Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to an electricity distribution system and particularly to a decentralized system for energy management.
Description of Related Art
[002] The increasing integration of distributed energy resources (DERs), such as solar panels and battery storage, has led to a shift from centralized energy management systems toward decentralized and autonomous control mechanisms. Traditional energy management systems rely on centralized controllers or cloud-based platforms to coordinate energy distribution and consumption. However, these systems often suffer from single points of failure, high latency, and scalability challenges when managing a growing number of interconnected appliances. Moreover, the rigid control structures of centralized systems make them less adaptable to the dynamic and unpredictable nature of energy supply and demand.
[003] Recent advancements in smart grid technologies and the Internet of Things (IoT) have enabled more sophisticated approaches to energy optimization, leveraging connected devices and data-driven decision-making. Blockchain-based platforms, transactive energy markets, and AI-driven automation have been explored to facilitate efficient energy sharing and resource allocation. While these approaches offer significant improvements in security and automation, they often face limitations such as high computational overhead, integration difficulties, and regulatory compliance barriers. Furthermore, many existing systems still depend on centralized intermediaries, restricting their ability to fully leverage the benefits of decentralized intelligence.
[004] A critical challenge in modern energy management is the need for autonomous, self-organizing systems that can optimize energy consumption based on real-time conditions, user behavior, and grid constraints. Current solutions struggle with balancing energy loads dynamically, ensuring security against cyber threats, and minimizing user intervention while maintaining efficiency. The evolving landscape of energy distribution calls for a robust, decentralized framework that allows appliances to interact intelligently, adapt to varying demands, and facilitate seamless peer-to-peer energy exchange without relying on a central authority.
[005] There is thus a need for an improved and advanced decentralized system for energy management that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a decentralized system for energy management. The system comprising electrical appliances comprising a reinforcement learning engine. The electrical appliances are interconnected using an application hive. The application hive establishes a peer-to-peer communication among the electrical appliances. The system further comprising a swarm intelligence unit adapted to collaborate energy from sources. The collaborated energy is adapted to operate the electrical appliances. The system further comprising a context engine adapted to monitor parameters relating to operation of the electrical appliances. The parameters are selected from ambient scenarios, availability of renewable energy sources, usage patterns, ambient monitors, renewable energy availability schemes, grid stability analyzer, user activity patterns to prioritize user comfort, cost reduction, energy efficiency, or a combination thereof. The system further comprising a microcontroller communicatively connected to the electrical appliances, the swarm intelligence unit, and to the context engine. The microcontroller is configured to receive the parameters monitored by the context engine; train the reinforcement learning engine based on the monitored parameters; select an operative status of the electrical appliances; and activate the swarm intelligence unit to supply the collaborated energy to the electrical appliances, when the operative status is selected as ‘ON’.
[007] Embodiments in accordance with the present invention further provide a method for operating a decentralized system for energy management. The method comprising steps of receiving parameters monitored by a context engine; training a reinforcement learning engine based on the monitored parameters; selecting an operative status of electrical appliances; and activating a swarm intelligence unit to supply collaborated energy to the electrical appliances, when the operative status is selected as ‘ON’.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a decentralized system for energy management.
[009] Next, embodiments of the present application may provide a decentralized system that enables appliances to autonomously communicate and optimize energy usage, reducing dependency on a single point of failure and enhancing overall system resilience.
[0010] Next, embodiments of the present application may provide a decentralized system that dynamically adjust energy distribution, ensuring optimal load balancing without requiring direct user intervention or a central coordinator.
[0011] Next, embodiments of the present application may provide a decentralized system that allows appliances to share energy locally, prioritizing stored power utilization and reducing reliance on the grid during peak demand, leading to improved efficiency and cost savings.
[0012] Next, embodiments of the present application may provide a decentralized system that features integration of reinforcement learning enables appliances to continuously adapt to user preferences, environmental conditions, and historical usage patterns, ensuring smarter energy consumption over time.
[0013] Next, embodiments of the present application may provide a decentralized system that reduces cybersecurity risks, ensuring more secure and private energy management compared to traditional IoT-enabled grids.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1A illustrates a decentralized system for energy management, according to an embodiment of the present invention;
[0018] FIG. 1B illustrates an exemplary embodiment of energy management, according to an embodiment of the present invention; and
[0019] FIG. 2 depicts a flowchart of a method for operating the decentralized system for energy management, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1A illustrates a decentralized system 100 (hereinafter referred to as the system 100) for energy management, according to an embodiment of the present invention. The system 100 may be installed in a premise, and may be adapted to learn about a routing of inhabitants in the premise. Further, the system 100 may be adapted to supply power to operate appliances as per the routine of the inhabitants. Further, the system 100 may be adapted to be in communication with a power supply grid to ensure receipt of power from renewable sources. Furthermore, the system 100 may be adapted to reroute the supplied power back to the power supply grid when received in excess.
[0025] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise electrical appliances 102, a reinforcement learning engine 104, an application hive 106, a swarm intelligence unit 108, sources 110, a context engine 112, and a microcontroller 114. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0026] In an embodiment of the present invention, the electrical appliances 102 may comprise the reinforcement learning engine 104. The reinforcement learning engine 104 of the electrical appliances 102 may be trained to enable a peer-to-peer energy-sharing mechanism during peak hours to reduce reliance on additional power sources. The reinforcement learning engine 104 may be adapted to deliver an optimal scheduling of the electrical appliances 102 exploiting user preferences and energy availability. The reinforcement learning engine 104 may allow peer-to-peer energy sharing to prioritize demand-based energy transfer considering storage availability and power supply grid status. The electrical appliances 102 may be, but not limited to, lights, a geyser, an air conditioner, a smart home plug-ins, and so forth. The electrical appliances 102 may be interconnected using the application hive 106. The application hive 106 may be adapted to establish a peer-to-peer communication among the electrical appliances 102.
[0027] In an embodiment of the present invention, the swarm intelligence unit 108 may be adapted to collaborate energy from the sources 110. The the swarm intelligence unit 108 may enabled by Internet of Things (IoT) based technology for collaborate the energy from the sources 110. The collaborated energy may be adapted to operate the electrical appliances 102. In an embodiment of the present invention, the swarm intelligence unit 108 may be adapted to enable decentralized decision-making among the electrical appliances 102 to collectively optimize energy consumption and avoid the power supply grid overloading.
[0028] In an embodiment of the present invention, the swarm intelligence unit 108 may be incorporated within the system 100 and may obliterate the need for a central controller by leveraging decentralized, local decision-making among the electrical appliances 102. In response to the collected ambient data, usage pattern, and grid status, the swarm intelligence unit 108 may ensure user comfort and proper energy management through a peer-to-peer protocol. Using simple heuristic rules - akin to those observed in natural swarms (e.g., ant colony optimization) - each of the electrical appliances 102 may independently assesses surplus or deficit and may adjust behavior accordingly. Through iterative local interactions and distributed consensus algorithms, autonomous agents collectively converge on an optimal energy distribution strategy. The distributed consensus algorithms may be, but not limited to, a Peer-2-Peer (P2P) messaging, a Message Queuing Telemetry Transport (MQTT), a Constrained Application Protocol (CoAP), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the distributed consensus algorithms, including known, related art, and/or later developed technologies.
[0029] In an embodiment of the present invention, the context engine 112 may be adapted to monitor parameters relating to operation of the electrical appliances 102. The parameters may be, but not limited to, ambient scenarios, availability of renewable energy sources, usage patterns, ambient monitors, renewable energy availability schemes, power supply grid stability analyzer, user activity patterns to prioritize user comfort, cost reduction, energy efficiency, and so forth.
[0030] In an embodiment of the present invention, the microcontroller 114 may be connected to the electrical appliances 102, the swarm intelligence unit 108, and the context engine 112. The microcontroller 114 may be configured to receive the parameters monitored by the context engine 112. The microcontroller 114 may be configured to train the reinforcement learning engine 104 based on the monitored parameters. The microcontroller 114 may be configured to select an operative status of the electrical appliances 102. If the operative status of the electrical appliances 102 may be ‘ON’, then the microcontroller 114 may be configured to activate the swarm intelligence unit 108 to supply the collaborated energy to the electrical appliances 102. Else, the microcontroller 114 may be configured to deactivate the swarm intelligence unit 108 to block the collaborated energy to the electrical appliances 102. The microcontroller 114 may be configured to record user preferences over time and modify the electrical appliance behavior to align with such preferences automatically.
[0031] FIG. 1B illustrates an exemplary embodiment of energy management using the system 100, according to an embodiment of the present invention. During peak demand, the electrical appliances 102 equipped with reinforcement learning engine 104 and participating in the application hive 106 focus to ensure user comfort. The electrical appliance 102 collects ambient data using the context engine 112. To enforce more reliability, the electrical appliances 102 may continuously monitor their own usage pattern, local storage status, and the power supply grid conditions utilizing the context engine 112. This data may further be processed in real time by reinforcement learning engine 104, which may further predict energy surplus or deficit based on historical patterns and current demand. When the electrical appliances 102 may identify the surplus, the electrical appliances 102 may broadcast the available reserve to the power supply grid. Conversely, the electrical appliances 102 facing a deficit may send out a request for supplemental power from the power supply grid. Utilizing the swarm intelligence unit 108, the application hive 106 may autonomously determine an optimal energy transfer routes and quantities without relying on a central controller. Energy sharing is executed through inverter controls (not shown) that may modulate power flow, ensuring safe, efficient, and balanced load distribution.
[0032] FIG. 2 depicts a flowchart of a method 200 for operating the system 100, according to an embodiment of the present invention.
[0033] At step 202, the system 100 may receive the parameters monitored by the context engine 112.
[0034] At step 204, the system 100 may train the reinforcement learning engine 104 based on the monitored parameters.
[0035] At step 206, the system 100 may select the operative status of the electrical appliances 102.
[0036] At step 208, if the operative status selected is ‘ON’, then the method 200 may proceed to a step 210. Else, the method 200 may proceed to a step 212.
[0037] At step 210, the system 100 may activate the swarm intelligence unit 108 to supply the collaborated energy to the electrical appliances 102.
[0038] At step 212, the system 100 may deactivate the swarm intelligence unit 108 to block the collaborated energy to the electrical appliances 102.
[0039] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0040] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A decentralized system (100) for energy management, the system (100) comprising:
electrical appliances (102) comprising a reinforcement learning engine (104), characterized in that the electrical appliances (102) are interconnected using an application hive (106), such that the application hive (106) establishes a peer-to-peer communication among the electrical appliances (102);
a swarm intelligence unit (108) adapted to collaborate energy from sources (110), wherein the collaborated energy is adapted to operate the electrical appliances (102);
a context engine (112) adapted to monitor parameters relating to operation of the electrical appliances (102), wherein the parameters are selected from ambient scenarios, availability of renewable energy sources, usage patterns, ambient monitors, renewable energy availability schemes, power supply grid stability analyzer, user activity pattern to prioritize user comfort, cost reduction, energy efficiency, or a combination thereof; and
a microcontroller (114) communicatively connected to the electrical appliances (102), the swarm intelligence unit (108), and to the context engine (112), wherein the microcontroller (114) is configured to:
receive the parameters monitored by the context engine (112);
train the reinforcement learning engine (104) based on the monitored parameters;
select an operative status of the electrical appliances (102); and
activate the swarm intelligence unit (108) to supply the collaborated energy to the electrical appliances (102), when the operative status is selected as ‘ON’.
2. The system (100) as claimed in claim 1, wherein the microcontroller (114) is configured to deactivate the swarm intelligence unit (108) to block the collaborated energy to the electrical appliances (102), when the operative status is selected as ‘OFF’.
3. The system (100) as claimed in claim 1, wherein the reinforcement learning engine (104) of the electrical appliances (102) is trained to enable a peer-to-peer energy-sharing mechanism during peak hours to reduce reliance on additional power sources.
4. The system (100) as claimed in claim 1, wherein the reinforcement learning engine (104) delivers an optimal scheduling of the electrical appliances (102) exploiting user preferences and energy availability.
5. The system (100) as claimed in claim 1, wherein the reinforcement learning engine (104) allows peer-to-peer energy sharing to prioritize demand-based energy transfer considering storage availability and power supply grid status.
6. The system (100) as claimed in claim 1, wherein the swarm intelligence unit (108) is adapted to enable decentralized decision-making among the electrical appliances (102) to collectively optimize energy consumption and avoid power supply grid overloading.
7. The system (100) as claimed in claim 1, wherein the microcontroller (114) is configured to record user preferences over time and modify the electrical appliance behavior to align with such preferences automatically.
8. A method (200) for operating a decentralized system (100) for energy management, the method (200) is characterized by steps of:
receiving parameters monitored by a context engine (112);
training a reinforcement learning engine (104) based on the monitored parameters;
selecting an operative status of electrical appliances (102); and
activating a swarm intelligence unit (108) to supply collaborated energy to the electrical appliances (102), when the operative status is selected as ‘ON’.
9. The method (200) as claimed in claim 8, comprising a step of deactivating the swarm intelligence unit (108) to block the collaborated energy to the electrical appliances (102), when the operative status is selected as ‘OFF’.
10. The method (200) as claimed in claim 8, wherein the electrical appliances (102) are selected from lights, a geyser, an air conditioner, a smart home plug-ins, or a combination thereof.
Date: March 12, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant