Electricity suppliers may charge a fee to their customers both for energy consumption, measured in kilowatt-hour (kWh), and for their peak demand, or maximum power rating, measured in kilowatt (kW), that may be assessed during a particular billing period. By and large, peak demand may be assessed and billed monthly, that is, a charge may be assessed based on the maximum demand by the customer for that particular month. Alternative billing schemes, for example, annual peak demand, may also be used in determining these assessments.
Embodiments include methods, systems, and devices where, for example a peak load management system embodiment may include: a battery system configured to store energy and discharge stored energy when actual load exceeds a target peak load; one or more managed loads configured to allow reduced energy consumption when needed to meet a target peak load; and a peak load management processor operatively coupled with the battery system and managed loads and configured to: continually measure actual load, determine predicted load data based at least in part on past actual load; determine if operation of battery system alone can allow target peak to be achieved; reduce energy consumption of managed loads only if battery system alone is not capable of maintaining target peak load; and discharge energy from battery system to maintain target peak load.
The peak load management system embodiment may further be configured to: calculate a target peak load based at least in part on: past actual load; energy storage and power capability of battery system; and estimated reduced energy consumption achievable by dispatching managed loads. Optionally the battery system may be engaged if the energy supply level of the battery system is greater than zero. In another embodiment, the battery system may be engaged if the actual load is greater than the target peak load. Additionally, the load reduction may be invoked if the energy supply level of the energy storage device is predicted to reach zero while attempting to meet target peak where the degree of load reduction invoked may be determined from a predicted peak load, based at least in part on past actual load and where the energy consumption may be reduced if the actual load is greater than the target peak load. Additionally, the system may periodically predict if the battery system can achieve target peak load at currently dispatched levels of load management, and additional load management resources may be dispatched as needed based on this prediction. The system embodiment may also have the target peak load further based on a peak demand energy usage of a real property. The system may also comprise electricity load. Additionally, they system may periodically predict if battery system can achieve target peak load at currently dispatched levels of load management, and additional load management resources may be dispatched as needed based on this prediction. The system embodiment may also have the target peak load further based on a peak demand energy usage of a real property. The system may also comprise electricity load.
Embodiments include methods, systems, and devices where, for example a method embodiment may include the steps of: determining a predicted load data based at least in part on measured actual load and past actual load; reducing energy consumption by one or more managed loads when needed to meet a target peak load; determining, by a peak load management processor operatively coupled with a battery system and the one or more managed loads, if operation of the battery system alone can allow target peak to be achieved; reducing energy consumption of the one or more managed loads if the battery system alone is not capable of maintaining target peak load; and discharging energy from the battery system to maintain target peak load; wherein the battery system is configured to store energy and discharge stored energy when actual load exceeds a target peak load. Optionally, the battery system may be engaged if the energy supply level of the battery system is greater than zero. Alternatively, the battery system may be engaged if the energy usage is greater than the target peak load.
The method embodiments may also comprise a scenario where the load reduction may be invoked if the energy supply level of the battery system is predicted to reach zero while attempting to meet target peak. Additionally, the degree of load reduction invoked may be determined from a predicted peak load, based at least in part on past actual load and the load reduction may be invoked if the energy usage is greater than the target peak load. Embodiments may also apply where it is periodically predicted if the battery system can achieve target peak load at currently dispatched levels of load management, and additional load management resources are dispatched as needed based on this prediction. Optionally, the target peak load is further based on a peak demand energy usage of a real property.
In some embodiments the method may further include the step of: calculating a target peak load based at least in part on: past actual load; energy storage and power capability of the battery system; and estimated reduced energy consumption achievable by dispatching managed loads. Optionally, the load may comprise electricity load.
Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, and in which:
An energy management system may comprise an energy monitoring and control system where the monitoring system monitors energy generating devices and energy consuming devices in the system and said control system effects changes in the operation of certain energy generating and energy consuming devices in the system to realize load management. Additionally, the energy monitoring system may check for the capacity and output of the energy generating devices along with the consumption of energy by the energy consuming devices. In one embodiment, the energy management system may calculate the net total energy usage of the system as the sum of energy consumed by all energy consuming devices in the system, minus the energy produced by all energy generating devices in the system. In one embodiment, this net total energy usage reflects the energy that must be supplied to the system by outside sources, such as the electricity grid. Some examples of energy consuming devices may include: appliances installed on the property, such as: a water heater; an air conditioning unit; lighting systems; elevators; pool pumps; a food refrigeration system; a multi-function peripheral (MFP) device; and a television set. Some examples of energy generating devices may include: a solar generation installation; and diesel generators. The energy management system may also comprise one or more energy storage devices where the energy storage device may act as either an energy generating device or energy consuming device. One exemplary embodiment of the energy storage device may be a battery storage system.
In one embodiment, net total energy usage of the system during the electricity billing period may be minimized to reduce peak demand charges assessed by the electricity service provider, and thereby reduce energy costs. Energy storage systems, e.g., battery system, in some cases coupled with solar generation systems, may reduce the peak demand. In the embodiment where an energy storage system, e.g., battery system, is used, the peak demand may be reduced by discharging the battery system during periods where the net energy usage of the system is at its highest. In some embodiments, the energy management system may actively control the discharge behavior of the battery system in order to realize a particular peak reduction strategy.
In some embodiments, the ability of a battery system to reduce demand may be dependent on the capacity of the battery (kWh), maximum power rating (kW), and the time dependent size and shape of the electric demand curve of the loads of the system. In an exemplary embodiment, the energy consumption of a system may have a regular, periodic daily shape of demand curve, where a usage peak load of each day may be in the afternoon or early evening. The exact magnitude of demand and precise shape of the curve, e.g., width of peaks, rate of change of power with time, may vary on a day to day basis. In some embodiments, this may depend on conditions such as: temperature or weather, which may dictate the requirements for energy consuming devices, for example, heating, ventilation, and air conditioning (HVAC) system and lighting system operation; occupant behavior, which may dictate which energy consuming devices are utilized; and day of the week, which may dictate usage characteristics of the property itself.
In one embodiment, the peak load management system comprises an energy storage system coupled with a load management system. The load management system may comprise one or more energy consuming devices at the site which may be controlled by the peak load management system to reduce their energy consumption, as needed, to realize a particular peak load management strategy. Examples of such managed energy consuming devices include: networked thermostats, networked load switches and networked lighting controls. In one embodiment, reducing the energy consumption of managed devices will result in an impact to the comfort or operations of the facility. That is, reducing energy consumption of HVAC systems, may for example result in higher temperatures at the property, potentially, reducing occupant comfort. Accordingly, it may be undesirable to regularly use such load management capability.
In an exemplary embodiment, by leveraging the load management with the battery system according to a particular strategy, more aggressive peak load and demand charge management may be accomplished while minimizing the usage of load management and effectively improving the overall battery system utilization.
The system controller 110 is configured to monitor local power generation level of the local power source 105, local power generation or consumption of local energy storage device 120, and the power consumption levels of the local devices 180. Based on the generation and consumption levels, the system controller 110 may:
In one embodiment, peak load across multiple day assessment periods, e.g., a billing month, may be managed, by: (i) predicting the worst case day expected for each month based at least in part on prior load data; (ii) assessing the peak load achieved after effective use of battery resources on that predicted worst day; and (iii) operating the battery storage system every day of the billing period only as needed to meet the expected peak load of that worst case day. This cycle efficient predictive system may result in the battery system remaining idle over a period of time, as the battery may only be engaged when needed to achieve a reduction in the monthly electricity usage of the system.
In one embodiment, load management may be used in conjunction with a cycle efficient predictive battery storage system, where the controller may engage load management to reduce the peak energy usage. Load management may be defined as the operation of system embodiments, at the customer site, that may result in managed energy consuming devices 185 by altering their state of operation to reduce the energy consuming devices' 185 electricity consumption. In one exemplary embodiment, the target devices may be operated in this adapted state over a defined period of time as determined by the system controller in order to reduce the effective energy use of the site. In this embodiment the system controller 110 will cause the managed energy consuming devices 185 to operate only on days with a sufficiently large anticipated and predicted peak load such that, by engaging the managed energy devices, the resulting load is reduced sufficiently to allow the battery system to be operated in a manner to achieve the target peak load where it would not have been able to do so without such load management.
Reducing operation of such systems may impact “quality” of operation of the customer site, and may not be desirable. In this embodiment, the battery storage and load management may be leveraged so as to together reduce the usage of load management while maintaining an aggressive target peak load. Accordingly, if the worst day usage is predicted along with battery system utilization and load management, the overall battery system operation may improve and the peak load reduction performance may increase.
In one embodiment, the energy management system may be used at a property comprising on-site loads and energy storage systems, where a portion of the on-site loads may be managed by the system. This management of loads may mean that the energy management system is capable of causing the loads to reduce their operation in order to reduce electricity demand. In some embodiments, it may be undesirable to reduce the operation of managed loads as this may reduce the operational quality of systems at the property, for example, reducing the HVAC load, at the cost of potentially uncomfortable temperatures. In addition to storage and loads, some of which are manageable, the site may include renewable generation of energy, for example, a solar PV system, which may have impact on the load shape at the property and may also have its output predicted by the energy management system. Accordingly, control of the storage device and use of load management on energy consuming assets and or devices may be utilized in order to achieve improved peak load reduction at the property, while maximizing the effective use of the storage asset and minimizing the usage of load reduction to maintain effective operation of systems at the property.
The embodiments of the system controller 210 may predict, for example, ahead of time, the worst case day of energy usage. Accordingly, the system controller 210 may leverage and use load management on the managed loads at the site 235 with the storage device 225 and achieve a more aggressive peak load. The system controller 210 may then target using up to, for example, 100%, of the storage device 225, i.e., the battery's full potential, before invoking, via the managed load controller 230, a load reduction scheme on the managed loads at the site 235. In one embodiment, the system controller 210 may function and predict in real-time so as to collect data and send control signals to achieve the aggressive peak load on each day of a billing period. Accordingly, the system controller 210 may determine, based on the billing period, an aggressive target, by first engaging and using the battery system and then invoking the load management scheme.
In one exemplary embodiment, an exemplary process of the energy management system may be carried out for predictive energy storage and load management, where the process may comprise the step of establishing a target peak load for a particular property during a billing period comprising a determined number of days. In some embodiments, the target peak load may be based on the following inputs: (a) predicted time varying demand, i.e., load shape, of the site, taking into account any on-site renewable generation, for the expected highest electricity demand day in the billing period; and (b) calculated achieved peak load reduction by utilizing the energy storage and load management assets on that worst case day. Optionally, the achievable load may then be used as the target peak load for the billing period. In some embodiments, once the target peak load for the billing period is established, the following steps may be carried out during operation of the system: (1) determine, via predicting, an expected peak load on each day; (2) engage the energy store, e.g., battery storage, where the available capacity is fully used in order to reduce the peak load from the predicted day's peak to the target peak, for example, cycle the battery storage in a manner where only the needed storage capacity is engaged; (3) if the target peak load may be achieved with the battery storage alone, no load management controls may be initiated; (4) if the battery system alone is unable to achieve the target peak load, load management, e.g., reduction of operation of certain manageable loads, may be invoked in addition to the battery storage use; and (5) use only the amount of load reduction necessary to achieve the target peak load for the billing period, for example, partial control of manageable loads may be used rather than the maximum available load management capacity.
Accordingly, in the exemplary system as depicted in the graph of
In the exemplary embodiment, depicted in
It is contemplated that various combinations and/or sub-combinations of the specific features and aspects of the above embodiments may be made and still fall within the scope of the invention. Accordingly, it should be understood that various features and aspects of the disclosed embodiments may be combined with or substituted for one another in order to form varying modes of the disclosed invention. Further it is intended that the scope of the present invention is herein disclosed by way of examples and should not be limited by the particular disclosed embodiments described above.
Number | Name | Date | Kind |
---|---|---|---|
4023043 | Stevenson | May 1977 | A |
6624532 | Davidow et al. | Sep 2003 | B1 |
6889122 | Perez | May 2005 | B2 |
7373222 | Wright et al. | May 2008 | B1 |
7715951 | Forbes, Jr. et al. | May 2010 | B2 |
8010812 | Forbes, Jr. et al. | Aug 2011 | B2 |
8019697 | Ozog | Sep 2011 | B2 |
8032233 | Forbes, Jr. et al. | Oct 2011 | B2 |
20040076860 | Aso | Apr 2004 | A1 |
20040260489 | Mansingh et al. | Dec 2004 | A1 |
20050107892 | Matsui et al. | May 2005 | A1 |
20060276938 | Miller | Dec 2006 | A1 |
20080040295 | Kaplan et al. | Feb 2008 | A1 |
20080046387 | Gopal et al. | Feb 2008 | A1 |
20080058998 | Breit | Mar 2008 | A1 |
20090292402 | Cruickshank, III | Nov 2009 | A1 |
20090296342 | Matteson et al. | Dec 2009 | A1 |
20090302681 | Yamada et al. | Dec 2009 | A1 |
20090326729 | Hakim et al. | Dec 2009 | A1 |
20100023176 | Imagawa et al. | Jan 2010 | A1 |
20100094476 | Hamilton et al. | Apr 2010 | A1 |
20100286937 | Hedley et al. | Nov 2010 | A1 |
20100292856 | Fujita | Nov 2010 | A1 |
20110172837 | Forbes, Jr. | Jul 2011 | A1 |
20110202910 | Venkatakrishnan et al. | Aug 2011 | A1 |
20110231028 | Ozog | Sep 2011 | A1 |
20110257803 | Yamamoto et al. | Oct 2011 | A1 |
20120032814 | Radovilsky et al. | Feb 2012 | A1 |
20120179301 | Aivaliotis et al. | Jul 2012 | A1 |
20120229081 | Kiuchi et al. | Sep 2012 | A1 |
20120245751 | Gow et al. | Sep 2012 | A1 |
20120296480 | Raman et al. | Nov 2012 | A1 |
20130018821 | Shao | Jan 2013 | A1 |
20130030590 | Prosser | Jan 2013 | A1 |
20130134962 | Kamel et al. | May 2013 | A1 |
20130190939 | Lenox | Jul 2013 | A1 |
20130221929 | Prosser | Aug 2013 | A1 |
20130261823 | Krok et al. | Oct 2013 | A1 |
20130285446 | Chow et al. | Oct 2013 | A1 |
20140067140 | Gow | Mar 2014 | A1 |
Entry |
---|
Shao, Vic “Commercial Buildings and Utilities—More in Common Than One Would Think” Environmental Leader News Environmentalleader.com Aug. 2, 2012. |
Green Charge Networks “Power Efficiency Case Study Intelligent Energy Storage System 7 eleven Case Study” Greenchargenet.com Sep. 2013. |
Number | Date | Country | |
---|---|---|---|
20140094979 A1 | Apr 2014 | US |