy equality conditions. This prediction has been done based on historical data and earlier prediction of loads and power sources. What is more, in historical data weather forecasts have been also taken into account. Steps of the method has been performed by means of a processing employing artificial intelligence. Further use of a machine learning techniques and/or at least one trained algorithm is possible. There are no limitations as far as scope of gathering or analyzing of historical data. Historical data might cover any period, in which 24 h period is considered as a period base. Hence historical data might be consisting of one hundred of 24 h periods or three hundred and sixty-five of 24 h periods. Historical data might also consist of any number of 24 h periods which are possible to register. It is favorable for artificial intelligence and/or machine learning because bigger scope of historical data enables better predictions.

[0100] In an alternative (second) embodiment, the method of controlling the battery energy storage system BESS is identical to the first embodiment, except that that depth of discharge DoD of the battery energy storage system BESS corresponds to the calculated required active power of the battery energy storage system BESS.

[0101] In an additional (third) embodiment, the method of controlling the battery energy storage system BESS is identical to the first embodiment, except that the battery energy storage system BESS charging and discharging sessions are also predicted in step of predicting 20 of the active power profiles for day+1. Therefore, artificial intelligence provides better utilization of all parts of the power system. Typical operation of the battery energy storage system BESS is additionally boosted by prediction of operation intervals—charging and discharging sessions.

[0102] In this embodiment, in order to consider financial aspects of the power system the battery energy storage system BESS supports operation of at least one high dynamic load, namely electric vehicle charger EVC when price of the energy during a day is high. While charging of the battery energy storage system BESS in order to store energy is realized when price of the energy during a day is low. Consequently, the battery energy storage system BESS injects power to the microgrid when price of kWh is highest and charge itself when price of kWh is lowest.

[0103] In an additional (fourth) embodiment, the method of controlling the battery energy storage system BESS is identical to the first embodiment. However, in this embodiment, this method has been used to develop the power system. Machine learning and historical data concerning: [0104] battery energy storage system BESS charging and discharging profiles, [0105] active power from renewable active power sources during 24 h day period [0106] active power demand from high dynamic loads [0107] active power demand from static load during 24 h day period
have been used to determine capacity of the microgrid components.

[0108] In other aspects, the present disclosure can be implemented as a computer program for performing all steps of the method. A computer program has been used to perform all steps of the method of controlling the battery energy storage system BESS. The computer program is running on the computer and is comprising means of program code for performing all steps of the method. A computer-readable medium storing computer-implemented instructions performing all steps of the method of controlling the battery energy storage system BESS has been implemented on the computer.

[0109] Main advantages of these embodiments are prevention of excessive oversizing of the power system components as well as extension of a battery energy storage system lifetime. Proper charging level of the battery energy storage system allows to define optimal state of charge levels and provide sufficient active power to cover entire day energy demand from static and high dynamic loads. It is possible to avoid disadvantages associated with extreme state of charges, i.e., when the battery is discharged or works with state of charge between 0% and 20% as well as when the battery is overcharged and when it is charged over 80% and over predicted demand. Such extreme state of charges leads to deterioration of battery lifetime.

[0110] Additionally, properly sized ratings of renewable active power sources as well as the battery energy storage system allows to run power system with quasi-constant active power received from the external power grid. The power system is meant as a microgrid. Thanks to this also the power system is a stable energy consumer from the external power grid. It is especially important because in the external power grid, energy usually comes from conventional energy sources. What is more power required from the external power grid to the power system at quasi-constant level can be significantly lower than resulting from load demand. Hence the power system is charged from the power grid with quasi-constant low power, which does not exceed maximum power.

[0111] Power generated by renewable active power sources are balanced in the power system by the battery energy storage system. Consequently, it is not necessary to establish bidirectional point of common coupling. Energy from the power system is not directed to the power grid.

[0112] Furthermore, all power system components can be optimally sized and utilized. Historical data and use of a machine learning and an artificial intelligence used in the inventions provide better utilization of all systems. Typical operation of the battery energy storage system is additionally boosted by prediction of operation intervals—charging and discharging sessions.

[0113] The present disclosure allows to decrease cost of installation especially in long-time perspective, due to a lifetime extension of batteries used in the battery energy storage system. Additionally, solution meets requirements of growing popularity of electric vehicles because electric vehicle chargers are part of the power system and high dynamic loads coming from them are satisfied in this power system. If such applications power system components don't need to be oversized to withstand high active power momentary peaks. Furthermore, it possible to use full power of high dynamic loads, for example full charging possibilities of electric vehicle chargers. Power of devices generating high dynamic loads is not limited by maximum power available in point of common coupling. Excessive power demands from high dynamic loads are satisfied by the battery energy storage system.

[0114] The embodiments described herein present an eco-friendly solution due to reduced sizing of components and prolonged life span. It allows also for energy cost optimization, because when price during a day is high the battery energy storage system supports operation of high dynamic loads, particularly electric vehicle chargers, otherwise the battery energy storage system will start charging to store energy.

[0115] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0116] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.

[0117] Preferred embodiments of this invention are described herein, including the best mode known to the inventors for carrying out the invention. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.