Description:FIELD OF THE INVENTION
This invention relates to Virtual Power Plant for Dynamic Hybrid Microgrid Operation
BACKGROUND OF THE INVENTION
Current hybrid microgrid systems face several challenges:
• Inefficient Energy Management: Traditional microgrids operate continuously, leading to unnecessary power losses.
• Grid Overload Issues: Unregulated energy dispatch can strain the main grid.
• Limited Demand-Driven Operation: Existing systems lack dynamic operation based on real-time demand.
• Battery Degradation: Constant energy cycling affects battery longevity.
• Lack of Integration with EVs: Existing solutions do not utilize electric vehicles as mobile energy storage.
EXISTING SOLUTIONS / PRIOR ART/RELATED APPLICATIONS & PATENTS:
Existing solutions include traditional microgrid control architectures that operate continuously rather than based on demand, leading to inefficiencies. They do not fully leverage the concept of Virtual Power Plants (VPPs) that activate only when required.
Patent Number Title Key Features
US10622834B2 Virtual Power Plant - Integration of Distributed Energy Resources (DERs): Combines multiple DERs, including renewables and storage systems, into a unified VPP.
- Real-time Monitoring and Control: Enables continuous monitoring and management of energy resources.
- Demand Response Capabilities: Adjusts power generation and consumption based on grid demands.

US20170373509A1 Virtual Power Plant - Scalable Architecture: Designed to manage a large number of DERs across various locations.
- Automated Energy Dispatch: Utilizes algorithms for optimal scheduling and dispatch of energy resources.
- Grid Services Provision: Offers ancillary services like frequency regulation and voltage support to the main grid.

US20170005474A1 Virtual Power Plant - Hybrid Energy Resource Management: Coordinates both renewable and non-renewable energy sources within a microgrid.
- User Participation: Incorporates consumer-owned energy assets into the VPP framework.
- Economic Optimization: Focuses on cost-effective operation by analyzing market prices and energy demands.

Patent Number Problems / Challenges
US10622834B2 - Limited adaptability to fast-changing grid dynamics in real-time
- Requires high-speed, secure communication infrastructure to maintain synchronization
US20170373509A1 -Scalability is constrained by centralized control logic
- Vulnerable to single-point failures; lacks decentralized decision-making for DER clusters
US20170005474A1 - Economic optimization model lacks real-time flexibility
- Integration of user-owned assets is complex due to non-standard communication protocols and privacy issues

Aspect Previous Problem (Patent Number) Proposed Solution
Efficiency Economic optimization model lacks real-time responsiveness (US20170005474A1) Integrate real-time pricing and load prediction using AI/ML to dynamically optimize dispatch strategies
Fault Tolerance Centralized architecture increases risk of single-point failure (US20170373509A1) Shift to a distributed/decentralized VPP control structure with redundant edge-level controllers
Energy Management Inflexible coordination among DERs leads to sub-optimal dispatch (US10622834B2) Employ agent-based control for localized decisions, integrated with a global coordinator to harmonize energy flows
Power Flow Delayed response in power reallocation due to centralized data processing (US10622834B2) Use fog/edge computing for local power decisions to reduce latency and improve responsiveness
Grid Stability Poor handling of frequency/voltage variations under dynamic load conditions (US20170373509A1) Integrate fast-responding energy storage systems (like supercapacitors) and real-time control loops for grid-forming behavior
Operational Reliability Complex DER integration leads to higher fault probability and downtime (US20170005474A1) Implement predictive diagnostics and digital twins to simulate and correct failures before actual deployment
Scalability Centralized VPPs face bottlenecks as the number of DERs grows (US20170373509A1) Adopt hierarchical or peer-to-peer VPP architecture; enable dynamic node discovery and plug-and-play resource registration

Traditional hybrid microgrid systems are faced with several operational inefficiencies due to their constant energy dispatching processes. These systems typically operate regardless of the available energy needs, leading to a lot of energy wastage, battery wear, and grid instability. Furthermore, integrating renewable energy sources such as solar and wind into microgrids poses challenges to ensuring a stable power supply because of their natural intermittent nature.
Existing microgrid deployments rely on pre-programmed or pre-defined energy management policies that are not responsive to demand changes and are not able to optimize energy storage resource utilization. Additionally, while electric vehicles (EVs) are becoming more and more the focus of efforts to integrate them into the energy system, existing microgrid architectures are not able to leverage EVs as flexible storage devices to improve grid reliability.
Below is an AI-based, demand-responsiveness driven Virtual Power Plant (VPP) for Hybrid Microgrid operation. Unlike regular microgrid systems, which run continuously, the invention dynamically shifts Distributed Energy Resources (DERs), EV-based storage, and grid interaction in accordance with actual demand in real-time. Usage of bidirectional Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) operations raises grid flexibility, optimizes battery life, and improves the harnessing of renewable energy.
The suggested Virtual Power Plant (VPP) with an artificial intelligence algorithm-based smart Energy Management System (EMS) can accurately forecast patterns of demand, prevent grid congestion, and increase the overall efficiency of hybrid microgrids. The suggested innovation addresses the shortcomings of existing systems by suggesting a scalable, flexible, and highly efficient energy management policy that effectively integrates renewable energy sources, energy storage systems, and electric vehicle-based power support.
OBJECTS OF INVENTION:
The main goals of the suggested Virtual Power Plant (VPP) for Hybrid Microgrid Operation are:
•Demand-Driven Energy Dispatch – Develop an intelligent energy management system that only runs when and as needed, thus saving wasted energy consumption.
• AI-Optimized Resource Allocation – Utilize artificial intelligence to forecast demand trends and maximize the activation of Distributed Energy Resources (DERs) like solar, wind, and battery storage.
• EV-Based Energy Storage Integration – Convert electric vehicles (EVs) into onboard storage units such that bidirectional Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) can be facilitated for grid stability.
• Grid Stability Enhancement – Prevent grid overload while dynamically managing energy distribution between grid, microgrid, and independent power generation modules.
•Battery Life Enhancement – Minimize unnecessary charge-discharge cycles, thereby extending battery life and enhancing overall system performance.
• Seamless Integration of Renewable Energy – Optimize the utilization of variable renewable energy sources through artificial intelligence-based demand response methods and dynamic grid management.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed Virtual Power Plant (VPP) for Hybrid Microgrid is a demand-response energy management system that uses artificial intelligence to optimize dynamic power allocation using distributed energy resources (DERs) and electric vehicles (EVs) as mobile storage units. Unlike conventional microgrid systems that operate continuously, resulting in inefficiencies and additional energy loss, the VPP only activates, when necessary, thereby ensuring optimal use of energy and enhancing grid stability. The operational framework employs an artificial intelligence-based decision mechanism, analyzing real-time energy demand and initiating resources such as solar power, wind power, batteries, and bidirectional electric vehicles accordingly. The virtual power plant is able to successfully integrate Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) functions, thereby preventing redundant battery cycling and system overall robustness. By means of real-time load balancing, demand response maximization, and predictive analytics, the VPP prevents grid congestion, optimizes battery life, and delivers sustainable, cost-efficient, and scale-up energy for use within the microgrid environment and the large-scale environment too.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
1. System Architecture:
The architecture of the proposed Virtual Power Plant (VPP) is designed to dynamically manage energy resources within a hybrid microgrid using AI-driven optimization. The system ensures efficient bidirectional power flow between distributed energy resources (DERs), battery storage, electric vehicles (EVs), smart loads, and the main grid. The key components and their interactions are as follows:
1. VPP Control Center (AI & Optimization)
• Acts as the brain of the system.
• Uses artificial intelligence (AI) and optimization algorithms to control energy dispatch.
• Monitors real-time energy demand and grid conditions.
• Determines when to activate/disconnect energy resources dynamically.
2. Energy Management System (EMS)
• Manages energy flow within the hybrid microgrid.
• Optimizes power exchange between EV chargers, battery storage, smart loads, and the main grid.
• Implements demand response strategies for optimal grid interaction.
3. Electric Vehicle (EV) Charger
• Enables bidirectional power transfer (Vehicle-to-Grid, V2G).
• Supplies power to the grid when demand is high.
• Charges EVs during off-peak hours or surplus energy availability.
4. Smart Loads
• Represents residential and industrial loads that adjust based on energy availability.
• Interacts with the demand response system to optimize power consumption.
5. Battery Storage
• Stores excess energy from renewable sources or grid supply.
• Supports microgrid-to-grid and grid-to-microgrid operations.
• Helps in stabilizing fluctuations in energy demand and supply.
6. Grid Interaction
• Allows seamless power exchange between the main grid and the hybrid microgrid.
• Supports microgrid-to-grid (exporting surplus energy) and grid-to-microgrid (importing energy when needed).
• Enhances grid resilience and flexibility by balancing supply-demand fluctuations.

This architecture ensures efficient, dynamic, and demand-driven operation of the VPP, optimizing energy distribution and minimizing power losses.

2. Operational Workflow:
Step-1: Start
• System initializes and monitors energy demand.
Step-2: Energy Demand Assessment
• AI-driven analytics predict real-time power requirements.
• Evaluate grid and microgrid conditions.
Step-3: Decision Point: Peak Demand Detected?
• Yes → Activate VPP resources.
• No → Maintain standby mode.
Step-4: Resource Activation (If Peak Demand Exists)
• Enable Distributed Energy Resources (DERs) (solar, wind, batteries).
• Initiate bidirectional Vehicle-to-Grid (V2G) operations.
• Optimize dispatch using AI-based algorithms.
Step-5: Grid Interaction & Load Balancing
• Dispatch energy based on real-time demand.
• Ensure optimal utilization of renewable sources.
• Adjust load dynamically to prevent grid overloading.
Step-6: Demand Response Optimization
• AI system continuously monitors consumption.
• Adjust output based on demand fluctuations.
Step-7: Decision Point: Demand Normalized?
• Yes → Deactivate excess resources and return to standby mode.
• No → Continue monitoring and adjusting power dispatch.
Step-8: System Health Check & Optimization
• Monitor battery and grid stability.
• Predict and prevent potential system failures.
Step-9: Shutdown or Standby Mode
• Deactivate resources when demand normalizes.
• Maintain essential grid operations.
Step-10: End Process
NOVELTY:
• Demand-Driven VPP: In comparison to operating conventional microgrids around the clock, the suggested VPP only calls for resources whenever necessary, thus minimizing the energy wastage.
• AI-Optimized Power Management: Utilizes real-time AI-based analytics to predict variability of demand to facilitate real-time activation of Distributed Energy Resources (DERs), Electric Vehicles (EVs), and grid integration.
• Installation of Bidirectional Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) systems enables the convenient utilization of electric vehicles (EVs) as mobile energy storage devices, enhancing grid stability and reducing dependency on stationary battery banks.
Increased Grid Flexibility with Dynamic Load Balancing: Compared to rigid fixed-schedule energy dispatch systems, the VPP optimizes grid loads balancing dynamically by modulating DER output.
• Battery Life Optimization: Minimizes overcharge-discharge cycles, keeping battery life optimized with maximum power output.
• Scalable and Modular Design: Designed to be appropriate for both grid-scale and microgrid deployments, thereby making it flexible to various energy systems.
• Smooth Integration of Renewable Energy: Optimizes the use of renewable energy through predictive dispatch, minimizing the intermittency issue of solar and wind energy.
• Real-Time Adaptive Demand Response: Utilizing a self-adapting artificial intelligence model that optimizes energy allocation in real-time, it facilitates efficient and cost-effective power management.
 
, Claims:1. A virtual power plant (VPP) system for managing energy within a hybrid microgrid, comprising: AI-based decision mechanism, solar power, wind power, batteries, and bidirectional electric vehicles.
2. The system as claimed as claim 1, wherein the system is designed to dynamically manage energy resources within a hybrid microgrid using AI-driven optimization.
3. The system as claimed as claim 1, wherein the system ensuring efficient bidirectional power flow between distributed energy resources (DERs), battery storage, electric vehicles (EVs), smart loads, and the main grid.
4. The system as claimed as claim 1, wherein the system is able to successfully integrate Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) functions, thereby preventing redundant battery cycling and system overall robustness.
5. The system as claimed as claim 1, wherein the system preventing grid congestion, optimizes battery life, and delivers sustainable, cost-efficient, and scale-up energy for use within the microgrid environment and the large-scale environment too.
6. The system as claimed as claim 1, wherein the system utilizing real-time AI-based analytics to predict variability of demand to facilitate real-time activation of Distributed Energy Resources (DERs), Electric Vehicles (EVs), and grid integration.