System and method for optimizing energy distribution in renewable energy plants

Abstract

The present invention discloses a system and method for optimizing energy distribution in renewable energy plants. The system (100) comprises a solar power plant or a wind power plant to generate electrical energy. A renewable energy controller (108) monitors real-time energy output and manages energy flow to a grid (112). The renewable energy controller (108) optimizes charging cycles based on forecasts. The renewable energy controller (108) generates dispatch schedules through an intelligent bidding unit (114), considering market prices and grid conditions, while ensuring grid code compliance. A battery digital twin unit (122) is operatively connected to the renewable energy controller (108), to simulate future states of the plant based on historical data, real-time operational data, and weather forecasts, providing inputs for the intelligent bidding unit (114) to optimize energy dispatch and storage.

Description

System and method for optimizing energy distribution in renewable energy plants

[0001] The field of invention generally relates to renewable energy systems. More specifically, it relates to a system and method for optimizing energy distribution in renewable energy plants.

[0002] Renewable energy plants are increasingly integrated into a power grid to reduce carbon emissions and promote sustainable energy sources.

[0003] Power Plant Controller (PPC) plays a critical role in this context by controlling specific generation assets, such as solar and wind, to meet grid codes and optimize energy dispatch.

[0004] Renewable energy sources, such as wind and solar, pose challenges for maintaining grid stability and optimizing energy dispatch.

[0005] Currently, existing systems do not succeed in providing effective integration of real-time monitoring, energy management, grid compliance, and market participation for the renewable energy plants. The existing systems often fail to optimize energy storage and dispatch, resulting in inefficiencies and potential grid instability.

[0006] Other existing systems have tried to address this problem. However, their scope was limited to either managing individual renewable energy sources, such as wind or solar, or providing basic energy storage solutions.

[0007] Additionally, the PPCs lack direct interaction with market platforms or participate in trading platforms; instead, they receive commands and setpoints from higher-level systems that coordinate market participation and long-term energy dispatch, further limiting the overall efficiency and profitability of energy dispatch in hybrid renewable energy plants.

[0008] Thus, in light of the above discussion, it is implied that there is a need for a system and method for optimizing energy distribution in renewable energy plants, which is reliable, efficient, stable and does not suffer from the problems discussed above.Object of Invention

[0009] The principal object of this invention is to provide a system and method to optimize energy distribution in renewable energy plants.

[0010] A further object of the invention is to provide the system and method for integrating multiple renewable energy sources, comprising wind and solar, into a unified control system.

[0011] Another object of the invention is to optimize charging and discharging cycles of battery energy storage units based on real-time data and forecasted data.

[0012] Another object of the invention is to ensure compliance with grid codes by dynamically regulating active and reactive power, voltage, frequency, and power factor.

[0013] Another object of the invention is to enhance grid stability by providing ancillary services such as frequency regulation and spinning reserves.

[0014] Another object of the invention is to maximize profitability of the renewable energy plants through intelligent bidding and market participation.

[0015] Another object of the invention is to simulate future plant states using digital twin for accurate forecasting and operational optimization.

[0016] Another object of the invention is to manage state of charge (SOC) and state of health (SOH) of batteries within the battery energy storage units to prolong their lifespan and maintain efficiency.

[0017] Another object of the invention is to provide real-time monitoring and control of energy generation and energy storage units.

[0018] Another object of the invention is to dynamically adjust energy dispatch schedules in response to changing grid conditions and market opportunities.

[0019] Another object of the invention is to prevent energy curtailment by efficiently managing excess generation through the battery energy storage unit’s integration.

[0020] Another object of the invention is to support black start capabilities, allowing the renewable energy plant to restart independently in event of the grid failure.

[0021] This invention is illustrated in the accompanying drawings, throughout which, like reference letters indicate corresponding parts in the various figures.

[0022] The embodiments herein will be better understood from the following description with reference to the drawings, in which:Fig. 1

[0023] depicts a system for optimizing energy distribution in renewable energy plant, in accordance with an embodiment of the present disclosure;Fig. 2

[0024] illustrates a detailed system diagram for optimizing energy distribution in renewable energy plants, in accordance with an embodiment of the present disclosure;Fig. 3

[0025] depicts sub-components of renewable energy controller, in accordance with an embodiment of the present disclosure; and;Fig. 4

[0026] illustrates a method for optimizing energy distribution in renewable energy plants, in accordance with an embodiment of the present disclosure.Statement of Invention

[0027] The present invention discloses a system and method for optimizing energy distribution in renewable energy plants

[0028] The system comprises a solar power plant controller operatively connected to at least one solar power plant. The solar power plant controller to monitor and communicate power output of the solar power plant to a renewable energy controller of the solar power plant.

[0029] Further, the system comprises a wind power plant controller operatively connected to at least one wind power plant. The wind power plant to monitor and communicate power output of the wind power plant to the renewable energy controller.

[0030] Subsequently, the system comprises a battery energy storage unit operatively connected to the solar power plant and the wind power plant. The battery energy storage unit to store excess renewable energy of the solar power plant and the wind power plant, supply stored energy during demand peaks, and provide ancillary services.

[0031] Furthermore, the system comprises a battery energy storage unit controller operatively connected to the battery energy storage unit. The battery energy storage unit controller to optimize charging and discharging cycles of the battery energy storage unit.

[0032] Subsequently, the system comprises the renewable energy controller operatively connected to at least one of the solar power plant controller, the wind power plant controller, and the battery energy storage unit controller. The renewable energy controller coordinates energy dispatch, ensure grid compliance, and optimize operations using at least one of forecast data, market prices, and grid condition, to enable optimized energy distribution in the renewable energy plants.

[0033] Thereafter, the system comprises an intelligent bidding unit communicatively connected to the renewable energy controller. The intelligent bidding unit to develop and execute bidding strategies in energy markets based on the forecast data, the market prices, and the grid condition, and generate a dispatch schedule.Detailed Description

[0034] 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 / or 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.

[0035] The present invention discloses a system and method for optimizing energy distribution in renewable energy plants, which comprises wind, solar, and battery energy storage units. The present renewable energy controller ensures compliance with grid codes, manages energy dispatch, and enhances grid stability through real-time monitoring, intelligent energy management, and advanced control features like power oscillation dampers and artificial inertia. Additionally, it integrates a digital twin for accurate forecasting and supports intelligent bidding for market participation, maximizing profitability. The system is adaptable for both grid-connected and off-grid applications, offering scalability, safety, and efficiency across various renewable energy scenarios.

[0036] depicts a system for optimizing energy distribution in renewable energy plants.

[0037] The system 100 comprises at least one solar power plant (not shown), at least one wind power plant (not shown), at least one solar power plant controller 102, at least one wind power plant controller 104, at least one battery energy storage unit controller 106, at least one renewable energy (RE) controller 108, at least one power quality meter 110, at least one grid 112 and at least one intelligent bidding unit 114.

[0038] In an embodiment, the solar power plant comprises multiple PV modules.

[0039] In an embodiment, the solar power plant is configured for converting solar energy into electrical power.

[0040] In an embodiment, the energy generated from the solar power plant is transmitted to the solar power plant controller 102.

[0041] In an embodiment, the wind power plant comprises multiple wind turbines.

[0042] In an embodiment, the wind power plant is configured for harnessing wind energy to generate electrical energy / power.

[0043] In an embodiment, the energy generated from the wind power plant is transmitted to the wind power plant controller 104.

[0044] In an embodiment, the solar power plant controller 102 may be operatively connected to the solar power plant. The solar power plant controller 102 may comprise at least one solar plant sensor and a solar control unit.

[0045] The at least one solar plant sensor is operatively connected to the solar power plant. The at least one solar plant sensor to monitor and communicate the solar data of the solar PV modules to a solar control unit. The at least one solar sensor comprises at least of a voltage sensor and a current sensor. The solar sensor data comprises at least one of electrical output and performance, active and reactive power output, and solar irradiance.

[0046] The solar sensor data further comprises plane of array (POA) irradiance, global horizontal irradiance (GHI), and module temperature. Inverter-level data comprise active power, reactive power, power factor, red-yellow (RY) line voltage, yellow-blue (YB) line voltage, blue-red (BR) line voltage, red (R) phase current, yellow (Y) phase current, blue (B) phase current, overall voltage, frequency, active power setpoint feedback, reactive power setpoint feedback, inverter work status, communication status, and running status. Power quality monitoring (PQM) data further comprises apparent power, active power, reactive power, power factor, overall voltage, red–yellow (RY) line voltage, yellow-blue (YB) line voltage, blue-red (BR) line voltage, frequency, red (R) phase current, yellow (Y) phase current, blue (B) phase current, active power export, active power import, reactive power export, reactive power import, and power quality monitoring communication status.

[0047] The solar control unit is operatively connected to the solar power plant.

[0048] The solar control unit processes the solar data using at least one control algorithm to optimize and communicate power output of the solar power plant to the renewable energy controller 108.

[0049] The solar power plant controller 102 to monitor and communicate power output of the solar power plant to a renewable energy controller 108 of the solar power plant.

[0050] In an embodiment, the solar power plant controller 102 receives direct current (DC) power from the solar panels of the solar power plant, optimizes the power, and converts it to alternate current (AC) power compatible with the grid 112 or for storage in the battery energy storage unit controller 106. Further, the solar power plant controller 102 may also send data to the renewable energy controller 108.

[0051] In an embodiment, the wind power plant controller 104 may be operatively connected to the wind power plant. The wind power plant controller 104 comprises at least one wind sensor and a wind control unit. The wind power plant controller 104 to monitor and communicate power output of the wind power plant to the renewable energy controller 108.

[0052] The at least one wind plant sensor is operatively connected to the wind power plant. The at least one wind plant sensor to monitor and communicate the wind data to the wind control unit. The at least one wind plant sensor comprises at least one of: an anemometer, a wind vane, a rotor speed sensor, a current sensor, a voltage sensor, and a pitch angle sensor. The wind plant sensor data comprises at least one wind speed, wind direction, rotational speed of turbine rotor, electrical output and performance, and blade pitch.

[0053] The wind plant sensor data may further comprise active power of the generator, reactive power of the generator, power factor of the grid, grid frequency, and red-yellow (RY), yellow-blue (YB), and blue-red (BR) line voltages, cumulative active power generation, available active power capability, number of turbines in production, cumulative reactive power generation, maximum available capacitive volt-ampere reactive (Vars), maximum available inductive volt-ampere reactive (Vars), power factor, average wind speed, active power feed to a wind server, reactive power feed to the wind server, active power control enable signal in slave power plant controller, reactive power control enable signal in slave power plant controller, enable feedback from slave wind power plant controller, active power setpoint feedback, reactive power setpoint feedback, and communication status of the slave wind power plant controller, apparent power, active power, reactive power, power factor, voltage, red (R) phase voltage, yellow (Y) phase voltage, blue (B) phase voltage, frequency, red (R) phase current, yellow (Y) phase current, blue (B) phase current, active power export, active power import, reactive power export, reactive power import, and power quality monitoring communication status. The wind control unit is operatively connected to the wind power plant. The wind control unit processes the wind data using the control algorithm to optimize and communicate power output of the wind power plant to the renewable energy controller 108. The control algorithm comprises at least one of a maximum power point tracking (MPPT) algorithm comprising perturb and observe, incremental conductance, fuzzy-logic MPPT, neural-network MPPT, and extremum-seeking control; a model predictive control (MPC) algorithm; droop-based grid-support algorithms including P–f control, Q–V control, Volt–VAR control, and Volt–Watt control; ramp-rate limiting and power-smoothing algorithms; forecast-integrated optimization strategies; hybrid rule-based and AI-enhanced curtailment and reserve controllers; and safety and protection logics.

[0054] In an embodiment, the wind power plant controller 104 is configured for optimizing operation of wind turbines, managing power generation based on wind conditions, and maintaining grid stability.

[0055] In an embodiment, the wind power plant controller 104 collects the AC power from the wind turbines, optimizes the power, and ensures it’s in the right form for the grid connection.

[0056] In an embodiment, a battery energy storage unit (not shown) is operatively connected to the solar power plant and the wind power plant. The battery energy storage unit to store excess renewable energy of the solar power plant and the wind power plant, supply stored energy during demand peaks, and provide ancillary services.

[0057] In an embodiment, the battery energy storage unit controller 106 may be operatively connected to the battery energy storage unit. The battery energy storage unit may also be referred to as a Battery Energy Storage System (BESS).

[0058] In an embodiment, the battery energy storage unit controller 106 may be a dedicated controller that manages operation of the battery energy storage unit.

[0059] In an embodiment, the battery energy storage unit controller 106 may provide at least one battery energy signal to the renewable energy controller (108) comprising at least one of converter operation mode, active power, reactive power, grid voltage, grid frequency, input voltage comprising battery voltage, active power limit to grid, active power limit from grid, positive reactive power limit, negative reactive power limit, code of the active fault, root mean square (RMS) phase voltage of phase 1, RMS phase voltage of phase 2, RMS phase voltage of phase 3, RMS phase current of phase 1, RMS phase current of phase 2, RMS phase current of phase 3, direct current (DC) input power, grounding current, isolation resistance, ambient temperature, highest insulated-gate bipolar transistor (IGBT) temperature, control section temperature, highest cabinet temperature, inductor capacitor inductor (LCL) section temperature, measured maximum inverter section humidity, kilowatt hour (kWh) energy charged to battery, megawatt hour (MWh) energy charged to battery, gigawatt hour (GWh) energy charged to battery, kilowatt hour (kWh) energy discharged from battery, megawatt hour (MWh) energy discharged from battery, gigawatt hour (GWh) energy discharged from battery, direct current (DC) input current, electromechanical switching device status word, inverter main status word, limitation status word, grid status word, fan status word, environmental status word, fault status word, warning status word, direct current (DC) fuse status word, inverter inhibits, total active import, daily active import, total active export, daily active export, communication status, control word, active power setpoint, reactive power setpoint, direct current (DC) contactor of battery energy storage system enable or disable, communication signal comprising heartbeat, system operating status, system voltage, system current, system state of charge, system state of health, system state of power or state of energy, system insulation resistance, system available energy for charging, system available energy for discharging, system available power for charging, system available power for discharging, system maximum charging current, system maximum discharging current, system charging power limit, system discharging power limit, container level alarm 1, container level alarm 2, rack operating status, rack contactor state, rack state of charge, rack state of health, total number of racks connected, total number of racks online, rack insulation value, rack maximum charge current, rack maximum discharge current, rack available power for charging, rack available power for discharging, rack fault, rack alarm 1, rack alarm 2, and rack alarm 3.

[0060] In an embodiment, the battery energy storage unit controller 106 is configured for optimizing the charging and discharging cycles of the battery energy storage unit, ensuring efficient energy storage, and extending battery life.

[0061] The battery energy storage unit controller 106 enforces various safety protocols to protect the battery storage unit from potential hazards such as overcharging, deep discharging, thermal runaway, short circuits, and overvoltage / undervoltage conditions.

[0062] In an embodiment, the battery energy storage unit controller 106 coordinates with the renewable energy controller 108 and an inverter (not shown) to manage energy storage based on real-time grid demands and market opportunities.

[0063] In an embodiment, the battery energy storage unit controller 106 provides feedback to the renewable energy controller 108 and the inverter on battery status and adjusts operations accordingly.

[0064] The battery energy storage unit controller 106 stabilizes the grid 112 by quickly responding to fluctuations in demand or supply, maintaining the grid balance through frequency regulation and peak shaving. The battery energy storage unit controller 106 also smooths out variability in the energy generated, known as renewable firming, by storing excess energy and releasing it during low generation periods. Additionally, it supports energy arbitrage, storing energy when prices are low and selling it when prices are high, maximizing economic returns.

[0065] The battery energy storage unit controller 106 provides feedback to the renewable energy controller 108 on battery status and adjusts operations based on the feedback.

[0066] The system 100 is also configured for backup power during outages and can assist in grid restarts (black start).

[0067] In an embodiment, the renewable energy controller 108 may be a centralized control system for the renewable power plant. The renewable power plant may be at least one of the solar power plant, the wind power plant, a hydroelectric power plant, and other renewable power plant. The renewable energy controller 108 is further configured to perform functions such as real-time monitoring, energy dispatch, and ensuring grid compliance. The grid code compliance is ensured by controlling active and reactive power, stabilizing the grid 112, and adhering to voltage, frequency, ramp rates, and power factor control within regulated limits.

[0068] The renewable energy controller 108 implements grid code compliance by receiving and processing grid data relating to active power, reactive power, frequency, voltage, ramp rates, droop functions, and power factor. The renewable energy controller 108 compares the received data and predefined thresholds to generate control signals. Further, the renewable energy controller 108 determines and communicates the control signals to the solar power plant controller 102, wind power plant controller 104, and battery energy storage unit controller 106 to regulate the active power, reactive power, frequency, voltage, ramp rates, droop functions, power factor to maintain grid stability and compliance.

[0069] The renewable energy controller 108 operatively connected to at least one of the solar power plant controller 102, the wind power plant controller 104, and the battery energy storage unit controller 106.

[0070] The renewable energy controller 108 to coordinate energy dispatch, ensure grid compliance, and optimize operations using at least one of forecast data, market prices, and grid condition, to enable optimized energy distribution in the renewable energy plants.

[0071] In one embodiment, the renewable energy controller 108 incorporates advanced features like artificial inertia and power oscillation dampers to boost the system’s resilience.

[0072] In an embodiment, the artificial inertia mimics inertial response of traditional generators, stabilizing the grid 112 by responding to sudden frequency changes, which helps in maintaining the grid stability during abrupt fluctuations in power demand or supply. On the other hand, the power oscillation dampers are designed to reduce oscillations in power output, which can occur due to disturbances in the grid 112. By actively damping these oscillations, the system ensures a more stable and smoother integration of renewable energy into the grid 112, reducing the risk of power instability or grid imbalances.

[0073] In an embodiment, the renewable energy controller 108 is designed to manage both on-grid which is connected to public electricity grid and off-grid which is independent of the grid 112 of the renewable energy power plants. The renewable energy controller 108 may also operate in at least one mode that integrates with energy storage units.

[0074] These at least one mode comprises services like energy arbitrage, energy shifting, providing assured peak power, scheduled dispatch, renewable firming.

[0075] The energy arbitrage may be referred as buying and storing energy when prices are low and selling it when prices are high.

[0076] The energy shifting may be referred as storing energy during low demand and releasing it during high demand.

[0077] The scheduled dispatch may be referred as planning energy output to meet demand.

[0078] The renewable firming may be referred as stabilizing variable renewable energy output, and round-the-clock services to ensure continuous energy supply.

[0079] The renewable energy controller 108 supports the ancillary services by monitoring the grid condition in real-time, coordinating charging and discharging of the battery energy storage unit, adjusting active and reactive power outputs of the renewable power plant, and executing control commands to maintain grid stability during disturbances or fluctuations.

[0080] The ancillary services comprise spinning reserves, energy imbalance support, power curve smoothing, black start, fast frequency response, and islanding / anti-islanding.

[0081] The spinning reserves may be referred as backup power that can be quickly brought online.

[0082] The power curve smoothing may be referred as reducing fluctuations in energy output.

[0083] The black start capabilities may be referred as starting up the plant without external power.

[0084] The fast frequency response may be referred as quickly stabilizing grid frequency.

[0085] The islanding / anti-islanding may be referred as isolating the plant from the grid during faults.

[0086] Additionally, the RE controller 108 optimizes the charging and discharging of battery energy storage units 106, ensuring efficient resource use and longevity.

[0087] The RE controller 108 manages battery state of charge (SOC) and state of health (SOH) in real-time, preventing performance issues and ensuring safety through protocols like thermal runaway prevention.

[0088] The RE controller's 108 integration with the solar power plant controller 102, the wind power plant controller 104 and the battery energy storage unit controller 106, along with its intelligent bidding (IB) unit 114 capabilities using forecast data, market prices, and grid conditions enables it to generate optimal dispatch schedules. This coordinated control enhances the renewable plant's efficiency, the grid reliability, and economic returns by dynamically adjusting operations to market conditions and the grid demands.

[0089] In an embodiment, the renewable energy controller 108 is configured for coordinating operations of the solar power plant, the wind power plant, and battery energy storage unit (not shown), ensuring optimal energy generation, grid compliance, and market participation. Further, the renewable energy controller 108 executes the dispatch schedule that interfaces with the grid 112.

[0090] In an embodiment, the renewable energy controller 108 monitors real-time output of the energy generated by the renewable power plants. Further, the renewable energy controller 108 receives data and operational statuses from the solar power plant controller 102 and the wind power plant controller 104 as well as the battery energy storage unit controller 106.

[0091] Further, flow of the energy generated to the grid 112 is controlled and the battery energy storage unit controller 106 using the renewable energy controller 108.

[0092] In an embodiment, the power quality meter 110 may be a monitoring device that measures the quality of power being generated and supplied to the grid 112.

[0093] In an embodiment, the power quality meter 110 is placed at point of interconnection. The point of connection (POI) refers to a specific location where the solar power plant or installation connects to the grid 112.

[0094] The power quality meter 110 is disposed at a point of interconnection POI with a grid 112, to monitor quality of power being generated and supplied to the grid 112 and ensure compliance of delivered power with grid standards.

[0095] The POI is typically located at a substation, a transmission line, or the distribution network, depending on the size and scale of the solar power plant installation.

[0096] For large utility-scale solar power plant farms, the POI is usually at a high-voltage transmission line or substation. For smaller, distributed solar power plants (e.g., residential or commercial installations), the POI is often at the low-voltage distribution level.

[0097] The POI must comply with technical and regulatory requirements of the grid operator or utility company. This comprises meeting standards for voltage, frequency, power quality, and safety.

[0098] Grid operators may require specific equipment, such as transformers, protection devices, and metering, to be installed at the POI to ensure compliance and safe operation.

[0099] An interconnection agreement is typically required between solar project developer / owner and the grid operator or utility. This agreement outlines the technical specifications, responsibilities, costs, and operational procedures related to the POI.

[0100] The agreement ensures that both parties understand terms under which the solar power plant will connect to the grid 112, including any potential impacts on the grid's stability and reliability.

[0101] The POI is where power flow between the solar power plant and the grid is monitored and measured. This comprises tracking the amount of electricity exported to the grid 112 and, in some cases, the electricity imported from the grid when the solar power plants output is insufficient to meet the load.

[0102] Accurate metering at the POI is essential for billing, performance monitoring, and regulatory reporting. In an embodiment, the power quality meter 110 may be configured for ensuring that the generated power meets regulatory standards for grid compliance.

[0103] In an embodiment, the grid 112 is operatively connected to the renewable energy controller 108 via the power quality meter 110, to receive and distribute electrical power generated by the solar power plant and the wind power plant.

[0104] The grid 112 may be an electrical power distribution network. The grid 112 comprises substations, transformers, transmission lines, and grid interface equipment.

[0105] In an embodiment, the grid 112 may be operatively connected to the RE controller 108 via the power quality meter 110.

[0106] In an embodiment, the grid 112 is configured for receiving and distributing electrical power generated by the solar power plant and the wind power plant, ensuring stable energy supply to consumers while maintaining grid stability and compliance.

[0107] In an embodiment, the grid 112 receives electricity from the renewable energy controller 108, which has optimized and adjusted the power output to meet the grid standards. The grid 112 also sends signals back to the renewable energy controller 108 regarding demand and supply conditions, influencing how the controller manages the energy resources.

[0108] In an embodiment, the intelligent bidding unit 114 is communicatively connected to the renewable energy controller 108. The intelligent bidding unit 114 to develop and execute bidding strategies in energy markets based on the forecast data, the market prices, and the grid condition, and generate a dispatch schedule.

[0109] The intelligent bidding unit 114 may comprise predictive algorithms, market data analysis tools, and decision-making software. In an embodiment, the intelligent bidding unit 114 may be present inside the centralized server. The intelligent bidding can be implemented using at least one of a machine learning or artificial intelligence algorithms.

[0110] In an embodiment, the intelligent bidding unit 114 may be configured for maximizing revenue by strategically bidding in energy markets based on forecasted data.

[0111] In an embodiment, the intelligent bidding unit 114 is communicatively connected with the renewable energy controller 108 to develop and execute bidding strategies. In an embodiment, the intelligent bidding unit 114 may take input simulations and real-time grid conditions to maximize revenue and ensure the grid stability.

[0112] In an embodiment, the intelligent bidding unit 114 generates a dispatch schedule by considering forecasted energy production, market prices, and grid condition.

[0113] illustrates a detailed system diagram for optimizing energy distribution in renewable energy plants.

[0114] The system comprises the solar power plant controller 102, the wind power plant controller 104, a battery energy storage unit controller 106, the renewable energy controller 108, power quality meter 110, intelligent bidding unit 114, battery unit 116, a SCADA (Supervisory Control and Data Acquisition) unit 118, a solar digital twin unit 120, a battery digital twin unit 122, a 3rd party Application Programming Interface (API) 124.

[0115] In an embodiment, the renewable energy controller 108 is operatively connected to the solar power plant controller 102, the wind power plant controller 104 and battery energy storage unit controller 106 to harmonize their operations to manage the flow of the power generated.

[0116] In an embodiment, the battery energy storage unit controller 106 may comprise monitoring state of charge (SOC), state of health (SOH), and thermal conditions of battery unit 116.

[0117] In an embodiment, the state of charge (SOC) refers to current charge level of batteries in the battery unit 116, expressed as a percentage of its total capacity.

[0118] The battery energy storage unit controller 106 continuously monitors the SOC to ensure the batteries are neither overcharged nor excessively discharged. Accurate SOC monitoring helps optimize energy storage and usage, allowing the system to efficiently manage energy flow based on demand and availability.

[0119] In an embodiment, the battery energy storage unit controller 106 works with both the solar power plant controller 102 and the wind power plant controller 104 to store excess energy and discharge energy when needed. The battery energy storage unit controller 106 receives instructions from the renewable energy controller 108 on when to charge or discharge based on grid demand and energy availability.

[0120] In an embodiment, the state of health (SOH) is an indicator of the overall condition of the batteries, reflecting their ability to store and deliver energy compared to when it was new.

[0121] The battery energy storage unit controller 106 continuously tracks the SOH by analyzing factors such as capacity fade, internal resistance, and self-discharge rates. This tracking helps in predicting the remaining useful life of the battery and planning maintenance or replacement schedules.

[0122] In an embodiment, the battery unit 116 may comprise multiple battery cells or batteries.

[0123] The battery unit 116 is communicatively connected to the battery energy storage unit controller 106 to manage charging and discharging cycles based on the grid 112 needs and the energy availability. The battery unit 116 stores energy when generation exceeds demand and discharges the energy during peak demand periods or grid instability.

[0124] In an embodiment, the battery unit 116 works in conjunction with the battery energy storage unit controller 106 to manage charging and discharging cycles based on the grid needs and the energy availability. In an embodiment, the battery unit 116 stores energy when generation exceeds demand and discharges it during peak demand periods or grid instability.

[0125] In an embodiment, the SCADA unit 118 may be a Supervisory Control and Data Acquisition system that monitors and controls the entire renewable energy plant.

[0126] In an embodiment, the SCADA unit 118 communicatively connected with the renewable energy controller 108, the solar power plant controller 102 and the wind power plant controller 104, the battery energy storage unit controller 106, the battery unit 116, and the power quality meter 110, to centralize control and data monitoring.

[0127] In an embodiment, the solar digital twin unit 120 acts as a virtual model that mirrors real-time operations and characteristics of the solar power plant.

[0128] The solar digital twin unit 120 is communicatively connected to the solar power plant to simulate operations of the solar power plant under various conditions, predict solar energy production, and optimize energy dispatch based on the predictions. The solar digital twin unit 120 integrates real-time operational data from the solar power plant with external inputs like weather forecasts and historical performance data.

[0129] The solar digital twin unit 120 predicts solar energy production and optimizes energy dispatch based on these predictions.

[0130] The solar digital twin unit 120 simulates future states of the solar plant, providing predictive analytics that inform decisions on energy dispatch, storage management, and grid interaction.

[0131] The solar digital twin unit 120 ensures that the solar power plant operates efficiently, predicting energy output under different weather conditions and optimizing energy dispatch accordingly.

[0132] The solar digital twin unit 120 predicts future energy production, the solar digital twin helps the renewable energy controller 108 to optimize the scheduling of energy dispatch to the grid 112.

[0133] The solar digital twin unit 120 maximizes energy utilization and minimizes wastage, ensuring that solar energy is efficiently used or stored. The solar digital twin unit 120 provides insights that help in planning maintenance activities for the solar plant, ensuring that any potential issues are addressed before they impact performance. The solar digital twin unit 120 reduces downtime and maintains high efficiency in solar energy production. The solar digital twin unit 120 integrates with the renewable energy controller 108, intelligent bidding unit 114, and SCADA unit 118 to provide comprehensive predictive analytics and simulation data. The solar digital twin unit 120 to enhance overall decision-making in energy management, ensuring optimized operations and profitability across the renewable energy plant.

[0134] In an embodiment, the battery digital twin unit 122 acts as a virtual model that mirrors real-time operations and characteristics of the battery energy storage unit controller 106.

[0135] The battery digital twin unit 122 combines real-time operational data derived from the battery energy storage unit controller 106 with external inputs to predict how the battery unit 116 will perform under varying conditions, such as different weather patterns that influence energy generation from the renewable power plant. The external inputs comprise weather forecasts and historical performance data. The real-time operational data comprises current state of charge, and temperature.

[0136] In an embodiment, the battery digital twin unit 122 may be communicatively connected to the battery energy storage unit controller 106 to simulate real-time and future performance of the battery energy storage unit and optimize charging and discharging schedules.

[0137] In an embodiment, the battery digital twin unit 122 is configured to provide a detailed and interactive simulation that enables advanced monitoring, control, and optimization of the battery's performance.

[0138] Further, the battery digital twin unit 122 combines real-time operational data with external inputs such as weather forecasts and historical performance data. In an embodiment, the real-time operational data may be current state of charge, and temperature.

[0139] The battery digital twin unit 122 is configured to predict how the battery will perform under varying conditions, such as different weather patterns that influence energy generation from renewable sources like solar or wind.

[0140] The battery digital twin unit 122 offers predictive capabilities by simulating how the battery energy storage unit controller 106 will behave in future, providing insights into energy storage availability and how to optimize charging and discharging schedules.

[0141] The battery digital twin unit 122 also ensures that the battery unit 116 is efficiently charged or discharged based on forecasted renewable energy generation and the grid demand, thus maximizing its utility.

[0142] The battery digital twin unit 122 helps the renewable energy controller 108 to optimize the battery unit 116 usage.

[0143] In an exemplary embodiment, if a sunny day is forecasted, the battery digital twin unit 122 can plan to fully charge the battery unit 116 in advance. Conversely, during low renewable generation periods, it can optimize discharging to meet the grid demands without overstraining the battery.

[0144] In an embodiment, the battery digital twin unit 122 is configured to align the battery unit 116 usage with predicted energy generation and the grid requirements, ensuring efficient energy management and the battery longevity.

[0145] In an embodiment, the battery digital twin unit 122 models different market scenarios, including fluctuations in energy prices and changes in grid conditions, to estimate the most profitable energy storage or discharge strategies.

[0146] The battery digital twin unit 122 aids the renewable energy controller 108 and the intelligent bidding unit 114 make informed decisions that maximize economic returns while maintaining the grid stability.

[0147] The battery digital twin unit 122 simulates how the battery can contribute to ancillary services like frequency regulation or spinning reserve, which are essential for grid stability.

[0148] The battery digital twin unit 122 optimizes the battery's participation in these services, enhancing its role in supporting the grid 112.

[0149] The RE controller 108 uses insights provided by the solar digital twin unit and the battery digital twin unit 122 to make informed decisions in the energy market. This results in optimized dispatch schedules plans for when and how much energy should be generated or stored and bids for ancillary services, ensuring that the renewable energy plant operates profitably while maintaining grid stability and complying with regulatory standards.

[0150] In an embodiment, the 3rd party API 124 may be an interface that allows external systems to communicate with the renewable energy controller 108. In an embodiment, the 3rd party API 124 may be present inside a centralized server and communicatively connected to the RE controller 108. In an embodiment, the 3rd party API 124 may be configured for enabling integration with external software, such as energy management units.

[0151] In an embodiment, the 3rd party API 124 interfaces with the renewable energy controller 108, the SCADA unit 118, and the intelligent bidding unit 114 to share data with external entities. The 3rd party API 124 may facilitate market participation, regulatory compliance, and coordination with the grid 112 operators.

[0152] depicts sub-components of the renewable energy controller 108.

[0153] The renewable energy controller 108 comprises a memory unit 302, a communication unit 304, a real-time monitoring module 306, an energy management module 308, a grid compliance module 310, intelligent bidding module 312, a digital twin module 314, and an ancillary services module 316.

[0154] In an embodiment, the memory unit 302 comprises one or more volatile and non-volatile memory components which are capable of storing data and instructions to be executed.

[0155] In an embodiment, the communication unit 304 may comprise wired and wireless communication, comprising but not limited to, GPS, GSM, LAN, Wi-fi compatibility, Bluetooth low energy as well as NFC. The wireless communication may comprise one or more of Bluetooth (registered trademark), ZigBee (registered trademark), a short-range wireless communication such as UWB, a medium-range wireless communication such as WiFi (registered trademark) or a long-range wireless communication such as 3G / 4G or WiMAX (registered trademark), according to the usage environment.

[0156] In an embodiment, the real-time monitoring module 306 may continuously track the performance and output of the renewable energy plants, such as wind turbines and solar panels.

[0157] In an embodiment, the real-time monitoring module 306 to track performance and output of the renewable plant using the solar plant sensor data and the wind plant sensor data, and analyze the forecasted data in real-time to detect using at least one anomaly detection algorithm, forecast energy production using at least one forecasting algorithm, and provide inputs for other modules within the RE controller

[0158] The least one anomaly detection algorithm comprises threshold-based checks, rule-based checks, statistical change-point detectors comprising cumulative sum (CUSUM) and exponentially weighted moving average (EWMA), multivariate statistical analysis comprising principal component analysis (PCA)-based monitoring, model-residual analysis using digital twin models, and machine learning models comprising isolation forest, one-class support vector machines (SVM), local outlier factor, autoencoders, and long short-term memory (LSTM).

[0159] The at least one forecasting algorithm comprises persistence models, physical models, numerical weather prediction (NWP) with bias correction, statistical time-series methods comprising autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and Prophet, machine learning regressors comprising random forest, XGBoost, and LightGBM, deep learning sequence models comprising LSTM, sequence-to-sequence (Seq2Seq) networks, and temporal convolutional networks (TCN), hybrid physics, machine learning ensemble models and quantile regression or bootstrapped ensembles.

[0160] In an embodiment, the energy management module 308 may optimize the distribution of energy generated by the renewable energy plants, as well as managing energy storage systems. In an embodiment, the energy management module 308 may comprise at least one energy control algorithm, energy flow regulators, and interfaces for communication with both the grid 112 and the battery energy storage unit controller 106.

[0161] The at least one energy control algorithm comprising rule-based control, heuristic control, proportional integral (PI) control, proportional integral derivative (PID) control, model predictive control (MPC), stochastic dynamic programming, scenario-based MPC, mixed integer linear programming (MILP), reinforcement learning algorithms comprising Q-learning, deep Q-networks (DQN), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO), degradation-aware optimization, Kalman filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF).

[0162] In an embodiment, the energy management module 308 may be configured for controlling the charging and discharging cycles of the battery energy storage unit controller 106, ensuring efficient energy use, and minimizing energy losses.

[0163] In an embodiment, the grid compliance module 310 may comprise control systems for regulating voltage, frequency, and power factor to meet grid codes.

[0164] The grid compliance module 310 to ensure that the renewable energy plant operates within regulatory limits and supports the grid stability by measuring operational parameters of the renewable energy plant against grid code limits, comparing the measured parameters with predefined thresholds, and determining deviations or anomalies for corrective action.

[0165] The grid compliance module 310 comprises control systems for regulating voltage, frequency, and power factor to meet the grid code.

[0166] In an embodiment, the intelligent bidding module 312 may be a software component that strategically plans energy dispatch and market bids.

[0167] In an embodiment, the intelligent bidding module 312 may comprise at least one bidding algorithm for analyzing market conditions, forecasting energy prices, and optimizing dispatch schedules.

[0168] The at least one bidding algorithm comprises autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), prophet, gradient boosted trees, XGBoost, LightGBM, random forests, long short-term memory (LSTM) networks, Seq2Seq models, ensemble predictors, mixed integer linear programming (MILP), stochastic optimization, scenario-based model predictive control (MPC), genetic algorithms, particle swarm optimization, chance-constrained optimization, conditional value at risk (CVaR) minimization, Q-learning, proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), actor-critic agents, and Monte-Carlo simulation frameworks.

[0169] In an embodiment, the intelligent bidding module 312 may be configured for maximizing revenue through optimized participation in energy markets, considering both energy production forecasts and market opportunities. In an embodiment, the renewable energy controller 108 features intelligent bidding capabilities that optimize power generation by automatically engaging in energy markets. The intelligent bidding module 312 utilizes forecasted data from the digital twin module 314, market prices, grid conditions, and operational constraints to create dispatch schedule focused on maximizing profitability.

[0170] In an embodiment, the dispatch schedule is a plan that outlines how and when different power generation units within a power plant will produce and deliver electricity to the grid. It specifies the amount of power that each unit (e.g., wind turbines, solar panels, battery storage) generate or discharge at specific times, based on factors such as energy demand forecasts, market prices, grid conditions, and operational constraints.

[0171] When there is excess generation, the intelligent bidding module 312 can reduce power output to prevent penalties or grid instability. This advanced integration of artificial inertia, power oscillation dampers, and intelligent bidding ensures that the renewable plant not only adheres to grid codes but also operates with efficiency, reliability, and profitability in a dynamic energy environment.

[0172] In an embodiment, the digital twin module 314 may be a component that connects the digital twin unit 114 to real-time data and operational systems.

[0173] In an embodiment, the digital twin module 314 may comprise data interfaces, simulation models, and analytical tools.

[0174] In an embodiment, the digital twin module 314 may be configured for providing real-time insights and predictive analytics to enhance decision-making and optimize plant operations.

[0175] In an embodiment, the ancillary services module 316 may provide grid 112 support functions such as frequency regulation and spinning reserves.

[0176] In an embodiment, the ancillary services module 316 may be configured for delivering essential grid 112 services, ensuring grid 112 reliability, and optimizing the operation of the renewable energy plant in response to grid 112 demands.

[0177] illustrates a method for optimizing energy distribution in renewable energy plants.

[0178] The method begins with monitoring and communicating, by a solar power plant controller 102 operatively connected to at least one solar power plant, power output of the solar power plant to a renewable energy controller 108 of the solar power plant, as depicted at step 402. Subsequently, the method 400 discloses monitoring and communicating, by a wind power plant controller 104 operatively connected to at least one wind power plant, power output of the wind power plant to the renewable energy controller 108, as depicted at step 404. Additionally, the method 400 discloses storing, by a battery energy storage unit operatively connected to the solar power plant and the wind power plant, excess renewable energy of the solar power plant and the wind power plant, supplying stored energy during demand peaks, and providing ancillary services, as depicted at step 406. Subsequently, the method 400 discloses optimizing, by a battery energy storage unit controller 106 operatively connected to the battery energy storage unit, charging and discharging cycles of the battery energy storage unit, as depicted at step 408. Subsequently, the method 400 discloses coordinating, by the renewable energy controller 108 operatively connected to at least one of the solar power plant controller 102, the wind power plant controller 104, and the battery energy storage unit controller 106, energy dispatch, ensuring grid compliance, and optimizing operations using at least one of forecast data, market prices, and grid condition, enabling optimized energy distribution in the renewable energy plants, as depicted at step 410. Thereafter, the method 400 discloses developing and executing, by an intelligent bidding unit 114 communicatively connected to the renewable energy controller 108, bidding strategies in energy markets based on the forecast data, the market prices, and the grid condition, and for generating a dispatch schedule, as depicted at step 412.

[0179] The advantages of the current invention include enhanced operational efficiency of renewable energy plants through integrated real-time monitoring, energy management, and grid compliance.

[0180] An additional advantage is that the invention allows intelligent energy dispatch scheduling based on market conditions, maximizing profitability.

[0181] An additional advantage is that the system ensures compliance with grid codes by regulating active and reactive power, voltage, frequency, and power factor.

[0182] An additional advantage is that the battery energy storage unit is managed optimally to extend battery life and maintain grid stability.

[0183] An additional advantage is that the integration of a digital twin allows for accurate forecasting and simulation, leading to more informed decision-making.

[0184] An additional advantage is that the invention provides ancillary services such as frequency regulation, spinning reserves, and black start capabilities, enhancing grid reliability.

[0185] An additional advantage is that modular design allows for scalability and adaptability in various renewable energy plant configurations.

[0186] An additional advantage is that the invention supports both on-grid and off-grid applications, making it versatile for different operational environments.

[0187] An additional advantage is that the system's intelligent bidding unit optimizes participation in energy markets, ensuring maximum economic returns.

[0188] An additional advantage is that the system enhances safety by continuously monitoring State of Charge (SOC) and State of Health (SOH) of the batteries, preventing potential risks.

[0189] An additional advantage is that the system mitigates power oscillations and improves grid stability through advanced control features like power oscillation dampers and artificial inertia.

[0190] An additional advantage is that the system reduces the need for curtailment by efficiently managing excess energy, either storing it in the battery energy storage units or intelligently dispatching it.

[0191] An additional advantage is that the renewable energy controller 108's feedback loop ensures continuous optimization of plant operations based on real-time data.

[0192] An additional advantage is that the system is designed to meet both current and future regulatory standards, providing long-term operational compliance.

[0193] Applications of the current invention include hybrid renewable energy plants, grid-connected and off-grid renewable systems, battery energy storage management, energy market participation, ancillary grid services, peak shaving, renewable firming, and black start capabilities.

[0194] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described here.

Claims

1.A system (100) for optimizing energy distribution in renewable energy plants, comprising:a solar power plant controller (102) operatively connected to at least one solar power plant, to monitor and communicate power output of the solar power plant to a renewable energy controller (108) of the solar power plant;a wind power plant controller (104) operatively connected to at least one wind power plant, to monitor and communicate power output of the wind power plant to the renewable energy controller (108);a battery energy storage unit operatively connected to the solar power plant and the wind power plant, to store excess renewable energy of the solar power plant and the wind power plant, supply stored energy during demand peaks, and provide ancillary services;a battery energy storage unit controller (106) operatively connected to the battery energy storage unit, to optimize charging and discharging cycles of the battery energy storage unit;the renewable energy controller (108) operatively connected to at least one of the solar power plant controller (102), the wind power plant controller (104), and the battery energy storage unit controller (106), to coordinate energy dispatch, ensure grid compliance, and optimize operations using at least one of forecast data, market prices, and grid condition, to enable optimized energy distribution in the renewable energy plants; andan intelligent bidding unit (114) communicatively connected to the renewable energy controller (108), to develop and execute bidding strategies in energy markets based on the forecast data, the market prices, and the grid condition, and generate a dispatch schedule.2.The system (100) as claimed in claim 1, comprising:a power quality meter (110) disposed at a point of interconnection (POI) with a grid (112), to monitor quality of power being generated and supplied to the grid (112) and ensure compliance of delivered power with grid standards;the grid (112) operatively connected to the renewable energy controller (108) via the power quality meter (110), to receive and distribute electrical power generated by the solar power plant and the wind power plant; anda battery unit (116) communicatively connected to the battery energy storage unit controller (106) to manage charging and discharging cycles based on the grid (112) needs and the energy availability, wherein the battery unit (116) stores energy when generation exceeds demand and discharges the energy during peak demand periods or grid instability.3.The system (100) as claimed in claim 1, wherein the solar power plant controller (102) comprises:at least one solar plant sensor operatively connected to the solar power plant, to monitor and communicate the solar data of the solar PV modules to a solar control unit, wherein the at least one solar sensor comprises at least of a voltage sensor and a current sensor, and wherein the solar sensor data comprises at least one of: electrical output and performance, active and reactive power output, solar irradiance, plane of array (POA) irradiance, global horizontal irradiance (GHI), and module temperature. Inverter-level data comprise active power, reactive power, power factor, red-yellow (RY) line voltage, yellow-blue (YB) line voltage, blue-red (BR) line voltage, red (R) phase current, yellow (Y) phase current, blue (B) phase current, overall voltage, frequency, active power setpoint feedback, reactive power setpoint feedback, inverter work status, communication status, and running status. Power quality monitoring (PQM) data further comprises apparent power, active power, reactive power, power factor, overall voltage, red–yellow (RY) line voltage, yellow-blue (YB) line voltage, blue-red (BR) line voltage, frequency, red (R) phase current, yellow (Y) phase current, blue (B) phase current, active power export, active power import, reactive power export, reactive power import, and power quality monitoring communication status; andthe solar control unit operatively connected to the solar power plant, to process the solar data using at least one control algorithm to optimize and communicate power output of the solar power plant to the renewable energy controller (108); andwherein the wind power plant controller (104) comprises:at least one wind plant sensor operatively connected to the wind power plant to monitor and communicate the wind data to a wind control unit, wherein the at least one wind plant sensor comprises at least one of: an anemometer, a wind vane, a rotor speed sensor, a current sensor, a voltage sensor, and a pitch angle sensor, and wherein the wind plant sensor data comprises at least one of wind speed, wind direction, rotational speed of turbine rotor, electrical output and performance, blade pitch, active power of the generator, reactive power of the generator, power factor of the grid, grid frequency, and red-yellow (RY), yellow-blue (YB), and blue-red (BR) line voltages, cumulative active power generation, available active power capability, number of turbines in production, cumulative reactive power generation, maximum available capacitive volt-ampere reactive (Vars), maximum available inductive volt-ampere reactive (Vars), power factor, average wind speed, active power feed to a wind server, reactive power feed to the wind server, active power control enable signal in slave power plant controller, reactive power control enable signal in slave power plant controller, enable feedback from slave wind power plant controller, active power setpoint feedback, reactive power setpoint feedback, and communication status of the slave wind power plant controller, apparent power, active power, reactive power, power factor, voltage, red (R) phase voltage, yellow (Y) phase voltage, blue (B) phase voltage, frequency, red (R) phase current, yellow (Y) phase current, blue (B) phase current, active power export, active power import, reactive power export, reactive power import, and power quality monitoring communication status; andthe wind control unit operatively connected to the wind power plant to process the wind data using the control algorithm to optimize and communicate power output of the wind power plant to the renewable energy controller (108), wherein the control algorithm comprises at least one of:a maximum power point tracking (MPPT) algorithm comprising perturb and observe, incremental conductance, fuzzy-logic MPPT, neural-network MPPT, and extremum-seeking control;a model predictive control (MPC);a droop-based grid-support algorithms comprising P–f control, Q–V control, Volt–VAR control, and Volt–Watt control;ramp-rate limiting and power-smoothing algorithms;forecast-integrated optimization strategies;hybrid rule-based and AI-enhanced curtailment and reserve controllers; andsafety and protection logics.4.The system (100) as claimed in claim 1, wherein the battery energy storage unit controller (106) provides renewable firming by storing excess energy and releasing stored energy during periods of low energy generation periods based on instructions received from the renewable energy controller (108);wherein the battery energy storage unit controller (106) supports energy arbitrage storing energy when prices are low and selling it when prices are high, maximizing economic returns;wherein the battery energy storage unit controller (106) provides feedback to the renewable energy controller (108) on battery status and adjusts operations based on the feedback; and wherein the battery energy storage unit controller 106 provides at least one battery energy signal to the renewable energy controller (108) comprising at least one of converter operation mode, active power, reactive power, grid voltage, grid frequency, input voltage comprising battery voltage, active power limit to grid, active power limit from grid, positive reactive power limit, negative reactive power limit, code of the active fault, root mean square (RMS) phase voltage of phase 1, RMS phase voltage of phase 2, RMS phase voltage of phase 3, RMS phase current of phase 1, RMS phase current of phase 2, RMS phase current of phase 3, direct current (DC) input power, grounding current, isolation resistance, ambient temperature, highest insulated-gate bipolar transistor (IGBT) temperature, control section temperature, highest cabinet temperature, inductor capacitor inductor (LCL) section temperature, measured maximum inverter section humidity, kilowatt hour (kWh) energy charged to battery, megawatt hour (MWh) energy charged to battery, gigawatt hour (GWh) energy charged to battery, kilowatt hour (kWh) energy discharged from battery, megawatt hour (MWh) energy discharged from battery, gigawatt hour (GWh) energy discharged from battery, direct current (DC) input current, electromechanical switching device status word, inverter main status word, limitation status word, grid status word, fan status word, environmental status word, fault status word, warning status word, direct current (DC) fuse status word, inverter inhibits, total active import, daily active import, total active export, daily active export, communication status, control word, active power setpoint, reactive power setpoint, direct current (DC) contactor of battery energy storage system enable or disable, communication signal comprising heartbeat, system operating status, system voltage, system current, system state of charge, system state of health, system state of power or state of energy, system insulation resistance, system available energy for charging, system available energy for discharging, system available power for charging, system available power for discharging, system maximum charging current, system maximum discharging current, system charging power limit, system discharging power limit, container level alarm 1, container level alarm 2, rack operating status, rack contactor state, rack state of charge, rack state of health, total number of racks connected, total number of racks online, rack insulation value, rack maximum charge current, rack maximum discharge current, rack available power for charging, rack available power for discharging, rack fault, rack alarm 1, rack alarm 2, and rack alarm 3.5.The system (100) as claimed in claim 1, wherein the renewable energy controller (108) implements grid code compliance by receive and processing grid data relating to active power, reactive power, frequency, voltage, ramp rates, droop functions, and power factor; compare the received data and predefined thresholds, to generate control signals; and determine and communicate the control signals to the solar power plant controller (102), wind power plant controller (104), and battery energy storage unit controller (106) to regulate the active power, reactive power, frequency, voltage, ramp rates, droop functions, power factor to maintain grid stability and compliance;wherein the renewable energy controller (108) comprises control features comprising at least one of artificial inertia and power oscillation dampers to reduce oscillations in the power output and stabilize grid operation during abrupt fluctuations in demand or supply;wherein the renewable energy controller (108) supports the ancillary services by monitoring the grid condition in real-time, coordinating charging and discharging of the battery energy storage unit, adjusting active and reactive power outputs of the renewable power plant, and executing control commands to maintain grid stability during disturbances or fluctuations, wherein the ancillary services comprise spinning reserves, energy imbalance support, power curve smoothing, black start, fast frequency response, and islanding / anti-islanding;wherein the renewable energy controller (108) is communicatively connected to a Supervisory Control and Data Acquisition (SCADA) unit (118) to monitor and control the renewable energy plant;wherein the renewable energy controller (108) is operatively connected to a third-party Application Programming Interface (API) (124) to enable integration with external entities such as energy management platforms and facilitate market participation, regulatory compliance, and coordination with grid operators;wherein the renewable energy controller (108) manages battery state of charge (SOC) and state of health (SOH) in real-time, preventing performance issues and ensuring safety through protocols like thermal runaway prevention; andwherein the renewable energy controller (108) generates a dispatch schedule to coordinate function of at least one of the solar power plant controller (102), the wind power plant controller (104) and the battery energy storage unit controller (106) to optimize grid reliability, the renewable plant’s efficiency, and economic returns.6.The system (100) as claimed in claim 1, wherein the renewable energy controller (108) comprises:a real-time monitoring module (306) to track performance and output of the renewable plant using the solar plant sensor data and the wind plant sensor data, and analyze the forecasted data in real-time to detect anomalies using at least one anomaly detection algorithm, forecast energy production using at least one forecasting algorithm, and provide inputs for other modules within the RE controller;an energy management module (308) to control charging and discharging cycles of the battery energy storage unit controller (106), wherein the energy management module (308) comprises at least one energy control algorithm, energy flow regulators, and interfaces for communication with both the grid (112) and the battery energy storage unit controller (106);a grid compliance module (310) to ensure that the renewable energy plant operates within regulatory limits and supports the grid stability by measuring operational parameters of the renewable energy plant against grid code limits, comparing the measured parameters with predefined thresholds, and determining deviations or anomalies for corrective action, wherein the grid compliance module (310) comprises control systems for regulating voltage, frequency, and power factor to meet the grid code;an intelligent bidding module (312) to maximize revenue through optimized participation in energy markets, considering both energy production forecasts and market opportunities, wherein the intelligent bidding module (312) comprises at least one bidding algorithm for analyzing market conditions, forecasting energy prices, and optimizing the dispatch schedule;a digital twin module (314) to provide real-time insights and predictive analytics to enhance decision-making and optimize plant operations; andan ancillary services module (316) to deliver essential grid services, ensuring the grid reliability, and optimizing the operation of the renewable energy plant in response to grid demands, wherein the least one anomaly detection algorithm comprises threshold-based checks, rule-based checks, statistical change-point detectors comprising cumulative sum (CUSUM) and exponentially weighted moving average (EWMA), multivariate statistical analysis comprising principal component analysis (PCA)-based monitoring, model-residual analysis using digital twin models, and machine learning models comprising isolation forest, one-class support vector machines (SVM), local outlier factor, autoencoders, and long short-term memory (LSTM);wherein the at least one forecasting algorithm comprises persistence models, physical models, numerical weather prediction (NWP) with bias correction, statistical time-series methods comprising autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and Prophet, machine learning regressors comprising random forest, XGBoost, and LightGBM, deep learning sequence models comprising LSTM, sequence-to-sequence (Seq2Seq) networks, and temporal convolutional networks (TCN), hybrid physics, machine learning ensemble models and quantile regression or bootstrapped ensembles;wherein the at least one energy control algorithm comprising rule-based control, heuristic control, proportional integral (PI) control, proportional integral derivative (PID) control, model predictive control (MPC), stochastic dynamic programming, scenario-based MPC, mixed integer linear programming (MILP), reinforcement learning algorithms comprising Q-learning, deep Q-networks (DQN), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO), degradation-aware optimization, Kalman filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF); andwherein the at least one bidding algorithm comprises autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), prophet, gradient boosted trees, XGBoost, LightGBM, random forests, long short-term memory (LSTM) networks, Seq2Seq models, ensemble predictors, mixed integer linear programming (MILP), stochastic optimization, scenario-based model predictive control (MPC), genetic algorithms, particle swarm optimization, chance-constrained optimization, conditional value at risk (CVaR) minimization, Q-learning, proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), actor-critic agents, and Monte-Carlo simulation frameworks.7.The system (100) as claimed in claim 1, wherein a solar digital twin unit (120) is communicatively connected to the solar power plant to simulate operations of the solar power plant under various conditions, predict solar energy production, and optimize energy dispatch based on the predictions;wherein a battery digital twin unit (122) is communicatively connected to the battery energy storage unit controller (106) to simulate real-time and future performance of the battery energy storage unit and optimize charging and discharging schedules;wherein the battery digital twin unit (122) combines real-time operational data derived from the battery energy storage unit controller (106) with external inputs to predict how the battery unit (116) will perform under varying conditions, such as different weather patterns that influence energy generation from the renewable power plant, wherein the external inputs comprise weather forecasts and historical performance data, wherein the real-time operational data comprises current state of charge, and temperature; andwherein the intelligent bidding unit (114) present inside a centralized server comprises predictive algorithms, market data analysis tools, machine learning and artificial intelligence algorithms.8.A method (400) for optimizing energy distribution in renewable energy plants, comprising:monitoring and communicating, by a solar power plant controller (102) operatively connected to at least one solar power plant, power output of the solar power plant to a renewable energy controller (108) of the solar power plant;monitoring and communicating, by a wind power plant controller (104) operatively connected to at least one wind power plant, power output of the wind power plant to the renewable energy controller (108);storing, by a battery energy storage unit operatively connected to the solar power plant and the wind power plant, excess renewable energy of the solar power plant and the wind power plant, supplying stored energy during demand peaks, and providing ancillary services;optimizing, by a battery energy storage unit controller (106) operatively connected to the battery energy storage unit, charging and discharging cycles of the battery energy storage unit;coordinating, by the renewable energy controller (108) operatively connected to at least one of the solar power plant controller (102), the wind power plant controller (104), and the battery energy storage unit controller (106), energy dispatch, ensuring grid compliance, and optimizing operations using at least one of forecast data, market prices, and grid condition, enabling optimized energy distribution in the renewable energy plants; anddeveloping and executing, by an intelligent bidding unit (114) communicatively connected to the renewable energy controller (108), bidding strategies in energy markets based on the forecast data, the market prices, and the grid condition, and for generating a dispatch schedule.9.The method (400) as claimed in claim 8, comprising:monitoring, by a power quality meter (110) disposed at a point of interconnection (POI) with a grid (112), quality of power being generated and supplied to the grid (112) and ensuring compliance of delivered power with grid standards;receiving and distributing, by the grid (112) operatively connected to the renewable energy controller (108) via the power quality meter (110), electrical power generated by the solar power plant and the wind power plant;managing, by a battery unit (116) communicatively connected to the battery energy storage unit controller (106), charging and discharging cycles based on the grid (112) needs and the energy availability; andstoring, by the battery unit (116), energy when generation exceeds demand and discharging the energy during peak demand periods or grid instability.10.The method (400) as claimed in claim 8, comprising providing the solar power plant controller (102) comprises:monitoring and communicating, by at least one solar plant sensor operatively connected to the solar power plant, the solar data of the solar PV modules to a solar control unit, wherein the at least one solar sensor comprises at least of a voltage sensor and a current sensor, and wherein the solar sensor data comprises at least one of: electrical output and performance, active and reactive power output, solar irradiance, plane of array (POA) irradiance, global horizontal irradiance (GHI), and module temperature. Inverter-level data comprise active power, reactive power, power factor, red-yellow (RY) line voltage, yellow-blue (YB) line voltage, blue-red (BR) line voltage, red (R) phase current, yellow (Y) phase current, blue (B) phase current, overall voltage, frequency, active power setpoint feedback, reactive power setpoint feedback, inverter work status, communication status, and running status. Power quality monitoring (PQM) data further comprises apparent power, active power, reactive power, power factor, overall voltage, red–yellow (RY) line voltage, yellow-blue (YB) line voltage, blue-red (BR) line voltage, frequency, red (R) phase current, yellow (Y) phase current, blue (B) phase current, active power export, active power import, reactive power export, reactive power import, and power quality monitoring communication status; andprocessing, by the solar control unit operatively connected to the solar power plant, the solar data using at least one control algorithm for optimizing and communicating power output of the solar power plant to the renewable energy controller (108), andwherein providing the wind power plant controller (104) comprises:monitoring and communicating, by at least one wind plant sensor operatively connected to the wind power plant, the wind data to a wind control unit, wherein the at least one wind plant sensor comprises at least one of: an anemometer, a wind vane, a rotor speed sensor, a current sensor, a voltage sensor, and a pitch angle sensor, and wherein the wind plant sensor data comprises at least one of wind speed, wind direction, rotational speed of turbine rotor, electrical output and performance, blade pitch, active power of the generator, reactive power of the generator, power factor of the grid, grid frequency, and red-yellow (RY), yellow-blue (YB), and blue-red (BR) line voltages, cumulative active power generation, available active power capability, number of turbines in production, cumulative reactive power generation, maximum available capacitive volt-ampere reactive (Vars), maximum available inductive volt-ampere reactive (Vars), power factor, average wind speed, active power feed to a wind server, reactive power feed to the wind server, active power control enable signal in slave power plant controller, reactive power control enable signal in slave power plant controller, enable feedback from slave wind power plant controller, active power setpoint feedback, reactive power setpoint feedback, and communication status of the slave wind power plant controller, apparent power, active power, reactive power, power factor, voltage, red (R) phase voltage, yellow (Y) phase voltage, blue (B) phase voltage, frequency, red (R) phase current, yellow (Y) phase current, blue (B) phase current, active power export, active power import, reactive power export, reactive power import, and power quality monitoring communication status; andprocessing, by the wind control unit operatively connected to the wind power plant, the wind data using the control algorithm for optimizing and communicating power output of the wind power plant to the renewable energy controller (108), wherein the control algorithm comprises at least one of:a maximum power point tracking (MPPT) algorithm comprising perturb and observe, incremental conductance, fuzzy-logic MPPT, neural-network MPPT, and extremum-seeking control;a model predictive control (MPC);a droop-based grid-support algorithms comprising P–f control, Q–V control, Volt–VAR control, and Volt–Watt control;ramp-rate limiting and power-smoothing algorithms;forecast-integrated optimization strategies;hybrid rule-based and AI-enhanced curtailment and reserve controllers; andsafety and protection logics.11.The method (400) as claimed in claim 8, comprising providing renewable firming, by the battery energy storage unit controller (106), by storing excess energy and releasing stored energy during periods of low energy generation periods based on instructions received from the renewable energy controller (108);supporting, by the battery energy storage unit controller (106), energy arbitrage storing energy when prices are low and selling it when prices are high, maximizing economic returns;providing, by the battery energy storage unit controller (106), feedback to the renewable energy controller (108) on battery status and adjusting operations based on the feedback; andwherein the battery energy storage unit controller 106 provides at least one battery energy signal to the renewable energy controller (108) comprising at least one of converter operation mode, active power, reactive power, grid voltage, grid frequency, input voltage comprising battery voltage, active power limit to grid, active power limit from grid, positive reactive power limit, negative reactive power limit, code of the active fault, root mean square (RMS) phase voltage of phase 1, RMS phase voltage of phase 2, RMS phase voltage of phase 3, RMS phase current of phase 1, RMS phase current of phase 2, RMS phase current of phase 3, direct current (DC) input power, grounding current, isolation resistance, ambient temperature, highest insulated-gate bipolar transistor (IGBT) temperature, control section temperature, highest cabinet temperature, inductor capacitor inductor (LCL) section temperature, measured maximum inverter section humidity, kilowatt hour (kWh) energy charged to battery, megawatt hour (MWh) energy charged to battery, gigawatt hour (GWh) energy charged to battery, kilowatt hour (kWh) energy discharged from battery, megawatt hour (MWh) energy discharged from battery, gigawatt hour (GWh) energy discharged from battery, direct current (DC) input current, electromechanical switching device status word, inverter main status word, limitation status word, grid status word, fan status word, environmental status word, fault status word, warning status word, direct current (DC) fuse status word, inverter inhibits, total active import, daily active import, total active export, daily active export, communication status, control word, active power setpoint, reactive power setpoint, direct current (DC) contactor of battery energy storage system enable or disable, communication signal comprising heartbeat, system operating status, system voltage, system current, system state of charge, system state of health, system state of power or state of energy, system insulation resistance, system available energy for charging, system available energy for discharging, system available power for charging, system available power for discharging, system maximum charging current, system maximum discharging current, system charging power limit, system discharging power limit, container level alarm 1, container level alarm 2, rack operating status, rack contactor state, rack state of charge, rack state of health, total number of racks connected, total number of racks online, rack insulation value, rack maximum charge current, rack maximum discharge current, rack available power for charging, rack available power for discharging, rack fault, rack alarm 1, rack alarm 2, and rack alarm 3.12.The method (400) as claimed in claim 8, comprising implementing by the renewable energy controller (108), code compliance by receiving and processing grid data relating to active power, reactive power, frequency, voltage, ramp rates, droop functions, and power factor; comparing the received data and predefined thresholds, for generating control signals; determining and communicating the control signals to the solar power plant controller (102), wind power plant controller (104), and battery energy storage unit controller (106) for regulating the active power, reactive power, frequency, voltage, ramp rates, droop functions, power factor to maintain grid stability and compliance;wherein the renewable energy controller (108) comprises control features comprising at least one of artificial inertia and power oscillation dampers for reducing oscillations in the power output and stabilizing grid operation during abrupt fluctuations in demand or supply, the control features;supporting, by the renewable energy controller (108), the ancillary services by monitoring the grid condition in real-time, coordinating charging and discharging of the battery energy storage unit, adjusting active and reactive power outputs of the renewable power plant, and executing control commands for maintain grid stability during disturbances or fluctuations, wherein the ancillary services comprise spinning reserves, energy imbalance support, power curve smoothing, black start, fast frequency response, and islanding / anti-islanding;monitoring and controlling, by the renewable energy controller (108) communicatively connected to a Supervisory Control and Data Acquisition (SCADA) unit (118), the renewable energy plant;enabling, by the renewable energy controller (108) is operatively connected to a third-party Application Programming Interface (API) (124), integration with external entities such as energy management platforms and facilitating market participation, regulatory compliance, and coordination with grid operators;managing, by the renewable energy controller (108), battery state of charge (SOC) and state of health (SOH) in real time, preventing performance issues and ensuring safety through protocols like thermal runaway prevention; andgenerating, by the renewable energy controller (108), a dispatch schedule for coordinating function of at least one of the solar power plant controller (102), the wind power plant controller (104) and the battery energy storage unit controller (106) for optimizing grid reliability, the renewable plant’s efficiency, and economic returns.13.The method (400) as claimed in claim 8, comprising providing the renewable energy controller (108) comprises:tracking, by a real-time monitoring module (306), performance and output of the renewable plant using the solar plant sensor data and the wind plant sensor data, and analyzing the forecasted data in real-time for detecting anomalies, using at least one anomaly detection algorithm, forecasting energy production using at least one forecasting algorithm, and providing inputs for other modules within the RE controller;controlling, by an energy management module (308), charging and discharging cycles of the battery energy storage unit controller (106), wherein the energy management module (308) comprises at least one energy control algorithm, energy flow regulators, and interfaces for communication with both the grid (112) and the battery energy storage unit controller (106);ensuring, by a grid compliance module (310), that the renewable energy plant operates within regulatory limits and supports the grid stability by measuring operational parameters of the renewable energy plant against grid code limits, comparing the measured parameters with predefined thresholds, and determining deviations or anomalies for corrective action, wherein the grid compliance module (310) comprises control systems for regulating voltage, frequency, and power factor to meet the grid code;maximizing, by an intelligent bidding module (312), revenue through optimized participation in energy markets, considering both energy production forecasts and market opportunities, wherein the intelligent bidding module (312) comprises at least one bidding algorithm for analyzing market conditions, forecasting energy prices, and optimizing the dispatch schedule;providing, by a digital twin module (314), real-time insights and predictive analytics to enhance decision-making and optimize plant operations; anddelivering, by an ancillary services module (316), essential grid services, ensuring the grid reliability, and optimizing the operation of the renewable energy plant in response to grid demands, wherein the least one anomaly detection algorithm comprises threshold-based checks, rule-based checks, statistical change-point detectors comprising cumulative sum (CUSUM) and exponentially weighted moving average (EWMA), multivariate statistical analysis comprising principal component analysis (PCA)-based monitoring, model-residual analysis using digital twin models, and machine learning models comprising isolation forest, one-class support vector machines (SVM), local outlier factor, autoencoders, and long short-term memory (LSTM);wherein the at least one forecasting algorithm comprises persistence models, physical models, numerical weather prediction (NWP) with bias correction, statistical time-series methods comprising autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and Prophet, machine learning regressors comprising random forest, XGBoost, and LightGBM, deep learning sequence models comprising LSTM, sequence-to-sequence (Seq2Seq) networks, and temporal convolutional networks (TCN), hybrid physics, machine learning ensemble models and quantile regression or bootstrapped ensembles;wherein the at least one energy control algorithm comprising rule-based control, heuristic control, proportional integral (PI) control, proportional integral derivative (PID) control, model predictive control (MPC), stochastic dynamic programming, scenario-based MPC, mixed integer linear programming (MILP), reinforcement learning algorithms comprising Q-learning, deep Q-networks (DQN), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO), degradation-aware optimization, Kalman filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF); and wherein the at least one bidding algorithm comprises autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), prophet, gradient boosted trees, XGBoost, LightGBM, random forests, long short-term memory (LSTM) networks, Seq2Seq models, ensemble predictors, mixed integer linear programming (MILP), stochastic optimization, scenario-based model predictive control (MPC), genetic algorithms, particle swarm optimization, chance-constrained optimization, conditional value at risk (CVaR) minimization, Q-learning, proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), actor-critic agents, and Monte-Carlo simulation frameworks.14.The method (400) as claimed in claim 8, comprising:simulating, by a solar digital twin unit (120) communicatively connected to the solar power plant, operations of the solar power plant under various conditions, predicting solar energy production, and optimizing energy dispatch based on the predictions;simulating, by a battery digital twin unit (122) is communicatively connected to the battery energy storage unit controller (106), real-time and future performance of the battery energy storage unit and optimizing charging and discharging schedules;wherein the battery digital twin unit (122) combines real-time operational data derived from the battery energy storage unit controller (106) with external inputs for predicting how the battery unit (116) will perform under varying conditions, such as different weather patterns that influence energy generation from the renewable power plant,wherein the external inputs comprise weather forecasts and historical performance data,wherein the real-time operational data comprises current state of charge, and temperature; andwherein the intelligent bidding unit (114) present inside a centralized server comprises predictive algorithms, market data analysis tools, machine learning and artificial intelligence algorithms.

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