DESC:ENERGY MANAGEMENT SYSTEM AND A METHOD FOR ENERGY MANAGEMENT THEREOF

TECHNICAL FIELD
[0001] The proposed invention relates to energy management system and more particularly, to the energy management system and a method for managing supply of energy from one or more energy sources.
BACKGROUND
[0002] The following description of related art is intended to provide background information pertaining to the field of the present disclosure. This section may include certain aspects of the art that may be related to various aspects of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
[0003] In general, one or more energy sources such as, grid, batteries, solar system, or the like, may be used for distributing electric power/energy to one or more loads in a building or an energy consuming system e.g., agro-pumps etc. Typically, an inverter receives a grid electricity as an Alternating Current (AC) input and converts it to a Direct Current (DC), which may be further stepped down using a transformer to charge the batteries and partially/fully bypass to power a building’s load. A Solar system/Photovoltaic (PV) systems harvest solar energy using a Pulse Width Modulation (PWM) or a Maximum Power Point Tracking (MPPT)-based controller and the harnessed solar energy may be used to charge the batteries after stepping up/down a DC voltage or to supply electricity to the building after converting it to the AC and stepping up the same. If electricity is unavailable, the same energy stored in the batteries is converted to the AC and the AC is stepped up using a transformer before supplying it to the building.
[0004] However, if such energy sources and inverters are not monitored in real-time, significant losses can be caused by abrupt power failures, insufficient backup capacity, unplanned replacements, and maintenance-heavy operations that organizations and personnel face.

SUMMARY
[0005] Accordingly, exemplary embodiments of the present disclosure address these and other difficulties relating to energy management of one or more energy distribution modules in any predefined area.
[0006] According to first aspect of the present disclosure provides a method implemented in an Energy Management System (EMS) for energy management. The method comprises receiving data, in real time, associated with one or more energy sources, one or more energy-controlling devices, or energy supply requirements of one or more loads and enabling delivery of energy from the one or more energy sources to the one or more loads according to a priority logic of a predefined set of priority logics and based on the data received in real time. The method further comprises processing data that comprises the data associated with the one or more energy sources, the one or more energy-controlling devices, or the energy supply requirements of the one or more loads received in real time; and instructing the one or more energy controlling devices to take one or more data-driven actions in real time based on the processed data, wherein the one or more data-driven actions control the delivery of the energy from the one or more energy sources.
[0007] According to second aspect of the present disclosure provides an Energy Management System (EMS), comprising a processor and a memory coupled to the processor and storing a set of instructions that when executed by the processor cause the processor to receive data, in real-time, associated with one or more energy sources, one or more energy-controlling devices, or energy supply requirements of one or more loads and enable delivery of energy from the one or more energy sources to the one or more loads according to a priority logic from a predefined set of priority logics and based on the data received in real time. The processor is further arranged to process data that comprises the data associated with the one or more energy sources, the one or more energy-controlling devices, or the energy supply requirements of the one or more loads received in real time and instruct the one or more energy controlling devices to take one or more data-driven actions in real time based on the processed data. The one or more data-driven actions control the delivery of the energy from the one or more energy sources.
[0008] Advantageously, the proposed EMS, and method optimizes energy usage, reduces energy waste, and improves energy efficiently by enabling real-time monitoring and controlling energy distribution through the plurality of energy sources.

BRIEF DESCRIPTION OF THE DRAWINGS
[0009] For a more complete understanding of the present invention and its features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:

[0010] Figure 1 schematically illustrates implementation of an Energy Management System (EMS) according to an embodiment herein;

[0011] Figure 2 illustrates a block diagram of the EMS according to an embodiment herein;

[0012] Figure 3 illustrates additional details of the EMS, according to an embodiment herein;

[0013] Figure 4 illustrates a flowchart illustrating a method for energy management, according to an embodiment herein; and

[0014] Figure 5 illustrates a flowchart illustrating a method for energy management, according to another embodiment herein.

DETAILED DESCRIPTION
[0015] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0016] Embodiments herein disclose an Energy Management System (EMS) (smart energy management system) for remotely and independently monitoring and controlling one or more energy sources such as grid, batteries, or the like and one or more energy controlling devices such as inverters solar system and enabling delivery (or supply) of energy from one or more energy sources to facilitate optimization of energy consumption and the management of energy sources and/or energy controlling devices. The EMS is connected to the plurality of energy sources, plurality of energy controlling devices or plurality of loads using a suitable communication protocol. In an embodiment, the EMS is configured to monitor the one or more energy sources and the energy controlling devices in real-time, and enable the delivery of energy from the one or more energy sources for operation of one or more loads according to a predefined set of priority logics.

[0017] Figure 1 discloses implementation of the Energy Management System (EMS) 100 that is arranged to communicate with at least one of: one or more energy sources 200, one or more energy controlling devices 300, one or more loads 400, an external entity 500 and one or more sensors 600. Figure 1 shows the implementation of the EMS 100 communicating with a single energy source 200, single energy controlling device 300, and one load 400, however this to be understood that implementation shown in Figure 1 is exemplary and the implementation may be extended to plurality of such energy sources 200, plurality of such energy controlling devices 300, and plurality of loads 400.

[0018] The external entity 500 may comprise a cloud server and a system coupled to the EMS 100. The system coupled to the EMS 100 may include a plurality of computing devices. The plurality of computing devices are configured to communicate with each other and with EMS 100 via a network (not shown in the Figures). The external entity 500 may further comprise a server connected to the EMS 100. The server may be further connected to one or more systems external to the EMS 100 through the network. The systems external to the EMS may comprise external databases supplying energy related data.

[0019] It may be understood that the server (local server/remote server/ cloud server) of the external entity 500 may also be implemented in a variety of computing systems/devices such as, a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a network server, a cloud-based computing environment, or a smart phone, and the like. It may be understood that the EMS 100 may correspond to a variety of portable device.

[0020] In an example implementation, the network for establishing the communication between the EMS 100 and the external entity 500 may be a wireless network, a wired network, or a combination thereof. The network may be implemented as one of the different types of networks, such as intranet, Local Area Network, LAN, Wireless Personal Area Network, WPAN, Wireless Local Area Network, WLAN, wide area network, WAN, the Internet, and the like. The network may be either a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, MQ Telemetry Transport, MQTT, Extensible Messaging and Presence Protocol, XMPP, Hypertext Transfer Protocol, HTTP, Transmission Control Protocol/Internet Protocol, TCP/IP, Wireless Application Protocol, WAP, and the like, to communicate with one another.

[0021] In accordance with embodiments disclosed herein, the server/cloud server is configured for establishing the communication between the EMS 100 and the external entity 500.The cloud server is configured to receive and store various data (discussed in subsequent paragraphs) from the EMS 100.

[0022] As shown in Figure 1, each of the energy source 200, the energy controlling device 300 and the load 400 are power connected such that the energy-controlling device 300 facilitates supply of energy from the energy source 200 to the load 400, whereas the EMS 100 is signal connected to each of the energy source 200, the energy controlling device 300, the load 400, the external entity 500 and the sensor 600. In embodiments where a plurality of energy sources 200, a plurality of energy-controlling devices 300 and a plurality of loads 400 are present, each energy source 200, each energy-controlling device 300 and each load 400 are power connected to one another such that one or more energy-controlling devices 300 facilitate supply of energy from one or more energy sources 200 to one or more loads 400.
[0023] In an example, the one or more energy sources 200, may be interfaced with the EMS 100 using communication protocols such as, but are not limited to, Universal Asynchronous Receiver/Transmitter (UART), Controller Area Network (CAN), RS485, and so on. In another example, the one or more energy sources 200 may be coupled to the one or more energy controlling devices 300 using a wired network, a wireless communication network, Over-The-Air (OTA) based Internet connection, or the like.

[0024] The sensors 600 as shown in Figure 1 herein may include, but are not limited to, temperature sensors, humidity sensors, GPS sensors or the like. In an example, the sensors 600 may be interfaced with the one or more energy sources 200, the one or more energy-controlling devices 300, the one or more loads 400 and EMS 100 to receive their performance and health parameters. The sensors 600 may be in physical contact (e.g., mounted), in operational proximity or communicatively coupled with the one or more energy sources 200, the one or more energy-controlling devices 300, the one or more loads 400 and EMS 100. The communicative coupling may be established using communication protocols such as, but are not limited to, UART, CAN, RS485, and so on. In an example, the sensors 600 may be signal coupled to the EMS 100 by using a wired network, a wireless communication network, OTA based Internet communication, or the like.

[0025] In accordance with an embodiment, Figure 2 illustrates a block diagram for the EMS 100. The EMS 100 referred herein may be used in residential, industrial, commercial, agricultural, and mobility sectors as a power backup or Radioisotope Thermophotovoltaic (RTPV) based generation system.

[0026] The EMS 100 comprises a memory 32, a processor 34 and an Input/output (I/O) interface 36. The memory (32) is coupled to the processor (34) and stores a set of instructions that when executed by the processor (34) cause the processor (34) to execute various function as described below. The EMS 100 is configured to remotely and independently monitor the one or more energy sources 200 and the one or more energy-controlling devices 300 and manage the supply of energy from the one or more energy sources 200 to the one or more loads 400.

[0027] The memory 32 may also serve as a repository for storing data that is received processed, and transmitted by the EMS 100. The memory 32 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as Static Random-Access Memory, SRAM, and Dynamic Random-Access Memory, DRAM, and/or non-volatile memory, such as Read Only Memory, ROM, Erasable Programmable ROM, EPROM, Electrically Erasable and Programmable ROM, EEPROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0028] The processor 34 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 34 may be configured to fetch and execute computer-readable instructions stored in the memory 32 and control all components of the EMS 100 as shown later in Figure 3.

[0029] In an example, the one or more energy sources 200 comprise grid, batteries, generator, solar system or the like. The one or more energy controlling devices 300 are the devices that control the supply of energy from the one or more energy sources 200 to the one or more loads 400. In an example, the one or more energy controlling devices 300 comprise an inverter, the generator such as Diesel Generator (DG), or thermo-electric generators (TEG), charging stations or the like. It should be noted that the generator acts as an energy source and an energy-controlling device.

[0030] In an example, the one or more loads 400 comprise home appliances (such as smart meters, television (TV), refrigerator, air conditioner, fans, motors, lights, or the like), machineries, Internet of Things (IoT) devices, computing devices, and so on.

[0031] In an exemplary embodiment, the processor 34 is arranged to receive data, in real-time, associated with the one or more energy sources 200, the one or more energy-controlling devices 300, or energy supply requirements of one or more loads 400.
[0032] In an example, the data comprises at least one of: energy distribution data of each energy source 200, energy supply requirements of the one or more loads 400, and defined data of each energy source 200. The defined data comprises amount of electricity/energy the one or more energy source may supply. For example, for a small set-up grid, estimated power that could be supplied to the one or more loads is in a range of 1,000 watts to 1,500 watts. The data further comprises amount of energy required for functioning of the one or more loads, for example a television (load 400) may use 50 watts to 200 watts of electricity for its functioning.

[0033] In an example, the defined data further comprises one of: performance data, health data and system parameters (e.g., charging modes of a battery, discharge mode of an inverter) of each energy sources 200 or each energy-controlling device 300, environmental data associated with each energy source 200 (e.g., predicted operating efficiency of solar system), or one or more parameters associated with each energy source 200 that affect the energy distribution of the one or more energy sources 200.
[0034] The data further comprises historical data associated with the one or more energy sources 200, the one or more energy-controlling devices 300, and energy supply requirements of one or more loads 400.

[0035] In another example, the data further comprises data related to user pattern, grid pattern, weather conditions, or publicly-available data; and dynamic data of each energy source 200 shared with one or more servers in the network for instructing the one or more energy controlling devices 300 to take one or more data-driven actions.

[0036] In an example, the one or more energy sources 200 are arranged to supply energy (or power) to the one or more loads 400 deployed in a predefined area comprising at least one of: a building, a factory floor, or the like. The EMS 100 is arranged to select the one or more energy sources 200 to supply the power to the one or more loads 400 according to the priority logic.

[0037] In an example, the data may also be received from the external entity 500 signal connected with the EMS 100. The external entity 500 referred herein may be a server (for example, a standalone server, a server deployed in cloud, or the like), a computing device, a mainframe electronic device, or the like. The external entity 500 maintains information about the one or more energy sources 200, the one or more parameters relevant to the energy sources, or the like. Examples of the parameters may include, but are not limited to, a current location where the one or more energy sources 200 are deployed, a historical location, prior failure events associated with the one or more energy sources 200, weather conditions (humidity/rain) associated with the said current location, grid energy availability patterns and solar energy availability in the said current location; and personalized energy usage patterns of the user.

[0038] In an embodiment, the external entity 500 may also be configured to receive, from the EMS 100 the data comprising the energy distribution data, the sensor data, the control action, or the like for further analysis and remote instructions.

[0039] The performance data of each energy source 200 comprises one of an amount of power being generated, a power being stored, a power output, a status of the energy source, an efficiency of the energy source, or health of the energy source 200.

[0040] In an example, the EMS 100 also receives data from one or more sensors 600 signal coupled to the EMS 100 and the one or more sensors 600 are arranged to generate the environmental data identifying temperature and humidity levels in a surrounding environment of the one or more energy sources 200, wherein the environmental data comprises light intensity in the predefined area.

[0041] As discussed above, the processor 34 receives the data in real time. In an example when the EMS 100 is implemented, it first receives the data in real time and then enables delivery of energy from the one or more energy sources 200 to the one or more loads 400 according to a priority logic from a predefined set of priority logics and based on the data received. Accordingly, the one or more energy sources 200 supply energy to the one or more loads 400.

[0042] After the implementation of the EMS 100, the processor 34 continue to receive the data in real time (e.g., every second, every microsecond or every nanosecond). The processor 34 then processes the data received in real-time. That means, the processor 34 receives the data in real time and process the received data in real time. In an example, the processing comprises monitoring and analysing the data to optimize a plurality of parameters comprising performance and the health of the one or more energy sources 200, the one or more energy controlling devices 300or the one or more loads 400 in real-time.

[0043] In an example, the analysing comprises calculating various parameters associated with the one or more energy sources 200 and the energy controlling devices 300 and determining performance of the one or more energy sources 200 and the energy controlling devices 300. Examples comprise determining state of charge (SOC), state of health (SOH) of a battery, efficiency of solar system, fuel predictions, backup requirement predictions, power cut predictions and alike. The term “state of charge (SOC)” of a battery is a measurement of the amount of energy available in the battery at a specific point in time expressed as a percentage. The term “state of health (SOH)” is defined as the ratio of the maximum battery charge to its rated capacity.

[0044] The processor 34 is then arranged to instruct the one or more energy controlling devices 300 to take one or more data-driven actions in real time based on the processed data. The one or more data-driven actions control the delivery of the energy from the one or more energy sources 200 to the one or more loads 400.
[0045] In an example, the data-driven actions comprise switching of the one or more energy sources 200, optimizing load distribution on the one or more energy sources 200, increasing or decreasing charging currents of the one or more energy sources 200, and changing settings of the or more energy controlling devices 300, functional and safety parameters of the one or more energy sources 200, or the one or more energy controlling devices 300. The data-driven actions may be executed through the EMS 100.

[0046] In an example, the predefined set of priority logics comprise a user set priority logic for supplying energy from one of the energy sources 200 to the one or more loads 400, an energy availability based priority logic , or a cost based priority logic for delivering the energy. In an embodiment, the EMS 100 may deliver the energy based on a combination of two or more of the user set priority logic, the energy availability based priority logic and the cost based priority logic.
[0047] In an example, the user set priority logic may be set by a user. The user set priority logic prioritizes the energy sources 200 for delivering energy to loads 400. For example, in an embodiment, the user set priority logic may prioritize the grid as the energy source 200 for supplying energy. In another embodiment, the user set priority logic may prioritize the battery as the energy source 200 for supplying the energy.
[0048] The energy availability based priority logic prioritizes an energy source from the one or more energy sources 200 based on a number of parameters for example fuel level, output power, service timing, state of charge (in case of a battery), planned down-time of energy sources, real-time and predicted power requirements or the like. For example, when a battery has 50% of SOC, the energy level of the battery is 50%. In an example, when the grid is unavailable and the energy level of the battery is lower than the predicted daily energy requirement, the processor 34 enables energy delivery from the generator during its operational hours and charge the battery at the same time to ensure sustained energy delivery for the one or more energy sources 200.
[0049] The cost priority logic prioritizes an energy source from the one or more energy sources 200 according to the ascending order of a total unit cost of delivering energy from the energy sources. The total unit cost of the energy source 200 includes an amortized unit cost and a running unit cost of the energy source 200. For example, if the total unit cost of delivering energy from the grid, DG, battery, solar system are Rs. 8, Rs. 30, Rs. 28, Rs. 5, respectively, based on the real time data (e.g., cost of energy from the grid, SOH of the battery, real time fuel prices etc.), the EMS 100 may prioritize the solar system as the energy source 200 for delivering energy.
[0050] Within the predefined set of priority logics, the user set priority logic, the energy availability based priority logic and the cost based priority logic may have priority. In an example, the user set priority logic may be preferred over the energy availability based priority logic followed by the cost based priority logic. If the user set priority exists, then the processor 34 only process the data (received, historic, web, news etc.) in real time and recommend data-driven actions according to the energy availability and predictions of energy optimization and cost optimization.
[0051] As discussed above, the one or more data-driven actions control the delivery of the energy. In an embodiment, in order to control the delivery of energy, the processor 34 is arranged to first reprioritize the priority logic or another priority logic of the predefined set of priority logics and then enable the delivery of energy from the one or more energy sources 200 based on the reprioritized priority logic, in some embodiments. In an example, if the user set priority does not exist, then the processor 34 may enable the delivery of energy according to the energy availability based priority logic at the time of implementation of the EMS 100. After a period of time, the processor 34 may reprioritize the same priority logic i.e., the energy availability based priority logic or another priority logic i.e., the cost based priority logic based on the processed data and the enable the delivery of energy from the one or more energy sources 200 according to the reprioritized priority logic.
[0052] Referring to Figure 3, additional details of the EMS 100 will now be discussed. The EMS 100 is communicatively signal coupled to an input 60 comprising the one or more energy sources 200, the one or more energy controlling devices 300, the one or more loads 400 and the sensors 600.
[0053] The EMS 100 comprises components for example, a microcontroller unit (MCU) 54 (or the processor 34 or communicatively coupled to the processor 34), an auxiliary battery 56, a Direct Current (DC)-DC converter 58, a communication unit 38, a Secure Digital Card (SD) card 40, Light Emitting Diode (LED) indicators 42, General Purpose Input/Output (GPIO) pins 44, a protocol selector unit 46, a CAN port 48, RS485 50, and UART 52. The components of the EMS as shown in Figure 3 are controlled through the processor 34.
[0054] Further, the EMS 100 supports different communication lines and ADC channels via which the energy controlling devices 300 (inverter, Battery Management System (BMS), and solar system) and the sensors 600 may be connected to the EMS 100.

[0055] In an example, the inverter may be connected to the EMS 100 via a communication line, which allows the EMS 100 to receive the data from the inverter. The data received from the inverter comprises default settings of operating parameters and performance data of the inverter. The performance data of the inverter indicates an amount of power being generated, a power output, a status of the inverter, and an efficiency.

[0056] The BMS may be connected to the EMS 100 via a communication line, which allows the EMS 100 to receive the data from the BMS. The data received from the BMS comprises default settings of operating parameters and performance data of the BMS. The performance data of the BMS indicates a status of battery(ies) such as a current state of charge, voltage, and temperature, which may be used to determine a health of the battery(ies) and optimizing its usage. Thereby, the performance data indicates the status and health of the battery(ies).

[0057] The solar system may also be connected to the EMS 100 via a communication line for providing the data to the EMS 100. The data received from the solar system comprises default settings of operating parameters and performance data of the solar system. The performance data of the solar system indicates an amount of energy being generated by the solar system.

[0058] The sensors 600 may be connected to the EMS 100 via a communication line for providing the sensor data to the EMS 100. The sensor data identifies temperature and humidity levels in a surrounding environment of the one or more energy sources 200.

[0059] The on-board protocol selector unit 49 may be configured to enable the user to switch any communication protocols such as UART, CAN, and RS485. Such a feature allows the EMS 100 to communicate with different energy sources, the energy controlling devices 300 (the inverter, the BMS, and the solar system) and the sensors 600 seamlessly.

[0060] The MCU 54 in the EMS 100 may be configured to process, i.e., monitor and control the one or more energy sources 200, the energy distribution modules (the inverter, the BMS, and the solar system) 300 and modify firmware through wired, wireless, and OTA internet-based communication. The MCU 54 controls the energy-controlling devices 300 and thereby the one or more energy sources 200 based on the data received from the one or more energy sources 200, and the energy controlling devices 300 (the inverter, the BMS, and the solar system) 300. The MCU 54 controls the energy source out of grid, solar system and battery to provide an optimized cost, efficiency, and performance for the user while optimizing device life by preserving battery life.

[0061] For controlling the energy sources 200, the MCU 54 obtains, from the external entity 500, the one or more parameters that affect energy distribution of the energy distribution sources 200. Examples of the parameters may include, but are not limited to, a current location where the energy sources 200 are deployed, a historical location, prior failure events associated with the one or more energy sources 200, weather conditions (humidity/rain) associated with the said current location, grid energy availability patterns and solar energy availability in the said current location; and personalized energy usage patterns of the user. Thereafter, the MCU 54 instructs the one or more energy controlling devices 300 to take one or more data-driven actions by evaluating the data received from the one or more energy sources 200, the one or more energy controlling devices 300 or the one or more loads 400 along with the sensor data and the one or more parameters received from the external entity 500.
[0062] In an example, referring to Figure 4, a flow chart for a method 4000 providing energy management is shown. The method 400 is a computer implemented method and is implemented in the EMS 100 as discussed above.
[0063] At step 402, the method 4000 provides receiving the data, in real time, associated with the one or more energy sources 200, the one or more energy-controlling devices 300, or energy supply requirements of the one or more loads 400.
[0064] At step 404, the method 4000 enables the delivery of energy from the one or more energy sources 200 in real-time to the one or more loads 400, based on a priority logic of the predefined set of priority logics based on the data received in real time.
[0065] At step 406, the method 4000 processes the data that comprises the data received in real time from the one or more energy sources 200, the one or more energy-controlling devices 300, or the one or more loads 400. The data that is received in real time is described in above embodiments in details.
[0066] In an example, the processing 406 comprises monitoring and analysing the data to optimize the plurality of parameters comprising performance and the health of the one or more energy sources 200, the one or more energy controlling devices 300 or the one or more loads 400 in real-time.
[0067] The method 4000 at step 408 instructs the one or more energy controlling devices 300 to take one or more data-driven actions in real time based on the processed data. The one or more data-driven actions controls the delivery of the energy from the one of more energy sources 200 to one or more loads 400.
[0068] In an embodiment, referring to Figure 5, a flow chart for a method 5000 providing the energy management is shown. The method 5000 is a computer implemented method and is implemented in the EMS 100 as discussed above. The method 4000 comprises the steps 502, 504, 506, 508, 510, and 512 out of which the steps 502, 504, 506 and 508 are similar to the steps 402, 404, 406 and 408 of the method 4000 of Figure 4, and are not described again with respect to Figure 5. The method 5000 at step 510, reprioritize the priority logic or another priority logic from the predefined set of priority logics based on the processed data in real-time, in order to control the delivery of energy. At step 512, the method 400 enables the delivery of energy from one or more energy sources 200 in real time according to the reprioritized priority logic.
[0069] In an example, the EMS 100 is configured to some additional functions. The additional functions comprise recommending one or more data-driven actions to the user based on the processing of the data, and then receiving, one or more informative parameters from the user based on the recommended one or more data-driven actions and then performing the one or more data-driven actions based on the received one or more informative parameters.
[0070] In an example, the EMS 100 is further configured to receive information related to software update of the EMS 100, the one or more energy sources 200, or the one or more energy controlling devices 300. Upon receiving such information, the EMS 100 is configured to perform the software update for the EMS 100, the one or more energy sources 200, or the one or more energy controlling devices 300 according to the information.
[0071] The details of the methods 4000 and 5000 are similar to the details of the EMS 100 and hence are not repeated for the sake of brevity.
[0072] Again referring to Figure3, in an example, functioning of the EMS 100 and the method 3000 will now be explained. Consider that the MCU 54 identifies the change in operating settings of the one or more energy sources 200 from the above described evaluation. In such a scenario, the MCU 54 generates the at least one data-driven action indicating controlling complete or partial operating parameters/functions of the one or more energy sources 200 and the one or more energy controlling devices 300.

[0073] In another example, consider that the MCU 54 determines solar energy availability from the above described evaluation. In such a scenario, the MCU 54 generates the data-driven action that indicates prioritization of the solar systems to act as the energy source according to a priority logic of the predefined set of priority logics as discussed above. Thus, generating the data-driven action based on the energy consumption patterns/energy usage patterns, the grid electricity availability patterns, and solar energy availability enhances the system’s battery life while ensuring ample battery capacity on a day when solar energy production is low by optimizing charge currents. As a result, a life of batteries may be increased up to 100% depending upon standard charge rates, consumption and availability of grid electricity, and solar energy harnesses the capability by slowing down the charging rates and increasing the charging rates from the grid when the solar energy availability is low. In addition, an overall infrastructure cost of ownership may be reduced by half by extending system life and saving upfront infrastructure costs by providing data-driven recommendations.

[0074] In yet another example, consider that the MCU 54 determines the probable anomaly in the inverter from the above described evaluation. In such a scenario, the MCU 54 generates the data-driven action that provides an indication about the anomaly and the remedies for solving anomaly.

[0075] In accordance with the instructed data-driven action, the MCU 54 controls the one or more energy sources 200 and the one or more energy controlling devices 300. The controlling comprises one or more of: providing corrective inputs for operating the energy distribution modules, controlling complete or partial operating parameters/functions of the energy distribution models, providing real-time alerts/warning of a probable anomaly, providing an indication for corrective maintenance, reducing downtime, reducing operating costs, or the like. Thereby, optimizing energy usage, reducing energy waste, and improving energy efficiency.

[0076] The auxiliary battery 56 may be connected to the DC-DC converter 58, which allows the EMS 100 to continue functioning even in an event of a shut down from the energy controlling device’s end (for example, the inverter end). Thereby, ensuring that the EMS 100 continues to monitor and control the energy sources/energy consumption, even during power outages.

[0077] The SD card 40/memory 32 may be used to store one or more of: the data received from the energy sources 200, the sensor data, the data-driven action, and so on, for pre-defined period. For example, up to 5 years of such data may be stored in the SD card 40.

[0078] The GPIO pins 42 (for example, 5 GPIO pins) may be used to control the one or more loads 400 such as lights, fans, pumps, or the like. Thus, the user may be allowed to enable/disable the one or more loads 400 using the GPIO pins 42.

[0079] The LED indicators 42 may be used to indicate a status of the EMS 100, network status, and status of the one or more energy controlling devices 300 for example, inverter status.

[0080] The communication unit 38 may support for example, but is not limited to, Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), 3G communication, 4G/5G communication or any other next generation communication. The communication unit 38 may be used to establish the communication between the EMS 100 and the external entity 500 and the one or more energy sources 200. The communication unit 38 may be configured to send the data comprising the at least one data-driven action, the data, the sensor data, and so on, to the external entity 500.

[0081] The EMS 100 may also communicate the data such as the at least one data-driven action, the data, the sensor data, and so on, to an output unit 62 comprising MQTT broker and is stored in a database (in the output 62) through a data pipeline. Such a feature enables the users to access the real-time data anywhere in the world to monitor the energy usage/consumption.

[0082] Further, the EMS 100 maintains an Application Programming Interface (API) server in the output unit 62 through which the user can access the above said data on a website and mobile applications. Such a feature provides the users with a convenient way to monitor and manage energy consumption remotely.

[0083] Embodiments herein disclose the EMS 100 for monitoring and controlling of multiple energy sources and the one or more energy controlling devices 300 such as inverters, batteries, solar system, or the like.

[0084] The EMS 100 interfaces with each of the energy source 200 independently and monitors settings and performance data, analyses available data of the location-specific humidity levels, expected power cut-off timings, solar energy production, and usage patterns of the users and controls the energy source 200 and the energy controlling unit 300. The EMS 100 controls the energy source 200 and the energy controlling unit 300 for prioritizing energy source(s), optimizing battery charging currents, energy distribution while electricity is available, remote maintenance by a manufacturer/service provider, and system diagnostics by detecting changes in default settings of all the operating parameters. The EMS 100 further helps by providing usage analytics, and recommendations to users on the correct system sizing for the solar/battery or inverters and maintaining transparency of performance between users and service providers solving an issue of unsettled warranty claims in the associated industry.

[0085] Advantageously, the EMS 100 may provide data identifying energy consumption to users based on rental/subscription based services.

[0086] Further, the EMS 100 may aid the organizations/personnel by
? generating a data-driven decision for generation/backup utility and plan replacements with a help of a predictive maintenance utility that works on pattern recognition from prior failure events of the energy distribution modules connected over the external entity, and
? generating alerts/warnings of a probable anomaly in the energy distribution modules 10a-10n;
? providing corrective actions to solve the anomaly. Thereby, the remote-controlled devices allow the organizations/personnel to get an immediate diagnosis and corrective maintenance, reducing downtime. Further, the operating cost of the energy distribution modules may be reduced by 90% while enabling the organizations to scale their business. Additionally, remote controlled nature of the smart energy management system allows a service provider to shut down the system in case of non-payments increasing overall repayment rates in the business.

[0087] In some embodiments, interfacing other home appliances like smart meters, TV, refrigerator, air conditioner, or the like, with the smart energy management system may enhance its capabilities by providing cost analytics, power-saving actions, and recommendations on system sizing for batteries, RTPV modules, and feasibility of going off-grid. Each combination of device interfaces with the modules may result in unique product value proposition, for example, smart inverters, smart batteries, smart hybrid inverters, smart generators, or the like.

[0088] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the spirit and scope of this disclosure.

,CLAIMS:We Claim:

1. A method (4000) implemented in an Energy Management system (EMS) (100) for energy management, the method (4000) comprising:
receiving (402) data, in real time, associated with one or more energy sources (200), one or more energy-controlling devices (300), or energy supply requirements of one or more loads (400);
enabling (404) delivery of energy from the one or more energy sources in real-time to the one or more loads (400) according to a priority logic of a predefined set of priority logics and based on the data received in real time;
processing (406) data comprising the data associated with the one or more energy sources (200), the one or more energy-controlling devices (300), or the one or more loads (400) received in real time; and
instructing (408) the one or more energy controlling devices (300) to take one or more data-driven actions in real time based on the processed data, wherein the one or more data-driven actions control the delivery of the energy from the one or more energy sources.
2. The method (4000) as claimed in claim 1, wherein the data comprises:
energy distribution data of each energy source, supply power requirements of the one or more loads, defined data of each energy source;
historical data associated with the one or more energy sources, the one or more energy-controlling devices, or energy supply requirements of the one or more loads;
data related to user pattern, grid pattern, weather conditions, or publicly-available data related to the one or more energy sources (200), the one or more energy controlling devices (300) or the energy supply requirements of the one or more loads (400); and
dynamic data of each energy source shared with one or more servers in a network.
3. The method (4000) as claimed in claim 1, wherein the predefined set of priority logics comprises a user set priority logic, an energy availability based priority logic, and a cost based priority logic.
4. The method (4000) as claimed in claim 1, wherein the data-driven actions comprise switching-off the one or more energy sources (200), optimizing load distribution on the one or more energy sources (200), increasing or decreasing charging currents of the one or more energy sources (200), changing settings of the one or more energy controlling devices (300), or changing functional and safety parameters of the one or more energy sources (200) or the one or more energy controlling devices (300).
5. The method (4000) as claimed in claim 1, wherein controlling the delivery of the energy comprises:
reprioritizing (510) the priority logic or another priority logic of the predefined set of priority logics in real time based on the processed data ; and
enabling (512) the delivery of energy from one or more energy sources (200) in real time according to the reprioritized priority logic.
6. The method (4000) as claimed in claim 1, wherein the processing (306) comprise:
monitoring and analyzing the data to optimize a plurality of parameters comprising performance and the health of the one or more energy sources (200), the one or more energy controlling devices (300) or the one or more loads (400) in real-time.
7. The method (4000) as claimed in claim 1, wherein the EMS is configured to:
recommend one or more actions to a user based on the processing of the data;
receive one or more informative parameters from the user based on the recommended one or more actions; and
perform the one or more actions based on the received one or more informative parameters.
8. The method (3000) as claimed in claim 1, wherein the EMS is configured to:
receive information related to software update of the EMS (100), the one or more energy sources (200), or the one or more energy controlling devices (300); and
enable to perform the software update for the EMS (100), the one or more energy sources (200), or the one or more energy controlling devices (300) according to the received information.
9. An Energy Management System (EMS) (100), comprising:
a processor (34); and
a memory (32) coupled to the processor (34) and storing a set of instructions that when executed by the processor (34) cause the processor (34) to:
receive data, in real time, associated with one or more energy sources (200), one or more energy-controlling devices (300), and energy supply requirements of one or more loads (400);
enable delivery of energy from the one or more energy sources (200) to the one or more loads according to a priority logic of a predefined set of priority logics and based on the data received;
process data comprising the data associated with the one or more energy sources (200), the one or more energy-controlling devices (300), and the one or more loads (400) received in real time; and
instruct the one or more energy controlling devices to take one or more data-driven actions in real time based on the processed data, wherein the one or more data-driven actions control the delivery of the energy from the one or more energy sources.
10. The EMS (100) as claimed in claim 9, wherein the data comprises:
energy distribution data of each energy source (200), supply requirements of the one or more loads (400), defined data of each energy source (200);
historical data associated with one or more energy sources (200), the one or more energy-controlling devices (300), and energy supply requirements of the one or more loads (400);
data related to user pattern, grid pattern, weather conditions, or publicly-available data related to the one or more energy sources (200), the one or more energy controlling devices (300) or the one or more loads (400); and
dynamic data of each energy source (200) shared with one or more servers in a network.
11. The EMS (100) as claimed in claim 9, wherein the predefined set of priority logics comprise a user set priority logic, an energy availability based priority logic, and a cost based priority logic.
12. The EMS (100) as claimed in claim 9, wherein the data-driven actions comprise switching-off the one or more energy sources (200), optimizing load distribution on the one or more energy sources (200), increasing or decreasing charging currents of the one or more energy sources (200), changing settings of the one or more energy controlling devices (300), or changing functional and safety parameters of the one or more energy sources (200), or the one or more energy controlling devices (300).
13. The EMS (100) as claimed in claim 9, wherein the set of instructions comprises instructions that when executed by the processor (34) cause the processor (34) to:
reprioritize (510) the priority logic or another priority logic from the predefined set of priority logics in real time based on the processed data; and
enable (512) the delivery of energy from one or more energy sources (200) in real time according to the reprioritized priority logic.

14. The EMS (200) as claimed in claim 9, wherein the instructions to process comprise instructions that when executed by the processor (34) cause the processor (34) to:
monitor and analyze the data received in real time to optimize a plurality of parameters comprising performance and health of the one or more energy sources (200), the one or more energy controlling devices (300) or the one or more loads (400) in real-time.
15. The EMS as claimed in claim 9, wherein the set of instructions comprises instructions that when executed by the processor (34) cause the processor (34) to:
recommend one or more actions to a user based on the processing of the data;
receive one or more informative parameters from the user based on the recommended one or more actions; and
perform the one or more actions based on the received one or more informative parameters.
16. The EMS as claimed in claim 9, wherein the set of instructions comprises instructions that when executed by the processor (34) cause the processor (34) to:
receive information related to software update of the EMS (100), the one or more energy sources (200), or the one or more energy controlling devices (300); and
enable to perform the software update for the EMS (100), the one or more energy sources (200), or the one or more energy controlling devices (300) according to the received information.